## Using numpy with Matplotlib

The previous article showed how to create a plot using Python lists to store the data. Matplotlib also works with numpy, and this can often simplify things.

Here we will look at how to use numpy to create the x values and y values more easily.

## Creating the x values

We want to create a set of 100 x values, equally spaced over the range 0 to 12. We can do this using Python lists by scaling the loop variable.

However, numpy has a function `linspace`

that is designed to do this job. It has two advantages:

- it is more readable
- it covers the exact range 0 to 12, with the intermediate values equally spaced

If you recall, our previous code went from 0 to *almost* 12, which wasn’t really much of a problem, but it wasn’t quite correct.

Here is how we use `linspace`

to create our range:

```
xa = np.linspace(0, 12, 100)
```

## Creating the y values

numpy supports univeral functions. These allow you to operate on entire arrays in one go:

```
ya = np.sin(xa)*np.exp(-xa/4)
```

This performs the same calculation on each of the 100 elements of `xa`

, creating a new 100 element array `ya`

containing the results. This avoids a loop, which makes the code more readable, but also more efficient.

## Complete code

Here is the complete code:

```
from matplotlib import pyplot as plt
import numpy as np
xa = np.linspace(0, 12, 100)
ya = np.sin(xa)*np.exp(-xa/4)
plt.plot(xa, ya)
plt.show()
```

This code is shorter and more readable, as well as being more efficient. This becomes more important with large data sets, and especially 2 dimensional data.

Here is the output, which look exactly the same as before:

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