xorbits.pandas.Series.sum#

Series.sum(axis=None, skipna=True, level=None, min_count=0, combine_size=None, method=None, **kwargs)#

Return the sum of the values over the requested axis.

This is equivalent to the method numpy.sum.

Parameters

  • axis ({index (0)}) –

    Axis for the function to be applied on. For Series this parameter is unused and defaults to 0.

    For DataFrames, specifying axis=None will apply the aggregation across both axes.

    New in version 2.0.0(pandas).

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

  • numeric_only (bool, default False (Not supported yet)) – 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

scalar or scalar

See also

Series.sum

Return the sum.

Series.min

Return the minimum.

Series.max

Return the maximum.

Series.idxmin

Return the index of the minimum.

Series.idxmax

Return the index of the maximum.

DataFrame.sum

Return the sum over the requested axis.

DataFrame.min

Return the minimum over the requested axis.

DataFrame.max

Return the maximum over the requested axis.

DataFrame.idxmin

Return the index of the minimum over the requested axis.

DataFrame.idxmax

Return the index of the maximum over the requested axis.

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

This docstring was copied from pandas.core.series.Series.