Constants — NumPy v2.2 Manual (original) (raw)
NumPy includes several constants:
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%27s_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.
Examples
import numpy as np np.inf inf np.array([1]) / 0. array([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.
Examples
import numpy as np np.nan nan np.log(-1) np.float64(nan) np.log([-1, 1, 2]) array([ nan, 0. , 0.69314718])
numpy.newaxis#
A convenient alias for None, useful for indexing arrays.
Examples
import numpy as np np.newaxis is None True x = np.arange(3) x array([0, 1, 2]) x[:, np.newaxis] array([[0], [1], [2]]) x[:, np.newaxis, np.newaxis] array([[[0]], [[1]], [[2]]]) x[:, np.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[:, np.newaxis] * y array([[ 0, 0, 0], [ 3, 4, 5], [ 6, 8, 10]])
x[np.newaxis, :] is equivalent to x[np.newaxis] and x[None]:
x[np.newaxis, :].shape (1, 3) x[np.newaxis].shape (1, 3) x[None].shape (1, 3) x[:, np.newaxis].shape (3, 1)
numpy.pi#
pi = 3.1415926535897932384626433...
References