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Module « numpy »

Fonction sign - module numpy

Signature de la fonction sign

def sign(*args, **kwargs) 

Description

help(numpy.sign)

sign(x, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature])

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``).

.. versionchanged:: 2.0.0
    Definition of complex sign changed to follow the Array API standard.

Parameters
----------
x : array_like
    Input values.
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
    :ref:`ufunc docs <ufuncs.kwargs>`.

Returns
-------
y : ndarray
    The sign of `x`.
    This is a scalar if `x` is a scalar.

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

Examples
--------
>>> 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 ])

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