pandas.DataFrame.sum

DataFrame.sum(*, axis=0, skipna=True, numeric_only=False, min_count=0, **kwargs)

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

Sum over requested axis.

See also

Series.sum

Return the sum over Series values.

DataFrame.mean

Return the mean of the values over the requested axis.

DataFrame.median

Return the median of the values over the requested axis.

DataFrame.mode

Get the mode(s) of each element along the requested axis.

DataFrame.std

Return the standard deviation of the values 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