Mastering Pandas DataFrames: Seamless Data Integration with Joins

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Joining data in Pandas is a crucial skill for combining information from different sources. Here’s everything you need to know about Pandas joins:

1. Types of Joins:

• Inner Join (pd.merge()): Returns only the rows with matching keys in both DataFrames.

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

• Left Join (pd.merge()): Returns all rows from the left DataFrame and the matched rows from the right DataFrame.

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

• Right Join (pd.merge()): Returns all rows from the right DataFrame and the matched rows from the left DataFrame.

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

• Outer Join (pd.merge()): Returns all rows when there is a match in either the left or right DataFrame.

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

2. Key Columns:

• Specify the columns on which the DataFrames should be joined using the on parameter.
• For joining on multiple columns, pass a list of column names to the on parameter.

3. Suffixes:

• Use the suffixes parameter to add custom…