Outer join behavior in Pandas - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

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

Pandas

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

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

Aouter

Bleft

Cright

Dinner

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'inner' join which only keeps matching rows.

Using 'left' or 'right' which keep only one side's rows.

Using how='outer' returns all rows from both DataFrames, matching where possible.

2fill in blank

medium

Complete the code to merge df1 and df2 on columns 'key1' and 'key2' with an outer join.

Pandas

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

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

A'key1', 'key2'

B'key2'

C'key1'

D['key1', 'key2']

Attempts:

3 left

💡 Hint

Common Mistakes

Passing a string with comma-separated keys instead of a list.

Passing only one key when two are needed.

The on parameter expects a list of column names for multiple keys.

3fill in blank

hard

Fix the error in the code to perform an outer join on 'id' column.

Pandas

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

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

A'id'

Bid

C['id']

Did'

Attempts:

3 left

💡 Hint

Common Mistakes

Passing the column name without quotes causing a NameError.

Using incorrect quotes or missing quotes.

The on parameter requires the column name as a string, so it must be quoted.

4fill in blank

hard

Fill both blanks to create an outer join and fill missing values with 0.

Pandas

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

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

Aouter

B{0}

C0

Dinner

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'inner' join which excludes unmatched rows.

Passing 0 directly to fillna instead of a dictionary.

Use how='outer' for full join and fillna({0}) to replace missing values with zero.

5fill in blank

hard

Fill all three blanks to perform an outer join on 'key', suffix overlapping columns with '_left' and '_right'.

Pandas

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

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

A'key'

B'outer'

C'_left'

D'_common'

Attempts:

3 left

💡 Hint

Common Mistakes

Not quoting the key name.

Using wrong join type.

Not providing suffixes as a tuple.

Use on='key', how='outer', and suffixes=('_left', '_right') to handle overlapping columns.