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Module « numpy »

Fonction nanmin - module numpy

Signature de la fonction nanmin

def nanmin(a, axis=None, out=None, keepdims=<no value>, initial=<no value>, where=<no value>) 

Description

help(numpy.nanmin)

Return minimum of an array or minimum along an axis, ignoring any NaNs.
When all-NaN slices are encountered a ``RuntimeWarning`` is raised and
Nan is returned for that slice.

Parameters
----------
a : array_like
    Array containing numbers whose minimum is desired. If `a` is not an
    array, a conversion is attempted.
axis : {int, tuple of int, None}, optional
    Axis or axes along which the minimum is computed. The default is to compute
    the minimum of the flattened array.
out : ndarray, optional
    Alternate output array in which to place the result.  The default
    is ``None``; if provided, it must have the same shape as the
    expected output, but the type will be cast if necessary. See
    :ref:`ufuncs-output-type` for more details.
keepdims : bool, optional
    If this is set to True, the axes which are reduced are left
    in the result as dimensions with size one. With this option,
    the result will broadcast correctly against the original `a`.

    If the value is anything but the default, then
    `keepdims` will be passed through to the `min` method
    of sub-classes of `ndarray`.  If the sub-classes methods
    does not implement `keepdims` any exceptions will be raised.
initial : scalar, optional
    The maximum value of an output element. Must be present to allow
    computation on empty slice. See `~numpy.ufunc.reduce` for details.

    .. versionadded:: 1.22.0
where : array_like of bool, optional
    Elements to compare for the minimum. See `~numpy.ufunc.reduce`
    for details.

    .. versionadded:: 1.22.0

Returns
-------
nanmin : ndarray
    An array with the same shape as `a`, with the specified axis
    removed.  If `a` is a 0-d array, or if axis is None, an ndarray
    scalar is returned.  The same dtype as `a` is returned.

See Also
--------
nanmax :
    The maximum value of an array along a given axis, ignoring any NaNs.
amin :
    The minimum value of an array along a given axis, propagating any NaNs.
fmin :
    Element-wise minimum of two arrays, ignoring any NaNs.
minimum :
    Element-wise minimum of two arrays, propagating any NaNs.
isnan :
    Shows which elements are Not a Number (NaN).
isfinite:
    Shows which elements are neither NaN nor infinity.

amax, fmax, maximum

Notes
-----
NumPy uses the IEEE Standard for Binary Floating-Point for Arithmetic
(IEEE 754). This means that Not a Number is not equivalent to infinity.
Positive infinity is treated as a very large number and negative
infinity is treated as a very small (i.e. negative) number.

If the input has a integer type the function is equivalent to np.min.

Examples
--------
>>> import numpy as np
>>> a = np.array([[1, 2], [3, np.nan]])
>>> np.nanmin(a)
1.0
>>> np.nanmin(a, axis=0)
array([1.,  2.])
>>> np.nanmin(a, axis=1)
array([1.,  3.])

When positive infinity and negative infinity are present:

>>> np.nanmin([1, 2, np.nan, np.inf])
1.0
>>> np.nanmin([1, 2, np.nan, -np.inf])
-inf

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