numpy.nan_to_num¶

numpy.
nan_to_num
(x, copy=True)[source]¶ Replace NaN with zero and infinity with large finite numbers.
If x is inexact, NaN is replaced by zero, and infinity and infinity replaced by the respectively largest and most negative finite floating point values representable by
x.dtype
.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:  x : scalar or array_like
Input data.
 copy : bool, 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.
Returns:  out : ndarray
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, 1.28000000e+002, 1.28000000e+002]) >>> y = np.array([complex(np.inf, np.nan), np.nan, complex(np.nan, np.inf)]) >>> np.nan_to_num(y) array([ 1.79769313e+308 +0.00000000e+000j, 0.00000000e+000 +0.00000000e+000j, 0.00000000e+000 +1.79769313e+308j])