modin.pandas.Series.sum

Series.sum(axis=None, skipna=True, numeric_only=False, min_count=0, **kwargs) → Scalar[source]

Return the sum of the values over the requested axis.

This is equivalent to the method numpy.sum.

Parameters:

  • axis ({index (0), columns (1)}) – Axis for the function to be applied on. For Series this parameter is unused and defaults to 0.

  • skipna (bool, default True) – Exclude NA/null values when computing the result.

  • numeric_only (bool, default False) – If True, Include only float, int, boolean columns. Not implemented for Series.

  • 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.

  • **kwargs – Additional keyword arguments to be passed to the function.

Return type:

Series

Examples

>>> idx = pd.MultiIndex.from_arrays([
...     ['warm', 'warm', 'cold', 'cold'],
...     ['dog', 'falcon', 'fish', 'spider']],
...     names=['blooded', 'animal'])
>>> s = pd.Series([4, 2, 0, 8], name='legs', index=idx)
>>> s
blooded  animal
warm     dog       4
         falcon    2
cold     fish      0
         spider    8
Name: legs, dtype: int64

>>> s.sum()  
14

By default, the sum of an empty or all-NA Series is 0.

>>> pd.Series([], dtype="float64").sum()  # min_count=0 is the default  
0.0

This can be controlled with the min_count parameter. For example, if you’d like the sum of an empty series to be NaN, pass min_count=1.

>>> pd.Series([], dtype="float64").sum(min_count=1)  
nan

Thanks to the skipna parameter, min_count handles all-NA and empty series identically.

>>> pd.Series([np.nan]).sum()  
0.0

>>> pd.Series([np.nan]).sum(min_count=1)  
nan