Constants

Euler's number, base of natural logarithms, Napier's constant.

``e = 2.71828182845904523536028747135266249775724709369995...``

.. rubric:: See Also

exp : Exponential function
log : Natural logarithm

.. rubric:: References

https://en.wikipedia.org/wiki/E_%28mathematical_constant%29

``γ = 0.5772156649015328606065120900824024310421...``

.. rubric:: References

https://en.wikipedia.org/wiki/Euler%27s_constant

IEEE 754 floating point representation of (positive) infinity.

.. rubric:: Returns

y : float
    A floating point representation of positive infinity.

.. rubric:: 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)

.. rubric:: 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.

.. rubric:: Examples

>>> import numpy as np
>>> np.inf
inf
>>> np.array([1]) / 0.
array([inf])

IEEE 754 floating point representation of Not a Number (NaN).

.. rubric:: Returns

y : A floating point representation of Not a Number.

.. rubric:: 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)

.. rubric:: 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.

.. rubric:: Examples

>>> import numpy as np
>>> np.nan
nan
>>> np.log(-1)
np.float64(nan)
>>> np.log([-1, 1, 2])
array([       nan, 0.        , 0.69314718])

A convenient alias for None, useful for indexing arrays.

.. rubric:: 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)

``pi = 3.1415926535897932384626433...``

.. rubric:: References

https://en.wikipedia.org/wiki/Pi