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:

Parameters:	x : array_like Input data.
Returns:	out : ndarray New Array with the same shape as `x` and dtype of the element in `x` with the greatest precision. If `x` is inexact, then 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. If `x` is not inexact, then a copy of `x` is returned.

x : array_like

Input data.

Returns:

out : ndarray

New Array with the same shape as x and dtype of the element in x with the greatest precision. If x is inexact, then 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. If x is not inexact, then a copy of x is returned.

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])

Links:

https://docs.scipy.org/doc/numpy-1.11.0/reference/generated/numpy.nan_to_num.html

doc_NumPy

2025-01-10 15:47:30

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