# 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.

`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)
```