Say we have a set of points generated by an unknown polynomial function, we can approximate the function using linear interpolation. To do this in Python, you can use the `np.interp()`

function from NumPy:

```
import numpy as np
points = [-2, -1, 0, 1, 2]
values = [4, 1, 0, 1, 4]
x = np.linspace(-2, 2, num=10)
y = np.interp(x, points, values)
```

Notice that you have to pass in:

- A set of points where you want the interpolated value (
`x`

) - A set of points with a known value (
`points`

) - The set of known values (
`values`

)

Let’s plot the known points in blue and the interpolated points in orange so we can see what’s happening:

```
import matplotlib.pyplot as plt
plt.plot(points, values, 'o')
plt.plot(x, y, 'o', alpha=0.5)
plt.xlabel("x")
plt.ylabel("y");
```

Easy enough.

### Hello, my name is Ben Cook

I help data scientists deploy their code. If there's any way I can serve you, don't hestitate to **reach out**.
You can also find out a little more **about me** or download my free guide: **8 Best Practices for Building Machine Learning Pipelines**.

Thanks for stopping by!