numpy.sign#

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

Returns an element-wise indication of the sign of a number.

The sign function returns -1 if x < 0, 0 if x==0, 1 if x > 0. nan is returned for nan inputs.

For complex inputs, the sign function returns x / abs(x), the generalization of the above (and 0 if x==0).

Changed in version 2.0.0: Definition of complex sign changed to follow the Array API standard.

Parameters:

xarray_like

Input values.

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:

yndarray

The sign of x. This is a scalar if x is a scalar.

See also

signbit

copysign

Notes

There is more than one definition of sign in common use for complex numbers. The definition used here, \(x/|x|\), is the more common and useful one, but is different from the one used in numpy prior to version 2.0, \(x/\sqrt{x*x}\), which is equivalent to sign(x.real) + 0j if x.real != 0 else sign(x.imag) + 0j.

Examples

Try it in your browser!

>>> import numpy as np
>>> np.sign([-5., 4.5])
array([-1.,  1.])
>>> np.sign(0)
0
>>> np.sign([3-4j, 8j])
array([0.6-0.8j, 0. +1.j ])

Go BackOpen In Tab