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. 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.
Exclude NA/null values when computing the result.
Include only float, int, boolean columns. 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 youâd 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
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