@@ -34,10 +34,8 @@ instance of the :class:`random.Random` class. You can instantiate your own
3434instances of:class: `Random ` to get generators that don't share state.
3535
3636Class:class: `Random ` can also be subclassed if you want to use a different
37- basic generator of your own devising: in that case, override the:meth: `~Random.random `,
38- :meth: `~Random.seed `,:meth: `~Random.getstate `, and:meth: `~Random.setstate ` methods.
39- Optionally, a new generator can supply a:meth: `~Random.getrandbits ` method --- this
40- allows:meth: `randrange ` to produce selections over an arbitrarily large range.
37+ basic generator of your own devising: see the documentation on that class for
38+ more details.
4139
4240The:mod: `random ` module also provides the:class: `SystemRandom ` class which
4341uses the system function:func: `os.urandom ` to generate random numbers
@@ -88,7 +86,7 @@ Bookkeeping functions
8886
8987 ..versionchanged ::3.11
9088 The *seed * must be one of the following types:
91- * NoneType * ,:class: `int `,:class: `float `,:class: `str `,
89+ `` None `` ,:class: `int `,:class: `float `,:class: `str `,
9290:class: `bytes `, or:class: `bytearray `.
9391
9492..function ::getstate()
@@ -412,6 +410,37 @@ Alternative Generator
412410 ``None ``,:class: `int `,:class: `float `,:class: `str `,
413411:class: `bytes `, or:class: `bytearray `.
414412
413+ Subclasses of:class: `!Random ` should override the following methods if they
414+ wish to make use of a different basic generator:
415+
416+ ..method ::Random.seed(a=None, version=2)
417+
418+ Override this method in subclasses to customise the:meth: `~random.seed `
419+ behaviour of:class: `!Random ` instances.
420+
421+ ..method ::Random.getstate()
422+
423+ Override this method in subclasses to customise the:meth: `~random.getstate `
424+ behaviour of:class: `!Random ` instances.
425+
426+ ..method ::Random.setstate(state)
427+
428+ Override this method in subclasses to customise the:meth: `~random.setstate `
429+ behaviour of:class: `!Random ` instances.
430+
431+ ..method ::Random.random()
432+
433+ Override this method in subclasses to customise the:meth: `~random.random `
434+ behaviour of:class: `!Random ` instances.
435+
436+ Optionally, a custom generator subclass can also supply the following method:
437+
438+ ..method ::Random.getrandbits(k)
439+
440+ Override this method in subclasses to customise the
441+ :meth: `~random.getrandbits ` behaviour of:class: `!Random ` instances.
442+
443+
415444..class ::SystemRandom([seed])
416445
417446 Class that uses the:func: `os.urandom ` function for generating random numbers
@@ -445,30 +474,30 @@ Examples
445474
446475Basic examples::
447476
448- >>> random()
477+ >>> random() # Random float: 0.0 <= x < 1.0
449478 0.37444887175646646
450479
451- >>> uniform(2.5, 10.0)
480+ >>> uniform(2.5, 10.0) # Random float: 2.5 <= x <= 10.0
452481 3.1800146073117523
453482
454- >>> expovariate(1 / 5)
483+ >>> expovariate(1 / 5) # Interval between arrivals averaging 5 seconds
455484 5.148957571865031
456485
457- >>> randrange(10)
486+ >>> randrange(10) # Integer from 0 to 9 inclusive
458487 7
459488
460- >>> randrange(0, 101, 2)
489+ >>> randrange(0, 101, 2) # Even integer from 0 to 100 inclusive
461490 26
462491
463- >>> choice(['win', 'lose', 'draw'])
492+ >>> choice(['win', 'lose', 'draw']) # Single random element from a sequence
464493 'draw'
465494
466495 >>> deck = 'ace two three four'.split()
467- >>> shuffle(deck)
496+ >>> shuffle(deck) # Shuffle a list
468497 >>> deck
469498 ['four', 'two', 'ace', 'three']
470499
471- >>> sample([10, 20, 30, 40, 50], k=4)
500+ >>> sample([10, 20, 30, 40, 50], k=4) # Four samples without replacement
472501 [40, 10, 50, 30]
473502
474503Simulations::
@@ -572,14 +601,14 @@ Simulation of arrival times and service deliveries for a multiserver queue::
572601 including simulation, sampling, shuffling, and cross-validation.
573602
574603 `Economics Simulation
575- <https://nbviewer.jupyter. org/url/norvig.com/ipython/Economics.ipynb> `_
604+ <https://nbviewer.org/url/norvig.com/ipython/Economics.ipynb> `_
576605 a simulation of a marketplace by
577606 `Peter Norvig <https://norvig.com/bio.html >`_ that shows effective
578607 use of many of the tools and distributions provided by this module
579608 (gauss, uniform, sample, betavariate, choice, triangular, and randrange).
580609
581610 `A Concrete Introduction to Probability (using Python)
582- <https://nbviewer.jupyter. org/url/norvig.com/ipython/Probability.ipynb> `_
611+ <https://nbviewer.org/url/norvig.com/ipython/Probability.ipynb> `_
583612 a tutorial by `Peter Norvig <https://norvig.com/bio.html >`_ covering
584613 the basics of probability theory, how to write simulations, and
585614 how to perform data analysis using Python.