Inner join in Data Analysis Python - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to perform an inner join of two DataFrames on the 'id' column.

Data Analysis Python

result = df1.merge(df2, on=[1])

Drag options to blanks, or click blank then click option'

A'name'

B'age'

C'id'

D'date'

Attempts:

3 left

💡 Hint

Common Mistakes

Using a column name that does not exist in both DataFrames.

Forgetting to put quotes around the column name.

The merge function joins two DataFrames on the specified column. Here, joining on 'id' matches rows with the same 'id' in both DataFrames.

2fill in blank

medium

Complete the code to perform an inner join and keep only rows with matching keys.

Data Analysis Python

joined = df1.merge(df2, on='id', how=[1])

Drag options to blanks, or click blank then click option'

A'inner'

B'left'

C'outer'

D'right'

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'left' or 'right' which keep unmatched rows from one side.

Using 'outer' which keeps all rows from both DataFrames.

The how='inner' option keeps only rows with matching keys in both DataFrames.

3fill in blank

hard

Fix the error in the code to correctly perform an inner join on 'user_id'.

Data Analysis Python

result = df1.merge(df2, on=[1])

Drag options to blanks, or click blank then click option'

A'userid'

B'id'

C'userId'

D'user_id'

Attempts:

3 left

💡 Hint

Common Mistakes

Using a column name with wrong case or spelling.

Using a column name that exists only in one DataFrame.

The column name must exactly match in both DataFrames. Here, 'user_id' is the correct column name.

4fill in blank

hard

Fill both blanks to perform an inner join on 'order_id' and keep only matching rows.

Data Analysis Python

joined = df_orders.merge(df_customers, on=[1], how=[2])

Drag options to blanks, or click blank then click option'

A'order_id'

B'inner'

C'outer'

D'customer_id'

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'customer_id' instead of 'order_id' as join key.

Using 'outer' join which keeps unmatched rows.

We join on 'order_id' and use how='inner' to keep only matching rows.

5fill in blank

hard

Fill all three blanks to perform an inner join on 'product_id', rename the joined column, and keep only matching rows.

Data Analysis Python

result = df1.merge(df2, on=[1], how=[2]).rename(columns=[3])

Drag options to blanks, or click blank then click option'

A'product_id'

B'inner'

C{'price_y': 'price'}

D'price_x'

Attempts:

3 left

💡 Hint

Common Mistakes

Using the wrong join column.

Using 'outer' or other join types instead of 'inner'.

Not renaming the duplicate column causing confusion.

We join on 'product_id' with an inner join, then rename the 'price_y' column to 'price' for clarity.

Practice

1. What does an inner join do when combining two tables?

easy

A. Returns only rows with matching keys in both tables

B. Returns all rows from the first table, matching or not

C. Returns all rows from the second table, matching or not

D. Returns all rows from both tables, matching or not

Solution

1. Step 1: Understand join types

   Inner join returns rows where keys match in both tables only.

2. Step 2: Compare options with definition

   The option 'Returns only rows with matching keys in both tables' describes this behavior correctly.

3. Final Answer:

   Returns only rows with matching keys in both tables -> Option A

4. Quick Check:

   Inner join = matching rows only [OK]

Hint: Inner join keeps only matching rows from both tables [OK]

Common Mistakes:

• Confusing inner join with left or right join
• Thinking inner join returns unmatched rows
• Mixing up join types and their results

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

easy

A. df1.join(df2, on='id', how='outer')

B. pd.concat([df1, df2], axis=1, join='inner')

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

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

Solution

1. Step 1: Identify pandas inner join syntax

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

2. Step 2: Check each option

   pd.merge(df1, df2, on='id', how='inner') matches this exactly; others use wrong methods or join types.

3. Final Answer:

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

4. Quick Check:

   pd.merge + how='inner' = inner join [OK]

Hint: Use pd.merge with how='inner' for inner joins [OK]

Common Mistakes:

• Using join() instead of merge() for inner join
• Using wrong join type like 'outer' or 'left'
• Using concat which does not perform join

3. Given two 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], 'val1': ['B', 'C'], 'val2': ['X', 'Y']}

B. {'id': [1, 4], 'val1': ['A', None], 'val2': [None, 'Z']}

C. {'id': [2, 3, 4], 'val1': ['B', 'C', None], 'val2': ['X', 'Y', 'Z']}

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

Solution

1. Step 1: Identify matching keys for inner join

   Keys in both df1 and df2 are 2 and 3 only.

2. Step 2: Merge rows with matching keys

   Rows with id 2 and 3 combine val1 and val2 columns.

3. Final Answer:

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

4. Quick Check:

   Inner join keeps ids 2 and 3 only [OK]

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

Common Mistakes:

• Including keys not present in both tables
• Confusing inner join with outer join
• Assuming all rows are kept

4. You wrote this code to inner join two DataFrames:

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

But it raises an error. What is the likely cause?

medium

A. Using 'how' parameter incorrectly

B. Missing the 'on' parameter to specify join key

C. DataFrames must be converted to lists first

D. Inner join is not supported by pandas

Solution

1. Step 1: Check required parameters for pd.merge

   pd.merge requires 'on' or 'left_on'/'right_on' to specify join keys.

2. Step 2: Identify missing 'on' parameter

   Code misses 'on' parameter, causing error.

3. Final Answer:

   Missing the 'on' parameter to specify join key -> Option B

4. Quick Check:

   pd.merge needs 'on' key [OK]

Hint: Always specify 'on' column for merge keys [OK]

Common Mistakes:

• Forgetting to specify join keys
• Assuming default join key exists
• Trying to join incompatible data types

5. You have two DataFrames:

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

How many rows will result contain after the inner join?

hard

A. 6

B. 4

C. 3

D. 5

Solution

1. Step 1: Identify matching keys and duplicates

   Keys 2 and 3 appear multiple times in both DataFrames.

2. Step 2: Calculate Cartesian product for duplicates

   For id=2: df1 has 2 rows, df2 has 2 rows -> 2*2=4 rows.
   For id=3: df1 has 1 row, df2 has 1 row -> 1*1=1 row.
   Total rows = 4 + 1 = 5 rows.

3. Step 3: Recount carefully

   Recount confirms 5 rows total.

4. Final Answer:

   5 -> Option D

5. Quick Check:

   Duplicates multiply rows in inner join [OK]

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

Common Mistakes:

• Ignoring duplicate keys causing more rows
• Counting unique keys only
• Assuming one-to-one join always