You can create scatter plots in Python by using the **matplotlib** as follows:

import matplotlib.pyplot as plt plt.scatter(x, y) plt.show()

Where **x** and **y** are lists of numbers or the data points for the plot.

For example, let’s create a scatter plot where **x** and **y** are lists of random numbers between **1** and **100**:

import matplotlib.pyplot as plt import random x = [random.randint(1, 100) for n in range(100)] y = [random.randint(1, 100) for n in range(100)] plt.scatter(x, y) plt.show()

Given randomized **x** and **y** data, the scatter plot looks something like this:

## Scatter Plots in Python

Generally, scatter plots are used to demonstrate the relationship between two variables. These relationships can be linear, non-linear, positive, negative, strong, or weak.

To create scatter plots for visualizing these relationships in Python, first install **matplotlib** on your machine.

### How to Install Matplotlib in Python

To create a scatter plot, you need to have **matplotlib** module installed.

If you don’t have it yet, install it by running the following command in your command line:

pip install matplotlib

## How to Create a Scatter Plot in Python

To create a scatter plot:

- Specify a group of data points
`x`

and`y`

. - Call
`matplotlib.pyplot.scatter(x, y)`

for creating a scatter plot.

For example, let’s create a scatter plot with **100** random **x** and **y** values as the data points:

import matplotlib.pyplot as plt import random x = [random.randint(1, 100) for n in range(100)] y = [random.randint(1, 100) for n in range(100)] plt.scatter(x, y) plt.show()

Here is the resulting scatter plot:

## Example—Randomly Distributed Data

This example uses **NumPy** to generate random data from a normal distribution. Make sure to have **NumPy** installed on your system:

pip install numpy

Let’s create two lists filled with **100** numbers picked from the normal distribution. Then let’s create a scatter plot from the randomized data:

import numpy import matplotlib.pyplot as plt x = numpy.random.normal(2.0, 1.0, 1000) y = numpy.random.normal(8.0, 3.0, 1000) plt.scatter(x, y) plt.show()

- The
**x**data is from a normal distribution where the mean is**2.0**and STD**1.0**. - The
**y**data is from a normal distribution where the mean is**8.0**and STD**3.0**.

This means we expect to see the `x`

values centered around **2.0**, and y values around **8.0**. Also, the **y** values are going to be spread more than the **x** values due to greater standard deviation.

Output:

## Conclusion

Today you learned how to produce a scatterplot in Python.

To recap, scatter plotting is a useful tool to observe relationships between two variables.

In Python, you can create a scatter plot with **matplotlib**:

import matplotlib.pyplot as plt plt.scatter(x, y)

Where **x** and **y** are lists of numbers that act as data points.

Thanks for reading. I hope you enjoy it.

Happy coding!