`numpy.ma.core:copy`

source: build-install/usr/lib/python3.14/site-packages/numpy/ma/core.py :7063

Summary

Return a copy of the array.

Parameters

order : {'C', 'F', 'A', 'K'}, optional: Controls the memory layout of the copy. 'C' means C-order, 'F' means F-order, 'A' means 'F' if a is Fortran contiguous, 'C' otherwise. 'K' means match the layout of a as closely as possible. (Note that this function and numpy.copy are very similar but have different default values for their order= arguments, and this function always passes sub-classes through.)

Notes

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.

Examples

import numpy as np
x = np.array([[1,2,3],[4,5,6]], order='F')

y = x.copy()

x.fill(0)

y.flags['C_CONTIGUOUS']

For arrays containing Python objects (e.g. dtype=object), the copy is a shallow one. The new array will contain the same object which may lead to surprises if that object can be modified (is mutable):

a = np.array([1, 'm', [2, 3, 4]], dtype=object)
b = a.copy()
b[2][0] = 10
a

To ensure all elements within an ``object`` array are copied, use `copy.deepcopy`:

import copy
a = np.array([1, 'm', [2, 3, 4]], dtype=object)
c = copy.deepcopy(a)
c[2][0] = 10
c
a

Aliases

numpy.ma.copy

Summary

Parameters

Notes

Examples

See also

Aliases