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# 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

    >>> import numpy as np
    >>> 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

    >>> import numpy as np
    >>> 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

    >>> 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)

numpy.pi

  `pi = 3.1415926535897932384626433...`

  References

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