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
See also
tensor.mean
,tensor.percentile
Notes
Given a vector
V
of lengthN
, the median ofV
is the middle value of a sorted copy ofV
,V_sorted
- i e.,V_sorted[(N-1)/2]
, whenN
is odd, and the average of the two middle values ofV_sorted
whenN
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