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.
Example :
Python3
joined_df = left.join(right, lsuffix='_')print(joined_df) |
Output :
Notice the index is preserved and we have four columns.We can also separately specify a specific column of the left dataframe with a parameter on to use as the join key, but it will still use the index from the right.
Example :
Python3
joined_df2 = left.reset_index().join(right, on='index', lsuffix='_')print(joined_df2) |
Output :
What is the difference between join and merge in Pandas?
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.
Let’s first create two dataframes to show the effect of the two methods.
Python3
import pandas as pd# Creating the two dataframesleft = pd.DataFrame([['a', 1], ['b', 2]], list('XY'), list('PQ'))right = pd.DataFrame([['c', 3], ['d', 4]], list('XY'), list('PR')) |
Output:
Now let’s see the effect of the two methods on the dataframes one by one.
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