Bin count and bin edges in Matplotlib

Introduction

We use bin count and bin edges to group data into ranges. This helps us see how data is spread out or clustered.

When you want to create a histogram to visualize data distribution.

When you need to summarize large data by grouping values into intervals.

When analyzing exam scores to see how many students fall into each grade range.

When checking sales data to find how many sales fall into different price ranges.

When comparing the frequency of different ranges in sensor measurements.

Syntax

Matplotlib

counts, bin_edges, patches = plt.hist(data, bins=number_of_bins)

counts shows how many data points fall into each bin.

bin_edges shows the boundaries of each bin.

Examples

This divides data into 3 bins and counts how many values fall into each.

Matplotlib

counts, bin_edges, patches = plt.hist([1, 2, 2, 3, 4], bins=3)

Here, bins are set manually with edges at 0, 2, 4, and 6.

Matplotlib

counts, bin_edges, patches = plt.hist(data, bins=[0, 2, 4, 6])

Sample Program

This code creates a histogram with 4 bins from the data array. It prints how many values fall into each bin and the edges of these bins. Then it shows the histogram plot.

Matplotlib

import matplotlib.pyplot as plt
import numpy as np

# Sample data
data = np.array([1, 2, 2, 3, 4, 5, 5, 6, 7, 8])

# Create histogram with 4 bins
counts, bin_edges, patches = plt.hist(data, bins=4)

# Print counts and bin edges
print('Counts:', counts)
print('Bin edges:', bin_edges)

# Show the plot
plt.show()

Important Notes

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

Bins include the left edge but exclude the right edge, except the last bin which includes both edges.

You can use either a number of bins or a list of bin edges to control binning.

Summary

Bin count tells how many data points fall into each range.

Bin edges define the boundaries of these ranges.

Using bins helps visualize and understand data distribution easily.