• Group data by a column.

df.groupby('col1')

• Group by a column and get the sum.

df.groupby('col1').sum()

• Apply multiple aggregation functions at once.

df.groupby('col1').agg(['mean', 'count'])

• Get the size of each group.

df.groupby('col1').size()

• Get the frequency counts of unique values in a Series.

df['col1'].value_counts()

• Create a pivot table.

pd.pivot_table(df, values='D', index=['A', 'B'], columns=['C'])

VI. Merging, Joining & Concatenating

• Merge two DataFrames (like a SQL join).

pd.merge(left_df, right_df, on='key_column')

• Concatenate (stack) DataFrames along an axis.

pd.concat([df1, df2]) # Stacks rows

• Join DataFrames on their indexes.

left_df.join(right_df, how='outer')

VII. Input & Output

• Write a DataFrame to a CSV file.

df.to_csv('output.csv', index=False)

• Write a DataFrame to an Excel file.

df.to_excel('output.xlsx', sheet_name='Sheet1')

• Read data from an Excel file.

pd.read_excel('input.xlsx', sheet_name='Sheet1')

• Read from a SQL database.

pd.read_sql_query('SELECT * FROM my_table', connection_object)

VIII. Time Series & Special Operations

• Use the string accessor (.str) for Series operations.

s.str.lower()
s.str.contains('pattern')

• Use the datetime accessor (.dt) for Series operations.

s.dt.year
s.dt.day_name()

• Create a rolling window calculation.

df['col1'].rolling(window=3).mean()

• Create a basic plot from a Series or DataFrame.

df['col1'].plot(kind='hist')

#Python #Pandas #DataAnalysis #DataScience #Programming

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By: @DataScienceM ✨

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