Inner join behavior in Pandas - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to perform an inner join of df1 and df2 on the 'key' column.

Pandas

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

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

A'value'

B'index'

C'key'

D'id'

Attempts:

3 left

💡 Hint

Common Mistakes

Using a column name that does not exist in both dataframes.

Using 'index' when the join is not on the index.

The on parameter specifies the column to join on. Here, 'key' is the correct column common to both dataframes.

2fill in blank

medium

Complete the code to specify the type of join as inner explicitly.

Pandas

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

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

A'outer'

B'inner'

C'left'

D'right'

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'outer' or 'left' which include non-matching rows.

Using 'right' which joins differently.

The how parameter defines the type of join. 'inner' returns only matching rows.

3fill in blank

hard

Fix the error in the code to join df1 and df2 on 'key' with inner join.

Pandas

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

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

A'name'

B'index'

C'value'

D'key'

Attempts:

3 left

💡 Hint

Common Mistakes

Using a column that does not exist in both dataframes.

Using 'index' when not joining on index.

The join column must exist in both dataframes. 'key' is the correct column here.

4fill in blank

hard

Fill both blanks to join df1 and df2 on 'key' and keep only matching rows.

Pandas

result = df1.merge(df2, on=[1], how=[2])

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

A'key'

B'inner'

C'outer'

D'left'

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'outer' or 'left' join which keep non-matching rows.

Using wrong column names.

Use 'key' for the join column and 'inner' to keep only rows with matching keys.

5fill in blank

hard

Fill all three blanks to join df1 and df2 on 'key', keep matching rows, and suffix overlapping columns with '_left' and '_right'.

Pandas

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

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

A'key'

B'inner'

C'_left', '_right'

D'_x', '_y'

Attempts:

3 left

💡 Hint

Common Mistakes

Not specifying suffixes causing column name conflicts.

Using wrong join type or column.

Use 'key' to join, 'inner' to keep matching rows, and suffixes ('_left', '_right') to distinguish overlapping columns.