Binning continuous variables in Data Analysis Python

Introduction

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

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

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

You want to reduce noise in data by grouping similar values.

You want to prepare data for models that work better with categories.

You want to create histograms or frequency tables.

Syntax

Data Analysis Python

pandas.cut(x, bins, labels=None, right=True, include_lowest=False)

x is the continuous data you want to bin.

bins defines the edges of the groups.

Examples

This splits ages into two groups: 0-30 and 30-60.

Data Analysis Python

import pandas as pd
ages = [22, 35, 58, 45, 18]
bins = [0, 30, 60]
categories = pd.cut(ages, bins)
print(categories)

This adds names to the groups instead of ranges.

Data Analysis Python

labels = ['Young', 'Old']
categories = pd.cut(ages, bins, labels=labels)
print(categories)

This changes whether the bins include the right edge and includes the lowest value.

Data Analysis Python

categories = pd.cut(ages, bins, right=False, include_lowest=True)
print(categories)

Sample Program

This program groups exam scores into letter grades using bins.

Data Analysis Python

import pandas as pd

# Sample continuous data: exam scores
scores = [55, 67, 89, 45, 72, 90, 33, 78, 84, 60]

# Define bins for grades
bins = [0, 60, 70, 80, 90, 100]
labels = ['F', 'D', 'C', 'B', 'A']

# Bin the scores into grade categories
grade_categories = pd.cut(scores, bins=bins, labels=labels, include_lowest=True)

# Create a DataFrame to show scores and their grades
df = pd.DataFrame({'Score': scores, 'Grade': grade_categories})
print(df)

Important Notes

Binning changes continuous data into categories, which can lose some detail.

Choose bins carefully to make meaningful groups.

Use include_lowest=True to include the smallest value in the first bin.

Summary

Binning groups continuous numbers into categories.

Use pandas.cut() with bins and optional labels.

Binning helps simplify data and prepare it for analysis.