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 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 …

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 …

# Imports
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
import pandas as pd
%matplotlib inline

# Sample Data
x = [1, 2, 3, 4, 5]
y = [10, 15, 13, 17, 20]

# DataFrame for seaborn
df = pd.DataFrame({
    'Day': ['Mon', 'Tue', 'Wed', 'Thu', 'Fri'],
    'Sales': [200, 300, 250 …

import pandas as pd  
from sklearn.model_selection import train_test_split
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.naive_bayes import MultinomialNB
import streamlit as st  

data = pd.read_csv('C:/Users/HP/Desktop/OIB-SIP/emailspam/dataset-mail/spam.csv', encoding='latin-1')

print("Original Shape:", data.shape)

Original Shape: (5572, 5)

print(data.head …

import pandas as pd
import matplotlib.pyplot as plt
df = pd.DataFrame({'values': [i*2 for i in range(10)]})
df.plot(kind='line')
plt.title('Line Plot 1')
plt.show()

import pandas as pd
import matplotlib.pyplot as plt
df = pd.DataFrame({'values': [i*3 for i in range …

import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
import streamlit as st

data = {
    "text": [
        "NASA confirms water on Mars",
        "You won't believe what this actor did!",
        "Government passes new education bill",
        "Scientists …

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

df = pd.read_csv(r'C:\datasets\ipl_2023_batting.csv')
df

import matplotlib.pyplot as plt
plt.plot([1, 2, 3, 4])
plt.show()

import matplotlib.pyplot as plt
plt.plot([1, 2, 3, 4], [1, 4, 9, 16])
plt.title('Plot 2: Line Plot')
plt.show()

import matplotlib.pyplot as plt
plt.bar([1,2,3], [3,7,5])
plt …