Inner join behavior in Pandas

Introduction

An inner join helps you combine two tables by keeping only the rows that match in both tables. It is useful to find common information between two sets of data.

You want to find customers who made orders by combining customer and order lists.

You need to match employee details with their department info where both exist.

You want to see products that have sales records by joining product and sales tables.

You want to compare two lists and keep only the common items.

Syntax

Pandas

pd.merge(left_dataframe, right_dataframe, how='inner', on='common_column')

The 'how' parameter set to 'inner' keeps only rows with matching keys in both tables.

The 'on' parameter specifies the column(s) to join on, which must exist in both dataframes.

Examples

Joins df1 and df2 on the 'id' column, keeping only rows where 'id' exists in both.

Pandas

pd.merge(df1, df2, how='inner', on='id')

Joins on multiple columns 'id' and 'date', keeping rows matching both columns.

Pandas

pd.merge(df1, df2, how='inner', on=['id', 'date'])

Sample Program

This code joins customers and orders on 'customer_id'. Only customers with orders appear in the result.

Pandas

import pandas as pd

# Create first dataframe
customers = pd.DataFrame({
    'customer_id': [1, 2, 3, 4],
    'name': ['Alice', 'Bob', 'Charlie', 'David']
})

# Create second dataframe
orders = pd.DataFrame({
    'order_id': [101, 102, 103],
    'customer_id': [2, 4, 5],
    'product': ['Book', 'Pen', 'Notebook']
})

# Perform inner join on 'customer_id'
result = pd.merge(customers, orders, how='inner', on='customer_id')
print(result)

Important Notes

Inner join drops rows that do not have matching keys in both dataframes.

If the join column has duplicates in either dataframe, the result will have all combinations of matches.

Use 'how'='inner' explicitly to avoid confusion with default merge behavior.

Summary

Inner join keeps only rows with matching keys in both tables.

Use pd.merge() with how='inner' and specify the join column(s).

It helps find common data between two datasets.

Practice

1. What does an inner join do when merging two pandas DataFrames?

easy

A. Keeps only rows with matching keys in both DataFrames

B. Keeps all rows from the left DataFrame and fills missing with NaN

C. Keeps all rows from the right DataFrame and fills missing with NaN

D. Keeps all rows from both DataFrames, filling missing with NaN

Solution

1. Step 1: Understand join types in pandas

   Inner join keeps only rows where the key exists in both DataFrames.

2. Step 2: Compare with other join types

   Left join keeps all left rows, right join keeps all right rows, outer join keeps all rows from both.

3. Final Answer:

   Keeps only rows with matching keys in both DataFrames -> Option A

4. Quick Check:

   Inner join = matching keys only [OK]

Hint: Inner join keeps only common keys in both tables [OK]

Common Mistakes:

• Confusing inner join with left or outer join
• Thinking inner join keeps all rows
• Assuming inner join fills missing with NaN

2. Which of the following is the correct syntax to perform an inner join on DataFrames df1 and df2 using the column 'id'?

easy

A. pd.merge(df1, df2, on='id', how='inner')

B. pd.merge(df1, df2, on='id', how='outer')

C. df1.join(df2, on='id')

D. df1.merge(df2, left_on='id', right_on='id', how='left')

Solution

1. Step 1: Identify correct function and parameters

   Use pd.merge() with on='id' and how='inner' for inner join.

2. Step 2: Check other options for errors

   df1.join(df2, on='id') defaults to left join, pd.merge(df1, df2, on='id', how='outer') uses outer join, df1.merge(df2, left_on='id', right_on='id', how='left') uses left join.

3. Final Answer:

   pd.merge(df1, df2, on='id', how='inner') -> Option A

4. Quick Check:

   pd.merge + how='inner' + on='id' = correct syntax [OK]

Hint: Use pd.merge with how='inner' and on='column' [OK]

Common Mistakes:

• Using wrong join type like 'outer' or 'left'
• Using df.join() without specifying correct parameters
• Mixing left_on and right_on incorrectly

3. Given the following DataFrames:

df1 = pd.DataFrame({'id': [1, 2, 3], 'val1': ['A', 'B', 'C']})
df2 = pd.DataFrame({'id': [2, 3, 4], 'val2': ['X', 'Y', 'Z']})
result = pd.merge(df1, df2, on='id', how='inner')

What is the content of result?

medium

A. {'id': [2, 3, 4], 'val1': ['B', 'C', 'NaN'], 'val2': ['X', 'Y', 'Z']}

B. {'id': [1, 2, 3], 'val1': ['A', 'B', 'C'], 'val2': ['NaN', 'X', 'Y']}

C. {'id': [2, 3], 'val1': ['B', 'C'], 'val2': ['X', 'Y']}

D. {'id': [1, 2, 3, 4], 'val1': ['A', 'B', 'C', 'NaN'], 'val2': ['NaN', 'X', 'Y', 'Z']}

Solution

1. Step 1: Identify matching keys for inner join

   Inner join keeps rows where 'id' is in both df1 and df2: ids 2 and 3.

2. Step 2: Combine matching rows

   Rows with id 2 and 3 are merged with their respective val1 and val2 values.

3. Final Answer:

   {'id': [2, 3], 'val1': ['B', 'C'], 'val2': ['X', 'Y']} -> Option C

4. Quick Check:

   Inner join = only common ids 2 and 3 [OK]

Hint: Inner join keeps only keys present in both DataFrames [OK]

Common Mistakes:

• Including rows with id 1 or 4 which are not common
• Confusing inner join with outer join results
• Expecting NaN values in inner join output

4. You run this code:

df1 = pd.DataFrame({'key': [1, 2], 'val': ['A', 'B']})
df2 = pd.DataFrame({'key': [2, 3], 'val': ['X', 'Y']})
result = pd.merge(df1, df2, on='key', how='inner')

But you get an error: ValueError: columns overlap but no suffix specified. What is the fix?

medium

A. Remove the 'on' parameter from pd.merge()

B. Change how='inner' to how='outer'

C. Rename columns before merge to avoid overlap

D. Add suffixes parameter like suffixes=('_left', '_right') in pd.merge()

Solution

1. Step 1: Understand cause of error

   Both DataFrames have a 'val' column causing overlap without suffixes.

2. Step 2: Apply fix by adding suffixes

   Use suffixes=('_left', '_right') in pd.merge() to differentiate overlapping columns.

3. Final Answer:

   Add suffixes parameter like suffixes=('_left', '_right') in pd.merge() -> Option D

4. Quick Check:

   Overlapping columns need suffixes to avoid error [OK]

Hint: Add suffixes to handle overlapping columns in merge [OK]

Common Mistakes:

• Changing join type does not fix column overlap
• Removing 'on' causes wrong join keys
• Ignoring need for suffixes with overlapping columns

5. You have two DataFrames:

df1 = pd.DataFrame({'id': [1, 2, 2, 3], 'score': [10, 20, 30, 40]})
df2 = pd.DataFrame({'id': [2, 2, 3], 'grade': ['A', 'B', 'C']})
result = pd.merge(df1, df2, on='id', how='inner')

How many rows will result have after the inner join?

hard

A. 4

B. 5

C. 6

D. 7

Solution

1. Step 1: Identify matching keys and duplicates

   Keys 2 and 3 are common. df1 has two rows with id=2, df2 has two rows with id=2.

2. Step 2: Calculate Cartesian product for duplicates

   For id=2: 2 rows in df1 x 2 rows in df2 = 4 rows; for id=3: 1 row x 1 row = 1 row; total = 5 rows.

3. Step 3: Count total rows

   Total rows = 4 (id=2) + 1 (id=3) = 5 rows.

4. Final Answer:

   5 -> Option B

5. Quick Check:

   Duplicates multiply rows in inner join [OK]

Hint: Duplicates multiply rows in inner join result [OK]

Common Mistakes:

• Counting unique keys instead of row combinations
• Ignoring duplicate rows in join keys
• Assuming row count equals unique keys count