How to Perform Different Types of Joins and Handle Missing Data in Pandas?

Pandas Merging 101

Merging Basics - Basic Types of Joins

How to perform a (INNER| (LEFT|RIGHT|FULL) OUTER) JOIN with pandas?

To perform a merge operation, use the merge method on a DataFrame. Specify the other DataFrame and the merge keys as arguments. The different types of joins are:

• INNER JOIN: Joins rows that share the same values in the merge key.
• LEFT OUTER JOIN: Retains all rows from the left DataFrame and inserts missing values for rows in the right DataFrame.
• RIGHT OUTER JOIN: Retains all rows from the right DataFrame and inserts missing values for rows in the left DataFrame.
• FULL OUTER JOIN: Combines all rows from both DataFrames, inserting missing values for any missing overlaps.

How do I add NaNs for missing rows after a merge?

Missing data in the right DataFrame after a LEFT OUTER JOIN or in the left DataFrame after a RIGHT OUTER JOIN are replaced with NaNs by default.

How do I get rid of NaNs after merging?

NaNs can be removed using filtering or by using the fillna() method to replace them with a desired value.

Can I merge on the index?

Yes, you can merge on the index by setting the index as the merge key using the left_index and right_index parameters.

How do I merge multiple DataFrames?

Multiple DataFrames can be merged by calling merge multiple times or by using the pd.concat function.

Cross join with pandas

To perform a cross join, which combines every row from one DataFrame with every row from another, use the pd.merge function without specifying a merge key.

merge? join? concat? update? Who? What? Why?!!

The following table summarizes the differences between these operations:

Operation

Purpose

Operation	Purpose
merge	Join DataFrames based on common keys
join	Alias for merge
concat	Concatenate DataFrames along a specific axis
update	Update one DataFrame with values from another

merge

Join DataFrames based on common keys

join

Alias for merge

concat

Concatenate DataFrames along a specific axis

update

Update one DataFrame with values from another

The above is the detailed content of How to Perform Different Types of Joins and Handle Missing Data in Pandas?. For more information, please follow other related articles on the PHP Chinese website!