Constants#
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
- numpy.euler_gamma#
γ = 0.5772156649015328606065120900824024310421...
References
- 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
>>> 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.
NaN and NAN are aliases of
nan
.Examples
>>> 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
>>> 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 tox[np.newaxis]
andx[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