Right join behavior in Pandas - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ query_result

intermediate

What is the output of this right join?

Given two DataFrames df1 and df2, what is the result of df1.merge(df2, how='right', on='key')?

Pandas

import pandas as pd

df1 = pd.DataFrame({'key': ['A', 'B', 'C'], 'val1': [1, 2, 3]})
df2 = pd.DataFrame({'key': ['B', 'C', 'D'], 'val2': [4, 5, 6]})

result = df1.merge(df2, how='right', on='key')
print(result)

A

  key  val1  val2
0   A   1.0     NaN
1   B   2.0     4
2   D   NaN     6

B

  key  val1  val2
0   A   1.0     NaN
1   B   2.0     4
2   C   3.0     5

C

  key  val1  val2
0   B   2.0     4
1   C   3.0     5
2   D   NaN     6

D

  key  val1  val2
0   B   2.0     4
1   C   3.0     5

💡 Hint

Remember, a right join keeps all rows from the right DataFrame and matches rows from the left.

The right join keeps all keys from df2. Keys 'B', 'C', and 'D' are in df2. For 'D', there is no match in df1, so val1 is NaN.

🧠 Conceptual

intermediate

Which statement best describes a right join?

Choose the correct description of what a right join does in pandas merge.

A Returns all rows from the left DataFrame and matching rows from the right DataFrame.

B Returns all rows from the right DataFrame and matching rows from the left DataFrame.

C Returns only rows that have matching keys in both DataFrames.

D Returns all rows from both DataFrames, filling missing matches with NaN.

💡 Hint

Think about which DataFrame's rows are always kept in a right join.

A right join keeps all rows from the right DataFrame and matches rows from the left DataFrame where keys match. Unmatched left rows are dropped.

📝 Syntax

advanced

Which code correctly performs a right join on columns 'id' and 'code'?

Given two DataFrames df1 and df2, which code snippet correctly performs a right join on columns 'id' and 'code'?

Pandas

import pandas as pd

# df1 and df2 are predefined DataFrames

A df1.merge(df2, how='right', left_on=['id', 'code'], right_on=['id', 'code'])

B df1.merge(df2, how='right', left_on='id', right_on='code')

C df1.merge(df2, how='right', on='id', on='code')

D df1.merge(df2, how='right', on=['id', 'code'])

💡 Hint

Check the correct syntax for joining on multiple columns with the same names.

When joining on multiple columns with the same names in both DataFrames, use on=[...]. Using left_on and right_on is for different column names.

❓ optimization

advanced

How to optimize a right join when the right DataFrame is very large?

You have two DataFrames: df1 (small) and df2 (very large). You want to perform a right join. Which approach optimizes performance?

A Perform a left join with <code>df2</code> as left and <code>df1</code> as right, then rename columns accordingly.

B Perform a right join directly with <code>df1.merge(df2, how='right')</code> without changes.

C Convert both DataFrames to dictionaries and merge manually in Python.

D Sort both DataFrames by join keys before merging.

💡 Hint

Think about which DataFrame should be on the left for better performance.

Left joins are generally faster when the left DataFrame is large. Since df2 is large, making it the left DataFrame and performing a left join is more efficient than a right join.

🔧 Debug

expert

Why does this right join produce unexpected NaNs?

Consider this code:

df1 = pd.DataFrame({'key': ['A', 'B'], 'val1': [1, 2]})
df2 = pd.DataFrame({'key': ['a', 'b', 'c'], 'val2': [3, 4, 5]})
result = df1.merge(df2, how='right', on='key')

Why does result contain NaNs in val1 for all rows?

A Because the keys have different cases ('A' vs 'a'), so no matches occur.

B Because right join only keeps rows from the left DataFrame.

C Because the 'on' parameter is missing in the merge call.

D Because the DataFrames have different column names for the join key.

💡 Hint

Check if the join keys match exactly including case sensitivity.

Join keys are case sensitive. 'A' is not equal to 'a', so no rows match. The right join keeps all rows from df2, but val1 is NaN because no matching keys in df1.