# Constants

NumPy includes several constants:

numpy.Inf

  IEEE 754 floating point representation of (positive) infinity.

  Use `inf` because `Inf`, `Infinity`, `PINF` and `infty` are aliases for `inf`. For more details, see `inf`.

  See Also

  inf

numpy.Infinity

  IEEE 754 floating point representation of (positive) infinity.

  Use `inf` because `Inf`, `Infinity`, `PINF` and `infty` are aliases for `inf`. For more details, see `inf`.

  See Also

  inf

numpy.NAN

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

  `NaN` and `NAN` are equivalent definitions of `nan`. Please use `nan` instead of `NAN`.

  See Also

  nan

numpy.NINF

  IEEE 754 floating point representation of negative infinity.

  Returns

  yfloat

    A floating point representation of negative 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.NINF
      -inf
      >>> np.log(0)
      -inf

numpy.NZERO

  IEEE 754 floating point representation of negative zero.

  Returns

  yfloat

    A floating point representation of negative zero.

  See Also

  PZERO : Defines positive zero.

  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.

  isfiniteShows 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). Negative zero is considered to be a finite number.

  Examples

      >>> np.NZERO
      -0.0
      >>> np.PZERO
      0.0

      >>> np.isfinite([np.NZERO])
      array([ True])
      >>> np.isnan([np.NZERO])
      array([False])
      >>> np.isinf([np.NZERO])
      array([False])

numpy.NaN

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

  `NaN` and `NAN` are equivalent definitions of `nan`. Please use `nan` instead of `NaN`.

  See Also

  nan

numpy.PINF

  IEEE 754 floating point representation of (positive) infinity.

  Use `inf` because `Inf`, `Infinity`, `PINF` and `infty` are aliases for `inf`. For more details, see `inf`.

  See Also

  inf

numpy.PZERO

  IEEE 754 floating point representation of positive zero.

  Returns

  yfloat

    A floating point representation of positive zero.

  See Also

  NZERO : Defines negative zero.

  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.

  isfiniteShows 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). Positive zero is considered to be a finite number.

  Examples

      >>> np.PZERO
      0.0
      >>> np.NZERO
      -0.0

      >>> np.isfinite([np.PZERO])
      array([ True])
      >>> np.isnan([np.PZERO])
      array([False])
      >>> np.isinf([np.PZERO])
      array([False])

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-Mascheroni_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.

  `Inf`, `Infinity`, `PINF` and `infty` are aliases for `inf`.

  Examples

      >>> np.inf
      inf
      >>> np.array([1]) / 0.
      array([ Inf])

numpy.infty

  IEEE 754 floating point representation of (positive) infinity.

  Use `inf` because `Inf`, `Infinity`, `PINF` and `infty` are aliases for `inf`. For more details, see `inf`.

  See Also

  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)
      nan
      >>> np.log([-1, 1, 2])
      array([        NaN,  0.        ,  0.69314718])

numpy.newaxis

  A convenient alias for None, useful for indexing arrays.

  Examples

      >>> newaxis is None
      True
      >>> x = np.arange(3)
      >>> x
      array([0, 1, 2])
      >>> x[:, newaxis]
      array([[0],
      [1],
      [2]])
      >>> x[:, newaxis, newaxis]
      array([[[0]],
      [[1]],
      [[2]]])
      >>> x[:, 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[:, newaxis] * y
      array([[ 0,  0,  0],
      [ 3,  4,  5],
      [ 6,  8, 10]])

  `x[newaxis, :]` is equivalent to `x[newaxis]` and `x[None]`:

      >>> x[newaxis, :].shape
      (1, 3)
      >>> x[newaxis].shape
      (1, 3)
      >>> x[None].shape
      (1, 3)
      >>> x[:, newaxis].shape
      (3, 1)

numpy.pi

  `pi = 3.1415926535897932384626433...`

  References

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