Constants#

NumPy includes several constants:

numpy.e#

Euler’s number, base of natural logarithms, Napier’s constant.

e=2.71828182845904523536028747135266249775724709369995...

See Also

exp : Exponential functionlog : 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. Butinfinity is equivalent to positive infinity.

Examples

>>>importnumpyasnp>>>np.infinf>>>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 ofNot 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

>>>importnumpyasnp>>>np.nannan>>>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

>>>importnumpyasnp>>>np.newaxisisNoneTrue>>>x=np.arange(3)>>>xarray([0, 1, 2])>>>x[:,np.newaxis]array([[0],[1],[2]])>>>x[:,np.newaxis,np.newaxis]array([[[0]],[[1]],[[2]]])>>>x[:,np.newaxis]*xarray([[0, 0, 0],    [0, 1, 2],    [0, 2, 4]])

Outer product, same asouter(x,y):

>>>y=np.arange(3,6)>>>x[:,np.newaxis]*yarray([[ 0,  0,  0],    [ 3,  4,  5],    [ 6,  8, 10]])

x[np.newaxis,:] is equivalent tox[np.newaxis] andx[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