Series.sum(axis=None, skipna=None, level=None, numeric_only=None, min_count=0, **kwargs)
[source]
Return the sum of the values for the requested axis
Parameters: |
skipna : boolean, default True Exclude NA/null values when computing the result. level : int or level name, default None If the axis is a MultiIndex (hierarchical), count along a particular level, collapsing into a scalar numeric_only : boolean, 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_count : int, default 0 The required number of valid values to perform the operation. If fewer than 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. |
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Returns: |
|
By default, the sum of an empty or all-NA Series is 0
.
>>> pd.Series([]).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([]).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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http://pandas.pydata.org/pandas-docs/version/0.23.4/generated/pandas.Series.sum.html