Matplotlib Histograms

Histograms vs. Bar Charts

A histogram looks like a bar chart, but it answers a different question. A bar chart compares a value across separate named categories, such as sales by region. A histogram instead takes one long list of numbers, such as everyone's height in a class, and shows how many values fall into each range, called a bin. There are no separate categories in the input data — Matplotlib creates the groups for you.

Basic Histogram

Pass a single array of numbers to plt.hist(). By default Matplotlib splits the data's range into 10 equal-width bins and draws a bar for the count of values in each one.

import matplotlib.pyplot as plt
import numpy as np

np.random.seed(1)
delivery_times = np.random.normal(loc=30, scale=5, size=200)

plt.hist(delivery_times)
plt.xlabel("Delivery time (minutes)")
plt.ylabel("Number of deliveries")
plt.title("Distribution of delivery times")
plt.show()

Choosing the Number of Bins

The bins argument controls how many equal-width groups the data is split into. Too few bins can hide real structure in the data, such as two overlapping peaks; too many bins can make the chart look noisy and jagged, dominated by random gaps. There is no single right number — it is common to try a few values and see which one best shows the shape of the data.

import matplotlib.pyplot as plt
import numpy as np

np.random.seed(0)
ages = np.random.randint(18, 70, size=150)

plt.hist(ages, bins=8, color="mediumpurple", edgecolor="white")
plt.xlabel("Age")
plt.ylabel("Number of customers")
plt.title("Customer age distribution (8 bins)")
plt.show()

Passing two arrays to plt.hist() in separate calls, with alpha set below 1 so both are visible, lets you compare how two groups are distributed on the same axes.

import matplotlib.pyplot as plt
import numpy as np

np.random.seed(2)
before_training = np.random.normal(loc=20, scale=4, size=100)
after_training = np.random.normal(loc=25, scale=3, size=100)

plt.hist(before_training, bins=10, alpha=0.6, label="Before training")
plt.hist(after_training, bins=10, alpha=0.6, label="After training")
plt.xlabel("5K run time (minutes)")
plt.ylabel("Number of runners")
plt.legend()
plt.show()