Aggregation with agg() in Pandas - Step-by-Step Execution

Concept Flow - Aggregation with agg()

Start with DataFrame

↓

Call agg() with functions

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Apply each function to columns

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Combine results into new DataFrame

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Return aggregated DataFrame

The agg() method applies one or more aggregation functions to DataFrame columns and returns a new DataFrame with the results.

Execution Sample

Pandas

import pandas as pd

df = pd.DataFrame({
  'A': [1, 2, 3],
  'B': [4, 5, 6]
})

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

This code creates a DataFrame and uses agg() to calculate sum and mean for each column.

Execution Table

Step	Action	Column	Function	Result	Intermediate Output
1	Start with DataFrame	A, B	-	-	{'A': [1,2,3], 'B': [4,5,6]}
2	Apply 'sum' to column A	A	sum	6	{'sum': {'A': 6}}
3	Apply 'sum' to column B	B	sum	15	{'sum': {'A': 6, 'B': 15}}
4	Apply 'mean' to column A	A	mean	2.0	{'sum': {'A': 6, 'B': 15}, 'mean': {'A': 2.0}}
5	Apply 'mean' to column B	B	mean	5.0	{'sum': {'A': 6, 'B': 15}, 'mean': {'A': 2.0, 'B': 5.0}}
6	Combine results into DataFrame	-	-	-	DataFrame with rows 'sum' and 'mean', columns 'A' and 'B'
7	Return aggregated DataFrame	-	-	-	A B sum 6 15 mean 2.0 5.0

💡 All specified aggregation functions applied to all columns; aggregation complete.

Variable Tracker

Variable	Start	After Step 2	After Step 4	Final
df	{'A': [1,2,3], 'B': [4,5,6]}	{'A': [1,2,3], 'B': [4,5,6]}	{'A': [1,2,3], 'B': [4,5,6]}	{'A': [1,2,3], 'B': [4,5,6]}
result	None	{'sum': {'A': 6}}	{'sum': {'A': 6, 'B': 15}, 'mean': {'A': 2.0}}	DataFrame with sum and mean rows

Key Moments - 2 Insights

Why does agg() return a DataFrame with function names as row labels?

Because agg() applies each function to all columns and combines results, it labels rows by function names to show which result belongs to which aggregation (see execution_table rows 6 and 7).

Can agg() apply different functions to different columns at once?

Yes, but in this example we used the same functions for all columns. To apply different functions per column, you pass a dictionary to agg(), which is not shown here.

Visual Quiz - 3 Questions

Look at the execution_table at step 3, what is the sum of column B?

A 15

B 5.0

C 6

D 4

💡 Hint

Check the 'Result' column in execution_table row 3 for sum of column B.

At which step does the mean of column A get calculated?

A Step 2

B Step 4

C Step 5

D Step 6

💡 Hint

Look at the 'Function' column in execution_table to find when 'mean' is applied to column A.

If we add 'max' to the agg() functions, how would the execution_table change?

A No change, max is ignored

B Fewer rows because max replaces sum

C More rows for steps applying 'max' to each column

D The DataFrame would have fewer columns

💡 Hint

Adding a function means agg() applies it to all columns, adding steps for each.

Concept Snapshot

agg() method in pandas:
- Applies one or more aggregation functions to DataFrame columns
- Syntax: df.agg(['func1', 'func2']) or df.agg({'col1': 'func1', 'col2': 'func2'})
- Returns a DataFrame with functions as row labels and columns as original columns
- Useful for quick summary statistics
- Supports built-in and custom aggregation functions

Full Transcript

This visual execution trace shows how pandas agg() method works on a DataFrame. Starting with a DataFrame with columns A and B, agg() applies the functions 'sum' and 'mean' to each column. Step by step, it calculates sum for A and B, then mean for A and B, storing results in an intermediate dictionary. Finally, it combines these results into a new DataFrame with rows labeled by function names and columns by original column names. Variables df and result are tracked through the steps. Key moments clarify why agg() returns a DataFrame with function names as rows and that different functions can be applied per column. The quiz tests understanding of specific steps and effects of adding functions. The snapshot summarizes agg() usage and behavior.