Multiple aggregation functions in Pandas - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of multiple aggregation functions on grouped data

What is the output of this code snippet that groups data by 'Category' and applies multiple aggregation functions on 'Value'?

Pandas

import pandas as pd

data = {'Category': ['A', 'A', 'B', 'B', 'C'], 'Value': [10, 20, 10, 30, 50]}
df = pd.DataFrame(data)
result = df.groupby('Category')['Value'].agg(['sum', 'mean']).reset_index()
print(result)

A [['Category', 'sum', 'mean'], ['A', 30, 15.0], ['B', 40, 20.0], ['C', 50, 50.0]]

B

  Category  Value
0        A  30
1        B  40
2        C  50

C

  Category  sum  mean
0        A  30.0  15
1        B  40.0  20
2        C  50.0  50

D

  Category  sum  mean
0        A   30  15.0
1        B   40  20.0
2        C   50  50.0

💡 Hint

Look at how pandas formats the output DataFrame with multiple aggregation functions.

The groupby with agg(['sum', 'mean']) returns a DataFrame with columns 'sum' and 'mean' for each group. The values are numeric with 'sum' as int and 'mean' as float. The reset_index() makes 'Category' a column again.

❓ data_output

intermediate

Number of rows in multi-aggregation result

After running this code, how many rows does the resulting DataFrame have?

Pandas

import pandas as pd

data = {'Group': ['X', 'X', 'Y', 'Y', 'Z', 'Z'], 'Score': [5, 10, 15, 20, 25, 30]}
df = pd.DataFrame(data)
result = df.groupby('Group')['Score'].agg(['min', 'max', 'mean'])

A 3

B 6

C 1

D 9

💡 Hint

Count unique groups in the 'Group' column.

The groupby groups data by unique values in 'Group'. There are 3 unique groups: X, Y, and Z. So the result has 3 rows.

🔧 Debug

advanced

Identify the error in applying multiple aggregation functions

What error does this code raise when trying to apply multiple aggregation functions to a DataFrame column?

Pandas

import pandas as pd

data = {'Type': ['A', 'B', 'A'], 'Value': [1, 2, 3]}
df = pd.DataFrame(data)
result = df.groupby('Type')['Value'].agg('sum', 'mean')

A No error, returns sum and mean aggregation

B SyntaxError: invalid syntax

C TypeError: agg() takes from 1 to 2 positional arguments but 3 were given

D ValueError: Function names must be strings or list of strings

💡 Hint

Check how agg() accepts multiple functions.

agg() expects a single function, a list of functions, or a dict. Passing multiple positional arguments causes a TypeError.

🚀 Application

advanced

Applying different aggregation functions to different columns

Given this DataFrame, which option correctly applies 'sum' to 'Sales' and 'mean' to 'Quantity' grouped by 'Region'?

Pandas

import pandas as pd

data = {'Region': ['East', 'East', 'West', 'West'], 'Sales': [100, 200, 150, 300], 'Quantity': [1, 2, 3, 4]}
df = pd.DataFrame(data)

A df.groupby('Region').agg({'Sales': ['sum'], 'Quantity': ['mean']})

B df.groupby('Region').agg({'Sales': 'sum', 'Quantity': 'mean'})

C df.groupby('Region').agg({'Sales': ['mean'], 'Quantity': ['sum']})

D df.groupby('Region').agg({'Sales': 'mean', 'Quantity': 'sum'})

💡 Hint

Check the dictionary keys and values for agg() to apply different functions per column.

agg() accepts a dictionary mapping column names to aggregation functions. 'Sales' should map to 'sum' and 'Quantity' to 'mean'.

🧠 Conceptual

expert

Understanding output structure of multiple aggregation functions with named aggregation

What is the structure of the DataFrame resulting from this code?

Pandas

import pandas as pd

data = {'Category': ['A', 'A', 'B', 'B'], 'Value': [10, 20, 30, 40]}
df = pd.DataFrame(data)
result = df.groupby('Category').agg(total_value=('Value', 'sum'), average_value=('Value', 'mean'))

A A DataFrame with columns 'total_value' and 'average_value' indexed by 'Category'

B A DataFrame with multi-level columns where 'Value' is the top level and 'sum' and 'mean' are sub-levels

C A Series with a MultiIndex of 'Category' and aggregation function names

D A DataFrame with columns 'Value_sum' and 'Value_mean' indexed by integers

💡 Hint

Named aggregation creates columns with the given names.

Using named aggregation with tuples assigns new column names. The result is a DataFrame indexed by 'Category' with columns 'total_value' and 'average_value'.