numpy.divide¶

`numpy.``divide`(x1, x2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj]) = <ufunc 'divide'>

Divide arguments element-wise.

Parameters: x1 : array_like Dividend array. x2 : array_like Divisor array. 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 Values of True indicate to calculate the ufunc at that position, values of False indicate to leave the value in the output alone. **kwargs For other keyword-only arguments, see the ufunc docs. y : ndarray or scalar The quotient `x1/x2`, element-wise. Returns a scalar if both `x1` and `x2` are scalars.

`seterr`
Set whether to raise or warn on overflow, underflow and division by zero.

Notes

Equivalent to `x1` / `x2` in terms of array-broadcasting.

Behavior on division by zero can be changed using `seterr`.

In Python 2, when both `x1` and `x2` are of an integer type, `divide` will behave like `floor_divide`. In Python 3, it behaves like `true_divide`.

Examples

```>>> np.divide(2.0, 4.0)
0.5
>>> x1 = np.arange(9.0).reshape((3, 3))
>>> x2 = np.arange(3.0)
>>> np.divide(x1, x2)
array([[ NaN,  1. ,  1. ],
[ Inf,  4. ,  2.5],
[ Inf,  7. ,  4. ]])
```

Note the behavior with integer types (Python 2 only):

```>>> np.divide(2, 4)
0
>>> np.divide(2, 4.)
0.5
```

Division by zero always yields zero in integer arithmetic (again, Python 2 only), and does not raise an exception or a warning:

```>>> np.divide(np.array([0, 1], dtype=int), np.array([0, 0], dtype=int))
array([0, 0])
```

Division by zero can, however, be caught using `seterr`:

```>>> old_err_state = np.seterr(divide='raise')
>>> np.divide(1, 0)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
FloatingPointError: divide by zero encountered in divide
```
```>>> ignored_states = np.seterr(**old_err_state)
>>> np.divide(1, 0)
0
```

numpy.multiply

numpy.power