Home >Backend Development >Python Tutorial >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!