Inner join behavior in Pandas - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ query_result

intermediate

What is the output of this inner join?

Given two dataframes df1 and df2, what is the result of the inner join on column key?

Pandas

import pandas as pd

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

result = pd.merge(df1, df2, on='key', how='inner')
print(result)

A [{'key': 'A', 'val1': 1, 'val2': 4}, {'key': 'B', 'val1': 2, 'val2': 5}]

B [{'key': 'B', 'val1': 2, 'val2': 4}, {'key': 'C', 'val1': 3, 'val2': 5}]

C [{'key': 'A', 'val1': 1, 'val2': 6}, {'key': 'D', 'val1': 3, 'val2': 6}]

D [{'key': 'A', 'val1': 1, 'val2': null}, {'key': 'B', 'val1': 2, 'val2': 4}, {'key': 'C', 'val1': 3, 'val2': 5}, {'key': 'D', 'val1': null, 'val2': 6}]

💡 Hint

Inner join returns only rows with matching keys in both dataframes.

The inner join keeps only keys present in both df1 and df2. Here, keys 'B' and 'C' are common.

🧠 Conceptual

intermediate

Which statement best describes an inner join?

Choose the correct description of an inner join operation between two tables or dataframes.

A Returns only rows where keys exist in both tables, excluding all others.

B Returns all rows from the left table and matching rows from the right table, filling with nulls if no match.

C Returns all rows from the right table and matching rows from the left table, filling with nulls if no match.

D Returns all rows from both tables, combining matching rows and filling nulls where no match exists.

💡 Hint

Think about which rows appear in the result when keys do not match.

An inner join returns only rows with keys present in both tables, excluding rows without matches.

📝 Syntax

advanced

Identify the correct syntax for an inner join in pandas

Which of the following code snippets correctly performs an inner join on column id between df_left and df_right?

A df_left.merge(df_right, on='id', join='inner')

B df_left.join(df_right, on='id', how='inner')

C pd.join(df_left, df_right, on='id', how='inner')

D pd.merge(df_left, df_right, on='id', how='inner')

💡 Hint

Recall the correct function and parameter names for pandas merge.

The pd.merge() function with how='inner' and on='id' is the correct syntax.

❓ optimization

advanced

Optimizing inner join performance on large dataframes

You have two large dataframes with millions of rows each. Which approach can improve the performance of an inner join on column user_id?

A Ensure both dataframes have <code>user_id</code> as an index before merging.

B Convert <code>user_id</code> columns to string type before merging.

C Drop duplicates from both dataframes before merging regardless of keys.

D Use <code>how='outer'</code> instead of <code>how='inner'</code> to reduce computation.

💡 Hint

Indexes can speed up join operations.

Setting the join key as index allows pandas to use faster join algorithms internally.

🔧 Debug

expert

Why does this inner join produce an empty dataframe?

Given the code below, why is the result of the inner join empty?

Pandas

import pandas as pd

df1 = pd.DataFrame({'key': ['a', 'b', 'c'], 'val': [1, 2, 3]})
df2 = pd.DataFrame({'key': ['A', 'B', 'C'], 'val': [4, 5, 6]})

result = pd.merge(df1, df2, on='key', how='inner')
print(result)

A The merge function requires specifying suffixes for overlapping columns.

B The 'val' columns have different names causing the join to fail.

C The keys differ in case sensitivity; 'a' != 'A', so no matches occur.

D The 'how' parameter should be 'left' instead of 'inner' to get results.

💡 Hint

Check if the keys match exactly including letter case.

Inner join matches keys exactly. Here, lowercase and uppercase letters differ, so no rows match.

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