NumPy

numpy.divide

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

Returns a true division of the inputs, element-wise.

Instead of the Python traditional ‘floor division’, this returns a true division. True division adjusts the output type to present the best answer, regardless of input types.

Parameters
x1array_like

Dividend array.

x2array_like

Divisor array. If x1.shape != x2.shape, they must be broadcastable to a common shape (which becomes the shape of the output).

outndarray, 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.

wherearray_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.

Returns
outndarray or scalar

This is a scalar if both x1 and x2 are scalars.

Notes

The floor division operator // was added in Python 2.2 making // and / equivalent operators. The default floor division operation of / can be replaced by true division with from __future__ import division.

In Python 3.0, // is the floor division operator and / the true division operator. The true_divide(x1, x2) function is equivalent to true division in Python.

Examples

>>> x = np.arange(5)
>>> np.true_divide(x, 4)
array([ 0.  ,  0.25,  0.5 ,  0.75,  1.  ])
>>> x//4
array([0, 0, 0, 0, 1])
>>> from __future__ import division
>>> x/4
array([ 0.  ,  0.25,  0.5 ,  0.75,  1.  ])
>>> x//4
array([0, 0, 0, 0, 1])

Previous topic

numpy.multiply

Next topic

numpy.power