merge() for SQL-like joins in Pandas - Mini Project: Build & Apply

Using merge() for SQL-like joins in pandas

📖 Scenario: You work in a small bookstore. You have two lists: one with book details and another with sales data. You want to combine these lists to see which books sold and their prices.

🎯 Goal: Learn how to use pandas.merge() to join two tables like in SQL. You will combine book details with sales data to get a full picture.

📋 What You'll Learn

Create two pandas DataFrames with exact data

Set a variable for the join key column

Use pandas.merge() to join the DataFrames on the key

Print the merged DataFrame

💡 Why This Matters

🌍 Real World

Combining data from different sources is common in business, like joining sales records with product details to analyze performance.

💼 Career

Data scientists and analysts often use joins to prepare data for reports, dashboards, and machine learning models.

1

Create the book details DataFrame

Create a pandas DataFrame called books with these exact columns and data: book_id as [101, 102, 103], title as ['Python Basics', 'Data Science 101', 'Machine Learning'], and price as [25.0, 30.0, 45.0].

Pandas

# Create the books DataFrame with book_id, title, and price columns
# Your code here

Hint

Use pd.DataFrame with a dictionary where keys are column names and values are lists of data.

2

Create the sales DataFrame and set join key

Create a pandas DataFrame called sales with columns book_id as [101, 103, 104] and copies_sold as [150, 200, 50]. Then create a variable called join_key and set it to the string 'book_id'.

Pandas

import pandas as pd

books = pd.DataFrame({
    'book_id': [101, 102, 103],
    'title': ['Python Basics', 'Data Science 101', 'Machine Learning'],
    'price': [25.0, 30.0, 45.0]
})

# Create the sales DataFrame with book_id and copies_sold columns
# Create the join_key variable with value 'book_id'
# Your code here

Hint

Use pd.DataFrame again for sales. Then assign join_key = 'book_id'.

3

Merge the DataFrames using merge()

Use pandas.merge() to join books and sales on the column stored in join_key. Store the result in a variable called merged_data. Use an inner join (default).

Pandas

import pandas as pd

books = pd.DataFrame({
    'book_id': [101, 102, 103],
    'title': ['Python Basics', 'Data Science 101', 'Machine Learning'],
    'price': [25.0, 30.0, 45.0]
})

sales = pd.DataFrame({
    'book_id': [101, 103, 104],
    'copies_sold': [150, 200, 50]
})

join_key = 'book_id'

# Merge books and sales on join_key using pandas.merge()
# Your code here

Hint

Use pd.merge(books, sales, on=join_key) and assign it to merged_data.

4

Print the merged DataFrame

Print the variable merged_data to see the combined table.

Pandas

import pandas as pd

books = pd.DataFrame({
    'book_id': [101, 102, 103],
    'title': ['Python Basics', 'Data Science 101', 'Machine Learning'],
    'price': [25.0, 30.0, 45.0]
})

sales = pd.DataFrame({
    'book_id': [101, 103, 104],
    'copies_sold': [150, 200, 50]
})

join_key = 'book_id'

merged_data = pd.merge(books, sales, on=join_key)

# Print the merged_data DataFrame
# Your code here

Hint

Use print(merged_data) to show the combined table.

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