Python:NumPy .copy()

The .copy() method in NumPy creates a new, independent copy of an array (ndarray). Unlike simple assignment, which creates a view that shares the same underlying data, it ensures that changes to the new array do not affect the original, and vice versa. This is useful when you need to modify an array while preserving the original data.

Syntax

ndarray.copy(order='C')

Parameters:

• order (optional, str): Controls the memory layout of the copy.
  • 'C' (default): C-style (row-major) order.
  • 'F': Fortran-style (column-major) order.
  • 'A': Preserves the array’s order — Fortran if the original is Fortran-contiguous, otherwise C.
  • 'K': Keeps the order as closely as possible to the original.

Return value:

Returns a new ndarray object that is an independent copy of the original array.

Example: Basic Usage of .copy()

The following example demonstrates the difference between assignment (which shares data) and using .copy() (which creates an independent copy):

import numpy as np

# Original array
original = np.array([10, 20, 30])
print(f"Original array: {original}")

# Assignment creates a reference (shares data)
view = original
view[0] = 111
print(f"Original after modifying view: {original}")

# Reset original for clarity
original = np.array([10, 20, 30])

# copy() creates an independent copy
copied_array = original.copy()
copied_array[0] = 999
print(f"Original after modifying copy: {original}")
print(f"Copied array: {copied_array}")

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Output of the above example:

Original array: [10 20 30]
Original after modifying view: [111 20 30]
Original after modifying copy: [10 20 30]
Copied array: [999 20 30]

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Codebyte Example: Copying a 2D Array Using .copy()

This example shows how .copy() works with a 2D array. It modifies the copy while leaving the original unchanged:

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