# xorbits.numpy.count_nonzero#

xorbits.numpy.count_nonzero(a, axis=None, combine_size=None)[source]#

Counts the number of non-zero values in the array `a`.

The word “non-zero” is in reference to the Python 2.x built-in method `__nonzero__()` (renamed `__bool__()` in Python 3.x) of Python objects that tests an object’s “truthfulness”. For example, any number is considered truthful if it is nonzero, whereas any string is considered truthful if it is not the empty string. Thus, this function (recursively) counts how many elements in `a` (and in sub-arrays thereof) have their `__nonzero__()` or `__bool__()` method evaluated to `True`.

Parameters
• a (array_like) – The array for which to count non-zeros.

• axis (int or tuple, optional) –

Axis or tuple of axes along which to count non-zeros. Default is None, meaning that non-zeros will be counted along a flattened version of `a`.

New in version 1.12.0(numpy).

• keepdims (bool, optional (Not supported yet)) –

If this is set to True, the axes that are counted are left in the result as dimensions with size one. With this option, the result will broadcast correctly against the input array.

New in version 1.19.0(numpy).

Returns

count – Number of non-zero values in the array along a given axis. Otherwise, the total number of non-zero values in the array is returned.

Return type

int or array of int

`nonzero`

Return the coordinates of all the non-zero values.

Examples

```>>> np.count_nonzero(np.eye(4))
4
>>> a = np.array([[0, 1, 7, 0],
...               [3, 0, 2, 19]])
>>> np.count_nonzero(a)
5
>>> np.count_nonzero(a, axis=0)
array([1, 1, 2, 1])
>>> np.count_nonzero(a, axis=1)
array([2, 3])
>>> np.count_nonzero(a, axis=1, keepdims=True)
array([[2],
[3]])
```
combine_size: int, optional

The number of chunks to combine.

This docstring was copied from numpy.