numpy.copy#

numpy.copy(a, order='K', subok=False)[source]#

Return an array copy of the given object.

Parameters:

aarray_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.)
subokbool, optional: If True, then sub-classes will be passed-through, otherwise the returned array will be forced to be a base-class array (defaults to False).

New in version 1.19.0.

Returns:

arrndarray: Array interpretation of a.

See also

ndarray.copy: Preferred method for creating an array copy

Notes

This is equivalent to:

>>> np.array(a, copy=True)  

The copy made of the data is shallow, i.e., for arrays with object dtype, the new array will point to the same objects. See Examples from ndarray.copy.

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

Note that, np.copy clears previously set WRITEABLE=False flag.

>>> a = np.array([1, 2, 3])
>>> a.flags["WRITEABLE"] = False
>>> b = np.copy(a)
>>> b.flags["WRITEABLE"]
True
>>> b[0] = 3
>>> b
array([3, 2, 3])