dask.array.nan_to_num
dask.array.nan_to_num¶
- dask.array.nan_to_num(*args, **kwargs)¶
Replace NaN with zero and infinity with large finite numbers (default behaviour) or with the numbers defined by the user using the nan, posinf and/or neginf keywords.
This docstring was copied from numpy.nan_to_num.
Some inconsistencies with the Dask version may exist.
If x is inexact, NaN is replaced by zero or by the user defined value in nan keyword, infinity is replaced by the largest finite floating point values representable by
x.dtype
or by the user defined value in posinf keyword and -infinity is replaced by the most negative finite floating point values representable byx.dtype
or by the user defined value in neginf keyword.For complex dtypes, the above is applied to each of the real and imaginary components of x separately.
If x is not inexact, then no replacements are made.
- Parameters
- xscalar or array_like (Not supported in Dask)
Input data.
- copybool, optional (Not supported in Dask)
Whether to create a copy of x (True) or to replace values in-place (False). The in-place operation only occurs if casting to an array does not require a copy. Default is True.
New in version 1.13.
- nanint, float, optional (Not supported in Dask)
Value to be used to fill NaN values. If no value is passed then NaN values will be replaced with 0.0.
New in version 1.17.
- posinfint, float, optional (Not supported in Dask)
Value to be used to fill positive infinity values. If no value is passed then positive infinity values will be replaced with a very large number.
New in version 1.17.
- neginfint, float, optional (Not supported in Dask)
Value to be used to fill negative infinity values. If no value is passed then negative infinity values will be replaced with a very small (or negative) number.
New in version 1.17.
- Returns
- outndarray
x, with the non-finite values replaced. If copy is False, this may be x itself.
See also
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.
Examples
>>> np.nan_to_num(np.inf) 1.7976931348623157e+308 >>> np.nan_to_num(-np.inf) -1.7976931348623157e+308 >>> np.nan_to_num(np.nan) 0.0 >>> x = np.array([np.inf, -np.inf, np.nan, -128, 128]) >>> np.nan_to_num(x) array([ 1.79769313e+308, -1.79769313e+308, 0.00000000e+000, # may vary -1.28000000e+002, 1.28000000e+002]) >>> np.nan_to_num(x, nan=-9999, posinf=33333333, neginf=33333333) array([ 3.3333333e+07, 3.3333333e+07, -9.9990000e+03, -1.2800000e+02, 1.2800000e+02]) >>> y = np.array([complex(np.inf, np.nan), np.nan, complex(np.nan, np.inf)]) array([ 1.79769313e+308, -1.79769313e+308, 0.00000000e+000, # may vary -1.28000000e+002, 1.28000000e+002]) >>> np.nan_to_num(y) array([ 1.79769313e+308 +0.00000000e+000j, # may vary 0.00000000e+000 +0.00000000e+000j, 0.00000000e+000 +1.79769313e+308j]) >>> np.nan_to_num(y, nan=111111, posinf=222222) array([222222.+111111.j, 111111. +0.j, 111111.+222222.j])