Selecting data with MultiIndex in Pandas - Mini Project: Build & Apply

Selecting data with MultiIndex

📖 Scenario: You work in a store that sells fruits in different cities. You have sales data organized by city and fruit type. You want to find sales numbers for specific cities and fruits easily.

🎯 Goal: Build a pandas DataFrame with a MultiIndex for city and fruit. Then select sales data for a specific city and fruit using MultiIndex selection.

📋 What You'll Learn

Create a pandas DataFrame with a MultiIndex from tuples of city and fruit

Add a sales column with integer values

Create a variable for the city to select

Use .loc with the MultiIndex to select sales data for the chosen city

Print the selected sales data

💡 Why This Matters

🌍 Real World

Stores and companies often have sales data organized by multiple categories like city and product. MultiIndex helps manage and analyze such data efficiently.

💼 Career

Data analysts and scientists use MultiIndex DataFrames to handle complex datasets with multiple levels of grouping, making data selection and aggregation easier.

1

Create a MultiIndex DataFrame

Import pandas as pd. Create a list of tuples called index_tuples with these exact pairs: ('New York', 'Apple'), ('New York', 'Banana'), ('Los Angeles', 'Apple'), ('Los Angeles', 'Banana'). Then create a MultiIndex called multi_index from index_tuples with names ['City', 'Fruit']. Finally, create a DataFrame called sales_data with this MultiIndex and a column 'Sales' with values [100, 150, 200, 250].

Pandas

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

Need a hint?

Use pd.MultiIndex.from_tuples() to create the MultiIndex. Then pass it as the index when creating the DataFrame.

2

Set the city to select

Create a variable called selected_city and set it to the string 'New York'.

Pandas

import pandas as pd
index_tuples = [('New York', 'Apple'), ('New York', 'Banana'), ('Los Angeles', 'Apple'), ('Los Angeles', 'Banana')]
multi_index = pd.MultiIndex.from_tuples(index_tuples, names=['City', 'Fruit'])
sales_data = pd.DataFrame({'Sales': [100, 150, 200, 250]}, index=multi_index)
# Create a variable called selected_city and set it to 'New York'
# Your code here

Need a hint?

Just assign the string 'New York' to the variable selected_city.

3

Select sales data for the chosen city

Use sales_data.loc with selected_city to select all fruit sales for that city. Store the result in a variable called city_sales.

Pandas

import pandas as pd
index_tuples = [('New York', 'Apple'), ('New York', 'Banana'), ('Los Angeles', 'Apple'), ('Los Angeles', 'Banana')]
multi_index = pd.MultiIndex.from_tuples(index_tuples, names=['City', 'Fruit'])
sales_data = pd.DataFrame({'Sales': [100, 150, 200, 250]}, index=multi_index)
selected_city = 'New York'
# Select sales data for selected_city using .loc and save to city_sales
# Your code here

Need a hint?

Use sales_data.loc[selected_city] to get all rows where the first level of the MultiIndex matches selected_city.

4

Print the selected sales data

Print the variable city_sales to display the sales data for the selected city.

Pandas

import pandas as pd
index_tuples = [('New York', 'Apple'), ('New York', 'Banana'), ('Los Angeles', 'Apple'), ('Los Angeles', 'Banana')]
multi_index = pd.MultiIndex.from_tuples(index_tuples, names=['City', 'Fruit'])
sales_data = pd.DataFrame({'Sales': [100, 150, 200, 250]}, index=multi_index)
selected_city = 'New York'
city_sales = sales_data.loc[selected_city]
# Print city_sales
# Your code here

Need a hint?

Use print(city_sales) to show the sales for the selected city.