Merging on different column names in Pandas - Step-by-Step Execution

Concept Flow - Merging on different column names

Start with two DataFrames

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Identify columns to merge

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Specify left_on and right_on

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Perform merge operation

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Get merged DataFrame with combined data

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End

We start with two tables having different column names for the same data. We tell pandas which columns to match, then merge them into one combined table.

Execution Sample

Pandas

import pandas as pd

left = pd.DataFrame({'key1': ['A', 'B'], 'val': [1, 2]})
right = pd.DataFrame({'key2': ['A', 'B'], 'val2': [3, 4]})
merged = pd.merge(left, right, left_on='key1', right_on='key2')
print(merged)

This code merges two DataFrames on columns with different names: 'key1' in left and 'key2' in right.

Execution Table

Step	Action	left DataFrame	right DataFrame	Merge Columns	Resulting DataFrame
1	Create left DataFrame	{'key1': ['A', 'B'], 'val': [1, 2]}	N/A	N/A	N/A
2	Create right DataFrame	N/A	{'key2': ['A', 'B'], 'val2': [3, 4]}	N/A	N/A
3	Specify merge columns	left_on='key1'	right_on='key2'	key1 and key2	N/A
4	Perform merge	left	right	key1=key2	{'key1': ['A', 'B'], 'val': [1, 2], 'key2': ['A', 'B'], 'val2': [3, 4]}
5	Print merged DataFrame	N/A	N/A	N/A	key1 val key2 val2 0 A 1 A 3 1 B 2 B 4

💡 Merge completed by matching 'key1' from left with 'key2' from right DataFrame.

Variable Tracker

Variable	Start	After Step 1	After Step 2	After Step 4	Final
left	undefined	{'key1': ['A', 'B'], 'val': [1, 2]}	{'key1': ['A', 'B'], 'val': [1, 2]}	{'key1': ['A', 'B'], 'val': [1, 2]}	{'key1': ['A', 'B'], 'val': [1, 2]}
right	undefined	undefined	{'key2': ['A', 'B'], 'val2': [3, 4]}	{'key2': ['A', 'B'], 'val2': [3, 4]}	{'key2': ['A', 'B'], 'val2': [3, 4]}
merged	undefined	undefined	undefined	{'key1': ['A', 'B'], 'val': [1, 2], 'key2': ['A', 'B'], 'val2': [3, 4]}	{'key1': ['A', 'B'], 'val': [1, 2], 'key2': ['A', 'B'], 'val2': [3, 4]}

Key Moments - 2 Insights

Why do we need to use left_on and right_on instead of just on?

Because the columns to join have different names in each DataFrame, we must specify left_on and right_on to tell pandas which columns to match. See execution_table step 3.

What happens if the values in the merge columns don't match?

Rows without matching keys will be excluded by default in an inner merge. Here, all keys match, so all rows appear. This is shown in execution_table step 4.

Visual Quiz - 3 Questions

Look at the execution_table at step 4, what columns does the merged DataFrame contain?

A key1, val

B key1, val, key2, val2

C key2, val2

D val, val2

💡 Hint

Check the 'Resulting DataFrame' column at step 4 in execution_table.

At which step do we specify which columns to merge on?

A Step 2

B Step 4

C Step 3

D Step 5

💡 Hint

Look at the 'Action' column in execution_table for when merge columns are specified.

If the left DataFrame had a column named 'key2' instead of 'key1', how would the merge call change?

A Use left_on='key2', right_on='key2'

B Use left_on='key1', right_on='key2'

C Use on='key2'

D No change needed

💡 Hint

Refer to variable_tracker and think about matching column names for merge.

Concept Snapshot

Merging on different column names:
Use pd.merge(left, right, left_on='left_col', right_on='right_col')
This joins two DataFrames on columns with different names.
Result includes all columns from both DataFrames.
Default merge type is inner join (only matching rows).

Full Transcript

We start with two DataFrames that have columns with different names but represent the same data to join on. We specify which columns to match using left_on and right_on parameters in pandas merge function. The merge combines rows where these columns have matching values, producing a new DataFrame with columns from both inputs. This method is useful when column names differ but data is related. The example shows merging on 'key1' from left and 'key2' from right, resulting in a combined table with all columns.