I prefer a Savitzky-Golay filter. It uses least squares to regress a small window of your data onto a polynomial, then uses the polynomial to estimate the point in the center of the window. Finally the window is shifted forward by one data point and the process repeats. This continues until every point has been optimally adjusted relative to its neighbors. It works great even with noisy samples from non-periodic and non-linear sources.

Here is a thorough cookbook example. See my code below to get an idea of how easy it is to use. Note: I left out the code for defining the `savitzky_golay()`

function because you can literally copy/paste it from the cookbook example I linked above.

```
import numpy as np
import matplotlib.pyplot as plt
x = np.linspace(0,2*np.pi,100)
y = np.sin(x) + np.random.random(100) * 0.2
yhat = savitzky_golay(y, 51, 3) # window size 51, polynomial order 3
plt.plot(x,y)
plt.plot(x,yhat, color="red")
plt.show()
```

**UPDATE:** It has come to my attention that the cookbook example I linked to has been taken down. Fortunately, the Savitzky-Golay filter has been incorporated into the SciPy library, as pointed out by @dodohjk (thanks @bicarlsen for the updated link).

To adapt the above code by using SciPy source, type:

```
from scipy.signal import savgol_filter
yhat = savgol_filter(y, 51, 3) # window size 51, polynomial order 3
```