Histogram computation with np.histogram() in NumPy

Introduction

A histogram helps us see how data is spread out by counting how many values fall into different ranges.

You want to understand the distribution of exam scores in a class.

You need to check how many customers bought products in different price ranges.

You want to see the frequency of different ages in a survey.

You want to visualize how often certain temperature ranges occur in a month.

Syntax

NumPy

numpy.histogram(data, bins=10, range=None, density=False, weights=None, cumulative=False)

data: The list or array of numbers you want to analyze.

bins: How many groups (bars) you want to split your data into. Default is 10.

Examples

This splits the data into 3 groups and counts how many numbers fall into each group.

NumPy

counts, bin_edges = np.histogram([1, 2, 2, 3, 4, 5], bins=3)

Here, bins are set by specific edges: 0 to 2, 2 to 4, and 4 to 6.

NumPy

counts, bin_edges = np.histogram(data, bins=[0, 2, 4, 6])

This returns the probability density instead of counts, useful for comparing different sized datasets.

NumPy

counts, bin_edges = np.histogram(data, density=True)

Sample Program

This code counts how many ages fall into 4 groups and shows the edges of these groups.

NumPy

import numpy as np

# Sample data: ages of 10 people
ages = np.array([22, 25, 27, 30, 22, 24, 29, 31, 35, 40])

# Compute histogram with 4 bins
counts, bin_edges = np.histogram(ages, bins=4)

print('Counts:', counts)
print('Bin edges:', bin_edges)

Important Notes

The bin_edges array shows the boundaries of each group.

The number of counts is always one less than the number of bin edges.

If you want to plot the histogram, you can use matplotlib.pyplot.hist() which does this internally.

Summary

Histograms count how many data points fall into different ranges.

Use np.histogram() to get counts and bin edges separately.

You can control the number or exact edges of bins to see data distribution clearly.