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

Concept Flow - Left join behavior

Start with Left DataFrame

↓

Match keys with Right DataFrame

↓

If match found

→Combine rows

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If no match

→Fill right columns with NaN

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Result: Left join DataFrame

Left join keeps all rows from the left table and adds matching rows from the right table. If no match, right side is filled with missing values.

Execution Sample

Pandas

import pandas as pd
left = pd.DataFrame({'key': ['A', 'B', 'C'], 'left_val': [1, 2, 3]})
right = pd.DataFrame({'key': ['B', 'C', 'D'], 'right_val': [4, 5, 6]})
result = pd.merge(left, right, on='key', how='left')
print(result)

This code merges two tables on 'key' using a left join, keeping all left rows and adding matching right rows.

Execution Table

Step	Left Row Key	Right Match Found?	Action	Result Row
1	A	No	Keep left row, fill right with NaN	{'key': 'A', 'left_val': 1, 'right_val': null}
2	B	Yes	Combine left and right rows	{'key': 'B', 'left_val': 2, 'right_val': 4}
3	C	Yes	Combine left and right rows	{'key': 'C', 'left_val': 3, 'right_val': 5}
4	End	-	All left rows processed	Result has 3 rows

💡 All rows from left DataFrame processed; left join complete.

Variable Tracker

Variable	Start	After Step 1	After Step 2	After Step 3	Final
result	Empty	[{'A',1,null}]	[{'A',1,null}, {'B',2,4}]	[{'A',1,null}, {'B',2,4}, {'C',3,5}]	[{'A',1,null}, {'B',2,4}, {'C',3,5}]

Key Moments - 2 Insights

Why does the row with key 'A' have NaN in the right_val column?

Because there is no matching key 'A' in the right DataFrame, left join keeps the left row and fills right columns with NaN as shown in execution_table step 1.

What happens to rows in the right DataFrame that do not match any left key?

They are ignored in a left join. For example, key 'D' in right DataFrame is not in left, so it does not appear in the result.

Visual Quiz - 3 Questions

Look at the execution table, what is the 'right_val' for the left key 'B' after step 2?

A 4

B NaN

C 2

D 5

💡 Hint

Check the 'Result Row' column for step 2 in the execution_table.

At which step does the left join fill right columns with NaN?

A Step 2

B Step 1

C Step 3

D Step 4

💡 Hint

Look for 'fill right with NaN' in the 'Action' column of execution_table.

If the right DataFrame had a matching key 'A', how would the result change at step 1?

A The right_val would be NaN

B The row would be dropped

C The right_val would have the matching value from right DataFrame

D The left_val would become NaN

💡 Hint

Consider how left join combines rows when a match is found, as shown in steps 2 and 3.

Concept Snapshot

Left join keeps all rows from the left table.
It adds matching rows from the right table based on key.
If no match, right columns are filled with NaN.
Syntax: pd.merge(left, right, on='key', how='left')
Useful to keep all left data and add info from right.

Full Transcript

This visual execution shows how a left join works in pandas. We start with two DataFrames, left and right, each with a key column. The left join keeps every row from the left DataFrame. For each left row, it looks for matching keys in the right DataFrame. If a match is found, it combines the rows. If not, it keeps the left row and fills right columns with NaN. The execution table traces each left row step-by-step, showing when matches occur and when NaNs appear. The variable tracker shows how the result DataFrame grows after each step. Key moments clarify why unmatched rows have NaN and what happens to unmatched right rows. The quiz tests understanding of these steps. This helps beginners see exactly how left join merges data visually and clearly.