PYTHON-matrix operation 1-X [:, 1] etc.

1. Conclusion:

# For a matrix X, X [:,:] This operation; 
# 1: represents all; 
# 2. count number from zero; 
# 3 may be a multi-dimensional, this test only to Three-dimensional. For more dimensions, please test yourself; 
# 4. [1D, 2D, 3D ...]

2. Code:

import numpy as np 
 
X = np.array ([[0,1], [2,3], [4,5], [6,7], [8,9], [10,11], [12, 13], [14,15], [16,17], [18,19]]) # 10 * 2 matrix 
print (X) 

# X [row, column] count from 0 
print (X [:, 0] ) #All rows and columns 0 
print (X [0,0]) # 0 rows and 0 columns 
print (X [:, 1]) #All rows and column 0 
print (X [1 ,:]) # 1 row All columns 

Print (X-[0: 2 ,:]) # 0 2-1 row to row, the column 

Y = [[[1,2], [3,4-], [5,6]], [[7,8], [9,10], [11,12]], [[13,14], [15,16], [17,18]]] # 3 * 3 * 2 matrix 
Y = np .array (y)
 print(of the type (the y-), of the type (the Y-))
 Print (the Y-[0,0,0]) # We know that 3-dimensional coordinate system (x, y, z), I took this to speak! Counting from 0! It's (0,0,0), understand it! 
Print (the Y [0,0 ,:]) # is (0,0, all)

#output
[[ 0  1]
 [ 2  3]
 [ 4  5]
 [ 6  7]
 [ 8  9]
 [10 11]
 [12 13]
 [14 15]
 [16 17]
 [18 19]]
[ 0  2  4  6  8 10 12 14 16 18]
0
[ 1  3  5  7  9 11 13 15 17 19]
[2 3]
[[0 1]
 [2 3]]
<class 'list'> <class 'numpy.ndarray'>
1
[1 2]

3. Reference URL:

https://blog.csdn.net/csj664103736/article/details/72828584/