Merging on different column names in Pandas - Cheat Sheet & Quick Revision

Recall & Review

beginner

What does merging on different column names mean in pandas?

It means joining two tables where the columns to match have different names in each table. You tell pandas which columns to use from each table.

beginner

Which pandas function is used to merge DataFrames on columns with different names?

The pandas function merge() is used with the parameters left_on and right_on to specify the different column names to join on.

beginner

How do you merge two DataFrames where the first has column 'A' and the second has column 'B' to join on?

Use pd.merge(df1, df2, left_on='A', right_on='B'). This tells pandas to match rows where df1's 'A' equals df2's 'B'.

intermediate

What happens if you merge on different column names without specifying left_on and right_on?

Pandas will look for columns with the same name in both DataFrames to merge on. If none match, it will raise an error.

intermediate

Can you merge on multiple columns with different names in pandas?

Yes. You can pass lists to left_on and right_on with matching positions, like left_on=['A','C'], right_on=['B','D'].

Which parameters do you use to merge on columns with different names in pandas?

A index and columns

B on and how

C left_on and right_on

D join and merge

Use left_on and right_on to specify different column names to merge on.

What happens if you try to merge two DataFrames with no common column names and don't specify left_on or right_on?

A Pandas merges on the index by default

B Pandas merges on the first column

C Pandas merges on all columns

D Pandas raises an error

Pandas raises an error because it cannot find columns to merge on.

How do you merge on multiple columns with different names?

A Pass lists to left_on and right_on

B Use on with a list of columns

C Use index_col parameter

D Merge twice

You pass lists of column names to left_on and right_on to merge on multiple columns.

If df1 has column 'key1' and df2 has column 'key2', how do you merge on these?

A pd.merge(df1, df2, left_on='key1', right_on='key2')

B pd.merge(df1, df2, on='key1')

C pd.merge(df1, df2, on='key2')

D pd.merge(df1, df2)

You specify left_on and right_on with the different column names.

Which of these is NOT a valid way to merge DataFrames on different column names?

A pd.merge(df1, df2, left_on='A', right_on='B')

B pd.merge(df1, df2, on=['A','B'])

C pd.merge(df1, df2, left_on=['A','C'], right_on=['B','D'])

D pd.merge(df1, df2, left_on='A', right_on='B', how='inner')

The 'on' parameter expects the same column names in both DataFrames, so using different names in a list is invalid.

Explain how to merge two pandas DataFrames when the columns to join on have different names.

Think about telling pandas which columns to match from each table.

Describe what happens if you try to merge DataFrames without common column names and without specifying left_on and right_on.

What does pandas do when it can't find matching columns?