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 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 x, with the non-finite values replaced. If copy is False, this may be x itself.

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.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])