any(a, axis=None, keepdims=False, split_every=None, out=None)
This docstring was copied from numpy.any.
Some inconsistencies with the Dask version may exist.
Returns single boolean unless :None:None:`axis`
is not None
Not a Number (NaN), positive infinity and negative infinity evaluate to :None:None:`True`
because these are not equal to zero.
Input array or object that can be converted to an array.
Axis or axes along which a logical OR reduction is performed. The default ( axis=None
) is to perform a logical OR over all the dimensions of the input array. :None:None:`axis`
may be negative, in which case it counts from the last to the first axis.
If this is a tuple of ints, a reduction is performed on multiple axes, instead of a single axis or all the axes as before.
Alternate output array in which to place the result. It must have the same shape as the expected output and its type is preserved (e.g., if it is of type float, then it will remain so, returning 1.0 for True and 0.0 for False, regardless of the type of a
). See ufuncs-output-type
for more details.
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 input array.
If the default value is passed, then keepdims
will not be passed through to the any
method of sub-classes of :None:None:`ndarray`
, however any non-default value will be. If the sub-class' method does not implement keepdims
any exceptions will be raised.
Elements to include in checking for any :None:None:`True`
values. See :None:None:`~numpy.ufunc.reduce`
for details.
A new boolean or :None:None:`ndarray`
is returned unless :None:None:`out`
is specified, in which case a reference to :None:None:`out`
is returned.
Test whether any array element along a given axis evaluates to True.
all
Test whether all elements along a given axis evaluate to True.
ndarray.any
equivalent method
>>> np.any([[True, False], [True, True]]) # doctest: +SKIP TrueThis example is valid syntax, but we were not able to check execution
>>> np.any([[True, False], [False, False]], axis=0) # doctest: +SKIP array([ True, False])This example is valid syntax, but we were not able to check execution
>>> np.any([-1, 0, 5]) # doctest: +SKIP TrueThis example is valid syntax, but we were not able to check execution
>>> np.any(np.nan) # doctest: +SKIP TrueThis example is valid syntax, but we were not able to check execution
>>> np.any([[True, False], [False, False]], where=[[False], [True]]) # doctest: +SKIP FalseThis example is valid syntax, but we were not able to check execution
>>> o=np.array(False) # doctest: +SKIPThis example is valid syntax, but we were not able to check execution
... z=np.any([-1, 4, 5], out=o) # doctest: +SKIP
... z, o # doctest: +SKIP (array(True), array(True))
>>> # Check now that z is a reference to oThis example is valid syntax, but we were not able to check execution
... z is o # doctest: +SKIP True
>>> id(z), id(o) # identity of z and o # doctest: +SKIP (191614240, 191614240)See :
The following pages refer to to this document either explicitly or contain code examples using this.
dask.array.reductions.all
dask.array.reductions.any
Hover to see nodes names; edges to Self not shown, Caped at 50 nodes.
Using a canvas is more power efficient and can get hundred of nodes ; but does not allow hyperlinks; , arrows or text (beyond on hover)
SVG is more flexible but power hungry; and does not scale well to 50 + nodes.
All aboves nodes referred to, (or are referred from) current nodes; Edges from Self to other have been omitted (or all nodes would be connected to the central node "self" which is not useful). Nodes are colored by the library they belong to, and scaled with the number of references pointing them