filter() for group-level filtering in Data Analysis Python - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to filter groups where the mean score is greater than 80.

Data Analysis Python

filtered = df.groupby('team').filter(lambda x: x['score'].[1]() > 80)

Drag options to blanks, or click blank then click option'

Amax

Bmin

Cmean

Dsum

Attempts:

3 left

💡 Hint

Common Mistakes

Using sum() instead of mean()

Using max() or min() which do not calculate average

The mean() function calculates the average score for each group, which is needed to filter groups with mean score above 80.

2fill in blank

medium

Complete the code to filter groups where the number of rows is at least 3.

Data Analysis Python

filtered = df.groupby('category').filter(lambda x: len(x) [1] 3)

Drag options to blanks, or click blank then click option'

A>=

B<

C==

D!=

Attempts:

3 left

💡 Hint

Common Mistakes

Using < instead of >=

Using == which only keeps groups with exactly 3 rows

The condition len(x) >= 3 keeps groups with 3 or more rows.

3fill in blank

hard

Fix the error in the code to filter groups where the maximum value in 'value' column is less than 50.

Data Analysis Python

filtered = df.groupby('group').filter(lambda x: x['value'].[1]() < 50)

Drag options to blanks, or click blank then click option'

Asum

Bmax

Cmean

Dmin

Attempts:

3 left

💡 Hint

Common Mistakes

Using min() which returns the smallest value

Using mean() or sum() which do not give the maximum

The max() function finds the highest value in the 'value' column for each group, which is needed to check if it's less than 50.

4fill in blank

hard

Fill both blanks to filter groups where the sum of 'points' is greater than 100 and the group size is at least 5.

Data Analysis Python

filtered = df.groupby('team').filter(lambda x: x['points'].[1]() [2] 100 and len(x) >= 5)

Drag options to blanks, or click blank then click option'

Asum

B>

C<

Dmean

Attempts:

3 left

💡 Hint

Common Mistakes

Using mean() instead of sum()

Using < instead of >

The sum() function calculates total points per group, and > checks if it's greater than 100.

5fill in blank

hard

Fill all three blanks to create a dictionary of group names and their average 'score' for groups with average score above 70.

Data Analysis Python

result = {group: data['score'].[1]() for group, data in df.groupby('[2]') if data['score'].[3]() > 70}

Drag options to blanks, or click blank then click option'

Amean

Bteam

Dcategory

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'category' instead of 'team' for grouping

Using sum() instead of mean() for averages

The mean() function calculates average scores. The groupby is done on 'team'. The condition checks if the average score is above 70.

Practice

1. What does the filter() function do when used with groupby in pandas?

easy

A. Filters individual rows inside groups without removing groups

B. Keeps or removes entire groups based on a condition function

C. Sorts groups by their size

D. Aggregates data by summing values in each group

Solution

1. Step 1: Understand filter() with groupby

   The filter() function applies a condition to each group and keeps or removes the whole group based on True or False.

2. Step 2: Differentiate from other functions

   Unlike aggregation or sorting, filter() does not change group content but decides if the group stays or goes.

3. Final Answer:

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

4. Quick Check:

   filter() = group-level keep/remove [OK]

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

Common Mistakes:

• Thinking filter() works on individual rows only
• 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' where the group size is greater than 3?

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(lambda x: len(x) > 3)

D. df.groupby('Category').select(lambda x: len(x) > 3)

Solution

1. Step 1: Recall filter() syntax with groupby

   The correct syntax is df.groupby('Category').filter(function) where the function returns True or False per group.

2. Step 2: Check each option

   df.groupby('Category').filter(lambda x: len(x) > 3) matches the correct syntax. Options A, C, and D use invalid methods or parameters.

3. Final Answer:

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

4. Quick Check:

   Correct syntax uses groupby().filter() [OK]

Hint: Use groupby().filter(lambda x: condition) syntax [OK]

Common Mistakes:

• Using filter() directly on DataFrame without groupby
• Using apply() instead of filter() for filtering groups
• Passing wrong parameters to filter()

3. Given the following code, what will be the output?

import pandas as pd
data = {'Team': ['A', 'A', 'B', 'B', 'B', 'C'], 'Points': [10, 15, 5, 7, 8, 20]}
df = pd.DataFrame(data)
filtered = df.groupby('Team').filter(lambda x: x['Points'].mean() > 10)
print(filtered)

medium

A. Rows where Team is 'A' or 'C' only

B. Rows where Team is 'B' only

C. All rows from all teams

D. Empty DataFrame

Solution

1. Step 1: Calculate mean Points per Team

   Team A mean = (10+15)/2 = 12.5, Team B mean = (5+7+8)/3 = 6.67, Team C mean = 20

2. Step 2: Apply filter condition

   Keep groups with mean Points > 10, so Teams A and C remain, Team B is removed.

3. Final Answer:

   Rows where Team is 'A' or 'C' only -> Option A

4. Quick Check:

   Mean > 10 keeps A and C [OK]

Hint: Calculate group mean, keep groups meeting condition [OK]

Common Mistakes:

• Forgetting to calculate mean per group
• Assuming filter keeps rows, not groups
• Confusing group means with total sums

4. Identify the error in this code snippet that tries to filter groups with sum of 'Sales' greater than 100:

df.groupby('Region').filter(lambda x: x['Sales'].sum > 100)

medium

A. Lambda function should return False instead of True

B. filter() cannot be used with groupby

C. Missing parentheses after sum method call

D. Wrong column name 'Sales'

Solution

1. Step 1: Check the lambda function syntax

   The code uses x['Sales'].sum which is a method, but it lacks parentheses to call it.

2. Step 2: Correct the method call

   It should be x['Sales'].sum() to get the sum value for comparison.

3. Final Answer:

   Missing parentheses after sum method call -> Option C

4. Quick Check:

   sum() needs parentheses to execute [OK]

Hint: Remember to call sum() with parentheses [OK]

Common Mistakes:

• Using sum without parentheses
• Thinking filter() is invalid with groupby
• Assuming wrong column name causes error

5. You have a sales DataFrame df with columns 'Store', 'Month', and 'Revenue'. You want to keep only stores where the total revenue in any month exceeds 5000. Which code correctly filters these stores using filter()?

hard

A. df.groupby('Store').filter(lambda g: g['Revenue'].mean() > 5000)

B. df.groupby('Store').filter(lambda g: g['Revenue'].sum() > 5000)

C. df.groupby('Month').filter(lambda g: g['Revenue'].max() > 5000)

D. df.groupby('Store').filter(lambda g: g['Revenue'].max() > 5000)

Solution

1. Step 1: Understand the condition

   We want stores where revenue in any month exceeds 5000, so check max revenue per store.

2. Step 2: Analyze each option

   df.groupby('Store').filter(lambda g: g['Revenue'].max() > 5000) uses max() per store, matching the condition. df.groupby('Store').filter(lambda g: g['Revenue'].sum() > 5000) sums all revenue, which is different. df.groupby('Month').filter(lambda g: g['Revenue'].max() > 5000) groups by Month, not Store. df.groupby('Store').filter(lambda g: g['Revenue'].mean() > 5000) uses mean, which is not the condition.

3. Final Answer:

   df.groupby('Store').filter(lambda g: g['Revenue'].max() > 5000) -> Option D

4. Quick Check:

   max() per store matches condition [OK]

Hint: Use max() to check if any month exceeds threshold [OK]

Common Mistakes:

• Using sum() instead of max() for condition
• Grouping by wrong column
• Using mean() which may not meet condition