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

Return an array copy of the given object.

  • 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.)

  • subok (bool, optional (Not supported yet)) –

    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(numpy).


arr – Array interpretation of a.

Return type


See also


Preferred method for creating an array copy


This is equivalent to:

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


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]  
>>> x[0] == z[0]  

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"]  
>>> b[0] = 3  
>>> b  
array([3, 2, 3])

Note that np.copy is a shallow copy and will not copy object elements within arrays. This is mainly important for arrays containing Python objects. 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 = np.copy(a)  
>>> b[2][0] = 10  
>>> a  
array([1, 'm', list([10, 3, 4])], dtype=object)

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  
array([1, 'm', list([10, 3, 4])], dtype=object)
>>> a  
array([1, 'm', list([2, 3, 4])], dtype=object)

This docstring was copied from numpy.