Plotting Histogram in Python using Matplotlib

Last Updated : 13 Jan, 2026

Histograms are one of the most fundamental tools in data visualization. They provide a graphical representation of data distribution, showing how frequently each value or range of values occurs. Histograms are especially useful for analyzing continuous numerical data, such as measurements, sensor readings or experimental results.

A histogram is a type of bar plot where:

The X-axis represents intervals (called bins) of the data.
The Y-axis represents the frequency of values within each bin.

Unlike regular bar plots, histograms group data into bins to summarize data distribution effectively.

Creating a Matplotlib Histogram

Divide the data range into consecutive, non-overlapping intervals called bins.
Count how many values fall into each bin.
Use the matplotlib.pyplot.hist() function to plot the histogram.

The following table shows the parameters accepted by matplotlib.pyplot.hist() function :

Attribute	Parameter
x	An array or sequence of numerical data.
bins	Number of bins (int) or specific intervals (array).
density	If True, normalises the histogram to show probability instead of frequency.
range	A tuple specifying lower and upper limits of bins.
histtype	Type of histogram: bar, barstacked, step, stepfilled. Default: bar.
align	Bin alignment: left, right, mid.
weights	An array of weights for each data point.
bottom	Baseline for bins.
rwidth	Relative width of bars (0–1).
color	Color of bars. Can be a single color or sequence.
label	Label for legend.
log	If True, uses logarithmic scale on Y-axis.

Plotting Histogram in Python using Matplotlib

Here we will see different methods of Plotting Histogram in Matplotlib in Python:

Basic Histogram
Customized Histogram with Density Plot
Customized Histogram with Watermark
Multiple Histograms with Subplots
Stacked Histogram
2D Histogram (Hexbin Plot)

1. Basic Histogram

Python

import matplotlib.pyplot as plt
import numpy as np

# Generate random data for the histogram
data = np.random.randn(1000)

# Plotting a basic histogram
plt.hist(data, bins=30, color='skyblue', edgecolor='black')

# Adding labels and title
plt.xlabel('Values')
plt.ylabel('Frequency')
plt.title('Basic Histogram')

# Display the plot
plt.show()

Output: