I have a list of grades like this:

```
grades = [[[4.0, 3.0], [3.0, 3.0], [4.0, 3.0], [3.33, 3.33], [3.0, 3.0], [4.0, 3.67], [3.0, 3.67], [4.0, 3.0], [4.0, 3.33], [4.0, 3.33], [3.0, 3.0], [3.67, 3.67], [3.33, 3.0], [4.0, 3.0], [3.0, 3.67], [3.33, 4.0], [4.0, 3.33], [4.0, 4.0], [3.0, 3.67], [3.0, 3.67], [3.67, 3.33], [4.0, 3.0], [3.0, 4.0], [3.67, 3.67], [4.0, 3.33], [2.33, 2.0], [3.0, 2.67], [2.67, 3.67], [2.33, 2.0]], [[3.0, 3.67], [3.33, 4.0]], [[2.33, 4.0]], [[4.0, 0], [3.67, 0], [3.67, 0], [4.0, 0], [3.0, 0], [3.67, 0], [3.33, 0], [3.0, 0], [3.67, 3.67], [3.0, 0], [4.0, 0], [4.0, 0], [3.0, 0], [3.67, 0], [3.0, 0], [4.0, 0], [3.0, 0], [3.33, 0], [4.0, 0], [4.0, 0], [3.33, 0], [0, 3.33], [3.33, 0], [4.0, 3.0], [4.0, 0], [4.0, 0]], [[3.0, 3.67], [3.67, 3.67], [4.0, 3.67], [3.0, 3.0], [3.0, 3.0], [3.67, 4.0], [4.0, 3.0], [3.33, 4.0], [3.67, 3.33], [3.0, 3.67], [3.67, 3.0], [3.0, 3.0], [3.0, 3.33], [3.33, 3.0], [4.0, 3.67], [3.33, 4.0], [4.0, 3.33], [3.67, 3.0], [3.67, 4.0], [3.0, 3.33], [3.0, 3.0], [3.67, 4.0], [3.67, 3.0], [3.33, 3.67], [3.0, 3.33], [3.33, 4.0], [3.0, 4.0], [3.0, 4.0], [3.33, 3.33], [3.33, 3.33], [3.67, 3.33], [4.0, 3.67], [3.33, 3.33], [4.0, 3.67], [3.33, 3.67], [4.0, 3.33], [3.67, 3.0], [3.33, 3.0], [3.67, 3.33], [3.0, 4.0], [3.67, 4.0], [3.67, 3.0], [3.67, 3.33], [3.33, 3.33], [3.67, 3.0], [4.0, 4.0], [3.0, 3.67], [3.0, 3.0], [3.0, 3.0], [3.33, 4.0], [3.67, 4.0], [3.0, 4.0], [3.67, 3.67], [3.0, 3.33], [3.67, 4.0], [3.33, 3.33], [4.0, 4.0], [3.33, 4.0], [3.33, 4.0], [3.0, 3.67], [4.0, 3.33], [3.33, 4.0], [3.0, 3.0], [3.33, 3.67], [3.33, 4.0], [3.67, 3.33], [3.67, 3.33], [4.0, 3.67], [3.67, 4.0], [3.67, 3.0], [3.33, 3.33], [3.0, 3.67], [3.67, 3.33], [3.33, 3.0], [3.33, 4.0], [3.0, 3.0], [3.33, 4.0], [3.33, 4.0], [4.0, 3.67], [4.0, 3.0], [3.67, 4.0], [3.33, 3.0], [3.67, 3.67], [3.67, 3.0], [3.33, 3.0], [4.0, 4.0], [3.33, 4.0], [3.0, 3.33], [3.67, 3.0], [4.0, 3.33], [3.67, 4.0], [3.0, 2.33], [2.0, 3.33], [2.0, 3.67], [2.0, 2.33], [3.67, 3.33], [2.0, 2.33], [3.67, 2.33], [2.33, 3.67], [3.67, 3.67], [3.33, 2.67], [3.33, 2.0], [3.67, 4.0], [2.0, 4.0], [4.0, 2.0], [3.0, 2.0], [2.33, 2.67], [4.0, 4.0], [3.0, 2.33], [3.0, 3.33], [2.33, 3.0], [4.0, 3.33], [3.67, 4.0], [2.0, 2.0], [2.33, 4.0], [4.0, 2.0], [2.67, 3.33], [2.0, 3.67], [3.33, 4.0]], [[3.0, 0], [3.33, 0], [3.67, 0], [3.67, 0], [3.67, 0], [3.33, 0], [3.0, 0], [3.0, 0], [3.33, 0], [3.0, 0], [4.0, 0], [3.0, 0], [3.0, 0], [3.67, 0], [3.67, 3.67], [3.67, 0], [3.67, 0], [4.0, 0], [3.67, 0], [3.0, 0], [3.0, 0], [4.0, 0], [3.0, 0], [3.0, 0], [3.0, 0], [3.33, 0], [3.0, 0], [3.67, 0], [4.0, 0], [3.0, 0], [3.0, 0], [3.0, 0], [3.33, 0], [4.0, 0], [4.0, 0], [3.67, 0], [4.0, 0], [3.33, 0], [3.0, 0], [3.67, 0], [3.67, 0], [4.0, 0], [3.33, 0], [3.33, 0], [4.0, 0], [3.33, 0], [3.33, 0], [3.0, 0], [3.0, 0], [3.67, 0], [4.0, 0], [0, 3.0], [3.33, 0], [4.0, 0], [3.67, 0], [4.0, 0], [0, 4.0], [0, 3.33], [0, 3.67], [0, 3.0], [0, 4.0], [0, 3.0], [0, 3.67], [0, 3.0], [0, 3.0], [4.0, 0], [0, 3.67], [0, 3.0], [0, 3.0], [0, 4.0], [3.0, 0], [0, 3.0], [0, 3.67], [0, 3.0], [0, 3.33], [0, 4.0], [3.33, 0], [3.0, 3.67], [3.67, 3.0], [3.0, 3.0], [4.0, 4.0], [3.67, 3.0], [3.0, 3.0], [3.67, 3.0], [3.0, 3.0], [3.67, 4.0], [3.33, 3.67], [3.33, 0], [4.0, 0], [3.67, 0], [3.67, 0], [4.0, 0], [3.33, 0], [4.0, 0], [4.0, 0], [3.0, 0], [3.67, 0], [3.0, 0], [4.0, 0], [4.0, 0], [3.0, 0], [3.33, 0], [3.33, 0], [3.33, 0], [3.0, 0], [3.67, 0], [4.0, 0], [3.0, 0], [3.67, 0], [3.67, 0], [3.67, 0], [3.67, 0], [3.0, 0], [3.33, 0], [4.0, 0], [3.0, 0], [3.33, 0], [3.33, 0], [4.0, 0], [4.0, 0], [4.0, 0], [4.0, 0], [3.0, 0], [3.67, 0], [3.67, 0], [3.33, 0], [4.0, 0], [4.0, 0], [3.0, 0], [3.0, 0], [4.0, 0], [3.0, 0], [3.33, 0], [3.33, 0], [3.0, 0], [3.67, 0], [3.67, 0], [3.0, 0], [3.0, 0], [3.67, 0], [3.0, 0], [3.33, 0], [4.0, 0], [4.0, 0], [3.67, 0], [3.33, 0], [3.0, 0], [3.67, 0], [2.33, 0], [0, 2.33]], [[3.67, 3.33], [4.0, 3.67], [3.0, 3.33], [3.33, 3.67], [3.0, 3.33], [3.0, 3.33], [3.0, 3.33], [4.0, 3.33], [3.33, 3.67], [3.0, 3.67], [3.0, 3.0], [3.33, 3.0], [3.0, 3.67], [4.0, 3.33], [3.33, 4.0], [3.0, 3.33], [4.0, 4.0], [3.67, 3.67], [4.0, 3.33], [3.0, 4.0], [3.0, 3.67], [4.0, 3.0], [3.67, 3.67], [4.0, 3.33], [3.0, 3.33], [3.0, 4.0], [3.0, 3.33], [3.33, 3.33], [3.67, 3.0], [3.33, 3.33], [3.33, 3.0], [3.33, 3.33], [3.0, 3.67], [3.0, 3.67], [4.0, 3.33], [3.0, 4.0], [4.0, 3.67], [3.0, 3.0], [3.67, 4.0], [4.0, 3.67], [3.0, 3.67], [2.33, 3.67], [3.67, 3.67], [3.0, 2.33], [3.67, 2.33], [3.0, 2.33], [3.67, 4.0], [2.33, 2.33], [3.0, 3.67], [4.0, 2.67], [4.0, 3.67], [2.0, 2.0], [4.0, 2.67], [2.0, 3.33], [3.0, 2.33], [2.0, 3.0], [3.67, 2.33], [3.67, 2.67], [3.33, 2.33], [2.67, 2.0], [4.0, 3.67], [4.0, 4.0], [2.67, 4.0], [4.0, 3.33], [2.67, 2.33], [2.0, 4.0], [3.67, 2.33], [3.0, 2.67], [2.67, 3.0], [2.67, 4.0], [3.67, 2.33], [2.0, 3.33], [2.0, 3.0], [3.0, 4.0], [3.33, 3.0]], [[3.67, 4.0], [4.0, 4.0], [4.0, 4.0], [3.67, 3.67], [4.0, 4.0], [3.0, 3.67], [3.67, 3.33], [3.0, 4.0], [3.0, 4.0], [4.0, 3.0], [3.33, 3.67], [3.33, 4.0], [3.33, 3.0], [3.0, 3.33], [3.33, 3.67], [3.67, 3.0], [3.33, 3.33], [3.33, 3.33], [3.33, 4.0], [3.33, 3.33], [3.67, 3.33], [3.0, 4.0], [3.33, 3.0], [3.33, 3.0], [3.0, 3.33], [3.33, 3.33], [3.67, 4.0], [3.33, 3.0], [3.67, 4.0], [3.0, 3.33], [3.33, 3.33], [3.67, 3.33], [3.0, 3.33], [3.67, 4.0], [4.0, 3.0], [3.67, 4.0], [3.67, 3.67], [3.33, 3.0], [4.0, 3.67], [4.0, 3.67], [4.0, 3.33], [3.67, 3.67], [4.0, 4.0], [4.0, 3.67], [4.0, 3.0], [3.33, 3.67], [3.33, 3.33], [3.67, 3.0], [3.33, 4.0], [3.67, 3.0], [3.0, 4.0], [3.67, 3.0], [4.0, 4.0], [3.33, 3.67], [3.33, 3.67], [3.0, 3.0], [3.0, 3.67], [3.33, 3.33], [3.67, 3.33], [3.67, 3.67], [3.67, 4.0], [3.67, 3.67], [3.0, 3.33], [3.33, 4.0], [4.0, 4.0], [3.67, 3.33], [4.0, 3.33], [3.67, 3.33], [3.67, 3.33], [4.0, 4.0], [4.0, 3.0], [3.67, 4.0], [3.67, 4.0], [3.67, 4.0], [3.0, 4.0], [3.0, 4.0], [3.33, 3.67], [3.33, 3.67], [3.0, 4.0], [3.0, 3.67], [3.33, 3.33], [3.0, 3.67], [3.0, 4.0], [3.0, 3.33], [3.0, 3.0], [4.0, 3.33], [3.33, 3.67], [4.0, 4.0], [3.33, 4.0], [3.0, 3.0], [3.33, 3.0], [4.0, 3.67], [3.33, 3.33], [4.0, 3.67], [4.0, 4.0], [3.33, 3.33], [3.33, 3.0], [3.67, 4.0], [3.67, 3.67], [3.67, 3.67], [4.0, 3.0], [3.0, 3.67], [3.0, 3.67], [4.0, 3.33], [3.0, 3.33], [3.67, 3.67], [4.0, 4.0], [3.67, 3.33], [3.0, 3.33], [3.0, 3.33], [4.0, 3.33], [3.67, 3.67], [3.67, 3.33], [3.67, 3.33], [3.0, 3.33], [3.67, 3.67], [3.0, 3.0], [3.0, 3.33], [3.0, 3.0], [3.67, 2.33], [3.67, 3.33], [3.33, 2.33], [3.33, 3.67], [3.67, 2.0], [3.0, 3.33]], [[4.0, 0], [3.33, 0], [3.33, 0], [3.67, 0], [3.33, 0], [3.0, 0], [3.33, 0], [3.33, 0], [4.0, 0], [3.67, 0], [3.33, 0], [3.33, 0], [2.0, 0]], [[4.0, 4.0], [3.67, 3.33], [3.33, 3.0], [3.0, 3.0], [4.0, 3.33], [3.33, 4.0], [3.33, 3.0], [3.33, 3.67], [4.0, 3.67], [4.0, 3.33], [3.67, 3.0], [3.0, 3.33], [3.33, 3.0], [3.33, 3.67], [3.0, 4.0], [3.67, 3.0], [4.0, 4.0], [4.0, 3.33], [3.33, 3.33], [3.33, 3.33], [3.0, 3.0], [3.33, 4.0], [4.0, 4.0], [4.0, 3.0], [3.67, 3.33], [3.33, 3.0], [4.0, 3.33], [3.0, 4.0], [3.67, 3.0], [4.0, 3.0], [3.67, 3.67], [4.0, 3.67], [4.0, 4.0], [4.0, 3.67], [3.0, 3.33], [3.33, 4.0], [3.0, 3.0], [4.0, 3.0], [3.67, 3.0], [4.0, 3.0], [3.67, 3.0], [3.0, 3.0], [3.33, 3.0], [3.33, 3.33], [3.33, 4.0], [3.33, 3.67], [3.33, 3.33], [3.33, 4.0], [4.0, 3.0], [3.33, 4.0], [3.33, 3.67], [3.33, 4.0], [3.33, 3.0], [3.67, 3.33], [3.33, 3.33], [3.0, 4.0], [4.0, 4.0], [4.0, 3.33], [4.0, 3.0], [4.0, 3.0], [3.0, 3.0], [4.0, 3.0], [3.33, 3.0], [4.0, 3.67], [4.0, 3.33], [3.67, 4.0], [3.0, 3.0], [4.0, 4.0], [3.33, 3.0], [3.33, 3.0], [3.33, 3.33], [3.0, 4.0], [3.33, 4.0], [4.0, 4.0], [3.67, 3.67], [3.0, 4.0], [3.0, 3.33], [4.0, 3.0], [3.33, 3.33], [4.0, 4.0], [3.33, 3.0], [4.0, 3.67], [3.33, 4.0], [3.67, 3.67], [4.0, 3.0], [3.33, 3.0], [4.0, 3.67], [3.0, 3.33], [4.0, 3.67], [3.33, 3.33], [3.67, 3.67], [3.67, 4.0], [3.67, 3.0], [3.33, 3.67], [4.0, 3.67], [3.67, 4.0], [3.33, 3.67], [3.0, 3.67], [3.0, 3.33], [3.0, 3.0], [3.33, 4.0], [3.33, 3.67], [3.33, 4.0], [3.33, 4.0], [3.67, 3.67], [4.0, 3.33], [3.67, 3.0], [3.0, 3.33], [3.67, 3.33], [3.0, 3.0], [3.0, 3.0], [3.0, 3.67], [4.0, 4.0], [3.67, 3.33], [4.0, 3.67], [4.0, 3.33], [4.0, 4.0], [3.33, 3.0], [3.67, 3.67], [3.33, 3.33], [3.67, 4.0], [3.67, 3.33], [3.67, 3.67], [4.0, 4.0], [4.0, 3.0], [3.67, 3.67], [3.0, 4.0], [3.33, 4.0], [4.0, 4.0], [3.33, 3.33], [3.0, 3.67], [3.0, 4.0], [3.0, 4.0], [3.33, 4.0], [3.67, 2.67], [3.67, 2.0], [3.0, 2.0], [2.33, 3.33], [3.0, 2.0], [3.0, 2.0], [4.0, 2.0], [2.67, 2.0], [4.0, 4.0], [2.67, 2.33], [2.33, 3.0], [4.0, 2.0], [2.0, 3.33], [3.0, 3.0], [2.67, 3.0], [2.33, 3.67], [2.0, 3.33], [2.67, 3.67], [3.67, 4.0], [3.0, 2.0], [3.0, 3.0], [3.0, 3.0], [4.0, 3.33], [4.0, 3.33], [4.0, 4.0], [3.33, 2.33], [2.67, 2.0], [2.33, 2.0], [3.0, 3.0], [2.0, 2.0], [4.0, 2.33], [2.0, 4.0], [2.0, 3.67], [2.67, 3.33], [2.67, 3.0], [3.33, 4.0], [2.33, 3.67], [3.67, 3.33], [2.33, 3.67], [2.0, 3.33], [2.0, 3.33], [2.67, 3.0], [3.0, 4.0], [3.0, 2.0], [3.0, 2.33], [2.33, 2.33], [4.0, 3.33], [3.0, 3.33]], [[3.33, 3.67]], [[3.67, 3.67], [3.0, 4.0]], [[4.0, 4.0]], [[0, 0], [0, 0], [0, 0], [3.67, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 4.0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 3.33], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [3.67, 0], [4.0, 0], [3.33, 0], [3.67, 0], [3.67, 0], [3.67, 0], [3.33, 0], [3.0, 0], [3.33, 0], [4.0, 0], [4.0, 0], [4.0, 0], [3.67, 0], [3.33, 0], [4.0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 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```

and I want to find the mean of each sublist. For that reason I did this:

```
gpa = [[mean(sub_list) for sub_list in list] for list in grades]

```

But the problem is that some sublists have the value 0.0 and of course the code takes it under consideration. Is there a way for me to ignore those 0.0 when they appear and calculate the mean based on the rest of the values? Based on this list, I only have 2 values per sublist, but there are others that have 4 and 5 elements inside them. It is just crucial for the thing I am calculating that is why I need those 0 not to be considered at all. 

I am using **statistics mean**