/NumPy 1.17

# numpy.nan_to_num

`numpy.nan_to_num(x, copy=True, nan=0.0, posinf=None, neginf=None)` [source]

Replace NaN with zero and infinity with large finite numbers (default behaviour) or with the numbers defined by the user using the `nan`, `posinf` and/or `neginf` keywords.

If `x` is inexact, NaN is replaced by zero or by the user defined value in `nan` keyword, infinity is replaced by the largest finite floating point values representable by `x.dtype` or by the user defined value in `posinf` keyword and -infinity is replaced by the most negative finite floating point values representable by `x.dtype` or by the user defined value in `neginf` keyword.

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. `nan : int, float, optional` Value to be used to fill NaN values. If no value is passed then NaN values will be replaced with 0.0. `posinf : int, float, optional` Value to be used to fill positive infinity values. If no value is passed then positive infinity values will be replaced with a very large number. `neginf : int, float, optional` Value to be used to fill negative infinity values. If no value is passed then negative infinity values will be replaced with a very small (or negative) number. New in version 1.13. `out : ndarray` `x`, with the non-finite values replaced. If `copy` is False, this may be `x` itself.

`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, # may vary
-1.28000000e+002,  1.28000000e+002])
>>> np.nan_to_num(x, nan=-9999, posinf=33333333, neginf=33333333)
array([ 3.3333333e+07,  3.3333333e+07, -9.9990000e+03,
-1.2800000e+02,  1.2800000e+02])
>>> y = np.array([complex(np.inf, np.nan), np.nan, complex(np.nan, np.inf)])
array([  1.79769313e+308,  -1.79769313e+308,   0.00000000e+000, # may vary
-1.28000000e+002,   1.28000000e+002])
>>> np.nan_to_num(y)
array([  1.79769313e+308 +0.00000000e+000j, # may vary
0.00000000e+000 +0.00000000e+000j,
0.00000000e+000 +1.79769313e+308j])
>>> np.nan_to_num(y, nan=111111, posinf=222222)
array([222222.+111111.j, 111111.     +0.j, 111111.+222222.j])
```