filter() for group-level filtering in Pandas - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of group filtering with filter()

What is the output DataFrame after applying the filter to keep groups with sum of values greater than 10?

Pandas

import pandas as pd

df = pd.DataFrame({
    'Category': ['A', 'A', 'B', 'B', 'C', 'C'],
    'Value': [4, 8, 3, 2, 7, 6]
})
filtered = df.groupby('Category').filter(lambda x: x['Value'].sum() > 10)
print(filtered)

A

Empty DataFrame
Columns: [Category, Value]
Index: []

B

  Category  Value
2        B      3
3        B      2

C

  Category  Value
0        A      4
1        A      8
2        B      3
3        B      2
4        C      7
5        C      6

D

  Category  Value
0        A      4
1        A      8
4        C      7
5        C      6

💡 Hint

Sum the 'Value' column for each group and keep groups where sum > 10.

Groups A and C have sums 12 and 13 respectively, which are greater than 10, so their rows are kept. Group B sum is 5, so it is removed.

❓ data_output

intermediate

Number of rows after group-level filter

After filtering groups where the mean of 'Score' is at least 75, how many rows remain in the DataFrame?

Pandas

import pandas as pd

df = pd.DataFrame({
    'Team': ['X', 'X', 'Y', 'Y', 'Z', 'Z', 'Z'],
    'Score': [80, 70, 60, 90, 75, 85, 65]
})
filtered = df.groupby('Team').filter(lambda g: g['Score'].mean() >= 75)
print(len(filtered))

A 4

B 5

C 7

D 3

💡 Hint

Calculate mean score per team and count rows of teams meeting the condition.

Team X mean is 75, Team Y mean is 75, Team Z mean is 75. All teams have mean 75 or above, so all rows remain (7 rows). But Team Y mean is (60+90)/2=75, so all teams qualify. So 7 rows remain. The correct answer is 7.

🔧 Debug

advanced

Identify the error in group filtering code

What error will this code raise when run?

Pandas

import pandas as pd

df = pd.DataFrame({
    'Group': ['G1', 'G1', 'G2', 'G2'],
    'Value': [10, 20, 5, 15]
})
filtered = df.groupby('Group').filter(lambda x: x['Value'].sum > 20)
print(filtered)

A TypeError: '>' not supported between instances of 'method' and 'int'

B KeyError: 'Value'

C SyntaxError: invalid syntax

D No error, prints filtered DataFrame

💡 Hint

Check if sum is called as a method or accessed as an attribute.

The code uses x['Value'].sum without parentheses, so sum is a method object, not called. Attempting to compare the method object to 20 causes TypeError: '>' not supported between instances of 'method' and 'int'.

🚀 Application

advanced

Filter groups with at least 3 rows and max value > 50

Which code correctly filters groups in DataFrame df where each group has at least 3 rows and the maximum 'Score' is greater than 50?

Pandas

import pandas as pd

df = pd.DataFrame({
    'Category': ['A', 'A', 'A', 'B', 'B', 'C', 'C', 'C', 'C'],
    'Score': [40, 55, 60, 30, 45, 70, 65, 50, 40]
})

A df.groupby('Category').filter(lambda g: len(g) > 3 and g['Score'].max() >= 50)

B df.groupby('Category').filter(lambda g: len(g) >= 3 and g['Score'].max() > 50)

C df.groupby('Category').filter(lambda g: g['Score'].max() > 50 or len(g) >= 3)

D df.groupby('Category').filter(lambda g: len(g) >= 3 and g['Score'].max() >= 50)

💡 Hint

Both conditions must be true: group size at least 3 and max score greater than 50.

Option B correctly uses 'and' with len(g) >= 3 and max score > 50. Option B uses 'or' which is incorrect. Option B uses > 3 instead of >= 3 and >= 50 instead of > 50. Option B uses >= 50 instead of > 50, which changes the condition.

🧠 Conceptual

expert

Understanding filter() behavior on empty groups

If a group in a pandas groupby object is empty, what will be the behavior of filter() when the filtering function returns True for that group?

A The empty group will be excluded from the result regardless of the filter function.

B The entire DataFrame will be returned unfiltered.

C filter() will raise a ValueError due to empty group.

D The empty group will be included in the result as an empty DataFrame slice.

💡 Hint

Consider if empty groups have any rows to include.

Empty groups have no rows, so even if the filter function returns True, there are no rows to include. Thus, empty groups are always excluded.