import pandas as pd

data = {
    'city' : ['Toronto', 'Montreal', 'Waterloo'],
    'points' : [80, 70, 90]
}

data

{'city': ['Toronto', 'Montreal', 'Waterloo'], 'points': [80, 70, 90]}

type(data)

dict

df = pd.DataFrame(data)

df

df = df.assign(code = [1, 2, 3])

df

import numpy as np
import pandas as pd
import time
from numpy.linalg import inv, det, eig, norm

arr_1 = np.arange(6).reshape(2, 3)  # 6 is divisible by 2
print(arr_1)

[[0 1 2]
 [3 4 5]]

# Cell 2 – Array Creation
arr_2 = np.arange(2*3).reshape(3, -1 …

from datetime import datetime

def get_age(d):
    d1 = datetime.now()
    months = (d1.year - d.year) * 12 + d1.month - d.month

    year = int(months / 12)
    return year

age = get_age(datetime(1991, 1, 1))

age

33

import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

df = pd.read_csv(r'C:\Users\HP\Desktop\amazon_products.csv')
df

import pandas as pd
import numpy as np
df = pd.DataFrame({'value': np.append(np.random.normal(0, 1, 20), [10])})
threshold = 3
anomalies = df[np.abs(df['value']) > threshold]
anomalies

import pandas as pd
import numpy as np
df = pd.DataFrame({'value': np.append(np.random.normal(0, 1 …

# Declare variable
x = 5

# Print variable
print(x)

5

# Update variable
x = 10

# Print new value
print("Updated x:", x)

Updated x: 10

text = "Hello Sara"

print(text)

Hello Sara

print("Length:", len(text))

Length: 10

print("Upper:", text.upper())

Upper: HELLO SARA

print("Replace:", text.replace("Sara", "Queen"))

Replace …

import streamlit as st

st.title("⚖️ BMI Calculator")

2025-06-29 18:18:21.810 WARNING streamlit.runtime.scriptrunner_utils.script_run_context: Thread 'MainThread': missing ScriptRunContext! This warning can be ignored when running in bare mode.
2025-06-29 18:18:22.090 
  Warning: to view this Streamlit app on …

# Cell 1 - Data Abstraction Example
class Example1:
    def __init__(self, value):
        self.__value = value

    def get_value(self):
        return self.__value * 1

obj1 = Example1(1)
print(obj1.get_value())

1

# Cell 2 - Data Abstraction Example
class Example2:
    def __init__(self, value):
        self.__value = value

    def get_value(self):
        return self.__value * 2

obj2 …

import pandas as pd
df = pd.DataFrame({'group': ['A', 'B', 'A', 'B'], 'value': [1, 2, 3, 2]})
df.groupby('group').agg(['sum', 'mean'])

import pandas as pd
df = pd.DataFrame({'group': ['A', 'B', 'A', 'B'], 'value': [1, 2, 3, 3]})
df.groupby('group').agg(['sum', 'mean'])

import pandas as pd …

# 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 …