Named aggregation in Pandas - Step-by-Step Execution

Concept Flow - Named aggregation

Start with DataFrame

↓

Group data by column

↓

Apply aggregation functions

↓

Use named aggregation syntax

↓

Create new DataFrame with named columns

↓

Output aggregated DataFrame

Group data by a column, apply aggregation functions with custom names, and get a new summarized DataFrame.

Execution Sample

Pandas

import pandas as pd

df = pd.DataFrame({
    'Team': ['A', 'A', 'B', 'B'],
    'Points': [10, 15, 10, 20]
})

result = df.groupby('Team').agg(
    total_points=pd.NamedAgg(column='Points', aggfunc='sum'),
    avg_points=pd.NamedAgg(column='Points', aggfunc='mean')
)

Groups the DataFrame by 'Team' and calculates total and average points with named columns.

Execution Table

Step	Action	Group	Aggregation Function	Resulting Value	Output DataFrame State
1	Group by 'Team'	A	-	-	Groups created: A, B
2	Aggregate sum of 'Points' for group A	A	sum	25	Partial result: total_points for A = 25
3	Aggregate mean of 'Points' for group A	A	mean	12.5	Partial result: avg_points for A = 12.5
4	Aggregate sum of 'Points' for group B	B	sum	30	Partial result: total_points for B = 30
5	Aggregate mean of 'Points' for group B	B	mean	15.0	Partial result: avg_points for B = 15.0
6	Combine results into new DataFrame	-	-	-	DataFrame with columns total_points and avg_points for each Team
7	Output final aggregated DataFrame	-	-	-	Team | total_points | avg_points A | 25 | 12.5 B | 30 | 15.0

💡 All groups aggregated and combined into final DataFrame.

Variable Tracker

Variable	Start	After Step 2	After Step 3	After Step 5	Final
df	{'Team': ['A','A','B','B'], 'Points': [10,15,10,20]}	Same	Same	Same	Same
groups	None	['A','B']	['A','B']	['A','B']	['A','B']
total_points	None	A:25	A:25	A:25, B:30	A:25, B:30
avg_points	None	None	A:12.5	A:12.5, B:15.0	A:12.5, B:15.0
result	None	Partial	Partial	Partial	DataFrame with named columns

Key Moments - 2 Insights

Why do we use pd.NamedAgg instead of just passing a dictionary?

pd.NamedAgg lets us give clear names to the output columns, as shown in steps 2-5 where total_points and avg_points are created separately.

What happens if we don't name the aggregation results?

Without names, pandas uses default names which can be unclear. Named aggregation (step 6) ensures the output columns have meaningful names.

Visual Quiz - 3 Questions

Look at the execution table at step 4. What is the aggregation function applied for group B?

A mean

B sum

C count

D max

💡 Hint

Check the 'Aggregation Function' column in step 4 of the execution table.

At which step is the average points for group A calculated?

A Step 3

B Step 5

C Step 2

D Step 6

💡 Hint

Look for 'mean' aggregation for group A in the execution table.

If we change the aggregation function for total_points to 'max', what will happen at step 2?

A Sum of points for group A is calculated

B Mean points for group A is calculated

C Maximum points for group A is calculated

D No aggregation happens

💡 Hint

Step 2 shows the aggregation function applied; changing it changes the result accordingly.

Concept Snapshot

Named aggregation in pandas:
Use df.groupby('col').agg(new_name=pd.NamedAgg(column='col', aggfunc='func'))
Allows multiple aggregations with clear output column names.
Results in a DataFrame with named columns for each aggregation.
Helps keep output readable and organized.

Full Transcript

This visual execution shows how named aggregation works in pandas. We start with a DataFrame containing teams and points. We group the data by the 'Team' column. Then, for each group, we calculate the sum and mean of the 'Points' column using named aggregation syntax. Each aggregation is given a clear name like 'total_points' and 'avg_points'. The execution table traces each step: grouping, aggregating sums and means for each team, and combining results into a new DataFrame. The variable tracker shows how variables like total_points and avg_points change after each aggregation step. Key moments clarify why naming aggregations is important for clarity. The quiz tests understanding of which functions run at each step and the effect of changing aggregation functions. The snapshot summarizes the syntax and purpose of named aggregation for quick reference.