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# Sorting Arrays
# Fast Sorting in NumPy: np.sort and np.argsort

import numpy as np

a = np.array([1, 9, 0, 7, 3, 5])
sort = np.sort(a)
print(sort)

[0 1 3 5 7 9]

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# If you want the sorted index then use np.argsort

index = np.argsort(a)
print(index)

[2 0 4 5 3 1]

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# Sorting along rows or columns

X = rand.randint(0, 10, (4, 6))      
print(X)
print("-----------------------------------")
# sort each column of X
a = np.sort(X, axis=0)
print(a)
print("-----------------------------------")
# sort each row of X 
b = np.sort(X, axis =1)
print(b)

[[7 8 7 3 4 8]
 [6 3 8 8 5 1]
 [3 3 3 2 6 5]
 [5 2 5 6 1 5]]
-----------------------------------
[[3 2 3 2 1 1]
 [5 3 5 3 4 5]
 [6 3 7 6 5 5]
 [7 8 8 8 6 8]]
-----------------------------------
[[3 4 7 7 8 8]
 [1 3 5 6 8 8]
 [2 3 3 3 5 6]
 [1 2 5 5 5 6]]

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# Partial Sorts: Partitioning
x = np.array([7, 2, 3, 1, 6, 5, 4])   
print(x)
print("------------------------------")
a = np.partition(x, 3)
print(a)

# NumPy provides this in the np.partition function. 
# np.partition takes an array and a number K; the result is a new array with the smallest K values to the left of the partition,
# and the remaining values to the right, in arbitrary order:

[7 2 3 1 6 5 4]
------------------------------
[2 1 3 4 6 5 7]

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b = np.partition(X, 2, axis=1)
print(b)

[[3 4 7 7 8 8]
 [1 3 5 8 8 6]
 [2 3 3 3 6 5]
 [1 2 5 6 5 5]]