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 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 by x.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: 


Returns: 

See also
NumPy uses the IEEE Standard for Binary FloatingPoint for Arithmetic (IEEE 754). This means that Not a Number is not equivalent to infinity.
>>> 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])
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https://docs.scipy.org/doc/numpy1.17.0/reference/generated/numpy.nan_to_num.html