Binning with cut() and qcut() in Pandas - Step-by-Step Execution

Concept Flow - Binning with cut() and qcut()

Start with numeric data

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Choose binning method

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Define bins

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Assign bins

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Analyze binned data

We start with numeric data, choose either cut() for fixed bins or qcut() for quantile-based bins, assign data to bins, then analyze the results.

Execution Sample

Pandas

import pandas as pd

ages = [22, 25, 47, 35, 46, 55, 21, 23, 37, 52]

bins = [20, 30, 40, 50, 60]
labels = ['20-29', '30-39', '40-49', '50-59']

age_bins = pd.cut(ages, bins=bins, labels=labels)

quantile_bins = pd.qcut(ages, q=4, labels=['Q1', 'Q2', 'Q3', 'Q4'])

This code bins ages into fixed ranges using cut(), and into four quantile groups using qcut().

Execution Table

Step	Data Point	cut() Bin	qcut() Quantile Bin
1	22	20-29	Q1
2	25	20-29	Q1
3	47	40-49	Q3
4	35	30-39	Q2
5	46	40-49	Q3
6	55	50-59	Q4
7	21	20-29	Q1
8	23	20-29	Q1
9	37	30-39	Q2
10	52	50-59	Q4
Exit	All data processed	-	-

💡 All data points assigned to bins; execution ends.

Variable Tracker

Variable	Start	After 1	After 2	After 3	After 4	After 5	After 6	After 7	After 8	After 9	After 10	Final
ages	[22,25,47,35,46,55,21,23,37,52]	[25,47,35,46,55,21,23,37,52]	[47,35,46,55,21,23,37,52]	[35,46,55,21,23,37,52]	[46,55,21,23,37,52]	[55,21,23,37,52]	[21,23,37,52]	[23,37,52]	[37,52]	[52]	[]	[]
age_bins	[]	['20-29']	['20-29', '20-29']	['20-29', '20-29', '40-49']	['20-29', '20-29', '40-49', '30-39']	['20-29', '20-29', '40-49', '30-39', '40-49']	['20-29', '20-29', '40-49', '30-39', '40-49', '50-59']	['20-29', '20-29', '40-49', '30-39', '40-49', '50-59', '20-29']	['20-29', '20-29', '40-49', '30-39', '40-49', '50-59', '20-29', '20-29']	['20-29', '20-29', '40-49', '30-39', '40-49', '50-59', '20-29', '20-29', '30-39']	['20-29', '20-29', '40-49', '30-39', '40-49', '50-59', '20-29', '20-29', '30-39', '50-59']	['20-29', '20-29', '40-49', '30-39', '40-49', '50-59', '20-29', '20-29', '30-39', '50-59']
quantile_bins	[]	['Q1']	['Q1', 'Q1']	['Q1', 'Q1', 'Q3']	['Q1', 'Q1', 'Q3', 'Q2']	['Q1', 'Q1', 'Q3', 'Q2', 'Q3']	['Q1', 'Q1', 'Q3', 'Q2', 'Q3', 'Q4']	['Q1', 'Q1', 'Q3', 'Q2', 'Q3', 'Q4', 'Q1']	['Q1', 'Q1', 'Q3', 'Q2', 'Q3', 'Q4', 'Q1', 'Q1']	['Q1', 'Q1', 'Q3', 'Q2', 'Q3', 'Q4', 'Q1', 'Q1', 'Q2']	['Q1', 'Q1', 'Q3', 'Q2', 'Q3', 'Q4', 'Q1', 'Q1', 'Q2', 'Q4']	['Q1', 'Q1', 'Q3', 'Q2', 'Q3', 'Q4', 'Q1', 'Q1', 'Q2', 'Q4']

Key Moments - 2 Insights

Why do some ages fall into the same bin in cut() but different bins in qcut()?

cut() uses fixed ranges (bins), so ages 22 and 25 both go to '20-29'. qcut() divides data into equal-sized groups (quantiles), so 22 is Q1 and 25 is also Q1 due to the data distribution. See execution_table rows 1-2.

What happens if a data point is exactly on a bin edge in cut()?

By default (right=True), cut() uses (a, b] intervals, so value == b goes into the bin ending at b (upper bin). Set right=False for [a, b). No exact edges in our example.

Visual Quiz - 3 Questions

Look at the execution_table, what bin does the age 37 fall into using cut()?

A 20-29

B 30-39

C 40-49

D 50-59

💡 Hint

Check the row where Data Point is 37 in the cut() Bin column.

At which step does qcut() assign the first data point to Q3?

A Step 4

B Step 2

C Step 3

D Step 5

💡 Hint

Look at qcut() Quantile Bin column in execution_table for the earliest Q3.

If we change bins in cut() to [20, 35, 50, 60], how would the bin for age 35 change?

A It would be in '35-50' bin

B It would be in '30-39' bin

C It would be in '20-29' bin

D It would be in '50-59' bin

💡 Hint

Bins define intervals; with default right=True, 35==35 goes to (20,35], but assuming adjusted labels or right=False for '35-50'.

Concept Snapshot

pandas cut() divides data into fixed bins by ranges.
pandas qcut() divides data into bins with equal counts (quantiles).
Use cut() when you want specific ranges.
Use qcut() when you want balanced groups.
Both return categorical bins for analysis.

Full Transcript

We start with a list of ages. Using pandas cut(), we define fixed bins like 20-29, 30-39, etc., and assign each age to these bins. Using qcut(), we split the data into four equal groups called quantiles labeled Q1 to Q4. Each age is assigned to a bin or quantile based on its value. The execution table shows each age's bin assignments step by step. Variables track how bins fill up as we process each age. Key moments clarify why cut() and qcut() differ and how bin edges are handled. The quiz tests understanding of bin assignments and effects of changing bins. The snapshot summarizes when to use cut() or qcut() for binning numeric data.