numpy.nan_to_num¶

numpy.nan_to_num(x)[source]

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. 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).

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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