# dask.dataframe.Series.sum

#### Series.sum(axis=0, skipna=True, numeric_only=False, min_count=0, split_every=False, \*\*kwargs)

Return the sum of the values over the requested axis.

This docstring was copied from pandas.DataFrame.sum.

Some inconsistencies with the Dask version may exist.

This is equivalent to the method `numpy.sum`.

* **Parameters:**
  **axis**
  : Axis for the function to be applied on.
    For Series this parameter is unused and defaults to 0.
    <br/>
    #### 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).
    <br/>
    #### Versionadded
    Added in version 2.0.0.

  **skipna**
  : Exclude NA/null values when computing the result.

  **numeric_only**
  : Include only float, int, boolean columns. Not implemented for Series.

  **min_count**
  : 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`](#dask.dataframe.Series.sum)
: Return the sum over Series values.

[`DataFrame.mean`](dask.dataframe.DataFrame.mean.html.md#dask.dataframe.DataFrame.mean)
: Return the mean of the values over the requested axis.

[`DataFrame.median`](dask.dataframe.DataFrame.median.html.md#dask.dataframe.DataFrame.median)
: Return the median of the values over the requested axis.

[`DataFrame.mode`](dask.dataframe.DataFrame.mode.html.md#dask.dataframe.DataFrame.mode)
: Get the mode(s) of each element along the requested axis.

[`DataFrame.std`](dask.dataframe.DataFrame.std.html.md#dask.dataframe.DataFrame.std)
: Return the standard deviation of the values over the requested axis.

### Examples

```pycon
>>> 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
```

```pycon
>>> s.sum()
14
```

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

```pycon
>>> 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`.

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

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

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

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

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