pandas.DataFrame.sum#

DataFrame.sum(axis=0, 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), columns (1)}: 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 than min_count non-NA values are present the result will be NA.
**kwargs: Additional keyword arguments to be passed to the function.

Returns:

Series or scalar

See also

Series.sum: Return the sum.
Series.min: Return the minimum.
Series.max: Return the maximum.
Series.idxmin: Return the index of the minimum.
Series.idxmax: Return the index of the maximum.
DataFrame.sum: Return the sum over the requested axis.
DataFrame.min: Return the minimum over the requested axis.
DataFrame.max: Return the maximum over the requested axis.
DataFrame.idxmin: Return the index of the minimum over the requested axis.
DataFrame.idxmax: Return the index of the maximum over 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

>>> s.sum()
14

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