# numpy.copy

numpy.copy(a, order='K', subok=False)

  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.)

    subok bool, 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:
    arr ndarray

      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)

  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])

  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)