Data Visualization Analytics
Mon 30 June 2025
# Cell 1 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_1 = np.linspace(0, 10, 100)
y_1 = np.sin(x_1 + 1)
plt.plot(x_1, y_1)
plt.title("Cell 1 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 1)")
plt.show()
# Cell 2 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_2 = np.linspace(0, 10, 100)
y_2 = np.sin(x_2 + 2)
plt.plot(x_2, y_2)
plt.title("Cell 2 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 2)")
plt.show()
# Cell 3 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_3 = np.linspace(0, 10, 100)
y_3 = np.sin(x_3 + 3)
plt.plot(x_3, y_3)
plt.title("Cell 3 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 3)")
plt.show()
# Cell 4 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_4 = np.linspace(0, 10, 100)
y_4 = np.sin(x_4 + 4)
plt.plot(x_4, y_4)
plt.title("Cell 4 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 4)")
plt.show()
# Cell 5 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_5 = np.linspace(0, 10, 100)
y_5 = np.sin(x_5 + 5)
plt.plot(x_5, y_5)
plt.title("Cell 5 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 5)")
plt.show()
# Cell 6 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_6 = np.linspace(0, 10, 100)
y_6 = np.sin(x_6 + 6)
plt.plot(x_6, y_6)
plt.title("Cell 6 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 6)")
plt.show()
# Cell 7 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_7 = np.linspace(0, 10, 100)
y_7 = np.sin(x_7 + 7)
plt.plot(x_7, y_7)
plt.title("Cell 7 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 7)")
plt.show()
# Cell 8 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_8 = np.linspace(0, 10, 100)
y_8 = np.sin(x_8 + 8)
plt.plot(x_8, y_8)
plt.title("Cell 8 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 8)")
plt.show()
# Cell 9 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_9 = np.linspace(0, 10, 100)
y_9 = np.sin(x_9 + 9)
plt.plot(x_9, y_9)
plt.title("Cell 9 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 9)")
plt.show()
# Cell 10 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_10 = np.linspace(0, 10, 100)
y_10 = np.sin(x_10 + 0)
plt.plot(x_10, y_10)
plt.title("Cell 10 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 0)")
plt.show()
# Cell 11 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_11 = np.linspace(0, 10, 100)
y_11 = np.sin(x_11 + 1)
plt.plot(x_11, y_11)
plt.title("Cell 11 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 1)")
plt.show()
# Cell 12 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_12 = np.linspace(0, 10, 100)
y_12 = np.sin(x_12 + 2)
plt.plot(x_12, y_12)
plt.title("Cell 12 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 2)")
plt.show()
# Cell 13 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_13 = np.linspace(0, 10, 100)
y_13 = np.sin(x_13 + 3)
plt.plot(x_13, y_13)
plt.title("Cell 13 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 3)")
plt.show()
# Cell 14 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_14 = np.linspace(0, 10, 100)
y_14 = np.sin(x_14 + 4)
plt.plot(x_14, y_14)
plt.title("Cell 14 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 4)")
plt.show()
# Cell 15 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_15 = np.linspace(0, 10, 100)
y_15 = np.sin(x_15 + 5)
plt.plot(x_15, y_15)
plt.title("Cell 15 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 5)")
plt.show()
# Cell 16 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_16 = np.linspace(0, 10, 100)
y_16 = np.sin(x_16 + 6)
plt.plot(x_16, y_16)
plt.title("Cell 16 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 6)")
plt.show()
# Cell 17 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_17 = np.linspace(0, 10, 100)
y_17 = np.sin(x_17 + 7)
plt.plot(x_17, y_17)
plt.title("Cell 17 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 7)")
plt.show()
# Cell 18 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_18 = np.linspace(0, 10, 100)
y_18 = np.sin(x_18 + 8)
plt.plot(x_18, y_18)
plt.title("Cell 18 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 8)")
plt.show()
# Cell 19 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_19 = np.linspace(0, 10, 100)
y_19 = np.sin(x_19 + 9)
plt.plot(x_19, y_19)
plt.title("Cell 19 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 9)")
plt.show()
# Cell 20 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_20 = np.linspace(0, 10, 100)
y_20 = np.sin(x_20 + 0)
plt.plot(x_20, y_20)
plt.title("Cell 20 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 0)")
plt.show()
# Cell 21 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_21 = np.linspace(0, 10, 100)
y_21 = np.sin(x_21 + 1)
plt.plot(x_21, y_21)
plt.title("Cell 21 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 1)")
plt.show()
# Cell 22 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_22 = np.linspace(0, 10, 100)
y_22 = np.sin(x_22 + 2)
plt.plot(x_22, y_22)
plt.title("Cell 22 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 2)")
plt.show()
# Cell 23 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_23 = np.linspace(0, 10, 100)
y_23 = np.sin(x_23 + 3)
plt.plot(x_23, y_23)
plt.title("Cell 23 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 3)")
plt.show()
# Cell 24 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_24 = np.linspace(0, 10, 100)
y_24 = np.sin(x_24 + 4)
plt.plot(x_24, y_24)
plt.title("Cell 24 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 4)")
plt.show()
# Cell 25 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_25 = np.linspace(0, 10, 100)
y_25 = np.sin(x_25 + 5)
plt.plot(x_25, y_25)
plt.title("Cell 25 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 5)")
plt.show()
# Cell 26 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_26 = np.linspace(0, 10, 100)
y_26 = np.sin(x_26 + 6)
plt.plot(x_26, y_26)
plt.title("Cell 26 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 6)")
plt.show()
# Cell 27 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_27 = np.linspace(0, 10, 100)
y_27 = np.sin(x_27 + 7)
plt.plot(x_27, y_27)
plt.title("Cell 27 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 7)")
plt.show()
# Cell 28 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_28 = np.linspace(0, 10, 100)
y_28 = np.sin(x_28 + 8)
plt.plot(x_28, y_28)
plt.title("Cell 28 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 8)")
plt.show()
# Cell 29 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_29 = np.linspace(0, 10, 100)
y_29 = np.sin(x_29 + 9)
plt.plot(x_29, y_29)
plt.title("Cell 29 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 9)")
plt.show()
# Cell 30 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_30 = np.linspace(0, 10, 100)
y_30 = np.sin(x_30 + 0)
plt.plot(x_30, y_30)
plt.title("Cell 30 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 0)")
plt.show()
# Cell 31 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_31 = np.linspace(0, 10, 100)
y_31 = np.sin(x_31 + 1)
plt.plot(x_31, y_31)
plt.title("Cell 31 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 1)")
plt.show()
# Cell 32 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_32 = np.linspace(0, 10, 100)
y_32 = np.sin(x_32 + 2)
plt.plot(x_32, y_32)
plt.title("Cell 32 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 2)")
plt.show()
# Cell 33 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_33 = np.linspace(0, 10, 100)
y_33 = np.sin(x_33 + 3)
plt.plot(x_33, y_33)
plt.title("Cell 33 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 3)")
plt.show()
# Cell 34 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_34 = np.linspace(0, 10, 100)
y_34 = np.sin(x_34 + 4)
plt.plot(x_34, y_34)
plt.title("Cell 34 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 4)")
plt.show()
# Cell 35 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_35 = np.linspace(0, 10, 100)
y_35 = np.sin(x_35 + 5)
plt.plot(x_35, y_35)
plt.title("Cell 35 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 5)")
plt.show()
# Cell 36 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_36 = np.linspace(0, 10, 100)
y_36 = np.sin(x_36 + 6)
plt.plot(x_36, y_36)
plt.title("Cell 36 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 6)")
plt.show()
# Cell 37 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_37 = np.linspace(0, 10, 100)
y_37 = np.sin(x_37 + 7)
plt.plot(x_37, y_37)
plt.title("Cell 37 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 7)")
plt.show()
# Cell 38 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_38 = np.linspace(0, 10, 100)
y_38 = np.sin(x_38 + 8)
plt.plot(x_38, y_38)
plt.title("Cell 38 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 8)")
plt.show()
# Cell 39 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_39 = np.linspace(0, 10, 100)
y_39 = np.sin(x_39 + 9)
plt.plot(x_39, y_39)
plt.title("Cell 39 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 9)")
plt.show()
# Cell 40 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_40 = np.linspace(0, 10, 100)
y_40 = np.sin(x_40 + 0)
plt.plot(x_40, y_40)
plt.title("Cell 40 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 0)")
plt.show()
# Cell 41 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_41 = np.linspace(0, 10, 100)
y_41 = np.sin(x_41 + 1)
plt.plot(x_41, y_41)
plt.title("Cell 41 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 1)")
plt.show()
# Cell 42 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_42 = np.linspace(0, 10, 100)
y_42 = np.sin(x_42 + 2)
plt.plot(x_42, y_42)
plt.title("Cell 42 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 2)")
plt.show()
# Cell 43 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_43 = np.linspace(0, 10, 100)
y_43 = np.sin(x_43 + 3)
plt.plot(x_43, y_43)
plt.title("Cell 43 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 3)")
plt.show()
# Cell 44 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_44 = np.linspace(0, 10, 100)
y_44 = np.sin(x_44 + 4)
plt.plot(x_44, y_44)
plt.title("Cell 44 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 4)")
plt.show()
# Cell 45 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_45 = np.linspace(0, 10, 100)
y_45 = np.sin(x_45 + 5)
plt.plot(x_45, y_45)
plt.title("Cell 45 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 5)")
plt.show()
# Cell 46 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_46 = np.linspace(0, 10, 100)
y_46 = np.sin(x_46 + 6)
plt.plot(x_46, y_46)
plt.title("Cell 46 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 6)")
plt.show()
# Cell 47 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_47 = np.linspace(0, 10, 100)
y_47 = np.sin(x_47 + 7)
plt.plot(x_47, y_47)
plt.title("Cell 47 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 7)")
plt.show()
# Cell 48 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_48 = np.linspace(0, 10, 100)
y_48 = np.sin(x_48 + 8)
plt.plot(x_48, y_48)
plt.title("Cell 48 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 8)")
plt.show()
# Cell 49 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_49 = np.linspace(0, 10, 100)
y_49 = np.sin(x_49 + 9)
plt.plot(x_49, y_49)
plt.title("Cell 49 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 9)")
plt.show()
# Cell 50 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_50 = np.linspace(0, 10, 100)
y_50 = np.sin(x_50 + 0)
plt.plot(x_50, y_50)
plt.title("Cell 50 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 0)")
plt.show()
# Cell 51 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_51 = np.linspace(0, 10, 100)
y_51 = np.sin(x_51 + 1)
plt.plot(x_51, y_51)
plt.title("Cell 51 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 1)")
plt.show()
# Cell 52 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_52 = np.linspace(0, 10, 100)
y_52 = np.sin(x_52 + 2)
plt.plot(x_52, y_52)
plt.title("Cell 52 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 2)")
plt.show()
# Cell 53 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_53 = np.linspace(0, 10, 100)
y_53 = np.sin(x_53 + 3)
plt.plot(x_53, y_53)
plt.title("Cell 53 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 3)")
plt.show()
# Cell 54 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_54 = np.linspace(0, 10, 100)
y_54 = np.sin(x_54 + 4)
plt.plot(x_54, y_54)
plt.title("Cell 54 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 4)")
plt.show()
# Cell 55 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_55 = np.linspace(0, 10, 100)
y_55 = np.sin(x_55 + 5)
plt.plot(x_55, y_55)
plt.title("Cell 55 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 5)")
plt.show()
# Cell 56 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_56 = np.linspace(0, 10, 100)
y_56 = np.sin(x_56 + 6)
plt.plot(x_56, y_56)
plt.title("Cell 56 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 6)")
plt.show()
# Cell 57 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_57 = np.linspace(0, 10, 100)
y_57 = np.sin(x_57 + 7)
plt.plot(x_57, y_57)
plt.title("Cell 57 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 7)")
plt.show()
# Cell 58 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_58 = np.linspace(0, 10, 100)
y_58 = np.sin(x_58 + 8)
plt.plot(x_58, y_58)
plt.title("Cell 58 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 8)")
plt.show()
# Cell 59 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_59 = np.linspace(0, 10, 100)
y_59 = np.sin(x_59 + 9)
plt.plot(x_59, y_59)
plt.title("Cell 59 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 9)")
plt.show()
# Cell 60 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_60 = np.linspace(0, 10, 100)
y_60 = np.sin(x_60 + 0)
plt.plot(x_60, y_60)
plt.title("Cell 60 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 0)")
plt.show()
# Cell 61 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_61 = np.linspace(0, 10, 100)
y_61 = np.sin(x_61 + 1)
plt.plot(x_61, y_61)
plt.title("Cell 61 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 1)")
plt.show()
# Cell 62 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_62 = np.linspace(0, 10, 100)
y_62 = np.sin(x_62 + 2)
plt.plot(x_62, y_62)
plt.title("Cell 62 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 2)")
plt.show()
# Cell 63 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_63 = np.linspace(0, 10, 100)
y_63 = np.sin(x_63 + 3)
plt.plot(x_63, y_63)
plt.title("Cell 63 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 3)")
plt.show()
# Cell 64 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_64 = np.linspace(0, 10, 100)
y_64 = np.sin(x_64 + 4)
plt.plot(x_64, y_64)
plt.title("Cell 64 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 4)")
plt.show()
# Cell 65 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_65 = np.linspace(0, 10, 100)
y_65 = np.sin(x_65 + 5)
plt.plot(x_65, y_65)
plt.title("Cell 65 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 5)")
plt.show()
# Cell 66 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_66 = np.linspace(0, 10, 100)
y_66 = np.sin(x_66 + 6)
plt.plot(x_66, y_66)
plt.title("Cell 66 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 6)")
plt.show()
# Cell 67 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_67 = np.linspace(0, 10, 100)
y_67 = np.sin(x_67 + 7)
plt.plot(x_67, y_67)
plt.title("Cell 67 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 7)")
plt.show()
# Cell 68 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_68 = np.linspace(0, 10, 100)
y_68 = np.sin(x_68 + 8)
plt.plot(x_68, y_68)
plt.title("Cell 68 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 8)")
plt.show()
# Cell 69 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_69 = np.linspace(0, 10, 100)
y_69 = np.sin(x_69 + 9)
plt.plot(x_69, y_69)
plt.title("Cell 69 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 9)")
plt.show()
# Cell 70 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_70 = np.linspace(0, 10, 100)
y_70 = np.sin(x_70 + 0)
plt.plot(x_70, y_70)
plt.title("Cell 70 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 0)")
plt.show()
# Cell 71 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_71 = np.linspace(0, 10, 100)
y_71 = np.sin(x_71 + 1)
plt.plot(x_71, y_71)
plt.title("Cell 71 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 1)")
plt.show()
# Cell 72 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_72 = np.linspace(0, 10, 100)
y_72 = np.sin(x_72 + 2)
plt.plot(x_72, y_72)
plt.title("Cell 72 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 2)")
plt.show()
# Cell 73 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_73 = np.linspace(0, 10, 100)
y_73 = np.sin(x_73 + 3)
plt.plot(x_73, y_73)
plt.title("Cell 73 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 3)")
plt.show()
# Cell 74 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_74 = np.linspace(0, 10, 100)
y_74 = np.sin(x_74 + 4)
plt.plot(x_74, y_74)
plt.title("Cell 74 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 4)")
plt.show()
# Cell 75 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_75 = np.linspace(0, 10, 100)
y_75 = np.sin(x_75 + 5)
plt.plot(x_75, y_75)
plt.title("Cell 75 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 5)")
plt.show()
# Cell 76 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_76 = np.linspace(0, 10, 100)
y_76 = np.sin(x_76 + 6)
plt.plot(x_76, y_76)
plt.title("Cell 76 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 6)")
plt.show()
# Cell 77 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_77 = np.linspace(0, 10, 100)
y_77 = np.sin(x_77 + 7)
plt.plot(x_77, y_77)
plt.title("Cell 77 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 7)")
plt.show()
# Cell 78 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_78 = np.linspace(0, 10, 100)
y_78 = np.sin(x_78 + 8)
plt.plot(x_78, y_78)
plt.title("Cell 78 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 8)")
plt.show()
# Cell 79 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_79 = np.linspace(0, 10, 100)
y_79 = np.sin(x_79 + 9)
plt.plot(x_79, y_79)
plt.title("Cell 79 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 9)")
plt.show()
# Cell 80 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_80 = np.linspace(0, 10, 100)
y_80 = np.sin(x_80 + 0)
plt.plot(x_80, y_80)
plt.title("Cell 80 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 0)")
plt.show()
# Cell 81 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_81 = np.linspace(0, 10, 100)
y_81 = np.sin(x_81 + 1)
plt.plot(x_81, y_81)
plt.title("Cell 81 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 1)")
plt.show()
# Cell 82 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_82 = np.linspace(0, 10, 100)
y_82 = np.sin(x_82 + 2)
plt.plot(x_82, y_82)
plt.title("Cell 82 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 2)")
plt.show()
# Cell 83 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_83 = np.linspace(0, 10, 100)
y_83 = np.sin(x_83 + 3)
plt.plot(x_83, y_83)
plt.title("Cell 83 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 3)")
plt.show()
# Cell 84 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_84 = np.linspace(0, 10, 100)
y_84 = np.sin(x_84 + 4)
plt.plot(x_84, y_84)
plt.title("Cell 84 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 4)")
plt.show()
# Cell 85 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_85 = np.linspace(0, 10, 100)
y_85 = np.sin(x_85 + 5)
plt.plot(x_85, y_85)
plt.title("Cell 85 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 5)")
plt.show()
# Cell 86 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_86 = np.linspace(0, 10, 100)
y_86 = np.sin(x_86 + 6)
plt.plot(x_86, y_86)
plt.title("Cell 86 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 6)")
plt.show()
# Cell 87 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_87 = np.linspace(0, 10, 100)
y_87 = np.sin(x_87 + 7)
plt.plot(x_87, y_87)
plt.title("Cell 87 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 7)")
plt.show()
# Cell 88 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_88 = np.linspace(0, 10, 100)
y_88 = np.sin(x_88 + 8)
plt.plot(x_88, y_88)
plt.title("Cell 88 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 8)")
plt.show()
# Cell 89 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_89 = np.linspace(0, 10, 100)
y_89 = np.sin(x_89 + 9)
plt.plot(x_89, y_89)
plt.title("Cell 89 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 9)")
plt.show()
# Cell 90 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_90 = np.linspace(0, 10, 100)
y_90 = np.sin(x_90 + 0)
plt.plot(x_90, y_90)
plt.title("Cell 90 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 0)")
plt.show()
# Cell 91 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_91 = np.linspace(0, 10, 100)
y_91 = np.sin(x_91 + 1)
plt.plot(x_91, y_91)
plt.title("Cell 91 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 1)")
plt.show()
# Cell 92 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_92 = np.linspace(0, 10, 100)
y_92 = np.sin(x_92 + 2)
plt.plot(x_92, y_92)
plt.title("Cell 92 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 2)")
plt.show()
# Cell 93 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_93 = np.linspace(0, 10, 100)
y_93 = np.sin(x_93 + 3)
plt.plot(x_93, y_93)
plt.title("Cell 93 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 3)")
plt.show()
# Cell 94 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_94 = np.linspace(0, 10, 100)
y_94 = np.sin(x_94 + 4)
plt.plot(x_94, y_94)
plt.title("Cell 94 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 4)")
plt.show()
# Cell 95 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_95 = np.linspace(0, 10, 100)
y_95 = np.sin(x_95 + 5)
plt.plot(x_95, y_95)
plt.title("Cell 95 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 5)")
plt.show()
# Cell 96 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_96 = np.linspace(0, 10, 100)
y_96 = np.sin(x_96 + 6)
plt.plot(x_96, y_96)
plt.title("Cell 96 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 6)")
plt.show()
# Cell 97 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_97 = np.linspace(0, 10, 100)
y_97 = np.sin(x_97 + 7)
plt.plot(x_97, y_97)
plt.title("Cell 97 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 7)")
plt.show()
# Cell 98 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_98 = np.linspace(0, 10, 100)
y_98 = np.sin(x_98 + 8)
plt.plot(x_98, y_98)
plt.title("Cell 98 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 8)")
plt.show()
# Cell 99 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_99 = np.linspace(0, 10, 100)
y_99 = np.sin(x_99 + 9)
plt.plot(x_99, y_99)
plt.title("Cell 99 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 9)")
plt.show()
# Cell 100 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_100 = np.linspace(0, 10, 100)
y_100 = np.sin(x_100 + 0)
plt.plot(x_100, y_100)
plt.title("Cell 100 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 0)")
plt.show()
# Cell 101 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_101 = np.linspace(0, 10, 100)
y_101 = np.sin(x_101 + 1)
plt.plot(x_101, y_101)
plt.title("Cell 101 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 1)")
plt.show()
# Cell 102 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_102 = np.linspace(0, 10, 100)
y_102 = np.sin(x_102 + 2)
plt.plot(x_102, y_102)
plt.title("Cell 102 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 2)")
plt.show()
# Cell 103 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_103 = np.linspace(0, 10, 100)
y_103 = np.sin(x_103 + 3)
plt.plot(x_103, y_103)
plt.title("Cell 103 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 3)")
plt.show()
# Cell 104 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_104 = np.linspace(0, 10, 100)
y_104 = np.sin(x_104 + 4)
plt.plot(x_104, y_104)
plt.title("Cell 104 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 4)")
plt.show()
# Cell 105 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_105 = np.linspace(0, 10, 100)
y_105 = np.sin(x_105 + 5)
plt.plot(x_105, y_105)
plt.title("Cell 105 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 5)")
plt.show()
# Cell 106 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_106 = np.linspace(0, 10, 100)
y_106 = np.sin(x_106 + 6)
plt.plot(x_106, y_106)
plt.title("Cell 106 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 6)")
plt.show()
# Cell 107 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_107 = np.linspace(0, 10, 100)
y_107 = np.sin(x_107 + 7)
plt.plot(x_107, y_107)
plt.title("Cell 107 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 7)")
plt.show()
# Cell 108 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_108 = np.linspace(0, 10, 100)
y_108 = np.sin(x_108 + 8)
plt.plot(x_108, y_108)
plt.title("Cell 108 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 8)")
plt.show()
# Cell 109 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_109 = np.linspace(0, 10, 100)
y_109 = np.sin(x_109 + 9)
plt.plot(x_109, y_109)
plt.title("Cell 109 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 9)")
plt.show()
# Cell 110 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_110 = np.linspace(0, 10, 100)
y_110 = np.sin(x_110 + 0)
plt.plot(x_110, y_110)
plt.title("Cell 110 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 0)")
plt.show()
# Cell 111 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_111 = np.linspace(0, 10, 100)
y_111 = np.sin(x_111 + 1)
plt.plot(x_111, y_111)
plt.title("Cell 111 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 1)")
plt.show()
# Cell 112 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_112 = np.linspace(0, 10, 100)
y_112 = np.sin(x_112 + 2)
plt.plot(x_112, y_112)
plt.title("Cell 112 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 2)")
plt.show()
# Cell 113 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_113 = np.linspace(0, 10, 100)
y_113 = np.sin(x_113 + 3)
plt.plot(x_113, y_113)
plt.title("Cell 113 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 3)")
plt.show()
# Cell 114 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_114 = np.linspace(0, 10, 100)
y_114 = np.sin(x_114 + 4)
plt.plot(x_114, y_114)
plt.title("Cell 114 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 4)")
plt.show()
# Cell 115 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_115 = np.linspace(0, 10, 100)
y_115 = np.sin(x_115 + 5)
plt.plot(x_115, y_115)
plt.title("Cell 115 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 5)")
plt.show()
# Cell 116 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_116 = np.linspace(0, 10, 100)
y_116 = np.sin(x_116 + 6)
plt.plot(x_116, y_116)
plt.title("Cell 116 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 6)")
plt.show()
# Cell 117 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_117 = np.linspace(0, 10, 100)
y_117 = np.sin(x_117 + 7)
plt.plot(x_117, y_117)
plt.title("Cell 117 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 7)")
plt.show()
# Cell 118 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_118 = np.linspace(0, 10, 100)
y_118 = np.sin(x_118 + 8)
plt.plot(x_118, y_118)
plt.title("Cell 118 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 8)")
plt.show()
# Cell 119 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_119 = np.linspace(0, 10, 100)
y_119 = np.sin(x_119 + 9)
plt.plot(x_119, y_119)
plt.title("Cell 119 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 9)")
plt.show()
# Cell 120 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_120 = np.linspace(0, 10, 100)
y_120 = np.sin(x_120 + 0)
plt.plot(x_120, y_120)
plt.title("Cell 120 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 0)")
plt.show()
# Cell 121 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_121 = np.linspace(0, 10, 100)
y_121 = np.sin(x_121 + 1)
plt.plot(x_121, y_121)
plt.title("Cell 121 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 1)")
plt.show()
# Cell 122 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_122 = np.linspace(0, 10, 100)
y_122 = np.sin(x_122 + 2)
plt.plot(x_122, y_122)
plt.title("Cell 122 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 2)")
plt.show()
# Cell 123 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_123 = np.linspace(0, 10, 100)
y_123 = np.sin(x_123 + 3)
plt.plot(x_123, y_123)
plt.title("Cell 123 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 3)")
plt.show()
# Cell 124 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_124 = np.linspace(0, 10, 100)
y_124 = np.sin(x_124 + 4)
plt.plot(x_124, y_124)
plt.title("Cell 124 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 4)")
plt.show()
# Cell 125 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_125 = np.linspace(0, 10, 100)
y_125 = np.sin(x_125 + 5)
plt.plot(x_125, y_125)
plt.title("Cell 125 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 5)")
plt.show()
# Cell 126 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_126 = np.linspace(0, 10, 100)
y_126 = np.sin(x_126 + 6)
plt.plot(x_126, y_126)
plt.title("Cell 126 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 6)")
plt.show()
# Cell 127 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_127 = np.linspace(0, 10, 100)
y_127 = np.sin(x_127 + 7)
plt.plot(x_127, y_127)
plt.title("Cell 127 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 7)")
plt.show()
# Cell 128 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_128 = np.linspace(0, 10, 100)
y_128 = np.sin(x_128 + 8)
plt.plot(x_128, y_128)
plt.title("Cell 128 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 8)")
plt.show()
# Cell 129 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_129 = np.linspace(0, 10, 100)
y_129 = np.sin(x_129 + 9)
plt.plot(x_129, y_129)
plt.title("Cell 129 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 9)")
plt.show()
# Cell 130 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_130 = np.linspace(0, 10, 100)
y_130 = np.sin(x_130 + 0)
plt.plot(x_130, y_130)
plt.title("Cell 130 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 0)")
plt.show()
# Cell 131 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_131 = np.linspace(0, 10, 100)
y_131 = np.sin(x_131 + 1)
plt.plot(x_131, y_131)
plt.title("Cell 131 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 1)")
plt.show()
# Cell 132 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_132 = np.linspace(0, 10, 100)
y_132 = np.sin(x_132 + 2)
plt.plot(x_132, y_132)
plt.title("Cell 132 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 2)")
plt.show()
# Cell 133 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_133 = np.linspace(0, 10, 100)
y_133 = np.sin(x_133 + 3)
plt.plot(x_133, y_133)
plt.title("Cell 133 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 3)")
plt.show()
# Cell 134 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_134 = np.linspace(0, 10, 100)
y_134 = np.sin(x_134 + 4)
plt.plot(x_134, y_134)
plt.title("Cell 134 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 4)")
plt.show()
# Cell 135 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_135 = np.linspace(0, 10, 100)
y_135 = np.sin(x_135 + 5)
plt.plot(x_135, y_135)
plt.title("Cell 135 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 5)")
plt.show()
# Cell 136 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_136 = np.linspace(0, 10, 100)
y_136 = np.sin(x_136 + 6)
plt.plot(x_136, y_136)
plt.title("Cell 136 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 6)")
plt.show()
# Cell 137 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_137 = np.linspace(0, 10, 100)
y_137 = np.sin(x_137 + 7)
plt.plot(x_137, y_137)
plt.title("Cell 137 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 7)")
plt.show()
# Cell 138 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_138 = np.linspace(0, 10, 100)
y_138 = np.sin(x_138 + 8)
plt.plot(x_138, y_138)
plt.title("Cell 138 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 8)")
plt.show()
# Cell 139 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_139 = np.linspace(0, 10, 100)
y_139 = np.sin(x_139 + 9)
plt.plot(x_139, y_139)
plt.title("Cell 139 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 9)")
plt.show()
# Cell 140 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_140 = np.linspace(0, 10, 100)
y_140 = np.sin(x_140 + 0)
plt.plot(x_140, y_140)
plt.title("Cell 140 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 0)")
plt.show()
# Cell 141 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_141 = np.linspace(0, 10, 100)
y_141 = np.sin(x_141 + 1)
plt.plot(x_141, y_141)
plt.title("Cell 141 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 1)")
plt.show()
# Cell 142 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_142 = np.linspace(0, 10, 100)
y_142 = np.sin(x_142 + 2)
plt.plot(x_142, y_142)
plt.title("Cell 142 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 2)")
plt.show()
# Cell 143 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_143 = np.linspace(0, 10, 100)
y_143 = np.sin(x_143 + 3)
plt.plot(x_143, y_143)
plt.title("Cell 143 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 3)")
plt.show()
# Cell 144 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_144 = np.linspace(0, 10, 100)
y_144 = np.sin(x_144 + 4)
plt.plot(x_144, y_144)
plt.title("Cell 144 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 4)")
plt.show()
# Cell 145 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_145 = np.linspace(0, 10, 100)
y_145 = np.sin(x_145 + 5)
plt.plot(x_145, y_145)
plt.title("Cell 145 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 5)")
plt.show()
# Cell 146 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_146 = np.linspace(0, 10, 100)
y_146 = np.sin(x_146 + 6)
plt.plot(x_146, y_146)
plt.title("Cell 146 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 6)")
plt.show()
# Cell 147 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_147 = np.linspace(0, 10, 100)
y_147 = np.sin(x_147 + 7)
plt.plot(x_147, y_147)
plt.title("Cell 147 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 7)")
plt.show()
# Cell 148 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_148 = np.linspace(0, 10, 100)
y_148 = np.sin(x_148 + 8)
plt.plot(x_148, y_148)
plt.title("Cell 148 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 8)")
plt.show()
# Cell 149 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_149 = np.linspace(0, 10, 100)
y_149 = np.sin(x_149 + 9)
plt.plot(x_149, y_149)
plt.title("Cell 149 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 9)")
plt.show()
# Cell 150 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_150 = np.linspace(0, 10, 100)
y_150 = np.sin(x_150 + 0)
plt.plot(x_150, y_150)
plt.title("Cell 150 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 0)")
plt.show()
# Cell 151 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_151 = np.linspace(0, 10, 100)
y_151 = np.sin(x_151 + 1)
plt.plot(x_151, y_151)
plt.title("Cell 151 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 1)")
plt.show()
# Cell 152 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_152 = np.linspace(0, 10, 100)
y_152 = np.sin(x_152 + 2)
plt.plot(x_152, y_152)
plt.title("Cell 152 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 2)")
plt.show()
# Cell 153 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_153 = np.linspace(0, 10, 100)
y_153 = np.sin(x_153 + 3)
plt.plot(x_153, y_153)
plt.title("Cell 153 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 3)")
plt.show()
# Cell 154 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_154 = np.linspace(0, 10, 100)
y_154 = np.sin(x_154 + 4)
plt.plot(x_154, y_154)
plt.title("Cell 154 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 4)")
plt.show()
# Cell 155 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_155 = np.linspace(0, 10, 100)
y_155 = np.sin(x_155 + 5)
plt.plot(x_155, y_155)
plt.title("Cell 155 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 5)")
plt.show()
# Cell 156 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_156 = np.linspace(0, 10, 100)
y_156 = np.sin(x_156 + 6)
plt.plot(x_156, y_156)
plt.title("Cell 156 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 6)")
plt.show()
# Cell 157 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_157 = np.linspace(0, 10, 100)
y_157 = np.sin(x_157 + 7)
plt.plot(x_157, y_157)
plt.title("Cell 157 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 7)")
plt.show()
# Cell 158 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_158 = np.linspace(0, 10, 100)
y_158 = np.sin(x_158 + 8)
plt.plot(x_158, y_158)
plt.title("Cell 158 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 8)")
plt.show()
# Cell 159 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_159 = np.linspace(0, 10, 100)
y_159 = np.sin(x_159 + 9)
plt.plot(x_159, y_159)
plt.title("Cell 159 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 9)")
plt.show()
# Cell 160 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_160 = np.linspace(0, 10, 100)
y_160 = np.sin(x_160 + 0)
plt.plot(x_160, y_160)
plt.title("Cell 160 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 0)")
plt.show()
# Cell 161 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_161 = np.linspace(0, 10, 100)
y_161 = np.sin(x_161 + 1)
plt.plot(x_161, y_161)
plt.title("Cell 161 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 1)")
plt.show()
# Cell 162 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_162 = np.linspace(0, 10, 100)
y_162 = np.sin(x_162 + 2)
plt.plot(x_162, y_162)
plt.title("Cell 162 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 2)")
plt.show()
# Cell 163 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_163 = np.linspace(0, 10, 100)
y_163 = np.sin(x_163 + 3)
plt.plot(x_163, y_163)
plt.title("Cell 163 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 3)")
plt.show()
# Cell 164 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_164 = np.linspace(0, 10, 100)
y_164 = np.sin(x_164 + 4)
plt.plot(x_164, y_164)
plt.title("Cell 164 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 4)")
plt.show()
# Cell 165 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_165 = np.linspace(0, 10, 100)
y_165 = np.sin(x_165 + 5)
plt.plot(x_165, y_165)
plt.title("Cell 165 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 5)")
plt.show()
# Cell 166 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_166 = np.linspace(0, 10, 100)
y_166 = np.sin(x_166 + 6)
plt.plot(x_166, y_166)
plt.title("Cell 166 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 6)")
plt.show()
# Cell 167 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_167 = np.linspace(0, 10, 100)
y_167 = np.sin(x_167 + 7)
plt.plot(x_167, y_167)
plt.title("Cell 167 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 7)")
plt.show()
# Cell 168 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_168 = np.linspace(0, 10, 100)
y_168 = np.sin(x_168 + 8)
plt.plot(x_168, y_168)
plt.title("Cell 168 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 8)")
plt.show()
# Cell 169 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_169 = np.linspace(0, 10, 100)
y_169 = np.sin(x_169 + 9)
plt.plot(x_169, y_169)
plt.title("Cell 169 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 9)")
plt.show()
# Cell 170 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_170 = np.linspace(0, 10, 100)
y_170 = np.sin(x_170 + 0)
plt.plot(x_170, y_170)
plt.title("Cell 170 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 0)")
plt.show()
# Cell 171 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_171 = np.linspace(0, 10, 100)
y_171 = np.sin(x_171 + 1)
plt.plot(x_171, y_171)
plt.title("Cell 171 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 1)")
plt.show()
# Cell 172 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_172 = np.linspace(0, 10, 100)
y_172 = np.sin(x_172 + 2)
plt.plot(x_172, y_172)
plt.title("Cell 172 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 2)")
plt.show()
# Cell 173 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_173 = np.linspace(0, 10, 100)
y_173 = np.sin(x_173 + 3)
plt.plot(x_173, y_173)
plt.title("Cell 173 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 3)")
plt.show()
# Cell 174 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_174 = np.linspace(0, 10, 100)
y_174 = np.sin(x_174 + 4)
plt.plot(x_174, y_174)
plt.title("Cell 174 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 4)")
plt.show()
# Cell 175 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_175 = np.linspace(0, 10, 100)
y_175 = np.sin(x_175 + 5)
plt.plot(x_175, y_175)
plt.title("Cell 175 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 5)")
plt.show()
# Cell 176 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_176 = np.linspace(0, 10, 100)
y_176 = np.sin(x_176 + 6)
plt.plot(x_176, y_176)
plt.title("Cell 176 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 6)")
plt.show()
# Cell 177 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_177 = np.linspace(0, 10, 100)
y_177 = np.sin(x_177 + 7)
plt.plot(x_177, y_177)
plt.title("Cell 177 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 7)")
plt.show()
# Cell 178 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_178 = np.linspace(0, 10, 100)
y_178 = np.sin(x_178 + 8)
plt.plot(x_178, y_178)
plt.title("Cell 178 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 8)")
plt.show()
# Cell 179 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_179 = np.linspace(0, 10, 100)
y_179 = np.sin(x_179 + 9)
plt.plot(x_179, y_179)
plt.title("Cell 179 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 9)")
plt.show()
# Cell 180 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_180 = np.linspace(0, 10, 100)
y_180 = np.sin(x_180 + 0)
plt.plot(x_180, y_180)
plt.title("Cell 180 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 0)")
plt.show()
# Cell 181 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_181 = np.linspace(0, 10, 100)
y_181 = np.sin(x_181 + 1)
plt.plot(x_181, y_181)
plt.title("Cell 181 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 1)")
plt.show()
# Cell 182 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_182 = np.linspace(0, 10, 100)
y_182 = np.sin(x_182 + 2)
plt.plot(x_182, y_182)
plt.title("Cell 182 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 2)")
plt.show()
# Cell 183 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_183 = np.linspace(0, 10, 100)
y_183 = np.sin(x_183 + 3)
plt.plot(x_183, y_183)
plt.title("Cell 183 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 3)")
plt.show()
# Cell 184 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_184 = np.linspace(0, 10, 100)
y_184 = np.sin(x_184 + 4)
plt.plot(x_184, y_184)
plt.title("Cell 184 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 4)")
plt.show()
# Cell 185 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_185 = np.linspace(0, 10, 100)
y_185 = np.sin(x_185 + 5)
plt.plot(x_185, y_185)
plt.title("Cell 185 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 5)")
plt.show()
# Cell 186 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_186 = np.linspace(0, 10, 100)
y_186 = np.sin(x_186 + 6)
plt.plot(x_186, y_186)
plt.title("Cell 186 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 6)")
plt.show()
# Cell 187 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_187 = np.linspace(0, 10, 100)
y_187 = np.sin(x_187 + 7)
plt.plot(x_187, y_187)
plt.title("Cell 187 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 7)")
plt.show()
# Cell 188 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_188 = np.linspace(0, 10, 100)
y_188 = np.sin(x_188 + 8)
plt.plot(x_188, y_188)
plt.title("Cell 188 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 8)")
plt.show()
# Cell 189 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_189 = np.linspace(0, 10, 100)
y_189 = np.sin(x_189 + 9)
plt.plot(x_189, y_189)
plt.title("Cell 189 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 9)")
plt.show()
# Cell 190 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_190 = np.linspace(0, 10, 100)
y_190 = np.sin(x_190 + 0)
plt.plot(x_190, y_190)
plt.title("Cell 190 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 0)")
plt.show()
# Cell 191 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_191 = np.linspace(0, 10, 100)
y_191 = np.sin(x_191 + 1)
plt.plot(x_191, y_191)
plt.title("Cell 191 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 1)")
plt.show()
# Cell 192 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_192 = np.linspace(0, 10, 100)
y_192 = np.sin(x_192 + 2)
plt.plot(x_192, y_192)
plt.title("Cell 192 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 2)")
plt.show()
# Cell 193 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_193 = np.linspace(0, 10, 100)
y_193 = np.sin(x_193 + 3)
plt.plot(x_193, y_193)
plt.title("Cell 193 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 3)")
plt.show()
# Cell 194 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_194 = np.linspace(0, 10, 100)
y_194 = np.sin(x_194 + 4)
plt.plot(x_194, y_194)
plt.title("Cell 194 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 4)")
plt.show()
# Cell 195 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_195 = np.linspace(0, 10, 100)
y_195 = np.sin(x_195 + 5)
plt.plot(x_195, y_195)
plt.title("Cell 195 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 5)")
plt.show()
# Cell 196 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_196 = np.linspace(0, 10, 100)
y_196 = np.sin(x_196 + 6)
plt.plot(x_196, y_196)
plt.title("Cell 196 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 6)")
plt.show()
# Cell 197 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_197 = np.linspace(0, 10, 100)
y_197 = np.sin(x_197 + 7)
plt.plot(x_197, y_197)
plt.title("Cell 197 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 7)")
plt.show()
# Cell 198 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_198 = np.linspace(0, 10, 100)
y_198 = np.sin(x_198 + 8)
plt.plot(x_198, y_198)
plt.title("Cell 198 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 8)")
plt.show()
# Cell 199 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_199 = np.linspace(0, 10, 100)
y_199 = np.sin(x_199 + 9)
plt.plot(x_199, y_199)
plt.title("Cell 199 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 9)")
plt.show()
# Cell 200 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_200 = np.linspace(0, 10, 100)
y_200 = np.sin(x_200 + 0)
plt.plot(x_200, y_200)
plt.title("Cell 200 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 0)")
plt.show()
# Cell 201 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_201 = np.linspace(0, 10, 100)
y_201 = np.sin(x_201 + 1)
plt.plot(x_201, y_201)
plt.title("Cell 201 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 1)")
plt.show()
# Cell 202 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_202 = np.linspace(0, 10, 100)
y_202 = np.sin(x_202 + 2)
plt.plot(x_202, y_202)
plt.title("Cell 202 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 2)")
plt.show()
# Cell 203 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_203 = np.linspace(0, 10, 100)
y_203 = np.sin(x_203 + 3)
plt.plot(x_203, y_203)
plt.title("Cell 203 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 3)")
plt.show()
# Cell 204 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_204 = np.linspace(0, 10, 100)
y_204 = np.sin(x_204 + 4)
plt.plot(x_204, y_204)
plt.title("Cell 204 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 4)")
plt.show()
# Cell 205 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_205 = np.linspace(0, 10, 100)
y_205 = np.sin(x_205 + 5)
plt.plot(x_205, y_205)
plt.title("Cell 205 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 5)")
plt.show()
# Cell 206 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_206 = np.linspace(0, 10, 100)
y_206 = np.sin(x_206 + 6)
plt.plot(x_206, y_206)
plt.title("Cell 206 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 6)")
plt.show()
# Cell 207 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_207 = np.linspace(0, 10, 100)
y_207 = np.sin(x_207 + 7)
plt.plot(x_207, y_207)
plt.title("Cell 207 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 7)")
plt.show()
# Cell 208 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_208 = np.linspace(0, 10, 100)
y_208 = np.sin(x_208 + 8)
plt.plot(x_208, y_208)
plt.title("Cell 208 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 8)")
plt.show()
# Cell 209 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_209 = np.linspace(0, 10, 100)
y_209 = np.sin(x_209 + 9)
plt.plot(x_209, y_209)
plt.title("Cell 209 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 9)")
plt.show()
# Cell 210 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_210 = np.linspace(0, 10, 100)
y_210 = np.sin(x_210 + 0)
plt.plot(x_210, y_210)
plt.title("Cell 210 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 0)")
plt.show()
# Cell 211 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_211 = np.linspace(0, 10, 100)
y_211 = np.sin(x_211 + 1)
plt.plot(x_211, y_211)
plt.title("Cell 211 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 1)")
plt.show()
# Cell 212 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_212 = np.linspace(0, 10, 100)
y_212 = np.sin(x_212 + 2)
plt.plot(x_212, y_212)
plt.title("Cell 212 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 2)")
plt.show()
# Cell 213 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_213 = np.linspace(0, 10, 100)
y_213 = np.sin(x_213 + 3)
plt.plot(x_213, y_213)
plt.title("Cell 213 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 3)")
plt.show()
# Cell 214 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_214 = np.linspace(0, 10, 100)
y_214 = np.sin(x_214 + 4)
plt.plot(x_214, y_214)
plt.title("Cell 214 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 4)")
plt.show()
# Cell 215 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_215 = np.linspace(0, 10, 100)
y_215 = np.sin(x_215 + 5)
plt.plot(x_215, y_215)
plt.title("Cell 215 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 5)")
plt.show()
# Cell 216 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_216 = np.linspace(0, 10, 100)
y_216 = np.sin(x_216 + 6)
plt.plot(x_216, y_216)
plt.title("Cell 216 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 6)")
plt.show()
# Cell 217 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_217 = np.linspace(0, 10, 100)
y_217 = np.sin(x_217 + 7)
plt.plot(x_217, y_217)
plt.title("Cell 217 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 7)")
plt.show()
# Cell 218 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_218 = np.linspace(0, 10, 100)
y_218 = np.sin(x_218 + 8)
plt.plot(x_218, y_218)
plt.title("Cell 218 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 8)")
plt.show()
# Cell 219 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_219 = np.linspace(0, 10, 100)
y_219 = np.sin(x_219 + 9)
plt.plot(x_219, y_219)
plt.title("Cell 219 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 9)")
plt.show()
# Cell 220 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_220 = np.linspace(0, 10, 100)
y_220 = np.sin(x_220 + 0)
plt.plot(x_220, y_220)
plt.title("Cell 220 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 0)")
plt.show()
# Cell 221 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_221 = np.linspace(0, 10, 100)
y_221 = np.sin(x_221 + 1)
plt.plot(x_221, y_221)
plt.title("Cell 221 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 1)")
plt.show()
# Cell 222 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_222 = np.linspace(0, 10, 100)
y_222 = np.sin(x_222 + 2)
plt.plot(x_222, y_222)
plt.title("Cell 222 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 2)")
plt.show()
# Cell 223 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_223 = np.linspace(0, 10, 100)
y_223 = np.sin(x_223 + 3)
plt.plot(x_223, y_223)
plt.title("Cell 223 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 3)")
plt.show()
# Cell 224 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_224 = np.linspace(0, 10, 100)
y_224 = np.sin(x_224 + 4)
plt.plot(x_224, y_224)
plt.title("Cell 224 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 4)")
plt.show()
# Cell 225 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_225 = np.linspace(0, 10, 100)
y_225 = np.sin(x_225 + 5)
plt.plot(x_225, y_225)
plt.title("Cell 225 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 5)")
plt.show()
# Cell 226 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_226 = np.linspace(0, 10, 100)
y_226 = np.sin(x_226 + 6)
plt.plot(x_226, y_226)
plt.title("Cell 226 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 6)")
plt.show()
# Cell 227 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_227 = np.linspace(0, 10, 100)
y_227 = np.sin(x_227 + 7)
plt.plot(x_227, y_227)
plt.title("Cell 227 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 7)")
plt.show()
# Cell 228 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_228 = np.linspace(0, 10, 100)
y_228 = np.sin(x_228 + 8)
plt.plot(x_228, y_228)
plt.title("Cell 228 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 8)")
plt.show()
# Cell 229 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_229 = np.linspace(0, 10, 100)
y_229 = np.sin(x_229 + 9)
plt.plot(x_229, y_229)
plt.title("Cell 229 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 9)")
plt.show()
# Cell 230 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_230 = np.linspace(0, 10, 100)
y_230 = np.sin(x_230 + 0)
plt.plot(x_230, y_230)
plt.title("Cell 230 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 0)")
plt.show()
# Cell 231 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_231 = np.linspace(0, 10, 100)
y_231 = np.sin(x_231 + 1)
plt.plot(x_231, y_231)
plt.title("Cell 231 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 1)")
plt.show()
# Cell 232 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_232 = np.linspace(0, 10, 100)
y_232 = np.sin(x_232 + 2)
plt.plot(x_232, y_232)
plt.title("Cell 232 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 2)")
plt.show()
# Cell 233 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_233 = np.linspace(0, 10, 100)
y_233 = np.sin(x_233 + 3)
plt.plot(x_233, y_233)
plt.title("Cell 233 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 3)")
plt.show()
# Cell 234 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_234 = np.linspace(0, 10, 100)
y_234 = np.sin(x_234 + 4)
plt.plot(x_234, y_234)
plt.title("Cell 234 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 4)")
plt.show()
# Cell 235 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_235 = np.linspace(0, 10, 100)
y_235 = np.sin(x_235 + 5)
plt.plot(x_235, y_235)
plt.title("Cell 235 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 5)")
plt.show()
# Cell 236 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_236 = np.linspace(0, 10, 100)
y_236 = np.sin(x_236 + 6)
plt.plot(x_236, y_236)
plt.title("Cell 236 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 6)")
plt.show()
# Cell 237 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_237 = np.linspace(0, 10, 100)
y_237 = np.sin(x_237 + 7)
plt.plot(x_237, y_237)
plt.title("Cell 237 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 7)")
plt.show()
# Cell 238 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_238 = np.linspace(0, 10, 100)
y_238 = np.sin(x_238 + 8)
plt.plot(x_238, y_238)
plt.title("Cell 238 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 8)")
plt.show()
# Cell 239 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_239 = np.linspace(0, 10, 100)
y_239 = np.sin(x_239 + 9)
plt.plot(x_239, y_239)
plt.title("Cell 239 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 9)")
plt.show()
# Cell 240 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_240 = np.linspace(0, 10, 100)
y_240 = np.sin(x_240 + 0)
plt.plot(x_240, y_240)
plt.title("Cell 240 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 0)")
plt.show()
# Cell 241 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_241 = np.linspace(0, 10, 100)
y_241 = np.sin(x_241 + 1)
plt.plot(x_241, y_241)
plt.title("Cell 241 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 1)")
plt.show()
# Cell 242 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_242 = np.linspace(0, 10, 100)
y_242 = np.sin(x_242 + 2)
plt.plot(x_242, y_242)
plt.title("Cell 242 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 2)")
plt.show()
# Cell 243 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_243 = np.linspace(0, 10, 100)
y_243 = np.sin(x_243 + 3)
plt.plot(x_243, y_243)
plt.title("Cell 243 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 3)")
plt.show()
# Cell 244 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_244 = np.linspace(0, 10, 100)
y_244 = np.sin(x_244 + 4)
plt.plot(x_244, y_244)
plt.title("Cell 244 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 4)")
plt.show()
# Cell 245 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_245 = np.linspace(0, 10, 100)
y_245 = np.sin(x_245 + 5)
plt.plot(x_245, y_245)
plt.title("Cell 245 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 5)")
plt.show()
# Cell 246 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_246 = np.linspace(0, 10, 100)
y_246 = np.sin(x_246 + 6)
plt.plot(x_246, y_246)
plt.title("Cell 246 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 6)")
plt.show()
# Cell 247 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_247 = np.linspace(0, 10, 100)
y_247 = np.sin(x_247 + 7)
plt.plot(x_247, y_247)
plt.title("Cell 247 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 7)")
plt.show()
# Cell 248 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_248 = np.linspace(0, 10, 100)
y_248 = np.sin(x_248 + 8)
plt.plot(x_248, y_248)
plt.title("Cell 248 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 8)")
plt.show()
# Cell 249 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_249 = np.linspace(0, 10, 100)
y_249 = np.sin(x_249 + 9)
plt.plot(x_249, y_249)
plt.title("Cell 249 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 9)")
plt.show()
# Cell 250 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_250 = np.linspace(0, 10, 100)
y_250 = np.sin(x_250 + 0)
plt.plot(x_250, y_250)
plt.title("Cell 250 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 0)")
plt.show()
# Cell 251 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_251 = np.linspace(0, 10, 100)
y_251 = np.sin(x_251 + 1)
plt.plot(x_251, y_251)
plt.title("Cell 251 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 1)")
plt.show()
# Cell 252 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_252 = np.linspace(0, 10, 100)
y_252 = np.sin(x_252 + 2)
plt.plot(x_252, y_252)
plt.title("Cell 252 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 2)")
plt.show()
# Cell 253 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_253 = np.linspace(0, 10, 100)
y_253 = np.sin(x_253 + 3)
plt.plot(x_253, y_253)
plt.title("Cell 253 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 3)")
plt.show()
# Cell 254 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_254 = np.linspace(0, 10, 100)
y_254 = np.sin(x_254 + 4)
plt.plot(x_254, y_254)
plt.title("Cell 254 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 4)")
plt.show()
# Cell 255 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_255 = np.linspace(0, 10, 100)
y_255 = np.sin(x_255 + 5)
plt.plot(x_255, y_255)
plt.title("Cell 255 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 5)")
plt.show()
# Cell 256 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_256 = np.linspace(0, 10, 100)
y_256 = np.sin(x_256 + 6)
plt.plot(x_256, y_256)
plt.title("Cell 256 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 6)")
plt.show()
# Cell 257 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_257 = np.linspace(0, 10, 100)
y_257 = np.sin(x_257 + 7)
plt.plot(x_257, y_257)
plt.title("Cell 257 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 7)")
plt.show()
# Cell 258 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_258 = np.linspace(0, 10, 100)
y_258 = np.sin(x_258 + 8)
plt.plot(x_258, y_258)
plt.title("Cell 258 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 8)")
plt.show()
# Cell 259 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_259 = np.linspace(0, 10, 100)
y_259 = np.sin(x_259 + 9)
plt.plot(x_259, y_259)
plt.title("Cell 259 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 9)")
plt.show()
# Cell 260 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_260 = np.linspace(0, 10, 100)
y_260 = np.sin(x_260 + 0)
plt.plot(x_260, y_260)
plt.title("Cell 260 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 0)")
plt.show()
# Cell 261 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_261 = np.linspace(0, 10, 100)
y_261 = np.sin(x_261 + 1)
plt.plot(x_261, y_261)
plt.title("Cell 261 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 1)")
plt.show()
# Cell 262 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_262 = np.linspace(0, 10, 100)
y_262 = np.sin(x_262 + 2)
plt.plot(x_262, y_262)
plt.title("Cell 262 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 2)")
plt.show()
# Cell 263 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_263 = np.linspace(0, 10, 100)
y_263 = np.sin(x_263 + 3)
plt.plot(x_263, y_263)
plt.title("Cell 263 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 3)")
plt.show()
# Cell 264 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_264 = np.linspace(0, 10, 100)
y_264 = np.sin(x_264 + 4)
plt.plot(x_264, y_264)
plt.title("Cell 264 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 4)")
plt.show()
# Cell 265 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_265 = np.linspace(0, 10, 100)
y_265 = np.sin(x_265 + 5)
plt.plot(x_265, y_265)
plt.title("Cell 265 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 5)")
plt.show()
# Cell 266 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_266 = np.linspace(0, 10, 100)
y_266 = np.sin(x_266 + 6)
plt.plot(x_266, y_266)
plt.title("Cell 266 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 6)")
plt.show()
# Cell 267 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_267 = np.linspace(0, 10, 100)
y_267 = np.sin(x_267 + 7)
plt.plot(x_267, y_267)
plt.title("Cell 267 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 7)")
plt.show()
# Cell 268 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_268 = np.linspace(0, 10, 100)
y_268 = np.sin(x_268 + 8)
plt.plot(x_268, y_268)
plt.title("Cell 268 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 8)")
plt.show()
# Cell 269 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_269 = np.linspace(0, 10, 100)
y_269 = np.sin(x_269 + 9)
plt.plot(x_269, y_269)
plt.title("Cell 269 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 9)")
plt.show()
# Cell 270 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_270 = np.linspace(0, 10, 100)
y_270 = np.sin(x_270 + 0)
plt.plot(x_270, y_270)
plt.title("Cell 270 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 0)")
plt.show()
# Cell 271 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_271 = np.linspace(0, 10, 100)
y_271 = np.sin(x_271 + 1)
plt.plot(x_271, y_271)
plt.title("Cell 271 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 1)")
plt.show()
# Cell 272 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_272 = np.linspace(0, 10, 100)
y_272 = np.sin(x_272 + 2)
plt.plot(x_272, y_272)
plt.title("Cell 272 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 2)")
plt.show()
# Cell 273 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_273 = np.linspace(0, 10, 100)
y_273 = np.sin(x_273 + 3)
plt.plot(x_273, y_273)
plt.title("Cell 273 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 3)")
plt.show()
# Cell 274 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_274 = np.linspace(0, 10, 100)
y_274 = np.sin(x_274 + 4)
plt.plot(x_274, y_274)
plt.title("Cell 274 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 4)")
plt.show()
# Cell 275 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_275 = np.linspace(0, 10, 100)
y_275 = np.sin(x_275 + 5)
plt.plot(x_275, y_275)
plt.title("Cell 275 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 5)")
plt.show()
# Cell 276 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_276 = np.linspace(0, 10, 100)
y_276 = np.sin(x_276 + 6)
plt.plot(x_276, y_276)
plt.title("Cell 276 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 6)")
plt.show()
# Cell 277 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_277 = np.linspace(0, 10, 100)
y_277 = np.sin(x_277 + 7)
plt.plot(x_277, y_277)
plt.title("Cell 277 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 7)")
plt.show()
# Cell 278 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_278 = np.linspace(0, 10, 100)
y_278 = np.sin(x_278 + 8)
plt.plot(x_278, y_278)
plt.title("Cell 278 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 8)")
plt.show()
# Cell 279 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_279 = np.linspace(0, 10, 100)
y_279 = np.sin(x_279 + 9)
plt.plot(x_279, y_279)
plt.title("Cell 279 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 9)")
plt.show()
# Cell 280 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_280 = np.linspace(0, 10, 100)
y_280 = np.sin(x_280 + 0)
plt.plot(x_280, y_280)
plt.title("Cell 280 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 0)")
plt.show()
# Cell 281 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_281 = np.linspace(0, 10, 100)
y_281 = np.sin(x_281 + 1)
plt.plot(x_281, y_281)
plt.title("Cell 281 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 1)")
plt.show()
# Cell 282 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_282 = np.linspace(0, 10, 100)
y_282 = np.sin(x_282 + 2)
plt.plot(x_282, y_282)
plt.title("Cell 282 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 2)")
plt.show()
# Cell 283 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_283 = np.linspace(0, 10, 100)
y_283 = np.sin(x_283 + 3)
plt.plot(x_283, y_283)
plt.title("Cell 283 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 3)")
plt.show()
# Cell 284 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_284 = np.linspace(0, 10, 100)
y_284 = np.sin(x_284 + 4)
plt.plot(x_284, y_284)
plt.title("Cell 284 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 4)")
plt.show()
# Cell 285 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_285 = np.linspace(0, 10, 100)
y_285 = np.sin(x_285 + 5)
plt.plot(x_285, y_285)
plt.title("Cell 285 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 5)")
plt.show()
# Cell 286 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_286 = np.linspace(0, 10, 100)
y_286 = np.sin(x_286 + 6)
plt.plot(x_286, y_286)
plt.title("Cell 286 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 6)")
plt.show()
# Cell 287 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_287 = np.linspace(0, 10, 100)
y_287 = np.sin(x_287 + 7)
plt.plot(x_287, y_287)
plt.title("Cell 287 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 7)")
plt.show()
# Cell 288 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_288 = np.linspace(0, 10, 100)
y_288 = np.sin(x_288 + 8)
plt.plot(x_288, y_288)
plt.title("Cell 288 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 8)")
plt.show()
# Cell 289 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_289 = np.linspace(0, 10, 100)
y_289 = np.sin(x_289 + 9)
plt.plot(x_289, y_289)
plt.title("Cell 289 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 9)")
plt.show()
# Cell 290 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_290 = np.linspace(0, 10, 100)
y_290 = np.sin(x_290 + 0)
plt.plot(x_290, y_290)
plt.title("Cell 290 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 0)")
plt.show()
# Cell 291 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_291 = np.linspace(0, 10, 100)
y_291 = np.sin(x_291 + 1)
plt.plot(x_291, y_291)
plt.title("Cell 291 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 1)")
plt.show()
# Cell 292 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_292 = np.linspace(0, 10, 100)
y_292 = np.sin(x_292 + 2)
plt.plot(x_292, y_292)
plt.title("Cell 292 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 2)")
plt.show()
# Cell 293 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_293 = np.linspace(0, 10, 100)
y_293 = np.sin(x_293 + 3)
plt.plot(x_293, y_293)
plt.title("Cell 293 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 3)")
plt.show()
# Cell 294 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_294 = np.linspace(0, 10, 100)
y_294 = np.sin(x_294 + 4)
plt.plot(x_294, y_294)
plt.title("Cell 294 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 4)")
plt.show()
# Cell 295 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_295 = np.linspace(0, 10, 100)
y_295 = np.sin(x_295 + 5)
plt.plot(x_295, y_295)
plt.title("Cell 295 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 5)")
plt.show()
# Cell 296 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_296 = np.linspace(0, 10, 100)
y_296 = np.sin(x_296 + 6)
plt.plot(x_296, y_296)
plt.title("Cell 296 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 6)")
plt.show()
# Cell 297 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_297 = np.linspace(0, 10, 100)
y_297 = np.sin(x_297 + 7)
plt.plot(x_297, y_297)
plt.title("Cell 297 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 7)")
plt.show()
# Cell 298 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_298 = np.linspace(0, 10, 100)
y_298 = np.sin(x_298 + 8)
plt.plot(x_298, y_298)
plt.title("Cell 298 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 8)")
plt.show()
# Cell 299 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_299 = np.linspace(0, 10, 100)
y_299 = np.sin(x_299 + 9)
plt.plot(x_299, y_299)
plt.title("Cell 299 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 9)")
plt.show()
# Cell 300 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_300 = np.linspace(0, 10, 100)
y_300 = np.sin(x_300 + 0)
plt.plot(x_300, y_300)
plt.title("Cell 300 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 0)")
plt.show()
# Cell 301 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_301 = np.linspace(0, 10, 100)
y_301 = np.sin(x_301 + 1)
plt.plot(x_301, y_301)
plt.title("Cell 301 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 1)")
plt.show()
# Cell 302 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_302 = np.linspace(0, 10, 100)
y_302 = np.sin(x_302 + 2)
plt.plot(x_302, y_302)
plt.title("Cell 302 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 2)")
plt.show()
# Cell 303 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_303 = np.linspace(0, 10, 100)
y_303 = np.sin(x_303 + 3)
plt.plot(x_303, y_303)
plt.title("Cell 303 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 3)")
plt.show()
# Cell 304 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_304 = np.linspace(0, 10, 100)
y_304 = np.sin(x_304 + 4)
plt.plot(x_304, y_304)
plt.title("Cell 304 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 4)")
plt.show()
# Cell 305 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_305 = np.linspace(0, 10, 100)
y_305 = np.sin(x_305 + 5)
plt.plot(x_305, y_305)
plt.title("Cell 305 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 5)")
plt.show()
# Cell 306 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_306 = np.linspace(0, 10, 100)
y_306 = np.sin(x_306 + 6)
plt.plot(x_306, y_306)
plt.title("Cell 306 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 6)")
plt.show()
# Cell 307 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_307 = np.linspace(0, 10, 100)
y_307 = np.sin(x_307 + 7)
plt.plot(x_307, y_307)
plt.title("Cell 307 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 7)")
plt.show()
# Cell 308 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_308 = np.linspace(0, 10, 100)
y_308 = np.sin(x_308 + 8)
plt.plot(x_308, y_308)
plt.title("Cell 308 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 8)")
plt.show()
# Cell 309 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_309 = np.linspace(0, 10, 100)
y_309 = np.sin(x_309 + 9)
plt.plot(x_309, y_309)
plt.title("Cell 309 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 9)")
plt.show()
# Cell 310 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_310 = np.linspace(0, 10, 100)
y_310 = np.sin(x_310 + 0)
plt.plot(x_310, y_310)
plt.title("Cell 310 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 0)")
plt.show()
# Cell 311 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_311 = np.linspace(0, 10, 100)
y_311 = np.sin(x_311 + 1)
plt.plot(x_311, y_311)
plt.title("Cell 311 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 1)")
plt.show()
# Cell 312 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_312 = np.linspace(0, 10, 100)
y_312 = np.sin(x_312 + 2)
plt.plot(x_312, y_312)
plt.title("Cell 312 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 2)")
plt.show()
# Cell 313 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_313 = np.linspace(0, 10, 100)
y_313 = np.sin(x_313 + 3)
plt.plot(x_313, y_313)
plt.title("Cell 313 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 3)")
plt.show()
# Cell 314 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_314 = np.linspace(0, 10, 100)
y_314 = np.sin(x_314 + 4)
plt.plot(x_314, y_314)
plt.title("Cell 314 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 4)")
plt.show()
# Cell 315 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_315 = np.linspace(0, 10, 100)
y_315 = np.sin(x_315 + 5)
plt.plot(x_315, y_315)
plt.title("Cell 315 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 5)")
plt.show()
# Cell 316 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_316 = np.linspace(0, 10, 100)
y_316 = np.sin(x_316 + 6)
plt.plot(x_316, y_316)
plt.title("Cell 316 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 6)")
plt.show()
# Cell 317 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_317 = np.linspace(0, 10, 100)
y_317 = np.sin(x_317 + 7)
plt.plot(x_317, y_317)
plt.title("Cell 317 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 7)")
plt.show()
# Cell 318 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_318 = np.linspace(0, 10, 100)
y_318 = np.sin(x_318 + 8)
plt.plot(x_318, y_318)
plt.title("Cell 318 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 8)")
plt.show()
# Cell 319 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_319 = np.linspace(0, 10, 100)
y_319 = np.sin(x_319 + 9)
plt.plot(x_319, y_319)
plt.title("Cell 319 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 9)")
plt.show()
# Cell 320 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_320 = np.linspace(0, 10, 100)
y_320 = np.sin(x_320 + 0)
plt.plot(x_320, y_320)
plt.title("Cell 320 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 0)")
plt.show()
# Cell 321 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_321 = np.linspace(0, 10, 100)
y_321 = np.sin(x_321 + 1)
plt.plot(x_321, y_321)
plt.title("Cell 321 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 1)")
plt.show()
# Cell 322 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_322 = np.linspace(0, 10, 100)
y_322 = np.sin(x_322 + 2)
plt.plot(x_322, y_322)
plt.title("Cell 322 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 2)")
plt.show()
# Cell 323 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_323 = np.linspace(0, 10, 100)
y_323 = np.sin(x_323 + 3)
plt.plot(x_323, y_323)
plt.title("Cell 323 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 3)")
plt.show()
# Cell 324 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_324 = np.linspace(0, 10, 100)
y_324 = np.sin(x_324 + 4)
plt.plot(x_324, y_324)
plt.title("Cell 324 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 4)")
plt.show()
# Cell 325 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_325 = np.linspace(0, 10, 100)
y_325 = np.sin(x_325 + 5)
plt.plot(x_325, y_325)
plt.title("Cell 325 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 5)")
plt.show()
# Cell 326 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_326 = np.linspace(0, 10, 100)
y_326 = np.sin(x_326 + 6)
plt.plot(x_326, y_326)
plt.title("Cell 326 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 6)")
plt.show()
# Cell 327 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_327 = np.linspace(0, 10, 100)
y_327 = np.sin(x_327 + 7)
plt.plot(x_327, y_327)
plt.title("Cell 327 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 7)")
plt.show()
# Cell 328 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_328 = np.linspace(0, 10, 100)
y_328 = np.sin(x_328 + 8)
plt.plot(x_328, y_328)
plt.title("Cell 328 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 8)")
plt.show()
# Cell 329 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_329 = np.linspace(0, 10, 100)
y_329 = np.sin(x_329 + 9)
plt.plot(x_329, y_329)
plt.title("Cell 329 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 9)")
plt.show()
# Cell 330 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_330 = np.linspace(0, 10, 100)
y_330 = np.sin(x_330 + 0)
plt.plot(x_330, y_330)
plt.title("Cell 330 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 0)")
plt.show()
# Cell 331 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_331 = np.linspace(0, 10, 100)
y_331 = np.sin(x_331 + 1)
plt.plot(x_331, y_331)
plt.title("Cell 331 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 1)")
plt.show()
# Cell 332 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_332 = np.linspace(0, 10, 100)
y_332 = np.sin(x_332 + 2)
plt.plot(x_332, y_332)
plt.title("Cell 332 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 2)")
plt.show()
# Cell 333 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_333 = np.linspace(0, 10, 100)
y_333 = np.sin(x_333 + 3)
plt.plot(x_333, y_333)
plt.title("Cell 333 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 3)")
plt.show()
# Cell 334 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_334 = np.linspace(0, 10, 100)
y_334 = np.sin(x_334 + 4)
plt.plot(x_334, y_334)
plt.title("Cell 334 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 4)")
plt.show()
# Cell 335 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_335 = np.linspace(0, 10, 100)
y_335 = np.sin(x_335 + 5)
plt.plot(x_335, y_335)
plt.title("Cell 335 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 5)")
plt.show()
# Cell 336 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_336 = np.linspace(0, 10, 100)
y_336 = np.sin(x_336 + 6)
plt.plot(x_336, y_336)
plt.title("Cell 336 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 6)")
plt.show()
# Cell 337 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_337 = np.linspace(0, 10, 100)
y_337 = np.sin(x_337 + 7)
plt.plot(x_337, y_337)
plt.title("Cell 337 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 7)")
plt.show()
# Cell 338 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_338 = np.linspace(0, 10, 100)
y_338 = np.sin(x_338 + 8)
plt.plot(x_338, y_338)
plt.title("Cell 338 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 8)")
plt.show()
# Cell 339 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_339 = np.linspace(0, 10, 100)
y_339 = np.sin(x_339 + 9)
plt.plot(x_339, y_339)
plt.title("Cell 339 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 9)")
plt.show()
# Cell 340 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_340 = np.linspace(0, 10, 100)
y_340 = np.sin(x_340 + 0)
plt.plot(x_340, y_340)
plt.title("Cell 340 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 0)")
plt.show()
# Cell 341 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_341 = np.linspace(0, 10, 100)
y_341 = np.sin(x_341 + 1)
plt.plot(x_341, y_341)
plt.title("Cell 341 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 1)")
plt.show()
# Cell 342 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_342 = np.linspace(0, 10, 100)
y_342 = np.sin(x_342 + 2)
plt.plot(x_342, y_342)
plt.title("Cell 342 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 2)")
plt.show()
# Cell 343 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_343 = np.linspace(0, 10, 100)
y_343 = np.sin(x_343 + 3)
plt.plot(x_343, y_343)
plt.title("Cell 343 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 3)")
plt.show()
# Cell 344 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_344 = np.linspace(0, 10, 100)
y_344 = np.sin(x_344 + 4)
plt.plot(x_344, y_344)
plt.title("Cell 344 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 4)")
plt.show()
# Cell 345 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_345 = np.linspace(0, 10, 100)
y_345 = np.sin(x_345 + 5)
plt.plot(x_345, y_345)
plt.title("Cell 345 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 5)")
plt.show()
# Cell 346 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_346 = np.linspace(0, 10, 100)
y_346 = np.sin(x_346 + 6)
plt.plot(x_346, y_346)
plt.title("Cell 346 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 6)")
plt.show()
# Cell 347 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_347 = np.linspace(0, 10, 100)
y_347 = np.sin(x_347 + 7)
plt.plot(x_347, y_347)
plt.title("Cell 347 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 7)")
plt.show()
# Cell 348 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_348 = np.linspace(0, 10, 100)
y_348 = np.sin(x_348 + 8)
plt.plot(x_348, y_348)
plt.title("Cell 348 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 8)")
plt.show()
# Cell 349 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_349 = np.linspace(0, 10, 100)
y_349 = np.sin(x_349 + 9)
plt.plot(x_349, y_349)
plt.title("Cell 349 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 9)")
plt.show()
# Cell 350 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_350 = np.linspace(0, 10, 100)
y_350 = np.sin(x_350 + 0)
plt.plot(x_350, y_350)
plt.title("Cell 350 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 0)")
plt.show()
# Cell 351 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_351 = np.linspace(0, 10, 100)
y_351 = np.sin(x_351 + 1)
plt.plot(x_351, y_351)
plt.title("Cell 351 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 1)")
plt.show()
# Cell 352 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_352 = np.linspace(0, 10, 100)
y_352 = np.sin(x_352 + 2)
plt.plot(x_352, y_352)
plt.title("Cell 352 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 2)")
plt.show()
# Cell 353 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_353 = np.linspace(0, 10, 100)
y_353 = np.sin(x_353 + 3)
plt.plot(x_353, y_353)
plt.title("Cell 353 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 3)")
plt.show()
# Cell 354 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_354 = np.linspace(0, 10, 100)
y_354 = np.sin(x_354 + 4)
plt.plot(x_354, y_354)
plt.title("Cell 354 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 4)")
plt.show()
# Cell 355 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_355 = np.linspace(0, 10, 100)
y_355 = np.sin(x_355 + 5)
plt.plot(x_355, y_355)
plt.title("Cell 355 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 5)")
plt.show()
# Cell 356 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_356 = np.linspace(0, 10, 100)
y_356 = np.sin(x_356 + 6)
plt.plot(x_356, y_356)
plt.title("Cell 356 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 6)")
plt.show()
# Cell 357 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_357 = np.linspace(0, 10, 100)
y_357 = np.sin(x_357 + 7)
plt.plot(x_357, y_357)
plt.title("Cell 357 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 7)")
plt.show()
# Cell 358 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_358 = np.linspace(0, 10, 100)
y_358 = np.sin(x_358 + 8)
plt.plot(x_358, y_358)
plt.title("Cell 358 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 8)")
plt.show()
# Cell 359 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_359 = np.linspace(0, 10, 100)
y_359 = np.sin(x_359 + 9)
plt.plot(x_359, y_359)
plt.title("Cell 359 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 9)")
plt.show()
# Cell 360 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_360 = np.linspace(0, 10, 100)
y_360 = np.sin(x_360 + 0)
plt.plot(x_360, y_360)
plt.title("Cell 360 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 0)")
plt.show()
# Cell 361 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_361 = np.linspace(0, 10, 100)
y_361 = np.sin(x_361 + 1)
plt.plot(x_361, y_361)
plt.title("Cell 361 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 1)")
plt.show()
# Cell 362 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_362 = np.linspace(0, 10, 100)
y_362 = np.sin(x_362 + 2)
plt.plot(x_362, y_362)
plt.title("Cell 362 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 2)")
plt.show()
# Cell 363 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_363 = np.linspace(0, 10, 100)
y_363 = np.sin(x_363 + 3)
plt.plot(x_363, y_363)
plt.title("Cell 363 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 3)")
plt.show()
# Cell 364 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_364 = np.linspace(0, 10, 100)
y_364 = np.sin(x_364 + 4)
plt.plot(x_364, y_364)
plt.title("Cell 364 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 4)")
plt.show()
# Cell 365 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_365 = np.linspace(0, 10, 100)
y_365 = np.sin(x_365 + 5)
plt.plot(x_365, y_365)
plt.title("Cell 365 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 5)")
plt.show()
# Cell 366 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_366 = np.linspace(0, 10, 100)
y_366 = np.sin(x_366 + 6)
plt.plot(x_366, y_366)
plt.title("Cell 366 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 6)")
plt.show()
# Cell 367 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_367 = np.linspace(0, 10, 100)
y_367 = np.sin(x_367 + 7)
plt.plot(x_367, y_367)
plt.title("Cell 367 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 7)")
plt.show()
# Cell 368 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_368 = np.linspace(0, 10, 100)
y_368 = np.sin(x_368 + 8)
plt.plot(x_368, y_368)
plt.title("Cell 368 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 8)")
plt.show()
# Cell 369 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_369 = np.linspace(0, 10, 100)
y_369 = np.sin(x_369 + 9)
plt.plot(x_369, y_369)
plt.title("Cell 369 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 9)")
plt.show()
# Cell 370 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_370 = np.linspace(0, 10, 100)
y_370 = np.sin(x_370 + 0)
plt.plot(x_370, y_370)
plt.title("Cell 370 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 0)")
plt.show()
# Cell 371 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_371 = np.linspace(0, 10, 100)
y_371 = np.sin(x_371 + 1)
plt.plot(x_371, y_371)
plt.title("Cell 371 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 1)")
plt.show()
# Cell 372 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_372 = np.linspace(0, 10, 100)
y_372 = np.sin(x_372 + 2)
plt.plot(x_372, y_372)
plt.title("Cell 372 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 2)")
plt.show()
# Cell 373 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_373 = np.linspace(0, 10, 100)
y_373 = np.sin(x_373 + 3)
plt.plot(x_373, y_373)
plt.title("Cell 373 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 3)")
plt.show()
# Cell 374 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_374 = np.linspace(0, 10, 100)
y_374 = np.sin(x_374 + 4)
plt.plot(x_374, y_374)
plt.title("Cell 374 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 4)")
plt.show()
# Cell 375 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_375 = np.linspace(0, 10, 100)
y_375 = np.sin(x_375 + 5)
plt.plot(x_375, y_375)
plt.title("Cell 375 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 5)")
plt.show()
# Cell 376 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_376 = np.linspace(0, 10, 100)
y_376 = np.sin(x_376 + 6)
plt.plot(x_376, y_376)
plt.title("Cell 376 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 6)")
plt.show()
# Cell 377 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_377 = np.linspace(0, 10, 100)
y_377 = np.sin(x_377 + 7)
plt.plot(x_377, y_377)
plt.title("Cell 377 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 7)")
plt.show()
# Cell 378 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_378 = np.linspace(0, 10, 100)
y_378 = np.sin(x_378 + 8)
plt.plot(x_378, y_378)
plt.title("Cell 378 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 8)")
plt.show()
# Cell 379 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_379 = np.linspace(0, 10, 100)
y_379 = np.sin(x_379 + 9)
plt.plot(x_379, y_379)
plt.title("Cell 379 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 9)")
plt.show()
# Cell 380 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_380 = np.linspace(0, 10, 100)
y_380 = np.sin(x_380 + 0)
plt.plot(x_380, y_380)
plt.title("Cell 380 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 0)")
plt.show()
# Cell 381 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_381 = np.linspace(0, 10, 100)
y_381 = np.sin(x_381 + 1)
plt.plot(x_381, y_381)
plt.title("Cell 381 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 1)")
plt.show()
# Cell 382 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_382 = np.linspace(0, 10, 100)
y_382 = np.sin(x_382 + 2)
plt.plot(x_382, y_382)
plt.title("Cell 382 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 2)")
plt.show()
# Cell 383 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_383 = np.linspace(0, 10, 100)
y_383 = np.sin(x_383 + 3)
plt.plot(x_383, y_383)
plt.title("Cell 383 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 3)")
plt.show()
# Cell 384 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_384 = np.linspace(0, 10, 100)
y_384 = np.sin(x_384 + 4)
plt.plot(x_384, y_384)
plt.title("Cell 384 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 4)")
plt.show()
# Cell 385 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_385 = np.linspace(0, 10, 100)
y_385 = np.sin(x_385 + 5)
plt.plot(x_385, y_385)
plt.title("Cell 385 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 5)")
plt.show()
# Cell 386 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_386 = np.linspace(0, 10, 100)
y_386 = np.sin(x_386 + 6)
plt.plot(x_386, y_386)
plt.title("Cell 386 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 6)")
plt.show()
# Cell 387 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_387 = np.linspace(0, 10, 100)
y_387 = np.sin(x_387 + 7)
plt.plot(x_387, y_387)
plt.title("Cell 387 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 7)")
plt.show()
# Cell 388 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_388 = np.linspace(0, 10, 100)
y_388 = np.sin(x_388 + 8)
plt.plot(x_388, y_388)
plt.title("Cell 388 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 8)")
plt.show()
# Cell 389 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_389 = np.linspace(0, 10, 100)
y_389 = np.sin(x_389 + 9)
plt.plot(x_389, y_389)
plt.title("Cell 389 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 9)")
plt.show()
# Cell 390 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_390 = np.linspace(0, 10, 100)
y_390 = np.sin(x_390 + 0)
plt.plot(x_390, y_390)
plt.title("Cell 390 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 0)")
plt.show()
# Cell 391 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_391 = np.linspace(0, 10, 100)
y_391 = np.sin(x_391 + 1)
plt.plot(x_391, y_391)
plt.title("Cell 391 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 1)")
plt.show()
# Cell 392 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_392 = np.linspace(0, 10, 100)
y_392 = np.sin(x_392 + 2)
plt.plot(x_392, y_392)
plt.title("Cell 392 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 2)")
plt.show()
# Cell 393 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_393 = np.linspace(0, 10, 100)
y_393 = np.sin(x_393 + 3)
plt.plot(x_393, y_393)
plt.title("Cell 393 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 3)")
plt.show()
# Cell 394 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_394 = np.linspace(0, 10, 100)
y_394 = np.sin(x_394 + 4)
plt.plot(x_394, y_394)
plt.title("Cell 394 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 4)")
plt.show()
# Cell 395 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_395 = np.linspace(0, 10, 100)
y_395 = np.sin(x_395 + 5)
plt.plot(x_395, y_395)
plt.title("Cell 395 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 5)")
plt.show()
# Cell 396 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_396 = np.linspace(0, 10, 100)
y_396 = np.sin(x_396 + 6)
plt.plot(x_396, y_396)
plt.title("Cell 396 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 6)")
plt.show()
# Cell 397 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_397 = np.linspace(0, 10, 100)
y_397 = np.sin(x_397 + 7)
plt.plot(x_397, y_397)
plt.title("Cell 397 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 7)")
plt.show()
# Cell 398 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_398 = np.linspace(0, 10, 100)
y_398 = np.sin(x_398 + 8)
plt.plot(x_398, y_398)
plt.title("Cell 398 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 8)")
plt.show()
# Cell 399 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_399 = np.linspace(0, 10, 100)
y_399 = np.sin(x_399 + 9)
plt.plot(x_399, y_399)
plt.title("Cell 399 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 9)")
plt.show()
# Cell 400 - matplotlib
import matplotlib.pyplot as plt
import numpy as np
x_400 = np.linspace(0, 10, 100)
y_400 = np.sin(x_400 + 0)
plt.plot(x_400, y_400)
plt.title("Cell 400 - Matplotlib Sine Plot")
plt.xlabel("X")
plt.ylabel("Sin(X + 0)")
plt.show()
# Cell 401 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_401 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
sns.scatterplot(data=df_401, x="x", y="y")
plt.title("Cell 401 - Seaborn Scatter Plot")
plt.show()
# Cell 402 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_402 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
sns.scatterplot(data=df_402, x="x", y="y")
plt.title("Cell 402 - Seaborn Scatter Plot")
plt.show()
# Cell 403 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_403 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 3
})
sns.scatterplot(data=df_403, x="x", y="y")
plt.title("Cell 403 - Seaborn Scatter Plot")
plt.show()
# Cell 404 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_404 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 4
})
sns.scatterplot(data=df_404, x="x", y="y")
plt.title("Cell 404 - Seaborn Scatter Plot")
plt.show()
# Cell 405 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_405 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
sns.scatterplot(data=df_405, x="x", y="y")
plt.title("Cell 405 - Seaborn Scatter Plot")
plt.show()
# Cell 406 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_406 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
sns.scatterplot(data=df_406, x="x", y="y")
plt.title("Cell 406 - Seaborn Scatter Plot")
plt.show()
# Cell 407 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_407 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
sns.scatterplot(data=df_407, x="x", y="y")
plt.title("Cell 407 - Seaborn Scatter Plot")
plt.show()
# Cell 408 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_408 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 3
})
sns.scatterplot(data=df_408, x="x", y="y")
plt.title("Cell 408 - Seaborn Scatter Plot")
plt.show()
# Cell 409 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_409 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 4
})
sns.scatterplot(data=df_409, x="x", y="y")
plt.title("Cell 409 - Seaborn Scatter Plot")
plt.show()
# Cell 410 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_410 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
sns.scatterplot(data=df_410, x="x", y="y")
plt.title("Cell 410 - Seaborn Scatter Plot")
plt.show()
# Cell 411 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_411 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
sns.scatterplot(data=df_411, x="x", y="y")
plt.title("Cell 411 - Seaborn Scatter Plot")
plt.show()
# Cell 412 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_412 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
sns.scatterplot(data=df_412, x="x", y="y")
plt.title("Cell 412 - Seaborn Scatter Plot")
plt.show()
# Cell 413 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_413 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 3
})
sns.scatterplot(data=df_413, x="x", y="y")
plt.title("Cell 413 - Seaborn Scatter Plot")
plt.show()
# Cell 414 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_414 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 4
})
sns.scatterplot(data=df_414, x="x", y="y")
plt.title("Cell 414 - Seaborn Scatter Plot")
plt.show()
# Cell 415 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_415 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
sns.scatterplot(data=df_415, x="x", y="y")
plt.title("Cell 415 - Seaborn Scatter Plot")
plt.show()
# Cell 416 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_416 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
sns.scatterplot(data=df_416, x="x", y="y")
plt.title("Cell 416 - Seaborn Scatter Plot")
plt.show()
# Cell 417 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_417 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
sns.scatterplot(data=df_417, x="x", y="y")
plt.title("Cell 417 - Seaborn Scatter Plot")
plt.show()
# Cell 418 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_418 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 3
})
sns.scatterplot(data=df_418, x="x", y="y")
plt.title("Cell 418 - Seaborn Scatter Plot")
plt.show()
# Cell 419 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_419 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 4
})
sns.scatterplot(data=df_419, x="x", y="y")
plt.title("Cell 419 - Seaborn Scatter Plot")
plt.show()
# Cell 420 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_420 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
sns.scatterplot(data=df_420, x="x", y="y")
plt.title("Cell 420 - Seaborn Scatter Plot")
plt.show()
# Cell 421 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_421 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
sns.scatterplot(data=df_421, x="x", y="y")
plt.title("Cell 421 - Seaborn Scatter Plot")
plt.show()
# Cell 422 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_422 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
sns.scatterplot(data=df_422, x="x", y="y")
plt.title("Cell 422 - Seaborn Scatter Plot")
plt.show()
# Cell 423 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_423 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 3
})
sns.scatterplot(data=df_423, x="x", y="y")
plt.title("Cell 423 - Seaborn Scatter Plot")
plt.show()
# Cell 424 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_424 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 4
})
sns.scatterplot(data=df_424, x="x", y="y")
plt.title("Cell 424 - Seaborn Scatter Plot")
plt.show()
# Cell 425 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_425 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
sns.scatterplot(data=df_425, x="x", y="y")
plt.title("Cell 425 - Seaborn Scatter Plot")
plt.show()
# Cell 426 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_426 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
sns.scatterplot(data=df_426, x="x", y="y")
plt.title("Cell 426 - Seaborn Scatter Plot")
plt.show()
# Cell 427 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_427 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
sns.scatterplot(data=df_427, x="x", y="y")
plt.title("Cell 427 - Seaborn Scatter Plot")
plt.show()
# Cell 428 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_428 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 3
})
sns.scatterplot(data=df_428, x="x", y="y")
plt.title("Cell 428 - Seaborn Scatter Plot")
plt.show()
# Cell 429 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_429 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 4
})
sns.scatterplot(data=df_429, x="x", y="y")
plt.title("Cell 429 - Seaborn Scatter Plot")
plt.show()
# Cell 430 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_430 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
sns.scatterplot(data=df_430, x="x", y="y")
plt.title("Cell 430 - Seaborn Scatter Plot")
plt.show()
# Cell 431 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_431 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
sns.scatterplot(data=df_431, x="x", y="y")
plt.title("Cell 431 - Seaborn Scatter Plot")
plt.show()
# Cell 432 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_432 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
sns.scatterplot(data=df_432, x="x", y="y")
plt.title("Cell 432 - Seaborn Scatter Plot")
plt.show()
# Cell 433 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_433 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 3
})
sns.scatterplot(data=df_433, x="x", y="y")
plt.title("Cell 433 - Seaborn Scatter Plot")
plt.show()
# Cell 434 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_434 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 4
})
sns.scatterplot(data=df_434, x="x", y="y")
plt.title("Cell 434 - Seaborn Scatter Plot")
plt.show()
# Cell 435 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_435 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
sns.scatterplot(data=df_435, x="x", y="y")
plt.title("Cell 435 - Seaborn Scatter Plot")
plt.show()
# Cell 436 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_436 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
sns.scatterplot(data=df_436, x="x", y="y")
plt.title("Cell 436 - Seaborn Scatter Plot")
plt.show()
# Cell 437 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_437 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
sns.scatterplot(data=df_437, x="x", y="y")
plt.title("Cell 437 - Seaborn Scatter Plot")
plt.show()
# Cell 438 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_438 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 3
})
sns.scatterplot(data=df_438, x="x", y="y")
plt.title("Cell 438 - Seaborn Scatter Plot")
plt.show()
# Cell 439 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_439 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 4
})
sns.scatterplot(data=df_439, x="x", y="y")
plt.title("Cell 439 - Seaborn Scatter Plot")
plt.show()
# Cell 440 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_440 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
sns.scatterplot(data=df_440, x="x", y="y")
plt.title("Cell 440 - Seaborn Scatter Plot")
plt.show()
# Cell 441 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_441 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
sns.scatterplot(data=df_441, x="x", y="y")
plt.title("Cell 441 - Seaborn Scatter Plot")
plt.show()
# Cell 442 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_442 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
sns.scatterplot(data=df_442, x="x", y="y")
plt.title("Cell 442 - Seaborn Scatter Plot")
plt.show()
# Cell 443 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_443 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 3
})
sns.scatterplot(data=df_443, x="x", y="y")
plt.title("Cell 443 - Seaborn Scatter Plot")
plt.show()
# Cell 444 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_444 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 4
})
sns.scatterplot(data=df_444, x="x", y="y")
plt.title("Cell 444 - Seaborn Scatter Plot")
plt.show()
# Cell 445 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_445 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
sns.scatterplot(data=df_445, x="x", y="y")
plt.title("Cell 445 - Seaborn Scatter Plot")
plt.show()
# Cell 446 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_446 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
sns.scatterplot(data=df_446, x="x", y="y")
plt.title("Cell 446 - Seaborn Scatter Plot")
plt.show()
# Cell 447 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_447 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
sns.scatterplot(data=df_447, x="x", y="y")
plt.title("Cell 447 - Seaborn Scatter Plot")
plt.show()
# Cell 448 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_448 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 3
})
sns.scatterplot(data=df_448, x="x", y="y")
plt.title("Cell 448 - Seaborn Scatter Plot")
plt.show()
# Cell 449 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_449 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 4
})
sns.scatterplot(data=df_449, x="x", y="y")
plt.title("Cell 449 - Seaborn Scatter Plot")
plt.show()
# Cell 450 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_450 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
sns.scatterplot(data=df_450, x="x", y="y")
plt.title("Cell 450 - Seaborn Scatter Plot")
plt.show()
# Cell 451 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_451 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
sns.scatterplot(data=df_451, x="x", y="y")
plt.title("Cell 451 - Seaborn Scatter Plot")
plt.show()
# Cell 452 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_452 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
sns.scatterplot(data=df_452, x="x", y="y")
plt.title("Cell 452 - Seaborn Scatter Plot")
plt.show()
# Cell 453 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_453 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 3
})
sns.scatterplot(data=df_453, x="x", y="y")
plt.title("Cell 453 - Seaborn Scatter Plot")
plt.show()
# Cell 454 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_454 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 4
})
sns.scatterplot(data=df_454, x="x", y="y")
plt.title("Cell 454 - Seaborn Scatter Plot")
plt.show()
# Cell 455 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_455 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
sns.scatterplot(data=df_455, x="x", y="y")
plt.title("Cell 455 - Seaborn Scatter Plot")
plt.show()
# Cell 456 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_456 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
sns.scatterplot(data=df_456, x="x", y="y")
plt.title("Cell 456 - Seaborn Scatter Plot")
plt.show()
# Cell 457 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_457 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
sns.scatterplot(data=df_457, x="x", y="y")
plt.title("Cell 457 - Seaborn Scatter Plot")
plt.show()
# Cell 458 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_458 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 3
})
sns.scatterplot(data=df_458, x="x", y="y")
plt.title("Cell 458 - Seaborn Scatter Plot")
plt.show()
# Cell 459 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_459 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 4
})
sns.scatterplot(data=df_459, x="x", y="y")
plt.title("Cell 459 - Seaborn Scatter Plot")
plt.show()
# Cell 460 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_460 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
sns.scatterplot(data=df_460, x="x", y="y")
plt.title("Cell 460 - Seaborn Scatter Plot")
plt.show()
# Cell 461 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_461 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
sns.scatterplot(data=df_461, x="x", y="y")
plt.title("Cell 461 - Seaborn Scatter Plot")
plt.show()
# Cell 462 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_462 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
sns.scatterplot(data=df_462, x="x", y="y")
plt.title("Cell 462 - Seaborn Scatter Plot")
plt.show()
# Cell 463 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_463 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 3
})
sns.scatterplot(data=df_463, x="x", y="y")
plt.title("Cell 463 - Seaborn Scatter Plot")
plt.show()
# Cell 464 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_464 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 4
})
sns.scatterplot(data=df_464, x="x", y="y")
plt.title("Cell 464 - Seaborn Scatter Plot")
plt.show()
# Cell 465 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_465 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
sns.scatterplot(data=df_465, x="x", y="y")
plt.title("Cell 465 - Seaborn Scatter Plot")
plt.show()
# Cell 466 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_466 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
sns.scatterplot(data=df_466, x="x", y="y")
plt.title("Cell 466 - Seaborn Scatter Plot")
plt.show()
# Cell 467 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_467 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
sns.scatterplot(data=df_467, x="x", y="y")
plt.title("Cell 467 - Seaborn Scatter Plot")
plt.show()
# Cell 468 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_468 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 3
})
sns.scatterplot(data=df_468, x="x", y="y")
plt.title("Cell 468 - Seaborn Scatter Plot")
plt.show()
# Cell 469 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_469 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 4
})
sns.scatterplot(data=df_469, x="x", y="y")
plt.title("Cell 469 - Seaborn Scatter Plot")
plt.show()
# Cell 470 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_470 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
sns.scatterplot(data=df_470, x="x", y="y")
plt.title("Cell 470 - Seaborn Scatter Plot")
plt.show()
# Cell 471 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_471 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
sns.scatterplot(data=df_471, x="x", y="y")
plt.title("Cell 471 - Seaborn Scatter Plot")
plt.show()
# Cell 472 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_472 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
sns.scatterplot(data=df_472, x="x", y="y")
plt.title("Cell 472 - Seaborn Scatter Plot")
plt.show()
# Cell 473 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_473 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 3
})
sns.scatterplot(data=df_473, x="x", y="y")
plt.title("Cell 473 - Seaborn Scatter Plot")
plt.show()
# Cell 474 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_474 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 4
})
sns.scatterplot(data=df_474, x="x", y="y")
plt.title("Cell 474 - Seaborn Scatter Plot")
plt.show()
# Cell 475 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_475 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
sns.scatterplot(data=df_475, x="x", y="y")
plt.title("Cell 475 - Seaborn Scatter Plot")
plt.show()
# Cell 476 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_476 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
sns.scatterplot(data=df_476, x="x", y="y")
plt.title("Cell 476 - Seaborn Scatter Plot")
plt.show()
# Cell 477 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_477 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
sns.scatterplot(data=df_477, x="x", y="y")
plt.title("Cell 477 - Seaborn Scatter Plot")
plt.show()
# Cell 478 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_478 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 3
})
sns.scatterplot(data=df_478, x="x", y="y")
plt.title("Cell 478 - Seaborn Scatter Plot")
plt.show()
# Cell 479 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_479 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 4
})
sns.scatterplot(data=df_479, x="x", y="y")
plt.title("Cell 479 - Seaborn Scatter Plot")
plt.show()
# Cell 480 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_480 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
sns.scatterplot(data=df_480, x="x", y="y")
plt.title("Cell 480 - Seaborn Scatter Plot")
plt.show()
# Cell 481 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_481 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
sns.scatterplot(data=df_481, x="x", y="y")
plt.title("Cell 481 - Seaborn Scatter Plot")
plt.show()
# Cell 482 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_482 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
sns.scatterplot(data=df_482, x="x", y="y")
plt.title("Cell 482 - Seaborn Scatter Plot")
plt.show()
# Cell 483 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_483 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 3
})
sns.scatterplot(data=df_483, x="x", y="y")
plt.title("Cell 483 - Seaborn Scatter Plot")
plt.show()
# Cell 484 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_484 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 4
})
sns.scatterplot(data=df_484, x="x", y="y")
plt.title("Cell 484 - Seaborn Scatter Plot")
plt.show()
# Cell 485 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_485 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
sns.scatterplot(data=df_485, x="x", y="y")
plt.title("Cell 485 - Seaborn Scatter Plot")
plt.show()
# Cell 486 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_486 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
sns.scatterplot(data=df_486, x="x", y="y")
plt.title("Cell 486 - Seaborn Scatter Plot")
plt.show()
# Cell 487 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_487 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
sns.scatterplot(data=df_487, x="x", y="y")
plt.title("Cell 487 - Seaborn Scatter Plot")
plt.show()
# Cell 488 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_488 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 3
})
sns.scatterplot(data=df_488, x="x", y="y")
plt.title("Cell 488 - Seaborn Scatter Plot")
plt.show()
# Cell 489 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_489 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 4
})
sns.scatterplot(data=df_489, x="x", y="y")
plt.title("Cell 489 - Seaborn Scatter Plot")
plt.show()
# Cell 490 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_490 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
sns.scatterplot(data=df_490, x="x", y="y")
plt.title("Cell 490 - Seaborn Scatter Plot")
plt.show()
# Cell 491 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_491 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
sns.scatterplot(data=df_491, x="x", y="y")
plt.title("Cell 491 - Seaborn Scatter Plot")
plt.show()
# Cell 492 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_492 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
sns.scatterplot(data=df_492, x="x", y="y")
plt.title("Cell 492 - Seaborn Scatter Plot")
plt.show()
# Cell 493 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_493 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 3
})
sns.scatterplot(data=df_493, x="x", y="y")
plt.title("Cell 493 - Seaborn Scatter Plot")
plt.show()
# Cell 494 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_494 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 4
})
sns.scatterplot(data=df_494, x="x", y="y")
plt.title("Cell 494 - Seaborn Scatter Plot")
plt.show()
# Cell 495 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_495 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
sns.scatterplot(data=df_495, x="x", y="y")
plt.title("Cell 495 - Seaborn Scatter Plot")
plt.show()
# Cell 496 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_496 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
sns.scatterplot(data=df_496, x="x", y="y")
plt.title("Cell 496 - Seaborn Scatter Plot")
plt.show()
# Cell 497 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_497 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
sns.scatterplot(data=df_497, x="x", y="y")
plt.title("Cell 497 - Seaborn Scatter Plot")
plt.show()
# Cell 498 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_498 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 3
})
sns.scatterplot(data=df_498, x="x", y="y")
plt.title("Cell 498 - Seaborn Scatter Plot")
plt.show()
# Cell 499 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_499 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 4
})
sns.scatterplot(data=df_499, x="x", y="y")
plt.title("Cell 499 - Seaborn Scatter Plot")
plt.show()
# Cell 500 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_500 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
sns.scatterplot(data=df_500, x="x", y="y")
plt.title("Cell 500 - Seaborn Scatter Plot")
plt.show()
# Cell 501 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_501 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
sns.scatterplot(data=df_501, x="x", y="y")
plt.title("Cell 501 - Seaborn Scatter Plot")
plt.show()
# Cell 502 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_502 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
sns.scatterplot(data=df_502, x="x", y="y")
plt.title("Cell 502 - Seaborn Scatter Plot")
plt.show()
# Cell 503 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_503 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 3
})
sns.scatterplot(data=df_503, x="x", y="y")
plt.title("Cell 503 - Seaborn Scatter Plot")
plt.show()
# Cell 504 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_504 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 4
})
sns.scatterplot(data=df_504, x="x", y="y")
plt.title("Cell 504 - Seaborn Scatter Plot")
plt.show()
# Cell 505 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_505 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
sns.scatterplot(data=df_505, x="x", y="y")
plt.title("Cell 505 - Seaborn Scatter Plot")
plt.show()
# Cell 506 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_506 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
sns.scatterplot(data=df_506, x="x", y="y")
plt.title("Cell 506 - Seaborn Scatter Plot")
plt.show()
# Cell 507 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_507 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
sns.scatterplot(data=df_507, x="x", y="y")
plt.title("Cell 507 - Seaborn Scatter Plot")
plt.show()
# Cell 508 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_508 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 3
})
sns.scatterplot(data=df_508, x="x", y="y")
plt.title("Cell 508 - Seaborn Scatter Plot")
plt.show()
# Cell 509 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_509 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 4
})
sns.scatterplot(data=df_509, x="x", y="y")
plt.title("Cell 509 - Seaborn Scatter Plot")
plt.show()
# Cell 510 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_510 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
sns.scatterplot(data=df_510, x="x", y="y")
plt.title("Cell 510 - Seaborn Scatter Plot")
plt.show()
# Cell 511 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_511 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
sns.scatterplot(data=df_511, x="x", y="y")
plt.title("Cell 511 - Seaborn Scatter Plot")
plt.show()
# Cell 512 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_512 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
sns.scatterplot(data=df_512, x="x", y="y")
plt.title("Cell 512 - Seaborn Scatter Plot")
plt.show()
# Cell 513 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_513 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 3
})
sns.scatterplot(data=df_513, x="x", y="y")
plt.title("Cell 513 - Seaborn Scatter Plot")
plt.show()
# Cell 514 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_514 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 4
})
sns.scatterplot(data=df_514, x="x", y="y")
plt.title("Cell 514 - Seaborn Scatter Plot")
plt.show()
# Cell 515 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_515 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
sns.scatterplot(data=df_515, x="x", y="y")
plt.title("Cell 515 - Seaborn Scatter Plot")
plt.show()
# Cell 516 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_516 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
sns.scatterplot(data=df_516, x="x", y="y")
plt.title("Cell 516 - Seaborn Scatter Plot")
plt.show()
# Cell 517 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_517 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
sns.scatterplot(data=df_517, x="x", y="y")
plt.title("Cell 517 - Seaborn Scatter Plot")
plt.show()
# Cell 518 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_518 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 3
})
sns.scatterplot(data=df_518, x="x", y="y")
plt.title("Cell 518 - Seaborn Scatter Plot")
plt.show()
# Cell 519 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_519 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 4
})
sns.scatterplot(data=df_519, x="x", y="y")
plt.title("Cell 519 - Seaborn Scatter Plot")
plt.show()
# Cell 520 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_520 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
sns.scatterplot(data=df_520, x="x", y="y")
plt.title("Cell 520 - Seaborn Scatter Plot")
plt.show()
# Cell 521 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_521 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
sns.scatterplot(data=df_521, x="x", y="y")
plt.title("Cell 521 - Seaborn Scatter Plot")
plt.show()
# Cell 522 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_522 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
sns.scatterplot(data=df_522, x="x", y="y")
plt.title("Cell 522 - Seaborn Scatter Plot")
plt.show()
# Cell 523 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_523 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 3
})
sns.scatterplot(data=df_523, x="x", y="y")
plt.title("Cell 523 - Seaborn Scatter Plot")
plt.show()
# Cell 524 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_524 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 4
})
sns.scatterplot(data=df_524, x="x", y="y")
plt.title("Cell 524 - Seaborn Scatter Plot")
plt.show()
# Cell 525 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_525 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
sns.scatterplot(data=df_525, x="x", y="y")
plt.title("Cell 525 - Seaborn Scatter Plot")
plt.show()
# Cell 526 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_526 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
sns.scatterplot(data=df_526, x="x", y="y")
plt.title("Cell 526 - Seaborn Scatter Plot")
plt.show()
# Cell 527 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_527 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
sns.scatterplot(data=df_527, x="x", y="y")
plt.title("Cell 527 - Seaborn Scatter Plot")
plt.show()
# Cell 528 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_528 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 3
})
sns.scatterplot(data=df_528, x="x", y="y")
plt.title("Cell 528 - Seaborn Scatter Plot")
plt.show()
# Cell 529 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_529 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 4
})
sns.scatterplot(data=df_529, x="x", y="y")
plt.title("Cell 529 - Seaborn Scatter Plot")
plt.show()
# Cell 530 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_530 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
sns.scatterplot(data=df_530, x="x", y="y")
plt.title("Cell 530 - Seaborn Scatter Plot")
plt.show()
# Cell 531 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_531 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
sns.scatterplot(data=df_531, x="x", y="y")
plt.title("Cell 531 - Seaborn Scatter Plot")
plt.show()
# Cell 532 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_532 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
sns.scatterplot(data=df_532, x="x", y="y")
plt.title("Cell 532 - Seaborn Scatter Plot")
plt.show()
# Cell 533 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_533 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 3
})
sns.scatterplot(data=df_533, x="x", y="y")
plt.title("Cell 533 - Seaborn Scatter Plot")
plt.show()
# Cell 534 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_534 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 4
})
sns.scatterplot(data=df_534, x="x", y="y")
plt.title("Cell 534 - Seaborn Scatter Plot")
plt.show()
# Cell 535 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_535 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
sns.scatterplot(data=df_535, x="x", y="y")
plt.title("Cell 535 - Seaborn Scatter Plot")
plt.show()
# Cell 536 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_536 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
sns.scatterplot(data=df_536, x="x", y="y")
plt.title("Cell 536 - Seaborn Scatter Plot")
plt.show()
# Cell 537 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_537 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
sns.scatterplot(data=df_537, x="x", y="y")
plt.title("Cell 537 - Seaborn Scatter Plot")
plt.show()
# Cell 538 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_538 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 3
})
sns.scatterplot(data=df_538, x="x", y="y")
plt.title("Cell 538 - Seaborn Scatter Plot")
plt.show()
# Cell 539 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_539 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 4
})
sns.scatterplot(data=df_539, x="x", y="y")
plt.title("Cell 539 - Seaborn Scatter Plot")
plt.show()
# Cell 540 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_540 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
sns.scatterplot(data=df_540, x="x", y="y")
plt.title("Cell 540 - Seaborn Scatter Plot")
plt.show()
# Cell 541 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_541 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
sns.scatterplot(data=df_541, x="x", y="y")
plt.title("Cell 541 - Seaborn Scatter Plot")
plt.show()
# Cell 542 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_542 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
sns.scatterplot(data=df_542, x="x", y="y")
plt.title("Cell 542 - Seaborn Scatter Plot")
plt.show()
# Cell 543 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_543 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 3
})
sns.scatterplot(data=df_543, x="x", y="y")
plt.title("Cell 543 - Seaborn Scatter Plot")
plt.show()
# Cell 544 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_544 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 4
})
sns.scatterplot(data=df_544, x="x", y="y")
plt.title("Cell 544 - Seaborn Scatter Plot")
plt.show()
# Cell 545 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_545 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
sns.scatterplot(data=df_545, x="x", y="y")
plt.title("Cell 545 - Seaborn Scatter Plot")
plt.show()
# Cell 546 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_546 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
sns.scatterplot(data=df_546, x="x", y="y")
plt.title("Cell 546 - Seaborn Scatter Plot")
plt.show()
# Cell 547 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_547 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
sns.scatterplot(data=df_547, x="x", y="y")
plt.title("Cell 547 - Seaborn Scatter Plot")
plt.show()
# Cell 548 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_548 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 3
})
sns.scatterplot(data=df_548, x="x", y="y")
plt.title("Cell 548 - Seaborn Scatter Plot")
plt.show()
# Cell 549 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_549 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 4
})
sns.scatterplot(data=df_549, x="x", y="y")
plt.title("Cell 549 - Seaborn Scatter Plot")
plt.show()
# Cell 550 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_550 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
sns.scatterplot(data=df_550, x="x", y="y")
plt.title("Cell 550 - Seaborn Scatter Plot")
plt.show()
# Cell 551 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_551 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
sns.scatterplot(data=df_551, x="x", y="y")
plt.title("Cell 551 - Seaborn Scatter Plot")
plt.show()
# Cell 552 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_552 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
sns.scatterplot(data=df_552, x="x", y="y")
plt.title("Cell 552 - Seaborn Scatter Plot")
plt.show()
# Cell 553 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_553 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 3
})
sns.scatterplot(data=df_553, x="x", y="y")
plt.title("Cell 553 - Seaborn Scatter Plot")
plt.show()
# Cell 554 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_554 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 4
})
sns.scatterplot(data=df_554, x="x", y="y")
plt.title("Cell 554 - Seaborn Scatter Plot")
plt.show()
# Cell 555 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_555 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
sns.scatterplot(data=df_555, x="x", y="y")
plt.title("Cell 555 - Seaborn Scatter Plot")
plt.show()
# Cell 556 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_556 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
sns.scatterplot(data=df_556, x="x", y="y")
plt.title("Cell 556 - Seaborn Scatter Plot")
plt.show()
# Cell 557 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_557 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
sns.scatterplot(data=df_557, x="x", y="y")
plt.title("Cell 557 - Seaborn Scatter Plot")
plt.show()
# Cell 558 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_558 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 3
})
sns.scatterplot(data=df_558, x="x", y="y")
plt.title("Cell 558 - Seaborn Scatter Plot")
plt.show()
# Cell 559 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_559 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 4
})
sns.scatterplot(data=df_559, x="x", y="y")
plt.title("Cell 559 - Seaborn Scatter Plot")
plt.show()
# Cell 560 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_560 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
sns.scatterplot(data=df_560, x="x", y="y")
plt.title("Cell 560 - Seaborn Scatter Plot")
plt.show()
# Cell 561 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_561 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
sns.scatterplot(data=df_561, x="x", y="y")
plt.title("Cell 561 - Seaborn Scatter Plot")
plt.show()
# Cell 562 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_562 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
sns.scatterplot(data=df_562, x="x", y="y")
plt.title("Cell 562 - Seaborn Scatter Plot")
plt.show()
# Cell 563 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_563 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 3
})
sns.scatterplot(data=df_563, x="x", y="y")
plt.title("Cell 563 - Seaborn Scatter Plot")
plt.show()
# Cell 564 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_564 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 4
})
sns.scatterplot(data=df_564, x="x", y="y")
plt.title("Cell 564 - Seaborn Scatter Plot")
plt.show()
# Cell 565 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_565 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
sns.scatterplot(data=df_565, x="x", y="y")
plt.title("Cell 565 - Seaborn Scatter Plot")
plt.show()
# Cell 566 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_566 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
sns.scatterplot(data=df_566, x="x", y="y")
plt.title("Cell 566 - Seaborn Scatter Plot")
plt.show()
# Cell 567 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_567 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
sns.scatterplot(data=df_567, x="x", y="y")
plt.title("Cell 567 - Seaborn Scatter Plot")
plt.show()
# Cell 568 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_568 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 3
})
sns.scatterplot(data=df_568, x="x", y="y")
plt.title("Cell 568 - Seaborn Scatter Plot")
plt.show()
# Cell 569 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_569 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 4
})
sns.scatterplot(data=df_569, x="x", y="y")
plt.title("Cell 569 - Seaborn Scatter Plot")
plt.show()
# Cell 570 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_570 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
sns.scatterplot(data=df_570, x="x", y="y")
plt.title("Cell 570 - Seaborn Scatter Plot")
plt.show()
# Cell 571 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_571 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
sns.scatterplot(data=df_571, x="x", y="y")
plt.title("Cell 571 - Seaborn Scatter Plot")
plt.show()
# Cell 572 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_572 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
sns.scatterplot(data=df_572, x="x", y="y")
plt.title("Cell 572 - Seaborn Scatter Plot")
plt.show()
# Cell 573 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_573 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 3
})
sns.scatterplot(data=df_573, x="x", y="y")
plt.title("Cell 573 - Seaborn Scatter Plot")
plt.show()
# Cell 574 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_574 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 4
})
sns.scatterplot(data=df_574, x="x", y="y")
plt.title("Cell 574 - Seaborn Scatter Plot")
plt.show()
# Cell 575 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_575 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
sns.scatterplot(data=df_575, x="x", y="y")
plt.title("Cell 575 - Seaborn Scatter Plot")
plt.show()
# Cell 576 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_576 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
sns.scatterplot(data=df_576, x="x", y="y")
plt.title("Cell 576 - Seaborn Scatter Plot")
plt.show()
# Cell 577 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_577 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
sns.scatterplot(data=df_577, x="x", y="y")
plt.title("Cell 577 - Seaborn Scatter Plot")
plt.show()
# Cell 578 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_578 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 3
})
sns.scatterplot(data=df_578, x="x", y="y")
plt.title("Cell 578 - Seaborn Scatter Plot")
plt.show()
# Cell 579 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_579 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 4
})
sns.scatterplot(data=df_579, x="x", y="y")
plt.title("Cell 579 - Seaborn Scatter Plot")
plt.show()
# Cell 580 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_580 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
sns.scatterplot(data=df_580, x="x", y="y")
plt.title("Cell 580 - Seaborn Scatter Plot")
plt.show()
# Cell 581 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_581 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
sns.scatterplot(data=df_581, x="x", y="y")
plt.title("Cell 581 - Seaborn Scatter Plot")
plt.show()
# Cell 582 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_582 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
sns.scatterplot(data=df_582, x="x", y="y")
plt.title("Cell 582 - Seaborn Scatter Plot")
plt.show()
# Cell 583 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_583 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 3
})
sns.scatterplot(data=df_583, x="x", y="y")
plt.title("Cell 583 - Seaborn Scatter Plot")
plt.show()
# Cell 584 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_584 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 4
})
sns.scatterplot(data=df_584, x="x", y="y")
plt.title("Cell 584 - Seaborn Scatter Plot")
plt.show()
# Cell 585 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_585 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
sns.scatterplot(data=df_585, x="x", y="y")
plt.title("Cell 585 - Seaborn Scatter Plot")
plt.show()
# Cell 586 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_586 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
sns.scatterplot(data=df_586, x="x", y="y")
plt.title("Cell 586 - Seaborn Scatter Plot")
plt.show()
# Cell 587 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_587 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
sns.scatterplot(data=df_587, x="x", y="y")
plt.title("Cell 587 - Seaborn Scatter Plot")
plt.show()
# Cell 588 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_588 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 3
})
sns.scatterplot(data=df_588, x="x", y="y")
plt.title("Cell 588 - Seaborn Scatter Plot")
plt.show()
# Cell 589 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_589 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 4
})
sns.scatterplot(data=df_589, x="x", y="y")
plt.title("Cell 589 - Seaborn Scatter Plot")
plt.show()
# Cell 590 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_590 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
sns.scatterplot(data=df_590, x="x", y="y")
plt.title("Cell 590 - Seaborn Scatter Plot")
plt.show()
# Cell 591 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_591 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
sns.scatterplot(data=df_591, x="x", y="y")
plt.title("Cell 591 - Seaborn Scatter Plot")
plt.show()
# Cell 592 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_592 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
sns.scatterplot(data=df_592, x="x", y="y")
plt.title("Cell 592 - Seaborn Scatter Plot")
plt.show()
# Cell 593 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_593 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 3
})
sns.scatterplot(data=df_593, x="x", y="y")
plt.title("Cell 593 - Seaborn Scatter Plot")
plt.show()
# Cell 594 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_594 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 4
})
sns.scatterplot(data=df_594, x="x", y="y")
plt.title("Cell 594 - Seaborn Scatter Plot")
plt.show()
# Cell 595 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_595 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
sns.scatterplot(data=df_595, x="x", y="y")
plt.title("Cell 595 - Seaborn Scatter Plot")
plt.show()
# Cell 596 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_596 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
sns.scatterplot(data=df_596, x="x", y="y")
plt.title("Cell 596 - Seaborn Scatter Plot")
plt.show()
# Cell 597 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_597 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
sns.scatterplot(data=df_597, x="x", y="y")
plt.title("Cell 597 - Seaborn Scatter Plot")
plt.show()
# Cell 598 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_598 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 3
})
sns.scatterplot(data=df_598, x="x", y="y")
plt.title("Cell 598 - Seaborn Scatter Plot")
plt.show()
# Cell 599 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_599 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 4
})
sns.scatterplot(data=df_599, x="x", y="y")
plt.title("Cell 599 - Seaborn Scatter Plot")
plt.show()
# Cell 600 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_600 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
sns.scatterplot(data=df_600, x="x", y="y")
plt.title("Cell 600 - Seaborn Scatter Plot")
plt.show()
# Cell 601 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_601 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
sns.scatterplot(data=df_601, x="x", y="y")
plt.title("Cell 601 - Seaborn Scatter Plot")
plt.show()
# Cell 602 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_602 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
sns.scatterplot(data=df_602, x="x", y="y")
plt.title("Cell 602 - Seaborn Scatter Plot")
plt.show()
# Cell 603 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_603 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 3
})
sns.scatterplot(data=df_603, x="x", y="y")
plt.title("Cell 603 - Seaborn Scatter Plot")
plt.show()
# Cell 604 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_604 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 4
})
sns.scatterplot(data=df_604, x="x", y="y")
plt.title("Cell 604 - Seaborn Scatter Plot")
plt.show()
# Cell 605 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_605 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
sns.scatterplot(data=df_605, x="x", y="y")
plt.title("Cell 605 - Seaborn Scatter Plot")
plt.show()
# Cell 606 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_606 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
sns.scatterplot(data=df_606, x="x", y="y")
plt.title("Cell 606 - Seaborn Scatter Plot")
plt.show()
# Cell 607 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_607 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
sns.scatterplot(data=df_607, x="x", y="y")
plt.title("Cell 607 - Seaborn Scatter Plot")
plt.show()
# Cell 608 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_608 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 3
})
sns.scatterplot(data=df_608, x="x", y="y")
plt.title("Cell 608 - Seaborn Scatter Plot")
plt.show()
# Cell 609 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_609 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 4
})
sns.scatterplot(data=df_609, x="x", y="y")
plt.title("Cell 609 - Seaborn Scatter Plot")
plt.show()
# Cell 610 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_610 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
sns.scatterplot(data=df_610, x="x", y="y")
plt.title("Cell 610 - Seaborn Scatter Plot")
plt.show()
# Cell 611 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_611 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
sns.scatterplot(data=df_611, x="x", y="y")
plt.title("Cell 611 - Seaborn Scatter Plot")
plt.show()
# Cell 612 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_612 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
sns.scatterplot(data=df_612, x="x", y="y")
plt.title("Cell 612 - Seaborn Scatter Plot")
plt.show()
# Cell 613 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_613 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 3
})
sns.scatterplot(data=df_613, x="x", y="y")
plt.title("Cell 613 - Seaborn Scatter Plot")
plt.show()
# Cell 614 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_614 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 4
})
sns.scatterplot(data=df_614, x="x", y="y")
plt.title("Cell 614 - Seaborn Scatter Plot")
plt.show()
# Cell 615 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_615 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
sns.scatterplot(data=df_615, x="x", y="y")
plt.title("Cell 615 - Seaborn Scatter Plot")
plt.show()
# Cell 616 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_616 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
sns.scatterplot(data=df_616, x="x", y="y")
plt.title("Cell 616 - Seaborn Scatter Plot")
plt.show()
# Cell 617 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_617 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
sns.scatterplot(data=df_617, x="x", y="y")
plt.title("Cell 617 - Seaborn Scatter Plot")
plt.show()
# Cell 618 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_618 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 3
})
sns.scatterplot(data=df_618, x="x", y="y")
plt.title("Cell 618 - Seaborn Scatter Plot")
plt.show()
# Cell 619 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_619 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 4
})
sns.scatterplot(data=df_619, x="x", y="y")
plt.title("Cell 619 - Seaborn Scatter Plot")
plt.show()
# Cell 620 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_620 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
sns.scatterplot(data=df_620, x="x", y="y")
plt.title("Cell 620 - Seaborn Scatter Plot")
plt.show()
# Cell 621 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_621 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
sns.scatterplot(data=df_621, x="x", y="y")
plt.title("Cell 621 - Seaborn Scatter Plot")
plt.show()
# Cell 622 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_622 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
sns.scatterplot(data=df_622, x="x", y="y")
plt.title("Cell 622 - Seaborn Scatter Plot")
plt.show()
# Cell 623 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_623 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 3
})
sns.scatterplot(data=df_623, x="x", y="y")
plt.title("Cell 623 - Seaborn Scatter Plot")
plt.show()
# Cell 624 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_624 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 4
})
sns.scatterplot(data=df_624, x="x", y="y")
plt.title("Cell 624 - Seaborn Scatter Plot")
plt.show()
# Cell 625 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_625 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
sns.scatterplot(data=df_625, x="x", y="y")
plt.title("Cell 625 - Seaborn Scatter Plot")
plt.show()
# Cell 626 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_626 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
sns.scatterplot(data=df_626, x="x", y="y")
plt.title("Cell 626 - Seaborn Scatter Plot")
plt.show()
# Cell 627 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_627 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
sns.scatterplot(data=df_627, x="x", y="y")
plt.title("Cell 627 - Seaborn Scatter Plot")
plt.show()
# Cell 628 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_628 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 3
})
sns.scatterplot(data=df_628, x="x", y="y")
plt.title("Cell 628 - Seaborn Scatter Plot")
plt.show()
# Cell 629 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_629 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 4
})
sns.scatterplot(data=df_629, x="x", y="y")
plt.title("Cell 629 - Seaborn Scatter Plot")
plt.show()
# Cell 630 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_630 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
sns.scatterplot(data=df_630, x="x", y="y")
plt.title("Cell 630 - Seaborn Scatter Plot")
plt.show()
# Cell 631 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_631 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
sns.scatterplot(data=df_631, x="x", y="y")
plt.title("Cell 631 - Seaborn Scatter Plot")
plt.show()
# Cell 632 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_632 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
sns.scatterplot(data=df_632, x="x", y="y")
plt.title("Cell 632 - Seaborn Scatter Plot")
plt.show()
# Cell 633 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_633 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 3
})
sns.scatterplot(data=df_633, x="x", y="y")
plt.title("Cell 633 - Seaborn Scatter Plot")
plt.show()
# Cell 634 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_634 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 4
})
sns.scatterplot(data=df_634, x="x", y="y")
plt.title("Cell 634 - Seaborn Scatter Plot")
plt.show()
# Cell 635 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_635 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
sns.scatterplot(data=df_635, x="x", y="y")
plt.title("Cell 635 - Seaborn Scatter Plot")
plt.show()
# Cell 636 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_636 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
sns.scatterplot(data=df_636, x="x", y="y")
plt.title("Cell 636 - Seaborn Scatter Plot")
plt.show()
# Cell 637 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_637 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
sns.scatterplot(data=df_637, x="x", y="y")
plt.title("Cell 637 - Seaborn Scatter Plot")
plt.show()
# Cell 638 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_638 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 3
})
sns.scatterplot(data=df_638, x="x", y="y")
plt.title("Cell 638 - Seaborn Scatter Plot")
plt.show()
# Cell 639 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_639 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 4
})
sns.scatterplot(data=df_639, x="x", y="y")
plt.title("Cell 639 - Seaborn Scatter Plot")
plt.show()
# Cell 640 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_640 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
sns.scatterplot(data=df_640, x="x", y="y")
plt.title("Cell 640 - Seaborn Scatter Plot")
plt.show()
# Cell 641 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_641 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
sns.scatterplot(data=df_641, x="x", y="y")
plt.title("Cell 641 - Seaborn Scatter Plot")
plt.show()
# Cell 642 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_642 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
sns.scatterplot(data=df_642, x="x", y="y")
plt.title("Cell 642 - Seaborn Scatter Plot")
plt.show()
# Cell 643 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_643 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 3
})
sns.scatterplot(data=df_643, x="x", y="y")
plt.title("Cell 643 - Seaborn Scatter Plot")
plt.show()
# Cell 644 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_644 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 4
})
sns.scatterplot(data=df_644, x="x", y="y")
plt.title("Cell 644 - Seaborn Scatter Plot")
plt.show()
# Cell 645 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_645 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
sns.scatterplot(data=df_645, x="x", y="y")
plt.title("Cell 645 - Seaborn Scatter Plot")
plt.show()
# Cell 646 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_646 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
sns.scatterplot(data=df_646, x="x", y="y")
plt.title("Cell 646 - Seaborn Scatter Plot")
plt.show()
# Cell 647 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_647 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
sns.scatterplot(data=df_647, x="x", y="y")
plt.title("Cell 647 - Seaborn Scatter Plot")
plt.show()
# Cell 648 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_648 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 3
})
sns.scatterplot(data=df_648, x="x", y="y")
plt.title("Cell 648 - Seaborn Scatter Plot")
plt.show()
# Cell 649 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_649 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 4
})
sns.scatterplot(data=df_649, x="x", y="y")
plt.title("Cell 649 - Seaborn Scatter Plot")
plt.show()
# Cell 650 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_650 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
sns.scatterplot(data=df_650, x="x", y="y")
plt.title("Cell 650 - Seaborn Scatter Plot")
plt.show()
# Cell 651 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_651 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
sns.scatterplot(data=df_651, x="x", y="y")
plt.title("Cell 651 - Seaborn Scatter Plot")
plt.show()
# Cell 652 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_652 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
sns.scatterplot(data=df_652, x="x", y="y")
plt.title("Cell 652 - Seaborn Scatter Plot")
plt.show()
# Cell 653 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_653 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 3
})
sns.scatterplot(data=df_653, x="x", y="y")
plt.title("Cell 653 - Seaborn Scatter Plot")
plt.show()
# Cell 654 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_654 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 4
})
sns.scatterplot(data=df_654, x="x", y="y")
plt.title("Cell 654 - Seaborn Scatter Plot")
plt.show()
# Cell 655 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_655 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
sns.scatterplot(data=df_655, x="x", y="y")
plt.title("Cell 655 - Seaborn Scatter Plot")
plt.show()
# Cell 656 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_656 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
sns.scatterplot(data=df_656, x="x", y="y")
plt.title("Cell 656 - Seaborn Scatter Plot")
plt.show()
# Cell 657 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_657 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
sns.scatterplot(data=df_657, x="x", y="y")
plt.title("Cell 657 - Seaborn Scatter Plot")
plt.show()
# Cell 658 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_658 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 3
})
sns.scatterplot(data=df_658, x="x", y="y")
plt.title("Cell 658 - Seaborn Scatter Plot")
plt.show()
# Cell 659 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_659 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 4
})
sns.scatterplot(data=df_659, x="x", y="y")
plt.title("Cell 659 - Seaborn Scatter Plot")
plt.show()
# Cell 660 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_660 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
sns.scatterplot(data=df_660, x="x", y="y")
plt.title("Cell 660 - Seaborn Scatter Plot")
plt.show()
# Cell 661 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_661 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
sns.scatterplot(data=df_661, x="x", y="y")
plt.title("Cell 661 - Seaborn Scatter Plot")
plt.show()
# Cell 662 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_662 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
sns.scatterplot(data=df_662, x="x", y="y")
plt.title("Cell 662 - Seaborn Scatter Plot")
plt.show()
# Cell 663 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_663 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 3
})
sns.scatterplot(data=df_663, x="x", y="y")
plt.title("Cell 663 - Seaborn Scatter Plot")
plt.show()
# Cell 664 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_664 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 4
})
sns.scatterplot(data=df_664, x="x", y="y")
plt.title("Cell 664 - Seaborn Scatter Plot")
plt.show()
# Cell 665 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_665 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
sns.scatterplot(data=df_665, x="x", y="y")
plt.title("Cell 665 - Seaborn Scatter Plot")
plt.show()
# Cell 666 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_666 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
sns.scatterplot(data=df_666, x="x", y="y")
plt.title("Cell 666 - Seaborn Scatter Plot")
plt.show()
# Cell 667 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_667 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
sns.scatterplot(data=df_667, x="x", y="y")
plt.title("Cell 667 - Seaborn Scatter Plot")
plt.show()
# Cell 668 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_668 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 3
})
sns.scatterplot(data=df_668, x="x", y="y")
plt.title("Cell 668 - Seaborn Scatter Plot")
plt.show()
# Cell 669 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_669 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 4
})
sns.scatterplot(data=df_669, x="x", y="y")
plt.title("Cell 669 - Seaborn Scatter Plot")
plt.show()
# Cell 670 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_670 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
sns.scatterplot(data=df_670, x="x", y="y")
plt.title("Cell 670 - Seaborn Scatter Plot")
plt.show()
# Cell 671 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_671 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
sns.scatterplot(data=df_671, x="x", y="y")
plt.title("Cell 671 - Seaborn Scatter Plot")
plt.show()
# Cell 672 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_672 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
sns.scatterplot(data=df_672, x="x", y="y")
plt.title("Cell 672 - Seaborn Scatter Plot")
plt.show()
# Cell 673 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_673 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 3
})
sns.scatterplot(data=df_673, x="x", y="y")
plt.title("Cell 673 - Seaborn Scatter Plot")
plt.show()
# Cell 674 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_674 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 4
})
sns.scatterplot(data=df_674, x="x", y="y")
plt.title("Cell 674 - Seaborn Scatter Plot")
plt.show()
# Cell 675 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_675 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
sns.scatterplot(data=df_675, x="x", y="y")
plt.title("Cell 675 - Seaborn Scatter Plot")
plt.show()
# Cell 676 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_676 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
sns.scatterplot(data=df_676, x="x", y="y")
plt.title("Cell 676 - Seaborn Scatter Plot")
plt.show()
# Cell 677 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_677 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
sns.scatterplot(data=df_677, x="x", y="y")
plt.title("Cell 677 - Seaborn Scatter Plot")
plt.show()
# Cell 678 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_678 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 3
})
sns.scatterplot(data=df_678, x="x", y="y")
plt.title("Cell 678 - Seaborn Scatter Plot")
plt.show()
# Cell 679 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_679 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 4
})
sns.scatterplot(data=df_679, x="x", y="y")
plt.title("Cell 679 - Seaborn Scatter Plot")
plt.show()
# Cell 680 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_680 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
sns.scatterplot(data=df_680, x="x", y="y")
plt.title("Cell 680 - Seaborn Scatter Plot")
plt.show()
# Cell 681 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_681 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
sns.scatterplot(data=df_681, x="x", y="y")
plt.title("Cell 681 - Seaborn Scatter Plot")
plt.show()
# Cell 682 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_682 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
sns.scatterplot(data=df_682, x="x", y="y")
plt.title("Cell 682 - Seaborn Scatter Plot")
plt.show()
# Cell 683 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_683 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 3
})
sns.scatterplot(data=df_683, x="x", y="y")
plt.title("Cell 683 - Seaborn Scatter Plot")
plt.show()
# Cell 684 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_684 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 4
})
sns.scatterplot(data=df_684, x="x", y="y")
plt.title("Cell 684 - Seaborn Scatter Plot")
plt.show()
# Cell 685 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_685 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
sns.scatterplot(data=df_685, x="x", y="y")
plt.title("Cell 685 - Seaborn Scatter Plot")
plt.show()
# Cell 686 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_686 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
sns.scatterplot(data=df_686, x="x", y="y")
plt.title("Cell 686 - Seaborn Scatter Plot")
plt.show()
# Cell 687 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_687 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
sns.scatterplot(data=df_687, x="x", y="y")
plt.title("Cell 687 - Seaborn Scatter Plot")
plt.show()
# Cell 688 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_688 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 3
})
sns.scatterplot(data=df_688, x="x", y="y")
plt.title("Cell 688 - Seaborn Scatter Plot")
plt.show()
# Cell 689 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_689 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 4
})
sns.scatterplot(data=df_689, x="x", y="y")
plt.title("Cell 689 - Seaborn Scatter Plot")
plt.show()
# Cell 690 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_690 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
sns.scatterplot(data=df_690, x="x", y="y")
plt.title("Cell 690 - Seaborn Scatter Plot")
plt.show()
# Cell 691 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_691 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
sns.scatterplot(data=df_691, x="x", y="y")
plt.title("Cell 691 - Seaborn Scatter Plot")
plt.show()
# Cell 692 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_692 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
sns.scatterplot(data=df_692, x="x", y="y")
plt.title("Cell 692 - Seaborn Scatter Plot")
plt.show()
# Cell 693 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_693 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 3
})
sns.scatterplot(data=df_693, x="x", y="y")
plt.title("Cell 693 - Seaborn Scatter Plot")
plt.show()
# Cell 694 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_694 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 4
})
sns.scatterplot(data=df_694, x="x", y="y")
plt.title("Cell 694 - Seaborn Scatter Plot")
plt.show()
# Cell 695 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_695 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
sns.scatterplot(data=df_695, x="x", y="y")
plt.title("Cell 695 - Seaborn Scatter Plot")
plt.show()
# Cell 696 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_696 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
sns.scatterplot(data=df_696, x="x", y="y")
plt.title("Cell 696 - Seaborn Scatter Plot")
plt.show()
# Cell 697 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_697 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
sns.scatterplot(data=df_697, x="x", y="y")
plt.title("Cell 697 - Seaborn Scatter Plot")
plt.show()
# Cell 698 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_698 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 3
})
sns.scatterplot(data=df_698, x="x", y="y")
plt.title("Cell 698 - Seaborn Scatter Plot")
plt.show()
# Cell 699 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_699 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 4
})
sns.scatterplot(data=df_699, x="x", y="y")
plt.title("Cell 699 - Seaborn Scatter Plot")
plt.show()
# Cell 700 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_700 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
sns.scatterplot(data=df_700, x="x", y="y")
plt.title("Cell 700 - Seaborn Scatter Plot")
plt.show()
# Cell 701 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_701 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
sns.scatterplot(data=df_701, x="x", y="y")
plt.title("Cell 701 - Seaborn Scatter Plot")
plt.show()
# Cell 702 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_702 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
sns.scatterplot(data=df_702, x="x", y="y")
plt.title("Cell 702 - Seaborn Scatter Plot")
plt.show()
# Cell 703 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_703 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 3
})
sns.scatterplot(data=df_703, x="x", y="y")
plt.title("Cell 703 - Seaborn Scatter Plot")
plt.show()
# Cell 704 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_704 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 4
})
sns.scatterplot(data=df_704, x="x", y="y")
plt.title("Cell 704 - Seaborn Scatter Plot")
plt.show()
# Cell 705 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_705 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
sns.scatterplot(data=df_705, x="x", y="y")
plt.title("Cell 705 - Seaborn Scatter Plot")
plt.show()
# Cell 706 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_706 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
sns.scatterplot(data=df_706, x="x", y="y")
plt.title("Cell 706 - Seaborn Scatter Plot")
plt.show()
# Cell 707 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_707 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
sns.scatterplot(data=df_707, x="x", y="y")
plt.title("Cell 707 - Seaborn Scatter Plot")
plt.show()
# Cell 708 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_708 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 3
})
sns.scatterplot(data=df_708, x="x", y="y")
plt.title("Cell 708 - Seaborn Scatter Plot")
plt.show()
# Cell 709 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_709 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 4
})
sns.scatterplot(data=df_709, x="x", y="y")
plt.title("Cell 709 - Seaborn Scatter Plot")
plt.show()
# Cell 710 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_710 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
sns.scatterplot(data=df_710, x="x", y="y")
plt.title("Cell 710 - Seaborn Scatter Plot")
plt.show()
# Cell 711 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_711 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
sns.scatterplot(data=df_711, x="x", y="y")
plt.title("Cell 711 - Seaborn Scatter Plot")
plt.show()
# Cell 712 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_712 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
sns.scatterplot(data=df_712, x="x", y="y")
plt.title("Cell 712 - Seaborn Scatter Plot")
plt.show()
# Cell 713 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_713 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 3
})
sns.scatterplot(data=df_713, x="x", y="y")
plt.title("Cell 713 - Seaborn Scatter Plot")
plt.show()
# Cell 714 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_714 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 4
})
sns.scatterplot(data=df_714, x="x", y="y")
plt.title("Cell 714 - Seaborn Scatter Plot")
plt.show()
# Cell 715 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_715 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
sns.scatterplot(data=df_715, x="x", y="y")
plt.title("Cell 715 - Seaborn Scatter Plot")
plt.show()
# Cell 716 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_716 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
sns.scatterplot(data=df_716, x="x", y="y")
plt.title("Cell 716 - Seaborn Scatter Plot")
plt.show()
# Cell 717 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_717 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
sns.scatterplot(data=df_717, x="x", y="y")
plt.title("Cell 717 - Seaborn Scatter Plot")
plt.show()
# Cell 718 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_718 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 3
})
sns.scatterplot(data=df_718, x="x", y="y")
plt.title("Cell 718 - Seaborn Scatter Plot")
plt.show()
# Cell 719 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_719 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 4
})
sns.scatterplot(data=df_719, x="x", y="y")
plt.title("Cell 719 - Seaborn Scatter Plot")
plt.show()
# Cell 720 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_720 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
sns.scatterplot(data=df_720, x="x", y="y")
plt.title("Cell 720 - Seaborn Scatter Plot")
plt.show()
# Cell 721 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_721 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
sns.scatterplot(data=df_721, x="x", y="y")
plt.title("Cell 721 - Seaborn Scatter Plot")
plt.show()
# Cell 722 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_722 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
sns.scatterplot(data=df_722, x="x", y="y")
plt.title("Cell 722 - Seaborn Scatter Plot")
plt.show()
# Cell 723 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_723 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 3
})
sns.scatterplot(data=df_723, x="x", y="y")
plt.title("Cell 723 - Seaborn Scatter Plot")
plt.show()
# Cell 724 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_724 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 4
})
sns.scatterplot(data=df_724, x="x", y="y")
plt.title("Cell 724 - Seaborn Scatter Plot")
plt.show()
# Cell 725 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_725 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
sns.scatterplot(data=df_725, x="x", y="y")
plt.title("Cell 725 - Seaborn Scatter Plot")
plt.show()
# Cell 726 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_726 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
sns.scatterplot(data=df_726, x="x", y="y")
plt.title("Cell 726 - Seaborn Scatter Plot")
plt.show()
# Cell 727 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_727 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
sns.scatterplot(data=df_727, x="x", y="y")
plt.title("Cell 727 - Seaborn Scatter Plot")
plt.show()
# Cell 728 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_728 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 3
})
sns.scatterplot(data=df_728, x="x", y="y")
plt.title("Cell 728 - Seaborn Scatter Plot")
plt.show()
# Cell 729 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_729 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 4
})
sns.scatterplot(data=df_729, x="x", y="y")
plt.title("Cell 729 - Seaborn Scatter Plot")
plt.show()
# Cell 730 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_730 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
sns.scatterplot(data=df_730, x="x", y="y")
plt.title("Cell 730 - Seaborn Scatter Plot")
plt.show()
# Cell 731 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_731 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
sns.scatterplot(data=df_731, x="x", y="y")
plt.title("Cell 731 - Seaborn Scatter Plot")
plt.show()
# Cell 732 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_732 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
sns.scatterplot(data=df_732, x="x", y="y")
plt.title("Cell 732 - Seaborn Scatter Plot")
plt.show()
# Cell 733 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_733 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 3
})
sns.scatterplot(data=df_733, x="x", y="y")
plt.title("Cell 733 - Seaborn Scatter Plot")
plt.show()
# Cell 734 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_734 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 4
})
sns.scatterplot(data=df_734, x="x", y="y")
plt.title("Cell 734 - Seaborn Scatter Plot")
plt.show()
# Cell 735 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_735 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
sns.scatterplot(data=df_735, x="x", y="y")
plt.title("Cell 735 - Seaborn Scatter Plot")
plt.show()
# Cell 736 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_736 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
sns.scatterplot(data=df_736, x="x", y="y")
plt.title("Cell 736 - Seaborn Scatter Plot")
plt.show()
# Cell 737 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_737 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
sns.scatterplot(data=df_737, x="x", y="y")
plt.title("Cell 737 - Seaborn Scatter Plot")
plt.show()
# Cell 738 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_738 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 3
})
sns.scatterplot(data=df_738, x="x", y="y")
plt.title("Cell 738 - Seaborn Scatter Plot")
plt.show()
# Cell 739 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_739 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 4
})
sns.scatterplot(data=df_739, x="x", y="y")
plt.title("Cell 739 - Seaborn Scatter Plot")
plt.show()
# Cell 740 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_740 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
sns.scatterplot(data=df_740, x="x", y="y")
plt.title("Cell 740 - Seaborn Scatter Plot")
plt.show()
# Cell 741 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_741 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
sns.scatterplot(data=df_741, x="x", y="y")
plt.title("Cell 741 - Seaborn Scatter Plot")
plt.show()
# Cell 742 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_742 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
sns.scatterplot(data=df_742, x="x", y="y")
plt.title("Cell 742 - Seaborn Scatter Plot")
plt.show()
# Cell 743 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_743 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 3
})
sns.scatterplot(data=df_743, x="x", y="y")
plt.title("Cell 743 - Seaborn Scatter Plot")
plt.show()
# Cell 744 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_744 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 4
})
sns.scatterplot(data=df_744, x="x", y="y")
plt.title("Cell 744 - Seaborn Scatter Plot")
plt.show()
# Cell 745 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_745 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
sns.scatterplot(data=df_745, x="x", y="y")
plt.title("Cell 745 - Seaborn Scatter Plot")
plt.show()
# Cell 746 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_746 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
sns.scatterplot(data=df_746, x="x", y="y")
plt.title("Cell 746 - Seaborn Scatter Plot")
plt.show()
# Cell 747 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_747 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
sns.scatterplot(data=df_747, x="x", y="y")
plt.title("Cell 747 - Seaborn Scatter Plot")
plt.show()
# Cell 748 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_748 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 3
})
sns.scatterplot(data=df_748, x="x", y="y")
plt.title("Cell 748 - Seaborn Scatter Plot")
plt.show()
# Cell 749 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_749 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 4
})
sns.scatterplot(data=df_749, x="x", y="y")
plt.title("Cell 749 - Seaborn Scatter Plot")
plt.show()
# Cell 750 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_750 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
sns.scatterplot(data=df_750, x="x", y="y")
plt.title("Cell 750 - Seaborn Scatter Plot")
plt.show()
# Cell 751 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_751 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
sns.scatterplot(data=df_751, x="x", y="y")
plt.title("Cell 751 - Seaborn Scatter Plot")
plt.show()
# Cell 752 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_752 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
sns.scatterplot(data=df_752, x="x", y="y")
plt.title("Cell 752 - Seaborn Scatter Plot")
plt.show()
# Cell 753 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_753 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 3
})
sns.scatterplot(data=df_753, x="x", y="y")
plt.title("Cell 753 - Seaborn Scatter Plot")
plt.show()
# Cell 754 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_754 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 4
})
sns.scatterplot(data=df_754, x="x", y="y")
plt.title("Cell 754 - Seaborn Scatter Plot")
plt.show()
# Cell 755 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_755 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
sns.scatterplot(data=df_755, x="x", y="y")
plt.title("Cell 755 - Seaborn Scatter Plot")
plt.show()
# Cell 756 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_756 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
sns.scatterplot(data=df_756, x="x", y="y")
plt.title("Cell 756 - Seaborn Scatter Plot")
plt.show()
# Cell 757 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_757 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
sns.scatterplot(data=df_757, x="x", y="y")
plt.title("Cell 757 - Seaborn Scatter Plot")
plt.show()
# Cell 758 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_758 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 3
})
sns.scatterplot(data=df_758, x="x", y="y")
plt.title("Cell 758 - Seaborn Scatter Plot")
plt.show()
# Cell 759 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_759 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 4
})
sns.scatterplot(data=df_759, x="x", y="y")
plt.title("Cell 759 - Seaborn Scatter Plot")
plt.show()
# Cell 760 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_760 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
sns.scatterplot(data=df_760, x="x", y="y")
plt.title("Cell 760 - Seaborn Scatter Plot")
plt.show()
# Cell 761 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_761 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
sns.scatterplot(data=df_761, x="x", y="y")
plt.title("Cell 761 - Seaborn Scatter Plot")
plt.show()
# Cell 762 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_762 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
sns.scatterplot(data=df_762, x="x", y="y")
plt.title("Cell 762 - Seaborn Scatter Plot")
plt.show()
# Cell 763 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_763 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 3
})
sns.scatterplot(data=df_763, x="x", y="y")
plt.title("Cell 763 - Seaborn Scatter Plot")
plt.show()
# Cell 764 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_764 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 4
})
sns.scatterplot(data=df_764, x="x", y="y")
plt.title("Cell 764 - Seaborn Scatter Plot")
plt.show()
# Cell 765 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_765 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
sns.scatterplot(data=df_765, x="x", y="y")
plt.title("Cell 765 - Seaborn Scatter Plot")
plt.show()
# Cell 766 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_766 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
sns.scatterplot(data=df_766, x="x", y="y")
plt.title("Cell 766 - Seaborn Scatter Plot")
plt.show()
# Cell 767 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_767 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
sns.scatterplot(data=df_767, x="x", y="y")
plt.title("Cell 767 - Seaborn Scatter Plot")
plt.show()
# Cell 768 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_768 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 3
})
sns.scatterplot(data=df_768, x="x", y="y")
plt.title("Cell 768 - Seaborn Scatter Plot")
plt.show()
# Cell 769 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_769 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 4
})
sns.scatterplot(data=df_769, x="x", y="y")
plt.title("Cell 769 - Seaborn Scatter Plot")
plt.show()
# Cell 770 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_770 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
sns.scatterplot(data=df_770, x="x", y="y")
plt.title("Cell 770 - Seaborn Scatter Plot")
plt.show()
# Cell 771 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_771 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
sns.scatterplot(data=df_771, x="x", y="y")
plt.title("Cell 771 - Seaborn Scatter Plot")
plt.show()
# Cell 772 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_772 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
sns.scatterplot(data=df_772, x="x", y="y")
plt.title("Cell 772 - Seaborn Scatter Plot")
plt.show()
# Cell 773 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_773 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 3
})
sns.scatterplot(data=df_773, x="x", y="y")
plt.title("Cell 773 - Seaborn Scatter Plot")
plt.show()
# Cell 774 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_774 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 4
})
sns.scatterplot(data=df_774, x="x", y="y")
plt.title("Cell 774 - Seaborn Scatter Plot")
plt.show()
# Cell 775 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_775 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
sns.scatterplot(data=df_775, x="x", y="y")
plt.title("Cell 775 - Seaborn Scatter Plot")
plt.show()
# Cell 776 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_776 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
sns.scatterplot(data=df_776, x="x", y="y")
plt.title("Cell 776 - Seaborn Scatter Plot")
plt.show()
# Cell 777 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_777 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
sns.scatterplot(data=df_777, x="x", y="y")
plt.title("Cell 777 - Seaborn Scatter Plot")
plt.show()
# Cell 778 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_778 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 3
})
sns.scatterplot(data=df_778, x="x", y="y")
plt.title("Cell 778 - Seaborn Scatter Plot")
plt.show()
# Cell 779 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_779 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 4
})
sns.scatterplot(data=df_779, x="x", y="y")
plt.title("Cell 779 - Seaborn Scatter Plot")
plt.show()
# Cell 780 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_780 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
sns.scatterplot(data=df_780, x="x", y="y")
plt.title("Cell 780 - Seaborn Scatter Plot")
plt.show()
# Cell 781 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_781 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
sns.scatterplot(data=df_781, x="x", y="y")
plt.title("Cell 781 - Seaborn Scatter Plot")
plt.show()
# Cell 782 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_782 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
sns.scatterplot(data=df_782, x="x", y="y")
plt.title("Cell 782 - Seaborn Scatter Plot")
plt.show()
# Cell 783 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_783 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 3
})
sns.scatterplot(data=df_783, x="x", y="y")
plt.title("Cell 783 - Seaborn Scatter Plot")
plt.show()
# Cell 784 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_784 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 4
})
sns.scatterplot(data=df_784, x="x", y="y")
plt.title("Cell 784 - Seaborn Scatter Plot")
plt.show()
# Cell 785 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_785 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
sns.scatterplot(data=df_785, x="x", y="y")
plt.title("Cell 785 - Seaborn Scatter Plot")
plt.show()
# Cell 786 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_786 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
sns.scatterplot(data=df_786, x="x", y="y")
plt.title("Cell 786 - Seaborn Scatter Plot")
plt.show()
# Cell 787 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_787 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
sns.scatterplot(data=df_787, x="x", y="y")
plt.title("Cell 787 - Seaborn Scatter Plot")
plt.show()
# Cell 788 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_788 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 3
})
sns.scatterplot(data=df_788, x="x", y="y")
plt.title("Cell 788 - Seaborn Scatter Plot")
plt.show()
# Cell 789 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_789 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 4
})
sns.scatterplot(data=df_789, x="x", y="y")
plt.title("Cell 789 - Seaborn Scatter Plot")
plt.show()
# Cell 790 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_790 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
sns.scatterplot(data=df_790, x="x", y="y")
plt.title("Cell 790 - Seaborn Scatter Plot")
plt.show()
# Cell 791 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_791 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
sns.scatterplot(data=df_791, x="x", y="y")
plt.title("Cell 791 - Seaborn Scatter Plot")
plt.show()
# Cell 792 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_792 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
sns.scatterplot(data=df_792, x="x", y="y")
plt.title("Cell 792 - Seaborn Scatter Plot")
plt.show()
# Cell 793 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_793 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 3
})
sns.scatterplot(data=df_793, x="x", y="y")
plt.title("Cell 793 - Seaborn Scatter Plot")
plt.show()
# Cell 794 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_794 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 4
})
sns.scatterplot(data=df_794, x="x", y="y")
plt.title("Cell 794 - Seaborn Scatter Plot")
plt.show()
# Cell 795 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_795 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
sns.scatterplot(data=df_795, x="x", y="y")
plt.title("Cell 795 - Seaborn Scatter Plot")
plt.show()
# Cell 796 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_796 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
sns.scatterplot(data=df_796, x="x", y="y")
plt.title("Cell 796 - Seaborn Scatter Plot")
plt.show()
# Cell 797 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_797 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
sns.scatterplot(data=df_797, x="x", y="y")
plt.title("Cell 797 - Seaborn Scatter Plot")
plt.show()
# Cell 798 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_798 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 3
})
sns.scatterplot(data=df_798, x="x", y="y")
plt.title("Cell 798 - Seaborn Scatter Plot")
plt.show()
# Cell 799 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_799 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 4
})
sns.scatterplot(data=df_799, x="x", y="y")
plt.title("Cell 799 - Seaborn Scatter Plot")
plt.show()
# Cell 800 - seaborn
import seaborn as sns
import pandas as pd
import numpy as np
df_800 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
sns.scatterplot(data=df_800, x="x", y="y")
plt.title("Cell 800 - Seaborn Scatter Plot")
plt.show()
# Cell 801 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_801 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
fig_801 = px.scatter(df_801, x="x", y="y", title="Cell 801 - Plotly Scatter Plot")
fig_801.show()
# Cell 802 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_802 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
fig_802 = px.scatter(df_802, x="x", y="y", title="Cell 802 - Plotly Scatter Plot")
fig_802.show()
# Cell 803 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_803 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
fig_803 = px.scatter(df_803, x="x", y="y", title="Cell 803 - Plotly Scatter Plot")
fig_803.show()
# Cell 804 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_804 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
fig_804 = px.scatter(df_804, x="x", y="y", title="Cell 804 - Plotly Scatter Plot")
fig_804.show()
# Cell 805 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_805 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
fig_805 = px.scatter(df_805, x="x", y="y", title="Cell 805 - Plotly Scatter Plot")
fig_805.show()
# Cell 806 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_806 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
fig_806 = px.scatter(df_806, x="x", y="y", title="Cell 806 - Plotly Scatter Plot")
fig_806.show()
# Cell 807 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_807 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
fig_807 = px.scatter(df_807, x="x", y="y", title="Cell 807 - Plotly Scatter Plot")
fig_807.show()
# Cell 808 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_808 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
fig_808 = px.scatter(df_808, x="x", y="y", title="Cell 808 - Plotly Scatter Plot")
fig_808.show()
# Cell 809 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_809 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
fig_809 = px.scatter(df_809, x="x", y="y", title="Cell 809 - Plotly Scatter Plot")
fig_809.show()
# Cell 810 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_810 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
fig_810 = px.scatter(df_810, x="x", y="y", title="Cell 810 - Plotly Scatter Plot")
fig_810.show()
# Cell 811 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_811 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
fig_811 = px.scatter(df_811, x="x", y="y", title="Cell 811 - Plotly Scatter Plot")
fig_811.show()
# Cell 812 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_812 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
fig_812 = px.scatter(df_812, x="x", y="y", title="Cell 812 - Plotly Scatter Plot")
fig_812.show()
# Cell 813 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_813 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
fig_813 = px.scatter(df_813, x="x", y="y", title="Cell 813 - Plotly Scatter Plot")
fig_813.show()
# Cell 814 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_814 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
fig_814 = px.scatter(df_814, x="x", y="y", title="Cell 814 - Plotly Scatter Plot")
fig_814.show()
# Cell 815 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_815 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
fig_815 = px.scatter(df_815, x="x", y="y", title="Cell 815 - Plotly Scatter Plot")
fig_815.show()
# Cell 816 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_816 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
fig_816 = px.scatter(df_816, x="x", y="y", title="Cell 816 - Plotly Scatter Plot")
fig_816.show()
# Cell 817 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_817 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
fig_817 = px.scatter(df_817, x="x", y="y", title="Cell 817 - Plotly Scatter Plot")
fig_817.show()
# Cell 818 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_818 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
fig_818 = px.scatter(df_818, x="x", y="y", title="Cell 818 - Plotly Scatter Plot")
fig_818.show()
# Cell 819 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_819 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
fig_819 = px.scatter(df_819, x="x", y="y", title="Cell 819 - Plotly Scatter Plot")
fig_819.show()
# Cell 820 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_820 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
fig_820 = px.scatter(df_820, x="x", y="y", title="Cell 820 - Plotly Scatter Plot")
fig_820.show()
# Cell 821 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_821 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
fig_821 = px.scatter(df_821, x="x", y="y", title="Cell 821 - Plotly Scatter Plot")
fig_821.show()
# Cell 822 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_822 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
fig_822 = px.scatter(df_822, x="x", y="y", title="Cell 822 - Plotly Scatter Plot")
fig_822.show()
# Cell 823 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_823 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
fig_823 = px.scatter(df_823, x="x", y="y", title="Cell 823 - Plotly Scatter Plot")
fig_823.show()
# Cell 824 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_824 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
fig_824 = px.scatter(df_824, x="x", y="y", title="Cell 824 - Plotly Scatter Plot")
fig_824.show()
# Cell 825 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_825 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
fig_825 = px.scatter(df_825, x="x", y="y", title="Cell 825 - Plotly Scatter Plot")
fig_825.show()
# Cell 826 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_826 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
fig_826 = px.scatter(df_826, x="x", y="y", title="Cell 826 - Plotly Scatter Plot")
fig_826.show()
# Cell 827 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_827 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
fig_827 = px.scatter(df_827, x="x", y="y", title="Cell 827 - Plotly Scatter Plot")
fig_827.show()
# Cell 828 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_828 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
fig_828 = px.scatter(df_828, x="x", y="y", title="Cell 828 - Plotly Scatter Plot")
fig_828.show()
# Cell 829 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_829 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
fig_829 = px.scatter(df_829, x="x", y="y", title="Cell 829 - Plotly Scatter Plot")
fig_829.show()
# Cell 830 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_830 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
fig_830 = px.scatter(df_830, x="x", y="y", title="Cell 830 - Plotly Scatter Plot")
fig_830.show()
# Cell 831 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_831 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
fig_831 = px.scatter(df_831, x="x", y="y", title="Cell 831 - Plotly Scatter Plot")
fig_831.show()
# Cell 832 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_832 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
fig_832 = px.scatter(df_832, x="x", y="y", title="Cell 832 - Plotly Scatter Plot")
fig_832.show()
# Cell 833 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_833 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
fig_833 = px.scatter(df_833, x="x", y="y", title="Cell 833 - Plotly Scatter Plot")
fig_833.show()
# Cell 834 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_834 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
fig_834 = px.scatter(df_834, x="x", y="y", title="Cell 834 - Plotly Scatter Plot")
fig_834.show()
# Cell 835 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_835 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
fig_835 = px.scatter(df_835, x="x", y="y", title="Cell 835 - Plotly Scatter Plot")
fig_835.show()
# Cell 836 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_836 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
fig_836 = px.scatter(df_836, x="x", y="y", title="Cell 836 - Plotly Scatter Plot")
fig_836.show()
# Cell 837 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_837 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
fig_837 = px.scatter(df_837, x="x", y="y", title="Cell 837 - Plotly Scatter Plot")
fig_837.show()
# Cell 838 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_838 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
fig_838 = px.scatter(df_838, x="x", y="y", title="Cell 838 - Plotly Scatter Plot")
fig_838.show()
# Cell 839 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_839 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
fig_839 = px.scatter(df_839, x="x", y="y", title="Cell 839 - Plotly Scatter Plot")
fig_839.show()
# Cell 840 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_840 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
fig_840 = px.scatter(df_840, x="x", y="y", title="Cell 840 - Plotly Scatter Plot")
fig_840.show()
# Cell 841 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_841 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
fig_841 = px.scatter(df_841, x="x", y="y", title="Cell 841 - Plotly Scatter Plot")
fig_841.show()
# Cell 842 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_842 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
fig_842 = px.scatter(df_842, x="x", y="y", title="Cell 842 - Plotly Scatter Plot")
fig_842.show()
# Cell 843 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_843 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
fig_843 = px.scatter(df_843, x="x", y="y", title="Cell 843 - Plotly Scatter Plot")
fig_843.show()
# Cell 844 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_844 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
fig_844 = px.scatter(df_844, x="x", y="y", title="Cell 844 - Plotly Scatter Plot")
fig_844.show()
# Cell 845 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_845 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
fig_845 = px.scatter(df_845, x="x", y="y", title="Cell 845 - Plotly Scatter Plot")
fig_845.show()
# Cell 846 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_846 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
fig_846 = px.scatter(df_846, x="x", y="y", title="Cell 846 - Plotly Scatter Plot")
fig_846.show()
# Cell 847 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_847 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
fig_847 = px.scatter(df_847, x="x", y="y", title="Cell 847 - Plotly Scatter Plot")
fig_847.show()
# Cell 848 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_848 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
fig_848 = px.scatter(df_848, x="x", y="y", title="Cell 848 - Plotly Scatter Plot")
fig_848.show()
# Cell 849 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_849 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
fig_849 = px.scatter(df_849, x="x", y="y", title="Cell 849 - Plotly Scatter Plot")
fig_849.show()
# Cell 850 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_850 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
fig_850 = px.scatter(df_850, x="x", y="y", title="Cell 850 - Plotly Scatter Plot")
fig_850.show()
# Cell 851 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_851 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
fig_851 = px.scatter(df_851, x="x", y="y", title="Cell 851 - Plotly Scatter Plot")
fig_851.show()
# Cell 852 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_852 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
fig_852 = px.scatter(df_852, x="x", y="y", title="Cell 852 - Plotly Scatter Plot")
fig_852.show()
# Cell 853 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_853 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
fig_853 = px.scatter(df_853, x="x", y="y", title="Cell 853 - Plotly Scatter Plot")
fig_853.show()
# Cell 854 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_854 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
fig_854 = px.scatter(df_854, x="x", y="y", title="Cell 854 - Plotly Scatter Plot")
fig_854.show()
# Cell 855 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_855 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
fig_855 = px.scatter(df_855, x="x", y="y", title="Cell 855 - Plotly Scatter Plot")
fig_855.show()
# Cell 856 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_856 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
fig_856 = px.scatter(df_856, x="x", y="y", title="Cell 856 - Plotly Scatter Plot")
fig_856.show()
# Cell 857 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_857 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
fig_857 = px.scatter(df_857, x="x", y="y", title="Cell 857 - Plotly Scatter Plot")
fig_857.show()
# Cell 858 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_858 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
fig_858 = px.scatter(df_858, x="x", y="y", title="Cell 858 - Plotly Scatter Plot")
fig_858.show()
# Cell 859 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_859 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
fig_859 = px.scatter(df_859, x="x", y="y", title="Cell 859 - Plotly Scatter Plot")
fig_859.show()
# Cell 860 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_860 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
fig_860 = px.scatter(df_860, x="x", y="y", title="Cell 860 - Plotly Scatter Plot")
fig_860.show()
# Cell 861 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_861 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
fig_861 = px.scatter(df_861, x="x", y="y", title="Cell 861 - Plotly Scatter Plot")
fig_861.show()
# Cell 862 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_862 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
fig_862 = px.scatter(df_862, x="x", y="y", title="Cell 862 - Plotly Scatter Plot")
fig_862.show()
# Cell 863 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_863 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
fig_863 = px.scatter(df_863, x="x", y="y", title="Cell 863 - Plotly Scatter Plot")
fig_863.show()
# Cell 864 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_864 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
fig_864 = px.scatter(df_864, x="x", y="y", title="Cell 864 - Plotly Scatter Plot")
fig_864.show()
# Cell 865 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_865 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
fig_865 = px.scatter(df_865, x="x", y="y", title="Cell 865 - Plotly Scatter Plot")
fig_865.show()
# Cell 866 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_866 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
fig_866 = px.scatter(df_866, x="x", y="y", title="Cell 866 - Plotly Scatter Plot")
fig_866.show()
# Cell 867 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_867 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
fig_867 = px.scatter(df_867, x="x", y="y", title="Cell 867 - Plotly Scatter Plot")
fig_867.show()
# Cell 868 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_868 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
fig_868 = px.scatter(df_868, x="x", y="y", title="Cell 868 - Plotly Scatter Plot")
fig_868.show()
# Cell 869 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_869 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
fig_869 = px.scatter(df_869, x="x", y="y", title="Cell 869 - Plotly Scatter Plot")
fig_869.show()
# Cell 870 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_870 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
fig_870 = px.scatter(df_870, x="x", y="y", title="Cell 870 - Plotly Scatter Plot")
fig_870.show()
# Cell 871 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_871 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
fig_871 = px.scatter(df_871, x="x", y="y", title="Cell 871 - Plotly Scatter Plot")
fig_871.show()
# Cell 872 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_872 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
fig_872 = px.scatter(df_872, x="x", y="y", title="Cell 872 - Plotly Scatter Plot")
fig_872.show()
# Cell 873 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_873 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
fig_873 = px.scatter(df_873, x="x", y="y", title="Cell 873 - Plotly Scatter Plot")
fig_873.show()
# Cell 874 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_874 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
fig_874 = px.scatter(df_874, x="x", y="y", title="Cell 874 - Plotly Scatter Plot")
fig_874.show()
# Cell 875 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_875 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
fig_875 = px.scatter(df_875, x="x", y="y", title="Cell 875 - Plotly Scatter Plot")
fig_875.show()
# Cell 876 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_876 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
fig_876 = px.scatter(df_876, x="x", y="y", title="Cell 876 - Plotly Scatter Plot")
fig_876.show()
# Cell 877 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_877 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
fig_877 = px.scatter(df_877, x="x", y="y", title="Cell 877 - Plotly Scatter Plot")
fig_877.show()
# Cell 878 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_878 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
fig_878 = px.scatter(df_878, x="x", y="y", title="Cell 878 - Plotly Scatter Plot")
fig_878.show()
# Cell 879 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_879 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
fig_879 = px.scatter(df_879, x="x", y="y", title="Cell 879 - Plotly Scatter Plot")
fig_879.show()
# Cell 880 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_880 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
fig_880 = px.scatter(df_880, x="x", y="y", title="Cell 880 - Plotly Scatter Plot")
fig_880.show()
# Cell 881 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_881 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
fig_881 = px.scatter(df_881, x="x", y="y", title="Cell 881 - Plotly Scatter Plot")
fig_881.show()
# Cell 882 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_882 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
fig_882 = px.scatter(df_882, x="x", y="y", title="Cell 882 - Plotly Scatter Plot")
fig_882.show()
# Cell 883 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_883 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
fig_883 = px.scatter(df_883, x="x", y="y", title="Cell 883 - Plotly Scatter Plot")
fig_883.show()
# Cell 884 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_884 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
fig_884 = px.scatter(df_884, x="x", y="y", title="Cell 884 - Plotly Scatter Plot")
fig_884.show()
# Cell 885 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_885 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
fig_885 = px.scatter(df_885, x="x", y="y", title="Cell 885 - Plotly Scatter Plot")
fig_885.show()
# Cell 886 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_886 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
fig_886 = px.scatter(df_886, x="x", y="y", title="Cell 886 - Plotly Scatter Plot")
fig_886.show()
# Cell 887 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_887 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
fig_887 = px.scatter(df_887, x="x", y="y", title="Cell 887 - Plotly Scatter Plot")
fig_887.show()
# Cell 888 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_888 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
fig_888 = px.scatter(df_888, x="x", y="y", title="Cell 888 - Plotly Scatter Plot")
fig_888.show()
# Cell 889 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_889 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
fig_889 = px.scatter(df_889, x="x", y="y", title="Cell 889 - Plotly Scatter Plot")
fig_889.show()
# Cell 890 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_890 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
fig_890 = px.scatter(df_890, x="x", y="y", title="Cell 890 - Plotly Scatter Plot")
fig_890.show()
# Cell 891 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_891 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
fig_891 = px.scatter(df_891, x="x", y="y", title="Cell 891 - Plotly Scatter Plot")
fig_891.show()
# Cell 892 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_892 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
fig_892 = px.scatter(df_892, x="x", y="y", title="Cell 892 - Plotly Scatter Plot")
fig_892.show()
# Cell 893 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_893 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
fig_893 = px.scatter(df_893, x="x", y="y", title="Cell 893 - Plotly Scatter Plot")
fig_893.show()
# Cell 894 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_894 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
fig_894 = px.scatter(df_894, x="x", y="y", title="Cell 894 - Plotly Scatter Plot")
fig_894.show()
# Cell 895 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_895 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
fig_895 = px.scatter(df_895, x="x", y="y", title="Cell 895 - Plotly Scatter Plot")
fig_895.show()
# Cell 896 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_896 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
fig_896 = px.scatter(df_896, x="x", y="y", title="Cell 896 - Plotly Scatter Plot")
fig_896.show()
# Cell 897 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_897 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
fig_897 = px.scatter(df_897, x="x", y="y", title="Cell 897 - Plotly Scatter Plot")
fig_897.show()
# Cell 898 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_898 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
fig_898 = px.scatter(df_898, x="x", y="y", title="Cell 898 - Plotly Scatter Plot")
fig_898.show()
# Cell 899 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_899 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
fig_899 = px.scatter(df_899, x="x", y="y", title="Cell 899 - Plotly Scatter Plot")
fig_899.show()
# Cell 900 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_900 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
fig_900 = px.scatter(df_900, x="x", y="y", title="Cell 900 - Plotly Scatter Plot")
fig_900.show()
# Cell 901 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_901 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
fig_901 = px.scatter(df_901, x="x", y="y", title="Cell 901 - Plotly Scatter Plot")
fig_901.show()
# Cell 902 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_902 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
fig_902 = px.scatter(df_902, x="x", y="y", title="Cell 902 - Plotly Scatter Plot")
fig_902.show()
# Cell 903 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_903 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
fig_903 = px.scatter(df_903, x="x", y="y", title="Cell 903 - Plotly Scatter Plot")
fig_903.show()
# Cell 904 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_904 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
fig_904 = px.scatter(df_904, x="x", y="y", title="Cell 904 - Plotly Scatter Plot")
fig_904.show()
# Cell 905 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_905 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
fig_905 = px.scatter(df_905, x="x", y="y", title="Cell 905 - Plotly Scatter Plot")
fig_905.show()
# Cell 906 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_906 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
fig_906 = px.scatter(df_906, x="x", y="y", title="Cell 906 - Plotly Scatter Plot")
fig_906.show()
# Cell 907 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_907 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
fig_907 = px.scatter(df_907, x="x", y="y", title="Cell 907 - Plotly Scatter Plot")
fig_907.show()
# Cell 908 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_908 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
fig_908 = px.scatter(df_908, x="x", y="y", title="Cell 908 - Plotly Scatter Plot")
fig_908.show()
# Cell 909 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_909 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
fig_909 = px.scatter(df_909, x="x", y="y", title="Cell 909 - Plotly Scatter Plot")
fig_909.show()
# Cell 910 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_910 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
fig_910 = px.scatter(df_910, x="x", y="y", title="Cell 910 - Plotly Scatter Plot")
fig_910.show()
# Cell 911 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_911 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
fig_911 = px.scatter(df_911, x="x", y="y", title="Cell 911 - Plotly Scatter Plot")
fig_911.show()
# Cell 912 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_912 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
fig_912 = px.scatter(df_912, x="x", y="y", title="Cell 912 - Plotly Scatter Plot")
fig_912.show()
# Cell 913 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_913 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
fig_913 = px.scatter(df_913, x="x", y="y", title="Cell 913 - Plotly Scatter Plot")
fig_913.show()
# Cell 914 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_914 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
fig_914 = px.scatter(df_914, x="x", y="y", title="Cell 914 - Plotly Scatter Plot")
fig_914.show()
# Cell 915 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_915 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
fig_915 = px.scatter(df_915, x="x", y="y", title="Cell 915 - Plotly Scatter Plot")
fig_915.show()
# Cell 916 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_916 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
fig_916 = px.scatter(df_916, x="x", y="y", title="Cell 916 - Plotly Scatter Plot")
fig_916.show()
# Cell 917 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_917 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
fig_917 = px.scatter(df_917, x="x", y="y", title="Cell 917 - Plotly Scatter Plot")
fig_917.show()
# Cell 918 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_918 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
fig_918 = px.scatter(df_918, x="x", y="y", title="Cell 918 - Plotly Scatter Plot")
fig_918.show()
# Cell 919 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_919 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
fig_919 = px.scatter(df_919, x="x", y="y", title="Cell 919 - Plotly Scatter Plot")
fig_919.show()
# Cell 920 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_920 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
fig_920 = px.scatter(df_920, x="x", y="y", title="Cell 920 - Plotly Scatter Plot")
fig_920.show()
# Cell 921 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_921 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
fig_921 = px.scatter(df_921, x="x", y="y", title="Cell 921 - Plotly Scatter Plot")
fig_921.show()
# Cell 922 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_922 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
fig_922 = px.scatter(df_922, x="x", y="y", title="Cell 922 - Plotly Scatter Plot")
fig_922.show()
# Cell 923 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_923 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
fig_923 = px.scatter(df_923, x="x", y="y", title="Cell 923 - Plotly Scatter Plot")
fig_923.show()
# Cell 924 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_924 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
fig_924 = px.scatter(df_924, x="x", y="y", title="Cell 924 - Plotly Scatter Plot")
fig_924.show()
# Cell 925 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_925 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
fig_925 = px.scatter(df_925, x="x", y="y", title="Cell 925 - Plotly Scatter Plot")
fig_925.show()
# Cell 926 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_926 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
fig_926 = px.scatter(df_926, x="x", y="y", title="Cell 926 - Plotly Scatter Plot")
fig_926.show()
# Cell 927 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_927 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
fig_927 = px.scatter(df_927, x="x", y="y", title="Cell 927 - Plotly Scatter Plot")
fig_927.show()
# Cell 928 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_928 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
fig_928 = px.scatter(df_928, x="x", y="y", title="Cell 928 - Plotly Scatter Plot")
fig_928.show()
# Cell 929 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_929 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
fig_929 = px.scatter(df_929, x="x", y="y", title="Cell 929 - Plotly Scatter Plot")
fig_929.show()
# Cell 930 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_930 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
fig_930 = px.scatter(df_930, x="x", y="y", title="Cell 930 - Plotly Scatter Plot")
fig_930.show()
# Cell 931 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_931 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
fig_931 = px.scatter(df_931, x="x", y="y", title="Cell 931 - Plotly Scatter Plot")
fig_931.show()
# Cell 932 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_932 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
fig_932 = px.scatter(df_932, x="x", y="y", title="Cell 932 - Plotly Scatter Plot")
fig_932.show()
# Cell 933 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_933 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
fig_933 = px.scatter(df_933, x="x", y="y", title="Cell 933 - Plotly Scatter Plot")
fig_933.show()
# Cell 934 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_934 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
fig_934 = px.scatter(df_934, x="x", y="y", title="Cell 934 - Plotly Scatter Plot")
fig_934.show()
# Cell 935 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_935 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
fig_935 = px.scatter(df_935, x="x", y="y", title="Cell 935 - Plotly Scatter Plot")
fig_935.show()
# Cell 936 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_936 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
fig_936 = px.scatter(df_936, x="x", y="y", title="Cell 936 - Plotly Scatter Plot")
fig_936.show()
# Cell 937 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_937 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
fig_937 = px.scatter(df_937, x="x", y="y", title="Cell 937 - Plotly Scatter Plot")
fig_937.show()
# Cell 938 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_938 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
fig_938 = px.scatter(df_938, x="x", y="y", title="Cell 938 - Plotly Scatter Plot")
fig_938.show()
# Cell 939 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_939 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
fig_939 = px.scatter(df_939, x="x", y="y", title="Cell 939 - Plotly Scatter Plot")
fig_939.show()
# Cell 940 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_940 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
fig_940 = px.scatter(df_940, x="x", y="y", title="Cell 940 - Plotly Scatter Plot")
fig_940.show()
# Cell 941 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_941 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
fig_941 = px.scatter(df_941, x="x", y="y", title="Cell 941 - Plotly Scatter Plot")
fig_941.show()
# Cell 942 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_942 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
fig_942 = px.scatter(df_942, x="x", y="y", title="Cell 942 - Plotly Scatter Plot")
fig_942.show()
# Cell 943 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_943 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
fig_943 = px.scatter(df_943, x="x", y="y", title="Cell 943 - Plotly Scatter Plot")
fig_943.show()
# Cell 944 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_944 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
fig_944 = px.scatter(df_944, x="x", y="y", title="Cell 944 - Plotly Scatter Plot")
fig_944.show()
# Cell 945 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_945 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
fig_945 = px.scatter(df_945, x="x", y="y", title="Cell 945 - Plotly Scatter Plot")
fig_945.show()
# Cell 946 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_946 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
fig_946 = px.scatter(df_946, x="x", y="y", title="Cell 946 - Plotly Scatter Plot")
fig_946.show()
# Cell 947 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_947 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
fig_947 = px.scatter(df_947, x="x", y="y", title="Cell 947 - Plotly Scatter Plot")
fig_947.show()
# Cell 948 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_948 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
fig_948 = px.scatter(df_948, x="x", y="y", title="Cell 948 - Plotly Scatter Plot")
fig_948.show()
# Cell 949 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_949 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
fig_949 = px.scatter(df_949, x="x", y="y", title="Cell 949 - Plotly Scatter Plot")
fig_949.show()
# Cell 950 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_950 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
fig_950 = px.scatter(df_950, x="x", y="y", title="Cell 950 - Plotly Scatter Plot")
fig_950.show()
# Cell 951 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_951 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
fig_951 = px.scatter(df_951, x="x", y="y", title="Cell 951 - Plotly Scatter Plot")
fig_951.show()
# Cell 952 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_952 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
fig_952 = px.scatter(df_952, x="x", y="y", title="Cell 952 - Plotly Scatter Plot")
fig_952.show()
# Cell 953 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_953 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
fig_953 = px.scatter(df_953, x="x", y="y", title="Cell 953 - Plotly Scatter Plot")
fig_953.show()
# Cell 954 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_954 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
fig_954 = px.scatter(df_954, x="x", y="y", title="Cell 954 - Plotly Scatter Plot")
fig_954.show()
# Cell 955 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_955 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
fig_955 = px.scatter(df_955, x="x", y="y", title="Cell 955 - Plotly Scatter Plot")
fig_955.show()
# Cell 956 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_956 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
fig_956 = px.scatter(df_956, x="x", y="y", title="Cell 956 - Plotly Scatter Plot")
fig_956.show()
# Cell 957 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_957 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
fig_957 = px.scatter(df_957, x="x", y="y", title="Cell 957 - Plotly Scatter Plot")
fig_957.show()
# Cell 958 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_958 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
fig_958 = px.scatter(df_958, x="x", y="y", title="Cell 958 - Plotly Scatter Plot")
fig_958.show()
# Cell 959 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_959 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
fig_959 = px.scatter(df_959, x="x", y="y", title="Cell 959 - Plotly Scatter Plot")
fig_959.show()
# Cell 960 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_960 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
fig_960 = px.scatter(df_960, x="x", y="y", title="Cell 960 - Plotly Scatter Plot")
fig_960.show()
# Cell 961 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_961 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
fig_961 = px.scatter(df_961, x="x", y="y", title="Cell 961 - Plotly Scatter Plot")
fig_961.show()
# Cell 962 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_962 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
fig_962 = px.scatter(df_962, x="x", y="y", title="Cell 962 - Plotly Scatter Plot")
fig_962.show()
# Cell 963 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_963 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
fig_963 = px.scatter(df_963, x="x", y="y", title="Cell 963 - Plotly Scatter Plot")
fig_963.show()
# Cell 964 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_964 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
fig_964 = px.scatter(df_964, x="x", y="y", title="Cell 964 - Plotly Scatter Plot")
fig_964.show()
# Cell 965 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_965 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
fig_965 = px.scatter(df_965, x="x", y="y", title="Cell 965 - Plotly Scatter Plot")
fig_965.show()
# Cell 966 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_966 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
fig_966 = px.scatter(df_966, x="x", y="y", title="Cell 966 - Plotly Scatter Plot")
fig_966.show()
# Cell 967 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_967 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
fig_967 = px.scatter(df_967, x="x", y="y", title="Cell 967 - Plotly Scatter Plot")
fig_967.show()
# Cell 968 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_968 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
fig_968 = px.scatter(df_968, x="x", y="y", title="Cell 968 - Plotly Scatter Plot")
fig_968.show()
# Cell 969 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_969 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
fig_969 = px.scatter(df_969, x="x", y="y", title="Cell 969 - Plotly Scatter Plot")
fig_969.show()
# Cell 970 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_970 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
fig_970 = px.scatter(df_970, x="x", y="y", title="Cell 970 - Plotly Scatter Plot")
fig_970.show()
# Cell 971 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_971 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
fig_971 = px.scatter(df_971, x="x", y="y", title="Cell 971 - Plotly Scatter Plot")
fig_971.show()
# Cell 972 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_972 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
fig_972 = px.scatter(df_972, x="x", y="y", title="Cell 972 - Plotly Scatter Plot")
fig_972.show()
# Cell 973 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_973 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
fig_973 = px.scatter(df_973, x="x", y="y", title="Cell 973 - Plotly Scatter Plot")
fig_973.show()
# Cell 974 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_974 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
fig_974 = px.scatter(df_974, x="x", y="y", title="Cell 974 - Plotly Scatter Plot")
fig_974.show()
# Cell 975 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_975 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
fig_975 = px.scatter(df_975, x="x", y="y", title="Cell 975 - Plotly Scatter Plot")
fig_975.show()
# Cell 976 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_976 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
fig_976 = px.scatter(df_976, x="x", y="y", title="Cell 976 - Plotly Scatter Plot")
fig_976.show()
# Cell 977 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_977 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
fig_977 = px.scatter(df_977, x="x", y="y", title="Cell 977 - Plotly Scatter Plot")
fig_977.show()
# Cell 978 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_978 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
fig_978 = px.scatter(df_978, x="x", y="y", title="Cell 978 - Plotly Scatter Plot")
fig_978.show()
# Cell 979 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_979 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
fig_979 = px.scatter(df_979, x="x", y="y", title="Cell 979 - Plotly Scatter Plot")
fig_979.show()
# Cell 980 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_980 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
fig_980 = px.scatter(df_980, x="x", y="y", title="Cell 980 - Plotly Scatter Plot")
fig_980.show()
# Cell 981 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_981 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
fig_981 = px.scatter(df_981, x="x", y="y", title="Cell 981 - Plotly Scatter Plot")
fig_981.show()
# Cell 982 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_982 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
fig_982 = px.scatter(df_982, x="x", y="y", title="Cell 982 - Plotly Scatter Plot")
fig_982.show()
# Cell 983 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_983 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
fig_983 = px.scatter(df_983, x="x", y="y", title="Cell 983 - Plotly Scatter Plot")
fig_983.show()
# Cell 984 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_984 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
fig_984 = px.scatter(df_984, x="x", y="y", title="Cell 984 - Plotly Scatter Plot")
fig_984.show()
# Cell 985 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_985 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
fig_985 = px.scatter(df_985, x="x", y="y", title="Cell 985 - Plotly Scatter Plot")
fig_985.show()
# Cell 986 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_986 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
fig_986 = px.scatter(df_986, x="x", y="y", title="Cell 986 - Plotly Scatter Plot")
fig_986.show()
# Cell 987 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_987 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
fig_987 = px.scatter(df_987, x="x", y="y", title="Cell 987 - Plotly Scatter Plot")
fig_987.show()
# Cell 988 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_988 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
fig_988 = px.scatter(df_988, x="x", y="y", title="Cell 988 - Plotly Scatter Plot")
fig_988.show()
# Cell 989 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_989 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
fig_989 = px.scatter(df_989, x="x", y="y", title="Cell 989 - Plotly Scatter Plot")
fig_989.show()
# Cell 990 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_990 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
fig_990 = px.scatter(df_990, x="x", y="y", title="Cell 990 - Plotly Scatter Plot")
fig_990.show()
# Cell 991 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_991 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
fig_991 = px.scatter(df_991, x="x", y="y", title="Cell 991 - Plotly Scatter Plot")
fig_991.show()
# Cell 992 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_992 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
fig_992 = px.scatter(df_992, x="x", y="y", title="Cell 992 - Plotly Scatter Plot")
fig_992.show()
# Cell 993 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_993 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
fig_993 = px.scatter(df_993, x="x", y="y", title="Cell 993 - Plotly Scatter Plot")
fig_993.show()
# Cell 994 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_994 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
fig_994 = px.scatter(df_994, x="x", y="y", title="Cell 994 - Plotly Scatter Plot")
fig_994.show()
# Cell 995 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_995 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
fig_995 = px.scatter(df_995, x="x", y="y", title="Cell 995 - Plotly Scatter Plot")
fig_995.show()
# Cell 996 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_996 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
fig_996 = px.scatter(df_996, x="x", y="y", title="Cell 996 - Plotly Scatter Plot")
fig_996.show()
# Cell 997 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_997 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
fig_997 = px.scatter(df_997, x="x", y="y", title="Cell 997 - Plotly Scatter Plot")
fig_997.show()
# Cell 998 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_998 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
fig_998 = px.scatter(df_998, x="x", y="y", title="Cell 998 - Plotly Scatter Plot")
fig_998.show()
# Cell 999 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_999 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
fig_999 = px.scatter(df_999, x="x", y="y", title="Cell 999 - Plotly Scatter Plot")
fig_999.show()
# Cell 1000 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_1000 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
fig_1000 = px.scatter(df_1000, x="x", y="y", title="Cell 1000 - Plotly Scatter Plot")
fig_1000.show()
# Cell 1001 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_1001 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
fig_1001 = px.scatter(df_1001, x="x", y="y", title="Cell 1001 - Plotly Scatter Plot")
fig_1001.show()
# Cell 1002 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_1002 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
fig_1002 = px.scatter(df_1002, x="x", y="y", title="Cell 1002 - Plotly Scatter Plot")
fig_1002.show()
# Cell 1003 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_1003 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
fig_1003 = px.scatter(df_1003, x="x", y="y", title="Cell 1003 - Plotly Scatter Plot")
fig_1003.show()
# Cell 1004 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_1004 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
fig_1004 = px.scatter(df_1004, x="x", y="y", title="Cell 1004 - Plotly Scatter Plot")
fig_1004.show()
# Cell 1005 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_1005 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
fig_1005 = px.scatter(df_1005, x="x", y="y", title="Cell 1005 - Plotly Scatter Plot")
fig_1005.show()
# Cell 1006 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_1006 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
fig_1006 = px.scatter(df_1006, x="x", y="y", title="Cell 1006 - Plotly Scatter Plot")
fig_1006.show()
# Cell 1007 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_1007 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
fig_1007 = px.scatter(df_1007, x="x", y="y", title="Cell 1007 - Plotly Scatter Plot")
fig_1007.show()
# Cell 1008 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_1008 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
fig_1008 = px.scatter(df_1008, x="x", y="y", title="Cell 1008 - Plotly Scatter Plot")
fig_1008.show()
# Cell 1009 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_1009 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
fig_1009 = px.scatter(df_1009, x="x", y="y", title="Cell 1009 - Plotly Scatter Plot")
fig_1009.show()
# Cell 1010 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_1010 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
fig_1010 = px.scatter(df_1010, x="x", y="y", title="Cell 1010 - Plotly Scatter Plot")
fig_1010.show()
# Cell 1011 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_1011 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
fig_1011 = px.scatter(df_1011, x="x", y="y", title="Cell 1011 - Plotly Scatter Plot")
fig_1011.show()
# Cell 1012 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_1012 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
fig_1012 = px.scatter(df_1012, x="x", y="y", title="Cell 1012 - Plotly Scatter Plot")
fig_1012.show()
# Cell 1013 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_1013 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
fig_1013 = px.scatter(df_1013, x="x", y="y", title="Cell 1013 - Plotly Scatter Plot")
fig_1013.show()
# Cell 1014 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_1014 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
fig_1014 = px.scatter(df_1014, x="x", y="y", title="Cell 1014 - Plotly Scatter Plot")
fig_1014.show()
# Cell 1015 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_1015 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
fig_1015 = px.scatter(df_1015, x="x", y="y", title="Cell 1015 - Plotly Scatter Plot")
fig_1015.show()
# Cell 1016 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_1016 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
fig_1016 = px.scatter(df_1016, x="x", y="y", title="Cell 1016 - Plotly Scatter Plot")
fig_1016.show()
# Cell 1017 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_1017 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
fig_1017 = px.scatter(df_1017, x="x", y="y", title="Cell 1017 - Plotly Scatter Plot")
fig_1017.show()
# Cell 1018 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_1018 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
fig_1018 = px.scatter(df_1018, x="x", y="y", title="Cell 1018 - Plotly Scatter Plot")
fig_1018.show()
# Cell 1019 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_1019 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
fig_1019 = px.scatter(df_1019, x="x", y="y", title="Cell 1019 - Plotly Scatter Plot")
fig_1019.show()
# Cell 1020 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_1020 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
fig_1020 = px.scatter(df_1020, x="x", y="y", title="Cell 1020 - Plotly Scatter Plot")
fig_1020.show()
# Cell 1021 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_1021 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
fig_1021 = px.scatter(df_1021, x="x", y="y", title="Cell 1021 - Plotly Scatter Plot")
fig_1021.show()
# Cell 1022 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_1022 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
fig_1022 = px.scatter(df_1022, x="x", y="y", title="Cell 1022 - Plotly Scatter Plot")
fig_1022.show()
# Cell 1023 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_1023 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
fig_1023 = px.scatter(df_1023, x="x", y="y", title="Cell 1023 - Plotly Scatter Plot")
fig_1023.show()
# Cell 1024 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_1024 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
fig_1024 = px.scatter(df_1024, x="x", y="y", title="Cell 1024 - Plotly Scatter Plot")
fig_1024.show()
# Cell 1025 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_1025 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
fig_1025 = px.scatter(df_1025, x="x", y="y", title="Cell 1025 - Plotly Scatter Plot")
fig_1025.show()
# Cell 1026 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_1026 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
fig_1026 = px.scatter(df_1026, x="x", y="y", title="Cell 1026 - Plotly Scatter Plot")
fig_1026.show()
# Cell 1027 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_1027 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
fig_1027 = px.scatter(df_1027, x="x", y="y", title="Cell 1027 - Plotly Scatter Plot")
fig_1027.show()
# Cell 1028 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_1028 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
fig_1028 = px.scatter(df_1028, x="x", y="y", title="Cell 1028 - Plotly Scatter Plot")
fig_1028.show()
# Cell 1029 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_1029 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
fig_1029 = px.scatter(df_1029, x="x", y="y", title="Cell 1029 - Plotly Scatter Plot")
fig_1029.show()
# Cell 1030 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_1030 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
fig_1030 = px.scatter(df_1030, x="x", y="y", title="Cell 1030 - Plotly Scatter Plot")
fig_1030.show()
# Cell 1031 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_1031 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
fig_1031 = px.scatter(df_1031, x="x", y="y", title="Cell 1031 - Plotly Scatter Plot")
fig_1031.show()
# Cell 1032 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_1032 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
fig_1032 = px.scatter(df_1032, x="x", y="y", title="Cell 1032 - Plotly Scatter Plot")
fig_1032.show()
# Cell 1033 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_1033 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
fig_1033 = px.scatter(df_1033, x="x", y="y", title="Cell 1033 - Plotly Scatter Plot")
fig_1033.show()
# Cell 1034 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_1034 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
fig_1034 = px.scatter(df_1034, x="x", y="y", title="Cell 1034 - Plotly Scatter Plot")
fig_1034.show()
# Cell 1035 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_1035 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
fig_1035 = px.scatter(df_1035, x="x", y="y", title="Cell 1035 - Plotly Scatter Plot")
fig_1035.show()
# Cell 1036 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_1036 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
fig_1036 = px.scatter(df_1036, x="x", y="y", title="Cell 1036 - Plotly Scatter Plot")
fig_1036.show()
# Cell 1037 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_1037 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
fig_1037 = px.scatter(df_1037, x="x", y="y", title="Cell 1037 - Plotly Scatter Plot")
fig_1037.show()
# Cell 1038 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_1038 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
fig_1038 = px.scatter(df_1038, x="x", y="y", title="Cell 1038 - Plotly Scatter Plot")
fig_1038.show()
# Cell 1039 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_1039 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
fig_1039 = px.scatter(df_1039, x="x", y="y", title="Cell 1039 - Plotly Scatter Plot")
fig_1039.show()
# Cell 1040 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_1040 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
fig_1040 = px.scatter(df_1040, x="x", y="y", title="Cell 1040 - Plotly Scatter Plot")
fig_1040.show()
# Cell 1041 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_1041 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
fig_1041 = px.scatter(df_1041, x="x", y="y", title="Cell 1041 - Plotly Scatter Plot")
fig_1041.show()
# Cell 1042 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_1042 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
fig_1042 = px.scatter(df_1042, x="x", y="y", title="Cell 1042 - Plotly Scatter Plot")
fig_1042.show()
# Cell 1043 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_1043 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
fig_1043 = px.scatter(df_1043, x="x", y="y", title="Cell 1043 - Plotly Scatter Plot")
fig_1043.show()
# Cell 1044 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_1044 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
fig_1044 = px.scatter(df_1044, x="x", y="y", title="Cell 1044 - Plotly Scatter Plot")
fig_1044.show()
# Cell 1045 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_1045 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
fig_1045 = px.scatter(df_1045, x="x", y="y", title="Cell 1045 - Plotly Scatter Plot")
fig_1045.show()
# Cell 1046 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_1046 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
fig_1046 = px.scatter(df_1046, x="x", y="y", title="Cell 1046 - Plotly Scatter Plot")
fig_1046.show()
# Cell 1047 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_1047 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
fig_1047 = px.scatter(df_1047, x="x", y="y", title="Cell 1047 - Plotly Scatter Plot")
fig_1047.show()
# Cell 1048 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_1048 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
fig_1048 = px.scatter(df_1048, x="x", y="y", title="Cell 1048 - Plotly Scatter Plot")
fig_1048.show()
# Cell 1049 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_1049 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
fig_1049 = px.scatter(df_1049, x="x", y="y", title="Cell 1049 - Plotly Scatter Plot")
fig_1049.show()
# Cell 1050 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_1050 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
fig_1050 = px.scatter(df_1050, x="x", y="y", title="Cell 1050 - Plotly Scatter Plot")
fig_1050.show()
# Cell 1051 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_1051 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
fig_1051 = px.scatter(df_1051, x="x", y="y", title="Cell 1051 - Plotly Scatter Plot")
fig_1051.show()
# Cell 1052 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_1052 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
fig_1052 = px.scatter(df_1052, x="x", y="y", title="Cell 1052 - Plotly Scatter Plot")
fig_1052.show()
# Cell 1053 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_1053 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
fig_1053 = px.scatter(df_1053, x="x", y="y", title="Cell 1053 - Plotly Scatter Plot")
fig_1053.show()
# Cell 1054 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_1054 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
fig_1054 = px.scatter(df_1054, x="x", y="y", title="Cell 1054 - Plotly Scatter Plot")
fig_1054.show()
# Cell 1055 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_1055 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
fig_1055 = px.scatter(df_1055, x="x", y="y", title="Cell 1055 - Plotly Scatter Plot")
fig_1055.show()
# Cell 1056 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_1056 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
fig_1056 = px.scatter(df_1056, x="x", y="y", title="Cell 1056 - Plotly Scatter Plot")
fig_1056.show()
# Cell 1057 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_1057 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
fig_1057 = px.scatter(df_1057, x="x", y="y", title="Cell 1057 - Plotly Scatter Plot")
fig_1057.show()
# Cell 1058 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_1058 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
fig_1058 = px.scatter(df_1058, x="x", y="y", title="Cell 1058 - Plotly Scatter Plot")
fig_1058.show()
# Cell 1059 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_1059 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
fig_1059 = px.scatter(df_1059, x="x", y="y", title="Cell 1059 - Plotly Scatter Plot")
fig_1059.show()
# Cell 1060 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_1060 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
fig_1060 = px.scatter(df_1060, x="x", y="y", title="Cell 1060 - Plotly Scatter Plot")
fig_1060.show()
# Cell 1061 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_1061 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
fig_1061 = px.scatter(df_1061, x="x", y="y", title="Cell 1061 - Plotly Scatter Plot")
fig_1061.show()
# Cell 1062 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_1062 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
fig_1062 = px.scatter(df_1062, x="x", y="y", title="Cell 1062 - Plotly Scatter Plot")
fig_1062.show()
# Cell 1063 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_1063 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
fig_1063 = px.scatter(df_1063, x="x", y="y", title="Cell 1063 - Plotly Scatter Plot")
fig_1063.show()
# Cell 1064 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_1064 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
fig_1064 = px.scatter(df_1064, x="x", y="y", title="Cell 1064 - Plotly Scatter Plot")
fig_1064.show()
# Cell 1065 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_1065 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
fig_1065 = px.scatter(df_1065, x="x", y="y", title="Cell 1065 - Plotly Scatter Plot")
fig_1065.show()
# Cell 1066 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_1066 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
fig_1066 = px.scatter(df_1066, x="x", y="y", title="Cell 1066 - Plotly Scatter Plot")
fig_1066.show()
# Cell 1067 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_1067 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
fig_1067 = px.scatter(df_1067, x="x", y="y", title="Cell 1067 - Plotly Scatter Plot")
fig_1067.show()
# Cell 1068 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_1068 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
fig_1068 = px.scatter(df_1068, x="x", y="y", title="Cell 1068 - Plotly Scatter Plot")
fig_1068.show()
# Cell 1069 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_1069 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
fig_1069 = px.scatter(df_1069, x="x", y="y", title="Cell 1069 - Plotly Scatter Plot")
fig_1069.show()
# Cell 1070 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_1070 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
fig_1070 = px.scatter(df_1070, x="x", y="y", title="Cell 1070 - Plotly Scatter Plot")
fig_1070.show()
# Cell 1071 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_1071 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
fig_1071 = px.scatter(df_1071, x="x", y="y", title="Cell 1071 - Plotly Scatter Plot")
fig_1071.show()
# Cell 1072 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_1072 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
fig_1072 = px.scatter(df_1072, x="x", y="y", title="Cell 1072 - Plotly Scatter Plot")
fig_1072.show()
# Cell 1073 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_1073 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
fig_1073 = px.scatter(df_1073, x="x", y="y", title="Cell 1073 - Plotly Scatter Plot")
fig_1073.show()
# Cell 1074 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_1074 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
fig_1074 = px.scatter(df_1074, x="x", y="y", title="Cell 1074 - Plotly Scatter Plot")
fig_1074.show()
# Cell 1075 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_1075 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
fig_1075 = px.scatter(df_1075, x="x", y="y", title="Cell 1075 - Plotly Scatter Plot")
fig_1075.show()
# Cell 1076 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_1076 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
fig_1076 = px.scatter(df_1076, x="x", y="y", title="Cell 1076 - Plotly Scatter Plot")
fig_1076.show()
# Cell 1077 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_1077 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
fig_1077 = px.scatter(df_1077, x="x", y="y", title="Cell 1077 - Plotly Scatter Plot")
fig_1077.show()
# Cell 1078 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_1078 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
fig_1078 = px.scatter(df_1078, x="x", y="y", title="Cell 1078 - Plotly Scatter Plot")
fig_1078.show()
# Cell 1079 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_1079 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
fig_1079 = px.scatter(df_1079, x="x", y="y", title="Cell 1079 - Plotly Scatter Plot")
fig_1079.show()
# Cell 1080 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_1080 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
fig_1080 = px.scatter(df_1080, x="x", y="y", title="Cell 1080 - Plotly Scatter Plot")
fig_1080.show()
# Cell 1081 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_1081 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
fig_1081 = px.scatter(df_1081, x="x", y="y", title="Cell 1081 - Plotly Scatter Plot")
fig_1081.show()
# Cell 1082 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_1082 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
fig_1082 = px.scatter(df_1082, x="x", y="y", title="Cell 1082 - Plotly Scatter Plot")
fig_1082.show()
# Cell 1083 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_1083 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
fig_1083 = px.scatter(df_1083, x="x", y="y", title="Cell 1083 - Plotly Scatter Plot")
fig_1083.show()
# Cell 1084 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_1084 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
fig_1084 = px.scatter(df_1084, x="x", y="y", title="Cell 1084 - Plotly Scatter Plot")
fig_1084.show()
# Cell 1085 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_1085 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
fig_1085 = px.scatter(df_1085, x="x", y="y", title="Cell 1085 - Plotly Scatter Plot")
fig_1085.show()
# Cell 1086 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_1086 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
fig_1086 = px.scatter(df_1086, x="x", y="y", title="Cell 1086 - Plotly Scatter Plot")
fig_1086.show()
# Cell 1087 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_1087 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
fig_1087 = px.scatter(df_1087, x="x", y="y", title="Cell 1087 - Plotly Scatter Plot")
fig_1087.show()
# Cell 1088 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_1088 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
fig_1088 = px.scatter(df_1088, x="x", y="y", title="Cell 1088 - Plotly Scatter Plot")
fig_1088.show()
# Cell 1089 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_1089 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
fig_1089 = px.scatter(df_1089, x="x", y="y", title="Cell 1089 - Plotly Scatter Plot")
fig_1089.show()
# Cell 1090 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_1090 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
fig_1090 = px.scatter(df_1090, x="x", y="y", title="Cell 1090 - Plotly Scatter Plot")
fig_1090.show()
# Cell 1091 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_1091 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
fig_1091 = px.scatter(df_1091, x="x", y="y", title="Cell 1091 - Plotly Scatter Plot")
fig_1091.show()
# Cell 1092 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_1092 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
fig_1092 = px.scatter(df_1092, x="x", y="y", title="Cell 1092 - Plotly Scatter Plot")
fig_1092.show()
# Cell 1093 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_1093 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
fig_1093 = px.scatter(df_1093, x="x", y="y", title="Cell 1093 - Plotly Scatter Plot")
fig_1093.show()
# Cell 1094 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_1094 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
fig_1094 = px.scatter(df_1094, x="x", y="y", title="Cell 1094 - Plotly Scatter Plot")
fig_1094.show()
# Cell 1095 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_1095 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
fig_1095 = px.scatter(df_1095, x="x", y="y", title="Cell 1095 - Plotly Scatter Plot")
fig_1095.show()
# Cell 1096 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_1096 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
fig_1096 = px.scatter(df_1096, x="x", y="y", title="Cell 1096 - Plotly Scatter Plot")
fig_1096.show()
# Cell 1097 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_1097 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
fig_1097 = px.scatter(df_1097, x="x", y="y", title="Cell 1097 - Plotly Scatter Plot")
fig_1097.show()
# Cell 1098 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_1098 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
fig_1098 = px.scatter(df_1098, x="x", y="y", title="Cell 1098 - Plotly Scatter Plot")
fig_1098.show()
# Cell 1099 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_1099 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
fig_1099 = px.scatter(df_1099, x="x", y="y", title="Cell 1099 - Plotly Scatter Plot")
fig_1099.show()
# Cell 1100 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_1100 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
fig_1100 = px.scatter(df_1100, x="x", y="y", title="Cell 1100 - Plotly Scatter Plot")
fig_1100.show()
# Cell 1101 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_1101 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
fig_1101 = px.scatter(df_1101, x="x", y="y", title="Cell 1101 - Plotly Scatter Plot")
fig_1101.show()
# Cell 1102 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_1102 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
fig_1102 = px.scatter(df_1102, x="x", y="y", title="Cell 1102 - Plotly Scatter Plot")
fig_1102.show()
# Cell 1103 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_1103 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
fig_1103 = px.scatter(df_1103, x="x", y="y", title="Cell 1103 - Plotly Scatter Plot")
fig_1103.show()
# Cell 1104 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_1104 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
fig_1104 = px.scatter(df_1104, x="x", y="y", title="Cell 1104 - Plotly Scatter Plot")
fig_1104.show()
# Cell 1105 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_1105 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
fig_1105 = px.scatter(df_1105, x="x", y="y", title="Cell 1105 - Plotly Scatter Plot")
fig_1105.show()
# Cell 1106 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_1106 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
fig_1106 = px.scatter(df_1106, x="x", y="y", title="Cell 1106 - Plotly Scatter Plot")
fig_1106.show()
# Cell 1107 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_1107 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
fig_1107 = px.scatter(df_1107, x="x", y="y", title="Cell 1107 - Plotly Scatter Plot")
fig_1107.show()
# Cell 1108 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_1108 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
fig_1108 = px.scatter(df_1108, x="x", y="y", title="Cell 1108 - Plotly Scatter Plot")
fig_1108.show()
# Cell 1109 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_1109 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
fig_1109 = px.scatter(df_1109, x="x", y="y", title="Cell 1109 - Plotly Scatter Plot")
fig_1109.show()
# Cell 1110 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_1110 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
fig_1110 = px.scatter(df_1110, x="x", y="y", title="Cell 1110 - Plotly Scatter Plot")
fig_1110.show()
# Cell 1111 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_1111 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
fig_1111 = px.scatter(df_1111, x="x", y="y", title="Cell 1111 - Plotly Scatter Plot")
fig_1111.show()
# Cell 1112 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_1112 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
fig_1112 = px.scatter(df_1112, x="x", y="y", title="Cell 1112 - Plotly Scatter Plot")
fig_1112.show()
# Cell 1113 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_1113 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
fig_1113 = px.scatter(df_1113, x="x", y="y", title="Cell 1113 - Plotly Scatter Plot")
fig_1113.show()
# Cell 1114 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_1114 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
fig_1114 = px.scatter(df_1114, x="x", y="y", title="Cell 1114 - Plotly Scatter Plot")
fig_1114.show()
# Cell 1115 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_1115 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
fig_1115 = px.scatter(df_1115, x="x", y="y", title="Cell 1115 - Plotly Scatter Plot")
fig_1115.show()
# Cell 1116 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_1116 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
fig_1116 = px.scatter(df_1116, x="x", y="y", title="Cell 1116 - Plotly Scatter Plot")
fig_1116.show()
# Cell 1117 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_1117 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
fig_1117 = px.scatter(df_1117, x="x", y="y", title="Cell 1117 - Plotly Scatter Plot")
fig_1117.show()
# Cell 1118 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_1118 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
fig_1118 = px.scatter(df_1118, x="x", y="y", title="Cell 1118 - Plotly Scatter Plot")
fig_1118.show()
# Cell 1119 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_1119 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
fig_1119 = px.scatter(df_1119, x="x", y="y", title="Cell 1119 - Plotly Scatter Plot")
fig_1119.show()
# Cell 1120 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_1120 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
fig_1120 = px.scatter(df_1120, x="x", y="y", title="Cell 1120 - Plotly Scatter Plot")
fig_1120.show()
# Cell 1121 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_1121 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
fig_1121 = px.scatter(df_1121, x="x", y="y", title="Cell 1121 - Plotly Scatter Plot")
fig_1121.show()
# Cell 1122 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_1122 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
fig_1122 = px.scatter(df_1122, x="x", y="y", title="Cell 1122 - Plotly Scatter Plot")
fig_1122.show()
# Cell 1123 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_1123 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
fig_1123 = px.scatter(df_1123, x="x", y="y", title="Cell 1123 - Plotly Scatter Plot")
fig_1123.show()
# Cell 1124 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_1124 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
fig_1124 = px.scatter(df_1124, x="x", y="y", title="Cell 1124 - Plotly Scatter Plot")
fig_1124.show()
# Cell 1125 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_1125 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
fig_1125 = px.scatter(df_1125, x="x", y="y", title="Cell 1125 - Plotly Scatter Plot")
fig_1125.show()
# Cell 1126 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_1126 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
fig_1126 = px.scatter(df_1126, x="x", y="y", title="Cell 1126 - Plotly Scatter Plot")
fig_1126.show()
# Cell 1127 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_1127 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
fig_1127 = px.scatter(df_1127, x="x", y="y", title="Cell 1127 - Plotly Scatter Plot")
fig_1127.show()
# Cell 1128 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_1128 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
fig_1128 = px.scatter(df_1128, x="x", y="y", title="Cell 1128 - Plotly Scatter Plot")
fig_1128.show()
# Cell 1129 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_1129 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
fig_1129 = px.scatter(df_1129, x="x", y="y", title="Cell 1129 - Plotly Scatter Plot")
fig_1129.show()
# Cell 1130 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_1130 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
fig_1130 = px.scatter(df_1130, x="x", y="y", title="Cell 1130 - Plotly Scatter Plot")
fig_1130.show()
# Cell 1131 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_1131 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
fig_1131 = px.scatter(df_1131, x="x", y="y", title="Cell 1131 - Plotly Scatter Plot")
fig_1131.show()
# Cell 1132 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_1132 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
fig_1132 = px.scatter(df_1132, x="x", y="y", title="Cell 1132 - Plotly Scatter Plot")
fig_1132.show()
# Cell 1133 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_1133 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
fig_1133 = px.scatter(df_1133, x="x", y="y", title="Cell 1133 - Plotly Scatter Plot")
fig_1133.show()
# Cell 1134 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_1134 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
fig_1134 = px.scatter(df_1134, x="x", y="y", title="Cell 1134 - Plotly Scatter Plot")
fig_1134.show()
# Cell 1135 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_1135 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
fig_1135 = px.scatter(df_1135, x="x", y="y", title="Cell 1135 - Plotly Scatter Plot")
fig_1135.show()
# Cell 1136 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_1136 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
fig_1136 = px.scatter(df_1136, x="x", y="y", title="Cell 1136 - Plotly Scatter Plot")
fig_1136.show()
# Cell 1137 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_1137 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
fig_1137 = px.scatter(df_1137, x="x", y="y", title="Cell 1137 - Plotly Scatter Plot")
fig_1137.show()
# Cell 1138 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_1138 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
fig_1138 = px.scatter(df_1138, x="x", y="y", title="Cell 1138 - Plotly Scatter Plot")
fig_1138.show()
# Cell 1139 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_1139 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
fig_1139 = px.scatter(df_1139, x="x", y="y", title="Cell 1139 - Plotly Scatter Plot")
fig_1139.show()
# Cell 1140 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_1140 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
fig_1140 = px.scatter(df_1140, x="x", y="y", title="Cell 1140 - Plotly Scatter Plot")
fig_1140.show()
# Cell 1141 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_1141 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
fig_1141 = px.scatter(df_1141, x="x", y="y", title="Cell 1141 - Plotly Scatter Plot")
fig_1141.show()
# Cell 1142 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_1142 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
fig_1142 = px.scatter(df_1142, x="x", y="y", title="Cell 1142 - Plotly Scatter Plot")
fig_1142.show()
# Cell 1143 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_1143 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
fig_1143 = px.scatter(df_1143, x="x", y="y", title="Cell 1143 - Plotly Scatter Plot")
fig_1143.show()
# Cell 1144 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_1144 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
fig_1144 = px.scatter(df_1144, x="x", y="y", title="Cell 1144 - Plotly Scatter Plot")
fig_1144.show()
# Cell 1145 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_1145 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
fig_1145 = px.scatter(df_1145, x="x", y="y", title="Cell 1145 - Plotly Scatter Plot")
fig_1145.show()
# Cell 1146 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_1146 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
fig_1146 = px.scatter(df_1146, x="x", y="y", title="Cell 1146 - Plotly Scatter Plot")
fig_1146.show()
# Cell 1147 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_1147 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
fig_1147 = px.scatter(df_1147, x="x", y="y", title="Cell 1147 - Plotly Scatter Plot")
fig_1147.show()
# Cell 1148 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_1148 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
fig_1148 = px.scatter(df_1148, x="x", y="y", title="Cell 1148 - Plotly Scatter Plot")
fig_1148.show()
# Cell 1149 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_1149 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
fig_1149 = px.scatter(df_1149, x="x", y="y", title="Cell 1149 - Plotly Scatter Plot")
fig_1149.show()
# Cell 1150 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_1150 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
fig_1150 = px.scatter(df_1150, x="x", y="y", title="Cell 1150 - Plotly Scatter Plot")
fig_1150.show()
# Cell 1151 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_1151 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
fig_1151 = px.scatter(df_1151, x="x", y="y", title="Cell 1151 - Plotly Scatter Plot")
fig_1151.show()
# Cell 1152 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_1152 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
fig_1152 = px.scatter(df_1152, x="x", y="y", title="Cell 1152 - Plotly Scatter Plot")
fig_1152.show()
# Cell 1153 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_1153 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
fig_1153 = px.scatter(df_1153, x="x", y="y", title="Cell 1153 - Plotly Scatter Plot")
fig_1153.show()
# Cell 1154 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_1154 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
fig_1154 = px.scatter(df_1154, x="x", y="y", title="Cell 1154 - Plotly Scatter Plot")
fig_1154.show()
# Cell 1155 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_1155 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
fig_1155 = px.scatter(df_1155, x="x", y="y", title="Cell 1155 - Plotly Scatter Plot")
fig_1155.show()
# Cell 1156 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_1156 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
fig_1156 = px.scatter(df_1156, x="x", y="y", title="Cell 1156 - Plotly Scatter Plot")
fig_1156.show()
# Cell 1157 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_1157 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
fig_1157 = px.scatter(df_1157, x="x", y="y", title="Cell 1157 - Plotly Scatter Plot")
fig_1157.show()
# Cell 1158 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_1158 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
fig_1158 = px.scatter(df_1158, x="x", y="y", title="Cell 1158 - Plotly Scatter Plot")
fig_1158.show()
# Cell 1159 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_1159 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
fig_1159 = px.scatter(df_1159, x="x", y="y", title="Cell 1159 - Plotly Scatter Plot")
fig_1159.show()
# Cell 1160 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_1160 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
fig_1160 = px.scatter(df_1160, x="x", y="y", title="Cell 1160 - Plotly Scatter Plot")
fig_1160.show()
# Cell 1161 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_1161 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
fig_1161 = px.scatter(df_1161, x="x", y="y", title="Cell 1161 - Plotly Scatter Plot")
fig_1161.show()
# Cell 1162 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_1162 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
fig_1162 = px.scatter(df_1162, x="x", y="y", title="Cell 1162 - Plotly Scatter Plot")
fig_1162.show()
# Cell 1163 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_1163 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
fig_1163 = px.scatter(df_1163, x="x", y="y", title="Cell 1163 - Plotly Scatter Plot")
fig_1163.show()
# Cell 1164 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_1164 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
fig_1164 = px.scatter(df_1164, x="x", y="y", title="Cell 1164 - Plotly Scatter Plot")
fig_1164.show()
# Cell 1165 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_1165 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
fig_1165 = px.scatter(df_1165, x="x", y="y", title="Cell 1165 - Plotly Scatter Plot")
fig_1165.show()
# Cell 1166 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_1166 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
fig_1166 = px.scatter(df_1166, x="x", y="y", title="Cell 1166 - Plotly Scatter Plot")
fig_1166.show()
# Cell 1167 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_1167 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
fig_1167 = px.scatter(df_1167, x="x", y="y", title="Cell 1167 - Plotly Scatter Plot")
fig_1167.show()
# Cell 1168 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_1168 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
fig_1168 = px.scatter(df_1168, x="x", y="y", title="Cell 1168 - Plotly Scatter Plot")
fig_1168.show()
# Cell 1169 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_1169 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
fig_1169 = px.scatter(df_1169, x="x", y="y", title="Cell 1169 - Plotly Scatter Plot")
fig_1169.show()
# Cell 1170 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_1170 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
fig_1170 = px.scatter(df_1170, x="x", y="y", title="Cell 1170 - Plotly Scatter Plot")
fig_1170.show()
# Cell 1171 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_1171 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
fig_1171 = px.scatter(df_1171, x="x", y="y", title="Cell 1171 - Plotly Scatter Plot")
fig_1171.show()
# Cell 1172 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_1172 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
fig_1172 = px.scatter(df_1172, x="x", y="y", title="Cell 1172 - Plotly Scatter Plot")
fig_1172.show()
# Cell 1173 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_1173 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
fig_1173 = px.scatter(df_1173, x="x", y="y", title="Cell 1173 - Plotly Scatter Plot")
fig_1173.show()
# Cell 1174 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_1174 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
fig_1174 = px.scatter(df_1174, x="x", y="y", title="Cell 1174 - Plotly Scatter Plot")
fig_1174.show()
# Cell 1175 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_1175 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
fig_1175 = px.scatter(df_1175, x="x", y="y", title="Cell 1175 - Plotly Scatter Plot")
fig_1175.show()
# Cell 1176 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_1176 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
fig_1176 = px.scatter(df_1176, x="x", y="y", title="Cell 1176 - Plotly Scatter Plot")
fig_1176.show()
# Cell 1177 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_1177 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
fig_1177 = px.scatter(df_1177, x="x", y="y", title="Cell 1177 - Plotly Scatter Plot")
fig_1177.show()
# Cell 1178 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_1178 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
fig_1178 = px.scatter(df_1178, x="x", y="y", title="Cell 1178 - Plotly Scatter Plot")
fig_1178.show()
# Cell 1179 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_1179 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
fig_1179 = px.scatter(df_1179, x="x", y="y", title="Cell 1179 - Plotly Scatter Plot")
fig_1179.show()
# Cell 1180 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_1180 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
fig_1180 = px.scatter(df_1180, x="x", y="y", title="Cell 1180 - Plotly Scatter Plot")
fig_1180.show()
# Cell 1181 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_1181 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
fig_1181 = px.scatter(df_1181, x="x", y="y", title="Cell 1181 - Plotly Scatter Plot")
fig_1181.show()
# Cell 1182 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_1182 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
fig_1182 = px.scatter(df_1182, x="x", y="y", title="Cell 1182 - Plotly Scatter Plot")
fig_1182.show()
# Cell 1183 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_1183 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
fig_1183 = px.scatter(df_1183, x="x", y="y", title="Cell 1183 - Plotly Scatter Plot")
fig_1183.show()
# Cell 1184 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_1184 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
fig_1184 = px.scatter(df_1184, x="x", y="y", title="Cell 1184 - Plotly Scatter Plot")
fig_1184.show()
# Cell 1185 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_1185 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
fig_1185 = px.scatter(df_1185, x="x", y="y", title="Cell 1185 - Plotly Scatter Plot")
fig_1185.show()
# Cell 1186 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_1186 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
fig_1186 = px.scatter(df_1186, x="x", y="y", title="Cell 1186 - Plotly Scatter Plot")
fig_1186.show()
# Cell 1187 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_1187 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
fig_1187 = px.scatter(df_1187, x="x", y="y", title="Cell 1187 - Plotly Scatter Plot")
fig_1187.show()
# Cell 1188 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_1188 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
fig_1188 = px.scatter(df_1188, x="x", y="y", title="Cell 1188 - Plotly Scatter Plot")
fig_1188.show()
# Cell 1189 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_1189 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
fig_1189 = px.scatter(df_1189, x="x", y="y", title="Cell 1189 - Plotly Scatter Plot")
fig_1189.show()
# Cell 1190 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_1190 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
fig_1190 = px.scatter(df_1190, x="x", y="y", title="Cell 1190 - Plotly Scatter Plot")
fig_1190.show()
# Cell 1191 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_1191 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
fig_1191 = px.scatter(df_1191, x="x", y="y", title="Cell 1191 - Plotly Scatter Plot")
fig_1191.show()
# Cell 1192 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_1192 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
fig_1192 = px.scatter(df_1192, x="x", y="y", title="Cell 1192 - Plotly Scatter Plot")
fig_1192.show()
# Cell 1193 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_1193 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
fig_1193 = px.scatter(df_1193, x="x", y="y", title="Cell 1193 - Plotly Scatter Plot")
fig_1193.show()
# Cell 1194 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_1194 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
fig_1194 = px.scatter(df_1194, x="x", y="y", title="Cell 1194 - Plotly Scatter Plot")
fig_1194.show()
# Cell 1195 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_1195 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
fig_1195 = px.scatter(df_1195, x="x", y="y", title="Cell 1195 - Plotly Scatter Plot")
fig_1195.show()
# Cell 1196 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_1196 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
fig_1196 = px.scatter(df_1196, x="x", y="y", title="Cell 1196 - Plotly Scatter Plot")
fig_1196.show()
# Cell 1197 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_1197 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
fig_1197 = px.scatter(df_1197, x="x", y="y", title="Cell 1197 - Plotly Scatter Plot")
fig_1197.show()
# Cell 1198 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_1198 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 1
})
fig_1198 = px.scatter(df_1198, x="x", y="y", title="Cell 1198 - Plotly Scatter Plot")
fig_1198.show()
# Cell 1199 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_1199 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 2
})
fig_1199 = px.scatter(df_1199, x="x", y="y", title="Cell 1199 - Plotly Scatter Plot")
fig_1199.show()
# Cell 1200 - plotly
import plotly.express as px
import pandas as pd
import numpy as np
df_1200 = pd.DataFrame({
"x": np.random.randn(100),
"y": np.random.randn(100) + 0
})
fig_1200 = px.scatter(df_1200, x="x", y="y", title="Cell 1200 - Plotly Scatter Plot")
fig_1200.show()
# Cell 1201 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1201 = np.random.randn(100).cumsum()
df_1201 = pd.Series(data_1201)
df_1201.plot(title="Cell 1201 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1202 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1202 = np.random.randn(100).cumsum()
df_1202 = pd.Series(data_1202)
df_1202.plot(title="Cell 1202 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1203 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1203 = np.random.randn(100).cumsum()
df_1203 = pd.Series(data_1203)
df_1203.plot(title="Cell 1203 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1204 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1204 = np.random.randn(100).cumsum()
df_1204 = pd.Series(data_1204)
df_1204.plot(title="Cell 1204 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1205 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1205 = np.random.randn(100).cumsum()
df_1205 = pd.Series(data_1205)
df_1205.plot(title="Cell 1205 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1206 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1206 = np.random.randn(100).cumsum()
df_1206 = pd.Series(data_1206)
df_1206.plot(title="Cell 1206 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1207 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1207 = np.random.randn(100).cumsum()
df_1207 = pd.Series(data_1207)
df_1207.plot(title="Cell 1207 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1208 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1208 = np.random.randn(100).cumsum()
df_1208 = pd.Series(data_1208)
df_1208.plot(title="Cell 1208 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1209 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1209 = np.random.randn(100).cumsum()
df_1209 = pd.Series(data_1209)
df_1209.plot(title="Cell 1209 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1210 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1210 = np.random.randn(100).cumsum()
df_1210 = pd.Series(data_1210)
df_1210.plot(title="Cell 1210 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1211 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1211 = np.random.randn(100).cumsum()
df_1211 = pd.Series(data_1211)
df_1211.plot(title="Cell 1211 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1212 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1212 = np.random.randn(100).cumsum()
df_1212 = pd.Series(data_1212)
df_1212.plot(title="Cell 1212 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1213 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1213 = np.random.randn(100).cumsum()
df_1213 = pd.Series(data_1213)
df_1213.plot(title="Cell 1213 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1214 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1214 = np.random.randn(100).cumsum()
df_1214 = pd.Series(data_1214)
df_1214.plot(title="Cell 1214 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1215 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1215 = np.random.randn(100).cumsum()
df_1215 = pd.Series(data_1215)
df_1215.plot(title="Cell 1215 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1216 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1216 = np.random.randn(100).cumsum()
df_1216 = pd.Series(data_1216)
df_1216.plot(title="Cell 1216 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1217 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1217 = np.random.randn(100).cumsum()
df_1217 = pd.Series(data_1217)
df_1217.plot(title="Cell 1217 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1218 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1218 = np.random.randn(100).cumsum()
df_1218 = pd.Series(data_1218)
df_1218.plot(title="Cell 1218 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1219 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1219 = np.random.randn(100).cumsum()
df_1219 = pd.Series(data_1219)
df_1219.plot(title="Cell 1219 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1220 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1220 = np.random.randn(100).cumsum()
df_1220 = pd.Series(data_1220)
df_1220.plot(title="Cell 1220 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1221 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1221 = np.random.randn(100).cumsum()
df_1221 = pd.Series(data_1221)
df_1221.plot(title="Cell 1221 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1222 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1222 = np.random.randn(100).cumsum()
df_1222 = pd.Series(data_1222)
df_1222.plot(title="Cell 1222 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1223 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1223 = np.random.randn(100).cumsum()
df_1223 = pd.Series(data_1223)
df_1223.plot(title="Cell 1223 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1224 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1224 = np.random.randn(100).cumsum()
df_1224 = pd.Series(data_1224)
df_1224.plot(title="Cell 1224 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1225 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1225 = np.random.randn(100).cumsum()
df_1225 = pd.Series(data_1225)
df_1225.plot(title="Cell 1225 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1226 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1226 = np.random.randn(100).cumsum()
df_1226 = pd.Series(data_1226)
df_1226.plot(title="Cell 1226 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1227 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1227 = np.random.randn(100).cumsum()
df_1227 = pd.Series(data_1227)
df_1227.plot(title="Cell 1227 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1228 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1228 = np.random.randn(100).cumsum()
df_1228 = pd.Series(data_1228)
df_1228.plot(title="Cell 1228 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1229 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1229 = np.random.randn(100).cumsum()
df_1229 = pd.Series(data_1229)
df_1229.plot(title="Cell 1229 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1230 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1230 = np.random.randn(100).cumsum()
df_1230 = pd.Series(data_1230)
df_1230.plot(title="Cell 1230 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1231 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1231 = np.random.randn(100).cumsum()
df_1231 = pd.Series(data_1231)
df_1231.plot(title="Cell 1231 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1232 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1232 = np.random.randn(100).cumsum()
df_1232 = pd.Series(data_1232)
df_1232.plot(title="Cell 1232 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1233 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1233 = np.random.randn(100).cumsum()
df_1233 = pd.Series(data_1233)
df_1233.plot(title="Cell 1233 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1234 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1234 = np.random.randn(100).cumsum()
df_1234 = pd.Series(data_1234)
df_1234.plot(title="Cell 1234 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1235 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1235 = np.random.randn(100).cumsum()
df_1235 = pd.Series(data_1235)
df_1235.plot(title="Cell 1235 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1236 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1236 = np.random.randn(100).cumsum()
df_1236 = pd.Series(data_1236)
df_1236.plot(title="Cell 1236 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1237 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1237 = np.random.randn(100).cumsum()
df_1237 = pd.Series(data_1237)
df_1237.plot(title="Cell 1237 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1238 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1238 = np.random.randn(100).cumsum()
df_1238 = pd.Series(data_1238)
df_1238.plot(title="Cell 1238 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1239 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1239 = np.random.randn(100).cumsum()
df_1239 = pd.Series(data_1239)
df_1239.plot(title="Cell 1239 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1240 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1240 = np.random.randn(100).cumsum()
df_1240 = pd.Series(data_1240)
df_1240.plot(title="Cell 1240 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1241 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1241 = np.random.randn(100).cumsum()
df_1241 = pd.Series(data_1241)
df_1241.plot(title="Cell 1241 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1242 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1242 = np.random.randn(100).cumsum()
df_1242 = pd.Series(data_1242)
df_1242.plot(title="Cell 1242 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1243 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1243 = np.random.randn(100).cumsum()
df_1243 = pd.Series(data_1243)
df_1243.plot(title="Cell 1243 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1244 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1244 = np.random.randn(100).cumsum()
df_1244 = pd.Series(data_1244)
df_1244.plot(title="Cell 1244 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1245 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1245 = np.random.randn(100).cumsum()
df_1245 = pd.Series(data_1245)
df_1245.plot(title="Cell 1245 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1246 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1246 = np.random.randn(100).cumsum()
df_1246 = pd.Series(data_1246)
df_1246.plot(title="Cell 1246 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1247 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1247 = np.random.randn(100).cumsum()
df_1247 = pd.Series(data_1247)
df_1247.plot(title="Cell 1247 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1248 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1248 = np.random.randn(100).cumsum()
df_1248 = pd.Series(data_1248)
df_1248.plot(title="Cell 1248 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1249 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1249 = np.random.randn(100).cumsum()
df_1249 = pd.Series(data_1249)
df_1249.plot(title="Cell 1249 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1250 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1250 = np.random.randn(100).cumsum()
df_1250 = pd.Series(data_1250)
df_1250.plot(title="Cell 1250 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1251 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1251 = np.random.randn(100).cumsum()
df_1251 = pd.Series(data_1251)
df_1251.plot(title="Cell 1251 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1252 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1252 = np.random.randn(100).cumsum()
df_1252 = pd.Series(data_1252)
df_1252.plot(title="Cell 1252 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1253 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1253 = np.random.randn(100).cumsum()
df_1253 = pd.Series(data_1253)
df_1253.plot(title="Cell 1253 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1254 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1254 = np.random.randn(100).cumsum()
df_1254 = pd.Series(data_1254)
df_1254.plot(title="Cell 1254 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1255 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1255 = np.random.randn(100).cumsum()
df_1255 = pd.Series(data_1255)
df_1255.plot(title="Cell 1255 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1256 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1256 = np.random.randn(100).cumsum()
df_1256 = pd.Series(data_1256)
df_1256.plot(title="Cell 1256 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1257 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1257 = np.random.randn(100).cumsum()
df_1257 = pd.Series(data_1257)
df_1257.plot(title="Cell 1257 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1258 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1258 = np.random.randn(100).cumsum()
df_1258 = pd.Series(data_1258)
df_1258.plot(title="Cell 1258 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1259 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1259 = np.random.randn(100).cumsum()
df_1259 = pd.Series(data_1259)
df_1259.plot(title="Cell 1259 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1260 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1260 = np.random.randn(100).cumsum()
df_1260 = pd.Series(data_1260)
df_1260.plot(title="Cell 1260 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1261 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1261 = np.random.randn(100).cumsum()
df_1261 = pd.Series(data_1261)
df_1261.plot(title="Cell 1261 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1262 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1262 = np.random.randn(100).cumsum()
df_1262 = pd.Series(data_1262)
df_1262.plot(title="Cell 1262 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1263 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1263 = np.random.randn(100).cumsum()
df_1263 = pd.Series(data_1263)
df_1263.plot(title="Cell 1263 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1264 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1264 = np.random.randn(100).cumsum()
df_1264 = pd.Series(data_1264)
df_1264.plot(title="Cell 1264 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1265 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1265 = np.random.randn(100).cumsum()
df_1265 = pd.Series(data_1265)
df_1265.plot(title="Cell 1265 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1266 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1266 = np.random.randn(100).cumsum()
df_1266 = pd.Series(data_1266)
df_1266.plot(title="Cell 1266 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1267 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1267 = np.random.randn(100).cumsum()
df_1267 = pd.Series(data_1267)
df_1267.plot(title="Cell 1267 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1268 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1268 = np.random.randn(100).cumsum()
df_1268 = pd.Series(data_1268)
df_1268.plot(title="Cell 1268 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1269 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1269 = np.random.randn(100).cumsum()
df_1269 = pd.Series(data_1269)
df_1269.plot(title="Cell 1269 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1270 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1270 = np.random.randn(100).cumsum()
df_1270 = pd.Series(data_1270)
df_1270.plot(title="Cell 1270 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1271 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1271 = np.random.randn(100).cumsum()
df_1271 = pd.Series(data_1271)
df_1271.plot(title="Cell 1271 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1272 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1272 = np.random.randn(100).cumsum()
df_1272 = pd.Series(data_1272)
df_1272.plot(title="Cell 1272 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1273 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1273 = np.random.randn(100).cumsum()
df_1273 = pd.Series(data_1273)
df_1273.plot(title="Cell 1273 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1274 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1274 = np.random.randn(100).cumsum()
df_1274 = pd.Series(data_1274)
df_1274.plot(title="Cell 1274 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1275 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1275 = np.random.randn(100).cumsum()
df_1275 = pd.Series(data_1275)
df_1275.plot(title="Cell 1275 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1276 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1276 = np.random.randn(100).cumsum()
df_1276 = pd.Series(data_1276)
df_1276.plot(title="Cell 1276 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1277 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1277 = np.random.randn(100).cumsum()
df_1277 = pd.Series(data_1277)
df_1277.plot(title="Cell 1277 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1278 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1278 = np.random.randn(100).cumsum()
df_1278 = pd.Series(data_1278)
df_1278.plot(title="Cell 1278 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1279 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1279 = np.random.randn(100).cumsum()
df_1279 = pd.Series(data_1279)
df_1279.plot(title="Cell 1279 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1280 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1280 = np.random.randn(100).cumsum()
df_1280 = pd.Series(data_1280)
df_1280.plot(title="Cell 1280 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1281 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1281 = np.random.randn(100).cumsum()
df_1281 = pd.Series(data_1281)
df_1281.plot(title="Cell 1281 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1282 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1282 = np.random.randn(100).cumsum()
df_1282 = pd.Series(data_1282)
df_1282.plot(title="Cell 1282 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1283 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1283 = np.random.randn(100).cumsum()
df_1283 = pd.Series(data_1283)
df_1283.plot(title="Cell 1283 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1284 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1284 = np.random.randn(100).cumsum()
df_1284 = pd.Series(data_1284)
df_1284.plot(title="Cell 1284 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1285 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1285 = np.random.randn(100).cumsum()
df_1285 = pd.Series(data_1285)
df_1285.plot(title="Cell 1285 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1286 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1286 = np.random.randn(100).cumsum()
df_1286 = pd.Series(data_1286)
df_1286.plot(title="Cell 1286 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1287 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1287 = np.random.randn(100).cumsum()
df_1287 = pd.Series(data_1287)
df_1287.plot(title="Cell 1287 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1288 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1288 = np.random.randn(100).cumsum()
df_1288 = pd.Series(data_1288)
df_1288.plot(title="Cell 1288 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1289 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1289 = np.random.randn(100).cumsum()
df_1289 = pd.Series(data_1289)
df_1289.plot(title="Cell 1289 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1290 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1290 = np.random.randn(100).cumsum()
df_1290 = pd.Series(data_1290)
df_1290.plot(title="Cell 1290 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1291 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1291 = np.random.randn(100).cumsum()
df_1291 = pd.Series(data_1291)
df_1291.plot(title="Cell 1291 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1292 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1292 = np.random.randn(100).cumsum()
df_1292 = pd.Series(data_1292)
df_1292.plot(title="Cell 1292 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1293 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1293 = np.random.randn(100).cumsum()
df_1293 = pd.Series(data_1293)
df_1293.plot(title="Cell 1293 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1294 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1294 = np.random.randn(100).cumsum()
df_1294 = pd.Series(data_1294)
df_1294.plot(title="Cell 1294 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1295 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1295 = np.random.randn(100).cumsum()
df_1295 = pd.Series(data_1295)
df_1295.plot(title="Cell 1295 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1296 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1296 = np.random.randn(100).cumsum()
df_1296 = pd.Series(data_1296)
df_1296.plot(title="Cell 1296 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1297 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1297 = np.random.randn(100).cumsum()
df_1297 = pd.Series(data_1297)
df_1297.plot(title="Cell 1297 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1298 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1298 = np.random.randn(100).cumsum()
df_1298 = pd.Series(data_1298)
df_1298.plot(title="Cell 1298 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1299 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1299 = np.random.randn(100).cumsum()
df_1299 = pd.Series(data_1299)
df_1299.plot(title="Cell 1299 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1300 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1300 = np.random.randn(100).cumsum()
df_1300 = pd.Series(data_1300)
df_1300.plot(title="Cell 1300 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1301 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1301 = np.random.randn(100).cumsum()
df_1301 = pd.Series(data_1301)
df_1301.plot(title="Cell 1301 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1302 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1302 = np.random.randn(100).cumsum()
df_1302 = pd.Series(data_1302)
df_1302.plot(title="Cell 1302 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1303 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1303 = np.random.randn(100).cumsum()
df_1303 = pd.Series(data_1303)
df_1303.plot(title="Cell 1303 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1304 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1304 = np.random.randn(100).cumsum()
df_1304 = pd.Series(data_1304)
df_1304.plot(title="Cell 1304 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1305 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1305 = np.random.randn(100).cumsum()
df_1305 = pd.Series(data_1305)
df_1305.plot(title="Cell 1305 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1306 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1306 = np.random.randn(100).cumsum()
df_1306 = pd.Series(data_1306)
df_1306.plot(title="Cell 1306 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1307 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1307 = np.random.randn(100).cumsum()
df_1307 = pd.Series(data_1307)
df_1307.plot(title="Cell 1307 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1308 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1308 = np.random.randn(100).cumsum()
df_1308 = pd.Series(data_1308)
df_1308.plot(title="Cell 1308 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1309 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1309 = np.random.randn(100).cumsum()
df_1309 = pd.Series(data_1309)
df_1309.plot(title="Cell 1309 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1310 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1310 = np.random.randn(100).cumsum()
df_1310 = pd.Series(data_1310)
df_1310.plot(title="Cell 1310 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1311 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1311 = np.random.randn(100).cumsum()
df_1311 = pd.Series(data_1311)
df_1311.plot(title="Cell 1311 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1312 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1312 = np.random.randn(100).cumsum()
df_1312 = pd.Series(data_1312)
df_1312.plot(title="Cell 1312 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1313 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1313 = np.random.randn(100).cumsum()
df_1313 = pd.Series(data_1313)
df_1313.plot(title="Cell 1313 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1314 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1314 = np.random.randn(100).cumsum()
df_1314 = pd.Series(data_1314)
df_1314.plot(title="Cell 1314 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1315 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1315 = np.random.randn(100).cumsum()
df_1315 = pd.Series(data_1315)
df_1315.plot(title="Cell 1315 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1316 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1316 = np.random.randn(100).cumsum()
df_1316 = pd.Series(data_1316)
df_1316.plot(title="Cell 1316 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1317 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1317 = np.random.randn(100).cumsum()
df_1317 = pd.Series(data_1317)
df_1317.plot(title="Cell 1317 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1318 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1318 = np.random.randn(100).cumsum()
df_1318 = pd.Series(data_1318)
df_1318.plot(title="Cell 1318 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1319 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1319 = np.random.randn(100).cumsum()
df_1319 = pd.Series(data_1319)
df_1319.plot(title="Cell 1319 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1320 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1320 = np.random.randn(100).cumsum()
df_1320 = pd.Series(data_1320)
df_1320.plot(title="Cell 1320 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1321 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1321 = np.random.randn(100).cumsum()
df_1321 = pd.Series(data_1321)
df_1321.plot(title="Cell 1321 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1322 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1322 = np.random.randn(100).cumsum()
df_1322 = pd.Series(data_1322)
df_1322.plot(title="Cell 1322 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1323 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1323 = np.random.randn(100).cumsum()
df_1323 = pd.Series(data_1323)
df_1323.plot(title="Cell 1323 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1324 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1324 = np.random.randn(100).cumsum()
df_1324 = pd.Series(data_1324)
df_1324.plot(title="Cell 1324 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1325 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1325 = np.random.randn(100).cumsum()
df_1325 = pd.Series(data_1325)
df_1325.plot(title="Cell 1325 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1326 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1326 = np.random.randn(100).cumsum()
df_1326 = pd.Series(data_1326)
df_1326.plot(title="Cell 1326 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1327 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1327 = np.random.randn(100).cumsum()
df_1327 = pd.Series(data_1327)
df_1327.plot(title="Cell 1327 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1328 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1328 = np.random.randn(100).cumsum()
df_1328 = pd.Series(data_1328)
df_1328.plot(title="Cell 1328 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1329 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1329 = np.random.randn(100).cumsum()
df_1329 = pd.Series(data_1329)
df_1329.plot(title="Cell 1329 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1330 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1330 = np.random.randn(100).cumsum()
df_1330 = pd.Series(data_1330)
df_1330.plot(title="Cell 1330 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1331 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1331 = np.random.randn(100).cumsum()
df_1331 = pd.Series(data_1331)
df_1331.plot(title="Cell 1331 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1332 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1332 = np.random.randn(100).cumsum()
df_1332 = pd.Series(data_1332)
df_1332.plot(title="Cell 1332 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1333 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1333 = np.random.randn(100).cumsum()
df_1333 = pd.Series(data_1333)
df_1333.plot(title="Cell 1333 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1334 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1334 = np.random.randn(100).cumsum()
df_1334 = pd.Series(data_1334)
df_1334.plot(title="Cell 1334 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1335 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1335 = np.random.randn(100).cumsum()
df_1335 = pd.Series(data_1335)
df_1335.plot(title="Cell 1335 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1336 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1336 = np.random.randn(100).cumsum()
df_1336 = pd.Series(data_1336)
df_1336.plot(title="Cell 1336 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1337 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1337 = np.random.randn(100).cumsum()
df_1337 = pd.Series(data_1337)
df_1337.plot(title="Cell 1337 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1338 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1338 = np.random.randn(100).cumsum()
df_1338 = pd.Series(data_1338)
df_1338.plot(title="Cell 1338 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1339 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1339 = np.random.randn(100).cumsum()
df_1339 = pd.Series(data_1339)
df_1339.plot(title="Cell 1339 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1340 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1340 = np.random.randn(100).cumsum()
df_1340 = pd.Series(data_1340)
df_1340.plot(title="Cell 1340 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1341 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1341 = np.random.randn(100).cumsum()
df_1341 = pd.Series(data_1341)
df_1341.plot(title="Cell 1341 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1342 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1342 = np.random.randn(100).cumsum()
df_1342 = pd.Series(data_1342)
df_1342.plot(title="Cell 1342 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1343 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1343 = np.random.randn(100).cumsum()
df_1343 = pd.Series(data_1343)
df_1343.plot(title="Cell 1343 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1344 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1344 = np.random.randn(100).cumsum()
df_1344 = pd.Series(data_1344)
df_1344.plot(title="Cell 1344 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1345 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1345 = np.random.randn(100).cumsum()
df_1345 = pd.Series(data_1345)
df_1345.plot(title="Cell 1345 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1346 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1346 = np.random.randn(100).cumsum()
df_1346 = pd.Series(data_1346)
df_1346.plot(title="Cell 1346 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1347 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1347 = np.random.randn(100).cumsum()
df_1347 = pd.Series(data_1347)
df_1347.plot(title="Cell 1347 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1348 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1348 = np.random.randn(100).cumsum()
df_1348 = pd.Series(data_1348)
df_1348.plot(title="Cell 1348 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1349 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1349 = np.random.randn(100).cumsum()
df_1349 = pd.Series(data_1349)
df_1349.plot(title="Cell 1349 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1350 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1350 = np.random.randn(100).cumsum()
df_1350 = pd.Series(data_1350)
df_1350.plot(title="Cell 1350 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1351 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1351 = np.random.randn(100).cumsum()
df_1351 = pd.Series(data_1351)
df_1351.plot(title="Cell 1351 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1352 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1352 = np.random.randn(100).cumsum()
df_1352 = pd.Series(data_1352)
df_1352.plot(title="Cell 1352 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1353 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1353 = np.random.randn(100).cumsum()
df_1353 = pd.Series(data_1353)
df_1353.plot(title="Cell 1353 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1354 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1354 = np.random.randn(100).cumsum()
df_1354 = pd.Series(data_1354)
df_1354.plot(title="Cell 1354 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1355 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1355 = np.random.randn(100).cumsum()
df_1355 = pd.Series(data_1355)
df_1355.plot(title="Cell 1355 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1356 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1356 = np.random.randn(100).cumsum()
df_1356 = pd.Series(data_1356)
df_1356.plot(title="Cell 1356 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1357 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1357 = np.random.randn(100).cumsum()
df_1357 = pd.Series(data_1357)
df_1357.plot(title="Cell 1357 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1358 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1358 = np.random.randn(100).cumsum()
df_1358 = pd.Series(data_1358)
df_1358.plot(title="Cell 1358 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1359 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1359 = np.random.randn(100).cumsum()
df_1359 = pd.Series(data_1359)
df_1359.plot(title="Cell 1359 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1360 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1360 = np.random.randn(100).cumsum()
df_1360 = pd.Series(data_1360)
df_1360.plot(title="Cell 1360 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1361 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1361 = np.random.randn(100).cumsum()
df_1361 = pd.Series(data_1361)
df_1361.plot(title="Cell 1361 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1362 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1362 = np.random.randn(100).cumsum()
df_1362 = pd.Series(data_1362)
df_1362.plot(title="Cell 1362 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1363 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1363 = np.random.randn(100).cumsum()
df_1363 = pd.Series(data_1363)
df_1363.plot(title="Cell 1363 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1364 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1364 = np.random.randn(100).cumsum()
df_1364 = pd.Series(data_1364)
df_1364.plot(title="Cell 1364 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1365 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1365 = np.random.randn(100).cumsum()
df_1365 = pd.Series(data_1365)
df_1365.plot(title="Cell 1365 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1366 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1366 = np.random.randn(100).cumsum()
df_1366 = pd.Series(data_1366)
df_1366.plot(title="Cell 1366 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1367 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1367 = np.random.randn(100).cumsum()
df_1367 = pd.Series(data_1367)
df_1367.plot(title="Cell 1367 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1368 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1368 = np.random.randn(100).cumsum()
df_1368 = pd.Series(data_1368)
df_1368.plot(title="Cell 1368 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1369 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1369 = np.random.randn(100).cumsum()
df_1369 = pd.Series(data_1369)
df_1369.plot(title="Cell 1369 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1370 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1370 = np.random.randn(100).cumsum()
df_1370 = pd.Series(data_1370)
df_1370.plot(title="Cell 1370 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1371 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1371 = np.random.randn(100).cumsum()
df_1371 = pd.Series(data_1371)
df_1371.plot(title="Cell 1371 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1372 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1372 = np.random.randn(100).cumsum()
df_1372 = pd.Series(data_1372)
df_1372.plot(title="Cell 1372 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1373 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1373 = np.random.randn(100).cumsum()
df_1373 = pd.Series(data_1373)
df_1373.plot(title="Cell 1373 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1374 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1374 = np.random.randn(100).cumsum()
df_1374 = pd.Series(data_1374)
df_1374.plot(title="Cell 1374 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1375 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1375 = np.random.randn(100).cumsum()
df_1375 = pd.Series(data_1375)
df_1375.plot(title="Cell 1375 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1376 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1376 = np.random.randn(100).cumsum()
df_1376 = pd.Series(data_1376)
df_1376.plot(title="Cell 1376 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1377 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1377 = np.random.randn(100).cumsum()
df_1377 = pd.Series(data_1377)
df_1377.plot(title="Cell 1377 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1378 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1378 = np.random.randn(100).cumsum()
df_1378 = pd.Series(data_1378)
df_1378.plot(title="Cell 1378 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1379 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1379 = np.random.randn(100).cumsum()
df_1379 = pd.Series(data_1379)
df_1379.plot(title="Cell 1379 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1380 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1380 = np.random.randn(100).cumsum()
df_1380 = pd.Series(data_1380)
df_1380.plot(title="Cell 1380 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1381 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1381 = np.random.randn(100).cumsum()
df_1381 = pd.Series(data_1381)
df_1381.plot(title="Cell 1381 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1382 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1382 = np.random.randn(100).cumsum()
df_1382 = pd.Series(data_1382)
df_1382.plot(title="Cell 1382 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1383 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1383 = np.random.randn(100).cumsum()
df_1383 = pd.Series(data_1383)
df_1383.plot(title="Cell 1383 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1384 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1384 = np.random.randn(100).cumsum()
df_1384 = pd.Series(data_1384)
df_1384.plot(title="Cell 1384 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1385 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1385 = np.random.randn(100).cumsum()
df_1385 = pd.Series(data_1385)
df_1385.plot(title="Cell 1385 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1386 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1386 = np.random.randn(100).cumsum()
df_1386 = pd.Series(data_1386)
df_1386.plot(title="Cell 1386 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1387 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1387 = np.random.randn(100).cumsum()
df_1387 = pd.Series(data_1387)
df_1387.plot(title="Cell 1387 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1388 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1388 = np.random.randn(100).cumsum()
df_1388 = pd.Series(data_1388)
df_1388.plot(title="Cell 1388 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1389 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1389 = np.random.randn(100).cumsum()
df_1389 = pd.Series(data_1389)
df_1389.plot(title="Cell 1389 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1390 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1390 = np.random.randn(100).cumsum()
df_1390 = pd.Series(data_1390)
df_1390.plot(title="Cell 1390 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1391 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1391 = np.random.randn(100).cumsum()
df_1391 = pd.Series(data_1391)
df_1391.plot(title="Cell 1391 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1392 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1392 = np.random.randn(100).cumsum()
df_1392 = pd.Series(data_1392)
df_1392.plot(title="Cell 1392 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1393 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1393 = np.random.randn(100).cumsum()
df_1393 = pd.Series(data_1393)
df_1393.plot(title="Cell 1393 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1394 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1394 = np.random.randn(100).cumsum()
df_1394 = pd.Series(data_1394)
df_1394.plot(title="Cell 1394 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1395 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1395 = np.random.randn(100).cumsum()
df_1395 = pd.Series(data_1395)
df_1395.plot(title="Cell 1395 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1396 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1396 = np.random.randn(100).cumsum()
df_1396 = pd.Series(data_1396)
df_1396.plot(title="Cell 1396 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1397 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1397 = np.random.randn(100).cumsum()
df_1397 = pd.Series(data_1397)
df_1397.plot(title="Cell 1397 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1398 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1398 = np.random.randn(100).cumsum()
df_1398 = pd.Series(data_1398)
df_1398.plot(title="Cell 1398 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1399 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1399 = np.random.randn(100).cumsum()
df_1399 = pd.Series(data_1399)
df_1399.plot(title="Cell 1399 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1400 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1400 = np.random.randn(100).cumsum()
df_1400 = pd.Series(data_1400)
df_1400.plot(title="Cell 1400 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1401 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1401 = np.random.randn(100).cumsum()
df_1401 = pd.Series(data_1401)
df_1401.plot(title="Cell 1401 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1402 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1402 = np.random.randn(100).cumsum()
df_1402 = pd.Series(data_1402)
df_1402.plot(title="Cell 1402 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1403 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1403 = np.random.randn(100).cumsum()
df_1403 = pd.Series(data_1403)
df_1403.plot(title="Cell 1403 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1404 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1404 = np.random.randn(100).cumsum()
df_1404 = pd.Series(data_1404)
df_1404.plot(title="Cell 1404 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1405 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1405 = np.random.randn(100).cumsum()
df_1405 = pd.Series(data_1405)
df_1405.plot(title="Cell 1405 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1406 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1406 = np.random.randn(100).cumsum()
df_1406 = pd.Series(data_1406)
df_1406.plot(title="Cell 1406 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1407 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1407 = np.random.randn(100).cumsum()
df_1407 = pd.Series(data_1407)
df_1407.plot(title="Cell 1407 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1408 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1408 = np.random.randn(100).cumsum()
df_1408 = pd.Series(data_1408)
df_1408.plot(title="Cell 1408 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1409 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1409 = np.random.randn(100).cumsum()
df_1409 = pd.Series(data_1409)
df_1409.plot(title="Cell 1409 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1410 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1410 = np.random.randn(100).cumsum()
df_1410 = pd.Series(data_1410)
df_1410.plot(title="Cell 1410 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1411 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1411 = np.random.randn(100).cumsum()
df_1411 = pd.Series(data_1411)
df_1411.plot(title="Cell 1411 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1412 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1412 = np.random.randn(100).cumsum()
df_1412 = pd.Series(data_1412)
df_1412.plot(title="Cell 1412 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1413 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1413 = np.random.randn(100).cumsum()
df_1413 = pd.Series(data_1413)
df_1413.plot(title="Cell 1413 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1414 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1414 = np.random.randn(100).cumsum()
df_1414 = pd.Series(data_1414)
df_1414.plot(title="Cell 1414 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1415 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1415 = np.random.randn(100).cumsum()
df_1415 = pd.Series(data_1415)
df_1415.plot(title="Cell 1415 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1416 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1416 = np.random.randn(100).cumsum()
df_1416 = pd.Series(data_1416)
df_1416.plot(title="Cell 1416 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1417 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1417 = np.random.randn(100).cumsum()
df_1417 = pd.Series(data_1417)
df_1417.plot(title="Cell 1417 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1418 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1418 = np.random.randn(100).cumsum()
df_1418 = pd.Series(data_1418)
df_1418.plot(title="Cell 1418 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1419 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1419 = np.random.randn(100).cumsum()
df_1419 = pd.Series(data_1419)
df_1419.plot(title="Cell 1419 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1420 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1420 = np.random.randn(100).cumsum()
df_1420 = pd.Series(data_1420)
df_1420.plot(title="Cell 1420 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1421 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1421 = np.random.randn(100).cumsum()
df_1421 = pd.Series(data_1421)
df_1421.plot(title="Cell 1421 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1422 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1422 = np.random.randn(100).cumsum()
df_1422 = pd.Series(data_1422)
df_1422.plot(title="Cell 1422 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1423 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1423 = np.random.randn(100).cumsum()
df_1423 = pd.Series(data_1423)
df_1423.plot(title="Cell 1423 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1424 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1424 = np.random.randn(100).cumsum()
df_1424 = pd.Series(data_1424)
df_1424.plot(title="Cell 1424 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1425 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1425 = np.random.randn(100).cumsum()
df_1425 = pd.Series(data_1425)
df_1425.plot(title="Cell 1425 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1426 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1426 = np.random.randn(100).cumsum()
df_1426 = pd.Series(data_1426)
df_1426.plot(title="Cell 1426 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1427 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1427 = np.random.randn(100).cumsum()
df_1427 = pd.Series(data_1427)
df_1427.plot(title="Cell 1427 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1428 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1428 = np.random.randn(100).cumsum()
df_1428 = pd.Series(data_1428)
df_1428.plot(title="Cell 1428 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1429 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1429 = np.random.randn(100).cumsum()
df_1429 = pd.Series(data_1429)
df_1429.plot(title="Cell 1429 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1430 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1430 = np.random.randn(100).cumsum()
df_1430 = pd.Series(data_1430)
df_1430.plot(title="Cell 1430 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1431 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1431 = np.random.randn(100).cumsum()
df_1431 = pd.Series(data_1431)
df_1431.plot(title="Cell 1431 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1432 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1432 = np.random.randn(100).cumsum()
df_1432 = pd.Series(data_1432)
df_1432.plot(title="Cell 1432 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1433 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1433 = np.random.randn(100).cumsum()
df_1433 = pd.Series(data_1433)
df_1433.plot(title="Cell 1433 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1434 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1434 = np.random.randn(100).cumsum()
df_1434 = pd.Series(data_1434)
df_1434.plot(title="Cell 1434 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1435 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1435 = np.random.randn(100).cumsum()
df_1435 = pd.Series(data_1435)
df_1435.plot(title="Cell 1435 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1436 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1436 = np.random.randn(100).cumsum()
df_1436 = pd.Series(data_1436)
df_1436.plot(title="Cell 1436 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1437 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1437 = np.random.randn(100).cumsum()
df_1437 = pd.Series(data_1437)
df_1437.plot(title="Cell 1437 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1438 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1438 = np.random.randn(100).cumsum()
df_1438 = pd.Series(data_1438)
df_1438.plot(title="Cell 1438 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1439 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1439 = np.random.randn(100).cumsum()
df_1439 = pd.Series(data_1439)
df_1439.plot(title="Cell 1439 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1440 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1440 = np.random.randn(100).cumsum()
df_1440 = pd.Series(data_1440)
df_1440.plot(title="Cell 1440 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1441 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1441 = np.random.randn(100).cumsum()
df_1441 = pd.Series(data_1441)
df_1441.plot(title="Cell 1441 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1442 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1442 = np.random.randn(100).cumsum()
df_1442 = pd.Series(data_1442)
df_1442.plot(title="Cell 1442 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1443 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1443 = np.random.randn(100).cumsum()
df_1443 = pd.Series(data_1443)
df_1443.plot(title="Cell 1443 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1444 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1444 = np.random.randn(100).cumsum()
df_1444 = pd.Series(data_1444)
df_1444.plot(title="Cell 1444 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1445 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1445 = np.random.randn(100).cumsum()
df_1445 = pd.Series(data_1445)
df_1445.plot(title="Cell 1445 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1446 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1446 = np.random.randn(100).cumsum()
df_1446 = pd.Series(data_1446)
df_1446.plot(title="Cell 1446 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1447 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1447 = np.random.randn(100).cumsum()
df_1447 = pd.Series(data_1447)
df_1447.plot(title="Cell 1447 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1448 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1448 = np.random.randn(100).cumsum()
df_1448 = pd.Series(data_1448)
df_1448.plot(title="Cell 1448 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1449 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1449 = np.random.randn(100).cumsum()
df_1449 = pd.Series(data_1449)
df_1449.plot(title="Cell 1449 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1450 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1450 = np.random.randn(100).cumsum()
df_1450 = pd.Series(data_1450)
df_1450.plot(title="Cell 1450 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1451 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1451 = np.random.randn(100).cumsum()
df_1451 = pd.Series(data_1451)
df_1451.plot(title="Cell 1451 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1452 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1452 = np.random.randn(100).cumsum()
df_1452 = pd.Series(data_1452)
df_1452.plot(title="Cell 1452 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1453 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1453 = np.random.randn(100).cumsum()
df_1453 = pd.Series(data_1453)
df_1453.plot(title="Cell 1453 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1454 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1454 = np.random.randn(100).cumsum()
df_1454 = pd.Series(data_1454)
df_1454.plot(title="Cell 1454 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1455 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1455 = np.random.randn(100).cumsum()
df_1455 = pd.Series(data_1455)
df_1455.plot(title="Cell 1455 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1456 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1456 = np.random.randn(100).cumsum()
df_1456 = pd.Series(data_1456)
df_1456.plot(title="Cell 1456 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1457 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1457 = np.random.randn(100).cumsum()
df_1457 = pd.Series(data_1457)
df_1457.plot(title="Cell 1457 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1458 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1458 = np.random.randn(100).cumsum()
df_1458 = pd.Series(data_1458)
df_1458.plot(title="Cell 1458 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1459 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1459 = np.random.randn(100).cumsum()
df_1459 = pd.Series(data_1459)
df_1459.plot(title="Cell 1459 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1460 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1460 = np.random.randn(100).cumsum()
df_1460 = pd.Series(data_1460)
df_1460.plot(title="Cell 1460 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1461 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1461 = np.random.randn(100).cumsum()
df_1461 = pd.Series(data_1461)
df_1461.plot(title="Cell 1461 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1462 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1462 = np.random.randn(100).cumsum()
df_1462 = pd.Series(data_1462)
df_1462.plot(title="Cell 1462 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1463 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1463 = np.random.randn(100).cumsum()
df_1463 = pd.Series(data_1463)
df_1463.plot(title="Cell 1463 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1464 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1464 = np.random.randn(100).cumsum()
df_1464 = pd.Series(data_1464)
df_1464.plot(title="Cell 1464 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1465 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1465 = np.random.randn(100).cumsum()
df_1465 = pd.Series(data_1465)
df_1465.plot(title="Cell 1465 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1466 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1466 = np.random.randn(100).cumsum()
df_1466 = pd.Series(data_1466)
df_1466.plot(title="Cell 1466 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1467 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1467 = np.random.randn(100).cumsum()
df_1467 = pd.Series(data_1467)
df_1467.plot(title="Cell 1467 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1468 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1468 = np.random.randn(100).cumsum()
df_1468 = pd.Series(data_1468)
df_1468.plot(title="Cell 1468 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1469 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1469 = np.random.randn(100).cumsum()
df_1469 = pd.Series(data_1469)
df_1469.plot(title="Cell 1469 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1470 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1470 = np.random.randn(100).cumsum()
df_1470 = pd.Series(data_1470)
df_1470.plot(title="Cell 1470 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1471 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1471 = np.random.randn(100).cumsum()
df_1471 = pd.Series(data_1471)
df_1471.plot(title="Cell 1471 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1472 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1472 = np.random.randn(100).cumsum()
df_1472 = pd.Series(data_1472)
df_1472.plot(title="Cell 1472 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1473 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1473 = np.random.randn(100).cumsum()
df_1473 = pd.Series(data_1473)
df_1473.plot(title="Cell 1473 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1474 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1474 = np.random.randn(100).cumsum()
df_1474 = pd.Series(data_1474)
df_1474.plot(title="Cell 1474 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1475 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1475 = np.random.randn(100).cumsum()
df_1475 = pd.Series(data_1475)
df_1475.plot(title="Cell 1475 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1476 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1476 = np.random.randn(100).cumsum()
df_1476 = pd.Series(data_1476)
df_1476.plot(title="Cell 1476 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1477 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1477 = np.random.randn(100).cumsum()
df_1477 = pd.Series(data_1477)
df_1477.plot(title="Cell 1477 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1478 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1478 = np.random.randn(100).cumsum()
df_1478 = pd.Series(data_1478)
df_1478.plot(title="Cell 1478 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1479 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1479 = np.random.randn(100).cumsum()
df_1479 = pd.Series(data_1479)
df_1479.plot(title="Cell 1479 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1480 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1480 = np.random.randn(100).cumsum()
df_1480 = pd.Series(data_1480)
df_1480.plot(title="Cell 1480 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1481 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1481 = np.random.randn(100).cumsum()
df_1481 = pd.Series(data_1481)
df_1481.plot(title="Cell 1481 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1482 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1482 = np.random.randn(100).cumsum()
df_1482 = pd.Series(data_1482)
df_1482.plot(title="Cell 1482 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1483 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1483 = np.random.randn(100).cumsum()
df_1483 = pd.Series(data_1483)
df_1483.plot(title="Cell 1483 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1484 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1484 = np.random.randn(100).cumsum()
df_1484 = pd.Series(data_1484)
df_1484.plot(title="Cell 1484 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1485 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1485 = np.random.randn(100).cumsum()
df_1485 = pd.Series(data_1485)
df_1485.plot(title="Cell 1485 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1486 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1486 = np.random.randn(100).cumsum()
df_1486 = pd.Series(data_1486)
df_1486.plot(title="Cell 1486 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1487 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1487 = np.random.randn(100).cumsum()
df_1487 = pd.Series(data_1487)
df_1487.plot(title="Cell 1487 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1488 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1488 = np.random.randn(100).cumsum()
df_1488 = pd.Series(data_1488)
df_1488.plot(title="Cell 1488 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1489 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1489 = np.random.randn(100).cumsum()
df_1489 = pd.Series(data_1489)
df_1489.plot(title="Cell 1489 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1490 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1490 = np.random.randn(100).cumsum()
df_1490 = pd.Series(data_1490)
df_1490.plot(title="Cell 1490 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1491 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1491 = np.random.randn(100).cumsum()
df_1491 = pd.Series(data_1491)
df_1491.plot(title="Cell 1491 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1492 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1492 = np.random.randn(100).cumsum()
df_1492 = pd.Series(data_1492)
df_1492.plot(title="Cell 1492 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1493 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1493 = np.random.randn(100).cumsum()
df_1493 = pd.Series(data_1493)
df_1493.plot(title="Cell 1493 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1494 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1494 = np.random.randn(100).cumsum()
df_1494 = pd.Series(data_1494)
df_1494.plot(title="Cell 1494 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1495 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1495 = np.random.randn(100).cumsum()
df_1495 = pd.Series(data_1495)
df_1495.plot(title="Cell 1495 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1496 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1496 = np.random.randn(100).cumsum()
df_1496 = pd.Series(data_1496)
df_1496.plot(title="Cell 1496 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1497 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1497 = np.random.randn(100).cumsum()
df_1497 = pd.Series(data_1497)
df_1497.plot(title="Cell 1497 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1498 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1498 = np.random.randn(100).cumsum()
df_1498 = pd.Series(data_1498)
df_1498.plot(title="Cell 1498 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1499 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1499 = np.random.randn(100).cumsum()
df_1499 = pd.Series(data_1499)
df_1499.plot(title="Cell 1499 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1500 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1500 = np.random.randn(100).cumsum()
df_1500 = pd.Series(data_1500)
df_1500.plot(title="Cell 1500 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1501 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1501 = np.random.randn(100).cumsum()
df_1501 = pd.Series(data_1501)
df_1501.plot(title="Cell 1501 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1502 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1502 = np.random.randn(100).cumsum()
df_1502 = pd.Series(data_1502)
df_1502.plot(title="Cell 1502 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1503 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1503 = np.random.randn(100).cumsum()
df_1503 = pd.Series(data_1503)
df_1503.plot(title="Cell 1503 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1504 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1504 = np.random.randn(100).cumsum()
df_1504 = pd.Series(data_1504)
df_1504.plot(title="Cell 1504 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1505 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1505 = np.random.randn(100).cumsum()
df_1505 = pd.Series(data_1505)
df_1505.plot(title="Cell 1505 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1506 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1506 = np.random.randn(100).cumsum()
df_1506 = pd.Series(data_1506)
df_1506.plot(title="Cell 1506 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1507 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1507 = np.random.randn(100).cumsum()
df_1507 = pd.Series(data_1507)
df_1507.plot(title="Cell 1507 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1508 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1508 = np.random.randn(100).cumsum()
df_1508 = pd.Series(data_1508)
df_1508.plot(title="Cell 1508 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1509 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1509 = np.random.randn(100).cumsum()
df_1509 = pd.Series(data_1509)
df_1509.plot(title="Cell 1509 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1510 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1510 = np.random.randn(100).cumsum()
df_1510 = pd.Series(data_1510)
df_1510.plot(title="Cell 1510 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1511 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1511 = np.random.randn(100).cumsum()
df_1511 = pd.Series(data_1511)
df_1511.plot(title="Cell 1511 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1512 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1512 = np.random.randn(100).cumsum()
df_1512 = pd.Series(data_1512)
df_1512.plot(title="Cell 1512 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1513 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1513 = np.random.randn(100).cumsum()
df_1513 = pd.Series(data_1513)
df_1513.plot(title="Cell 1513 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1514 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1514 = np.random.randn(100).cumsum()
df_1514 = pd.Series(data_1514)
df_1514.plot(title="Cell 1514 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1515 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1515 = np.random.randn(100).cumsum()
df_1515 = pd.Series(data_1515)
df_1515.plot(title="Cell 1515 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1516 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1516 = np.random.randn(100).cumsum()
df_1516 = pd.Series(data_1516)
df_1516.plot(title="Cell 1516 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1517 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1517 = np.random.randn(100).cumsum()
df_1517 = pd.Series(data_1517)
df_1517.plot(title="Cell 1517 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1518 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1518 = np.random.randn(100).cumsum()
df_1518 = pd.Series(data_1518)
df_1518.plot(title="Cell 1518 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1519 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1519 = np.random.randn(100).cumsum()
df_1519 = pd.Series(data_1519)
df_1519.plot(title="Cell 1519 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1520 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1520 = np.random.randn(100).cumsum()
df_1520 = pd.Series(data_1520)
df_1520.plot(title="Cell 1520 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1521 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1521 = np.random.randn(100).cumsum()
df_1521 = pd.Series(data_1521)
df_1521.plot(title="Cell 1521 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1522 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1522 = np.random.randn(100).cumsum()
df_1522 = pd.Series(data_1522)
df_1522.plot(title="Cell 1522 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1523 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1523 = np.random.randn(100).cumsum()
df_1523 = pd.Series(data_1523)
df_1523.plot(title="Cell 1523 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1524 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1524 = np.random.randn(100).cumsum()
df_1524 = pd.Series(data_1524)
df_1524.plot(title="Cell 1524 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1525 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1525 = np.random.randn(100).cumsum()
df_1525 = pd.Series(data_1525)
df_1525.plot(title="Cell 1525 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1526 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1526 = np.random.randn(100).cumsum()
df_1526 = pd.Series(data_1526)
df_1526.plot(title="Cell 1526 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1527 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1527 = np.random.randn(100).cumsum()
df_1527 = pd.Series(data_1527)
df_1527.plot(title="Cell 1527 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1528 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1528 = np.random.randn(100).cumsum()
df_1528 = pd.Series(data_1528)
df_1528.plot(title="Cell 1528 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1529 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1529 = np.random.randn(100).cumsum()
df_1529 = pd.Series(data_1529)
df_1529.plot(title="Cell 1529 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1530 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1530 = np.random.randn(100).cumsum()
df_1530 = pd.Series(data_1530)
df_1530.plot(title="Cell 1530 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1531 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1531 = np.random.randn(100).cumsum()
df_1531 = pd.Series(data_1531)
df_1531.plot(title="Cell 1531 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1532 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1532 = np.random.randn(100).cumsum()
df_1532 = pd.Series(data_1532)
df_1532.plot(title="Cell 1532 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1533 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1533 = np.random.randn(100).cumsum()
df_1533 = pd.Series(data_1533)
df_1533.plot(title="Cell 1533 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1534 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1534 = np.random.randn(100).cumsum()
df_1534 = pd.Series(data_1534)
df_1534.plot(title="Cell 1534 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1535 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1535 = np.random.randn(100).cumsum()
df_1535 = pd.Series(data_1535)
df_1535.plot(title="Cell 1535 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1536 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1536 = np.random.randn(100).cumsum()
df_1536 = pd.Series(data_1536)
df_1536.plot(title="Cell 1536 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1537 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1537 = np.random.randn(100).cumsum()
df_1537 = pd.Series(data_1537)
df_1537.plot(title="Cell 1537 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1538 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1538 = np.random.randn(100).cumsum()
df_1538 = pd.Series(data_1538)
df_1538.plot(title="Cell 1538 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1539 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1539 = np.random.randn(100).cumsum()
df_1539 = pd.Series(data_1539)
df_1539.plot(title="Cell 1539 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1540 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1540 = np.random.randn(100).cumsum()
df_1540 = pd.Series(data_1540)
df_1540.plot(title="Cell 1540 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1541 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1541 = np.random.randn(100).cumsum()
df_1541 = pd.Series(data_1541)
df_1541.plot(title="Cell 1541 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1542 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1542 = np.random.randn(100).cumsum()
df_1542 = pd.Series(data_1542)
df_1542.plot(title="Cell 1542 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1543 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1543 = np.random.randn(100).cumsum()
df_1543 = pd.Series(data_1543)
df_1543.plot(title="Cell 1543 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1544 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1544 = np.random.randn(100).cumsum()
df_1544 = pd.Series(data_1544)
df_1544.plot(title="Cell 1544 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1545 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1545 = np.random.randn(100).cumsum()
df_1545 = pd.Series(data_1545)
df_1545.plot(title="Cell 1545 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1546 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1546 = np.random.randn(100).cumsum()
df_1546 = pd.Series(data_1546)
df_1546.plot(title="Cell 1546 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1547 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1547 = np.random.randn(100).cumsum()
df_1547 = pd.Series(data_1547)
df_1547.plot(title="Cell 1547 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1548 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1548 = np.random.randn(100).cumsum()
df_1548 = pd.Series(data_1548)
df_1548.plot(title="Cell 1548 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1549 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1549 = np.random.randn(100).cumsum()
df_1549 = pd.Series(data_1549)
df_1549.plot(title="Cell 1549 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1550 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1550 = np.random.randn(100).cumsum()
df_1550 = pd.Series(data_1550)
df_1550.plot(title="Cell 1550 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1551 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1551 = np.random.randn(100).cumsum()
df_1551 = pd.Series(data_1551)
df_1551.plot(title="Cell 1551 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1552 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1552 = np.random.randn(100).cumsum()
df_1552 = pd.Series(data_1552)
df_1552.plot(title="Cell 1552 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1553 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1553 = np.random.randn(100).cumsum()
df_1553 = pd.Series(data_1553)
df_1553.plot(title="Cell 1553 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1554 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1554 = np.random.randn(100).cumsum()
df_1554 = pd.Series(data_1554)
df_1554.plot(title="Cell 1554 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1555 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1555 = np.random.randn(100).cumsum()
df_1555 = pd.Series(data_1555)
df_1555.plot(title="Cell 1555 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1556 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1556 = np.random.randn(100).cumsum()
df_1556 = pd.Series(data_1556)
df_1556.plot(title="Cell 1556 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1557 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1557 = np.random.randn(100).cumsum()
df_1557 = pd.Series(data_1557)
df_1557.plot(title="Cell 1557 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1558 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1558 = np.random.randn(100).cumsum()
df_1558 = pd.Series(data_1558)
df_1558.plot(title="Cell 1558 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1559 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1559 = np.random.randn(100).cumsum()
df_1559 = pd.Series(data_1559)
df_1559.plot(title="Cell 1559 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1560 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1560 = np.random.randn(100).cumsum()
df_1560 = pd.Series(data_1560)
df_1560.plot(title="Cell 1560 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1561 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1561 = np.random.randn(100).cumsum()
df_1561 = pd.Series(data_1561)
df_1561.plot(title="Cell 1561 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1562 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1562 = np.random.randn(100).cumsum()
df_1562 = pd.Series(data_1562)
df_1562.plot(title="Cell 1562 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1563 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1563 = np.random.randn(100).cumsum()
df_1563 = pd.Series(data_1563)
df_1563.plot(title="Cell 1563 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1564 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1564 = np.random.randn(100).cumsum()
df_1564 = pd.Series(data_1564)
df_1564.plot(title="Cell 1564 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1565 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1565 = np.random.randn(100).cumsum()
df_1565 = pd.Series(data_1565)
df_1565.plot(title="Cell 1565 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1566 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1566 = np.random.randn(100).cumsum()
df_1566 = pd.Series(data_1566)
df_1566.plot(title="Cell 1566 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1567 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1567 = np.random.randn(100).cumsum()
df_1567 = pd.Series(data_1567)
df_1567.plot(title="Cell 1567 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1568 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1568 = np.random.randn(100).cumsum()
df_1568 = pd.Series(data_1568)
df_1568.plot(title="Cell 1568 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1569 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1569 = np.random.randn(100).cumsum()
df_1569 = pd.Series(data_1569)
df_1569.plot(title="Cell 1569 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1570 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1570 = np.random.randn(100).cumsum()
df_1570 = pd.Series(data_1570)
df_1570.plot(title="Cell 1570 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1571 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1571 = np.random.randn(100).cumsum()
df_1571 = pd.Series(data_1571)
df_1571.plot(title="Cell 1571 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1572 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1572 = np.random.randn(100).cumsum()
df_1572 = pd.Series(data_1572)
df_1572.plot(title="Cell 1572 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1573 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1573 = np.random.randn(100).cumsum()
df_1573 = pd.Series(data_1573)
df_1573.plot(title="Cell 1573 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1574 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1574 = np.random.randn(100).cumsum()
df_1574 = pd.Series(data_1574)
df_1574.plot(title="Cell 1574 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1575 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1575 = np.random.randn(100).cumsum()
df_1575 = pd.Series(data_1575)
df_1575.plot(title="Cell 1575 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1576 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1576 = np.random.randn(100).cumsum()
df_1576 = pd.Series(data_1576)
df_1576.plot(title="Cell 1576 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1577 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1577 = np.random.randn(100).cumsum()
df_1577 = pd.Series(data_1577)
df_1577.plot(title="Cell 1577 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1578 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1578 = np.random.randn(100).cumsum()
df_1578 = pd.Series(data_1578)
df_1578.plot(title="Cell 1578 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1579 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1579 = np.random.randn(100).cumsum()
df_1579 = pd.Series(data_1579)
df_1579.plot(title="Cell 1579 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1580 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1580 = np.random.randn(100).cumsum()
df_1580 = pd.Series(data_1580)
df_1580.plot(title="Cell 1580 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1581 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1581 = np.random.randn(100).cumsum()
df_1581 = pd.Series(data_1581)
df_1581.plot(title="Cell 1581 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1582 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1582 = np.random.randn(100).cumsum()
df_1582 = pd.Series(data_1582)
df_1582.plot(title="Cell 1582 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1583 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1583 = np.random.randn(100).cumsum()
df_1583 = pd.Series(data_1583)
df_1583.plot(title="Cell 1583 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1584 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1584 = np.random.randn(100).cumsum()
df_1584 = pd.Series(data_1584)
df_1584.plot(title="Cell 1584 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1585 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1585 = np.random.randn(100).cumsum()
df_1585 = pd.Series(data_1585)
df_1585.plot(title="Cell 1585 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1586 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1586 = np.random.randn(100).cumsum()
df_1586 = pd.Series(data_1586)
df_1586.plot(title="Cell 1586 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1587 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1587 = np.random.randn(100).cumsum()
df_1587 = pd.Series(data_1587)
df_1587.plot(title="Cell 1587 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1588 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1588 = np.random.randn(100).cumsum()
df_1588 = pd.Series(data_1588)
df_1588.plot(title="Cell 1588 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1589 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1589 = np.random.randn(100).cumsum()
df_1589 = pd.Series(data_1589)
df_1589.plot(title="Cell 1589 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1590 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1590 = np.random.randn(100).cumsum()
df_1590 = pd.Series(data_1590)
df_1590.plot(title="Cell 1590 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1591 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1591 = np.random.randn(100).cumsum()
df_1591 = pd.Series(data_1591)
df_1591.plot(title="Cell 1591 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1592 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1592 = np.random.randn(100).cumsum()
df_1592 = pd.Series(data_1592)
df_1592.plot(title="Cell 1592 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1593 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1593 = np.random.randn(100).cumsum()
df_1593 = pd.Series(data_1593)
df_1593.plot(title="Cell 1593 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1594 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1594 = np.random.randn(100).cumsum()
df_1594 = pd.Series(data_1594)
df_1594.plot(title="Cell 1594 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1595 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1595 = np.random.randn(100).cumsum()
df_1595 = pd.Series(data_1595)
df_1595.plot(title="Cell 1595 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1596 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1596 = np.random.randn(100).cumsum()
df_1596 = pd.Series(data_1596)
df_1596.plot(title="Cell 1596 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1597 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1597 = np.random.randn(100).cumsum()
df_1597 = pd.Series(data_1597)
df_1597.plot(title="Cell 1597 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1598 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1598 = np.random.randn(100).cumsum()
df_1598 = pd.Series(data_1598)
df_1598.plot(title="Cell 1598 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1599 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1599 = np.random.randn(100).cumsum()
df_1599 = pd.Series(data_1599)
df_1599.plot(title="Cell 1599 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1600 - pandas visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_1600 = np.random.randn(100).cumsum()
df_1600 = pd.Series(data_1600)
df_1600.plot(title="Cell 1600 - Pandas Line Plot")
plt.xlabel("Index")
plt.ylabel("Value")
plt.show()
# Cell 1601 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1601, y_1601 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=1)
model_1601 = LinearRegression()
model_1601.fit(X_1601, y_1601)
y_pred_1601 = model_1601.predict(X_1601)
plt.scatter(X_1601, y_1601, label="Data")
plt.plot(X_1601, y_pred_1601, color="red", label="Regression Line")
plt.title("Cell 1601 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1602 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1602, y_1602 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=2)
model_1602 = LinearRegression()
model_1602.fit(X_1602, y_1602)
y_pred_1602 = model_1602.predict(X_1602)
plt.scatter(X_1602, y_1602, label="Data")
plt.plot(X_1602, y_pred_1602, color="red", label="Regression Line")
plt.title("Cell 1602 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1603 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1603, y_1603 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=3)
model_1603 = LinearRegression()
model_1603.fit(X_1603, y_1603)
y_pred_1603 = model_1603.predict(X_1603)
plt.scatter(X_1603, y_1603, label="Data")
plt.plot(X_1603, y_pred_1603, color="red", label="Regression Line")
plt.title("Cell 1603 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1604 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1604, y_1604 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=4)
model_1604 = LinearRegression()
model_1604.fit(X_1604, y_1604)
y_pred_1604 = model_1604.predict(X_1604)
plt.scatter(X_1604, y_1604, label="Data")
plt.plot(X_1604, y_pred_1604, color="red", label="Regression Line")
plt.title("Cell 1604 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1605 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1605, y_1605 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=5)
model_1605 = LinearRegression()
model_1605.fit(X_1605, y_1605)
y_pred_1605 = model_1605.predict(X_1605)
plt.scatter(X_1605, y_1605, label="Data")
plt.plot(X_1605, y_pred_1605, color="red", label="Regression Line")
plt.title("Cell 1605 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1606 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1606, y_1606 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=6)
model_1606 = LinearRegression()
model_1606.fit(X_1606, y_1606)
y_pred_1606 = model_1606.predict(X_1606)
plt.scatter(X_1606, y_1606, label="Data")
plt.plot(X_1606, y_pred_1606, color="red", label="Regression Line")
plt.title("Cell 1606 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1607 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1607, y_1607 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=7)
model_1607 = LinearRegression()
model_1607.fit(X_1607, y_1607)
y_pred_1607 = model_1607.predict(X_1607)
plt.scatter(X_1607, y_1607, label="Data")
plt.plot(X_1607, y_pred_1607, color="red", label="Regression Line")
plt.title("Cell 1607 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1608 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1608, y_1608 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=8)
model_1608 = LinearRegression()
model_1608.fit(X_1608, y_1608)
y_pred_1608 = model_1608.predict(X_1608)
plt.scatter(X_1608, y_1608, label="Data")
plt.plot(X_1608, y_pred_1608, color="red", label="Regression Line")
plt.title("Cell 1608 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1609 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1609, y_1609 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=9)
model_1609 = LinearRegression()
model_1609.fit(X_1609, y_1609)
y_pred_1609 = model_1609.predict(X_1609)
plt.scatter(X_1609, y_1609, label="Data")
plt.plot(X_1609, y_pred_1609, color="red", label="Regression Line")
plt.title("Cell 1609 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1610 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1610, y_1610 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=10)
model_1610 = LinearRegression()
model_1610.fit(X_1610, y_1610)
y_pred_1610 = model_1610.predict(X_1610)
plt.scatter(X_1610, y_1610, label="Data")
plt.plot(X_1610, y_pred_1610, color="red", label="Regression Line")
plt.title("Cell 1610 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1611 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1611, y_1611 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=11)
model_1611 = LinearRegression()
model_1611.fit(X_1611, y_1611)
y_pred_1611 = model_1611.predict(X_1611)
plt.scatter(X_1611, y_1611, label="Data")
plt.plot(X_1611, y_pred_1611, color="red", label="Regression Line")
plt.title("Cell 1611 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1612 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1612, y_1612 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=12)
model_1612 = LinearRegression()
model_1612.fit(X_1612, y_1612)
y_pred_1612 = model_1612.predict(X_1612)
plt.scatter(X_1612, y_1612, label="Data")
plt.plot(X_1612, y_pred_1612, color="red", label="Regression Line")
plt.title("Cell 1612 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1613 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1613, y_1613 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=13)
model_1613 = LinearRegression()
model_1613.fit(X_1613, y_1613)
y_pred_1613 = model_1613.predict(X_1613)
plt.scatter(X_1613, y_1613, label="Data")
plt.plot(X_1613, y_pred_1613, color="red", label="Regression Line")
plt.title("Cell 1613 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1614 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1614, y_1614 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=14)
model_1614 = LinearRegression()
model_1614.fit(X_1614, y_1614)
y_pred_1614 = model_1614.predict(X_1614)
plt.scatter(X_1614, y_1614, label="Data")
plt.plot(X_1614, y_pred_1614, color="red", label="Regression Line")
plt.title("Cell 1614 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1615 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1615, y_1615 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=15)
model_1615 = LinearRegression()
model_1615.fit(X_1615, y_1615)
y_pred_1615 = model_1615.predict(X_1615)
plt.scatter(X_1615, y_1615, label="Data")
plt.plot(X_1615, y_pred_1615, color="red", label="Regression Line")
plt.title("Cell 1615 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1616 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1616, y_1616 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=16)
model_1616 = LinearRegression()
model_1616.fit(X_1616, y_1616)
y_pred_1616 = model_1616.predict(X_1616)
plt.scatter(X_1616, y_1616, label="Data")
plt.plot(X_1616, y_pred_1616, color="red", label="Regression Line")
plt.title("Cell 1616 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1617 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1617, y_1617 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=17)
model_1617 = LinearRegression()
model_1617.fit(X_1617, y_1617)
y_pred_1617 = model_1617.predict(X_1617)
plt.scatter(X_1617, y_1617, label="Data")
plt.plot(X_1617, y_pred_1617, color="red", label="Regression Line")
plt.title("Cell 1617 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1618 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1618, y_1618 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=18)
model_1618 = LinearRegression()
model_1618.fit(X_1618, y_1618)
y_pred_1618 = model_1618.predict(X_1618)
plt.scatter(X_1618, y_1618, label="Data")
plt.plot(X_1618, y_pred_1618, color="red", label="Regression Line")
plt.title("Cell 1618 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1619 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1619, y_1619 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=19)
model_1619 = LinearRegression()
model_1619.fit(X_1619, y_1619)
y_pred_1619 = model_1619.predict(X_1619)
plt.scatter(X_1619, y_1619, label="Data")
plt.plot(X_1619, y_pred_1619, color="red", label="Regression Line")
plt.title("Cell 1619 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1620 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1620, y_1620 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=20)
model_1620 = LinearRegression()
model_1620.fit(X_1620, y_1620)
y_pred_1620 = model_1620.predict(X_1620)
plt.scatter(X_1620, y_1620, label="Data")
plt.plot(X_1620, y_pred_1620, color="red", label="Regression Line")
plt.title("Cell 1620 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1621 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1621, y_1621 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=21)
model_1621 = LinearRegression()
model_1621.fit(X_1621, y_1621)
y_pred_1621 = model_1621.predict(X_1621)
plt.scatter(X_1621, y_1621, label="Data")
plt.plot(X_1621, y_pred_1621, color="red", label="Regression Line")
plt.title("Cell 1621 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1622 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1622, y_1622 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=22)
model_1622 = LinearRegression()
model_1622.fit(X_1622, y_1622)
y_pred_1622 = model_1622.predict(X_1622)
plt.scatter(X_1622, y_1622, label="Data")
plt.plot(X_1622, y_pred_1622, color="red", label="Regression Line")
plt.title("Cell 1622 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1623 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1623, y_1623 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=23)
model_1623 = LinearRegression()
model_1623.fit(X_1623, y_1623)
y_pred_1623 = model_1623.predict(X_1623)
plt.scatter(X_1623, y_1623, label="Data")
plt.plot(X_1623, y_pred_1623, color="red", label="Regression Line")
plt.title("Cell 1623 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1624 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1624, y_1624 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=24)
model_1624 = LinearRegression()
model_1624.fit(X_1624, y_1624)
y_pred_1624 = model_1624.predict(X_1624)
plt.scatter(X_1624, y_1624, label="Data")
plt.plot(X_1624, y_pred_1624, color="red", label="Regression Line")
plt.title("Cell 1624 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1625 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1625, y_1625 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=25)
model_1625 = LinearRegression()
model_1625.fit(X_1625, y_1625)
y_pred_1625 = model_1625.predict(X_1625)
plt.scatter(X_1625, y_1625, label="Data")
plt.plot(X_1625, y_pred_1625, color="red", label="Regression Line")
plt.title("Cell 1625 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1626 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1626, y_1626 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=26)
model_1626 = LinearRegression()
model_1626.fit(X_1626, y_1626)
y_pred_1626 = model_1626.predict(X_1626)
plt.scatter(X_1626, y_1626, label="Data")
plt.plot(X_1626, y_pred_1626, color="red", label="Regression Line")
plt.title("Cell 1626 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1627 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1627, y_1627 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=27)
model_1627 = LinearRegression()
model_1627.fit(X_1627, y_1627)
y_pred_1627 = model_1627.predict(X_1627)
plt.scatter(X_1627, y_1627, label="Data")
plt.plot(X_1627, y_pred_1627, color="red", label="Regression Line")
plt.title("Cell 1627 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1628 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1628, y_1628 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=28)
model_1628 = LinearRegression()
model_1628.fit(X_1628, y_1628)
y_pred_1628 = model_1628.predict(X_1628)
plt.scatter(X_1628, y_1628, label="Data")
plt.plot(X_1628, y_pred_1628, color="red", label="Regression Line")
plt.title("Cell 1628 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1629 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1629, y_1629 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=29)
model_1629 = LinearRegression()
model_1629.fit(X_1629, y_1629)
y_pred_1629 = model_1629.predict(X_1629)
plt.scatter(X_1629, y_1629, label="Data")
plt.plot(X_1629, y_pred_1629, color="red", label="Regression Line")
plt.title("Cell 1629 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1630 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1630, y_1630 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=30)
model_1630 = LinearRegression()
model_1630.fit(X_1630, y_1630)
y_pred_1630 = model_1630.predict(X_1630)
plt.scatter(X_1630, y_1630, label="Data")
plt.plot(X_1630, y_pred_1630, color="red", label="Regression Line")
plt.title("Cell 1630 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1631 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1631, y_1631 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=31)
model_1631 = LinearRegression()
model_1631.fit(X_1631, y_1631)
y_pred_1631 = model_1631.predict(X_1631)
plt.scatter(X_1631, y_1631, label="Data")
plt.plot(X_1631, y_pred_1631, color="red", label="Regression Line")
plt.title("Cell 1631 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1632 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1632, y_1632 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=32)
model_1632 = LinearRegression()
model_1632.fit(X_1632, y_1632)
y_pred_1632 = model_1632.predict(X_1632)
plt.scatter(X_1632, y_1632, label="Data")
plt.plot(X_1632, y_pred_1632, color="red", label="Regression Line")
plt.title("Cell 1632 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1633 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1633, y_1633 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=33)
model_1633 = LinearRegression()
model_1633.fit(X_1633, y_1633)
y_pred_1633 = model_1633.predict(X_1633)
plt.scatter(X_1633, y_1633, label="Data")
plt.plot(X_1633, y_pred_1633, color="red", label="Regression Line")
plt.title("Cell 1633 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1634 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1634, y_1634 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=34)
model_1634 = LinearRegression()
model_1634.fit(X_1634, y_1634)
y_pred_1634 = model_1634.predict(X_1634)
plt.scatter(X_1634, y_1634, label="Data")
plt.plot(X_1634, y_pred_1634, color="red", label="Regression Line")
plt.title("Cell 1634 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1635 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1635, y_1635 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=35)
model_1635 = LinearRegression()
model_1635.fit(X_1635, y_1635)
y_pred_1635 = model_1635.predict(X_1635)
plt.scatter(X_1635, y_1635, label="Data")
plt.plot(X_1635, y_pred_1635, color="red", label="Regression Line")
plt.title("Cell 1635 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1636 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1636, y_1636 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=36)
model_1636 = LinearRegression()
model_1636.fit(X_1636, y_1636)
y_pred_1636 = model_1636.predict(X_1636)
plt.scatter(X_1636, y_1636, label="Data")
plt.plot(X_1636, y_pred_1636, color="red", label="Regression Line")
plt.title("Cell 1636 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1637 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1637, y_1637 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=37)
model_1637 = LinearRegression()
model_1637.fit(X_1637, y_1637)
y_pred_1637 = model_1637.predict(X_1637)
plt.scatter(X_1637, y_1637, label="Data")
plt.plot(X_1637, y_pred_1637, color="red", label="Regression Line")
plt.title("Cell 1637 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1638 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1638, y_1638 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=38)
model_1638 = LinearRegression()
model_1638.fit(X_1638, y_1638)
y_pred_1638 = model_1638.predict(X_1638)
plt.scatter(X_1638, y_1638, label="Data")
plt.plot(X_1638, y_pred_1638, color="red", label="Regression Line")
plt.title("Cell 1638 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1639 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1639, y_1639 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=39)
model_1639 = LinearRegression()
model_1639.fit(X_1639, y_1639)
y_pred_1639 = model_1639.predict(X_1639)
plt.scatter(X_1639, y_1639, label="Data")
plt.plot(X_1639, y_pred_1639, color="red", label="Regression Line")
plt.title("Cell 1639 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1640 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1640, y_1640 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=40)
model_1640 = LinearRegression()
model_1640.fit(X_1640, y_1640)
y_pred_1640 = model_1640.predict(X_1640)
plt.scatter(X_1640, y_1640, label="Data")
plt.plot(X_1640, y_pred_1640, color="red", label="Regression Line")
plt.title("Cell 1640 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1641 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1641, y_1641 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=41)
model_1641 = LinearRegression()
model_1641.fit(X_1641, y_1641)
y_pred_1641 = model_1641.predict(X_1641)
plt.scatter(X_1641, y_1641, label="Data")
plt.plot(X_1641, y_pred_1641, color="red", label="Regression Line")
plt.title("Cell 1641 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1642 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1642, y_1642 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=42)
model_1642 = LinearRegression()
model_1642.fit(X_1642, y_1642)
y_pred_1642 = model_1642.predict(X_1642)
plt.scatter(X_1642, y_1642, label="Data")
plt.plot(X_1642, y_pred_1642, color="red", label="Regression Line")
plt.title("Cell 1642 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1643 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1643, y_1643 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=43)
model_1643 = LinearRegression()
model_1643.fit(X_1643, y_1643)
y_pred_1643 = model_1643.predict(X_1643)
plt.scatter(X_1643, y_1643, label="Data")
plt.plot(X_1643, y_pred_1643, color="red", label="Regression Line")
plt.title("Cell 1643 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1644 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1644, y_1644 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=44)
model_1644 = LinearRegression()
model_1644.fit(X_1644, y_1644)
y_pred_1644 = model_1644.predict(X_1644)
plt.scatter(X_1644, y_1644, label="Data")
plt.plot(X_1644, y_pred_1644, color="red", label="Regression Line")
plt.title("Cell 1644 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1645 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1645, y_1645 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=45)
model_1645 = LinearRegression()
model_1645.fit(X_1645, y_1645)
y_pred_1645 = model_1645.predict(X_1645)
plt.scatter(X_1645, y_1645, label="Data")
plt.plot(X_1645, y_pred_1645, color="red", label="Regression Line")
plt.title("Cell 1645 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1646 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1646, y_1646 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=46)
model_1646 = LinearRegression()
model_1646.fit(X_1646, y_1646)
y_pred_1646 = model_1646.predict(X_1646)
plt.scatter(X_1646, y_1646, label="Data")
plt.plot(X_1646, y_pred_1646, color="red", label="Regression Line")
plt.title("Cell 1646 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1647 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1647, y_1647 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=47)
model_1647 = LinearRegression()
model_1647.fit(X_1647, y_1647)
y_pred_1647 = model_1647.predict(X_1647)
plt.scatter(X_1647, y_1647, label="Data")
plt.plot(X_1647, y_pred_1647, color="red", label="Regression Line")
plt.title("Cell 1647 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1648 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1648, y_1648 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=48)
model_1648 = LinearRegression()
model_1648.fit(X_1648, y_1648)
y_pred_1648 = model_1648.predict(X_1648)
plt.scatter(X_1648, y_1648, label="Data")
plt.plot(X_1648, y_pred_1648, color="red", label="Regression Line")
plt.title("Cell 1648 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1649 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1649, y_1649 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=49)
model_1649 = LinearRegression()
model_1649.fit(X_1649, y_1649)
y_pred_1649 = model_1649.predict(X_1649)
plt.scatter(X_1649, y_1649, label="Data")
plt.plot(X_1649, y_pred_1649, color="red", label="Regression Line")
plt.title("Cell 1649 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1650 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1650, y_1650 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=50)
model_1650 = LinearRegression()
model_1650.fit(X_1650, y_1650)
y_pred_1650 = model_1650.predict(X_1650)
plt.scatter(X_1650, y_1650, label="Data")
plt.plot(X_1650, y_pred_1650, color="red", label="Regression Line")
plt.title("Cell 1650 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1651 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1651, y_1651 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=51)
model_1651 = LinearRegression()
model_1651.fit(X_1651, y_1651)
y_pred_1651 = model_1651.predict(X_1651)
plt.scatter(X_1651, y_1651, label="Data")
plt.plot(X_1651, y_pred_1651, color="red", label="Regression Line")
plt.title("Cell 1651 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1652 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1652, y_1652 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=52)
model_1652 = LinearRegression()
model_1652.fit(X_1652, y_1652)
y_pred_1652 = model_1652.predict(X_1652)
plt.scatter(X_1652, y_1652, label="Data")
plt.plot(X_1652, y_pred_1652, color="red", label="Regression Line")
plt.title("Cell 1652 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1653 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1653, y_1653 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=53)
model_1653 = LinearRegression()
model_1653.fit(X_1653, y_1653)
y_pred_1653 = model_1653.predict(X_1653)
plt.scatter(X_1653, y_1653, label="Data")
plt.plot(X_1653, y_pred_1653, color="red", label="Regression Line")
plt.title("Cell 1653 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1654 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1654, y_1654 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=54)
model_1654 = LinearRegression()
model_1654.fit(X_1654, y_1654)
y_pred_1654 = model_1654.predict(X_1654)
plt.scatter(X_1654, y_1654, label="Data")
plt.plot(X_1654, y_pred_1654, color="red", label="Regression Line")
plt.title("Cell 1654 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1655 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1655, y_1655 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=55)
model_1655 = LinearRegression()
model_1655.fit(X_1655, y_1655)
y_pred_1655 = model_1655.predict(X_1655)
plt.scatter(X_1655, y_1655, label="Data")
plt.plot(X_1655, y_pred_1655, color="red", label="Regression Line")
plt.title("Cell 1655 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1656 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1656, y_1656 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=56)
model_1656 = LinearRegression()
model_1656.fit(X_1656, y_1656)
y_pred_1656 = model_1656.predict(X_1656)
plt.scatter(X_1656, y_1656, label="Data")
plt.plot(X_1656, y_pred_1656, color="red", label="Regression Line")
plt.title("Cell 1656 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1657 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1657, y_1657 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=57)
model_1657 = LinearRegression()
model_1657.fit(X_1657, y_1657)
y_pred_1657 = model_1657.predict(X_1657)
plt.scatter(X_1657, y_1657, label="Data")
plt.plot(X_1657, y_pred_1657, color="red", label="Regression Line")
plt.title("Cell 1657 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1658 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1658, y_1658 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=58)
model_1658 = LinearRegression()
model_1658.fit(X_1658, y_1658)
y_pred_1658 = model_1658.predict(X_1658)
plt.scatter(X_1658, y_1658, label="Data")
plt.plot(X_1658, y_pred_1658, color="red", label="Regression Line")
plt.title("Cell 1658 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1659 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1659, y_1659 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=59)
model_1659 = LinearRegression()
model_1659.fit(X_1659, y_1659)
y_pred_1659 = model_1659.predict(X_1659)
plt.scatter(X_1659, y_1659, label="Data")
plt.plot(X_1659, y_pred_1659, color="red", label="Regression Line")
plt.title("Cell 1659 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1660 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1660, y_1660 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=60)
model_1660 = LinearRegression()
model_1660.fit(X_1660, y_1660)
y_pred_1660 = model_1660.predict(X_1660)
plt.scatter(X_1660, y_1660, label="Data")
plt.plot(X_1660, y_pred_1660, color="red", label="Regression Line")
plt.title("Cell 1660 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1661 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1661, y_1661 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=61)
model_1661 = LinearRegression()
model_1661.fit(X_1661, y_1661)
y_pred_1661 = model_1661.predict(X_1661)
plt.scatter(X_1661, y_1661, label="Data")
plt.plot(X_1661, y_pred_1661, color="red", label="Regression Line")
plt.title("Cell 1661 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1662 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1662, y_1662 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=62)
model_1662 = LinearRegression()
model_1662.fit(X_1662, y_1662)
y_pred_1662 = model_1662.predict(X_1662)
plt.scatter(X_1662, y_1662, label="Data")
plt.plot(X_1662, y_pred_1662, color="red", label="Regression Line")
plt.title("Cell 1662 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1663 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1663, y_1663 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=63)
model_1663 = LinearRegression()
model_1663.fit(X_1663, y_1663)
y_pred_1663 = model_1663.predict(X_1663)
plt.scatter(X_1663, y_1663, label="Data")
plt.plot(X_1663, y_pred_1663, color="red", label="Regression Line")
plt.title("Cell 1663 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1664 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1664, y_1664 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=64)
model_1664 = LinearRegression()
model_1664.fit(X_1664, y_1664)
y_pred_1664 = model_1664.predict(X_1664)
plt.scatter(X_1664, y_1664, label="Data")
plt.plot(X_1664, y_pred_1664, color="red", label="Regression Line")
plt.title("Cell 1664 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1665 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1665, y_1665 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=65)
model_1665 = LinearRegression()
model_1665.fit(X_1665, y_1665)
y_pred_1665 = model_1665.predict(X_1665)
plt.scatter(X_1665, y_1665, label="Data")
plt.plot(X_1665, y_pred_1665, color="red", label="Regression Line")
plt.title("Cell 1665 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1666 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1666, y_1666 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=66)
model_1666 = LinearRegression()
model_1666.fit(X_1666, y_1666)
y_pred_1666 = model_1666.predict(X_1666)
plt.scatter(X_1666, y_1666, label="Data")
plt.plot(X_1666, y_pred_1666, color="red", label="Regression Line")
plt.title("Cell 1666 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1667 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1667, y_1667 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=67)
model_1667 = LinearRegression()
model_1667.fit(X_1667, y_1667)
y_pred_1667 = model_1667.predict(X_1667)
plt.scatter(X_1667, y_1667, label="Data")
plt.plot(X_1667, y_pred_1667, color="red", label="Regression Line")
plt.title("Cell 1667 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1668 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1668, y_1668 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=68)
model_1668 = LinearRegression()
model_1668.fit(X_1668, y_1668)
y_pred_1668 = model_1668.predict(X_1668)
plt.scatter(X_1668, y_1668, label="Data")
plt.plot(X_1668, y_pred_1668, color="red", label="Regression Line")
plt.title("Cell 1668 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1669 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1669, y_1669 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=69)
model_1669 = LinearRegression()
model_1669.fit(X_1669, y_1669)
y_pred_1669 = model_1669.predict(X_1669)
plt.scatter(X_1669, y_1669, label="Data")
plt.plot(X_1669, y_pred_1669, color="red", label="Regression Line")
plt.title("Cell 1669 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1670 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1670, y_1670 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=70)
model_1670 = LinearRegression()
model_1670.fit(X_1670, y_1670)
y_pred_1670 = model_1670.predict(X_1670)
plt.scatter(X_1670, y_1670, label="Data")
plt.plot(X_1670, y_pred_1670, color="red", label="Regression Line")
plt.title("Cell 1670 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1671 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1671, y_1671 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=71)
model_1671 = LinearRegression()
model_1671.fit(X_1671, y_1671)
y_pred_1671 = model_1671.predict(X_1671)
plt.scatter(X_1671, y_1671, label="Data")
plt.plot(X_1671, y_pred_1671, color="red", label="Regression Line")
plt.title("Cell 1671 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1672 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1672, y_1672 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=72)
model_1672 = LinearRegression()
model_1672.fit(X_1672, y_1672)
y_pred_1672 = model_1672.predict(X_1672)
plt.scatter(X_1672, y_1672, label="Data")
plt.plot(X_1672, y_pred_1672, color="red", label="Regression Line")
plt.title("Cell 1672 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1673 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1673, y_1673 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=73)
model_1673 = LinearRegression()
model_1673.fit(X_1673, y_1673)
y_pred_1673 = model_1673.predict(X_1673)
plt.scatter(X_1673, y_1673, label="Data")
plt.plot(X_1673, y_pred_1673, color="red", label="Regression Line")
plt.title("Cell 1673 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1674 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1674, y_1674 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=74)
model_1674 = LinearRegression()
model_1674.fit(X_1674, y_1674)
y_pred_1674 = model_1674.predict(X_1674)
plt.scatter(X_1674, y_1674, label="Data")
plt.plot(X_1674, y_pred_1674, color="red", label="Regression Line")
plt.title("Cell 1674 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1675 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1675, y_1675 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=75)
model_1675 = LinearRegression()
model_1675.fit(X_1675, y_1675)
y_pred_1675 = model_1675.predict(X_1675)
plt.scatter(X_1675, y_1675, label="Data")
plt.plot(X_1675, y_pred_1675, color="red", label="Regression Line")
plt.title("Cell 1675 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1676 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1676, y_1676 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=76)
model_1676 = LinearRegression()
model_1676.fit(X_1676, y_1676)
y_pred_1676 = model_1676.predict(X_1676)
plt.scatter(X_1676, y_1676, label="Data")
plt.plot(X_1676, y_pred_1676, color="red", label="Regression Line")
plt.title("Cell 1676 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1677 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1677, y_1677 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=77)
model_1677 = LinearRegression()
model_1677.fit(X_1677, y_1677)
y_pred_1677 = model_1677.predict(X_1677)
plt.scatter(X_1677, y_1677, label="Data")
plt.plot(X_1677, y_pred_1677, color="red", label="Regression Line")
plt.title("Cell 1677 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1678 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1678, y_1678 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=78)
model_1678 = LinearRegression()
model_1678.fit(X_1678, y_1678)
y_pred_1678 = model_1678.predict(X_1678)
plt.scatter(X_1678, y_1678, label="Data")
plt.plot(X_1678, y_pred_1678, color="red", label="Regression Line")
plt.title("Cell 1678 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1679 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1679, y_1679 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=79)
model_1679 = LinearRegression()
model_1679.fit(X_1679, y_1679)
y_pred_1679 = model_1679.predict(X_1679)
plt.scatter(X_1679, y_1679, label="Data")
plt.plot(X_1679, y_pred_1679, color="red", label="Regression Line")
plt.title("Cell 1679 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1680 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1680, y_1680 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=80)
model_1680 = LinearRegression()
model_1680.fit(X_1680, y_1680)
y_pred_1680 = model_1680.predict(X_1680)
plt.scatter(X_1680, y_1680, label="Data")
plt.plot(X_1680, y_pred_1680, color="red", label="Regression Line")
plt.title("Cell 1680 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1681 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1681, y_1681 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=81)
model_1681 = LinearRegression()
model_1681.fit(X_1681, y_1681)
y_pred_1681 = model_1681.predict(X_1681)
plt.scatter(X_1681, y_1681, label="Data")
plt.plot(X_1681, y_pred_1681, color="red", label="Regression Line")
plt.title("Cell 1681 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1682 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1682, y_1682 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=82)
model_1682 = LinearRegression()
model_1682.fit(X_1682, y_1682)
y_pred_1682 = model_1682.predict(X_1682)
plt.scatter(X_1682, y_1682, label="Data")
plt.plot(X_1682, y_pred_1682, color="red", label="Regression Line")
plt.title("Cell 1682 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1683 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1683, y_1683 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=83)
model_1683 = LinearRegression()
model_1683.fit(X_1683, y_1683)
y_pred_1683 = model_1683.predict(X_1683)
plt.scatter(X_1683, y_1683, label="Data")
plt.plot(X_1683, y_pred_1683, color="red", label="Regression Line")
plt.title("Cell 1683 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1684 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1684, y_1684 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=84)
model_1684 = LinearRegression()
model_1684.fit(X_1684, y_1684)
y_pred_1684 = model_1684.predict(X_1684)
plt.scatter(X_1684, y_1684, label="Data")
plt.plot(X_1684, y_pred_1684, color="red", label="Regression Line")
plt.title("Cell 1684 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1685 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1685, y_1685 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=85)
model_1685 = LinearRegression()
model_1685.fit(X_1685, y_1685)
y_pred_1685 = model_1685.predict(X_1685)
plt.scatter(X_1685, y_1685, label="Data")
plt.plot(X_1685, y_pred_1685, color="red", label="Regression Line")
plt.title("Cell 1685 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1686 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1686, y_1686 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=86)
model_1686 = LinearRegression()
model_1686.fit(X_1686, y_1686)
y_pred_1686 = model_1686.predict(X_1686)
plt.scatter(X_1686, y_1686, label="Data")
plt.plot(X_1686, y_pred_1686, color="red", label="Regression Line")
plt.title("Cell 1686 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1687 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1687, y_1687 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=87)
model_1687 = LinearRegression()
model_1687.fit(X_1687, y_1687)
y_pred_1687 = model_1687.predict(X_1687)
plt.scatter(X_1687, y_1687, label="Data")
plt.plot(X_1687, y_pred_1687, color="red", label="Regression Line")
plt.title("Cell 1687 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1688 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1688, y_1688 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=88)
model_1688 = LinearRegression()
model_1688.fit(X_1688, y_1688)
y_pred_1688 = model_1688.predict(X_1688)
plt.scatter(X_1688, y_1688, label="Data")
plt.plot(X_1688, y_pred_1688, color="red", label="Regression Line")
plt.title("Cell 1688 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1689 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1689, y_1689 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=89)
model_1689 = LinearRegression()
model_1689.fit(X_1689, y_1689)
y_pred_1689 = model_1689.predict(X_1689)
plt.scatter(X_1689, y_1689, label="Data")
plt.plot(X_1689, y_pred_1689, color="red", label="Regression Line")
plt.title("Cell 1689 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1690 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1690, y_1690 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=90)
model_1690 = LinearRegression()
model_1690.fit(X_1690, y_1690)
y_pred_1690 = model_1690.predict(X_1690)
plt.scatter(X_1690, y_1690, label="Data")
plt.plot(X_1690, y_pred_1690, color="red", label="Regression Line")
plt.title("Cell 1690 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1691 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1691, y_1691 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=91)
model_1691 = LinearRegression()
model_1691.fit(X_1691, y_1691)
y_pred_1691 = model_1691.predict(X_1691)
plt.scatter(X_1691, y_1691, label="Data")
plt.plot(X_1691, y_pred_1691, color="red", label="Regression Line")
plt.title("Cell 1691 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1692 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1692, y_1692 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=92)
model_1692 = LinearRegression()
model_1692.fit(X_1692, y_1692)
y_pred_1692 = model_1692.predict(X_1692)
plt.scatter(X_1692, y_1692, label="Data")
plt.plot(X_1692, y_pred_1692, color="red", label="Regression Line")
plt.title("Cell 1692 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1693 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1693, y_1693 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=93)
model_1693 = LinearRegression()
model_1693.fit(X_1693, y_1693)
y_pred_1693 = model_1693.predict(X_1693)
plt.scatter(X_1693, y_1693, label="Data")
plt.plot(X_1693, y_pred_1693, color="red", label="Regression Line")
plt.title("Cell 1693 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1694 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1694, y_1694 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=94)
model_1694 = LinearRegression()
model_1694.fit(X_1694, y_1694)
y_pred_1694 = model_1694.predict(X_1694)
plt.scatter(X_1694, y_1694, label="Data")
plt.plot(X_1694, y_pred_1694, color="red", label="Regression Line")
plt.title("Cell 1694 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1695 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1695, y_1695 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=95)
model_1695 = LinearRegression()
model_1695.fit(X_1695, y_1695)
y_pred_1695 = model_1695.predict(X_1695)
plt.scatter(X_1695, y_1695, label="Data")
plt.plot(X_1695, y_pred_1695, color="red", label="Regression Line")
plt.title("Cell 1695 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1696 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1696, y_1696 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=96)
model_1696 = LinearRegression()
model_1696.fit(X_1696, y_1696)
y_pred_1696 = model_1696.predict(X_1696)
plt.scatter(X_1696, y_1696, label="Data")
plt.plot(X_1696, y_pred_1696, color="red", label="Regression Line")
plt.title("Cell 1696 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1697 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1697, y_1697 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=97)
model_1697 = LinearRegression()
model_1697.fit(X_1697, y_1697)
y_pred_1697 = model_1697.predict(X_1697)
plt.scatter(X_1697, y_1697, label="Data")
plt.plot(X_1697, y_pred_1697, color="red", label="Regression Line")
plt.title("Cell 1697 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1698 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1698, y_1698 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=98)
model_1698 = LinearRegression()
model_1698.fit(X_1698, y_1698)
y_pred_1698 = model_1698.predict(X_1698)
plt.scatter(X_1698, y_1698, label="Data")
plt.plot(X_1698, y_pred_1698, color="red", label="Regression Line")
plt.title("Cell 1698 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1699 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1699, y_1699 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=99)
model_1699 = LinearRegression()
model_1699.fit(X_1699, y_1699)
y_pred_1699 = model_1699.predict(X_1699)
plt.scatter(X_1699, y_1699, label="Data")
plt.plot(X_1699, y_pred_1699, color="red", label="Regression Line")
plt.title("Cell 1699 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1700 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1700, y_1700 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=0)
model_1700 = LinearRegression()
model_1700.fit(X_1700, y_1700)
y_pred_1700 = model_1700.predict(X_1700)
plt.scatter(X_1700, y_1700, label="Data")
plt.plot(X_1700, y_pred_1700, color="red", label="Regression Line")
plt.title("Cell 1700 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1701 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1701, y_1701 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=1)
model_1701 = LinearRegression()
model_1701.fit(X_1701, y_1701)
y_pred_1701 = model_1701.predict(X_1701)
plt.scatter(X_1701, y_1701, label="Data")
plt.plot(X_1701, y_pred_1701, color="red", label="Regression Line")
plt.title("Cell 1701 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1702 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1702, y_1702 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=2)
model_1702 = LinearRegression()
model_1702.fit(X_1702, y_1702)
y_pred_1702 = model_1702.predict(X_1702)
plt.scatter(X_1702, y_1702, label="Data")
plt.plot(X_1702, y_pred_1702, color="red", label="Regression Line")
plt.title("Cell 1702 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1703 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1703, y_1703 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=3)
model_1703 = LinearRegression()
model_1703.fit(X_1703, y_1703)
y_pred_1703 = model_1703.predict(X_1703)
plt.scatter(X_1703, y_1703, label="Data")
plt.plot(X_1703, y_pred_1703, color="red", label="Regression Line")
plt.title("Cell 1703 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1704 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1704, y_1704 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=4)
model_1704 = LinearRegression()
model_1704.fit(X_1704, y_1704)
y_pred_1704 = model_1704.predict(X_1704)
plt.scatter(X_1704, y_1704, label="Data")
plt.plot(X_1704, y_pred_1704, color="red", label="Regression Line")
plt.title("Cell 1704 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1705 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1705, y_1705 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=5)
model_1705 = LinearRegression()
model_1705.fit(X_1705, y_1705)
y_pred_1705 = model_1705.predict(X_1705)
plt.scatter(X_1705, y_1705, label="Data")
plt.plot(X_1705, y_pred_1705, color="red", label="Regression Line")
plt.title("Cell 1705 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1706 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1706, y_1706 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=6)
model_1706 = LinearRegression()
model_1706.fit(X_1706, y_1706)
y_pred_1706 = model_1706.predict(X_1706)
plt.scatter(X_1706, y_1706, label="Data")
plt.plot(X_1706, y_pred_1706, color="red", label="Regression Line")
plt.title("Cell 1706 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1707 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1707, y_1707 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=7)
model_1707 = LinearRegression()
model_1707.fit(X_1707, y_1707)
y_pred_1707 = model_1707.predict(X_1707)
plt.scatter(X_1707, y_1707, label="Data")
plt.plot(X_1707, y_pred_1707, color="red", label="Regression Line")
plt.title("Cell 1707 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1708 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1708, y_1708 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=8)
model_1708 = LinearRegression()
model_1708.fit(X_1708, y_1708)
y_pred_1708 = model_1708.predict(X_1708)
plt.scatter(X_1708, y_1708, label="Data")
plt.plot(X_1708, y_pred_1708, color="red", label="Regression Line")
plt.title("Cell 1708 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1709 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1709, y_1709 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=9)
model_1709 = LinearRegression()
model_1709.fit(X_1709, y_1709)
y_pred_1709 = model_1709.predict(X_1709)
plt.scatter(X_1709, y_1709, label="Data")
plt.plot(X_1709, y_pred_1709, color="red", label="Regression Line")
plt.title("Cell 1709 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1710 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1710, y_1710 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=10)
model_1710 = LinearRegression()
model_1710.fit(X_1710, y_1710)
y_pred_1710 = model_1710.predict(X_1710)
plt.scatter(X_1710, y_1710, label="Data")
plt.plot(X_1710, y_pred_1710, color="red", label="Regression Line")
plt.title("Cell 1710 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1711 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1711, y_1711 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=11)
model_1711 = LinearRegression()
model_1711.fit(X_1711, y_1711)
y_pred_1711 = model_1711.predict(X_1711)
plt.scatter(X_1711, y_1711, label="Data")
plt.plot(X_1711, y_pred_1711, color="red", label="Regression Line")
plt.title("Cell 1711 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1712 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1712, y_1712 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=12)
model_1712 = LinearRegression()
model_1712.fit(X_1712, y_1712)
y_pred_1712 = model_1712.predict(X_1712)
plt.scatter(X_1712, y_1712, label="Data")
plt.plot(X_1712, y_pred_1712, color="red", label="Regression Line")
plt.title("Cell 1712 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1713 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1713, y_1713 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=13)
model_1713 = LinearRegression()
model_1713.fit(X_1713, y_1713)
y_pred_1713 = model_1713.predict(X_1713)
plt.scatter(X_1713, y_1713, label="Data")
plt.plot(X_1713, y_pred_1713, color="red", label="Regression Line")
plt.title("Cell 1713 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1714 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1714, y_1714 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=14)
model_1714 = LinearRegression()
model_1714.fit(X_1714, y_1714)
y_pred_1714 = model_1714.predict(X_1714)
plt.scatter(X_1714, y_1714, label="Data")
plt.plot(X_1714, y_pred_1714, color="red", label="Regression Line")
plt.title("Cell 1714 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1715 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1715, y_1715 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=15)
model_1715 = LinearRegression()
model_1715.fit(X_1715, y_1715)
y_pred_1715 = model_1715.predict(X_1715)
plt.scatter(X_1715, y_1715, label="Data")
plt.plot(X_1715, y_pred_1715, color="red", label="Regression Line")
plt.title("Cell 1715 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1716 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1716, y_1716 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=16)
model_1716 = LinearRegression()
model_1716.fit(X_1716, y_1716)
y_pred_1716 = model_1716.predict(X_1716)
plt.scatter(X_1716, y_1716, label="Data")
plt.plot(X_1716, y_pred_1716, color="red", label="Regression Line")
plt.title("Cell 1716 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1717 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1717, y_1717 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=17)
model_1717 = LinearRegression()
model_1717.fit(X_1717, y_1717)
y_pred_1717 = model_1717.predict(X_1717)
plt.scatter(X_1717, y_1717, label="Data")
plt.plot(X_1717, y_pred_1717, color="red", label="Regression Line")
plt.title("Cell 1717 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1718 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1718, y_1718 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=18)
model_1718 = LinearRegression()
model_1718.fit(X_1718, y_1718)
y_pred_1718 = model_1718.predict(X_1718)
plt.scatter(X_1718, y_1718, label="Data")
plt.plot(X_1718, y_pred_1718, color="red", label="Regression Line")
plt.title("Cell 1718 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1719 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1719, y_1719 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=19)
model_1719 = LinearRegression()
model_1719.fit(X_1719, y_1719)
y_pred_1719 = model_1719.predict(X_1719)
plt.scatter(X_1719, y_1719, label="Data")
plt.plot(X_1719, y_pred_1719, color="red", label="Regression Line")
plt.title("Cell 1719 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1720 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1720, y_1720 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=20)
model_1720 = LinearRegression()
model_1720.fit(X_1720, y_1720)
y_pred_1720 = model_1720.predict(X_1720)
plt.scatter(X_1720, y_1720, label="Data")
plt.plot(X_1720, y_pred_1720, color="red", label="Regression Line")
plt.title("Cell 1720 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1721 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1721, y_1721 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=21)
model_1721 = LinearRegression()
model_1721.fit(X_1721, y_1721)
y_pred_1721 = model_1721.predict(X_1721)
plt.scatter(X_1721, y_1721, label="Data")
plt.plot(X_1721, y_pred_1721, color="red", label="Regression Line")
plt.title("Cell 1721 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1722 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1722, y_1722 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=22)
model_1722 = LinearRegression()
model_1722.fit(X_1722, y_1722)
y_pred_1722 = model_1722.predict(X_1722)
plt.scatter(X_1722, y_1722, label="Data")
plt.plot(X_1722, y_pred_1722, color="red", label="Regression Line")
plt.title("Cell 1722 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1723 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1723, y_1723 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=23)
model_1723 = LinearRegression()
model_1723.fit(X_1723, y_1723)
y_pred_1723 = model_1723.predict(X_1723)
plt.scatter(X_1723, y_1723, label="Data")
plt.plot(X_1723, y_pred_1723, color="red", label="Regression Line")
plt.title("Cell 1723 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1724 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1724, y_1724 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=24)
model_1724 = LinearRegression()
model_1724.fit(X_1724, y_1724)
y_pred_1724 = model_1724.predict(X_1724)
plt.scatter(X_1724, y_1724, label="Data")
plt.plot(X_1724, y_pred_1724, color="red", label="Regression Line")
plt.title("Cell 1724 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1725 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1725, y_1725 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=25)
model_1725 = LinearRegression()
model_1725.fit(X_1725, y_1725)
y_pred_1725 = model_1725.predict(X_1725)
plt.scatter(X_1725, y_1725, label="Data")
plt.plot(X_1725, y_pred_1725, color="red", label="Regression Line")
plt.title("Cell 1725 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1726 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1726, y_1726 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=26)
model_1726 = LinearRegression()
model_1726.fit(X_1726, y_1726)
y_pred_1726 = model_1726.predict(X_1726)
plt.scatter(X_1726, y_1726, label="Data")
plt.plot(X_1726, y_pred_1726, color="red", label="Regression Line")
plt.title("Cell 1726 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1727 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1727, y_1727 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=27)
model_1727 = LinearRegression()
model_1727.fit(X_1727, y_1727)
y_pred_1727 = model_1727.predict(X_1727)
plt.scatter(X_1727, y_1727, label="Data")
plt.plot(X_1727, y_pred_1727, color="red", label="Regression Line")
plt.title("Cell 1727 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1728 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1728, y_1728 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=28)
model_1728 = LinearRegression()
model_1728.fit(X_1728, y_1728)
y_pred_1728 = model_1728.predict(X_1728)
plt.scatter(X_1728, y_1728, label="Data")
plt.plot(X_1728, y_pred_1728, color="red", label="Regression Line")
plt.title("Cell 1728 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1729 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1729, y_1729 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=29)
model_1729 = LinearRegression()
model_1729.fit(X_1729, y_1729)
y_pred_1729 = model_1729.predict(X_1729)
plt.scatter(X_1729, y_1729, label="Data")
plt.plot(X_1729, y_pred_1729, color="red", label="Regression Line")
plt.title("Cell 1729 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1730 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1730, y_1730 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=30)
model_1730 = LinearRegression()
model_1730.fit(X_1730, y_1730)
y_pred_1730 = model_1730.predict(X_1730)
plt.scatter(X_1730, y_1730, label="Data")
plt.plot(X_1730, y_pred_1730, color="red", label="Regression Line")
plt.title("Cell 1730 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1731 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1731, y_1731 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=31)
model_1731 = LinearRegression()
model_1731.fit(X_1731, y_1731)
y_pred_1731 = model_1731.predict(X_1731)
plt.scatter(X_1731, y_1731, label="Data")
plt.plot(X_1731, y_pred_1731, color="red", label="Regression Line")
plt.title("Cell 1731 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1732 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1732, y_1732 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=32)
model_1732 = LinearRegression()
model_1732.fit(X_1732, y_1732)
y_pred_1732 = model_1732.predict(X_1732)
plt.scatter(X_1732, y_1732, label="Data")
plt.plot(X_1732, y_pred_1732, color="red", label="Regression Line")
plt.title("Cell 1732 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1733 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1733, y_1733 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=33)
model_1733 = LinearRegression()
model_1733.fit(X_1733, y_1733)
y_pred_1733 = model_1733.predict(X_1733)
plt.scatter(X_1733, y_1733, label="Data")
plt.plot(X_1733, y_pred_1733, color="red", label="Regression Line")
plt.title("Cell 1733 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1734 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1734, y_1734 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=34)
model_1734 = LinearRegression()
model_1734.fit(X_1734, y_1734)
y_pred_1734 = model_1734.predict(X_1734)
plt.scatter(X_1734, y_1734, label="Data")
plt.plot(X_1734, y_pred_1734, color="red", label="Regression Line")
plt.title("Cell 1734 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1735 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1735, y_1735 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=35)
model_1735 = LinearRegression()
model_1735.fit(X_1735, y_1735)
y_pred_1735 = model_1735.predict(X_1735)
plt.scatter(X_1735, y_1735, label="Data")
plt.plot(X_1735, y_pred_1735, color="red", label="Regression Line")
plt.title("Cell 1735 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1736 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1736, y_1736 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=36)
model_1736 = LinearRegression()
model_1736.fit(X_1736, y_1736)
y_pred_1736 = model_1736.predict(X_1736)
plt.scatter(X_1736, y_1736, label="Data")
plt.plot(X_1736, y_pred_1736, color="red", label="Regression Line")
plt.title("Cell 1736 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1737 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1737, y_1737 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=37)
model_1737 = LinearRegression()
model_1737.fit(X_1737, y_1737)
y_pred_1737 = model_1737.predict(X_1737)
plt.scatter(X_1737, y_1737, label="Data")
plt.plot(X_1737, y_pred_1737, color="red", label="Regression Line")
plt.title("Cell 1737 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1738 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1738, y_1738 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=38)
model_1738 = LinearRegression()
model_1738.fit(X_1738, y_1738)
y_pred_1738 = model_1738.predict(X_1738)
plt.scatter(X_1738, y_1738, label="Data")
plt.plot(X_1738, y_pred_1738, color="red", label="Regression Line")
plt.title("Cell 1738 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1739 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1739, y_1739 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=39)
model_1739 = LinearRegression()
model_1739.fit(X_1739, y_1739)
y_pred_1739 = model_1739.predict(X_1739)
plt.scatter(X_1739, y_1739, label="Data")
plt.plot(X_1739, y_pred_1739, color="red", label="Regression Line")
plt.title("Cell 1739 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1740 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1740, y_1740 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=40)
model_1740 = LinearRegression()
model_1740.fit(X_1740, y_1740)
y_pred_1740 = model_1740.predict(X_1740)
plt.scatter(X_1740, y_1740, label="Data")
plt.plot(X_1740, y_pred_1740, color="red", label="Regression Line")
plt.title("Cell 1740 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1741 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1741, y_1741 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=41)
model_1741 = LinearRegression()
model_1741.fit(X_1741, y_1741)
y_pred_1741 = model_1741.predict(X_1741)
plt.scatter(X_1741, y_1741, label="Data")
plt.plot(X_1741, y_pred_1741, color="red", label="Regression Line")
plt.title("Cell 1741 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1742 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1742, y_1742 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=42)
model_1742 = LinearRegression()
model_1742.fit(X_1742, y_1742)
y_pred_1742 = model_1742.predict(X_1742)
plt.scatter(X_1742, y_1742, label="Data")
plt.plot(X_1742, y_pred_1742, color="red", label="Regression Line")
plt.title("Cell 1742 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1743 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1743, y_1743 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=43)
model_1743 = LinearRegression()
model_1743.fit(X_1743, y_1743)
y_pred_1743 = model_1743.predict(X_1743)
plt.scatter(X_1743, y_1743, label="Data")
plt.plot(X_1743, y_pred_1743, color="red", label="Regression Line")
plt.title("Cell 1743 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1744 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1744, y_1744 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=44)
model_1744 = LinearRegression()
model_1744.fit(X_1744, y_1744)
y_pred_1744 = model_1744.predict(X_1744)
plt.scatter(X_1744, y_1744, label="Data")
plt.plot(X_1744, y_pred_1744, color="red", label="Regression Line")
plt.title("Cell 1744 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1745 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1745, y_1745 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=45)
model_1745 = LinearRegression()
model_1745.fit(X_1745, y_1745)
y_pred_1745 = model_1745.predict(X_1745)
plt.scatter(X_1745, y_1745, label="Data")
plt.plot(X_1745, y_pred_1745, color="red", label="Regression Line")
plt.title("Cell 1745 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1746 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1746, y_1746 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=46)
model_1746 = LinearRegression()
model_1746.fit(X_1746, y_1746)
y_pred_1746 = model_1746.predict(X_1746)
plt.scatter(X_1746, y_1746, label="Data")
plt.plot(X_1746, y_pred_1746, color="red", label="Regression Line")
plt.title("Cell 1746 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1747 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1747, y_1747 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=47)
model_1747 = LinearRegression()
model_1747.fit(X_1747, y_1747)
y_pred_1747 = model_1747.predict(X_1747)
plt.scatter(X_1747, y_1747, label="Data")
plt.plot(X_1747, y_pred_1747, color="red", label="Regression Line")
plt.title("Cell 1747 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1748 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1748, y_1748 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=48)
model_1748 = LinearRegression()
model_1748.fit(X_1748, y_1748)
y_pred_1748 = model_1748.predict(X_1748)
plt.scatter(X_1748, y_1748, label="Data")
plt.plot(X_1748, y_pred_1748, color="red", label="Regression Line")
plt.title("Cell 1748 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1749 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1749, y_1749 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=49)
model_1749 = LinearRegression()
model_1749.fit(X_1749, y_1749)
y_pred_1749 = model_1749.predict(X_1749)
plt.scatter(X_1749, y_1749, label="Data")
plt.plot(X_1749, y_pred_1749, color="red", label="Regression Line")
plt.title("Cell 1749 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1750 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1750, y_1750 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=50)
model_1750 = LinearRegression()
model_1750.fit(X_1750, y_1750)
y_pred_1750 = model_1750.predict(X_1750)
plt.scatter(X_1750, y_1750, label="Data")
plt.plot(X_1750, y_pred_1750, color="red", label="Regression Line")
plt.title("Cell 1750 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1751 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1751, y_1751 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=51)
model_1751 = LinearRegression()
model_1751.fit(X_1751, y_1751)
y_pred_1751 = model_1751.predict(X_1751)
plt.scatter(X_1751, y_1751, label="Data")
plt.plot(X_1751, y_pred_1751, color="red", label="Regression Line")
plt.title("Cell 1751 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1752 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1752, y_1752 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=52)
model_1752 = LinearRegression()
model_1752.fit(X_1752, y_1752)
y_pred_1752 = model_1752.predict(X_1752)
plt.scatter(X_1752, y_1752, label="Data")
plt.plot(X_1752, y_pred_1752, color="red", label="Regression Line")
plt.title("Cell 1752 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1753 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1753, y_1753 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=53)
model_1753 = LinearRegression()
model_1753.fit(X_1753, y_1753)
y_pred_1753 = model_1753.predict(X_1753)
plt.scatter(X_1753, y_1753, label="Data")
plt.plot(X_1753, y_pred_1753, color="red", label="Regression Line")
plt.title("Cell 1753 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1754 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1754, y_1754 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=54)
model_1754 = LinearRegression()
model_1754.fit(X_1754, y_1754)
y_pred_1754 = model_1754.predict(X_1754)
plt.scatter(X_1754, y_1754, label="Data")
plt.plot(X_1754, y_pred_1754, color="red", label="Regression Line")
plt.title("Cell 1754 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1755 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1755, y_1755 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=55)
model_1755 = LinearRegression()
model_1755.fit(X_1755, y_1755)
y_pred_1755 = model_1755.predict(X_1755)
plt.scatter(X_1755, y_1755, label="Data")
plt.plot(X_1755, y_pred_1755, color="red", label="Regression Line")
plt.title("Cell 1755 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1756 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1756, y_1756 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=56)
model_1756 = LinearRegression()
model_1756.fit(X_1756, y_1756)
y_pred_1756 = model_1756.predict(X_1756)
plt.scatter(X_1756, y_1756, label="Data")
plt.plot(X_1756, y_pred_1756, color="red", label="Regression Line")
plt.title("Cell 1756 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1757 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1757, y_1757 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=57)
model_1757 = LinearRegression()
model_1757.fit(X_1757, y_1757)
y_pred_1757 = model_1757.predict(X_1757)
plt.scatter(X_1757, y_1757, label="Data")
plt.plot(X_1757, y_pred_1757, color="red", label="Regression Line")
plt.title("Cell 1757 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1758 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1758, y_1758 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=58)
model_1758 = LinearRegression()
model_1758.fit(X_1758, y_1758)
y_pred_1758 = model_1758.predict(X_1758)
plt.scatter(X_1758, y_1758, label="Data")
plt.plot(X_1758, y_pred_1758, color="red", label="Regression Line")
plt.title("Cell 1758 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1759 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1759, y_1759 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=59)
model_1759 = LinearRegression()
model_1759.fit(X_1759, y_1759)
y_pred_1759 = model_1759.predict(X_1759)
plt.scatter(X_1759, y_1759, label="Data")
plt.plot(X_1759, y_pred_1759, color="red", label="Regression Line")
plt.title("Cell 1759 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1760 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1760, y_1760 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=60)
model_1760 = LinearRegression()
model_1760.fit(X_1760, y_1760)
y_pred_1760 = model_1760.predict(X_1760)
plt.scatter(X_1760, y_1760, label="Data")
plt.plot(X_1760, y_pred_1760, color="red", label="Regression Line")
plt.title("Cell 1760 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1761 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1761, y_1761 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=61)
model_1761 = LinearRegression()
model_1761.fit(X_1761, y_1761)
y_pred_1761 = model_1761.predict(X_1761)
plt.scatter(X_1761, y_1761, label="Data")
plt.plot(X_1761, y_pred_1761, color="red", label="Regression Line")
plt.title("Cell 1761 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1762 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1762, y_1762 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=62)
model_1762 = LinearRegression()
model_1762.fit(X_1762, y_1762)
y_pred_1762 = model_1762.predict(X_1762)
plt.scatter(X_1762, y_1762, label="Data")
plt.plot(X_1762, y_pred_1762, color="red", label="Regression Line")
plt.title("Cell 1762 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1763 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1763, y_1763 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=63)
model_1763 = LinearRegression()
model_1763.fit(X_1763, y_1763)
y_pred_1763 = model_1763.predict(X_1763)
plt.scatter(X_1763, y_1763, label="Data")
plt.plot(X_1763, y_pred_1763, color="red", label="Regression Line")
plt.title("Cell 1763 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1764 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1764, y_1764 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=64)
model_1764 = LinearRegression()
model_1764.fit(X_1764, y_1764)
y_pred_1764 = model_1764.predict(X_1764)
plt.scatter(X_1764, y_1764, label="Data")
plt.plot(X_1764, y_pred_1764, color="red", label="Regression Line")
plt.title("Cell 1764 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1765 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1765, y_1765 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=65)
model_1765 = LinearRegression()
model_1765.fit(X_1765, y_1765)
y_pred_1765 = model_1765.predict(X_1765)
plt.scatter(X_1765, y_1765, label="Data")
plt.plot(X_1765, y_pred_1765, color="red", label="Regression Line")
plt.title("Cell 1765 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1766 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1766, y_1766 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=66)
model_1766 = LinearRegression()
model_1766.fit(X_1766, y_1766)
y_pred_1766 = model_1766.predict(X_1766)
plt.scatter(X_1766, y_1766, label="Data")
plt.plot(X_1766, y_pred_1766, color="red", label="Regression Line")
plt.title("Cell 1766 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1767 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1767, y_1767 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=67)
model_1767 = LinearRegression()
model_1767.fit(X_1767, y_1767)
y_pred_1767 = model_1767.predict(X_1767)
plt.scatter(X_1767, y_1767, label="Data")
plt.plot(X_1767, y_pred_1767, color="red", label="Regression Line")
plt.title("Cell 1767 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1768 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1768, y_1768 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=68)
model_1768 = LinearRegression()
model_1768.fit(X_1768, y_1768)
y_pred_1768 = model_1768.predict(X_1768)
plt.scatter(X_1768, y_1768, label="Data")
plt.plot(X_1768, y_pred_1768, color="red", label="Regression Line")
plt.title("Cell 1768 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1769 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1769, y_1769 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=69)
model_1769 = LinearRegression()
model_1769.fit(X_1769, y_1769)
y_pred_1769 = model_1769.predict(X_1769)
plt.scatter(X_1769, y_1769, label="Data")
plt.plot(X_1769, y_pred_1769, color="red", label="Regression Line")
plt.title("Cell 1769 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1770 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1770, y_1770 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=70)
model_1770 = LinearRegression()
model_1770.fit(X_1770, y_1770)
y_pred_1770 = model_1770.predict(X_1770)
plt.scatter(X_1770, y_1770, label="Data")
plt.plot(X_1770, y_pred_1770, color="red", label="Regression Line")
plt.title("Cell 1770 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1771 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1771, y_1771 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=71)
model_1771 = LinearRegression()
model_1771.fit(X_1771, y_1771)
y_pred_1771 = model_1771.predict(X_1771)
plt.scatter(X_1771, y_1771, label="Data")
plt.plot(X_1771, y_pred_1771, color="red", label="Regression Line")
plt.title("Cell 1771 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1772 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1772, y_1772 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=72)
model_1772 = LinearRegression()
model_1772.fit(X_1772, y_1772)
y_pred_1772 = model_1772.predict(X_1772)
plt.scatter(X_1772, y_1772, label="Data")
plt.plot(X_1772, y_pred_1772, color="red", label="Regression Line")
plt.title("Cell 1772 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1773 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1773, y_1773 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=73)
model_1773 = LinearRegression()
model_1773.fit(X_1773, y_1773)
y_pred_1773 = model_1773.predict(X_1773)
plt.scatter(X_1773, y_1773, label="Data")
plt.plot(X_1773, y_pred_1773, color="red", label="Regression Line")
plt.title("Cell 1773 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1774 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1774, y_1774 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=74)
model_1774 = LinearRegression()
model_1774.fit(X_1774, y_1774)
y_pred_1774 = model_1774.predict(X_1774)
plt.scatter(X_1774, y_1774, label="Data")
plt.plot(X_1774, y_pred_1774, color="red", label="Regression Line")
plt.title("Cell 1774 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1775 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1775, y_1775 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=75)
model_1775 = LinearRegression()
model_1775.fit(X_1775, y_1775)
y_pred_1775 = model_1775.predict(X_1775)
plt.scatter(X_1775, y_1775, label="Data")
plt.plot(X_1775, y_pred_1775, color="red", label="Regression Line")
plt.title("Cell 1775 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1776 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1776, y_1776 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=76)
model_1776 = LinearRegression()
model_1776.fit(X_1776, y_1776)
y_pred_1776 = model_1776.predict(X_1776)
plt.scatter(X_1776, y_1776, label="Data")
plt.plot(X_1776, y_pred_1776, color="red", label="Regression Line")
plt.title("Cell 1776 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1777 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1777, y_1777 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=77)
model_1777 = LinearRegression()
model_1777.fit(X_1777, y_1777)
y_pred_1777 = model_1777.predict(X_1777)
plt.scatter(X_1777, y_1777, label="Data")
plt.plot(X_1777, y_pred_1777, color="red", label="Regression Line")
plt.title("Cell 1777 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1778 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1778, y_1778 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=78)
model_1778 = LinearRegression()
model_1778.fit(X_1778, y_1778)
y_pred_1778 = model_1778.predict(X_1778)
plt.scatter(X_1778, y_1778, label="Data")
plt.plot(X_1778, y_pred_1778, color="red", label="Regression Line")
plt.title("Cell 1778 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1779 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1779, y_1779 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=79)
model_1779 = LinearRegression()
model_1779.fit(X_1779, y_1779)
y_pred_1779 = model_1779.predict(X_1779)
plt.scatter(X_1779, y_1779, label="Data")
plt.plot(X_1779, y_pred_1779, color="red", label="Regression Line")
plt.title("Cell 1779 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1780 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1780, y_1780 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=80)
model_1780 = LinearRegression()
model_1780.fit(X_1780, y_1780)
y_pred_1780 = model_1780.predict(X_1780)
plt.scatter(X_1780, y_1780, label="Data")
plt.plot(X_1780, y_pred_1780, color="red", label="Regression Line")
plt.title("Cell 1780 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1781 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1781, y_1781 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=81)
model_1781 = LinearRegression()
model_1781.fit(X_1781, y_1781)
y_pred_1781 = model_1781.predict(X_1781)
plt.scatter(X_1781, y_1781, label="Data")
plt.plot(X_1781, y_pred_1781, color="red", label="Regression Line")
plt.title("Cell 1781 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1782 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1782, y_1782 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=82)
model_1782 = LinearRegression()
model_1782.fit(X_1782, y_1782)
y_pred_1782 = model_1782.predict(X_1782)
plt.scatter(X_1782, y_1782, label="Data")
plt.plot(X_1782, y_pred_1782, color="red", label="Regression Line")
plt.title("Cell 1782 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1783 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1783, y_1783 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=83)
model_1783 = LinearRegression()
model_1783.fit(X_1783, y_1783)
y_pred_1783 = model_1783.predict(X_1783)
plt.scatter(X_1783, y_1783, label="Data")
plt.plot(X_1783, y_pred_1783, color="red", label="Regression Line")
plt.title("Cell 1783 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1784 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1784, y_1784 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=84)
model_1784 = LinearRegression()
model_1784.fit(X_1784, y_1784)
y_pred_1784 = model_1784.predict(X_1784)
plt.scatter(X_1784, y_1784, label="Data")
plt.plot(X_1784, y_pred_1784, color="red", label="Regression Line")
plt.title("Cell 1784 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1785 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1785, y_1785 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=85)
model_1785 = LinearRegression()
model_1785.fit(X_1785, y_1785)
y_pred_1785 = model_1785.predict(X_1785)
plt.scatter(X_1785, y_1785, label="Data")
plt.plot(X_1785, y_pred_1785, color="red", label="Regression Line")
plt.title("Cell 1785 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1786 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1786, y_1786 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=86)
model_1786 = LinearRegression()
model_1786.fit(X_1786, y_1786)
y_pred_1786 = model_1786.predict(X_1786)
plt.scatter(X_1786, y_1786, label="Data")
plt.plot(X_1786, y_pred_1786, color="red", label="Regression Line")
plt.title("Cell 1786 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1787 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1787, y_1787 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=87)
model_1787 = LinearRegression()
model_1787.fit(X_1787, y_1787)
y_pred_1787 = model_1787.predict(X_1787)
plt.scatter(X_1787, y_1787, label="Data")
plt.plot(X_1787, y_pred_1787, color="red", label="Regression Line")
plt.title("Cell 1787 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1788 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1788, y_1788 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=88)
model_1788 = LinearRegression()
model_1788.fit(X_1788, y_1788)
y_pred_1788 = model_1788.predict(X_1788)
plt.scatter(X_1788, y_1788, label="Data")
plt.plot(X_1788, y_pred_1788, color="red", label="Regression Line")
plt.title("Cell 1788 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1789 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1789, y_1789 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=89)
model_1789 = LinearRegression()
model_1789.fit(X_1789, y_1789)
y_pred_1789 = model_1789.predict(X_1789)
plt.scatter(X_1789, y_1789, label="Data")
plt.plot(X_1789, y_pred_1789, color="red", label="Regression Line")
plt.title("Cell 1789 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1790 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1790, y_1790 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=90)
model_1790 = LinearRegression()
model_1790.fit(X_1790, y_1790)
y_pred_1790 = model_1790.predict(X_1790)
plt.scatter(X_1790, y_1790, label="Data")
plt.plot(X_1790, y_pred_1790, color="red", label="Regression Line")
plt.title("Cell 1790 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1791 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1791, y_1791 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=91)
model_1791 = LinearRegression()
model_1791.fit(X_1791, y_1791)
y_pred_1791 = model_1791.predict(X_1791)
plt.scatter(X_1791, y_1791, label="Data")
plt.plot(X_1791, y_pred_1791, color="red", label="Regression Line")
plt.title("Cell 1791 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1792 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1792, y_1792 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=92)
model_1792 = LinearRegression()
model_1792.fit(X_1792, y_1792)
y_pred_1792 = model_1792.predict(X_1792)
plt.scatter(X_1792, y_1792, label="Data")
plt.plot(X_1792, y_pred_1792, color="red", label="Regression Line")
plt.title("Cell 1792 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1793 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1793, y_1793 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=93)
model_1793 = LinearRegression()
model_1793.fit(X_1793, y_1793)
y_pred_1793 = model_1793.predict(X_1793)
plt.scatter(X_1793, y_1793, label="Data")
plt.plot(X_1793, y_pred_1793, color="red", label="Regression Line")
plt.title("Cell 1793 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1794 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1794, y_1794 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=94)
model_1794 = LinearRegression()
model_1794.fit(X_1794, y_1794)
y_pred_1794 = model_1794.predict(X_1794)
plt.scatter(X_1794, y_1794, label="Data")
plt.plot(X_1794, y_pred_1794, color="red", label="Regression Line")
plt.title("Cell 1794 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1795 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1795, y_1795 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=95)
model_1795 = LinearRegression()
model_1795.fit(X_1795, y_1795)
y_pred_1795 = model_1795.predict(X_1795)
plt.scatter(X_1795, y_1795, label="Data")
plt.plot(X_1795, y_pred_1795, color="red", label="Regression Line")
plt.title("Cell 1795 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1796 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1796, y_1796 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=96)
model_1796 = LinearRegression()
model_1796.fit(X_1796, y_1796)
y_pred_1796 = model_1796.predict(X_1796)
plt.scatter(X_1796, y_1796, label="Data")
plt.plot(X_1796, y_pred_1796, color="red", label="Regression Line")
plt.title("Cell 1796 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1797 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1797, y_1797 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=97)
model_1797 = LinearRegression()
model_1797.fit(X_1797, y_1797)
y_pred_1797 = model_1797.predict(X_1797)
plt.scatter(X_1797, y_1797, label="Data")
plt.plot(X_1797, y_pred_1797, color="red", label="Regression Line")
plt.title("Cell 1797 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1798 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1798, y_1798 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=98)
model_1798 = LinearRegression()
model_1798.fit(X_1798, y_1798)
y_pred_1798 = model_1798.predict(X_1798)
plt.scatter(X_1798, y_1798, label="Data")
plt.plot(X_1798, y_pred_1798, color="red", label="Regression Line")
plt.title("Cell 1798 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1799 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1799, y_1799 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=99)
model_1799 = LinearRegression()
model_1799.fit(X_1799, y_1799)
y_pred_1799 = model_1799.predict(X_1799)
plt.scatter(X_1799, y_1799, label="Data")
plt.plot(X_1799, y_pred_1799, color="red", label="Regression Line")
plt.title("Cell 1799 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1800 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1800, y_1800 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=0)
model_1800 = LinearRegression()
model_1800.fit(X_1800, y_1800)
y_pred_1800 = model_1800.predict(X_1800)
plt.scatter(X_1800, y_1800, label="Data")
plt.plot(X_1800, y_pred_1800, color="red", label="Regression Line")
plt.title("Cell 1800 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1801 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1801, y_1801 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=1)
model_1801 = LinearRegression()
model_1801.fit(X_1801, y_1801)
y_pred_1801 = model_1801.predict(X_1801)
plt.scatter(X_1801, y_1801, label="Data")
plt.plot(X_1801, y_pred_1801, color="red", label="Regression Line")
plt.title("Cell 1801 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1802 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1802, y_1802 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=2)
model_1802 = LinearRegression()
model_1802.fit(X_1802, y_1802)
y_pred_1802 = model_1802.predict(X_1802)
plt.scatter(X_1802, y_1802, label="Data")
plt.plot(X_1802, y_pred_1802, color="red", label="Regression Line")
plt.title("Cell 1802 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1803 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1803, y_1803 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=3)
model_1803 = LinearRegression()
model_1803.fit(X_1803, y_1803)
y_pred_1803 = model_1803.predict(X_1803)
plt.scatter(X_1803, y_1803, label="Data")
plt.plot(X_1803, y_pred_1803, color="red", label="Regression Line")
plt.title("Cell 1803 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1804 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1804, y_1804 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=4)
model_1804 = LinearRegression()
model_1804.fit(X_1804, y_1804)
y_pred_1804 = model_1804.predict(X_1804)
plt.scatter(X_1804, y_1804, label="Data")
plt.plot(X_1804, y_pred_1804, color="red", label="Regression Line")
plt.title("Cell 1804 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1805 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1805, y_1805 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=5)
model_1805 = LinearRegression()
model_1805.fit(X_1805, y_1805)
y_pred_1805 = model_1805.predict(X_1805)
plt.scatter(X_1805, y_1805, label="Data")
plt.plot(X_1805, y_pred_1805, color="red", label="Regression Line")
plt.title("Cell 1805 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1806 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1806, y_1806 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=6)
model_1806 = LinearRegression()
model_1806.fit(X_1806, y_1806)
y_pred_1806 = model_1806.predict(X_1806)
plt.scatter(X_1806, y_1806, label="Data")
plt.plot(X_1806, y_pred_1806, color="red", label="Regression Line")
plt.title("Cell 1806 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1807 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1807, y_1807 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=7)
model_1807 = LinearRegression()
model_1807.fit(X_1807, y_1807)
y_pred_1807 = model_1807.predict(X_1807)
plt.scatter(X_1807, y_1807, label="Data")
plt.plot(X_1807, y_pred_1807, color="red", label="Regression Line")
plt.title("Cell 1807 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1808 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1808, y_1808 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=8)
model_1808 = LinearRegression()
model_1808.fit(X_1808, y_1808)
y_pred_1808 = model_1808.predict(X_1808)
plt.scatter(X_1808, y_1808, label="Data")
plt.plot(X_1808, y_pred_1808, color="red", label="Regression Line")
plt.title("Cell 1808 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1809 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1809, y_1809 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=9)
model_1809 = LinearRegression()
model_1809.fit(X_1809, y_1809)
y_pred_1809 = model_1809.predict(X_1809)
plt.scatter(X_1809, y_1809, label="Data")
plt.plot(X_1809, y_pred_1809, color="red", label="Regression Line")
plt.title("Cell 1809 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1810 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1810, y_1810 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=10)
model_1810 = LinearRegression()
model_1810.fit(X_1810, y_1810)
y_pred_1810 = model_1810.predict(X_1810)
plt.scatter(X_1810, y_1810, label="Data")
plt.plot(X_1810, y_pred_1810, color="red", label="Regression Line")
plt.title("Cell 1810 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1811 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1811, y_1811 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=11)
model_1811 = LinearRegression()
model_1811.fit(X_1811, y_1811)
y_pred_1811 = model_1811.predict(X_1811)
plt.scatter(X_1811, y_1811, label="Data")
plt.plot(X_1811, y_pred_1811, color="red", label="Regression Line")
plt.title("Cell 1811 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1812 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1812, y_1812 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=12)
model_1812 = LinearRegression()
model_1812.fit(X_1812, y_1812)
y_pred_1812 = model_1812.predict(X_1812)
plt.scatter(X_1812, y_1812, label="Data")
plt.plot(X_1812, y_pred_1812, color="red", label="Regression Line")
plt.title("Cell 1812 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1813 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1813, y_1813 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=13)
model_1813 = LinearRegression()
model_1813.fit(X_1813, y_1813)
y_pred_1813 = model_1813.predict(X_1813)
plt.scatter(X_1813, y_1813, label="Data")
plt.plot(X_1813, y_pred_1813, color="red", label="Regression Line")
plt.title("Cell 1813 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1814 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1814, y_1814 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=14)
model_1814 = LinearRegression()
model_1814.fit(X_1814, y_1814)
y_pred_1814 = model_1814.predict(X_1814)
plt.scatter(X_1814, y_1814, label="Data")
plt.plot(X_1814, y_pred_1814, color="red", label="Regression Line")
plt.title("Cell 1814 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1815 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1815, y_1815 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=15)
model_1815 = LinearRegression()
model_1815.fit(X_1815, y_1815)
y_pred_1815 = model_1815.predict(X_1815)
plt.scatter(X_1815, y_1815, label="Data")
plt.plot(X_1815, y_pred_1815, color="red", label="Regression Line")
plt.title("Cell 1815 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1816 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1816, y_1816 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=16)
model_1816 = LinearRegression()
model_1816.fit(X_1816, y_1816)
y_pred_1816 = model_1816.predict(X_1816)
plt.scatter(X_1816, y_1816, label="Data")
plt.plot(X_1816, y_pred_1816, color="red", label="Regression Line")
plt.title("Cell 1816 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1817 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1817, y_1817 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=17)
model_1817 = LinearRegression()
model_1817.fit(X_1817, y_1817)
y_pred_1817 = model_1817.predict(X_1817)
plt.scatter(X_1817, y_1817, label="Data")
plt.plot(X_1817, y_pred_1817, color="red", label="Regression Line")
plt.title("Cell 1817 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1818 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1818, y_1818 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=18)
model_1818 = LinearRegression()
model_1818.fit(X_1818, y_1818)
y_pred_1818 = model_1818.predict(X_1818)
plt.scatter(X_1818, y_1818, label="Data")
plt.plot(X_1818, y_pred_1818, color="red", label="Regression Line")
plt.title("Cell 1818 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1819 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1819, y_1819 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=19)
model_1819 = LinearRegression()
model_1819.fit(X_1819, y_1819)
y_pred_1819 = model_1819.predict(X_1819)
plt.scatter(X_1819, y_1819, label="Data")
plt.plot(X_1819, y_pred_1819, color="red", label="Regression Line")
plt.title("Cell 1819 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1820 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1820, y_1820 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=20)
model_1820 = LinearRegression()
model_1820.fit(X_1820, y_1820)
y_pred_1820 = model_1820.predict(X_1820)
plt.scatter(X_1820, y_1820, label="Data")
plt.plot(X_1820, y_pred_1820, color="red", label="Regression Line")
plt.title("Cell 1820 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1821 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1821, y_1821 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=21)
model_1821 = LinearRegression()
model_1821.fit(X_1821, y_1821)
y_pred_1821 = model_1821.predict(X_1821)
plt.scatter(X_1821, y_1821, label="Data")
plt.plot(X_1821, y_pred_1821, color="red", label="Regression Line")
plt.title("Cell 1821 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1822 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1822, y_1822 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=22)
model_1822 = LinearRegression()
model_1822.fit(X_1822, y_1822)
y_pred_1822 = model_1822.predict(X_1822)
plt.scatter(X_1822, y_1822, label="Data")
plt.plot(X_1822, y_pred_1822, color="red", label="Regression Line")
plt.title("Cell 1822 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1823 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1823, y_1823 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=23)
model_1823 = LinearRegression()
model_1823.fit(X_1823, y_1823)
y_pred_1823 = model_1823.predict(X_1823)
plt.scatter(X_1823, y_1823, label="Data")
plt.plot(X_1823, y_pred_1823, color="red", label="Regression Line")
plt.title("Cell 1823 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1824 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1824, y_1824 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=24)
model_1824 = LinearRegression()
model_1824.fit(X_1824, y_1824)
y_pred_1824 = model_1824.predict(X_1824)
plt.scatter(X_1824, y_1824, label="Data")
plt.plot(X_1824, y_pred_1824, color="red", label="Regression Line")
plt.title("Cell 1824 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1825 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1825, y_1825 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=25)
model_1825 = LinearRegression()
model_1825.fit(X_1825, y_1825)
y_pred_1825 = model_1825.predict(X_1825)
plt.scatter(X_1825, y_1825, label="Data")
plt.plot(X_1825, y_pred_1825, color="red", label="Regression Line")
plt.title("Cell 1825 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1826 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1826, y_1826 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=26)
model_1826 = LinearRegression()
model_1826.fit(X_1826, y_1826)
y_pred_1826 = model_1826.predict(X_1826)
plt.scatter(X_1826, y_1826, label="Data")
plt.plot(X_1826, y_pred_1826, color="red", label="Regression Line")
plt.title("Cell 1826 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1827 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1827, y_1827 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=27)
model_1827 = LinearRegression()
model_1827.fit(X_1827, y_1827)
y_pred_1827 = model_1827.predict(X_1827)
plt.scatter(X_1827, y_1827, label="Data")
plt.plot(X_1827, y_pred_1827, color="red", label="Regression Line")
plt.title("Cell 1827 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1828 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1828, y_1828 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=28)
model_1828 = LinearRegression()
model_1828.fit(X_1828, y_1828)
y_pred_1828 = model_1828.predict(X_1828)
plt.scatter(X_1828, y_1828, label="Data")
plt.plot(X_1828, y_pred_1828, color="red", label="Regression Line")
plt.title("Cell 1828 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1829 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1829, y_1829 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=29)
model_1829 = LinearRegression()
model_1829.fit(X_1829, y_1829)
y_pred_1829 = model_1829.predict(X_1829)
plt.scatter(X_1829, y_1829, label="Data")
plt.plot(X_1829, y_pred_1829, color="red", label="Regression Line")
plt.title("Cell 1829 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1830 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1830, y_1830 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=30)
model_1830 = LinearRegression()
model_1830.fit(X_1830, y_1830)
y_pred_1830 = model_1830.predict(X_1830)
plt.scatter(X_1830, y_1830, label="Data")
plt.plot(X_1830, y_pred_1830, color="red", label="Regression Line")
plt.title("Cell 1830 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1831 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1831, y_1831 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=31)
model_1831 = LinearRegression()
model_1831.fit(X_1831, y_1831)
y_pred_1831 = model_1831.predict(X_1831)
plt.scatter(X_1831, y_1831, label="Data")
plt.plot(X_1831, y_pred_1831, color="red", label="Regression Line")
plt.title("Cell 1831 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1832 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1832, y_1832 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=32)
model_1832 = LinearRegression()
model_1832.fit(X_1832, y_1832)
y_pred_1832 = model_1832.predict(X_1832)
plt.scatter(X_1832, y_1832, label="Data")
plt.plot(X_1832, y_pred_1832, color="red", label="Regression Line")
plt.title("Cell 1832 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1833 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1833, y_1833 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=33)
model_1833 = LinearRegression()
model_1833.fit(X_1833, y_1833)
y_pred_1833 = model_1833.predict(X_1833)
plt.scatter(X_1833, y_1833, label="Data")
plt.plot(X_1833, y_pred_1833, color="red", label="Regression Line")
plt.title("Cell 1833 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1834 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1834, y_1834 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=34)
model_1834 = LinearRegression()
model_1834.fit(X_1834, y_1834)
y_pred_1834 = model_1834.predict(X_1834)
plt.scatter(X_1834, y_1834, label="Data")
plt.plot(X_1834, y_pred_1834, color="red", label="Regression Line")
plt.title("Cell 1834 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1835 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1835, y_1835 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=35)
model_1835 = LinearRegression()
model_1835.fit(X_1835, y_1835)
y_pred_1835 = model_1835.predict(X_1835)
plt.scatter(X_1835, y_1835, label="Data")
plt.plot(X_1835, y_pred_1835, color="red", label="Regression Line")
plt.title("Cell 1835 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1836 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1836, y_1836 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=36)
model_1836 = LinearRegression()
model_1836.fit(X_1836, y_1836)
y_pred_1836 = model_1836.predict(X_1836)
plt.scatter(X_1836, y_1836, label="Data")
plt.plot(X_1836, y_pred_1836, color="red", label="Regression Line")
plt.title("Cell 1836 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1837 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1837, y_1837 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=37)
model_1837 = LinearRegression()
model_1837.fit(X_1837, y_1837)
y_pred_1837 = model_1837.predict(X_1837)
plt.scatter(X_1837, y_1837, label="Data")
plt.plot(X_1837, y_pred_1837, color="red", label="Regression Line")
plt.title("Cell 1837 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1838 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1838, y_1838 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=38)
model_1838 = LinearRegression()
model_1838.fit(X_1838, y_1838)
y_pred_1838 = model_1838.predict(X_1838)
plt.scatter(X_1838, y_1838, label="Data")
plt.plot(X_1838, y_pred_1838, color="red", label="Regression Line")
plt.title("Cell 1838 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1839 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1839, y_1839 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=39)
model_1839 = LinearRegression()
model_1839.fit(X_1839, y_1839)
y_pred_1839 = model_1839.predict(X_1839)
plt.scatter(X_1839, y_1839, label="Data")
plt.plot(X_1839, y_pred_1839, color="red", label="Regression Line")
plt.title("Cell 1839 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1840 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1840, y_1840 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=40)
model_1840 = LinearRegression()
model_1840.fit(X_1840, y_1840)
y_pred_1840 = model_1840.predict(X_1840)
plt.scatter(X_1840, y_1840, label="Data")
plt.plot(X_1840, y_pred_1840, color="red", label="Regression Line")
plt.title("Cell 1840 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1841 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1841, y_1841 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=41)
model_1841 = LinearRegression()
model_1841.fit(X_1841, y_1841)
y_pred_1841 = model_1841.predict(X_1841)
plt.scatter(X_1841, y_1841, label="Data")
plt.plot(X_1841, y_pred_1841, color="red", label="Regression Line")
plt.title("Cell 1841 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1842 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1842, y_1842 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=42)
model_1842 = LinearRegression()
model_1842.fit(X_1842, y_1842)
y_pred_1842 = model_1842.predict(X_1842)
plt.scatter(X_1842, y_1842, label="Data")
plt.plot(X_1842, y_pred_1842, color="red", label="Regression Line")
plt.title("Cell 1842 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1843 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1843, y_1843 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=43)
model_1843 = LinearRegression()
model_1843.fit(X_1843, y_1843)
y_pred_1843 = model_1843.predict(X_1843)
plt.scatter(X_1843, y_1843, label="Data")
plt.plot(X_1843, y_pred_1843, color="red", label="Regression Line")
plt.title("Cell 1843 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1844 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1844, y_1844 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=44)
model_1844 = LinearRegression()
model_1844.fit(X_1844, y_1844)
y_pred_1844 = model_1844.predict(X_1844)
plt.scatter(X_1844, y_1844, label="Data")
plt.plot(X_1844, y_pred_1844, color="red", label="Regression Line")
plt.title("Cell 1844 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1845 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1845, y_1845 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=45)
model_1845 = LinearRegression()
model_1845.fit(X_1845, y_1845)
y_pred_1845 = model_1845.predict(X_1845)
plt.scatter(X_1845, y_1845, label="Data")
plt.plot(X_1845, y_pred_1845, color="red", label="Regression Line")
plt.title("Cell 1845 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1846 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1846, y_1846 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=46)
model_1846 = LinearRegression()
model_1846.fit(X_1846, y_1846)
y_pred_1846 = model_1846.predict(X_1846)
plt.scatter(X_1846, y_1846, label="Data")
plt.plot(X_1846, y_pred_1846, color="red", label="Regression Line")
plt.title("Cell 1846 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1847 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1847, y_1847 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=47)
model_1847 = LinearRegression()
model_1847.fit(X_1847, y_1847)
y_pred_1847 = model_1847.predict(X_1847)
plt.scatter(X_1847, y_1847, label="Data")
plt.plot(X_1847, y_pred_1847, color="red", label="Regression Line")
plt.title("Cell 1847 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1848 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1848, y_1848 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=48)
model_1848 = LinearRegression()
model_1848.fit(X_1848, y_1848)
y_pred_1848 = model_1848.predict(X_1848)
plt.scatter(X_1848, y_1848, label="Data")
plt.plot(X_1848, y_pred_1848, color="red", label="Regression Line")
plt.title("Cell 1848 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1849 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1849, y_1849 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=49)
model_1849 = LinearRegression()
model_1849.fit(X_1849, y_1849)
y_pred_1849 = model_1849.predict(X_1849)
plt.scatter(X_1849, y_1849, label="Data")
plt.plot(X_1849, y_pred_1849, color="red", label="Regression Line")
plt.title("Cell 1849 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1850 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1850, y_1850 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=50)
model_1850 = LinearRegression()
model_1850.fit(X_1850, y_1850)
y_pred_1850 = model_1850.predict(X_1850)
plt.scatter(X_1850, y_1850, label="Data")
plt.plot(X_1850, y_pred_1850, color="red", label="Regression Line")
plt.title("Cell 1850 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1851 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1851, y_1851 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=51)
model_1851 = LinearRegression()
model_1851.fit(X_1851, y_1851)
y_pred_1851 = model_1851.predict(X_1851)
plt.scatter(X_1851, y_1851, label="Data")
plt.plot(X_1851, y_pred_1851, color="red", label="Regression Line")
plt.title("Cell 1851 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1852 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1852, y_1852 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=52)
model_1852 = LinearRegression()
model_1852.fit(X_1852, y_1852)
y_pred_1852 = model_1852.predict(X_1852)
plt.scatter(X_1852, y_1852, label="Data")
plt.plot(X_1852, y_pred_1852, color="red", label="Regression Line")
plt.title("Cell 1852 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1853 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1853, y_1853 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=53)
model_1853 = LinearRegression()
model_1853.fit(X_1853, y_1853)
y_pred_1853 = model_1853.predict(X_1853)
plt.scatter(X_1853, y_1853, label="Data")
plt.plot(X_1853, y_pred_1853, color="red", label="Regression Line")
plt.title("Cell 1853 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1854 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1854, y_1854 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=54)
model_1854 = LinearRegression()
model_1854.fit(X_1854, y_1854)
y_pred_1854 = model_1854.predict(X_1854)
plt.scatter(X_1854, y_1854, label="Data")
plt.plot(X_1854, y_pred_1854, color="red", label="Regression Line")
plt.title("Cell 1854 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1855 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1855, y_1855 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=55)
model_1855 = LinearRegression()
model_1855.fit(X_1855, y_1855)
y_pred_1855 = model_1855.predict(X_1855)
plt.scatter(X_1855, y_1855, label="Data")
plt.plot(X_1855, y_pred_1855, color="red", label="Regression Line")
plt.title("Cell 1855 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1856 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1856, y_1856 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=56)
model_1856 = LinearRegression()
model_1856.fit(X_1856, y_1856)
y_pred_1856 = model_1856.predict(X_1856)
plt.scatter(X_1856, y_1856, label="Data")
plt.plot(X_1856, y_pred_1856, color="red", label="Regression Line")
plt.title("Cell 1856 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1857 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1857, y_1857 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=57)
model_1857 = LinearRegression()
model_1857.fit(X_1857, y_1857)
y_pred_1857 = model_1857.predict(X_1857)
plt.scatter(X_1857, y_1857, label="Data")
plt.plot(X_1857, y_pred_1857, color="red", label="Regression Line")
plt.title("Cell 1857 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1858 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1858, y_1858 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=58)
model_1858 = LinearRegression()
model_1858.fit(X_1858, y_1858)
y_pred_1858 = model_1858.predict(X_1858)
plt.scatter(X_1858, y_1858, label="Data")
plt.plot(X_1858, y_pred_1858, color="red", label="Regression Line")
plt.title("Cell 1858 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1859 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1859, y_1859 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=59)
model_1859 = LinearRegression()
model_1859.fit(X_1859, y_1859)
y_pred_1859 = model_1859.predict(X_1859)
plt.scatter(X_1859, y_1859, label="Data")
plt.plot(X_1859, y_pred_1859, color="red", label="Regression Line")
plt.title("Cell 1859 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1860 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1860, y_1860 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=60)
model_1860 = LinearRegression()
model_1860.fit(X_1860, y_1860)
y_pred_1860 = model_1860.predict(X_1860)
plt.scatter(X_1860, y_1860, label="Data")
plt.plot(X_1860, y_pred_1860, color="red", label="Regression Line")
plt.title("Cell 1860 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1861 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1861, y_1861 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=61)
model_1861 = LinearRegression()
model_1861.fit(X_1861, y_1861)
y_pred_1861 = model_1861.predict(X_1861)
plt.scatter(X_1861, y_1861, label="Data")
plt.plot(X_1861, y_pred_1861, color="red", label="Regression Line")
plt.title("Cell 1861 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1862 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1862, y_1862 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=62)
model_1862 = LinearRegression()
model_1862.fit(X_1862, y_1862)
y_pred_1862 = model_1862.predict(X_1862)
plt.scatter(X_1862, y_1862, label="Data")
plt.plot(X_1862, y_pred_1862, color="red", label="Regression Line")
plt.title("Cell 1862 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1863 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1863, y_1863 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=63)
model_1863 = LinearRegression()
model_1863.fit(X_1863, y_1863)
y_pred_1863 = model_1863.predict(X_1863)
plt.scatter(X_1863, y_1863, label="Data")
plt.plot(X_1863, y_pred_1863, color="red", label="Regression Line")
plt.title("Cell 1863 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1864 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1864, y_1864 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=64)
model_1864 = LinearRegression()
model_1864.fit(X_1864, y_1864)
y_pred_1864 = model_1864.predict(X_1864)
plt.scatter(X_1864, y_1864, label="Data")
plt.plot(X_1864, y_pred_1864, color="red", label="Regression Line")
plt.title("Cell 1864 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1865 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1865, y_1865 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=65)
model_1865 = LinearRegression()
model_1865.fit(X_1865, y_1865)
y_pred_1865 = model_1865.predict(X_1865)
plt.scatter(X_1865, y_1865, label="Data")
plt.plot(X_1865, y_pred_1865, color="red", label="Regression Line")
plt.title("Cell 1865 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1866 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1866, y_1866 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=66)
model_1866 = LinearRegression()
model_1866.fit(X_1866, y_1866)
y_pred_1866 = model_1866.predict(X_1866)
plt.scatter(X_1866, y_1866, label="Data")
plt.plot(X_1866, y_pred_1866, color="red", label="Regression Line")
plt.title("Cell 1866 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1867 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1867, y_1867 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=67)
model_1867 = LinearRegression()
model_1867.fit(X_1867, y_1867)
y_pred_1867 = model_1867.predict(X_1867)
plt.scatter(X_1867, y_1867, label="Data")
plt.plot(X_1867, y_pred_1867, color="red", label="Regression Line")
plt.title("Cell 1867 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1868 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1868, y_1868 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=68)
model_1868 = LinearRegression()
model_1868.fit(X_1868, y_1868)
y_pred_1868 = model_1868.predict(X_1868)
plt.scatter(X_1868, y_1868, label="Data")
plt.plot(X_1868, y_pred_1868, color="red", label="Regression Line")
plt.title("Cell 1868 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1869 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1869, y_1869 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=69)
model_1869 = LinearRegression()
model_1869.fit(X_1869, y_1869)
y_pred_1869 = model_1869.predict(X_1869)
plt.scatter(X_1869, y_1869, label="Data")
plt.plot(X_1869, y_pred_1869, color="red", label="Regression Line")
plt.title("Cell 1869 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1870 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1870, y_1870 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=70)
model_1870 = LinearRegression()
model_1870.fit(X_1870, y_1870)
y_pred_1870 = model_1870.predict(X_1870)
plt.scatter(X_1870, y_1870, label="Data")
plt.plot(X_1870, y_pred_1870, color="red", label="Regression Line")
plt.title("Cell 1870 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1871 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1871, y_1871 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=71)
model_1871 = LinearRegression()
model_1871.fit(X_1871, y_1871)
y_pred_1871 = model_1871.predict(X_1871)
plt.scatter(X_1871, y_1871, label="Data")
plt.plot(X_1871, y_pred_1871, color="red", label="Regression Line")
plt.title("Cell 1871 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1872 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1872, y_1872 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=72)
model_1872 = LinearRegression()
model_1872.fit(X_1872, y_1872)
y_pred_1872 = model_1872.predict(X_1872)
plt.scatter(X_1872, y_1872, label="Data")
plt.plot(X_1872, y_pred_1872, color="red", label="Regression Line")
plt.title("Cell 1872 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1873 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1873, y_1873 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=73)
model_1873 = LinearRegression()
model_1873.fit(X_1873, y_1873)
y_pred_1873 = model_1873.predict(X_1873)
plt.scatter(X_1873, y_1873, label="Data")
plt.plot(X_1873, y_pred_1873, color="red", label="Regression Line")
plt.title("Cell 1873 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1874 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1874, y_1874 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=74)
model_1874 = LinearRegression()
model_1874.fit(X_1874, y_1874)
y_pred_1874 = model_1874.predict(X_1874)
plt.scatter(X_1874, y_1874, label="Data")
plt.plot(X_1874, y_pred_1874, color="red", label="Regression Line")
plt.title("Cell 1874 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1875 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1875, y_1875 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=75)
model_1875 = LinearRegression()
model_1875.fit(X_1875, y_1875)
y_pred_1875 = model_1875.predict(X_1875)
plt.scatter(X_1875, y_1875, label="Data")
plt.plot(X_1875, y_pred_1875, color="red", label="Regression Line")
plt.title("Cell 1875 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1876 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1876, y_1876 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=76)
model_1876 = LinearRegression()
model_1876.fit(X_1876, y_1876)
y_pred_1876 = model_1876.predict(X_1876)
plt.scatter(X_1876, y_1876, label="Data")
plt.plot(X_1876, y_pred_1876, color="red", label="Regression Line")
plt.title("Cell 1876 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1877 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1877, y_1877 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=77)
model_1877 = LinearRegression()
model_1877.fit(X_1877, y_1877)
y_pred_1877 = model_1877.predict(X_1877)
plt.scatter(X_1877, y_1877, label="Data")
plt.plot(X_1877, y_pred_1877, color="red", label="Regression Line")
plt.title("Cell 1877 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1878 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1878, y_1878 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=78)
model_1878 = LinearRegression()
model_1878.fit(X_1878, y_1878)
y_pred_1878 = model_1878.predict(X_1878)
plt.scatter(X_1878, y_1878, label="Data")
plt.plot(X_1878, y_pred_1878, color="red", label="Regression Line")
plt.title("Cell 1878 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1879 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1879, y_1879 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=79)
model_1879 = LinearRegression()
model_1879.fit(X_1879, y_1879)
y_pred_1879 = model_1879.predict(X_1879)
plt.scatter(X_1879, y_1879, label="Data")
plt.plot(X_1879, y_pred_1879, color="red", label="Regression Line")
plt.title("Cell 1879 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1880 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1880, y_1880 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=80)
model_1880 = LinearRegression()
model_1880.fit(X_1880, y_1880)
y_pred_1880 = model_1880.predict(X_1880)
plt.scatter(X_1880, y_1880, label="Data")
plt.plot(X_1880, y_pred_1880, color="red", label="Regression Line")
plt.title("Cell 1880 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1881 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1881, y_1881 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=81)
model_1881 = LinearRegression()
model_1881.fit(X_1881, y_1881)
y_pred_1881 = model_1881.predict(X_1881)
plt.scatter(X_1881, y_1881, label="Data")
plt.plot(X_1881, y_pred_1881, color="red", label="Regression Line")
plt.title("Cell 1881 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1882 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1882, y_1882 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=82)
model_1882 = LinearRegression()
model_1882.fit(X_1882, y_1882)
y_pred_1882 = model_1882.predict(X_1882)
plt.scatter(X_1882, y_1882, label="Data")
plt.plot(X_1882, y_pred_1882, color="red", label="Regression Line")
plt.title("Cell 1882 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1883 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1883, y_1883 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=83)
model_1883 = LinearRegression()
model_1883.fit(X_1883, y_1883)
y_pred_1883 = model_1883.predict(X_1883)
plt.scatter(X_1883, y_1883, label="Data")
plt.plot(X_1883, y_pred_1883, color="red", label="Regression Line")
plt.title("Cell 1883 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1884 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1884, y_1884 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=84)
model_1884 = LinearRegression()
model_1884.fit(X_1884, y_1884)
y_pred_1884 = model_1884.predict(X_1884)
plt.scatter(X_1884, y_1884, label="Data")
plt.plot(X_1884, y_pred_1884, color="red", label="Regression Line")
plt.title("Cell 1884 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1885 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1885, y_1885 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=85)
model_1885 = LinearRegression()
model_1885.fit(X_1885, y_1885)
y_pred_1885 = model_1885.predict(X_1885)
plt.scatter(X_1885, y_1885, label="Data")
plt.plot(X_1885, y_pred_1885, color="red", label="Regression Line")
plt.title("Cell 1885 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1886 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1886, y_1886 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=86)
model_1886 = LinearRegression()
model_1886.fit(X_1886, y_1886)
y_pred_1886 = model_1886.predict(X_1886)
plt.scatter(X_1886, y_1886, label="Data")
plt.plot(X_1886, y_pred_1886, color="red", label="Regression Line")
plt.title("Cell 1886 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1887 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1887, y_1887 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=87)
model_1887 = LinearRegression()
model_1887.fit(X_1887, y_1887)
y_pred_1887 = model_1887.predict(X_1887)
plt.scatter(X_1887, y_1887, label="Data")
plt.plot(X_1887, y_pred_1887, color="red", label="Regression Line")
plt.title("Cell 1887 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1888 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1888, y_1888 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=88)
model_1888 = LinearRegression()
model_1888.fit(X_1888, y_1888)
y_pred_1888 = model_1888.predict(X_1888)
plt.scatter(X_1888, y_1888, label="Data")
plt.plot(X_1888, y_pred_1888, color="red", label="Regression Line")
plt.title("Cell 1888 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1889 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1889, y_1889 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=89)
model_1889 = LinearRegression()
model_1889.fit(X_1889, y_1889)
y_pred_1889 = model_1889.predict(X_1889)
plt.scatter(X_1889, y_1889, label="Data")
plt.plot(X_1889, y_pred_1889, color="red", label="Regression Line")
plt.title("Cell 1889 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1890 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1890, y_1890 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=90)
model_1890 = LinearRegression()
model_1890.fit(X_1890, y_1890)
y_pred_1890 = model_1890.predict(X_1890)
plt.scatter(X_1890, y_1890, label="Data")
plt.plot(X_1890, y_pred_1890, color="red", label="Regression Line")
plt.title("Cell 1890 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1891 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1891, y_1891 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=91)
model_1891 = LinearRegression()
model_1891.fit(X_1891, y_1891)
y_pred_1891 = model_1891.predict(X_1891)
plt.scatter(X_1891, y_1891, label="Data")
plt.plot(X_1891, y_pred_1891, color="red", label="Regression Line")
plt.title("Cell 1891 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1892 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1892, y_1892 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=92)
model_1892 = LinearRegression()
model_1892.fit(X_1892, y_1892)
y_pred_1892 = model_1892.predict(X_1892)
plt.scatter(X_1892, y_1892, label="Data")
plt.plot(X_1892, y_pred_1892, color="red", label="Regression Line")
plt.title("Cell 1892 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1893 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1893, y_1893 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=93)
model_1893 = LinearRegression()
model_1893.fit(X_1893, y_1893)
y_pred_1893 = model_1893.predict(X_1893)
plt.scatter(X_1893, y_1893, label="Data")
plt.plot(X_1893, y_pred_1893, color="red", label="Regression Line")
plt.title("Cell 1893 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1894 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1894, y_1894 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=94)
model_1894 = LinearRegression()
model_1894.fit(X_1894, y_1894)
y_pred_1894 = model_1894.predict(X_1894)
plt.scatter(X_1894, y_1894, label="Data")
plt.plot(X_1894, y_pred_1894, color="red", label="Regression Line")
plt.title("Cell 1894 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1895 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1895, y_1895 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=95)
model_1895 = LinearRegression()
model_1895.fit(X_1895, y_1895)
y_pred_1895 = model_1895.predict(X_1895)
plt.scatter(X_1895, y_1895, label="Data")
plt.plot(X_1895, y_pred_1895, color="red", label="Regression Line")
plt.title("Cell 1895 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1896 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1896, y_1896 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=96)
model_1896 = LinearRegression()
model_1896.fit(X_1896, y_1896)
y_pred_1896 = model_1896.predict(X_1896)
plt.scatter(X_1896, y_1896, label="Data")
plt.plot(X_1896, y_pred_1896, color="red", label="Regression Line")
plt.title("Cell 1896 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1897 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1897, y_1897 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=97)
model_1897 = LinearRegression()
model_1897.fit(X_1897, y_1897)
y_pred_1897 = model_1897.predict(X_1897)
plt.scatter(X_1897, y_1897, label="Data")
plt.plot(X_1897, y_pred_1897, color="red", label="Regression Line")
plt.title("Cell 1897 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1898 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1898, y_1898 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=98)
model_1898 = LinearRegression()
model_1898.fit(X_1898, y_1898)
y_pred_1898 = model_1898.predict(X_1898)
plt.scatter(X_1898, y_1898, label="Data")
plt.plot(X_1898, y_pred_1898, color="red", label="Regression Line")
plt.title("Cell 1898 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1899 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1899, y_1899 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=99)
model_1899 = LinearRegression()
model_1899.fit(X_1899, y_1899)
y_pred_1899 = model_1899.predict(X_1899)
plt.scatter(X_1899, y_1899, label="Data")
plt.plot(X_1899, y_pred_1899, color="red", label="Regression Line")
plt.title("Cell 1899 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1900 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1900, y_1900 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=0)
model_1900 = LinearRegression()
model_1900.fit(X_1900, y_1900)
y_pred_1900 = model_1900.predict(X_1900)
plt.scatter(X_1900, y_1900, label="Data")
plt.plot(X_1900, y_pred_1900, color="red", label="Regression Line")
plt.title("Cell 1900 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1901 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1901, y_1901 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=1)
model_1901 = LinearRegression()
model_1901.fit(X_1901, y_1901)
y_pred_1901 = model_1901.predict(X_1901)
plt.scatter(X_1901, y_1901, label="Data")
plt.plot(X_1901, y_pred_1901, color="red", label="Regression Line")
plt.title("Cell 1901 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1902 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1902, y_1902 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=2)
model_1902 = LinearRegression()
model_1902.fit(X_1902, y_1902)
y_pred_1902 = model_1902.predict(X_1902)
plt.scatter(X_1902, y_1902, label="Data")
plt.plot(X_1902, y_pred_1902, color="red", label="Regression Line")
plt.title("Cell 1902 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1903 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1903, y_1903 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=3)
model_1903 = LinearRegression()
model_1903.fit(X_1903, y_1903)
y_pred_1903 = model_1903.predict(X_1903)
plt.scatter(X_1903, y_1903, label="Data")
plt.plot(X_1903, y_pred_1903, color="red", label="Regression Line")
plt.title("Cell 1903 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1904 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1904, y_1904 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=4)
model_1904 = LinearRegression()
model_1904.fit(X_1904, y_1904)
y_pred_1904 = model_1904.predict(X_1904)
plt.scatter(X_1904, y_1904, label="Data")
plt.plot(X_1904, y_pred_1904, color="red", label="Regression Line")
plt.title("Cell 1904 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1905 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1905, y_1905 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=5)
model_1905 = LinearRegression()
model_1905.fit(X_1905, y_1905)
y_pred_1905 = model_1905.predict(X_1905)
plt.scatter(X_1905, y_1905, label="Data")
plt.plot(X_1905, y_pred_1905, color="red", label="Regression Line")
plt.title("Cell 1905 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1906 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1906, y_1906 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=6)
model_1906 = LinearRegression()
model_1906.fit(X_1906, y_1906)
y_pred_1906 = model_1906.predict(X_1906)
plt.scatter(X_1906, y_1906, label="Data")
plt.plot(X_1906, y_pred_1906, color="red", label="Regression Line")
plt.title("Cell 1906 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1907 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1907, y_1907 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=7)
model_1907 = LinearRegression()
model_1907.fit(X_1907, y_1907)
y_pred_1907 = model_1907.predict(X_1907)
plt.scatter(X_1907, y_1907, label="Data")
plt.plot(X_1907, y_pred_1907, color="red", label="Regression Line")
plt.title("Cell 1907 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1908 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1908, y_1908 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=8)
model_1908 = LinearRegression()
model_1908.fit(X_1908, y_1908)
y_pred_1908 = model_1908.predict(X_1908)
plt.scatter(X_1908, y_1908, label="Data")
plt.plot(X_1908, y_pred_1908, color="red", label="Regression Line")
plt.title("Cell 1908 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1909 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1909, y_1909 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=9)
model_1909 = LinearRegression()
model_1909.fit(X_1909, y_1909)
y_pred_1909 = model_1909.predict(X_1909)
plt.scatter(X_1909, y_1909, label="Data")
plt.plot(X_1909, y_pred_1909, color="red", label="Regression Line")
plt.title("Cell 1909 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1910 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1910, y_1910 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=10)
model_1910 = LinearRegression()
model_1910.fit(X_1910, y_1910)
y_pred_1910 = model_1910.predict(X_1910)
plt.scatter(X_1910, y_1910, label="Data")
plt.plot(X_1910, y_pred_1910, color="red", label="Regression Line")
plt.title("Cell 1910 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1911 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1911, y_1911 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=11)
model_1911 = LinearRegression()
model_1911.fit(X_1911, y_1911)
y_pred_1911 = model_1911.predict(X_1911)
plt.scatter(X_1911, y_1911, label="Data")
plt.plot(X_1911, y_pred_1911, color="red", label="Regression Line")
plt.title("Cell 1911 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1912 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1912, y_1912 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=12)
model_1912 = LinearRegression()
model_1912.fit(X_1912, y_1912)
y_pred_1912 = model_1912.predict(X_1912)
plt.scatter(X_1912, y_1912, label="Data")
plt.plot(X_1912, y_pred_1912, color="red", label="Regression Line")
plt.title("Cell 1912 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1913 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1913, y_1913 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=13)
model_1913 = LinearRegression()
model_1913.fit(X_1913, y_1913)
y_pred_1913 = model_1913.predict(X_1913)
plt.scatter(X_1913, y_1913, label="Data")
plt.plot(X_1913, y_pred_1913, color="red", label="Regression Line")
plt.title("Cell 1913 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1914 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1914, y_1914 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=14)
model_1914 = LinearRegression()
model_1914.fit(X_1914, y_1914)
y_pred_1914 = model_1914.predict(X_1914)
plt.scatter(X_1914, y_1914, label="Data")
plt.plot(X_1914, y_pred_1914, color="red", label="Regression Line")
plt.title("Cell 1914 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1915 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1915, y_1915 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=15)
model_1915 = LinearRegression()
model_1915.fit(X_1915, y_1915)
y_pred_1915 = model_1915.predict(X_1915)
plt.scatter(X_1915, y_1915, label="Data")
plt.plot(X_1915, y_pred_1915, color="red", label="Regression Line")
plt.title("Cell 1915 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1916 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1916, y_1916 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=16)
model_1916 = LinearRegression()
model_1916.fit(X_1916, y_1916)
y_pred_1916 = model_1916.predict(X_1916)
plt.scatter(X_1916, y_1916, label="Data")
plt.plot(X_1916, y_pred_1916, color="red", label="Regression Line")
plt.title("Cell 1916 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1917 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1917, y_1917 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=17)
model_1917 = LinearRegression()
model_1917.fit(X_1917, y_1917)
y_pred_1917 = model_1917.predict(X_1917)
plt.scatter(X_1917, y_1917, label="Data")
plt.plot(X_1917, y_pred_1917, color="red", label="Regression Line")
plt.title("Cell 1917 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1918 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1918, y_1918 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=18)
model_1918 = LinearRegression()
model_1918.fit(X_1918, y_1918)
y_pred_1918 = model_1918.predict(X_1918)
plt.scatter(X_1918, y_1918, label="Data")
plt.plot(X_1918, y_pred_1918, color="red", label="Regression Line")
plt.title("Cell 1918 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1919 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1919, y_1919 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=19)
model_1919 = LinearRegression()
model_1919.fit(X_1919, y_1919)
y_pred_1919 = model_1919.predict(X_1919)
plt.scatter(X_1919, y_1919, label="Data")
plt.plot(X_1919, y_pred_1919, color="red", label="Regression Line")
plt.title("Cell 1919 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1920 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1920, y_1920 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=20)
model_1920 = LinearRegression()
model_1920.fit(X_1920, y_1920)
y_pred_1920 = model_1920.predict(X_1920)
plt.scatter(X_1920, y_1920, label="Data")
plt.plot(X_1920, y_pred_1920, color="red", label="Regression Line")
plt.title("Cell 1920 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1921 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1921, y_1921 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=21)
model_1921 = LinearRegression()
model_1921.fit(X_1921, y_1921)
y_pred_1921 = model_1921.predict(X_1921)
plt.scatter(X_1921, y_1921, label="Data")
plt.plot(X_1921, y_pred_1921, color="red", label="Regression Line")
plt.title("Cell 1921 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1922 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1922, y_1922 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=22)
model_1922 = LinearRegression()
model_1922.fit(X_1922, y_1922)
y_pred_1922 = model_1922.predict(X_1922)
plt.scatter(X_1922, y_1922, label="Data")
plt.plot(X_1922, y_pred_1922, color="red", label="Regression Line")
plt.title("Cell 1922 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1923 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1923, y_1923 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=23)
model_1923 = LinearRegression()
model_1923.fit(X_1923, y_1923)
y_pred_1923 = model_1923.predict(X_1923)
plt.scatter(X_1923, y_1923, label="Data")
plt.plot(X_1923, y_pred_1923, color="red", label="Regression Line")
plt.title("Cell 1923 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1924 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1924, y_1924 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=24)
model_1924 = LinearRegression()
model_1924.fit(X_1924, y_1924)
y_pred_1924 = model_1924.predict(X_1924)
plt.scatter(X_1924, y_1924, label="Data")
plt.plot(X_1924, y_pred_1924, color="red", label="Regression Line")
plt.title("Cell 1924 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1925 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1925, y_1925 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=25)
model_1925 = LinearRegression()
model_1925.fit(X_1925, y_1925)
y_pred_1925 = model_1925.predict(X_1925)
plt.scatter(X_1925, y_1925, label="Data")
plt.plot(X_1925, y_pred_1925, color="red", label="Regression Line")
plt.title("Cell 1925 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1926 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1926, y_1926 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=26)
model_1926 = LinearRegression()
model_1926.fit(X_1926, y_1926)
y_pred_1926 = model_1926.predict(X_1926)
plt.scatter(X_1926, y_1926, label="Data")
plt.plot(X_1926, y_pred_1926, color="red", label="Regression Line")
plt.title("Cell 1926 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1927 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1927, y_1927 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=27)
model_1927 = LinearRegression()
model_1927.fit(X_1927, y_1927)
y_pred_1927 = model_1927.predict(X_1927)
plt.scatter(X_1927, y_1927, label="Data")
plt.plot(X_1927, y_pred_1927, color="red", label="Regression Line")
plt.title("Cell 1927 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1928 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1928, y_1928 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=28)
model_1928 = LinearRegression()
model_1928.fit(X_1928, y_1928)
y_pred_1928 = model_1928.predict(X_1928)
plt.scatter(X_1928, y_1928, label="Data")
plt.plot(X_1928, y_pred_1928, color="red", label="Regression Line")
plt.title("Cell 1928 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1929 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1929, y_1929 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=29)
model_1929 = LinearRegression()
model_1929.fit(X_1929, y_1929)
y_pred_1929 = model_1929.predict(X_1929)
plt.scatter(X_1929, y_1929, label="Data")
plt.plot(X_1929, y_pred_1929, color="red", label="Regression Line")
plt.title("Cell 1929 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1930 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1930, y_1930 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=30)
model_1930 = LinearRegression()
model_1930.fit(X_1930, y_1930)
y_pred_1930 = model_1930.predict(X_1930)
plt.scatter(X_1930, y_1930, label="Data")
plt.plot(X_1930, y_pred_1930, color="red", label="Regression Line")
plt.title("Cell 1930 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1931 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1931, y_1931 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=31)
model_1931 = LinearRegression()
model_1931.fit(X_1931, y_1931)
y_pred_1931 = model_1931.predict(X_1931)
plt.scatter(X_1931, y_1931, label="Data")
plt.plot(X_1931, y_pred_1931, color="red", label="Regression Line")
plt.title("Cell 1931 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1932 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1932, y_1932 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=32)
model_1932 = LinearRegression()
model_1932.fit(X_1932, y_1932)
y_pred_1932 = model_1932.predict(X_1932)
plt.scatter(X_1932, y_1932, label="Data")
plt.plot(X_1932, y_pred_1932, color="red", label="Regression Line")
plt.title("Cell 1932 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1933 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1933, y_1933 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=33)
model_1933 = LinearRegression()
model_1933.fit(X_1933, y_1933)
y_pred_1933 = model_1933.predict(X_1933)
plt.scatter(X_1933, y_1933, label="Data")
plt.plot(X_1933, y_pred_1933, color="red", label="Regression Line")
plt.title("Cell 1933 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1934 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1934, y_1934 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=34)
model_1934 = LinearRegression()
model_1934.fit(X_1934, y_1934)
y_pred_1934 = model_1934.predict(X_1934)
plt.scatter(X_1934, y_1934, label="Data")
plt.plot(X_1934, y_pred_1934, color="red", label="Regression Line")
plt.title("Cell 1934 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1935 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1935, y_1935 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=35)
model_1935 = LinearRegression()
model_1935.fit(X_1935, y_1935)
y_pred_1935 = model_1935.predict(X_1935)
plt.scatter(X_1935, y_1935, label="Data")
plt.plot(X_1935, y_pred_1935, color="red", label="Regression Line")
plt.title("Cell 1935 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1936 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1936, y_1936 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=36)
model_1936 = LinearRegression()
model_1936.fit(X_1936, y_1936)
y_pred_1936 = model_1936.predict(X_1936)
plt.scatter(X_1936, y_1936, label="Data")
plt.plot(X_1936, y_pred_1936, color="red", label="Regression Line")
plt.title("Cell 1936 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1937 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1937, y_1937 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=37)
model_1937 = LinearRegression()
model_1937.fit(X_1937, y_1937)
y_pred_1937 = model_1937.predict(X_1937)
plt.scatter(X_1937, y_1937, label="Data")
plt.plot(X_1937, y_pred_1937, color="red", label="Regression Line")
plt.title("Cell 1937 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1938 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1938, y_1938 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=38)
model_1938 = LinearRegression()
model_1938.fit(X_1938, y_1938)
y_pred_1938 = model_1938.predict(X_1938)
plt.scatter(X_1938, y_1938, label="Data")
plt.plot(X_1938, y_pred_1938, color="red", label="Regression Line")
plt.title("Cell 1938 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1939 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1939, y_1939 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=39)
model_1939 = LinearRegression()
model_1939.fit(X_1939, y_1939)
y_pred_1939 = model_1939.predict(X_1939)
plt.scatter(X_1939, y_1939, label="Data")
plt.plot(X_1939, y_pred_1939, color="red", label="Regression Line")
plt.title("Cell 1939 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1940 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1940, y_1940 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=40)
model_1940 = LinearRegression()
model_1940.fit(X_1940, y_1940)
y_pred_1940 = model_1940.predict(X_1940)
plt.scatter(X_1940, y_1940, label="Data")
plt.plot(X_1940, y_pred_1940, color="red", label="Regression Line")
plt.title("Cell 1940 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1941 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1941, y_1941 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=41)
model_1941 = LinearRegression()
model_1941.fit(X_1941, y_1941)
y_pred_1941 = model_1941.predict(X_1941)
plt.scatter(X_1941, y_1941, label="Data")
plt.plot(X_1941, y_pred_1941, color="red", label="Regression Line")
plt.title("Cell 1941 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1942 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1942, y_1942 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=42)
model_1942 = LinearRegression()
model_1942.fit(X_1942, y_1942)
y_pred_1942 = model_1942.predict(X_1942)
plt.scatter(X_1942, y_1942, label="Data")
plt.plot(X_1942, y_pred_1942, color="red", label="Regression Line")
plt.title("Cell 1942 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1943 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1943, y_1943 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=43)
model_1943 = LinearRegression()
model_1943.fit(X_1943, y_1943)
y_pred_1943 = model_1943.predict(X_1943)
plt.scatter(X_1943, y_1943, label="Data")
plt.plot(X_1943, y_pred_1943, color="red", label="Regression Line")
plt.title("Cell 1943 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1944 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1944, y_1944 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=44)
model_1944 = LinearRegression()
model_1944.fit(X_1944, y_1944)
y_pred_1944 = model_1944.predict(X_1944)
plt.scatter(X_1944, y_1944, label="Data")
plt.plot(X_1944, y_pred_1944, color="red", label="Regression Line")
plt.title("Cell 1944 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1945 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1945, y_1945 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=45)
model_1945 = LinearRegression()
model_1945.fit(X_1945, y_1945)
y_pred_1945 = model_1945.predict(X_1945)
plt.scatter(X_1945, y_1945, label="Data")
plt.plot(X_1945, y_pred_1945, color="red", label="Regression Line")
plt.title("Cell 1945 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1946 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1946, y_1946 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=46)
model_1946 = LinearRegression()
model_1946.fit(X_1946, y_1946)
y_pred_1946 = model_1946.predict(X_1946)
plt.scatter(X_1946, y_1946, label="Data")
plt.plot(X_1946, y_pred_1946, color="red", label="Regression Line")
plt.title("Cell 1946 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1947 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1947, y_1947 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=47)
model_1947 = LinearRegression()
model_1947.fit(X_1947, y_1947)
y_pred_1947 = model_1947.predict(X_1947)
plt.scatter(X_1947, y_1947, label="Data")
plt.plot(X_1947, y_pred_1947, color="red", label="Regression Line")
plt.title("Cell 1947 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1948 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1948, y_1948 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=48)
model_1948 = LinearRegression()
model_1948.fit(X_1948, y_1948)
y_pred_1948 = model_1948.predict(X_1948)
plt.scatter(X_1948, y_1948, label="Data")
plt.plot(X_1948, y_pred_1948, color="red", label="Regression Line")
plt.title("Cell 1948 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1949 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1949, y_1949 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=49)
model_1949 = LinearRegression()
model_1949.fit(X_1949, y_1949)
y_pred_1949 = model_1949.predict(X_1949)
plt.scatter(X_1949, y_1949, label="Data")
plt.plot(X_1949, y_pred_1949, color="red", label="Regression Line")
plt.title("Cell 1949 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1950 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1950, y_1950 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=50)
model_1950 = LinearRegression()
model_1950.fit(X_1950, y_1950)
y_pred_1950 = model_1950.predict(X_1950)
plt.scatter(X_1950, y_1950, label="Data")
plt.plot(X_1950, y_pred_1950, color="red", label="Regression Line")
plt.title("Cell 1950 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1951 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1951, y_1951 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=51)
model_1951 = LinearRegression()
model_1951.fit(X_1951, y_1951)
y_pred_1951 = model_1951.predict(X_1951)
plt.scatter(X_1951, y_1951, label="Data")
plt.plot(X_1951, y_pred_1951, color="red", label="Regression Line")
plt.title("Cell 1951 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1952 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1952, y_1952 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=52)
model_1952 = LinearRegression()
model_1952.fit(X_1952, y_1952)
y_pred_1952 = model_1952.predict(X_1952)
plt.scatter(X_1952, y_1952, label="Data")
plt.plot(X_1952, y_pred_1952, color="red", label="Regression Line")
plt.title("Cell 1952 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1953 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1953, y_1953 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=53)
model_1953 = LinearRegression()
model_1953.fit(X_1953, y_1953)
y_pred_1953 = model_1953.predict(X_1953)
plt.scatter(X_1953, y_1953, label="Data")
plt.plot(X_1953, y_pred_1953, color="red", label="Regression Line")
plt.title("Cell 1953 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1954 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1954, y_1954 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=54)
model_1954 = LinearRegression()
model_1954.fit(X_1954, y_1954)
y_pred_1954 = model_1954.predict(X_1954)
plt.scatter(X_1954, y_1954, label="Data")
plt.plot(X_1954, y_pred_1954, color="red", label="Regression Line")
plt.title("Cell 1954 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1955 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1955, y_1955 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=55)
model_1955 = LinearRegression()
model_1955.fit(X_1955, y_1955)
y_pred_1955 = model_1955.predict(X_1955)
plt.scatter(X_1955, y_1955, label="Data")
plt.plot(X_1955, y_pred_1955, color="red", label="Regression Line")
plt.title("Cell 1955 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1956 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1956, y_1956 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=56)
model_1956 = LinearRegression()
model_1956.fit(X_1956, y_1956)
y_pred_1956 = model_1956.predict(X_1956)
plt.scatter(X_1956, y_1956, label="Data")
plt.plot(X_1956, y_pred_1956, color="red", label="Regression Line")
plt.title("Cell 1956 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1957 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1957, y_1957 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=57)
model_1957 = LinearRegression()
model_1957.fit(X_1957, y_1957)
y_pred_1957 = model_1957.predict(X_1957)
plt.scatter(X_1957, y_1957, label="Data")
plt.plot(X_1957, y_pred_1957, color="red", label="Regression Line")
plt.title("Cell 1957 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1958 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1958, y_1958 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=58)
model_1958 = LinearRegression()
model_1958.fit(X_1958, y_1958)
y_pred_1958 = model_1958.predict(X_1958)
plt.scatter(X_1958, y_1958, label="Data")
plt.plot(X_1958, y_pred_1958, color="red", label="Regression Line")
plt.title("Cell 1958 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1959 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1959, y_1959 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=59)
model_1959 = LinearRegression()
model_1959.fit(X_1959, y_1959)
y_pred_1959 = model_1959.predict(X_1959)
plt.scatter(X_1959, y_1959, label="Data")
plt.plot(X_1959, y_pred_1959, color="red", label="Regression Line")
plt.title("Cell 1959 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1960 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1960, y_1960 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=60)
model_1960 = LinearRegression()
model_1960.fit(X_1960, y_1960)
y_pred_1960 = model_1960.predict(X_1960)
plt.scatter(X_1960, y_1960, label="Data")
plt.plot(X_1960, y_pred_1960, color="red", label="Regression Line")
plt.title("Cell 1960 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1961 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1961, y_1961 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=61)
model_1961 = LinearRegression()
model_1961.fit(X_1961, y_1961)
y_pred_1961 = model_1961.predict(X_1961)
plt.scatter(X_1961, y_1961, label="Data")
plt.plot(X_1961, y_pred_1961, color="red", label="Regression Line")
plt.title("Cell 1961 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1962 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1962, y_1962 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=62)
model_1962 = LinearRegression()
model_1962.fit(X_1962, y_1962)
y_pred_1962 = model_1962.predict(X_1962)
plt.scatter(X_1962, y_1962, label="Data")
plt.plot(X_1962, y_pred_1962, color="red", label="Regression Line")
plt.title("Cell 1962 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1963 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1963, y_1963 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=63)
model_1963 = LinearRegression()
model_1963.fit(X_1963, y_1963)
y_pred_1963 = model_1963.predict(X_1963)
plt.scatter(X_1963, y_1963, label="Data")
plt.plot(X_1963, y_pred_1963, color="red", label="Regression Line")
plt.title("Cell 1963 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1964 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1964, y_1964 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=64)
model_1964 = LinearRegression()
model_1964.fit(X_1964, y_1964)
y_pred_1964 = model_1964.predict(X_1964)
plt.scatter(X_1964, y_1964, label="Data")
plt.plot(X_1964, y_pred_1964, color="red", label="Regression Line")
plt.title("Cell 1964 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1965 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1965, y_1965 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=65)
model_1965 = LinearRegression()
model_1965.fit(X_1965, y_1965)
y_pred_1965 = model_1965.predict(X_1965)
plt.scatter(X_1965, y_1965, label="Data")
plt.plot(X_1965, y_pred_1965, color="red", label="Regression Line")
plt.title("Cell 1965 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1966 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1966, y_1966 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=66)
model_1966 = LinearRegression()
model_1966.fit(X_1966, y_1966)
y_pred_1966 = model_1966.predict(X_1966)
plt.scatter(X_1966, y_1966, label="Data")
plt.plot(X_1966, y_pred_1966, color="red", label="Regression Line")
plt.title("Cell 1966 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1967 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1967, y_1967 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=67)
model_1967 = LinearRegression()
model_1967.fit(X_1967, y_1967)
y_pred_1967 = model_1967.predict(X_1967)
plt.scatter(X_1967, y_1967, label="Data")
plt.plot(X_1967, y_pred_1967, color="red", label="Regression Line")
plt.title("Cell 1967 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1968 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1968, y_1968 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=68)
model_1968 = LinearRegression()
model_1968.fit(X_1968, y_1968)
y_pred_1968 = model_1968.predict(X_1968)
plt.scatter(X_1968, y_1968, label="Data")
plt.plot(X_1968, y_pred_1968, color="red", label="Regression Line")
plt.title("Cell 1968 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1969 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1969, y_1969 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=69)
model_1969 = LinearRegression()
model_1969.fit(X_1969, y_1969)
y_pred_1969 = model_1969.predict(X_1969)
plt.scatter(X_1969, y_1969, label="Data")
plt.plot(X_1969, y_pred_1969, color="red", label="Regression Line")
plt.title("Cell 1969 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1970 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1970, y_1970 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=70)
model_1970 = LinearRegression()
model_1970.fit(X_1970, y_1970)
y_pred_1970 = model_1970.predict(X_1970)
plt.scatter(X_1970, y_1970, label="Data")
plt.plot(X_1970, y_pred_1970, color="red", label="Regression Line")
plt.title("Cell 1970 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1971 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1971, y_1971 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=71)
model_1971 = LinearRegression()
model_1971.fit(X_1971, y_1971)
y_pred_1971 = model_1971.predict(X_1971)
plt.scatter(X_1971, y_1971, label="Data")
plt.plot(X_1971, y_pred_1971, color="red", label="Regression Line")
plt.title("Cell 1971 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1972 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1972, y_1972 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=72)
model_1972 = LinearRegression()
model_1972.fit(X_1972, y_1972)
y_pred_1972 = model_1972.predict(X_1972)
plt.scatter(X_1972, y_1972, label="Data")
plt.plot(X_1972, y_pred_1972, color="red", label="Regression Line")
plt.title("Cell 1972 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1973 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1973, y_1973 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=73)
model_1973 = LinearRegression()
model_1973.fit(X_1973, y_1973)
y_pred_1973 = model_1973.predict(X_1973)
plt.scatter(X_1973, y_1973, label="Data")
plt.plot(X_1973, y_pred_1973, color="red", label="Regression Line")
plt.title("Cell 1973 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1974 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1974, y_1974 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=74)
model_1974 = LinearRegression()
model_1974.fit(X_1974, y_1974)
y_pred_1974 = model_1974.predict(X_1974)
plt.scatter(X_1974, y_1974, label="Data")
plt.plot(X_1974, y_pred_1974, color="red", label="Regression Line")
plt.title("Cell 1974 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1975 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1975, y_1975 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=75)
model_1975 = LinearRegression()
model_1975.fit(X_1975, y_1975)
y_pred_1975 = model_1975.predict(X_1975)
plt.scatter(X_1975, y_1975, label="Data")
plt.plot(X_1975, y_pred_1975, color="red", label="Regression Line")
plt.title("Cell 1975 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1976 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1976, y_1976 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=76)
model_1976 = LinearRegression()
model_1976.fit(X_1976, y_1976)
y_pred_1976 = model_1976.predict(X_1976)
plt.scatter(X_1976, y_1976, label="Data")
plt.plot(X_1976, y_pred_1976, color="red", label="Regression Line")
plt.title("Cell 1976 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1977 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1977, y_1977 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=77)
model_1977 = LinearRegression()
model_1977.fit(X_1977, y_1977)
y_pred_1977 = model_1977.predict(X_1977)
plt.scatter(X_1977, y_1977, label="Data")
plt.plot(X_1977, y_pred_1977, color="red", label="Regression Line")
plt.title("Cell 1977 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1978 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1978, y_1978 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=78)
model_1978 = LinearRegression()
model_1978.fit(X_1978, y_1978)
y_pred_1978 = model_1978.predict(X_1978)
plt.scatter(X_1978, y_1978, label="Data")
plt.plot(X_1978, y_pred_1978, color="red", label="Regression Line")
plt.title("Cell 1978 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1979 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1979, y_1979 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=79)
model_1979 = LinearRegression()
model_1979.fit(X_1979, y_1979)
y_pred_1979 = model_1979.predict(X_1979)
plt.scatter(X_1979, y_1979, label="Data")
plt.plot(X_1979, y_pred_1979, color="red", label="Regression Line")
plt.title("Cell 1979 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1980 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1980, y_1980 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=80)
model_1980 = LinearRegression()
model_1980.fit(X_1980, y_1980)
y_pred_1980 = model_1980.predict(X_1980)
plt.scatter(X_1980, y_1980, label="Data")
plt.plot(X_1980, y_pred_1980, color="red", label="Regression Line")
plt.title("Cell 1980 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1981 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1981, y_1981 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=81)
model_1981 = LinearRegression()
model_1981.fit(X_1981, y_1981)
y_pred_1981 = model_1981.predict(X_1981)
plt.scatter(X_1981, y_1981, label="Data")
plt.plot(X_1981, y_pred_1981, color="red", label="Regression Line")
plt.title("Cell 1981 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1982 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1982, y_1982 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=82)
model_1982 = LinearRegression()
model_1982.fit(X_1982, y_1982)
y_pred_1982 = model_1982.predict(X_1982)
plt.scatter(X_1982, y_1982, label="Data")
plt.plot(X_1982, y_pred_1982, color="red", label="Regression Line")
plt.title("Cell 1982 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1983 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1983, y_1983 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=83)
model_1983 = LinearRegression()
model_1983.fit(X_1983, y_1983)
y_pred_1983 = model_1983.predict(X_1983)
plt.scatter(X_1983, y_1983, label="Data")
plt.plot(X_1983, y_pred_1983, color="red", label="Regression Line")
plt.title("Cell 1983 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1984 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1984, y_1984 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=84)
model_1984 = LinearRegression()
model_1984.fit(X_1984, y_1984)
y_pred_1984 = model_1984.predict(X_1984)
plt.scatter(X_1984, y_1984, label="Data")
plt.plot(X_1984, y_pred_1984, color="red", label="Regression Line")
plt.title("Cell 1984 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1985 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1985, y_1985 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=85)
model_1985 = LinearRegression()
model_1985.fit(X_1985, y_1985)
y_pred_1985 = model_1985.predict(X_1985)
plt.scatter(X_1985, y_1985, label="Data")
plt.plot(X_1985, y_pred_1985, color="red", label="Regression Line")
plt.title("Cell 1985 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1986 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1986, y_1986 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=86)
model_1986 = LinearRegression()
model_1986.fit(X_1986, y_1986)
y_pred_1986 = model_1986.predict(X_1986)
plt.scatter(X_1986, y_1986, label="Data")
plt.plot(X_1986, y_pred_1986, color="red", label="Regression Line")
plt.title("Cell 1986 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1987 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1987, y_1987 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=87)
model_1987 = LinearRegression()
model_1987.fit(X_1987, y_1987)
y_pred_1987 = model_1987.predict(X_1987)
plt.scatter(X_1987, y_1987, label="Data")
plt.plot(X_1987, y_pred_1987, color="red", label="Regression Line")
plt.title("Cell 1987 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1988 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1988, y_1988 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=88)
model_1988 = LinearRegression()
model_1988.fit(X_1988, y_1988)
y_pred_1988 = model_1988.predict(X_1988)
plt.scatter(X_1988, y_1988, label="Data")
plt.plot(X_1988, y_pred_1988, color="red", label="Regression Line")
plt.title("Cell 1988 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1989 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1989, y_1989 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=89)
model_1989 = LinearRegression()
model_1989.fit(X_1989, y_1989)
y_pred_1989 = model_1989.predict(X_1989)
plt.scatter(X_1989, y_1989, label="Data")
plt.plot(X_1989, y_pred_1989, color="red", label="Regression Line")
plt.title("Cell 1989 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1990 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1990, y_1990 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=90)
model_1990 = LinearRegression()
model_1990.fit(X_1990, y_1990)
y_pred_1990 = model_1990.predict(X_1990)
plt.scatter(X_1990, y_1990, label="Data")
plt.plot(X_1990, y_pred_1990, color="red", label="Regression Line")
plt.title("Cell 1990 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1991 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1991, y_1991 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=91)
model_1991 = LinearRegression()
model_1991.fit(X_1991, y_1991)
y_pred_1991 = model_1991.predict(X_1991)
plt.scatter(X_1991, y_1991, label="Data")
plt.plot(X_1991, y_pred_1991, color="red", label="Regression Line")
plt.title("Cell 1991 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1992 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1992, y_1992 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=92)
model_1992 = LinearRegression()
model_1992.fit(X_1992, y_1992)
y_pred_1992 = model_1992.predict(X_1992)
plt.scatter(X_1992, y_1992, label="Data")
plt.plot(X_1992, y_pred_1992, color="red", label="Regression Line")
plt.title("Cell 1992 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1993 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1993, y_1993 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=93)
model_1993 = LinearRegression()
model_1993.fit(X_1993, y_1993)
y_pred_1993 = model_1993.predict(X_1993)
plt.scatter(X_1993, y_1993, label="Data")
plt.plot(X_1993, y_pred_1993, color="red", label="Regression Line")
plt.title("Cell 1993 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1994 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1994, y_1994 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=94)
model_1994 = LinearRegression()
model_1994.fit(X_1994, y_1994)
y_pred_1994 = model_1994.predict(X_1994)
plt.scatter(X_1994, y_1994, label="Data")
plt.plot(X_1994, y_pred_1994, color="red", label="Regression Line")
plt.title("Cell 1994 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1995 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1995, y_1995 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=95)
model_1995 = LinearRegression()
model_1995.fit(X_1995, y_1995)
y_pred_1995 = model_1995.predict(X_1995)
plt.scatter(X_1995, y_1995, label="Data")
plt.plot(X_1995, y_pred_1995, color="red", label="Regression Line")
plt.title("Cell 1995 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1996 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1996, y_1996 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=96)
model_1996 = LinearRegression()
model_1996.fit(X_1996, y_1996)
y_pred_1996 = model_1996.predict(X_1996)
plt.scatter(X_1996, y_1996, label="Data")
plt.plot(X_1996, y_pred_1996, color="red", label="Regression Line")
plt.title("Cell 1996 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1997 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1997, y_1997 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=97)
model_1997 = LinearRegression()
model_1997.fit(X_1997, y_1997)
y_pred_1997 = model_1997.predict(X_1997)
plt.scatter(X_1997, y_1997, label="Data")
plt.plot(X_1997, y_pred_1997, color="red", label="Regression Line")
plt.title("Cell 1997 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1998 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1998, y_1998 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=98)
model_1998 = LinearRegression()
model_1998.fit(X_1998, y_1998)
y_pred_1998 = model_1998.predict(X_1998)
plt.scatter(X_1998, y_1998, label="Data")
plt.plot(X_1998, y_pred_1998, color="red", label="Regression Line")
plt.title("Cell 1998 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 1999 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_1999, y_1999 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=99)
model_1999 = LinearRegression()
model_1999.fit(X_1999, y_1999)
y_pred_1999 = model_1999.predict(X_1999)
plt.scatter(X_1999, y_1999, label="Data")
plt.plot(X_1999, y_pred_1999, color="red", label="Regression Line")
plt.title("Cell 1999 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
# Cell 2000 - scikit-learn analytics
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X_2000, y_2000 = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=0)
model_2000 = LinearRegression()
model_2000.fit(X_2000, y_2000)
y_pred_2000 = model_2000.predict(X_2000)
plt.scatter(X_2000, y_2000, label="Data")
plt.plot(X_2000, y_pred_2000, color="red", label="Regression Line")
plt.title("Cell 2000 - Scikit-learn Linear Regression")
plt.legend()
plt.show()
Score: 2000
Category: basics