Binning with cut() and qcut() in Pandas

Introduction

Binning helps group continuous numbers into categories. It makes data easier to understand and analyze.

You want to turn ages into groups like 'young', 'middle', 'old'.

You need to split sales amounts into low, medium, and high ranges.

You want to create equal-sized groups from test scores.

You want to simplify data for charts by grouping values.

You want to find patterns by grouping continuous data.

Syntax

Pandas

pandas.cut(x, bins, right=True, labels=None, retbins=False, precision=3, include_lowest=False)
pandas.qcut(x, q, labels=None, retbins=False, precision=3, duplicates='raise')

cut() divides data into fixed intervals you define.

qcut() divides data into equal-sized groups based on data distribution.

Examples

This cuts ages into groups based on fixed bins: 20-30, 30-40, etc.

Pandas

import pandas as pd
ages = [22, 25, 47, 35, 46, 55]
bins = [20, 30, 40, 50, 60]
categories = pd.cut(ages, bins)
print(categories)

This splits scores into 3 groups with roughly equal number of items.

Pandas

import pandas as pd
scores = [55, 89, 76, 45, 92, 67, 88]
quantiles = pd.qcut(scores, 3)
print(quantiles)

Sample Program

This program shows how to group ages into categories using fixed ranges with cut() and equal-sized groups with qcut().

Pandas

import pandas as pd

# Sample data: ages
ages = [18, 22, 27, 35, 42, 48, 55, 60, 70, 75]

# Using cut() to create age groups with fixed bins
bins = [0, 25, 50, 75]
age_groups = pd.cut(ages, bins, labels=['Young', 'Middle-aged', 'Senior'])
print('Age groups with cut():')
print(age_groups)

# Using qcut() to create 3 equal-sized groups
age_quantiles = pd.qcut(ages, 3, labels=['Low', 'Medium', 'High'])
print('\nAge groups with qcut():')
print(age_quantiles)

Important Notes

With cut(), bins must cover all data values or some values become NaN.

qcut() may fail if there are many duplicate values at bin edges; use duplicates='drop' to fix.

Labels help name groups but are optional; if omitted, intervals are shown.

Summary

cut() groups data into fixed ranges you choose.

qcut() groups data into equal-sized buckets based on data distribution.

Binning makes continuous data easier to analyze and visualize.