sum(self, axis=None, skipna=True, level=None, numeric_only=None, min_count=0, **kwargs)

This is equivalent to the method numpy.sum .

Parameters

axis : {index (0)}

Axis for the function to be applied on.

skipna : bool, 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 : bool, 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.

**kwargs :

Additional keyword arguments to be passed to the function.

Returns

scalar or Series (if level specified)

Return the sum of the values over the requested axis.

See Also

DataFrame.idxmax

Return the index of the maximum over the requested axis.

DataFrame.idxmin

Return the index of the minimum over the requested axis.

DataFrame.max

Return the maximum over the requested axis.

DataFrame.min

Return the minimum over the requested axis.

DataFrame.sum

Return the sum over the requested axis.

Series.idxmax

Return the index of the maximum.

Series.idxmin

Return the index of the minimum.

Series.max

Return the maximum.

Series.min

Return the minimum.

Series.sum

Return the sum.

Examples

This example is valid syntax, but we were not able to check execution

>>> 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

This example is valid syntax, but we were not able to check execution

>>> s.sum()
14

By default, the sum of an empty or all-NA Series is 0 .

This example is valid syntax, but we were not able to check execution

>>> 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 .

This example is valid syntax, but we were not able to check execution

>>> pd.Series([], dtype="float64").sum(min_count=1)
nan

Thanks to the skipna parameter, min_count handles all-NA and empty series identically.

This example is valid syntax, but we were not able to check execution

>>> pd.Series([np.nan]).sum()
0.0

This example is valid syntax, but we were not able to check execution

>>> pd.Series([np.nan]).sum(min_count=1)
nan

See :

Local connectivity graph

Hover to see nodes names; edges to Self not shown, Caped at 50 nodes.

Using a canvas is more power efficient and can get hundred of nodes ; but does not allow hyperlinks; , arrows or text (beyond on hover)

SVG is more flexible but power hungry; and does not scale well to 50 + nodes.

All aboves nodes referred to, (or are referred from) current nodes; Edges from Self to other have been omitted (or all nodes would be connected to the central node "self" which is not useful). Nodes are colored by the library they belong to, and scaled with the number of references pointing them

---

File: /pandas/core/generic.py#11047
type: <class 'function'>
Commit: