ADAP2

import pandas as pd

df = pd.DataFrame({
    'A': [10, 20, 30],
    'B': [1, 2, 3]
})
print(df)
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df = pd.DataFrame({
    'A': [10, 20, 30]})
df['A_squared'] = df['A'].apply(lambda x: x**2)
print(df['A_squared'])
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# Sum of A and B row-wise
df = pd.DataFrame({
    'A': [10, 20, 30],
    'B': [1, 2, 3]
})
df['Row_Sum'] = df.apply(lambda row: row['A'] + row['B'], axis=1)
print(df['Row_Sum'])
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df_csv = pd.read_csv('data.csv')
print(df_csv)
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df_excel = pd.read_excel('data.xlsx')
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df_json = pd.read_json('data.json')
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import sqlite3
conn = sqlite3.connect('data.db')
df_sql = pd.read_sql_query("SELECT * FROM my_table", conn)
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import pandas as pd
df = pd.DataFrame({
    'ID': [1, 2, 4],
    'Marks': [85, 90, 78]
})
df.describe()      # Summary statistics
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df.mean()
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df.median()          # Median
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df.std()             # Standard deviation
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df.var()             # Variance
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df.min(), df.max()   # Min, Max
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df.corr()            # Correlation matrix
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import pandas as pd

# Sample DataFrame with Category
df3 = pd.DataFrame({
    'Item': ['Apple', 'Banana', 'Carrot',
             'Banana','Apple', 'Carrot'],
    'Category': ['Fruit', 'Fruit', 'Vegetable',
                 'Fruit', 'Fruit', 'Vegetable'],
    'Quantity': [10, 5, 8, 6, 7, 3],
    'Price': [15, 10, 12, 10, 15, 12]
})

print(df3)
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grouped = df3.groupby('Category').sum()
print(grouped)
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df1 = pd.DataFrame({
    'ID': [1, 2, 3],
    'Name': ['Alice', 'Bob', 'Charlie']
})

df2 = pd.DataFrame({
    'ID': [1, 2, 4],
    'Marks': [85, 90, 78]
})
df_combined = pd.concat([df1, df2], axis=0)   # Row-wise
print(df_combined)
df_merged = pd.concat([df1, df2], axis=1)     # Column-wise
print(df_merged)
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