Swapping index levels in Pandas - Mini Project: Build & Apply

Swapping Index Levels in a MultiIndex DataFrame

📖 Scenario: You work in a sales department and have a table showing sales data indexed by Region and Year. You want to rearrange the table so that Year comes before Region in the index.

🎯 Goal: Create a pandas DataFrame with a MultiIndex of Region and Year, then swap the index levels so that Year is the first level and Region is the second.

📋 What You'll Learn

Create a pandas DataFrame with MultiIndex using Region and Year

Add a column Sales with given values

Swap the index levels so that Year is the first level

Ensure the DataFrame index reflects the swapped levels

💡 Why This Matters

🌍 Real World

Swapping index levels helps when you want to reorganize data views, such as changing the priority of grouping in sales reports or time series data.

💼 Career

Data analysts and scientists often need to manipulate MultiIndex DataFrames to prepare data for analysis or visualization.

1

Create the initial MultiIndex DataFrame

Import pandas as pd. Create a MultiIndex from the tuples [('North', 2020), ('North', 2021), ('South', 2020), ('South', 2021)] with names 'Region' and 'Year'. Then create a DataFrame called df with this MultiIndex and a Sales column containing [250, 300, 200, 400].

Pandas

# Import pandas and create the MultiIndex DataFrame df
# Your code here

Need a hint?

Use pd.MultiIndex.from_tuples() to create the index and pass it to pd.DataFrame() with the sales data.

2

Add a variable for the new index order

Create a list called new_order with the values ['Year', 'Region'] to represent the desired order of index levels.

Pandas

import pandas as pd

index = pd.MultiIndex.from_tuples(
    [('North', 2020), ('North', 2021), ('South', 2020), ('South', 2021)],
    names=['Region', 'Year']
)
df = pd.DataFrame({'Sales': [250, 300, 200, 400]}, index=index)

# Create a list new_order with ['Year', 'Region']
# Your code here

Need a hint?

Just create a list with the new order of index level names.

3

Swap the index levels using the new order

Use the df.reorder_levels() method with the new_order list to reorder the index levels of df. Assign the result back to df.

Pandas

import pandas as pd

index = pd.MultiIndex.from_tuples(
    [('North', 2020), ('North', 2021), ('South', 2020), ('South', 2021)],
    names=['Region', 'Year']
)
df = pd.DataFrame({'Sales': [250, 300, 200, 400]}, index=index)

new_order = ['Year', 'Region']

# Reorder the index levels of df using new_order
# Your code here

Need a hint?

Use df.reorder_levels(new_order) to swap the index levels.

4

Verify the index level names after swapping

Assign the list of index level names of df to a variable called index_names using df.index.names.

Pandas

import pandas as pd

index = pd.MultiIndex.from_tuples(
    [('North', 2020), ('North', 2021), ('South', 2020), ('South', 2021)],
    names=['Region', 'Year']
)
df = pd.DataFrame({'Sales': [250, 300, 200, 400]}, index=index)

new_order = ['Year', 'Region']

df = df.reorder_levels(new_order)

# Assign the index level names of df to index_names
# Your code here

Need a hint?

Use df.index.names to get the list of index level names.