Merging on multiple keys in Data Analysis Python - Cheat Sheet & Quick Revision

Recall & Review

beginner

What does merging on multiple keys mean in data analysis?

It means combining two tables or datasets by matching rows based on more than one column, like matching by both 'city' and 'year' together.

beginner

How do you specify multiple keys when merging two pandas DataFrames?

You pass a list of column names to the 'on' parameter, like df1.merge(df2, on=['key1', 'key2']).

intermediate

What happens if the keys you merge on have different names in the two DataFrames?

You use 'left_on' and 'right_on' parameters to specify the key columns separately for each DataFrame.

beginner

Why is merging on multiple keys useful?

It helps to join data more precisely when one key alone is not enough to uniquely identify matching rows.

beginner

What type of join can you perform when merging on multiple keys?

You can perform inner, left, right, or outer joins, just like merging on a single key.

How do you merge two DataFrames on columns 'A' and 'B' in pandas?

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

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

C df1.merge(df2, keys=['A', 'B'])

D df1.merge(df2, columns=['A', 'B'])

You must pass a list of column names to the 'on' parameter.

If the key columns have different names in two DataFrames, which parameters do you use?

A on and how

B left_on and right_on

C keys and values

D columns and index

Use 'left_on' for the left DataFrame keys and 'right_on' for the right DataFrame keys.

What join type returns only rows with matching keys in both DataFrames?

A left join

B outer join

C right join

D inner join

Inner join returns rows where keys match in both DataFrames.

Why merge on multiple keys instead of just one?

A To match rows more precisely when one key is not unique

B To speed up the merge

C To reduce memory usage

D To avoid using indexes

Multiple keys help uniquely identify matching rows.

What happens if you merge on keys that do not exist in both DataFrames?

A Pandas merges only on existing keys

B Pandas ignores missing keys

C Pandas raises an error

D Pandas merges anyway with NaNs

Pandas raises a KeyError if keys are missing.

Explain how to merge two DataFrames on multiple keys with different column names.

Think about specifying keys separately for each DataFrame.

Describe why merging on multiple keys can improve data matching accuracy.

Consider when one key alone is not enough.