Left join behavior in Pandas - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to perform a left join of df1 and df2 on the 'key' column.

Pandas

result = df1.merge(df2, how=[1], on='key')

Drag options to blanks, or click blank then click option'

A'outer'

B'inner'

C'right'

D'left'

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'inner' instead of 'left' will drop rows not matching in df2.

Using 'right' or 'outer' will change which rows are kept.

The left join keeps all rows from the left DataFrame (df1) and adds matching rows from df2.

2fill in blank

medium

Complete the code to join df1 and df2 on columns 'key1' and 'key2' using a left join.

Pandas

result = df1.merge(df2, how='left', on=[1])

Drag options to blanks, or click blank then click option'

A'key'

B'key2'

C['key1', 'key2']

D['key']

Attempts:

3 left

💡 Hint

Common Mistakes

Passing a single string instead of a list for multiple keys.

Using incorrect column names.

To join on multiple columns, pass a list of column names to the on parameter.

3fill in blank

hard

Fix the error in the code to perform a left join on 'id' column.

Pandas

result = df1.merge(df2, how='[1]', on='id')

Drag options to blanks, or click blank then click option'

Aleftt

Bleft

Clef

Dright

Attempts:

3 left

💡 Hint

Common Mistakes

Typo in the join type string.

Using 'right' instead of 'left' changes the join behavior.

The correct spelling for the left join is 'left'. Misspelling causes an error.

4fill in blank

hard

Fill both blanks to create a left join that adds suffix '_df2' to overlapping columns from df2.

Pandas

result = df1.merge(df2, how=[1], on='key', suffixes=([2], '_df2'))

Drag options to blanks, or click blank then click option'

A'left'

B'_df1'

C'_left'

D'_right'

Attempts:

3 left

💡 Hint

Common Mistakes

Using wrong join type like 'right' or 'inner'.

Not providing suffixes or using wrong suffix format.

Use how='left' for left join and suffixes=('_df1', '_df2') to rename overlapping columns.

5fill in blank

hard

Fill all three blanks to left join df1 and df2 on 'key', keep all df1 rows, and fill missing values in 'value' column with 0.

Pandas

result = df1.merge(df2, how=[1], on=[2])
result['value'] = result['value'].fillna([3])

Drag options to blanks, or click blank then click option'

A'left'

B'key'

C0

D'value'

Attempts:

3 left

💡 Hint

Common Mistakes

Using wrong join type like 'inner'.

Joining on wrong column name.

Not filling missing values or using wrong fill value.

Use how='left' to keep all rows from df1, join on 'key', and fill missing 'value' with 0 using fillna(0).