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

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

>>> 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

>>> 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 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

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