groupby() basics in Pandas - Step-by-Step Execution

Concept Flow - groupby() basics

Start with DataFrame

↓

Choose column(s) to group by

↓

Split data into groups

↓

Apply aggregation or function to each group

↓

Combine results into new DataFrame or Series

↓

Output grouped result

The groupby() process splits data by chosen columns, applies a function to each group, then combines the results.

Execution Sample

Pandas

import pandas as pd

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

This code groups rows by 'Team' and sums the 'Points' for each team.

Execution Table

Step	Action	Data State	Result
1	Create DataFrame	{'Team': ['A', 'A', 'B', 'B'], 'Points': [10, 15, 10, 20]}	4 rows with Teams and Points
2	Call groupby('Team')	DataFrame unchanged	Groups created: 'A' with 2 rows, 'B' with 2 rows
3	Apply sum() to groups	Groups split	Sum Points for 'A': 25, for 'B': 30
4	Combine results	Aggregated sums	New DataFrame with Teams as index and summed Points
5	Output grouped result	Final grouped DataFrame	Team A: 25, Team B: 30

💡 All groups processed and aggregated sums calculated

Variable Tracker

Variable	Start	After groupby	After sum	Final
df	Original DataFrame	Unchanged	Unchanged	Unchanged
grouped	Not defined	GroupBy object with groups 'A' and 'B'	DataFrame with sums per group	DataFrame with sums per group

Key Moments - 3 Insights

Why does groupby() not immediately show results?

groupby() creates a grouping object but does not compute results until an aggregation like sum() is called, as seen in steps 2 and 3 of the execution_table.

What happens if we group by a column but don't aggregate?

Without aggregation, groupby() returns a GroupBy object that holds groups but no summarized data, so no output is shown until an aggregation function is applied (step 3).

Why is the result indexed by the group column?

After aggregation, pandas uses the group column as the index to clearly show each group's result, as in step 4 where 'Team' becomes the index.

Visual Quiz - 3 Questions

Look at the execution_table at step 3, what is the sum of Points for group 'B'?

A 30

B 20

C 10

D 40

💡 Hint

Check the 'Result' column at step 3 where sums are calculated for each group.

At which step does the DataFrame change from original to aggregated sums?

A Step 2

B Step 3

C Step 4

D Step 5

💡 Hint

Look at the 'Data State' column to see when aggregation results are combined into a new DataFrame.

If we remove the sum() call, what type of object is 'grouped' after step 2?

A DataFrame

B GroupBy object

C Series

D List

💡 Hint

Refer to the 'Result' column at step 2 describing the groupby() output.

Concept Snapshot

pandas groupby() basics:
- Use df.groupby('col') to split data by 'col'
- Aggregation like sum(), mean() applies to each group
- Result is combined into new DataFrame or Series
- Group column becomes index in result
- groupby() alone returns a GroupBy object, no output until aggregation

Full Transcript

This lesson shows how pandas groupby() works step-by-step. First, a DataFrame is created with teams and points. Then groupby('Team') splits the data into groups by team. Next, sum() adds points within each group. Finally, results combine into a new DataFrame indexed by team. The key is groupby() creates groups but does not compute until aggregation is called. The output shows total points per team clearly. This helps summarize data by categories easily.