Table Of Contents


Enter search terms or a module, class or function name.


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.

axis : {index (0)}

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 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.

sum : scalar or Series (if level specified)
``MultiIndex`` series example of monthly rainfall
>>> index = pd.MultiIndex.from_product(
… [[‘London’, ‘New York’], [‘Jun’, ‘Jul’, ‘Aug’]],
… names=[‘city’, ‘month’])
>>> s = pd.Series([47, 35, 54, 112, 117, 113], index=index)
>>> s
city month

London Jun 47

Jul 35 Aug 54

New York Jun 112

Jul 117 Aug 113

dtype: int64
>>> s.sum()
Sum using level names, as well as indices
>>> s.sum(level=’city’)
London 136
New York 342
dtype: int64
>>> s.sum(level=1)
Jun 159
Jul 152
Aug 167
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)
Scroll To Top