numpy.nditer

# numpy.fill_diagonal¶

`numpy.``fill_diagonal`(a, val, wrap=False)[source]

Fill the main diagonal of the given array of any dimensionality.

For an array a with `a.ndim >= 2`, the diagonal is the list of locations with indices `a[i, ..., i]` all identical. This function modifies the input array in-place, it does not return a value.

Parameters: a : array, at least 2-D. Array whose diagonal is to be filled, it gets modified in-place. val : scalar Value to be written on the diagonal, its type must be compatible with that of the array a. wrap : bool For tall matrices in NumPy version up to 1.6.2, the diagonal “wrapped” after N columns. You can have this behavior with this option. This affects only tall matrices.

Notes

New in version 1.4.0.

This functionality can be obtained via `diag_indices`, but internally this version uses a much faster implementation that never constructs the indices and uses simple slicing.

Examples

```>>> a = np.zeros((3, 3), int)
>>> np.fill_diagonal(a, 5)
>>> a
array([[5, 0, 0],
[0, 5, 0],
[0, 0, 5]])
```

The same function can operate on a 4-D array:

```>>> a = np.zeros((3, 3, 3, 3), int)
>>> np.fill_diagonal(a, 4)
```

We only show a few blocks for clarity:

```>>> a[0, 0]
array([[4, 0, 0],
[0, 0, 0],
[0, 0, 0]])
>>> a[1, 1]
array([[0, 0, 0],
[0, 4, 0],
[0, 0, 0]])
>>> a[2, 2]
array([[0, 0, 0],
[0, 0, 0],
[0, 0, 4]])
```

The wrap option affects only tall matrices:

```>>> # tall matrices no wrap
>>> a = np.zeros((5, 3),int)
>>> fill_diagonal(a, 4)
>>> a
array([[4, 0, 0],
[0, 4, 0],
[0, 0, 4],
[0, 0, 0],
[0, 0, 0]])
```
```>>> # tall matrices wrap
>>> a = np.zeros((5, 3),int)
>>> fill_diagonal(a, 4, wrap=True)
>>> a
array([[4, 0, 0],
[0, 4, 0],
[0, 0, 4],
[0, 0, 0],
[4, 0, 0]])
```
```>>> # wide matrices
>>> a = np.zeros((3, 5),int)
>>> fill_diagonal(a, 4, wrap=True)
>>> a
array([[4, 0, 0, 0, 0],
[0, 4, 0, 0, 0],
[0, 0, 4, 0, 0]])
```