How to perform JOIN and MERGE in Pandas DataFrame in Python

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How to perform JOIN and MERGE in Pandas DataFrame in Python

JOIN and MERGE are two commonly used operations when working with multiple DataFrames in Pandas. These operations allow you to combine multiple DataFrames based on the values in one or more columns. In this blog, we will go over the basic concepts of JOIN and MERGE, and how to use them in Pandas.

The basic difference between JOIN and MERGE is the way they combine DataFrames. JOIN is used to combine DataFrames based on the values in one or more columns, while MERGE is used to combine DataFrames based on one or more common columns.

JOIN can be accomplished using the .merge() method in Pandas. The basic syntax is:

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

 

This will join the DataFrames df1 and df2 on the ‘key’ column. By default, it will perform an inner join, meaning that only the rows that have matching values in the ‘key’ column will be included in the resulting DataFrame.

You can also perform left, right, and outer joins by setting the how parameter to 'left', 'right', or 'outer' respectively. For example, to perform a left join, you would use the following code:

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

 

On the other hand, MERGE is used to combine DataFrames based on one or more common columns. This can be accomplished using the .merge() method as well. The basic syntax is:

pd.merge(df1, df2, left_on='key1', right_on='key2')

 

This will merge the DataFrames df1 and df2 based on the values in the ‘key1’ column in df1 and the ‘key2’ column in df2.

You can also specify more than one column for the left_on and right_on parameters, in this case, both DataFrames need to have these columns with the same name, otherwise, you should pass a list of columns to left_on, right_on or suffixes

pd.merge(df1, df2, left_on=['key1','key2'], right_on=['key3','key4'])

 

It’s worth noting that when working with large datasets, the JOIN and MERGE operations can be computationally expensive, it’s important to choose the right columns to join on and to optimize the performance of these operations. Also, it’s important to validate the correctness of the resulting DataFrame after these operations.

 

In conclusion, JOIN and MERGE are two commonly used operations when working with multiple DataFrames in Pandas. They allow you to combine DataFrames based on the values in one or more columns. The .merge() method is used to JOIN and MERGE DataFrames, and it provides several options to control the way the DataFrames are combined, such as the on, left_on, right_on, how, and suffixes parameters.

 

In this Learn through Codes example, you will learn: How to perform JOIN and MERGE in Pandas DataFrame in Python.



 

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