numpy.nan_to_num¶

numpy.
nan_to_num
(x, copy=True, nan=0.0, posinf=None, neginf=None)[source]¶ 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.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 byx.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
Input data.
 copybool, optional
Whether to create a copy of x (True) or to replace values inplace (False). The inplace operation only occurs if casting to an array does not require a copy. Default is True.
New in version 1.13.
 nanint, float, optional
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
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
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 nonfinite values replaced. If
copy
is False, this may be x itself.
See also
Notes
NumPy uses the IEEE Standard for Binary FloatingPoint 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])