dpnp.nan_to_num
- dpnp.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 behavior) 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 byx.dtype
or by the user defined value in posinf keyword and -infinity is replaced by the most negative finite floating point values representable byx.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.
For full documentation refer to
numpy.nan_to_num
.- Parameters:
x ({dpnp.ndarray, usm_ndarray}) -- 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.nan ({int, float, bool}, optional) -- Value to be used to fill
NaN
values. Default:0.0
.posinf ({int, float, bool, None}, 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. Default:
None
.neginf ({int, float, bool, None} 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. Default:
None
.
- Returns:
out -- x, with the non-finite values replaced. If copy is
False
, this may be x itself.- Return type:
dpnp.ndarray
See also
dpnp.isinf
Shows which elements are positive or negative infinity.
dpnp.isneginf
Shows which elements are negative infinity.
dpnp.isposinf
Shows which elements are positive infinity.
dpnp.isnan
Shows which elements are Not a Number (NaN).
dpnp.isfinite
Shows which elements are finite (not NaN, not infinity)
Examples
>>> import dpnp as np >>> np.nan_to_num(np.array(np.inf)) array(1.79769313e+308) >>> np.nan_to_num(np.array(-np.inf)) array(-1.79769313e+308) >>> np.nan_to_num(np.array(np.nan)) array(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]) >>> 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)]) >>> 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])