Grouping by multiple columns in Pandas - Mini Project: Build & Apply

Grouping Data by Multiple Columns with pandas

📖 Scenario: You work in a small store that sells different types of fruits in various cities. You have a list of sales records showing the city, fruit type, and quantity sold. You want to find out how many fruits were sold for each fruit type in each city.

🎯 Goal: Build a pandas DataFrame with sales data, then group the data by City and Fruit columns to find the total quantity sold for each group.

📋 What You'll Learn

Create a pandas DataFrame named sales_data with columns City, Fruit, and Quantity using the exact data provided.

Create a variable named group_columns that holds a list of the column names 'City' and 'Fruit'.

Use the groupby method on sales_data with group_columns and sum the Quantity for each group, saving the result in a variable named grouped_sales.

Print the grouped_sales DataFrame to display the total quantity sold for each fruit in each city.

💡 Why This Matters

🌍 Real World

Grouping data by multiple columns is common in sales analysis, customer segmentation, and many business reports where you want to summarize data by categories.

💼 Career

Data analysts and data scientists often use grouping to aggregate data and find insights from large datasets in fields like marketing, finance, and operations.

1

Create the sales data DataFrame

Create a pandas DataFrame called sales_data with these exact rows and columns: City, Fruit, and Quantity. Use the data: ('New York', 'Apple', 10), ('New York', 'Banana', 5), ('Los Angeles', 'Apple', 7), ('Los Angeles', 'Banana', 3), ('New York', 'Apple', 4).

Pandas

import pandas as pd
# Create the sales_data DataFrame with the exact data
# Your code here

Hint

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

2

Create the grouping columns list

Create a variable called group_columns and set it to a list containing the strings 'City' and 'Fruit'.

Pandas

import pandas as pd
sales_data = pd.DataFrame({
    'City': ['New York', 'New York', 'Los Angeles', 'Los Angeles', 'New York'],
    'Fruit': ['Apple', 'Banana', 'Apple', 'Banana', 'Apple'],
    'Quantity': [10, 5, 7, 3, 4]
})
# Create the group_columns list with 'City' and 'Fruit'
# Your code here

Hint

Use square brackets to create a list with the two column names as strings.

3

Group the data and sum quantities

Use the groupby method on sales_data with group_columns and sum the Quantity column for each group. Save the result in a variable called grouped_sales.

Pandas

import pandas as pd
sales_data = pd.DataFrame({
    'City': ['New York', 'New York', 'Los Angeles', 'Los Angeles', 'New York'],
    'Fruit': ['Apple', 'Banana', 'Apple', 'Banana', 'Apple'],
    'Quantity': [10, 5, 7, 3, 4]
})
group_columns = ['City', 'Fruit']
# Group sales_data by group_columns and sum Quantity, save as grouped_sales
# Your code here

Hint

Use sales_data.groupby(group_columns)['Quantity'].sum() to group and sum.

4

Print the grouped sales result

Print the grouped_sales variable to display the total quantity sold for each fruit in each city.

Pandas

import pandas as pd
sales_data = pd.DataFrame({
    'City': ['New York', 'New York', 'Los Angeles', 'Los Angeles', 'New York'],
    'Fruit': ['Apple', 'Banana', 'Apple', 'Banana', 'Apple'],
    'Quantity': [10, 5, 7, 3, 4]
})
group_columns = ['City', 'Fruit']
grouped_sales = sales_data.groupby(group_columns)['Quantity'].sum()
# Print grouped_sales to show the result
# Your code here

Hint

Use print(grouped_sales) to display the grouped sums.

Practice

1. What does grouping by multiple columns in pandas allow you to do?

easy

A. Filter rows based on a single condition

B. Sort data alphabetically by one column

C. Analyze data by several categories at the same time

D. Change the data type of multiple columns

Solution

1. Step 1: Understand grouping concept

   Grouping by multiple columns means splitting data into groups based on combinations of those columns.

2. Step 2: Identify the purpose of grouping

   This helps analyze data by several categories at once, like sales by region and product.

3. Final Answer:

   Analyze data by several categories at the same time -> Option C

4. Quick Check:

   Grouping multiple columns = multi-category analysis [OK]

Hint: Grouping by list of columns splits data by combined categories [OK]

Common Mistakes:

• Thinking grouping changes data order
• Confusing grouping with filtering
• Assuming grouping changes data types

2. Which of the following is the correct syntax to group a DataFrame df by columns 'A' and 'B'?

easy

A. df.groupby(['A', 'B'])

B. df.groupby('A', 'B')

C. df.group_by(['A', 'B'])

D. df.groupby(['A' & 'B'])

Solution

1. Step 1: Recall pandas groupby syntax

   To group by multiple columns, pass a list of column names inside groupby().

2. Step 2: Check each option

   df.groupby(['A', 'B']) uses a list ['A', 'B'], which is correct syntax. Others have syntax errors or wrong method names.

3. Final Answer:

   df.groupby(['A', 'B']) -> Option A

4. Quick Check:

   List of columns inside groupby() = correct syntax [OK]

Hint: Use a list of column names inside groupby() [OK]

Common Mistakes:

• Passing columns as separate arguments instead of a list
• Using wrong method name like group_by
• Using bitwise operators inside list

3. Given the DataFrame df below:

   City  Product  Sales
0  NY    A        10
1  NY    B        20
2  LA    A        15
3  LA    B        25
4  NY    A        5

What is the output of df.groupby(['City', 'Product']).sum()?

medium

A. A DataFrame with total sales for each City and Product combination

B. A DataFrame with average sales for each City

C. A Series with sales summed only by City

D. An error because sum() cannot be used after groupby

Solution

1. Step 1: Group by City and Product

   The data is grouped by both City and Product columns, creating groups like (NY, A), (NY, B), (LA, A), (LA, B).

2. Step 2: Apply sum() aggregation

   Sum sales within each group: (NY, A) = 10+5=15, (NY, B) = 20, (LA, A) = 15, (LA, B) = 25.

3. Final Answer:

   A DataFrame with total sales for each City and Product combination -> Option A

4. Quick Check:

   Group by multiple columns + sum() = grouped sums [OK]

Hint: Sum after groupby sums values per group combination [OK]

Common Mistakes:

• Expecting average instead of sum
• Grouping by only one column
• Thinking sum() causes error

4. What is wrong with this code snippet?

df.groupby('City', 'Product').mean()

medium

A. groupby() requires column names as integers

B. mean() cannot be used after groupby

C. The columns 'City' and 'Product' do not exist

D. groupby() expects a single argument, so multiple columns must be passed as a list

Solution

1. Step 1: Check groupby argument format

   groupby() accepts one argument for columns; multiple columns must be inside a list.

2. Step 2: Identify error cause

   Passing 'City', 'Product' as separate arguments causes a TypeError.

3. Final Answer:

   groupby() expects a single argument, so multiple columns must be passed as a list -> Option D

4. Quick Check:

   Multiple columns in groupby() = list argument [OK]

Hint: Pass multiple columns as a list inside groupby() [OK]

Common Mistakes:

• Passing multiple columns as separate arguments
• Thinking mean() is invalid after groupby
• Assuming column names must be integers

5. You have a sales DataFrame df with columns Region, Category, and Revenue. You want to find the average revenue per region and category, but only for categories where the total revenue exceeds 1000. Which code correctly does this?

hard

A. df.groupby(['Region', 'Category']).Revenue.mean()[df.groupby(['Region', 'Category']).Revenue.mean() > 1000]

B. df.groupby(['Region', 'Category']).filter(lambda x: x['Revenue'].sum() > 1000).groupby(['Region', 'Category']).Revenue.mean()

C. df.groupby(['Region', 'Category']).Revenue.mean().filter(lambda x: x > 1000)

D. df.groupby(['Region', 'Category']).Revenue.sum().mean() > 1000

Solution

1. Step 1: Filter groups by total revenue

   Use groupby with filter and a lambda to keep only groups where sum of Revenue > 1000.

2. Step 2: Calculate average revenue per filtered group

   After filtering, group again by Region and Category and calculate mean Revenue.

3. Final Answer:

   df.groupby(['Region', 'Category']).filter(lambda x: x['Revenue'].sum() > 1000).groupby(['Region', 'Category']).Revenue.mean() -> Option B

4. Quick Check:

   Filter groups by sum, then compute mean [OK]

Hint: Use filter() on groupby to keep groups before aggregation [OK]

Common Mistakes:

• Trying to filter after mean instead of before
• Using boolean indexing on Series with mismatched index
• Calculating mean of sums instead of per group