numpy.nan_to_num

numpy.nan_to_num(x)

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, float

Array with the same shape as x and dtype of the element in x with the greatest precision. 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. All finite numbers are upcast to the output dtype (default float64).

See also

isinf

Shows which elements are negative 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])