groupby() basics in Data Analysis Python - Step-by-Step Execution

Concept Flow - groupby() basics

Start with DataFrame

↓

Choose column(s) to group by

↓

Split data into groups

↓

Apply aggregation function

↓

Combine results into new DataFrame

↓

Output grouped data

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

Execution Sample

Data Analysis Python

import pandas as pd

data = {'Team': ['A', 'A', 'B', 'B'], 'Points': [10, 15, 10, 5]}
df = pd.DataFrame(data)
grouped = df.groupby('Team').sum()

This code groups data 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, 5]}	4 rows with Team and Points columns
2	Group by 'Team'	DataFrame	Groups: 'A' with 2 rows, 'B' with 2 rows
3	Sum Points in each group	Groups	Group 'A': 10+15=25, Group 'B': 10+5=15
4	Combine results	Aggregated sums	New DataFrame with Team as index and Points summed
5	Output grouped data	Grouped DataFrame	Team A: 25 points, Team B: 15 points

💡 All groups processed and summed, output DataFrame ready

Variable Tracker

Variable	Start	After Step 1	After Step 2	After Step 3	Final
df	None	{'Team': ['A', 'A', 'B', 'B'], 'Points': [10, 15, 10, 5]}	Same	Same	Same
grouped	None	None	Groups by Team	Sum applied per group	DataFrame with sums

Key Moments - 2 Insights

Why does groupby() create groups before applying sum()?

groupby() first splits the data into groups based on the column, then sum() is applied to each group separately, as shown in execution_table step 3.

Is the original DataFrame changed after groupby()?

No, the original DataFrame stays the same; groupby() returns a new grouped object, as seen in variable_tracker where df remains unchanged.

Visual Quiz - 3 Questions

Look at the execution table, what is the sum of Points for group 'A' after step 3?

A 15

B 25

C 10

D 35

💡 Hint

Check execution_table row 3 under Result for group 'A' sum

At which step does the data get split into groups?

A Step 1

B Step 3

C Step 2

D Step 4

💡 Hint

Look at execution_table row 2 Action column

If we group by 'Team' and use mean() instead of sum(), what changes in the execution table?

A Step 3 result changes to average Points per group

B Step 2 changes to grouping by Points

C Step 4 combines results differently

D No change at all

💡 Hint

Aggregation function in step 3 affects the result calculation

Concept Snapshot

groupby() basics:
- Use df.groupby('column') to split data into groups
- Apply aggregation like sum(), mean() on groups
- Returns new DataFrame with grouped results
- Original DataFrame stays unchanged
- Useful for summarizing data by categories

Full Transcript

The groupby() function in pandas splits a DataFrame into groups based on one or more columns. Then, you apply an aggregation function like sum() to each group separately. The results combine into a new DataFrame showing the summary per group. The original data does not change. This process helps summarize data by categories, like summing points per team.