# COO.std¶

`COO.``std`(axis=None, dtype=None, out=None, ddof=0, keepdims=False)

Compute the standard deviation along the given axes. Uses all axes by default.

Parameters
• axis (Union[int, Iterable[int]], optional) – The axes along which to compute the standard deviation. Uses all axes by default.

• dtype (numpy.dtype, optional) – The output datatype.

• out (SparseArray, optional) – The array to write the output to.

• ddof (int) – The degrees of freedom.

• keepdims (bool, optional) – Whether or not to keep the dimensions of the original array.

Returns

The reduced output sparse array.

Return type

SparseArray

`numpy.ndarray.std`

Equivalent numpy method.

Notes

• This function internally calls `COO.sum_duplicates` to bring the array into canonical form.

Examples

You can use `COO.std` to compute the standard deviation of an array across any dimension.

```>>> from sparse import COO
>>> x = np.array([[1, 2, 0, 0],
...               [0, 1, 0, 0]], dtype='i8')
>>> s = COO.from_numpy(x)
>>> s2 = s.std(axis=1)
>>> s2.todense()
array([0.8291562, 0.4330127])
```

You can also use the `keepdims` argument to keep the dimensions after the standard deviation.

```>>> s3 = s.std(axis=0, keepdims=True)
>>> s3.shape
(1, 4)
```

You can pass in an output datatype, if needed.

```>>> s4 = s.std(axis=0, dtype=np.float16)
>>> s4.dtype
dtype('float16')
```

By default, this reduces the array down to one number, computing the standard deviation along all axes.

```>>> s.std()
0.7071067811865476
```