How to Create a Normalized Histogram Using Python Matplotlib

A histogram is a frequency distribution that depicts the frequencies of different elements in a dataset. This graph is generally used to study frequencies and determine how the values are distributed in a dataset.

Normalization of histogram refers to mapping the frequencies of a dataset between the range [0, 1] both inclusive. In this article, we will learn how to create a normalized histogram in Python.

Create a Normalized Histogram Using the Matplotlib Library in Python

The Matplotlib module is a comprehensive Python module for creating static and interactive plots. It is a very robust and straightforward package that is widely used in data science for visualization purposes. Matplotlib can be used to create a normalized histogram. This module has a hist() function. that is used for creating histograms. Following is the function definition of the hist() method.

matplotlib.pyplot.hist(x, bins=None, range=None, density=False, weights=None, cumulative=False, bottom=None, histtype='bar', align='mid', orientation='vertical', rwidth=None, log=False, color=None, label=None, stacked=False, *, data=None, **kwargs)

Following is a brief explanation of the arguments we will use to generate a normalized histogram.

• x: A list, a tuple, or a NumPy array of input values.
• density: A boolean flag for plotting normalized values. By default, it is False.
• color: The colour of the bars in the histogram.
• label: A label for the plotted values.

Refer to the following Python code to create a normalized histogram.

import matplotlib.pyplot as plt

x = [1, 9, 5, 7, 1, 1, 2, 4, 9, 9, 9, 3, 4, 5, 5, 5, 6, 5, 5, 7]
plt.hist(x, density=True, color="green", label="Numbers")
plt.legend()
plt.show()

Output: