Counting non zero values in each column of a dataframe in python

Question

I have a python-pandas-DataFrame in which first column is "user_id" and rest of the columns are tags("Tag_0" to "Tag_122").

I have the data in the following format:

UserId  Tag_0   Tag_1 7867688 0   5 7867688 0   3 7867688 3   0 7867688 3.5 3.5 7867688 4   4 7867688 3.5 0 

My aim is to achieve Sum(Tag)/Count(NonZero(Tags)) for each user_id

df.groupby('user_id').sum(), gives me sum(tag), however I am clueless about counting non zero values

Is it possible to achieve Sum(Tag)/Count(NonZero(Tags)) in one command?

In MySQL I could achieve this as follows:-

select user_id, sum(tag)/count(nullif(tag,0)) from table group by 1 

Any help shall be appreciated.

Answer 1

My favorite way of getting number of nonzeros in each column is

df.astype(bool).sum(axis=0) 

For the number of non-zeros in each row use

df.astype(bool).sum(axis=1) 

(Thanks to Skulas)

If you have nans in your df you should make these zero first, otherwise they will be counted as 1.

df.fillna(0).astype(bool).sum(axis=1) 

(Thanks to SirC)