I need to get basic statistics values, such as mean(), var(), std() from some columns in different dataframes. I want to do it by creating a function, and then apply it to my dataframes. I am t... More on stackoverflow.com

if I understand correctly, each element in the Series is a list of 2 numbers. s = Series([ [1.5, 2], [3.4, 15] ]) So you should be able to apply a function to perform the calculation you need s.apply(lambda x: sum(x) / len(x)) More on reddit.com

I think a way to go about this is : import numpy as np pd['BasePay'] = pd['BasePay'].replace("Not provided", np.nan).astype(float) # You can't use int because nan's are of type float. Now pd['BasePay'].mean() doesn't throw any error anymore, it'll compute the mean without taking the "Nan" (not a number) into account. Note that it does so quietly, you're not seeing any message telling you that the nan's weren't taken into account so don't forget about it because in some cases this might not be the behavior you're expecting. pd.to_numeric(df['BasePay']).mean() Does exactly the same but doesn't change the initial column as I did above and it also behaves quietly, you won't get any message telling you that the strings "Not provided" were replaced by Nan's. In any case, be aware of the types of your columns and always check the expected behavior of your operations. More on reddit.com

It's syntax for the dunder invert protocol. https://docs.python.org/3/reference/datamodel.html?highlight=data%20model#object.__invert__ More on reddit.com