Sum across all NaNs in pandas returns zero?

Question

I'm trying to sum across columns of a Pandas dataframe, and when I have NaNs in every column I'm getting sum = zero; I'd expected sum = NaN based on the docs. Here's what I've got:

In [136]: df = pd.DataFrame()  In [137]: df['a'] = [1,2,np.nan,3]  In [138]: df['b'] = [4,5,np.nan,6]  In [139]: df Out[139]:      a   b 0   1   4 1   2   5 2 NaN NaN 3   3   6  In [140]: df['total'] = df.sum(axis=1)  In [141]: df Out[141]:      a   b  total 0   1   4      5 1   2   5      7 2 NaN NaN      0 3   3   6      9 

The pandas.DataFrame.sum docs say "If an entire row/column is NA, the result will be NA", so I don't understand why "total" = 0 and not NaN for index 2. What am I missing?

Answer 1

pandas documentation » API Reference » DataFrame » pandas.DataFrame »

DataFrame.sum(self, axis=None, skipna=None, level=None, numeric_only=None, min_count=0, **kwargs)

min_count: int, default 0

The required number of valid values to perform the operation. If fewer than min_count non-NA values are present the result will be NA.

New in version 0.22.0: Added with the default being 0. This means the sum of an all-NA or empty Series is 0, and the product of an all-NA or empty Series is 1.

Quoting from pandas latest docs it says the min_count will be 0 for the all-NA series.

If you say min_count=1 then the result of the sum will be a NaN.

Answer 2

Great link provided by Jeff.

Here you can find a example:

df1 = pd.DataFrame();  df1['a'] = [1,2,np.nan,3]; df1['b'] = [np.nan,2,np.nan,3]  df1 Out[4]:       a    b 0  1.0  NaN 1  2.0  2.0 2  NaN  NaN 3  3.0  3.0   df1.sum(axis=1, skipna=False) Out[6]:  0    NaN 1    4.0 2    NaN 3    6.0 dtype: float64  df1.sum(axis=1, skipna=True) Out[7]:  0    1.0 1    4.0 2    0.0 3    6.0 dtype: float64