一分钟学会如何查看Python内置函数的用法及其源码

import math

dir(math)

['__doc__',

 '__loader__',

 '__name__',

 '__package__',

 '__spec__',

 'acos',

 'acosh',

 'asin',

 'asinh',

 'atan',

 'atan2',

 'atanh',

 'ceil',

 'copysign',

 'cos',

 'cosh',

 'degrees',

 'e',

 'erf',

 'erfc',

 'exp',

 'expm1',

 'fabs',

 'factorial',

 'floor',

 'fmod',

 'frexp',

 'fsum',

 'gamma',

 'gcd',

 'hypot',

 'inf',

 'isclose',

 'isfinite',

 'isinf',

 'isnan',

 'ldexp',

 'lgamma',

 'log',

 'log10',

 'log1p',

 'log2',

 'modf',

 'nan',

 'pi',

 'pow',

 'radians',

 'sin',

 'sinh',

 'sqrt',

 'tan',

 'tanh',

 'tau',

 'trunc']

import math

help(math)

import random

random.__file__

'D:\\Anaconda2\\envs\\py3\\lib\\random.py'

def expovariate(self, lambd):

        """Exponential distribution.

        lambd is 1.0 divided by the desired mean.  It should be

        nonzero.  (The parameter would be called "lambda", but that is

        a reserved word in Python.)  Returned values range from 0 to

        positive infinity if lambd is positive, and from negative

        infinity to 0 if lambd is negative.

        """

        # lambd: rate lambd = 1/mean

        # ('lambda' is a Python reserved word)

        # we use 1-random() instead of random() to preclude the

        # possibility of taking the log of zero.

        return -_log(1.0 - self.random())/lambd