Multiple aggregation functions in Pandas - Step-by-Step Execution

Concept Flow - Multiple aggregation functions

Start with DataFrame

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Choose column(s) to aggregate

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Apply multiple aggregation functions

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Get aggregated results for each function

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Combine results into one DataFrame or Series

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Use or display aggregated output

We start with data, select columns, apply several aggregation functions at once, and get combined results.

Execution Sample

Pandas

import pandas as pd

df = pd.DataFrame({'A': [1, 2, 3, 4], 'B': [5, 6, 7, 8]})
result = df.agg({'A': ['sum', 'mean'], 'B': ['min', 'max']})
print(result)

This code applies sum and mean to column A, min and max to column B, and prints the combined results.

Execution Table

Step	Action	Column	Aggregation Function	Result
1	Select column 'A'	A	-	[1, 2, 3, 4]
2	Select column 'B'	B	-	[5, 6, 7, 8]
3	Apply 'sum' to 'A'	A	sum	10
4	Apply 'mean' to 'A'	A	mean	2.5
5	Apply 'min' to 'B'	B	min	5
6	Apply 'max' to 'B'	B	max	8
7	Combine all results	A, B	sum, mean, min, max	DataFrame with results
8	Print result	-	-	A B sum 10.0 NaN mean 2.5 NaN min NaN 5.0 max NaN 8.0

💡 All specified aggregation functions applied and results combined into one output.

Variable Tracker

Variable	Start	After Step 3	After Step 4	After Step 5	After Step 6	Final
df	{'A':[1,2,3,4],'B':[5,6,7,8]}	Same	Same	Same	Same	Same
result	None	Partial sum for A=10	Sum and mean for A=10,2.5	Sum, mean A; min B=5	Sum, mean A; min, max B=5,8	Full DataFrame with all aggregations

Key Moments - 2 Insights

Why do some cells in the result DataFrame show NaN?

Because each aggregation function applies only to specified columns, other columns have no value for that function, so pandas fills with NaN (see execution_table rows 7 and 8).

Can we apply different aggregation functions to different columns at once?

Yes, by passing a dictionary to agg() with column names as keys and list of functions as values, as shown in the code and execution_table steps 3-6.

Visual Quiz - 3 Questions

Look at the execution table at step 4. What is the mean of column 'A'?

A 10

B 3

C 2.5

D NaN

💡 Hint

Check the 'Result' column at step 4 in the execution_table.

At which step is the minimum value of column 'B' calculated?

A Step 3

B Step 5

C Step 6

D Step 7

💡 Hint

Look for 'min' aggregation on column 'B' in the execution_table.

If we add 'max' aggregation to column 'A', how will the final result change?

A A new row with max value for 'A' will appear

B The 'max' value will replace the 'sum' value for 'A'

C No change, max is ignored

D The entire result will be empty

💡 Hint

Adding an aggregation function adds a new row for that function in the result (see execution_table step 7).

Concept Snapshot

Use df.agg() to apply multiple aggregation functions at once.
Pass a dict: keys are columns, values are list of functions.
Result is a DataFrame with functions as rows, columns as columns.
NaN appears where function doesn't apply to a column.
Useful for quick summary stats on multiple columns.

Full Transcript

We start with a DataFrame containing columns A and B. We want to apply multiple aggregation functions to these columns at once. We select columns A and B, then apply sum and mean to A, and min and max to B using the agg() method with a dictionary. Each function is applied step-by-step, producing partial results. These results are combined into one DataFrame where rows represent aggregation functions and columns represent original columns. Some cells show NaN because not all functions apply to all columns. This combined output is printed as the final result. This method helps quickly get multiple summary statistics in one call.