DataFrame.sum(self, axis=None, skipna=None, level=None, numeric_only=None, min_count=0, **kwargs)[source]

Return the sum of the values for the requested axis.

This is equivalent to the method numpy.sum.

axis{index (0), columns (1)}

Axis for the function to be applied on.

skipnabool, default True

Exclude NA/null values when computing the result.

levelint or level name, default None

If the axis is a MultiIndex (hierarchical), count along a particular level, collapsing into a Series.

numeric_onlybool, default None

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

min_countint, 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.

New in version 0.22.0: Added with the default being 0. This means the sum of an all-NA or empty Series is 0, and the product of an all-NA or empty Series is 1.


Additional keyword arguments to be passed to the function.

Series or DataFrame (if level specified)
See Also
Series.sumReturn the sum.
Series.minReturn the minimum.
Series.maxReturn the maximum.
Series.idxminReturn the index of the minimum.
Series.idxmaxReturn the index of the maximum.
DataFrame.sumReturn the sum over the requested axis.
DataFrame.minReturn the minimum over the requested axis.
DataFrame.maxReturn the maximum over the requested axis.
DataFrame.idxminReturn the index of the minimum over the requested axis.
DataFrame.idxmaxReturn the index of the maximum over the requested axis.


>>> 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()

Sum using level names, as well as indices.

>>> s.sum(level='blooded')
warm    6
cold    8
Name: legs, dtype: int64
>>> s.sum(level=0)
warm    6
cold    8
Name: legs, dtype: int64

By default, the sum of an empty or all-NA Series is 0.

>>> pd.Series([]).sum()  # min_count=0 is the default

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([]).sum(min_count=1)

Thanks to the skipna parameter, min_count handles all-NA and empty series identically.

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