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 Corder, ‘F’ means Forder, ‘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 subclasses will be passedthrough, otherwise the returned array will be forced to be a baseclass 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)
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 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, usecopy.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)