Numpy array calculation

Array calculation

View array shapes

import numpy as np
t1=np.arange(3,12,2)
print(t1)#[ 3 5 7 9 11]
print(t1.shape)#(5,)

t2=np.array([[1,2,3],[4,5,6]])
print(t2)#[[1 2 3]
         # [4 5 6]]
print(t2.shape)#(2, 3) Two lines and three columns 

t3=np.array([[[1,2,3],[4,5,6]],[[7,8,9],[10,11,12]]])
print(t3)#[[[ 1 2 3]
  # [ 4 5 6]]

 # [[ 7 8 9]
  # [10 11 12]]]
print(t3.shape)#(2, 2, 3)

Modify array shape

t4=np.arange(12)
print(t4)#[ 0 1 2 3 4 5 6 7 8 9 10 11]

print(t4.reshape((3,4)))
"""[[ 0 1 2 3] [ 4 5 6 7] [ 8 9 10 11]]"""
print('-------------------')

3D array becomes 2D and 1D

t5=np.arange(24).reshape((2,3,4))
print(t5)#[[[ 0 1 2 3]
   # [ 4 5 6 7]
  # [ 8 9 10 11]]

 # [[12 13 14 15]
 # [16 17 18 19]
 # [20 21 22 23]]]
print(t5.reshape((4,6)))
"""[[ 0 1 2 3 4 5] [ 6 7 8 9 10 11] [12 13 14 15 16 17] [18 19 20 21 22 23]]"""
# The assignment operation is to return the return value to the left end of the equation , It is essentially the same as returning directly to the display output device , It's just that the objects are different 
print(t5.reshape((24,)))#[ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23]
print(t5.reshape((24,1)))
"""[[ 0] [ 1] [ 2] [ 3] [ 4] [ 5] [ 6] [ 7] [ 8] [ 9] [10] [11] [12] [13] [14] [15] [16] [17] [18] [19] [20] [21] [22] [23]]"""
print(t5.reshape((1,24)))#[[ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23]]
print(t5.flatten())# Data is converted into one dimension [ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23]
print('-----------------------')

Array addition, subtraction, multiplication and division

t6=np.arange(12)
print(t4)#[ 0 1 2 3 4 5 6 7 8 9 10 11]
print(t6)#[ 0 1 2 3 4 5 6 7 8 9 10 11]
print(t6+2)#[ 2 3 4 5 6 7 8 9 10 11 12 13]
print(t6+t4)#[ 0 2 4 6 8 10 12 14 16 18 20 22]
print(t6+t4-1)#[-1 1 3 5 7 9 11 13 15 17 19 21]
print(t6*2)#[ 0 2 4 6 8 10 12 14 16 18 20 22]
print(t6/2)#[0. 0.5 1. 1.5 2. 2.5 3. 3.5 4. 4.5 5. 5.5]
#print(t6/0)#[nan inf inf inf inf inf inf inf inf inf inf inf]
# nan You mean for not a number inf Meaning infinity 
print(t4*t6)#[ 0 1 4 9 16 25 36 49 64 81 100 121]
print(t2)#[[1 2 3]
         # [4 5 6]]
print(t3)#[[[ 1 2 3]
  # [ 4 5 6]]

 # [[ 7 8 9]
  # [10 11 12]]]
print(t2+t3)
'''[[[ 2 4 6] [ 8 10 12]] [[ 8 10 12] [14 16 18]]]'''

# Broadcasting principles , The shapes of the two arrays are different , It is not completely impossible to calculate , When one of the array shapes is （a,b) when , Another array shape is （b) perhaps （a,1） You can perform the numerical operation of the corresponding position element , And broadcast to other dimensions .
#331 You can talk to anyone （3,x） Calculation , On the contrary, except （3,3）, The rest （X,3） Can't calculate 
# Add a little more   Add up :(a,b,c) You can talk to (b,c) as well as (1.c) /(b,1) Calculation 

When expanding to three dimensions , For example, the shape of an array is （a,b,c), Then when the shape of another array is （a,b,1),(b,c),,(b,1) The broadcast calculation can be completed in all kinds of situations .