Understand numpy.copy(): Return a New Array Copy – NumPy Tutorial

By | October 20, 2020

numpy.copy() function can return a new array copy of the given object. In this tutorial, we will use some examples to show you how to use it.

numpy.copy()  Vs =

For example:

>>> import numpy as np
>>> x = np.array([1, 2, 3])
>>> y = x
>>> y
array([1, 2, 3])

You can find y is the same to x, same means x and y have the same value and memory address.

>>> id(x)
2788039846608
>>> id(y)
2788039846608

if we change the value in y, x is also be changed.

>>> y[0]=5
>>> y
array([5, 2, 3])
>>> x
array([5, 2, 3])

Notice: x is not array([1, 2, 3])

However, numpy.copy() only make the value of y is same to x, the memory address is different.

Look at this example:

>>> x = np.array([1, 2, 3])
>>> y = np.copy(x)
>>> y
array([1, 2, 3])
>>> id(x)
2788038511712
>>> id(y)
2788154247728

The value of y is same to x, but memory address is not.

If we change the value of y, x will not be changed.

>>> y[0]=5
>>> y
array([5, 2, 3])
>>> x
array([1, 2, 3])

numpy.copy() is same to numpy.ndarray.copy()

For example:

>>> x = np.array([1, 2, 3])
>>> y = x.copy()
>>> x
array([1, 2, 3])
>>> y
array([1, 2, 3])
>>> id(x)
2788039846608
>>> id(y)
2788154247808

From this example, we can find:

np.copy(x) is same to x.copy()

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