GroupBy performance considerations in Pandas - Mini Project: Build & Apply

GroupBy Performance Considerations

📖 Scenario: You work as a data analyst for a retail company. You have sales data for different products and stores. You want to find the total sales per store. But you also want to learn how to do this efficiently using pandas groupby.

🎯 Goal: Build a small pandas DataFrame with sales data, set a configuration for filtering, use groupby to calculate total sales per store efficiently, and print the result.

📋 What You'll Learn

Create a pandas DataFrame with exact sales data

Create a filter threshold variable

Use groupby on the DataFrame with the exact column names

Print the grouped result showing total sales per store

💡 Why This Matters

🌍 Real World

Retail companies often analyze sales data by store to understand performance and make decisions.

💼 Career

Data analysts and data scientists use pandas groupby to summarize and analyze data efficiently.

1

Create the sales DataFrame

Create a pandas DataFrame called sales_data with these exact columns and rows:
Store: ['Store A', 'Store B', 'Store A', 'Store C', 'Store B']
Product: ['Apples', 'Bananas', 'Oranges', 'Apples', 'Oranges']
Sales: [100, 150, 200, 130, 170]

Pandas

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

Need a hint?

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

2

Set a sales filter threshold

Create a variable called min_sales and set it to 150. This will help filter stores with sales above this value later.

Pandas

import pandas as pd
sales_data = pd.DataFrame({
    'Store': ['Store A', 'Store B', 'Store A', 'Store C', 'Store B'],
    'Product': ['Apples', 'Bananas', 'Oranges', 'Apples', 'Oranges'],
    'Sales': [100, 150, 200, 130, 170]
})
# Create min_sales variable and set it to 150
# Your code here

Need a hint?

Just assign the number 150 to the variable min_sales.

3

Group by Store and calculate total sales

Use groupby on sales_data by the 'Store' column. Then calculate the sum of 'Sales' for each store. Store the result in a variable called total_sales_per_store. Then filter total_sales_per_store to keep only stores with sales greater than or equal to min_sales.

Pandas

import pandas as pd
sales_data = pd.DataFrame({
    'Store': ['Store A', 'Store B', 'Store A', 'Store C', 'Store B'],
    'Product': ['Apples', 'Bananas', 'Oranges', 'Apples', 'Oranges'],
    'Sales': [100, 150, 200, 130, 170]
})
min_sales = 150
# Group sales_data by 'Store' and sum 'Sales'
# Filter stores with total sales >= min_sales
# Your code here

Need a hint?

Use groupby('Store')['Sales'].sum() to get total sales per store. Then filter with total_sales_per_store[total_sales_per_store >= min_sales].

4

Print the total sales per store

Print the total_sales_per_store variable to see the total sales for each store that meets the sales threshold.

Pandas

import pandas as pd
sales_data = pd.DataFrame({
    'Store': ['Store A', 'Store B', 'Store A', 'Store C', 'Store B'],
    'Product': ['Apples', 'Bananas', 'Oranges', 'Apples', 'Oranges'],
    'Sales': [100, 150, 200, 130, 170]
})
min_sales = 150
total_sales_per_store = sales_data.groupby('Store')['Sales'].sum()
total_sales_per_store = total_sales_per_store[total_sales_per_store >= min_sales]
# Print the total_sales_per_store
# Your code here

Need a hint?

Use print(total_sales_per_store) to display the result.