How can we reject a window containing an outlier during rolling average using python?

The problem that I am facing is how i can reject a window of 10 rows if one or many of the rows consist of an outlier while computing rolling average using python pandas?

For clarification:

df = df['speed'].rolling(10).mean() 
outlier_lower_bound = 0
outlier_upper_bound = 15

df.max()

Now how do I reject/ not consider the average value of those 10 period window if it consists an outlier?

The end goal is to get the max moving average without accounting/ considering the window of 10 period if it contains an outlier Thanks in advance!

Solution

You can do fix your issue in just one line like so:

_filter = lambda x: float("inf") if x > outlier_upper_bound or x < outlier_lower_bound else x

df["speed"].apply(_filter).rolling(10).mean().dropna()

The idea behind my code can be understood in these steps:

I create a lambda function called _filter that converts any value outside your boundaries into inf.
When applying mean over a window that has inf in it, the result will be Nan.
Finally, I drop all Nan values which will mimic the same effect.