Sorting MultiIndex in Pandas - Mini Project: Build & Apply

Sorting MultiIndex

📖 Scenario: You work in a company that tracks sales data by region and product category. The data is stored in a table with two levels of row labels: the region and the product category. You want to organize this data so it is easier to analyze by sorting the rows based on these labels.

🎯 Goal: Create a pandas DataFrame with a MultiIndex for regions and product categories, then sort the DataFrame by the MultiIndex to organize the data.

📋 What You'll Learn

Create a pandas DataFrame with a MultiIndex using the exact regions and product categories given.

Add a configuration variable to specify sorting order.

Sort the DataFrame by the MultiIndex using the specified order.

Print the sorted DataFrame to show the result.

💡 Why This Matters

🌍 Real World

Companies often store data with multiple levels of categories, like region and product type. Sorting this data helps in clear reporting and analysis.

💼 Career

Data analysts and scientists frequently work with MultiIndex data in pandas to organize and analyze complex datasets efficiently.

1

Create a MultiIndex DataFrame

Create a pandas DataFrame called sales with a MultiIndex from the tuples [('East', 'Furniture'), ('East', 'Technology'), ('West', 'Furniture'), ('West', 'Technology')]. Use the sales values [1000, 1500, 1200, 1300] for a column named 'Sales'.

Pandas

# Create the MultiIndex DataFrame called sales
# Your code here

Need a hint?

Use pd.MultiIndex.from_tuples to create the MultiIndex and then create the DataFrame with the Sales column.

2

Set sorting order configuration

Create a variable called sort_ascending and set it to True to specify that sorting should be in ascending order.

Pandas

import pandas as pd

index = pd.MultiIndex.from_tuples([
    ('East', 'Furniture'),
    ('East', 'Technology'),
    ('West', 'Furniture'),
    ('West', 'Technology')
], names=['Region', 'Category'])

sales = pd.DataFrame({'Sales': [1000, 1500, 1200, 1300]}, index=index)

# Create the variable sort_ascending and set it to True
# Your code here

Need a hint?

Just create a variable named sort_ascending and assign it the value True.

3

Sort the MultiIndex DataFrame

Sort the DataFrame sales by its MultiIndex using the variable sort_ascending to control ascending order. Assign the sorted DataFrame back to sales.

Pandas

import pandas as pd

index = pd.MultiIndex.from_tuples([
    ('East', 'Furniture'),
    ('East', 'Technology'),
    ('West', 'Furniture'),
    ('West', 'Technology')
], names=['Region', 'Category'])

sales = pd.DataFrame({'Sales': [1000, 1500, 1200, 1300]}, index=index)

sort_ascending = True

# Sort the sales DataFrame by its MultiIndex using sort_ascending
# Your code here

Need a hint?

Use sales.sort_index(ascending=sort_ascending) and assign it back to sales.

4

Display the sorted DataFrame

Print the sorted DataFrame sales to display the organized sales data.

Pandas

import pandas as pd

index = pd.MultiIndex.from_tuples([
    ('East', 'Furniture'),
    ('East', 'Technology'),
    ('West', 'Furniture'),
    ('West', 'Technology')
], names=['Region', 'Category'])

sales = pd.DataFrame({'Sales': [1000, 1500, 1200, 1300]}, index=index)

sort_ascending = True

sales = sales.sort_index(ascending=sort_ascending)

# Print the sales DataFrame
# Your code here

Need a hint?

Use print(sales) to show the sorted DataFrame.