How to Merge DataFrames by Index in Python?

Merge Dataframes by Index

When working with dataframes, it is often necessary to combine them based on matching indices. While merge operations typically rely on column matches, it is possible to merge dataframes based on their indices.

Inner Join on Indices

To merge two dataframes by index using an inner join, you can use the merge function with the left_index and right_index arguments set to True:

pd.merge(left_dataframe, right_dataframe, left_index=True, right_index=True)

This operation will create a new dataframe that contains only the rows where the indices of the two dataframes match.

Example:

Consider the following dataframes:

df1
  id  begin  conditional  confidence  discoveryTechnique
0  278    56       false        0.0                  1   
1  421    18       false        0.0                  1 

df2
   concept
0     A 
1     B

Merging these dataframes by index would result in:

  id  begin  conditional  confidence  discoveryTechnique  concept 
0  278    56       false        0.0                  1       A 
1  421    18       false        0.0                  1       B

Left Join on Indices

For a left join by index, you can use the join method on the left dataframe:

left_dataframe.join(right_dataframe, on='index')

Outer Join on Indices

To perform an outer join on indices, you can use the concat function with the axis argument set to 1:

pd.concat([left_dataframe, right_dataframe], axis=1)

Considerations

While it is generally possible to merge dataframes by index, it is important to note that this can result in duplicate rows if the indices are not unique across both dataframes. In such cases, it may be necessary to first ensure that the indices are unique before merging.

The above is the detailed content of How to Merge DataFrames by Index in Python?. For more information, please follow other related articles on the PHP Chinese website!