argsort#

argmax, argmin and argsort for array_like and Python iterables.

This module was written by Matthias Cuntz while at Department of Computational Hydrosystems, Helmholtz Centre for Environmental Research - UFZ, Leipzig, Germany, and continued while at Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Nancy, France.

copyright:: Copyright 2014-2022 Matthias Cuntz, see AUTHORS.rst for details.
license:: MIT License, see LICENSE for details.

The following functions are provided

`argmax`(a, args, *kwargs)	Wrapper for numpy.argmax, numpy.ma.argmax, and max for Python iterables
`argmin`(a, args, *kwargs)	Wrapper for numpy.argmin, numpy.ma.argmin, and min for Python iterables
`argsort`(a, args, *kwargs)	Wrapper for numpy.argsort, numpy.ma.argsort, and sorted for Python iterables

History

Written Dec 2014 by Matthias Cuntz (mc (at) macu (dot) de)
Added argmin, argmax, Jul 2019, Matthias Cuntz
Using numpy docstring format, extending examples from numpy docstrings, May 2020, Matthias Cuntz
More consistent docstrings, Jan 2022, Matthias Cuntz
Support pandas.Series, Jun 2023, Matthias Cuntz

argmax(a, *args, **kwargs)[source]#

Wrapper for numpy.argmax, numpy.ma.argmax, and max for Python iterables

Passes all keywords directly to the individual routines, i.e.

numpy.argmax(a, axis=None, out=None)
numpy.ma.argmax(self, axis=None, fill_value=None, out=None)

No keyword will be passed to the max routine for Python iterables.

Parameters:

a (array_like) – input array, masked array, or Python iterable
*args (optional) – all arguments of numpy.argmax or numpy.ma.argmax
**kwargs (optional) – all keyword arguments of numpy.argmax or numpy.ma.argmax

Returns:

index_array – Array of indices of the largest element in input array a. It has the same shape as a.shape with the dimension along axis removed. a[np.unravel_index(argmax(a), a.shape)] is the maximum value of a.

Return type:

ndarray, int

Notes

argmax for iterables was taken from https://stackoverflow.com/questions/16945518/finding-the-index-of-the-value-which-is-the-min-or-max-in-python

Examples

One-dimensional array

>>> import numpy as np
>>> a = np.array([0,4,6,2,1,5,3,5])
>>> ii = argmax(a)
>>> print(ii)
2
>>> print(a[ii])
6

One-dimensional masked array

>>> a = np.ma.array([0,4,6,2,1,5,3,5], mask=[0,0,1,1,0,0,0,0])
>>> ii = argmax(a)
>>> print(ii)
5
>>> print(a[ii])
5
>>> ii = argmax(a, fill_value=6)
>>> print(ii)
2

List

>>> a = [0,4,6,2,1,5,3,5]
>>> ii = argmax(a)
>>> print(ii)
2
>>> print(a[ii])
6

Examples from numpy.argmax docstring

>>> a = np.arange(6).reshape(2,3) + 10
>>> a
array([[10, 11, 12],
       [13, 14, 15]])
>>> argmax(a)
5
>>> argmax(a, axis=0)
array([1, 1, 1])
>>> argmax(a, axis=1)
array([2, 2])

>>> # Indexes of the maximal elements of a N-dimensional array:
>>> ind = np.unravel_index(np.argmax(a, axis=None), a.shape)
>>> ind
(1, 2)
>>> a[ind]
15

>>> b = np.arange(6)
>>> b[1] = 5
>>> b
array([0, 5, 2, 3, 4, 5])
>>> argmax(b)  # Only the first occurrence is returned.
1

argmin(a, *args, **kwargs)[source]#

Wrapper for numpy.argmin, numpy.ma.argmin, and min for Python iterables

Passes all keywords directly to the individual routines, i.e.

numpy.argmin(a, axis=None, out=None)
numpy.ma.argmin(self, axis=None, fill_value=None, out=None)

No keyword will be passed to the min routine for Python iterables.

Parameters:

a (array_like) – input array, masked array, or Python iterable
*args (optional) – all arguments of numpy.argmin or numpy.ma.argmin
**kwargs (optional) – all keyword arguments of numpy.argmin or numpy.ma.argmin

Returns:

index_array – Array of indices of the largest element in input array a. It has the same shape as a.shape with the dimension along axis removed. a[np.unravel_index(argmin(a), a.shape)] is the minimum value of a.

Return type:

ndarray, int

Notes

argmin for iterables was taken from https://stackoverflow.com/questions/16945518/finding-the-index-of-the-value-which-is-the-min-or-max-in-python

Examples

One-dimensional array

>>> import numpy as np
>>> a = np.array([0,4,6,2,1,5,3,5])
>>> ii = argmin(a)
>>> print(ii)
0
>>> print(a[ii])
0

One-dimensional masked array

>>> a = np.ma.array([0,4,6,2,1,5,3,5], mask=[1,0,1,1,0,0,0,0])
>>> ii = argmin(a)
>>> print(ii)
4
>>> print(a[ii])
1
>>> ii = argmin(a, fill_value=1)
>>> print(ii)
0

List

>>> a = [0,4,6,2,1,5,3,5]
>>> ii = argmin(a)
>>> print(ii)
0
>>> print(a[ii])
0

Examples from numpy.argmin docstring

>>> a = np.arange(6).reshape(2,3) + 10
>>> a
array([[10, 11, 12],
       [13, 14, 15]])
>>> argmin(a)
0
>>> argmin(a, axis=0)
array([0, 0, 0])
>>> argmin(a, axis=1)
array([0, 0])

>>> # Indices of the minimum elements of a N-dimensional array:
>>> ind = np.unravel_index(argmin(a, axis=None), a.shape)
>>> ind
(0, 0)
>>> a[ind]
10

>>> b = np.arange(6) + 10
>>> b[4] = 10
>>> b
array([10, 11, 12, 13, 10, 15])
>>> argmin(b)  # Only the first occurrence is returned.
0

argsort(a, *args, **kwargs)[source]#

Wrapper for numpy.argsort, numpy.ma.argsort, and sorted for Python iterables

Passes all keywords directly to the individual routines, i.e.

numpy.argsort(a, axis=-1, kind='quicksort', order=None)
numpy.ma.argsort(a, axis=None, kind='quicksort', order=None,
                 fill_value=None)
sorted(iterable[, cmp[, key[, reverse]]])

Only key cannot be given for Python iterables because the input array is used as key in the sorted function.

Parameters:

a (array_like) – input array, masked array, or Python iterable
*args (optional) – all arguments of numpy.argsort, numpy.ma.argsort, and sorted (except key argument)
**kwargs (optional) – all keyword arguments of numpy.argsort, numpy.ma.argsort, and sorted (except key argument)

Returns:

index_array – Array of indices that sort a along the specified axis. If a is one-dimensional, a[index_array] yields a sorted a.

Return type:

ndarray, int

Notes

argsort for iterables was taken from http://stackoverflow.com/questions/3382352/equivalent-of-numpy-argsort-in-basic-python http://stackoverflow.com/questions/3071415/efficient-method-to-calculate-the-rank-vector-of-a-list-in-python

Examples

1D array

>>> import numpy as np
>>> a = np.array([0,4,6,2,1,5,3,5])
>>> ii = argsort(a)
>>> print(a[ii])
[0 1 2 3 4 5 5 6]

>>> ii = argsort(a, kind='quicksort')
>>> print(a[ii])
[0 1 2 3 4 5 5 6]

1D masked array

>>> a = np.ma.array([0,4,6,2,1,5,3,5], mask=[0,0,1,1,0,0,0,0])
>>> ii = argsort(a)
>>> print(a[ii])
[0 1 3 4 5 5 -- --]

>>> ii = argsort(a, fill_value=1)
>>> print(a[ii])
[0 -- -- 1 3 4 5 5]

list

>>> a = [0,4,6,2,1,5,3,5]
>>> ii = argsort(a)
>>> b = [ a[i] for i in ii ]
>>> print(b)
[0, 1, 2, 3, 4, 5, 5, 6]

>>> a = [0,4,6,2,1,5,3,5]
>>> ii = argsort(a, reverse=True)
>>> b = [ a[i] for i in ii ]
>>> print(b)
[6, 5, 5, 4, 3, 2, 1, 0]

Examples from numpy.argsort docstring

>>> # One-dimensional array:
>>> x = np.array([3, 1, 2])
>>> argsort(x)
array([1, 2, 0])

>>> # Two-dimensional array:
>>> x = np.array([[0, 3], [2, 2]])
>>> x
array([[0, 3],
       [2, 2]])
>>> ind = argsort(x, axis=0)  # sorts along first axis (down)
>>> ind
array([[0, 1],
       [1, 0]])
>>> np.take_along_axis(x, ind, axis=0)  # same as np.sort(x, axis=0)
array([[0, 2],
       [2, 3]])
>>> ind = argsort(x, axis=1)  # sorts along last axis (across)
>>> ind
array([[0, 1],
       [0, 1]])
>>> np.take_along_axis(x, ind, axis=1)  # same as np.sort(x, axis=1)
array([[0, 3],
       [2, 2]])

>>> # Indices of the sorted elements of a N-dimensional array:
>>> ind = np.unravel_index(argsort(x, axis=None), x.shape)
>>> ind
(array([0, 1, 1, 0]), array([0, 0, 1, 1]))
>>> x[ind]  # same as np.sort(x, axis=None)
array([0, 2, 2, 3])

>>> # Sorting with keys:
>>> x = np.array([(1, 0), (0, 1)], dtype=[('x', '<i4'), ('y', '<i4')])
>>> x
array([(1, 0), (0, 1)],
      dtype=[('x', '<i4'), ('y', '<i4')])
>>> argsort(x, order=('x','y'))
array([1, 0])
>>> argsort(x, order=('y','x'))
array([0, 1])

argmax/argmin/argsort

Contents

argmax/argmin/argsort#