[Python] [numpy-summary 3] Sample code for all matrix functions

1. Sort function

Sort function	illustrate
np.sort( ndarray)	sort, return a copy
np.unique(ndarray)	Returns the elements in the ndarray, excluding duplicate elements, and sorted
np.intersect1d( ndarray1, ndarray2) e.g.union1d (ndarray1, ndarray2) e.g. setdiff1d (ndarray1, ndarray2) e.g. setxor1d (ndarray1, ndarray2)	Returns the intersection of the two and sorts. Returns the union of the two and sorts. Returns the difference between the two. Returns the symmetric difference between the two

2. Example of the use of the function

np.sort

See also the official manual: https://docs.scipy.org/doc/numpy/reference/generated/numpy.sort.html

np.sort(a, axis=-1, kind='quicksort', order=None)

# -*- coding: utf-8 -*-
"""
@author: tom
Talk is cheap, show me the code
Aim: numpy sort function example
"""

import numpy as np

#sort, return copy
#np.sort(ndarray, axis=-1, kind='quicksort', order=None)
a = np.array([[1,9,2],
              [7,5,6],
              [4,8,3]])

The axis where the single element element in #a is located is 0, such as 1, 7, 4 and 9, 5, 8, and 2, 6, 3 are all on axis=0
The axis of the innermost array in #a is all 1, such as [1,9,2] and [7,5,6] and [4,8,3] are all on axis=1
#Description: a is a 2-dimensional array, the maximum axis is 1, and the minimum axis is 0
ret = np.sort(a) #sort along the last axis
'''
[[1 2 9]
 [5 6 7]
 [3 4 8]]
'''
ret = np.sort(a, axis=0) #sort along the first axis
'''
[[1 5 2]
 [4 8 3]
 [7 9 6]]
'''
ret = np.sort(a, axis=None) #sort the flattened array
'''
[1 2 3 4 5 6 7 8 9]
'''

# Sort by a property in a given element
sampleType = [('name', 'S10'), ('height', float), ('age', int)] #custom element structure
samples = [('Tom',   1.8, 36),
          ('Kitty', 1.9, 35),
          ('John',  1.9, 39),
          ('Anna',  1.7, 32)]
a = np.array(samples, dtype=sampleType)
ret = np.sort(a, order='height') #sort by height
'''
[(b'Anna',  1.7, 32)
 (b'Tom',   1.8, 36)
 (b'John',  1.9, 39)
 (b'Kitty', 1.9, 35)]
'''
ret = np.sort(a, order=['height','age']) #Sort by height, and then sort by age when the heights are equal
'''
[(b'Anna',  1.7, 32)
 (b'Tom',   1.8, 36)
 (b'Kitty', 1.9, 35)
 (b'John',  1.9, 39)]
'''
print(id(a),id(ret)) #184520576 184520416

np.unique

#Return the elements in the ndarray, excluding duplicate elements, and sort
#np.unique(ar, return_index=False, return_inverse=False, return_counts=False)
ret = np.unique([1, 1, 2, 2, 3, 3])  #[1 2 3]
ret = np.unique([3, 3, 2, 2, 1, 1])  #[1 2 3]
ret = np.unique([[1,1],[2,2],[3,3]]) #[1 2 3]
ret = np.unique([[3,3],[2,2],[1,1]]) #[1 2 3]

a = np.array([1, 1, 2, 2, 3, 3])
u,indices = np.unique(a, return_index=True)#u=a[indices]
print (u) # [1 2 3]
print(indices)    #[0 2 4]
print(a[indices]) #[1 2 3]

u,indices = np.unique([1, 1, 2, 2, 3, 3], return_inverse=True)#a=u[indices]
print (u) # [1 2 3]
print(indices)    #[0 0 1 1 2 2]
print (u [indices]) # [1 1 2 2 3 3] = a

np.intersect1d

#Return the intersection of the two and sort.
#np.intersect1d( ndarray1, ndarray2, assume_unique=False)
a = np.array([1,2,3,4,5])
b = np.array([4,5,6,7,8])
c = np.array([4,5,6,7,8,9,10,11])
d = np.array([[4,5,6,7],[8,9,10,11]])
ret = np.intersect1d(a, b)#[4 5]
ret = np.intersect1d(a, c)#[4 5]
ret = np.intersect1d(a, d)#[4 5]

e.g.union1d

#Return the union of two sequences and sort.
# e.g.union1d (ndarray1, ndarray2)
a = [0, 1, 2]
b = [1, 2, 3]
c = [3, 4, 5]
ret = np.union1d(a, b) #[0 1 2 3]
ret = np.union1d(b, c) #[1 2 3 4 5]

#Return the union of multiple sequences and sort, you can use the functools.reduce function at the same time
from functools import reduce
ret = reduce(np.union1d, (a, b, c)) #[0 1 2 3 4 5]

e.g. setdiff1d

#Return the difference between the two.
# e.g. setdiff1d (ndarray1, ndarray2)
#Return the sorted, unique values in ar1 that are not in ar2.
a = np.array([1,2,3,4,5])
b = np.array([4,5,6,7,8])
c = np.array([[4,5,6,7,8],[9,10,11,12,13]])
ret = np.setdiff1d (a, b) # [1 2 3]
ret = np.setdiff1d (a, c) # [1 2 3]

e.g. setxor1d

#Return the symmetric difference between the two (unique & sorted of ar1 and ar2's own unique set of elements)
#Find the set exclusive-or of two arrays.
#Return the sorted, unique values that are in only one (not both) of the input arrays.
# e.g. setxor1d (ndarray1, ndarray2)

a = np.array([1,2,3,4,5])
b = np.array([4,5,6,7,8])
ret = np.setxor1d (a, b) # [1 2 3 6 7 8]

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