Multiple aggregation functions in Pandas - Cheat Sheet & Quick Revision

Recall & Review

beginner

What does applying multiple aggregation functions to a pandas DataFrame groupby object mean?

It means calculating more than one summary statistic (like mean, sum, count) at the same time for each group in the data.

beginner

How do you apply multiple aggregation functions to a single column in pandas?

Use the .agg() method with a list of functions, for example: df.groupby('col').agg({'data_col': ['mean', 'sum']})

intermediate

What is the difference between using a list and a dictionary inside the agg() function?

A list applies multiple functions to the same column(s). A dictionary lets you apply different functions to different columns.

beginner

Can you name three common aggregation functions used in pandas?

Yes, common functions include mean (average), sum (total), and count (number of items).

intermediate

What type of output do you get when you use multiple aggregation functions on a groupby object?

You get a DataFrame with hierarchical columns showing each aggregation function result for each group.

Which pandas method allows you to apply multiple aggregation functions to grouped data?

A filter()

B apply()

C transform()

D agg()

The agg() method is designed to apply one or more aggregation functions to grouped data.

What will df.groupby('A').agg({'B': ['mean', 'sum']}) return?

A Mean and sum of column A for each group in B

B Mean and sum of column B for each group in A

C Sum of columns A and B

D Count of rows in each group

It calculates mean and sum of column B for each group defined by column A.

If you want to apply different functions to different columns, what should you pass to agg()?

A A dictionary mapping columns to functions

B A list of functions

C A single function

D A boolean mask

A dictionary lets you specify which functions to apply to which columns.

What kind of columns does the output DataFrame have after multiple aggregations?

A Only index columns

B Flat columns with single names

C Hierarchical (multi-level) columns

D No columns, just a Series

Multiple aggregations create multi-level columns showing each function applied.

Which of these is NOT a typical aggregation function in pandas?

A plot

B sum

C count

D mean

plot is for visualization, not aggregation.

Explain how to use pandas to calculate both the average and total sales per product category.

Think about grouping by category and applying multiple functions to sales.

Describe the structure of the DataFrame output when multiple aggregation functions are applied to grouped data.

Focus on how columns are labeled after aggregation.

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