sort(self, axis=-1, kind=None, order=None, endwith=True, fill_value=None)
See sort
for notes on the different sorting algorithms.
Array to be sorted.
Axis along which to sort. If None, the array is flattened before sorting. The default is -1, which sorts along the last axis.
The sorting algorithm used.
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.
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.
Value used internally for the masked values. If fill_value
is not None, it supersedes endwith
.
Array of the same type and shape as a.
Sort the array, in-place
argsort
Indirect sort.
lexsort
Indirect stable sort on multiple keys.
numpy.ndarray.sort
Method to sort an array in-place.
searchsorted
Find elements in a sorted array.
>>> a = np.ma.array([1, 2, 5, 4, 3],mask=[0, 1, 0, 1, 0])This example is valid syntax, but we were not able to check execution
... # 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])This example is valid syntax, but we were not able to check execution
... # 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])See :
... # 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)
The following pages refer to to this document either explicitly or contain code examples using this.
numpy.ma.core.sort
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