isnan(x, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])
Some inconsistencies with the Dask version may exist.
Test element-wise for NaN and return result as a boolean array.
NumPy uses the IEEE Standard for Binary Floating-Point for Arithmetic (IEEE 754). This means that Not a Number is not equivalent to infinity.
Input array.
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.
This condition is broadcast over the input. At locations where the condition is True, the :None:None:`out`
array will be set to the ufunc result. Elsewhere, the :None:None:`out`
array will retain its original value. Note that if an uninitialized :None:None:`out`
array is created via the default out=None
, locations within it where the condition is False will remain uninitialized.
For other keyword-only arguments, see the ufunc docs <ufuncs.kwargs>
.
This docstring was copied from numpy.isnan.
>>> np.isnan(np.nan) # doctest: +SKIP TrueThis example is valid syntax, but we were not able to check execution
>>> np.isnan(np.inf) # doctest: +SKIP FalseThis example is valid syntax, but we were not able to check execution
>>> np.isnan([np.log(-1.),1.,np.log(0)]) # doctest: +SKIP array([ True, False, False])See :
The following pages refer to to this document either explicitly or contain code examples using this.
dask.array.reductions.nanmax
dask.array.ufunc.isinf
dask.array.reductions.nancumsum
dask.array.ufunc.isfinite
dask.array.reductions.make_arg_reduction.<locals>.wrapped
dask.array.reductions.nancumprod
dask.array.reductions.nanprod
dask.array.reductions.nansum
dask.array.reductions.nanmin
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