Multiple aggregation functions in Pandas

Introduction

We use multiple aggregation functions to get different summary numbers from data at once. It helps us understand data better by showing many insights in one step.

When you want to see both the average and the total of sales for each product.

When you need to find the minimum and maximum temperatures recorded each day.

When summarizing survey results by calculating count, mean, and standard deviation.

When analyzing student scores to get the average, highest, and lowest marks per class.

Syntax

Pandas

df.groupby('column').agg(['func1', 'func2', ...])

You can use built-in functions like 'mean', 'sum', 'min', 'max', or your own functions.

The result is a DataFrame with multiple columns for each aggregation function.

Examples

This groups data by 'Category' and calculates the mean and sum for each group.

Pandas

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

This groups by 'Team' and calculates minimum, maximum, and average points.

Pandas

df.groupby('Team')['Points'].agg(['min', 'max', 'mean'])

This applies different functions to different columns after grouping by 'City'.

Pandas

df.groupby('City').agg({'Temperature': ['min', 'max'], 'Rainfall': 'sum'})

Sample Program

This code groups the data by 'Team' and calculates the mean and sum for 'Points' and 'Assists'. It shows how multiple aggregation functions work together.

Pandas

import pandas as pd

data = {'Team': ['A', 'A', 'B', 'B', 'C', 'C'],
        'Points': [10, 15, 10, 20, 15, 25],
        'Assists': [5, 7, 8, 6, 7, 9]}
df = pd.DataFrame(data)

result = df.groupby('Team').agg(['mean', 'sum'])
print(result)

Important Notes

Aggregation functions are applied to each column separately.

You can pass a dictionary to apply different functions to different columns.

Result columns become multi-level with function names as second level.

Summary

Multiple aggregation functions let you get many summaries in one step.

Use agg() with a list or dictionary of functions after grouping.

Results show each function's output clearly for each group.

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