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. 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,
-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])
```

numpy.fmin

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numpy.real_if_close