How would you deal with inf. or NA for rate or ratio as a feature variable I'm trying to create a feature for a churn model (binary classifier). 
The feature is mean of sales growth rates for several months. But if I just take the mean of sales for several months, I often get NAN or inf. since sales are often zeros. I could impute some numbers like 0 or mean as the missing sales but I feel I'm modifying the pattern/underling distribution. How would you go by creating such a feature for a classification model? 

Thanks!
It depends on how would you define sales growth from a business point of view. For example if you trying to detect sales peaks with considering zero sales as normal,  you can fill zeroes, nans or infs with its most recent previous that has an ok number.