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

Concept Flow - Inner join behavior

Start with two tables

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Identify common columns

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Match rows where keys are equal

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Combine matched rows into new table

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Discard rows without matches

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Result: Inner join table

Inner join takes two tables and keeps only rows where the join keys match in both tables.

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='inner')

This code merges two tables on the 'key' column, keeping only rows with keys in both tables.

Execution Table

Step	Action	Left Table Rows	Right Table Rows	Matched Keys	Result Rows
1	Start with left and right tables	[{'A',1}, {'B',2}, {'C',3}]	[{'B',4}, {'C',5}, {'D',6}]	None yet	Empty
2	Identify common keys	Keys: A, B, C	Keys: B, C, D	B, C	Empty
3	Match rows on keys B and C	Rows with B and C	Rows with B and C	B, C	Empty
4	Combine matched rows	Row B: (B,2)	Row B: (B,4)	B	Row: (B,2,4)
5	Combine matched rows	Row C: (C,3)	Row C: (C,5)	C	Row: (C,3,5)
6	Discard unmatched rows (A, D)	Row A discarded	Row D discarded	B, C	Rows: (B,2,4), (C,3,5)
7	Final result	N/A	N/A	B, C	2 rows: (B,2,4), (C,3,5)

💡 No more matching keys; inner join returns only rows with keys B and C.

Variable Tracker

Variable	Start	After Step 2	After Step 4	After Step 6	Final
left	[{'A',1}, {'B',2}, {'C',3}]	Same	Same	Same	Same
right	[{'B',4}, {'C',5}, {'D',6}]	Same	Same	Same	Same
matched_keys	None	['B', 'C']	['B', 'C']	['B', 'C']	['B', 'C']
result	Empty	Empty	[{'B',2,4}]	[{'B',2,4}, {'C',3,5}]	[{'B',2,4}, {'C',3,5}]

Key Moments - 2 Insights

Why are rows with keys 'A' and 'D' missing in the result?

Because inner join keeps only rows where keys exist in both tables. Rows with 'A' only in left and 'D' only in right have no match, so they are excluded (see execution_table rows 6 and 7).

How does pandas know which rows to combine?

Pandas matches rows by comparing the values in the join column 'key'. Only rows with the same key value in both tables are combined (see execution_table rows 3 and 4).

Visual Quiz - 3 Questions

Look at the execution table, what keys are matched during the inner join?

A B and C

B A and D

C A, B, C, and D

D Only A

💡 Hint

Check the 'Matched Keys' column in execution_table rows 2 and 3.

At which step are unmatched rows discarded?

A Step 3

B Step 6

C Step 1

D Step 7

💡 Hint

Look at the 'Action' column in execution_table row 6.

If the right table had key 'A' instead of 'D', how many rows would the result have?

A 2 rows

B 1 row

C 3 rows

D 0 rows

💡 Hint

Think about which keys would match in both tables if right had 'A' instead of 'D'.

Concept Snapshot

Inner join merges two tables on matching keys.
Only rows with keys in both tables appear in the result.
Unmatched rows are dropped.
Use pd.merge(left, right, on='key', how='inner') in pandas.
Result columns combine data from both tables.

Full Transcript

Inner join in pandas combines two tables by matching rows where the join key is the same in both tables. It discards rows that do not have a matching key in the other table. For example, if the left table has keys A, B, C and the right table has keys B, C, D, the inner join keeps only rows with keys B and C. The result table contains combined columns from both tables for these matched keys. Rows with keys A and D are excluded because they do not have matches. This behavior is shown step-by-step in the execution table, where matched keys are identified, rows combined, and unmatched rows discarded. The variable tracker shows how the result builds up after each step. Understanding this helps avoid confusion about why some rows disappear after an inner join.