numpy.nan_to_num¶

numpy.
nan_to_num
(x, copy=True)[source]¶ Replace nan with zero and inf with finite numbers.
Returns an array or scalar replacing Not a Number (NaN) with zero, (positive) infinity with a very large number and negative infinity with a very small (or negative) number.
Parameters: x : 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
New Array with the same shape as x and dtype of the element in x with the greatest precision. If x is inexact, then NaN is replaced by zero, and infinity (infinity) is replaced by the largest (smallest or most negative) floating point value that fits in the output dtype. If x is not inexact, then a copy of x is returned.
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.set_printoptions(precision=8) >>> 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])