/NumPy 1.17

# numpy.floor

`numpy.floor(x, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj]) = <ufunc 'floor'>`

Return the floor of the input, element-wise.

The floor of the scalar `x` is the largest integer `i`, such that `i <= x`. It is often denoted as .

Parameters: `x : array_like` Input data. `out : ndarray, None, or tuple of ndarray and None, optional` A location into which the result is stored. If provided, it must have a shape that the inputs broadcast to. If not provided or `None`, a freshly-allocated array is returned. A tuple (possible only as a keyword argument) must have length equal to the number of outputs. `where : array_like, optional` This condition is broadcast over the input. At locations where the condition is True, the `out` array will be set to the ufunc result. Elsewhere, the `out` array will retain its original value. Note that if an uninitialized `out` array is created via the default `out=None`, locations within it where the condition is False will remain uninitialized. **kwargs For other keyword-only arguments, see the ufunc docs. `y : ndarray or scalar` The floor of each element in `x`. This is a scalar if `x` is a scalar.

#### Notes

Some spreadsheet programs calculate the “floor-towards-zero”, in other words `floor(-2.5) == -2`. NumPy instead uses the definition of `floor` where `floor(-2.5) == -3`.

#### Examples

```>>> a = np.array([-1.7, -1.5, -0.2, 0.2, 1.5, 1.7, 2.0])
>>> np.floor(a)
array([-2., -2., -1.,  0.,  1.,  1.,  2.])
```