Miscellaneous#

IEEE 754 floating point special values#

Special values defined in numpy: nan, 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:

  • isnan - True if value is nan

  • isinf - True if value is inf

  • isfinite - True if not nan or inf

  • 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:

>>> x = np.arange(10.)
>>> x[3] = np.nan
>>> x.sum()
nan
>>> np.nansum(x)
42.0