Matplotlib.pyplot.hist() in Python

The matplotlib.pyplot.hist() function in Python is used to create histograms, which are graphical representations of data distribution. It divides the data into bins (non-overlapping intervals) and counts the frequency of values in each bin, plotting them as bars. Lets consider the data values and visualise histogram with help of an example:

import matplotlib.pyplot as plt

data = [32, 96, 45, 67, 76, 28, 79, 62, 43, 81, 70,61, 95, 44, 60, 69, 71, 23
       ,69, 54, 76, 67,82, 97, 26, 34, 18, 16, 59, 88, 29, 30, 66,23, 65, 72, 
       20, 78, 49, 73, 62, 87, 37, 68,81, 80, 77, 92, 81, 52, 43, 68, 71, 86]
plt.hist(data)
plt.show()

Output:

Histogram with hist() with default parameters

Understanding the syntax and parameters

Syntax: matplotlib.pyplot.hist(x, bins=None, range=None, density=False, histtype=’bar’, color=None, label=None)

• x: The data to be represented in the histogram.
• bins: Specifies the number of bins or the bin edges for the histogram.
• range: The lower and upper range of the bins.
• density: If True, the histogram is normalized to form a probability density.
• histtype: Defines the type of histogram (e.g., ‘bar’ for a traditional bar histogram).
• color: Sets the color of the bars.
• label: Label for the histogram, used in legends.

Create a Histogram in Matplotlib

Using the Matplotlib library in python, we can create many types of histograms. Let us see a few examples to better understand the functionality of hist() function. In this example, we will create a histogram and pass the necessary parameters such as bins, color, density, etc.

import matplotlib.pyplot as plt
import numpy as np

mu, sigma = 121, 21
x = np.random.normal(mu, sigma, 1000)

num_bins = 100
n, bins, _ = plt.hist(x, num_bins, density=True, color='green', alpha=0.7)

plt.xlabel('X-Axis')
plt.ylabel('Y-Axis')
plt.title('matplotlib.pyplot.hist() Example', fontweight='bold')

plt.show()

Output:

Creating the histogram

Example 2: Visualize the Specific Bars of Histogram

In this example, we will create a histogram with different attributes using matplotlib.pyplot.hist() function. We define a specific set of colors for the bars of the histogram bars.

import numpy as np
import matplotlib.pyplot as plt

x = np.random.randn(10000, 3)
colors = ['green', 'blue', 'lime']

plt.hist(x, bins=20, density=True, histtype='bar', color=colors, label=colors)

plt.legend(fontsize=10)
plt.title('matplotlib.pyplot.hist() Example', fontweight='bold')

plt.show()

Output:

different color bars in matplot.pyplot.hist()

Frequently Asked Questions (FAQs)

What is the purpose of the pyplot.hist() function in Matplotlib?

The pyplot.hist() function is used to create histograms in Matplotlib. It visualizes the distribution of numerical data by grouping values into bins and displaying the frequency of data points within each bin, making it easier to analyze data distributions.

How do I create a histogram with custom bin sizes?

To create a histogram with custom bin sizes, you can specify the bins parameter in the hist() function.

This can either be an integer indicating the number of bins or a sequence defining specific bin edges. For example, using bins=20 will divide your data into 20 equal-width bins.

Can I overlay multiple histograms on the same plot?

Yes, you can overlay multiple histograms by calling hist() multiple times with different datasets and using different colors or styles. You can also use the label parameter to differentiate between datasets in a legend, enhancing clarity in your visualizations.

How can I save my histogram as an image file?

You can save your histogram as an image file by using the savefig() function after plotting your histogram. For instance, after calling plt.hist(), you can use plt.savefig('my_histogram.png') to save it in PNG format before calling plt.show(). This allows you to keep a record of your visualizations for reports or presentations.

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Article Tags :

• AI-ML-DS
• Data Visualization
• Python-matplotlib