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# 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])

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