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 in-place (False). The in-place 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

isinf

Shows which elements are positive or negative infinity.

isneginf

Shows which elements are negative infinity.

isposinf

Shows which elements are positive infinity.

isnan

Shows which elements are Not a Number (NaN).

isfinite

Shows which elements are finite (not NaN, not infinity)

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])