Return an array copy of the given object.

The main difference between a copy and a view of an array is that the copy is a new array, and the view is just a view of the original array.

This function is the preferred method for creating an array copy. The function numpy.copy is similar, but it defaults to using order ‘K’, and will not pass sub-classes through by default.

Return an array copy of the given object.

Return an array copy of the given object.

January 14, 2026 - While working with NumPy, you may notice that some operations return a copy, while others return a view. A copy creates a new, independent array with its own memory, while a view shares the same memory as the original array.

Return an array copy of the given object.

Return an array copy of the given object.

Return an array copy of the given object.

Return an array copy of the given object.

It must be noted here that during the assignment of x[[1, 2]] no view or copy is created as the assignment happens in-place. The numpy.reshape function creates a view where possible or a copy otherwise. In most cases, the strides can be modified to reshape the array with a view.

October 31, 2025 - Returns a new ndarray object that is an independent copy of the original array.

January 31, 2021 - This function is the preferred method for creating an array copy. The function numpy.copy is similar, but it defaults to using order ‘K’, and will not pass sub-classes through by default.

It ensures that changes made to the copy do not affect the original array, and vice versa. ... Use numpy.copy() when you need to modify an array independently of the original.

Copies values from one array to another, broadcasting as necessary.

This function is the preferred method for creating an array copy. The function numpy.copy is similar, but it defaults to using order ‘K’, and will not pass sub-classes through by default.

In this syntax, np.copy() creates a new array with its own data, known as a deep copy, while .view() creates a new array object that looks at the same data as the original array, acting as a shallow copy. import numpy as np arr = np.array([1, 2, 3]) copy_arr = np.copy(arr) copy_arr[0] = 99 ...

Return a copy of the array.

A deep copy, on the other hand, creates a new array object along with copies of all the data it contains. This means that any changes made to the new array will not affect the original array, and vice versa. The data in the new array is completely independent of the data in the original array. Full Duplication − In the context of NumPy, a deep copy involves duplicating the entire data buffer of the array, ensuring that the new array is entirely separate from the original.

Return an array copy of the given object.