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

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

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

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

Hint

Use print(filtered_sales) to show the filtered DataFrame.

Practice

1. What does the filter() function do when applied to a grouped pandas DataFrame?

easy

A. Sorts groups by their size

B. Filters rows inside each group individually

C. Keeps or removes entire groups based on a condition

D. Aggregates data by calculating group means

Solution

1. Step 1: Understand the purpose of filter() on groups

   The filter() function is used on grouped data to keep or remove whole groups based on a condition applied to the group as a whole.

2. Step 2: Differentiate from row-level filtering

   Unlike filtering rows inside groups, filter() returns entire groups only if the condition is True for that group.

3. Final Answer:

   Keeps or removes entire groups based on a condition -> Option C

4. Quick Check:

   Group-level filtering = Keeps/removes groups [OK]

Hint: Remember: filter() keeps or drops whole groups [OK]

Common Mistakes:

• Thinking filter() filters rows inside groups
• Confusing filter() with aggregation functions
• Assuming filter() sorts groups

2. Which of the following is the correct syntax to filter groups in a pandas DataFrame df grouped by column 'Category' to keep groups with more than 3 rows?

easy

A. df.groupby('Category').filter(lambda x: len(x) > 3)

B. df.filter(groupby='Category', condition=lambda x: len(x) > 3)

C. df.groupby('Category').apply(filter(len(x) > 3))

D. df.filter(lambda x: x['Category'] > 3)

Solution

1. Step 1: Recall correct filter() syntax on grouped data

   The correct syntax is to group by a column, then call filter() with a function that takes each group and returns True or False.

2. Step 2: Check each option

   df.groupby('Category').filter(lambda x: len(x) > 3) correctly groups by 'Category' and filters groups with length > 3. Options A, B, and D use incorrect syntax or wrong method calls.

3. Final Answer:

   df.groupby('Category').filter(lambda x: len(x) > 3) -> Option A

4. Quick Check:

   Correct syntax = groupby + filter(lambda x: condition) [OK]

Hint: Use groupby().filter(lambda x: condition) for group filtering [OK]

Common Mistakes:

• Using filter() without groupby()
• Passing wrong arguments to filter()
• Confusing apply() with filter()

3. Given the DataFrame df below:

import pandas as pd
data = {'Team': ['A', 'A', 'B', 'B', 'B', 'C'], 'Points': [10, 15, 10, 5, 7, 20]}
df = pd.DataFrame(data)
filtered = df.groupby('Team').filter(lambda x: x['Points'].sum() > 20)
print(filtered)

What will be the output?

medium

A. Rows where Team is 'A' and 'C'

B. Rows where Team is 'A' and 'B'

C. Rows where Team is 'B' only

D. All rows

Solution

1. Step 1: Calculate sum of Points per Team

   Team A: 10 + 15 = 25, Team B: 10 + 5 + 7 = 22, Team C: 20

2. Step 2: Apply filter condition sum > 20

   Teams A and B have sums > 20, Team C has 20 (not > 20), so only A and B groups pass.

3. Step 3: Check printed rows

   Filtered DataFrame includes rows from Teams A and B only.

4. Final Answer:

   Rows where Team is 'A' and 'B' -> Option B

5. Quick Check:

   Sum > 20 keeps Teams A and B [OK]

Hint: Sum group values, keep groups where sum > 20 [OK]

Common Mistakes:

• Forgetting to include Team A rows
• Including Team C because sum equals 20
• Confusing sum with mean

4. Consider this code snippet:

df.groupby('Category').filter(lambda x: x['Value'] > 10)

Why will this code raise an error?

medium

A. Because the lambda returns a Series, not a single True/False value

B. Because filter() cannot be used after groupby()

C. Because 'Value' column does not exist

D. Because filter() requires a list, not a lambda

Solution

1. Step 1: Understand filter() expects a boolean scalar per group

   The function inside filter() must return a single True or False for the whole group.

2. Step 2: Analyze the lambda function

   The lambda returns a Series of booleans (x['Value'] > 10) for each row, not a single boolean.

3. Final Answer:

   Because the lambda returns a Series, not a single True/False value -> Option A

4. Quick Check:

   filter() needs single True/False per group [OK]

Hint: Lambda must return one True/False per group, not Series [OK]

Common Mistakes:

• Returning a Series instead of a scalar boolean
• Thinking filter() works like row-level filter
• Misunderstanding filter() usage after groupby

5. You have a sales DataFrame df with columns 'Store', 'Sales', and 'Month'. You want to keep only stores where the average monthly sales are above 5000. Which code correctly filters the DataFrame using filter()?

hard

A. df.filter(lambda x: x['Sales'].mean() > 5000, groupby='Store')

B. df.groupby('Store').filter(lambda x: x['Sales'] > 5000)

C. df.groupby('Store').apply(lambda x: x['Sales'].mean() > 5000)

D. df.groupby('Store').filter(lambda x: x['Sales'].mean() > 5000)

Solution

1. Step 1: Identify correct use of filter() with groupby

   We must group by 'Store' and use filter() with a lambda returning True if the group's average sales > 5000.

2. Step 2: Check each option

   df.groupby('Store').filter(lambda x: x['Sales'].mean() > 5000) correctly uses filter() with lambda x: x['Sales'].mean() > 5000. df.filter(lambda x: x['Sales'].mean() > 5000, groupby='Store') has wrong syntax, C uses apply which returns a Series, and D compares sales values directly, not the mean.

3. Final Answer:

   df.groupby('Store').filter(lambda x: x['Sales'].mean() > 5000) -> Option D

4. Quick Check:

   Use groupby + filter with mean condition [OK]

Hint: Filter groups by mean with groupby().filter(lambda x: x['col'].mean() > val) [OK]

Common Mistakes:

• Using filter() without groupby()
• Comparing individual sales instead of group mean
• Using apply() instead of filter() for filtering groups