Programming tips for everyone

# Scatter Plots in Python 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

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, install the `matplotlib` library on your machine.

### How to Install Matplotlib in Python

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

In case you don’t have it, 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, you need to have a group of data points. Then use `matplotlib.pyplot.scatter()` for creating a scatter plot of the data.

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()```

The result looks like this:

## 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 `x` values due to greater standard deviation.

Output:

## Conclusion

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!