These are all different types for sorting techniques that behave very differently. Let’s study which technique works how and which one to use.
Let ‘a’ be a numpy array
-
a.sort()
(i) Sorts the array in-place & returns None
(ii) Return type is None
(iii) Occupies less space. No copy created as it directly sorts the original array
(iv) Faster than sorted(a)# Python code to sort an array in-place
# using a.sort
import
numpy as np
# Numpy array created
a
=
np.array([
9
,
3
,
1
,
7
,
4
,
3
,
6
])
# unsorted array print
print
(
'Original array:\n'
, a)
# Return type is None
print
(
'Return type:'
, a.sort())
# Sorted array output
print
(
'Original array sorted->'
, a)
OUTPUT: For a.sort() Original array: [9 3 1 7 4 3 6] Return type: None Original array sorted-> [1 3 3 4 6 7 9]
-
sorted(a)
(i) Creates a new list from the old & returns the new one, sorted
(ii) Return type is a list
(iii) Occupies more space as copy of original array is created and then sorting is done
(iv) Slower than a.sort()# Python code to create a sorted copy using
# sorted()
import
numpy as np
# Numpy array created
a
=
np.array([
9
,
3
,
1
,
7
,
4
,
3
,
6
])
# unsorted array print
print
(
'Original array:\n'
, a)
b
=
sorted
(a)
# sorted list returned to b, b type is
# <class 'list'>
print
(
'New array sorted->'
, b)
# original array no change
print
(
'Original array->'
, a)
OUTPUT:a.sorted() Original array: [9 3 1 7 4 3 6] New array sorted-> [1, 3, 3, 4, 6, 7, 9] Original array-> [9 3 1 7 4 3 6]
-
np.argsort(a)
(i) Returns the indices that would sort an array
(ii) Return type is numpy array
(iii) Occupies space as a new array of sorted indices is returned# Python code to demonstrate working of np.argsort
import
numpy as np
# Numpy array created
a
=
np.array([
9
,
3
,
1
,
7
,
4
,
3
,
6
])
# unsorted array print
print
(
'Original array:\n'
, a)
# Sort array indices
b
=
np.argsort(a)
print
(
'Sorted indices of original array->'
, b)
# To get sorted array using sorted indices
# c is temp array created of same len as of b
c
=
np.zeros(
len
(b), dtype
=
int
)
for
i
in
range
(
0
,
len
(b)):
c[i]
=
a[b[i]]
print
(
'Sorted array->'
, c)
OUTPUT:np.argsort(a) Original array: [9 3 1 7 4 3 6] Sorted indices of original array-> [2 1 5 4 6 3 0] Sorted array-> [1 3 3 4 6 7 9]
-
np.lexsort((b, a))
(i) Perform an indirect sort using a sequence of keys
(ii) Sort by a, then by b
(iii) Return type ndarray of ints Array of indices that sort the keys along the specified axis
(iv) Occupies space as a new array of sorted indices pair wise is returned.# Python code to demonstrate working of
# np.lexsort()
import
numpy as np
# Numpy array created
a
=
np.array([
9
,
3
,
1
,
3
,
4
,
3
,
6
])
# First column
b
=
np.array([
4
,
6
,
9
,
2
,
1
,
8
,
7
])
# Second column
print
(
'column a, column b'
)
for
(i, j)
in
zip
(a, b):
print
(i,
' '
, j)
ind
=
np.lexsort((b, a))
# Sort by a then by b
print
(
'Sorted indices->'
, ind)
OUTPUT:np.lexsort((b, a)) column a, column b 9 4 3 6 1 9 3 2 4 1 3 8 6 7 Sorted indices-> [2 3 1 5 4 6 0]