Constants — NumPy v1.24 Manual (original) (raw)
NumPy includes several constants:
numpy.Inf#
IEEE 754 floating point representation of (positive) infinity.
Use inf because Inf, Infinity, PINF and infty are aliases forinf. For more details, see inf.
See Also
inf
numpy.Infinity#
IEEE 754 floating point representation of (positive) infinity.
Use inf because Inf, Infinity, PINF and infty are aliases forinf. For more details, see inf.
See Also
inf
numpy.NAN#
IEEE 754 floating point representation of Not a Number (NaN).
NaN and NAN are equivalent definitions of nan. Please usenan instead of NAN.
See Also
nan
numpy.NINF#
IEEE 754 floating point representation of negative infinity.
Returns
yfloat
A floating point representation of negative infinity.
See Also
isinf : Shows which elements are positive or negative infinity
isposinf : Shows which elements are positive infinity
isneginf : Shows which elements are negative infinity
isnan : Shows which elements are Not a Number
isfinite : Shows which elements are finite (not one of Not a Number, positive infinity and negative 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. Also that positive infinity is not equivalent to negative infinity. But infinity is equivalent to positive infinity.
Examples
np.NINF -inf np.log(0) -inf
numpy.NZERO#
IEEE 754 floating point representation of negative zero.
Returns
yfloat
A floating point representation of negative zero.
See Also
PZERO : Defines positive zero.
isinf : Shows which elements are positive or negative infinity.
isposinf : Shows which elements are positive infinity.
isneginf : Shows which elements are negative infinity.
isnan : Shows which elements are Not a Number.
isfiniteShows which elements are finite - not one of
Not a Number, positive infinity and negative infinity.
Notes
NumPy uses the IEEE Standard for Binary Floating-Point for Arithmetic (IEEE 754). Negative zero is considered to be a finite number.
Examples
np.NZERO -0.0 np.PZERO 0.0
np.isfinite([np.NZERO]) array([ True]) np.isnan([np.NZERO]) array([False]) np.isinf([np.NZERO]) array([False])
numpy.NaN#
IEEE 754 floating point representation of Not a Number (NaN).
NaN and NAN are equivalent definitions of nan. Please usenan instead of NaN.
See Also
nan
numpy.PINF#
IEEE 754 floating point representation of (positive) infinity.
Use inf because Inf, Infinity, PINF and infty are aliases forinf. For more details, see inf.
See Also
inf
numpy.PZERO#
IEEE 754 floating point representation of positive zero.
Returns
yfloat
A floating point representation of positive zero.
See Also
NZERO : Defines negative zero.
isinf : Shows which elements are positive or negative infinity.
isposinf : Shows which elements are positive infinity.
isneginf : Shows which elements are negative infinity.
isnan : Shows which elements are Not a Number.
isfiniteShows which elements are finite - not one of
Not a Number, positive infinity and negative infinity.
Notes
NumPy uses the IEEE Standard for Binary Floating-Point for Arithmetic (IEEE 754). Positive zero is considered to be a finite number.
Examples
np.PZERO 0.0 np.NZERO -0.0
np.isfinite([np.PZERO]) array([ True]) np.isnan([np.PZERO]) array([False]) np.isinf([np.PZERO]) array([False])
numpy.e#
Euler’s constant, base of natural logarithms, Napier’s constant.
e = 2.71828182845904523536028747135266249775724709369995...
See Also
exp : Exponential function log : Natural logarithm
References
https://en.wikipedia.org/wiki/E_%28mathematical_constant%29
numpy.euler_gamma#
γ = 0.5772156649015328606065120900824024310421...
References
https://en.wikipedia.org/wiki/Euler-Mascheroni_constant
numpy.inf#
IEEE 754 floating point representation of (positive) infinity.
Returns
yfloat
A floating point representation of positive infinity.
See Also
isinf : Shows which elements are positive or negative infinity
isposinf : Shows which elements are positive infinity
isneginf : Shows which elements are negative infinity
isnan : Shows which elements are Not a Number
isfinite : Shows which elements are finite (not one of Not a Number, positive infinity and negative 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. Also that positive infinity is not equivalent to negative infinity. But infinity is equivalent to positive infinity.
Inf, Infinity, PINF and infty are aliases for inf.
Examples
np.inf inf np.array([1]) / 0. array([ Inf])
numpy.infty#
IEEE 754 floating point representation of (positive) infinity.
Use inf because Inf, Infinity, PINF and infty are aliases forinf. For more details, see inf.
See Also
inf
numpy.nan#
IEEE 754 floating point representation of Not a Number (NaN).
Returns
y : A floating point representation of Not a Number.
See Also
isnan : Shows which elements are Not a Number.
isfinite : Shows which elements are finite (not one of Not a Number, positive infinity and negative 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.
NaN and NAN are aliases of nan.
Examples
np.nan nan np.log(-1) nan np.log([-1, 1, 2]) array([ NaN, 0. , 0.69314718])
numpy.newaxis#
A convenient alias for None, useful for indexing arrays.
Examples
newaxis is None True x = np.arange(3) x array([0, 1, 2]) x[:, newaxis] array([[0], [1], [2]]) x[:, newaxis, newaxis] array([[[0]], [[1]], [[2]]]) x[:, newaxis] * x array([[0, 0, 0], [0, 1, 2], [0, 2, 4]])
Outer product, same as outer(x, y):
y = np.arange(3, 6) x[:, newaxis] * y array([[ 0, 0, 0], [ 3, 4, 5], [ 6, 8, 10]])
x[newaxis, :] is equivalent to x[newaxis] and x[None]:
x[newaxis, :].shape (1, 3) x[newaxis].shape (1, 3) x[None].shape (1, 3) x[:, newaxis].shape (3, 1)
numpy.pi#
pi = 3.1415926535897932384626433...
References