numpy.ma.masked_array.sort#

method

ma.masked_array.sort(axis=-1, kind=None, order=None, endwith=True, fill_value=None, *, stable=False)[source]#

Sort the array, in-place

Parameters:
aarray_like

Array to be sorted.

axisint, optional

Axis along which to sort. If None, the array is flattened before sorting. The default is -1, which sorts along the last axis.

kind{‘quicksort’, ‘mergesort’, ‘heapsort’, ‘stable’}, optional

The sorting algorithm used.

orderlist, optional

When a is a structured array, this argument specifies which fields to compare first, second, and so on. This list does not need to include all of the fields.

endwith{True, False}, optional

Whether missing values (if any) should be treated as the largest values (True) or the smallest values (False) When the array contains unmasked values sorting at the same extremes of the datatype, the ordering of these values and the masked values is undefined.

fill_valuescalar or None, optional

Value used internally for the masked values. If fill_value is not None, it supersedes endwith.

stablebool, optional

Only for compatibility with np.sort. Ignored.

Returns:
sorted_arrayndarray

Array of the same type and shape as a.

See also

numpy.ndarray.sort

Method to sort an array in-place.

argsort

Indirect sort.

lexsort

Indirect stable sort on multiple keys.

searchsorted

Find elements in a sorted array.

Notes

See sort for notes on the different sorting algorithms.

Examples

>>> import numpy as np
>>> a = np.ma.array([1, 2, 5, 4, 3],mask=[0, 1, 0, 1, 0])
>>> # Default
>>> a.sort()
>>> a
masked_array(data=[1, 3, 5, --, --],
             mask=[False, False, False,  True,  True],
       fill_value=999999)

>>> a = np.ma.array([1, 2, 5, 4, 3],mask=[0, 1, 0, 1, 0])
>>> # Put missing values in the front
>>> a.sort(endwith=False)
>>> a
masked_array(data=[--, --, 1, 3, 5],
             mask=[ True,  True, False, False, False],
       fill_value=999999)

>>> a = np.ma.array([1, 2, 5, 4, 3],mask=[0, 1, 0, 1, 0])
>>> # fill_value takes over endwith
>>> a.sort(endwith=False, fill_value=3)
>>> a
masked_array(data=[1, --, --, 3, 5],
             mask=[False,  True,  True, False, False],
       fill_value=999999)
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