In Pandas join() and merge() functions are used to combine dataframes. If both have the same functionality then what is the difference between those functions?
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In Pandas join() and merge() functions are used to combine dataframes. If both have the same functionality then what is the difference between those functions?
Merge is a pandas namespace function that is also available as a DataFrame instance method, with the caller DataFrame being considered the left object in the join.
The DataFrame.join function, which is closely related to merge, uses merge internally for index-on-index and index-on-column(s) joins, but joins on indexes by default rather than trying to join on common columns (the default behavior for merge). If you’re joining on index, you can save yourself some typing by using DataFrame.join.
DataFrames provide the
pandas.DataFrame.merge()and pandas.DataFrame.join() methods as a convenient way to access the capabilities ofpandas.merge().The main difference between them are:
When using lookup on right table
df1.join(df2) always joins via the index of df2, butdf1.merge(df2)can join to one or more columns of df2 (default) or to the index ofdf2 (with right_index=True).While using lookup on left table: by default, df1.join(df2) uses the index of df1 and
df1.merge(df2)uses column(s) of df1. That can be overridden by specifyingdf1.join(df2, on=key_or_keys)ordf1.merge(df2, left_index=True).The main difference is that merge creates a new index, whereas join keeps the left side index. If you incorrectly assume that merge does not affect your index, it can have a significant impact on your subsequent changes.
As the entry point for all typical database join operations between DataFrame objects, pandas provides a single function, merge. Whereas,
DataFrame.join is an easy way to combine the columns of two potentially different-indexed DataFrames into a single result DataFrame.
Both join and merge can be used to combine two dataframes, but the join method combines two dataframes based on their indexes, whereas the merge technique is more adaptable and allows us to select columns to join on for both dataframes in addition to the index.
When we compare this with SQL Pandas merge is to Outer/Inner Join and Pandas join is to Natural Join. As a result, when you use pandas merge, you should indicate the type of sql join you want to use, whereas when you use pandas join, you should have a matching column label to ensure it joins.