How Do I Perform Different Types of Pandas DataFrames Joins?

Pandas Merging 101: The Basics

JOINing DataFrames

Merging DataFrames in Pandas involves combining two or more DataFrames based on common key columns. There are several types of joins, including:

• INNER JOIN: Returns only rows where keys match in both DataFrames.

  • df1.merge(df2, on='key', how='inner')

• LEFT JOIN: Includes all rows from the left DataFrame, and matching rows from the right DataFrame. Missing values from the right are filled with NaNs.

  • df1.merge(df2, on='key', how='left')

• RIGHT JOIN: Includes all rows from the right DataFrame, and matching rows from the left DataFrame. Missing values from the left are filled with NaNs.

  • df1.merge(df2, on='key', how='right')

• FULL OUTER JOIN: Includes all rows from both DataFrames, filling missing values with NaNs.

  • df1.merge(df2, on='key', how='outer')

Different Key Column Names

If key columns have different names, use left_on and right_on arguments:

• df1.merge(df2, left_on='key1', right_on='key2', how='inner')

Avoiding Duplicate Key Columns in Output

When merging on different key columns, set the index as a preliminary step:

• df1.set_index('key1').merge(df2, left_index=True, right_on='key2')

Merging on Multiple Columns

Join on multiple columns by passing a list to on (or left_on and right_on):

• df1.merge(df2, on=['key1', 'key2'], how='inner')

Generalizing to Multiple DataFrames

To merge multiple DataFrames, use pd.merge_asof for approximate joins or pd.merge_ordered for ordered joins.

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