# mars.dataframe.Series.median#

Series.median(axis=None, skipna=True, out=None, overwrite_input=False, keepdims=False)[source]#

Return the median of the values over the requested axis.

Parameters
• axis ({index (0)}) – Axis or axes along which the medians are computed. The default is to compute the median along a flattened version of the tensor. A sequence of axes is supported since version 1.9.0.

• skipna (bool, optional, default True) – Exclude NA/null values when computing the result.

• out (Tensor, default None) – Output tensor in which to place the result. It must have the same shape and buffer length as the expected output, but the type (of the output) will be cast if necessary.

• overwrite_input (bool, default False) – Just for compatibility with Numpy, would not take effect.

• keepdims (bool, default False) – If this is set to True, the axes which are reduced are left in the result as dimensions with size one. With this option, the result will broadcast correctly against the original arr.

Returns

median – Return the median of the values over the requested axis.

Return type

scalar

`tensor.mean`, `tensor.percentile`

Notes

Given a vector `V` of length `N`, the median of `V` is the middle value of a sorted copy of `V`, `V_sorted` - i e., `V_sorted[(N-1)/2]`, when `N` is odd, and the average of the two middle values of `V_sorted` when `N` is even.

Examples

```>>> import mars.dataframe as md
>>> a = md.Series([10, 7, 4, 3, 2, 1])
>>> a.median().execute()
2.0
>>> mt.median(a).execute()
3.5
>>> a = md.Series([10, 7, 4, None, 2, 1])
>>> a.median().execute()
4.0
>>> a.median(skipna=False).execute()
nan
```