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钢铁龘齾

操作方法我再明确一下：

代码：help()

输出：

Welcome to Python 3.7's help utility!

If this is your first time using Python, you should definitely check out
the tutorial on the Internet at ……

To get a list of available modules, keywords, symbols, or topics, type
"modules", "keywords", "symbols", or "topics". Each module also comes
with a one-line summary of what it does; to list the modules whose name
or summary contain a given string such as "spam", type "modules spam".

help>

现在输入“modules”，注意不要换行再输入，打完了之后按回车键。

电脑输出：

Please wait a moment while I gather a list of all available modules...

__future__             atexit           http           sched
__main__           audioop           hyperparser           scrolledlist
_abc           autocomplete           idle           search
_ast            autocomplete_w           idle_test           searchbase
_asyncio           autoexpand           idlelib           searchengine
_bisect           base喵           imaplib           secrets
_blake2           bdb           imghdr           select
_bootlocale           binascii           imp           selectors
_bz2           binhex           importlib           setuptools
……

Enter any module name to get more help. Or, type "modules spam" to search
for modules whose name or summary contain the string "spam".

help>

这时输入turtle，它会停一停，然后打出一个有字的方框：

Squeezed text (8126 lines).

这是Python官方版的回答，双击它会问是否要打开，确认之后出现的是跟海龟编辑器里一样的回答：

Help on module turtle:

NAME
turtle

DESCRIPTION
Turtle graphics is a popular way for introducing programming to
kids. It was part of the original Logo programming language developed
by Wally Feurzig and Seymour Papert in 1966.

Imagine a robotic turtle starting at (0, 0) in the x-y plane. After an ``import turtle``, give it
t……

内容过长，其他的我就不说了，你知道什么模块就打它的名字进去吧！

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钢铁龘齾

输入random，它会弹出：

Help on module random:

NAME
random - Random variable generators.

DESCRIPTION
integers
--------
uniform within range

sequences
---------
pick random element
pick random sample
pick weighted random sample
generate random permutation

distributions on the real line:
------------------------------
uniform
triangular
normal (Gaussian)
lognormal
negative exponential
gamma
beta
pareto
Weibull

distributions on the circle (angles 0 to 2pi)
---------------------------------------------
circular uniform
von Mises

General notes on the underlying Mersenne Twister core generator:

* The period is 2**19937-1.
* It is one of the most extensively tested generators in existence.
* The random() method is implemented in C, executes in a single Python step,
and is, therefore, threadsafe.

CLASSES
_random.Random(builtins.object)
Random
SystemRandom

class Random(_random.Random)
| Random(x=None)
|
| Random number generator base class used by bound module functions.
|
| Used to instantiate instances of Random to get generators that don't
| share state.
|
| Class Random can also be subclassed if you want to use a different basic
| generator of your own devising: in that case, override the following
| methods: random(), seed(), getstate(), and setstate().
| Optionally, implement a getrandbits() method so that randrange()
| can cover arbitrarily large ranges.
|
| Method resolution order:
| Random
| _random.Random
| builtins.object
|
| Methods defined here:
|
| __getstate__(self)
| # Issue 17489: Since __reduce__ was defined to fix #759889 this is no
| # longer called; we leave it here because it has been here since random was
| # rewritten back in 2001 and why risk breaking something.
|
| __init__(self, x=None)
| Initialize an instance.
|
| Optional argument x controls seeding, as for Random.seed().
|
| __reduce__(self)
| Helper for pickle.
|
| __setstate__(self, state)
|
| betavariate(self, alpha, beta)
| Beta distribution.
|
| Conditions on the parameters are alpha > 0 and beta > 0.
| Returned values range between 0 and 1.
|
| choice(self, seq)
| Choose a random element from a non-empty sequence.
|
| choices(self, population, weights=None, *, cum_weights=None, k=1)
| Return a k sized list of population elements chosen with replacement.
|
| If the relative weights or cumulative weights are not specified,
| the selections are made with equal probability.
|
| 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.
|
| gammavariate(self, alpha, beta)
| Gamma distribution. Not the gamma function!
|
| Conditions on the parameters are alpha > 0 and beta > 0.
|
| The probability distribution function is:
|
| x ** (alpha - 1) * math.exp(-x / beta)
| pdf(x) = --------------------------------------
| math.gamma(alpha) * beta ** alpha
|
| gauss(self, mu, sigma)
| Gaussian distribution.
|
| mu is the mean, and sigma is the standard deviation. This is
| slightly faster than the normalvariate() function.
|
| Not thread-safe without a lock around calls.
|
| getstate(self)
| Return internal state; can be passed to setstate() later.
|
| lognormvariate(self, mu, sigma)
| Log normal distribution.
|
| If you take the natural logarithm of this distribution, you'll get a
| normal distribution with mean mu and standard deviation sigma.
| mu can have any value, and sigma must be greater than zero.
|
| normalvariate(self, mu, sigma)
| Normal distribution.
|
| mu is the mean, and sigma is the standard deviation.
|
| paretovariate(self, alpha)
| Pareto distribution. alpha is the shape parameter.
|
| randint(self, a, b)
| Return random integer in range [a, b], including both end points.
|
| randrange(self, start, stop=None, step=1, _int=<class 'int'>)
| Choose a random item from range(start, stop[, step]).
|
| This fixes the problem with randint() which includes the
| endpoint; in Python this is usually not what you want.
|
| sample(self, population, k)
| Chooses k unique random elements from a population sequence or set.
|
| Returns a new list containing elements from the population while
| leaving the original population unchanged. The resulting list is
| in selection order so that all sub-slices will also be valid random
| samples. This allows raffle winners (the sample) to be partitioned
| into grand prize and second place winners (the subslices).
|
| Members of the population need not be hashable or unique. If the
| population contains repeats, then each occurrence is a possible
| selection in the sample.
|
| To choose a sample in a range of integers, use range as an argument.
| This is especially fast and space efficient for sampling from a
| large population: sample(range(10000000), 60)
|
| seed(self, a=None, version=2)

……不说了，太长了

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