How to realize Multi-dimensional Array assignment after slicing in NumPy

This article will explain in detail how to achieve multi-dimensional array slice assignment in NumPy. The content of the article is of high quality, so the editor shares it for you as a reference. I hope you will have a certain understanding of the relevant knowledge after reading this article.

Python 3.7.4 (tags/v3.7.4:e09359112e, Jul 8 2019, 20:34:20) [MSC v.1916 64 bit (AMD64)] on win32Type "help", "copyright", "credits" or "license ()" for more information. > > import numpy as np > a = np.zeros (3Mague 5)) > aarray ([[0.,0.,0.,0.,0.], [0.,0.0.0.0.0.], [0.,0.,0.]. > > barray ([[1.1,1.1.1.1.1.1.1], [1.1.1.1.1.1.1.1], [1.1.1.1.1.1.1.1]) > > a [: 2] = b [: 2jue:] > > aarray ([[1.1,1.1.1.1.1.1.1], [1.1.1.1.1.1]) > > aarray. 1.], [0, 0, 0, 0, 0.]) > a = np.zeros (3) 5)) > > a [: 2 recorder 3] = b [: 2 recuperation 3] > aarray ([[1, 1, 1, 1, 0, 0.], [1, 1, 1, 0, 0.], [0, 0, 0, 0. 0.]]) > a = np.zeros ((3d5)) > a [:: 2] = b [:: 2] > > aarray ([[1.1,1.1.1.1.1.1.1.], [0.1,0.,0.,0.,0.], [1.1.1.1.1.1.1.1.]) > this is where we share the assignment of multi-dimensional array slices in NumPy. I hope the above content can be of some help to you and learn more knowledge. If you think the article is good, you can share it for more people to see.