-
numpy.nan_to_num(x)
[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.
Returns: out : ndarray
New Array with the same shape as
x
and dtype of the element inx
with the greatest precision. Ifx
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. Ifx
is not inexact, then a copy ofx
is returned.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.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])
numpy.nan_to_num()
2017-01-10 18:16:15
Please login to continue.