{"id":4303,"date":"2022-08-09T07:05:10","date_gmt":"2022-08-09T07:05:10","guid":{"rendered":"https:\/\/www.pythontutorial.net\/?page_id=4303"},"modified":"2022-08-10T04:17:11","modified_gmt":"2022-08-10T04:17:11","slug":"numpy-copy","status":"publish","type":"page","link":"https:\/\/www.pythontutorial.net\/python-numpy\/numpy-copy\/","title":{"rendered":"NumPy copy()"},"content":{"rendered":"\n

Summary<\/strong>: in this tutorial, you’ll learn how to use the NumPy copy()<\/code> method to create a copy of an array rather than a view.<\/p>\n\n\n\n

Introduction to the NumPy copy() method #<\/a><\/h2>\n\n\n\n

When you slice<\/a> an array, you get a subarray. The subarray is a view of the original array. In other words, if you change elements in the subarray, the change will be reflected in the original array. For example:<\/p>\n\n\n

import<\/span> numpy as<\/span> np\n\na = np.array([\n    [1<\/span>, 2<\/span>, 3<\/span>],\n    [4<\/span>, 5<\/span>, 6<\/span>]\n])\n\nb = a[0<\/span>:, 0<\/span>:2<\/span>]\nprint(b)\n\nb[0<\/span>, 0<\/span>] = 0<\/span>\nprint(b)\nprint(a)<\/code><\/span>Code language:<\/span> Python<\/span> (<\/span>python<\/span>)<\/span><\/small><\/pre>\n\n\n
How it works.<\/p>\n\n\n\n
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First, create a 2D array:<\/p>\n\n\n
a = np.array([\n    [1<\/span>, 2<\/span>, 3<\/span>],\n    [4<\/span>, 5<\/span>, 6<\/span>]\n])<\/code><\/span>Code language:<\/span> Python<\/span> (<\/span>python<\/span>)<\/span><\/small><\/pre>\n\n\n
Second, slice the array a and assign the subarray to the variable b:<\/p>\n\n\n
b = a[0<\/span>:, 0<\/span>:2<\/span>]<\/code><\/span>Code language:<\/span> Python<\/span> (<\/span>python<\/span>)<\/span><\/small><\/pre>\n\n\n
The variable b is:<\/p>\n\n\n
[[1<\/span> 2<\/span>]\n [4<\/span> 5<\/span>]]<\/code><\/span>Code language:<\/span> Python<\/span> (<\/span>python<\/span>)<\/span><\/small><\/pre>\n\n\n
Third, change the element at index [0,0] in the subarray b to zero and display the variable b:<\/p>\n\n\n
b[0<\/span>, 0<\/span>] = 0<\/span>\nprint(b)<\/code><\/span>Code language:<\/span> Python<\/span> (<\/span>python<\/span>)<\/span><\/small><\/pre>\n\n
[[0<\/span> 2<\/span>]\n [4<\/span> 5<\/span>]]<\/code><\/span>Code language:<\/span> Python<\/span> (<\/span>python<\/span>)<\/span><\/small><\/pre>\n\n\n
Since b is a view of array a, the change is also reflected in array a:<\/p>\n\n\n
print(a)<\/code><\/span>Code language:<\/span> Python<\/span> (<\/span>python<\/span>)<\/span><\/small><\/pre>\n\n
[[0<\/span> 2<\/span> 3<\/span>]\n [4<\/span> 5<\/span> 6<\/span>]]<\/code><\/span>Code language:<\/span> Python<\/span> (<\/span>python<\/span>)<\/span><\/small><\/pre>\n\n\n
The reason numpy creates a view instead of a new array is that it doesn’t have to copy data therefore improving performance.<\/p>\n\n\n\n
However, if you want a copy of an array rather than a view, you can use copy()<\/code> method. For example:<\/p>\n\n\n
import<\/span> numpy as<\/span> np\n\na = np.array([\n    [1<\/span>, 2<\/span>, 3<\/span>],\n    [4<\/span>, 5<\/span>, 6<\/span>]\n])\n\n# make a copy<\/span>\nb = a[0<\/span>:, 0<\/span>:2<\/span>].copy()\nprint(b)\n\nb[0<\/span>, 0<\/span>] = 0<\/span>\nprint(b)\n\nprint(a)<\/code><\/span>Code language:<\/span> Python<\/span> (<\/span>python<\/span>)<\/span><\/small><\/pre>\n\n\n
In this example: <\/p>\n\n\n\n
First, call the copy()<\/code> method of array a to make a copy of a subarray and assign it to the variable b. <\/p>\n\n\n\n
Second, change the element at index [0,0] of the array b, because both arrays are independent, the change doesn’t affect array a.<\/p>\n\n\n\n
Summary #<\/a><\/h2>\n\n\n\n
  • When you slice an array, you’ll get a view of the array.<\/li>
  • Use the copy()<\/code> method to make a copy of an array rather than a view.<\/li><\/ul>\n
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