pandas.Series.sum — pandas 2.2.3 documentation (original) (raw)

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 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.

Warning

The behavior of DataFrame.sum with axis=None is deprecated, in a future version this will reduce over both axes and return a scalar To 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 scalar

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

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