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:orphan:
*************
Miscellaneous
*************
IEEE 754 floating point special values
--------------------------------------
Special values defined in numpy: :data:`~numpy.nan`, :data:`~numpy.inf`
NaNs can be used as a poor-man's mask (if you don't care what the
original value was)
Note: cannot use equality to test NaNs. E.g.: ::
>>> myarr = np.array([1., 0., np.nan, 3.])
>>> np.nonzero(myarr == np.nan)
(array([], dtype=int64),)
::
>>> np.nan == np.nan # is always False! Use special numpy functions instead.
False
::
>>> myarr[myarr == np.nan] = 0. # doesn't work
>>> myarr
array([ 1., 0., nan, 3.])
::
>>> myarr[np.isnan(myarr)] = 0. # use this instead find
>>> myarr
array([1., 0., 0., 3.])
Other related special value functions:
- :func:`~numpy.isnan` - True if value is nan
- :func:`~numpy.isinf` - True if value is inf
- :func:`~numpy.isfinite` - True if not nan or inf
- :func:`~numpy.nan_to_num` - Map nan to 0, inf to max float, -inf to min float
The following corresponds to the usual functions except that nans are excluded
from the results:
- :func:`~numpy.nansum`
- :func:`~numpy.nanmax`
- :func:`~numpy.nanmin`
- :func:`~numpy.nanargmax`
- :func:`~numpy.nanargmin`
>>> x = np.arange(10.)
>>> x[3] = np.nan
>>> x.sum()
nan
>>> np.nansum(x)
42.0
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