Right join behavior in Pandas - Step-by-Step Execution

Concept Flow - Right join behavior

Start with two tables

↓

Identify keys to join on

↓

Match keys from right table to left table

↓

Keep all rows from right table

↓

Add matching rows from left table

↓

Fill missing left data with NaN

↓

Output combined table

Right join keeps all rows from the right table and matches rows from the left table where keys match, filling missing left data with NaN.

Execution Sample

Pandas

import pandas as pd
left = pd.DataFrame({'key': ['A', 'B'], 'val': [1, 2]})
right = pd.DataFrame({'key': ['B', 'C'], 'val': [3, 4]})
result = pd.merge(left, right, on='key', how='right', suffixes=('_left', '_right'))
print(result)

This code performs a right join on two small dataframes by the 'key' column.

Execution Table

Step	Action	Left Table Row	Right Table Row	Match?	Result Row
1	Check right row key 'B'	Left row with key 'B' exists	Right row key 'B'	Yes	{key: 'B', val_left: 2, val_right: 3}
2	Check right row key 'C'	Left row with key 'C' does not exist	Right row key 'C'	No	{key: 'C', val_left: NaN, val_right: 4}
3	No more right rows			Exit	Final result table formed

💡 All right table rows processed; right join includes all right rows with matched or NaN left data.

Variable Tracker

Variable	Start	After Step 1	After Step 2	Final
result_rows	[]	[{key: 'B', val_left: 2, val_right: 3}]	[{key: 'B', val_left: 2, val_right: 3}, {key: 'C', val_left: NaN, val_right: 4}]	[{key: 'B', val_left: 2, val_right: 3}, {key: 'C', val_left: NaN, val_right: 4}]

Key Moments - 2 Insights

Why does the row with key 'C' have NaN in the left table columns?

Because right join keeps all rows from the right table even if there is no matching key in the left table, so missing left data is filled with NaN as shown in execution_table row 2.

Does the right join include rows from the left table that have no matching key in the right table?

No, right join only guarantees all rows from the right table are included; unmatched left rows are excluded, as seen in execution_table where left row with key 'A' is missing.

Visual Quiz - 3 Questions

Look at the execution table, what is the result row for the right table key 'B' at step 1?

A {key: 'B', val_left: 1, val_right: 3}

B {key: 'B', val_left: NaN, val_right: 3}

C {key: 'B', val_left: 2, val_right: 3}

D {key: 'B', val_left: 2, val_right: NaN}

💡 Hint

Check execution_table row 1 under 'Result Row'

At which step does the join add a row with NaN for left table columns?

A Step 1

B Step 2

C Step 3

D No step adds NaN

💡 Hint

Look at execution_table row 2 for NaN in left columns

If the join type changed to 'left', which key would definitely appear in the result?

A 'A'

B 'B'

C 'C'

D 'D'

💡 Hint

Variable_tracker shows 'A' is missing in right join but would appear in left join

Concept Snapshot

Right join in pandas merges two tables keeping all rows from the right table.
Matching rows from the left table are added; unmatched left rows are excluded.
Missing left data for unmatched keys is filled with NaN.
Use pd.merge(left, right, on='key', how='right') to perform right join.

Full Transcript

Right join behavior in pandas means combining two tables by a key, keeping all rows from the right table. For each row in the right table, pandas looks for matching rows in the left table. If a match is found, it combines the data. If not, it fills the left table's columns with NaN. Rows in the left table without matching keys in the right table are not included. This is shown step-by-step in the execution table where the right table's keys 'B' and 'C' are processed. The key 'B' matches a left row, so values from both tables appear. The key 'C' has no left match, so left columns are NaN. This behavior helps keep all data from the right table while adding left data when available.