Shulou(Shulou.com)06/01 Report--

Today, I will talk to you about the deep copy and shallow copy of NumPy in data analysis, which may not be well understood by many people. in order to make you understand better, the editor has summarized the following contents for you. I hope you can get something according to this article.

Background introduction

Today we learn about deep and shallow copies of NumPy arrays and the use of array properties. Let's go on to implement all the code demonstrations using Jupyter Notebook, and let's start with:

Getting started exampl

The following is the execution process in Jupyter Notebook:

Code process:

# # NumPy# Deep copy and shallow copy Learning

Import numpy as npx = np.array ([- 45,-31,-12, 0, 2, 25, 51, 99]) y = x # check whether the element group is the same # reference the same x is yid (x) id (y) x = = yy [4] = 1010yxtree_house = np.array ([- 45,-31,-12, 0, 2, 25, 51) 99]) tree_house = = yid (tree_house) id (x) tree_house [0] = 214tree_housextree_house = = xtree_house is x

# shallow copy farm_house = tree_house.view () farm_house.shape = (2,4) tree_housefarm_housetree_house [3] =-111farm_house

# Deep copy dog_house = np.copy (tree_house) dog_house [0] =-121dog_housetree_house after reading the above content, do you have any further understanding of the deep copy and shallow copy of NumPy in data analysis? If you want to know more knowledge or related content, please follow the industry information channel, thank you for your support.

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