# numpy.ndarray.copy() in Python

`numpy.ndarray.copy()` returns a copy of the array.

Syntax : numpy.ndarray.copy(order=’C’)

Parameters:
order : 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.

Code #1:

 `# Python program explaining   ` `# numpy.ndarray.copy() function ` ` `  `import` `numpy as geek ` ` `  ` `  `x ``=` `geek.array([[``0``, ``1``, ``2``, ``3``], [``4``, ``5``, ``6``, ``7``]], ` `                                 ``order ``=``'F'``) ` `print``(``"x is: \n"``, x) ` ` `  `# copying x to y ` `y ``=` `x.copy() ` `print``(``"y is :\n"``, y) ` `print``(``"\nx is copied to y"``) `

Output:

```x is:
[[0 1 2 3]
[4 5 6 7]]
y is :
[[0 1 2 3]
[4 5 6 7]]

x is copied to y
```

Code #2:

 `# Python program explaining   ` `# numpy.ndarray.copy() function ` ` `  `import` `numpy as geek ` ` `  ` `  `x ``=` `geek.array([[``0``, ``1``, ], [``2``, ``3``]]) ` `print``(``"x is:\n"``, x) ` ` `  `# copying x to y ` `y ``=` `x.copy() ` ` `  `# filling x with 1's ` `x.fill(``1``) ` `print``(``"\n Now x is : \n"``, x) ` ` `  `print``(``"\n y is: \n"``, y) `

Output:

```x is:
[[0 1]
[2 3]]

Now x is :
[[1 1]
[1 1]]

y is:
[[0 1]
[2 3]]
```

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