filter() for group-level filtering in Pandas - Mini Project: Build & Apply

Filter Groups in a DataFrame Using filter()

📖 Scenario: You work in a small bookstore. You have sales data for different book genres. You want to find which genres have total sales above a certain number.

🎯 Goal: Build a program that uses pandas to group sales by genre and then filter groups where total sales are more than 100.

📋 What You'll Learn

Create a pandas DataFrame with given sales data

Set a sales threshold variable

Use groupby and filter() to keep only genres with total sales above threshold

Print the filtered DataFrame

💡 Why This Matters

🌍 Real World

Filtering grouped data is common in sales analysis, marketing, and finance to focus on important categories.

💼 Career

Data analysts and scientists often use group-level filtering to prepare data for reports and decision-making.

1

Create the sales DataFrame

Import pandas as pd. Create a DataFrame called sales_data with columns 'Genre' and 'Sales' using this exact data: 'Fiction' with 50, 'Non-Fiction' with 120, 'Fiction' with 70, 'Sci-Fi' with 90, and 'Non-Fiction' with 30.

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 the sales threshold

Create a variable called sales_threshold and set it to 100.

Pandas

import pandas as pd
sales_data = pd.DataFrame({
    'Genre': ['Fiction', 'Non-Fiction', 'Fiction', 'Sci-Fi', 'Non-Fiction'],
    'Sales': [50, 120, 70, 90, 30]
})
# Set sales_threshold to 100
# Your code here

Need a hint?

Just assign the number 100 to the variable sales_threshold.

3

Filter genres with total sales above threshold

Use groupby('Genre') on sales_data and then use filter() with a lambda function that keeps groups where the sum of 'Sales' is greater than sales_threshold. Save the result in a variable called filtered_sales.

Pandas

import pandas as pd
sales_data = pd.DataFrame({
    'Genre': ['Fiction', 'Non-Fiction', 'Fiction', 'Sci-Fi', 'Non-Fiction'],
    'Sales': [50, 120, 70, 90, 30]
})
sales_threshold = 100
# Use groupby and filter to keep genres with total sales > sales_threshold
# Your code here

Need a hint?

Use groupby('Genre') then filter() with a lambda that sums 'Sales' and compares to sales_threshold.

4

Print the filtered sales data

Print the variable filtered_sales to see the genres with total sales above 100.

Pandas

import pandas as pd
sales_data = pd.DataFrame({
    'Genre': ['Fiction', 'Non-Fiction', 'Fiction', 'Sci-Fi', 'Non-Fiction'],
    'Sales': [50, 120, 70, 90, 30]
})
sales_threshold = 100
filtered_sales = sales_data.groupby('Genre').filter(lambda x: x['Sales'].sum() > sales_threshold)
# Print filtered_sales
# Your code here

Need a hint?

Use print(filtered_sales) to show the filtered DataFrame.