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Module « numpy »

Fonction divide - module numpy

Signature de la fonction divide

def divide(*args, **kwargs) 

Description

help(numpy.divide)

divide(x1, x2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature])

Divide arguments element-wise.

Parameters
----------
x1 : array_like
    Dividend array.
x2 : array_like
    Divisor array.
    If ``x1.shape != x2.shape``, they must be broadcastable to a common
    shape (which becomes the shape of the output).
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 or scalar
    The quotient ``x1/x2``, element-wise.
    This is a scalar if both `x1` and `x2` are scalars.

See Also
--------
seterr : Set whether to raise or warn on overflow, underflow and
         division by zero.

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

The ``true_divide(x1, x2)`` function is an alias for
``divide(x1, x2)``.

Examples
--------
>>> import numpy as np
>>> 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. ]])

The ``/`` operator can be used as a shorthand for ``np.divide`` on
ndarrays.

>>> x1 = np.arange(9.0).reshape((3, 3))
>>> x2 = 2 * np.ones(3)
>>> x1 / x2
array([[0. , 0.5, 1. ],
       [1.5, 2. , 2.5],
       [3. , 3.5, 4. ]])

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