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 ofa
as closely as possible. (Note that this function andndarray.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)
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)
© 2005–2021 NumPy Developers
Licensed under the 3-clause BSD License.
https://numpy.org/doc/1.20/reference/generated/numpy.copy.html