Pandas Joining and merging DataFrame
Pandas provide various facilities for easily combining Series or DataFrame with various kinds of set logic for the indexes and relational algebra functionality in the case of join / merge-type operations.
Both join and merge can be used to combines two dataframes but the join method combines two dataframes on the basis of their indexes whereas the merge method is more versatile and allows us to specify columns beside the index to join on for both dataframes.
join
The join method takes two dataframes and joins them on their indexes (technically, you can pick the column to join on for the left dataframe). If there are overlapping columns, the join will want you to add a suffix to the overlapping column name from the left dataframe. Our two dataframes do have an overlapping column name P.
merge
At a basic level, merge more or less does the same thing as join. Both methods are used to combine two dataframes together, but merge is more versatile, it requires specifying the columns as a merge key. We can specify the overlapping columns with parameter on, or can separately specify it with left_on and right_on parameters.
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👉EXAMPLE PROGRAMS ON PANDAS JOINING AND MERGING DATA FRAME👈
