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An array is a grid of values and it contains information about the raw data, how to locate an element, and how to interpret an element.（ Like learning pytorch）

import numpy as np
import torch



a = np.array([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]])
print(a)
print(a.dtype)
print(a.shape)

''''
In NumPy, dimensions are called axes.
[[ 1  2  3  4]
 [ 5  6  7  8]
 [ 9 10 11 12]]
 
 This matrix can be said to be ,3 Row four column 
 It can also be said that 
The first axis has a length of 3 and the second axis has a length of 4.
'''
print(a[0])

narray

N-dimensional array

The NumPy ndarray class is used to represent both matrices（ matrix ） and vectors（ vector ）.

vector: an array with a single dimension

matrix: an array with two dimensions

tensor（ tensor ）: 3-D or higher dimensional array

2、create a basic array


import numpy as np
import torch


a = np.zeros(2)
print(a)

a = np.ones(2)
print(a)


a = np.arange(4) #an array with a range of elements
print(a)
b = np.arange(1,8,3) #start end step
print(b)


a = np.linspace(0, 10, num=5) # branch 5 branch   Linear increase 
print(a)

a = np.ones(2, dtype=np.int64) #specify the type
print(a)

Horizontal and vertical operation

Horizontal and vertical merge

import numpy as np

a1 = np.array([[1, 1],
            [2, 2]])

a2= np.array([[3, 3],
            [4, 4]])

a_h = np.hstack((a1,a2)) # level 
print(a_h)
a_v = np.vstack((a1,a2)) #vertical  vertical 
print(a_v)

Split horizontally and vertically

import numpy as np

a = np.arange(1,25).reshape(2,12)
print(a)
print(a.shape)
'''
 Return a list 
 return 3 individual array 
 Every array  Of shape （2*4） 4 = 12/3,  The original 12 Column 
'''
a_hsp = np.hsplit(a, 3)
print(a_hsp)
print(a_hsp[0].shape)

3、Adding, removing, and sorting elements

Sort

import numpy as np
import torch


arr = np.array([2, 1, 5, 3, 7, 4, 6, 8])
print(arr)

arr_sort = np.sort(arr)
print(arr_sort)
print(arr)

It can be seen that , Sorting does not change the elements of the original sequence

such as

import numpy as np
import torch


'''
 Returns the index of the elements of an ordered sequence 
'''

x = np.array([3, 1, 2])
index = np.argsort(x)
print(index)  #3-0,1-1,2-2,    The index returned 1,2,0

concatenate Connect

import numpy as np
import torch


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

c = np.concatenate((a, b))
print(c)

x = np.array([[1, 2], [3, 4]])
print(x) # 2*2
y = np.array([[5, 6]])
z = np.concatenate((x, y), axis=0) # Same number of columns   Connect 
print(z)

x = np.array([[1, 2, 3]])
print(x) # 2*2
y = np.array([[5, 6]])
z = np.concatenate((x, y), axis=1) # The same number of lines   Connect 
print(z)

4、shape of array

get the shape of array

'''
 The three-dimensional  array  3x2x4
'''
array_example = np.array([[[0, 1, 2, 3],
                           [4, 5, 6, 7]],

                          [[0, 1, 2, 3],
                            [4, 5, 6, 7]],

                         [[0 ,1 ,2, 3],
                          [4, 5, 6, 7]]])



print(array_example)
print(array_example.ndim) # Print dimension 
print(array_example.shape) 
print(array_example.size) # Total number of print elements

reshape

import numpy as np


'''
 The three-dimensional  array  3x2x4
'''
array_example = np.array([[[0, 1, 2, 3],
                           [4, 5, 6, 7]],

                          [[0, 1, 2, 3],
                            [4, 5, 6, 7]],

                         [[0 ,1 ,2, 3],
                          [4, 5, 6, 7]]])


print(array_example)
array_example1 = np.reshape(array_example,newshape=(3,4,2),order='C')
print(array_example1.shape)
print(array_example1)

5、convert array( such as A one-dimensional To A two-dimensional )

import numpy as np


a = np.array([1,2,3,4,5,6])
print(a)
print(a.shape)
a2 = a[np.newaxis, :] #np.newaxis will increase the dimensions of your array
print(a2)
print(a2.shape)

a3 = np.expand_dims(a, axis=1) # expand dim  Expand dimensions 
print(a3)
print(a3.shape)

6、index of array

index

import numpy as np


data = np.array([1, 2, 3])

print(data[1])
print(data[1:])
print(data[-2:])

To qualify elements in a sequence

import numpy as np

a = np.array([[1 , 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]])
# Establish a conditional discriminant 
five_blow = (a <= 5)
print(a[five_blow])

divisible_by_2 = (a % 2 == 0)
print(a[divisible_by_2])

c = a[(a > 2) & (a < 11)]
print(c)

return boolean values

import numpy as np

a = np.array([[1 , 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]])
five_up = (a > 5) | (a == 5)
print(five_up)

7、Basic array operations

Basic operation

addition, subtraction, multiplication, division, and more

import numpy as np

a = np.array([1,2])
b = np.ones(2,dtype=int)

print(a)
print(b)

c = a + b
print(c)
c1 = a - b
print(c1)
c2 = a * b
print(c2)
c3 = a // a
print(c3)

Sum up Find the maximum value

import numpy as np

a = np.array([1,2])
print(a.max())
print(a.min())
print(a.sum())

reverse an array Take the opposite

A one-dimensional

import numpy as np

arr = np.array([1, 2, 3, 4, 5, 6, 7, 8])
reversed_arr = np.flip(arr)
print(reversed_arr)

Multidimensional

import numpy as np

arr_2d = np.array([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]])
arr_2d_re = np.flip(arr_2d,axis=0) #  Press   That's ok   Take the opposite 
print(arr_2d_re)

arr_2d_re1 = np.flip(arr_2d,axis=1) #  Press   That's ok   Take the opposite 
print(arr_2d_re1)

arr_2d_re2 = np.flip(arr_2d) 
print(arr_2d_re2)

Specifies that the row and column are negated

import numpy as np

arr_2d = np.array([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]])
arr_2d[1] = np.flip(arr_2d[1]) #  The second line   Take the opposite 
print(arr_2d)

arr_2d = np.array([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]])
arr_2d[:,1] = np.flip(arr_2d[:,1]) #  Second column   Take the opposite 
print(arr_2d)

Multidimensional sequence flat

Reshaping and flattening multidimensional arrays

import numpy as np

arr_2d = np.array([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]])
a_flat = arr_2d.flatten()
print(a_flat)

import numpy as np

arr_2d = np.array([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]])
a_flat = arr_2d.flatten()
print(a_flat)
'''
 To the original array  No impact 
'''
a_flat[0] = 99
print(arr_2d)
print(a_flat)

'''
 use ravel, the changes you make to the new array
 will affect the parent array.
 
  Flattened   And with the original array  There is a direct impact 
'''
a_flat2 = arr_2d.ravel()
print(a_flat2)
a_flat2[0] = 99
print(a_flat2)
print(arr_2d)

8、matrices matrix

Create a matrix

import numpy as np

matrix = np.array([[1, 2], [3, 4], [5, 6]])
print(matrix)


print(matrix[0,1])
print(matrix[1:2])
print(matrix[1:2,1])

import numpy as np

matrix = np.array([[1, 2], [3, 4], [5, 6]])
print(matrix)

print(matrix.max())
print(matrix.min())
print(matrix.sum())

print(matrix.max(axis=0)) # Maximum value per column 
print(matrix.max(axis=1)) # The maximum value of each line

Matrix addition

import numpy as np

data = np.array([[1, 2], [3, 4]])
ones = np.array([[1, 1], [1, 1]])

sumArray = data + ones
print(sumArray)

broadcast - expand

use its broadcast rules for the operation.

It will be automatically replenished

import numpy as np

data = np.array([[1, 2], [3, 4], [5, 6]])
ones_row = np.array([[1, 1]])

sumArray = data + ones_row
print(sumArray)

Random number matrix

import numpy as np

rng = np.random.default_rng(0)
n = rng.random((2,3))
print(n)

Create a larger than 1, Less than 5 Of 2*4 Matrix

import numpy as np

rng = np.random.default_rng(1)
n = rng.integers(5,size=(2,4))
print(n)

Transposing（ Transposition ） and reshaping a matrix

import numpy as np

arr = np.array([[1,2,3],[4,5,6]])
print(arr)

arr1 = arr.reshape(3,2)
print(arr1)

# The transpose of the matrix , The two methods 
arr2 = arr.transpose()
print(arr2)
arr3 = arr.T
print(arr3)

9、unique items and count

A one-dimensional

Return non repeating elements And its index

import numpy as np

a = np.array([11, 11, 12, 13, 14, 15, 16, 17, 12, 13, 11, 14, 18, 19, 20])
unique_values, indices_list = np.unique(a, return_index=True)
print(unique_values)
print(indices_list)

Return non repeating elements And its Number of element repetitions

import numpy as np

a = np.array([11, 11, 12, 13, 14, 15, 16, 17, 12, 13, 11, 14, 18, 19, 20])
unique_values, occurrence_count = np.unique(a, return_counts=True)
print(unique_values)
print(occurrence_count)

Multidimensional

import numpy as np

a_2d = np.array([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12], [1, 2, 3, 4]])
print(a_2d.shape)

unique_rows, indices, occurrence_count \
    = np.unique( a_2d, axis=0, return_counts=True, return_index=True)
print(unique_rows) #  Returns a unique row vector 
print(indices) #  That's ok   Corresponding   coordinate 
print(occurrence_count) # That's ok   The emergence of   frequency