numpy.copy — NumPy v1.13 Manual (original) (raw)

numpy. copy(a, order='K')[source]¶

Return an array copy of the given object.

Parameters:	a : array_like Input data. 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 ndarray.copy are very similar, but have different default values for their order= arguments.)
Returns:	arr : ndarray Array interpretation of a.

Parameters:

a : array_like Input data. 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 ndarray.copy are very similar, but have different default values for their order= arguments.)

Returns:

arr : ndarray Array interpretation of a.

Notes

This is equivalent to:

np.array(a, copy=True)

Examples

Create an array x, with a reference y and a copy z:

x = np.array([1, 2, 3]) y = x z = np.copy(x)

Note that, when we modify x, y changes, but not z:

x[0] = 10 x[0] == y[0] True x[0] == z[0] False