How do you sum a pandas series in python?

Return the sum of the values over the requested axis.

This is equivalent to the method numpy.sum.

Axis for the function to be applied on.

Exclude NA/null values when computing the result.

If the axis is a MultiIndex [hierarchical], count along a particular level, collapsing into a scalar.

Include only float, int, boolean columns. If None, will attempt to use everything, then use only numeric data. Not implemented for Series.

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.

Additional keyword arguments to be passed to the function.

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