NumPy includes several constants:
numpy.e
Euler’s number, 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
yfloatA 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
>>> importnumpyasnp>>> np.infinf>>> 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
>>> importnumpyasnp>>> np.nannan>>> 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
>>> importnumpyasnp>>> np.newaxisisNoneTrue>>> x=np.arange(3)>>> xarray([0, 1, 2])>>> x[:,np.newaxis]array([[0],[1],[2]])>>> x[:,np.newaxis,np.newaxis]array([[[0]],[[1]],[[2]]])>>> x[:,np.newaxis]*xarray([[0, 0, 0], [0, 1, 2], [0, 2, 4]])Outer product, same as outer(x,y):
>>> y=np.arange(3,6)>>> x[:,np.newaxis]*yarray([[ 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