Merging on different column names in Pandas - Cheat Sheet & Quick Revision

Recall & Review

beginner

What does merging on different column names mean in pandas?

It means joining two tables where the columns to match have different names in each table. You tell pandas which columns to use from each table.

beginner

Which pandas function is used to merge DataFrames on columns with different names?

The pandas function merge() is used with the parameters left_on and right_on to specify the different column names to join on.

beginner

How do you merge two DataFrames where the first has column 'A' and the second has column 'B' to join on?

Use pd.merge(df1, df2, left_on='A', right_on='B'). This tells pandas to match rows where df1's 'A' equals df2's 'B'.

intermediate

What happens if you merge on different column names without specifying left_on and right_on?

Pandas will look for columns with the same name in both DataFrames to merge on. If none match, it will raise an error.

intermediate

Can you merge on multiple columns with different names in pandas?

Yes. You can pass lists to left_on and right_on with matching positions, like left_on=['A','C'], right_on=['B','D'].

Which parameters do you use to merge on columns with different names in pandas?

A index and columns

B on and how

C left_on and right_on

D join and merge

Use left_on and right_on to specify different column names to merge on.

What happens if you try to merge two DataFrames with no common column names and don't specify left_on or right_on?

A Pandas merges on the index by default

B Pandas merges on the first column

C Pandas merges on all columns

D Pandas raises an error

Pandas raises an error because it cannot find columns to merge on.

How do you merge on multiple columns with different names?

A Pass lists to left_on and right_on

B Use on with a list of columns

C Use index_col parameter

D Merge twice

You pass lists of column names to left_on and right_on to merge on multiple columns.

If df1 has column 'key1' and df2 has column 'key2', how do you merge on these?

A pd.merge(df1, df2, left_on='key1', right_on='key2')

B pd.merge(df1, df2, on='key1')

C pd.merge(df1, df2, on='key2')

D pd.merge(df1, df2)

You specify left_on and right_on with the different column names.

Which of these is NOT a valid way to merge DataFrames on different column names?

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

B pd.merge(df1, df2, on=['A','B'])

C pd.merge(df1, df2, left_on=['A','C'], right_on=['B','D'])

D pd.merge(df1, df2, left_on='A', right_on='B', how='inner')

The 'on' parameter expects the same column names in both DataFrames, so using different names in a list is invalid.

Explain how to merge two pandas DataFrames when the columns to join on have different names.

Think about telling pandas which columns to match from each table.

Describe what happens if you try to merge DataFrames without common column names and without specifying left_on and right_on.

What does pandas do when it can't find matching 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