# dask.dataframe.Series.sem

#### Series.sem(axis=None, skipna=True, ddof=1, split_every=False, numeric_only=False)

Return unbiased standard error of the mean over requested axis.

This docstring was copied from pandas.DataFrame.sem.

Some inconsistencies with the Dask version may exist.

Normalized by N-1 by default. This can be changed using the ddof argument

* **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.sem 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).

  **skipna**
  : Exclude NA/null values. If an entire row/column is NA, the result
    will be NA.

  **ddof**
  : Delta Degrees of Freedom. The divisor used in calculations is N - ddof,
    where N represents the number of elements.

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

  **\*\*kwargs**
  : Additional keywords passed.
* **Returns:**
  Series
  : Unbiased standard error of the mean over requested axis.

#### SEE ALSO
[`DataFrame.var`](dask.dataframe.DataFrame.var.html.md#dask.dataframe.DataFrame.var)
: Return unbiased variance over requested axis.

[`DataFrame.std`](dask.dataframe.DataFrame.std.html.md#dask.dataframe.DataFrame.std)
: Returns sample standard deviation over requested axis.

### Examples

```pycon
>>> s = pd.Series([1, 2, 3])
>>> round(s.sem(), 6)
0.57735
```

With a DataFrame

```pycon
>>> df = pd.DataFrame({"a": [1, 2], "b": [2, 3]}, index=["tiger", "zebra"])
>>> df
       a   b
tiger  1   2
zebra  2   3
>>> df.sem()
a   0.5
b   0.5
dtype: float64
```

Using axis=1

```pycon
>>> df.sem(axis=1)
tiger   0.5
zebra   0.5
dtype: float64
```

In this case, numeric_only should be set to True
to avoid getting an error.

```pycon
>>> df = pd.DataFrame({"a": [1, 2], "b": ["T", "Z"]}, index=["tiger", "zebra"])
>>> df.sem(numeric_only=True)
a   0.5
dtype: float64
```

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