merge() for SQL-like joins in Pandas - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to merge two DataFrames on the 'id' column.

Pandas

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

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

A'name'

B'age'

C'id'

D'score'

Attempts:

3 left

💡 Hint

Common Mistakes

Using a column that does not exist in both DataFrames.

Using a column that is not unique or relevant for merging.

We merge on the 'id' column because it is the common key in both DataFrames.

2fill in blank

medium

Complete the code to perform a left join using merge() on 'user_id'.

Pandas

result = df_left.merge(df_right, on=[1], how=[2])

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

A'customer_id'

B'user_id'

C'product_id'

D'order_id'

E'left'

Attempts:

3 left

💡 Hint

Common Mistakes

Using the wrong column name for the join key.

Forgetting to specify the join type for a left join.

The 'user_id' column is the key to join on. The default join type is inner, but here we want a left join, so we specify how='left'.

3fill in blank

hard

Fix the error in the merge code to join on columns with different names: 'id_left' and 'id_right'.

Pandas

merged = df1.merge(df2, left_on=[1], right_on=[2])

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

A'id_left'

B'id_right'

C'id'

D'user_id'

Attempts:

3 left

💡 Hint

Common Mistakes

Using only 'on' when column names differ.

Mixing up left_on and right_on values.

When the key columns have different names, use left_on and right_on to specify them.

4fill in blank

hard

Fill both blanks to create a dictionary comprehension that maps words to their lengths only if length is greater than 3.

Pandas

{word: [1] for word in words if [2]

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

Alen(word)

Blen(word) > 3

Cword.startswith('a')

Dword

Attempts:

3 left

💡 Hint

Common Mistakes

Using the word itself as the value instead of its length.

Using the wrong condition in the if clause.

The dictionary maps each word to its length, but only includes words longer than 3 characters.

5fill in blank

hard

Fill all three blanks to create a dictionary comprehension that maps uppercase words to their counts if count is greater than 1.

Pandas

{ [1]: [2] for [3], count in word_counts.items() if count > 1 }

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

Aword.upper()

Bcount

Cword

Dword.lower()

Attempts:

3 left

💡 Hint

Common Mistakes

Using the word itself as the key without converting to uppercase.

Mixing up the key and value in the comprehension.

The dictionary maps each word in uppercase to its count, but only includes words with count greater than 1.

Practice

1. What does the merge() function in pandas do?

easy

A. It filters rows based on a condition.

B. It sorts a DataFrame by column values.

C. It changes the data type of a column.

D. It combines two DataFrames based on matching columns.

Solution

1. Step 1: Understand the purpose of merge()

   The merge() function is designed to combine two tables (DataFrames) by matching values in specified columns.

2. Step 2: Compare with other options

   Sorting, filtering, and changing data types are different operations and not the main purpose of merge().

3. Final Answer:

   It combines two DataFrames based on matching columns. -> Option D

4. Quick Check:

   merge() joins tables on columns [OK]

Hint: merge() joins tables by matching columns [OK]

Common Mistakes:

• Confusing merge() with sort or filter functions
• Thinking merge() changes data types
• Assuming merge() works without matching columns

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

easy

A. df1.merge(df2, by='id', join='inner')

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

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

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

Solution

1. Step 1: Recall correct merge syntax

   The correct pandas syntax for merging is pd.merge(left, right, on=column, how=type). Here, how='inner' specifies an inner join.

2. Step 2: Check each option

   pd.merge(df1, df2, on='id', how='inner') matches the correct syntax. df1.merge(df2, by='id', join='inner') uses wrong parameter names. pd.join(df1, df2, on='id', how='inner') uses pd.join which does not exist. df1.merge(df2, on='id', how='outer') uses how='outer' which is not inner join.

3. Final Answer:

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

4. Quick Check:

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

Hint: Use pd.merge(df1, df2, on='col', how='inner') [OK]

Common Mistakes:

• Using wrong parameter names like 'by' or 'join'
• Confusing merge() with join() method
• Mixing up 'inner' and 'outer' join types

3. Given the following code:

import pandas as pd

df1 = pd.DataFrame({'id': [1, 2, 3], 'value1': ['A', 'B', 'C']})
df2 = pd.DataFrame({'id': [2, 3, 4], 'value2': ['X', 'Y', 'Z']})
result = pd.merge(df1, df2, on='id', how='inner')
print(result)

What will be the output?

medium

A. id value1 value2
0 2 B X
1 3 C Y

B. id value1 value2
0 1 A NaN
1 2 B X
2 3 C Y

C. id value1 value2
0 2 B X
1 3 C Y
2 4 NaN Z

D. id value1
0 1 A
1 2 B
2 3 C

Solution

1. Step 1: Understand inner join behavior

   An inner join keeps only rows with matching 'id' values in both DataFrames. Here, common ids are 2 and 3.

2. Step 2: Check matching rows and columns

   Rows with id 2 and 3 appear in both df1 and df2, so their columns combine. Rows with id 1 or 4 are excluded.

3. Final Answer:

   id value1 value2 0 2 B X 1 3 C Y -> Option A

4. Quick Check:

   Inner join keeps only matching ids [OK]

Hint: Inner join keeps only rows with matching keys [OK]

Common Mistakes:

• Expecting all rows from both DataFrames
• Confusing inner join with left or outer join
• Ignoring NaN values in output

4. You run this code but get a KeyError:

import pandas as pd

df1 = pd.DataFrame({'key1': [1, 2], 'val': ['A', 'B']})
df2 = pd.DataFrame({'key2': [1, 2], 'val': ['X', 'Y']})
result = pd.merge(df1, df2, on='key1', how='inner')

What is the cause of the error?

medium

A. Column 'key1' does not exist in df2.

B. The merge function requires 'how' to be 'outer'.

C. DataFrames must have the same number of rows to merge.

D. You cannot merge on columns with different names.

Solution

1. Step 1: Check columns in both DataFrames

   df1 has column 'key1', but df2 has 'key2'. The merge tries to join on 'key1' which df2 lacks.

2. Step 2: Understand KeyError cause

   Because 'key1' is missing in df2, pandas raises a KeyError indicating the column is not found.

3. Final Answer:

   Column 'key1' does not exist in df2. -> Option A

4. Quick Check:

   Merge keys must exist in both DataFrames [OK]

Hint: Ensure join columns exist in both DataFrames [OK]

Common Mistakes:

• Assuming merge works with different column names without specifying
• Thinking 'how' parameter causes KeyError
• Believing row counts must match to merge

5. You have two DataFrames:

df_customers = pd.DataFrame({'customer_id': [1, 2, 3], 'name': ['Alice', 'Bob', 'Charlie']})
df_orders = pd.DataFrame({'order_id': [101, 102, 103], 'customer_id': [2, 2, 4], 'amount': [250, 125, 300]})

You want to create a DataFrame showing all customers and their orders, including customers with no orders and orders with no matching customer. Which merge() call achieves this?

hard

A. pd.merge(df_customers, df_orders, on='customer_id', how='left')

B. pd.merge(df_customers, df_orders, on='customer_id', how='inner')

C. pd.merge(df_customers, df_orders, on='customer_id', how='outer')

D. pd.merge(df_customers, df_orders, on='customer_id', how='right')

Solution

1. Step 1: Understand join types

   An outer join keeps all rows from both DataFrames, filling missing matches with NaN.

2. Step 2: Apply to problem context

   We want all customers (even without orders) and all orders (even without matching customers). Only outer join includes all rows from both sides.

3. Final Answer:

   pd.merge(df_customers, df_orders, on='customer_id', how='outer') -> Option C

4. Quick Check:

   Outer join includes all rows from both tables [OK]

Hint: Use how='outer' to keep all rows from both DataFrames [OK]

Common Mistakes:

• Using inner join excludes unmatched rows
• Left join excludes orders without customers
• Right join excludes customers without orders