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'.

Practice

1. What does the agg() function do when used after groupby() in pandas?

easy

A. It merges two DataFrames together.

B. It sorts the groups in ascending order.

C. It filters rows based on a condition.

D. It applies multiple aggregation functions to each group and returns their results.

Solution

1. Step 1: Understand groupby operation

   The groupby() function splits data into groups based on column values.

2. Step 2: Apply agg() after grouping

   The agg() function applies one or more aggregation functions like sum, mean, count to each group and returns the results.

3. Final Answer:

   It applies multiple aggregation functions to each group and returns their results. -> Option D

4. Quick Check:

   agg() summarizes groups [OK]

Hint: Remember: agg() summarizes groups with many functions [OK]

Common Mistakes:

• Thinking agg() sorts data
• Confusing agg() with filtering
• Assuming agg() merges DataFrames

2. Which of the following is the correct syntax to apply both sum and mean aggregations on a grouped DataFrame df grouped by column 'Category'?

easy

A. df.groupby('Category').agg(['sum', 'mean'])

B. df.agg(['sum', 'mean']).groupby('Category')

C. df.groupby('Category').agg('sum', 'mean')

D. df.groupby('Category').aggregate(sum, mean)

Solution

1. Step 1: Identify correct method chaining

   Grouping must happen first with groupby('Category'), then aggregation with agg().

2. Step 2: Use list of functions inside agg()

   The correct syntax uses a list of function names: agg(['sum', 'mean']).

3. Final Answer:

   df.groupby('Category').agg(['sum', 'mean']) -> Option A

4. Quick Check:

   Group first, then agg with list [OK]

Hint: Use list inside agg() after groupby() [OK]

Common Mistakes:

• Calling agg() before groupby()
• Passing multiple args instead of list to agg()
• Using aggregate() without list syntax

3. Given the DataFrame:

import pandas as pd
data = {'Team': ['A', 'A', 'B', 'B'], 'Points': [10, 15, 10, 20], 'Assists': [5, 7, 8, 6]}
df = pd.DataFrame(data)
result = df.groupby('Team').agg({'Points': ['sum', 'mean'], 'Assists': 'max'})
print(result)

What is the output?

medium

A. A DataFrame with sum and mean of Points and max of Assists for each Team.

B. A DataFrame with sum and mean of Assists and max of Points for each Team.

C. An error because agg() cannot mix list and string in dict.

D. A Series with aggregated values for all columns combined.

Solution

1. Step 1: Group by 'Team'

   The data is grouped into two groups: Team A and Team B.

2. Step 2: Apply multiple aggregations

   For 'Points', sum and mean are calculated; for 'Assists', max is calculated for each group.

3. Final Answer:

   A DataFrame with sum and mean of Points and max of Assists for each Team. -> Option A

4. Quick Check:

   agg() supports dict with list and string [OK]

Hint: Use dict with list/string in agg() for different columns [OK]

Common Mistakes:

• Expecting error mixing list and string in agg dict
• Confusing which function applies to which column
• Thinking output is a Series, not DataFrame

4. What is wrong with this code snippet?

df.groupby('Category').agg({'Sales': 'sum', 'Profit': ['mean', 'max']})

It raises an error. Why?

medium

A. agg() does not accept multiple functions for different columns.

B. The column names 'Sales' and 'Profit' must be passed as a list, not dict.

C. The code is correct and runs without error.

D. The dictionary mixes single string and list of functions for different columns, which is invalid.

Solution

1. Step 1: Check agg() dictionary syntax

   agg() accepts a dictionary where values can be a string or list of functions per column.

2. Step 2: Verify code correctness

   The code correctly applies 'sum' to 'Sales' and ['mean', 'max'] to 'Profit' without error.

3. Final Answer:

   The code is correct and runs without error. -> Option C

4. Quick Check:

   agg() accepts mixed string and list in dict [OK]

Hint: agg() allows string or list per column in dict [OK]

Common Mistakes:

• Thinking mixing string and list causes error
• Expecting all functions must be in a list
• Confusing agg() with apply() syntax

5. You have a DataFrame df with columns 'Store', 'Sales', and 'Expenses'. You want to group by 'Store' and get the total sales, average expenses, and count of sales entries per store in one step. Which code correctly does this?

hard

A. df.groupby('Store').agg({'Sales': 'sum', 'Expenses': ['mean', 'count']})

B. df.groupby('Store').agg({'Sales': ['sum', 'count'], 'Expenses': 'mean'})

C. df.groupby('Store').agg({'Sales': 'mean', 'Expenses': 'sum', 'Store': 'count'})

D. df.groupby('Store').agg(['sum', 'mean', 'count'])

Solution

1. Step 1: Identify required aggregations per column

   We want total sales (sum), average expenses (mean), and count of sales entries (count).

2. Step 2: Map functions correctly in agg()

   Use a dict: 'Sales' gets ['sum', 'count'], 'Expenses' gets 'mean'. This matches df.groupby('Store').agg({'Sales': ['sum', 'count'], 'Expenses': 'mean'}).

3. Final Answer:

   df.groupby('Store').agg({'Sales': ['sum', 'count'], 'Expenses': 'mean'}) -> Option B

4. Quick Check:

   Dict with list and string per column [OK]

Hint: Use dict with list for multiple funcs per column [OK]

Common Mistakes:

• Putting count under wrong column
• Using agg() without dict for columns
• Applying all functions to all columns