pandas.Series.sum — pandas 3.0.0rc0+33.g1fd184de2a 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 Series (if level specified)

Sum of the values for 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

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