How to create histogram in Matplotlib and Numpy the easiest way?

Learn the simplest method to create a histogram using Python’s Matplotlib and Numpy libraries. These powerful libraries provide all the necessary functions for effortless histogram generation.

numpy matplotlib histogram in python

Generating a Histogram

To create a histogram in Python the easy way, you only need to import Matplotlib and Numpy Python libraries. They come equipped with all the essential functions.

Let’s generate a histogram based on randomly generated numbers with the following Python code:

import numpy as np
from matplotlib import pyplot as plt

histogram = np.random.randn(1000000)
plt.hist(histogram, bins=2000)
plt.title("Histogram by Pythoneo.com")
plt.show()

We use NumPy’s random.randn function to generate a normal distribution of random numbers, which forms the basis of our histogram. Python effortlessly generates even a million numbers in seconds.

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The hist function generates the histogram based on the generated data. You can adjust the number of bins to achieve the desired level of detail. It’s advisable to choose a number of bins that balances detail with readability for clearer interpretation.

By following these straightforward steps, you can create informative histograms using Matplotlib and Numpy in Python.

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Customizing Your Histogram

Now that you’ve created a basic histogram, let’s explore how to customize it to suit your needs.

You can enhance your histogram by:

Changing Colors: Use the color parameter in the hist function to select a different color for your bars.

Adding Labels: Include labels for the X and Y axes using plt.xlabel() and plt.ylabel().

Adjusting Bin Width: Modify the width of the bins with the binwidth parameter.

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Setting Titles: Customize the title of your histogram with plt.title().

Adding Legends: If you have multiple datasets, include legends to distinguish them with plt.legend().

import numpy as np
from matplotlib import pyplot as plt

histogram = np.random.randn(1000000)

plt.hist(histogram, bins=2000, color='skyblue', alpha=0.7)
plt.xlabel('Values')
plt.ylabel('Frequency')
plt.title('Customized Histogram by Pythoneo.com')
plt.legend(['Data Distribution'], loc='upper right')
plt.show()

Explore these customization options to tailor your histogram precisely to your analytical needs and enhance data visualization. Matplotlib offers a wide range of customization possibilities to make your histogram visually appealing and informative.