pandas.Series.sum#

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

Return the sum of the values over the requested axis.

This is equivalent to the methodnumpy.sum.

Parameters:

axis{index (0)}

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

Warning

The behavior of DataFrame.sum withaxis=None is deprecated,in a future version this will reduce over both axes and return a scalarTo retain the old behavior, pass axis=0 (or do not pass axis).

Added in version 2.0.0.

skipnabool, default True

Exclude NA/null values when computing the result.

numeric_onlybool, default False

Include only float, int, boolean columns. Not implemented for Series.

min_countint, default 0

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

**kwargs

Additional keyword arguments to be passed to the function.

Returns:

scalar or Series (if level specified)

Sum of the values for the requested axis.

See also

numpy.sum

Equivalent numpy function for computing sum.

Series.mean

Mean of the values.

Series.median

Median of the values.

Series.std

Standard deviation of the values.

Series.var

Variance of the values.

Series.min

Minimum value.

Series.max

Maximum value.

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)>>>sblooded  animalwarm     dog       4         falcon    2cold     fish      0         spider    8Name: legs, dtype: int64

>>>s.sum()14

By default, the sum of an empty or all-NA Series is0.

>>>pd.Series([],dtype="float64").sum()# min_count=0 is the default0.0

This can be controlled with themin_count parameter. For example, ifyou’d like the sum of an empty series to be NaN, passmin_count=1.

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

Thanks to theskipna parameter,min_count handles all-NA andempty series identically.

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

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