The difference between array and asarray in numpy

The following is a detailed explanation of the difference between array and asarray in numpy. It has a good reference value and I hope it will be helpful to everyone. Let’s take a look together

Both array and asarray can convert structural data into ndarray, but the main difference is that when the data source is ndarray, array will still copy a copy and occupy new memory, but asarray does not meeting.

Example:

##

import numpy as np 
 
#example 1: 
data1=[[1,1,1],[1,1,1],[1,1,1]] 
arr2=np.array(data1) 
arr3=np.asarray(data1) 
data1[1][1]=2 
print 'data1:\n',data1 
print 'arr2:\n',arr2 
print 'arr3:\n',arr3

Output:

data1: 
[[1, 1, 1], [1, 2, 1], [1, 1, 1]] 
arr2: 
[[1 1 1] 
 [1 1 1] 
 [1 1 1]] 
arr3: 
[[1 1 1] 
 [1 1 1] 
 [1 1 1]]

It can be seen that there is no difference between array and asarray, both of which copy the metadata.

import numpy as np 
 
#example 2: 
arr1=np.ones((3,3)) 
arr2=np.array(arr1) 
arr3=np.asarray(arr1) 
arr1[1]=2 
print 'arr1:\n',arr1 
print 'arr2:\n',arr2 
print 'arr3:\n',arr3

Output:

arr1: 
[[ 1. 1. 1.] 
 [ 2. 2. 2.] 
 [ 1. 1. 1.]] 
arr2: 
[[ 1. 1. 1.] 
 [ 1. 1. 1.] 
 [ 1. 1. 1.]] 
arr3: 
[[ 1. 1. 1.] 
 [ 2. 2. 2.] 
 [ 1. 1. 1.]]

The difference between the two is only shown at this time

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