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.. currentmodule:: numpy
*********
Constants
*********
NumPy includes several constants:
.. data:: e
Euler's constant, 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
.. data:: euler_gamma
``γ = 0.5772156649015328606065120900824024310421...``
.. rubric:: References
https://en.wikipedia.org/wiki/Euler%27s_constant
.. data:: inf
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
.. try_examples::
>>> import numpy as np
>>> np.inf
inf
>>> np.array([1]) / 0.
array([inf])
.. data:: nan
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
.. try_examples::
>>> import numpy as np
>>> np.nan
nan
>>> np.log(-1)
np.float64(nan)
>>> np.log([-1, 1, 2])
array([ nan, 0. , 0.69314718])
.. data:: newaxis
A convenient alias for None, useful for indexing arrays.
.. rubric:: Examples
.. try_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)
.. data:: pi
``pi = 3.1415926535897932384626433...``
.. rubric:: References
https://en.wikipedia.org/wiki/Pi
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