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

    y : float (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

    y : float 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.

    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). 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

    y : float (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.

    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). 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 y : float (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