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

Concept Flow - Merging on different column names

Start with two DataFrames

↓

Identify columns to merge

↓

Specify left_on and right_on

↓

Perform merge operation

↓

Get merged DataFrame with combined data

↓

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.

Practice

1.

What does the left_on and right_on parameters do in pandas.merge()?

easy

A. They specify columns with different names to join on from left and right DataFrames.

B. They rename columns in the DataFrames before merging.

C. They filter rows based on column values before merging.

D. They sort the DataFrames before merging.

Solution

1. Step 1: Understand merge parameters

   The left_on and right_on parameters specify which columns to join on when the column names differ between DataFrames.

2. Step 2: Clarify their purpose

   They do not rename or filter data but tell pandas which columns to match for merging.

3. Final Answer:

   They specify columns with different names to join on from left and right DataFrames. -> Option A

4. Quick Check:

   left_on/right_on = specify join columns [OK]

Hint: Use left_on and right_on to join different column names [OK]

Common Mistakes:

• Thinking they rename columns
• Confusing with filtering rows
• Assuming they sort data

2.

Which of the following is the correct syntax to merge df1 and df2 on columns 'id' in df1 and 'user_id' in df2?

df1 = pd.DataFrame({'id': [1, 2], 'val': ['A', 'B']})
df2 = pd.DataFrame({'user_id': [1, 3], 'score': [10, 20]})

easy

A. pd.merge(df1, df2, left_on='user_id', right_on='id')

B. pd.merge(df1, df2, on='id', right_on='user_id')

C. pd.merge(df1, df2, left_on='id', right_on='user_id')

D. pd.merge(df1, df2, on=['id', 'user_id'])

Solution

1. Step 1: Identify correct parameters

   Since columns have different names, use left_on='id' and right_on='user_id' to specify join keys.

2. Step 2: Check syntax correctness

   pd.merge(df1, df2, left_on='id', right_on='user_id') correctly uses pd.merge(df1, df2, left_on='id', right_on='user_id'). Others misuse on or swap keys.

3. Final Answer:

   pd.merge(df1, df2, left_on='id', right_on='user_id') -> Option C

4. Quick Check:

   left_on='id', right_on='user_id' = correct syntax [OK]

Hint: Use left_on for left, right_on for right column names [OK]

Common Mistakes:

• Using on with different column names
• Swapping left_on and right_on
• Passing both columns in on as a list

3.

What is the output of the following code?

import pandas as pd

df1 = pd.DataFrame({'key1': ['A', 'B', 'C'], 'val1': [1, 2, 3]})
df2 = pd.DataFrame({'key2': ['B', 'C', 'D'], 'val2': [4, 5, 6]})

result = pd.merge(df1, df2, left_on='key1', right_on='key2', how='inner')
print(result)

medium

A. key1 val1 key2 val2
0 A 1 A 4
1 B 2 B 5

B. key1 val1 key2 val2
0 A 1 B 4
1 B 2 C 5

C. Empty DataFrame
Columns: [key1, val1, key2, val2]
Index: []

D. key1 val1 key2 val2
0 B 2 B 4
1 C 3 C 5

Solution

1. Step 1: Understand merge keys and type

   We merge on key1 from df1 and key2 from df2 with how='inner', so only matching keys appear.

2. Step 2: Find matching keys

   Matching keys are 'B' and 'C'. Rows with 'A' and 'D' are excluded.

3. Final Answer:

   key1 val1 key2 val2 0 B 2 B 4 1 C 3 C 5 -> Option D

4. Quick Check:

   Inner join on matching keys = rows with B and C [OK]

Hint: Inner merge keeps only matching keys from both sides [OK]

Common Mistakes:

• Expecting all rows to appear
• Mixing up left_on and right_on
• Confusing inner with outer join

4.

What is wrong with this code snippet?

import pandas as pd

df1 = pd.DataFrame({'A': [1, 2], 'B': [3, 4]})
df2 = pd.DataFrame({'X': [1, 3], 'Y': [5, 6]})

merged = pd.merge(df1, df2, left_on='A', right_on='B')
print(merged)

medium

A. It will raise a KeyError because 'B' is not in df2.

B. It uses wrong parameter names for merge.

C. It tries to merge on columns that do not match logically.

D. It merges correctly without errors.

Solution

1. Step 1: Check columns in each DataFrame

   df1 has columns 'A' and 'B'. df2 has columns 'X' and 'Y'. The code tries to merge on left_on='A' and right_on='B'.

2. Step 2: Identify error cause

   Since df2 does not have column 'B', pandas will raise a KeyError.

3. Final Answer:

   It will raise a KeyError because 'B' is not in df2. -> Option A

4. Quick Check:

   right_on column must exist in right DataFrame [OK]

Hint: Check if merge columns exist in respective DataFrames [OK]

Common Mistakes:

• Assuming columns exist in both DataFrames
• Confusing left_on and right_on columns
• Ignoring error messages

5.

You have two DataFrames:

df_customers = pd.DataFrame({'cust_id': [101, 102, 103], 'name': ['Alice', 'Bob', 'Charlie']})
df_orders = pd.DataFrame({'customer': [102, 101, 104], 'order_id': [5001, 5002, 5003]})

You want to merge these to get customer names with their orders, including customers without orders and orders without customers. Which code correctly does this?

hard

A. pd.merge(df_customers, df_orders, left_on='cust_id', right_on='customer', how='inner')

B. pd.merge(df_customers, df_orders, left_on='cust_id', right_on='customer', how='outer')

C. pd.merge(df_customers, df_orders, left_on='name', right_on='customer', how='left')

D. pd.merge(df_customers, df_orders, on='cust_id', how='inner')

Solution

1. Step 1: Identify columns to join on

   Customer IDs are cust_id in df_customers and customer in df_orders. So use left_on='cust_id' and right_on='customer'.

2. Step 2: Choose join type for all data

   To include all customers and all orders (even unmatched), use how='outer' for full outer join.

3. Final Answer:

   pd.merge(df_customers, df_orders, left_on='cust_id', right_on='customer', how='outer') -> Option B

4. Quick Check:

   Outer join + left_on/right_on = all data combined [OK]

Hint: Use how='outer' with left_on/right_on for full merge [OK]

Common Mistakes:

• Using inner join loses unmatched rows
• Merging on wrong columns
• Using name column instead of ID