GroupBy with custom functions in Pandas - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of GroupBy with a custom aggregation function

What is the output of this code snippet that groups data by 'Category' and applies a custom function to the 'Value' column?

Pandas

import pandas as pd

data = pd.DataFrame({
    'Category': ['A', 'A', 'B', 'B', 'C'],
    'Value': [10, 20, 10, 30, 50]
})

def custom_func(x):
    return x.max() - x.min()

result = data.groupby('Category')['Value'].agg(custom_func)
print(result)

A

Category
A    10
B    20
C     0
Name: Value, dtype: int64

B

Category
A    10
B    10
C    50
Name: Value, dtype: int64

C

Category
A    20
B    30
C    50
Name: Value, dtype: int64

D

Category
A    30
B    40
C    50
Name: Value, dtype: int64

💡 Hint

Think about what the custom function does: max minus min for each group.

The custom function calculates the difference between the maximum and minimum values in each group. For group A, max is 20 and min is 10, difference 10. For B, max 30, min 10, difference 20. For C, only one value 50, difference 0.

❓ data_output

intermediate

Number of groups after grouping with a custom function

Given this DataFrame, how many groups are formed when grouping by the 'Type' column?

Pandas

import pandas as pd

data = pd.DataFrame({
    'Type': ['X', 'Y', 'X', 'Z', 'Y', 'Z', 'Z'],
    'Score': [5, 10, 15, 10, 20, 30, 40]
})
groups = data.groupby('Type')

count = len(groups)

A 4

B 7

C 3

D 2

💡 Hint

Count unique values in the 'Type' column.

The 'Type' column has three unique values: 'X', 'Y', and 'Z'. Grouping by this column creates 3 groups.

🔧 Debug

advanced

Identify the error in custom aggregation function usage

What error does this code raise when trying to apply a custom function to a groupby object?

Pandas

import pandas as pd

data = pd.DataFrame({
    'Group': ['A', 'A', 'B'],
    'Value': [1, 2, 3]
})

def faulty_func(x):
    return x + 1

result = data.groupby('Group').agg(faulty_func)
print(result)

A ValueError: Function does not reduce

B AttributeError: 'DataFrameGroupBy' object has no attribute 'agg'

C TypeError: unsupported operand type(s) for +: 'DataFrame' and 'int'

D No error, prints incremented values

💡 Hint

Check what the function receives and what operation it tries to do.

The agg function expects the custom function to reduce the group's Series to a scalar value. Here, the function returns x + 1, which is still a Series of the same length, causing ValueError: Function does not reduce.

❓ visualization

advanced

Visualizing group means with a custom function

Which option correctly produces a bar plot of the mean 'Score' per 'Category' using a custom aggregation function?

Pandas

import pandas as pd
import matplotlib.pyplot as plt

data = pd.DataFrame({
    'Category': ['A', 'A', 'B', 'B', 'C'],
    'Score': [10, 20, 30, 40, 50]
})

def mean_func(x):
    return x.mean()

result = data.groupby('Category')['Score'].agg(mean_func)
result.plot(kind='bar')
plt.show()

A No plot, code raises AttributeError

B A line plot showing scores over categories

C A scatter plot with categories on x-axis and scores on y-axis

D A bar plot showing bars at heights 15, 35, and 50 for categories A, B, and C respectively

💡 Hint

Check the plot kind and aggregation function used.

The code groups by 'Category', calculates mean scores, then plots a bar chart with correct heights for each category.

🚀 Application

expert

Applying multiple custom functions with groupby

Given this DataFrame, which option correctly applies two custom functions to the 'Value' column grouped by 'Category' and returns a DataFrame with both results?

Pandas

import pandas as pd

data = pd.DataFrame({
    'Category': ['X', 'X', 'Y', 'Y', 'Z'],
    'Value': [1, 3, 5, 7, 9]
})

def range_func(x):
    return x.max() - x.min()

def mean_plus_one(x):
    return x.mean() + 1

result = data.groupby('Category')['Value'].agg([range_func, mean_plus_one])
print(result)

A

          range_func  mean_plus_one
Category                            
X                  3           2.0
Y                  2           6.0
Z                  1           9.0

B

          range_func  mean_plus_one
Category                            
X                  2           3.0
Y                  2           7.0
Z                  0          10.0

C TypeError: agg() takes 2 positional arguments but 3 were given

D KeyError: 'Value'

💡 Hint

Check how agg accepts a list of functions and the output format.

The agg method accepts a list of functions and applies each to the grouped column, returning a DataFrame with columns named after the functions. The calculations match the values shown in option B.