median(a, axis=None, keepdims=False, out=None)
This docstring was copied from numpy.median.
Some inconsistencies with the Dask version may exist.
This works by automatically chunking the reduced axes to a single chunk if necessary and then calling numpy.median
function across the remaining dimensions
Returns the median of the array elements.
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.
Input array or object that can be converted to an array.
Axis or axes along which the medians are computed. The default is to compute the median along a flattened version of the array. A sequence of axes is supported since version 1.9.0.
Alternative output array 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.
If True, then allow use of memory of input array a for calculations. The input array will be modified by the call to median
. This will save memory when you do not need to preserve the contents of the input array. Treat the input as undefined, but it will probably be fully or partially sorted. Default is False. If :None:None:`overwrite_input` is True
and a is not already an :None:None:`ndarray`, an error will be raised.
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 :None:None:`arr`.
A new array holding the result. If the input contains integers or floats smaller than float64
, then the output data-type is np.float64
. Otherwise, the data-type of the output is the same as that of the input. If :None:None:`out` is specified, that array is returned instead.
Compute the median along the specified axis.
>>> a = np.array([[10, 7, 4], [3, 2, 1]]) # doctest: +SKIPThis example is valid syntax, but we were not able to check execution
... a # doctest: +SKIP array([[10, 7, 4], [ 3, 2, 1]])
>>> np.median(a) # doctest: +SKIP 3.5This example is valid syntax, but we were not able to check execution
>>> np.median(a, axis=0) # doctest: +SKIP array([6.5, 4.5, 2.5])This example is valid syntax, but we were not able to check execution
>>> np.median(a, axis=1) # doctest: +SKIP array([7., 2.])This example is valid syntax, but we were not able to check execution
>>> m = np.median(a, axis=0) # doctest: +SKIPThis example is valid syntax, but we were not able to check execution
... out = np.zeros_like(m) # doctest: +SKIP
... np.median(a, axis=0, out=m) # doctest: +SKIP array([6.5, 4.5, 2.5])
>>> m # doctest: +SKIP array([6.5, 4.5, 2.5])This example is valid syntax, but we were not able to check execution
>>> b = a.copy() # doctest: +SKIPThis example is valid syntax, but we were not able to check execution
... np.median(b, axis=1, overwrite_input=True) # doctest: +SKIP array([7., 2.])
>>> assert not np.all(a==b) # doctest: +SKIPThis example is valid syntax, but we were not able to check execution
... b = a.copy() # doctest: +SKIP
... np.median(b, axis=None, overwrite_input=True) # doctest: +SKIP 3.5
>>> assert not np.all(a==b) # doctest: +SKIPSee :
The following pages refer to to this document either explicitly or contain code examples using this.
dask.array.reductions.median
dask.array.reductions.nanmedian
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