Dec 28, 2023 · Dec 28, 2023 · Dec 28, 2023
diff --git a/Doc/library/random.rst b/Doc/library/random.rst
 instances of :class:`Random` to get generators that don't share state.

 Class :class:`Random` can also be subclassed if you want to use a different
 basic generator of your own devising: in that case, override the :meth:`~Random.random`,
 :meth:`~Random.seed`, :meth:`~Random.getstate`, and :meth:`~Random.setstate` methods.
 Optionally, a new generator can supply a :meth:`~Random.getrandbits` method --- this
 allows :meth:`randrange` to produce selections over an arbitrarily large range.
 basic generator of your own devising: see the documentation on that class for
 more details.

 The :mod:`random` module also provides the :class:`SystemRandom` class which
 uses the system function :func:`os.urandom` to generate random numbers

   .. versionchanged:: 3.11
      The *seed* must be one of the following types:
 *NoneType*, :class:`int`, :class:`float`, :class:`str`,
 ``None``, :class:`int`, :class:`float`, :class:`str`,
      :class:`bytes`, or :class:`bytearray`.

 .. function:: getstate()
      ``None``, :class:`int`, :class:`float`, :class:`str`,
      :class:`bytes`, or :class:`bytearray`.

   Subclasses of :class:`!Random` should override the following methods if they
   wish to make use of a different basic generator:

   .. method:: Random.seed(a=None, version=2)

      Override this method in subclasses to customise the :meth:`~random.seed`
      behaviour of :class:`!Random` instances.

   .. method:: Random.getstate()

      Override this method in subclasses to customise the :meth:`~random.getstate`
      behaviour of :class:`!Random` instances.

   .. method:: Random.setstate(state)

      Override this method in subclasses to customise the :meth:`~random.setstate`
      behaviour of :class:`!Random` instances.

   .. method:: Random.random()

      Override this method in subclasses to customise the :meth:`~random.random`
      behaviour of :class:`!Random` instances.

   Optionally, a custom generator subclass can also supply the following method:

   .. method:: Random.getrandbits(k)

      Override this method in subclasses to customise the
      :meth:`~random.getrandbits` behaviour of :class:`!Random` instances.


 .. class:: SystemRandom([seed])

   Class that uses the :func:`os.urandom` function for generating random numbers

 Basic examples::

   >>> random()# Random float:  0.0 <= x < 1.0
   >>> random()                          # Random float:  0.0 <= x < 1.0
   0.37444887175646646

   >>> uniform(2.5, 10.0)# Random float:  2.5 <= x <= 10.0
   >>> uniform(2.5, 10.0)                # Random float:  2.5 <= x <= 10.0
   3.1800146073117523

   >>> expovariate(1 / 5)# Interval between arrivals averaging 5 seconds
   >>> expovariate(1 / 5)                # Interval between arrivals averaging 5 seconds
   5.148957571865031

   >>> randrange(10)# Integer from 0 to 9 inclusive
   >>> randrange(10)                     # Integer from 0 to 9 inclusive
   7

   >>> randrange(0, 101, 2)# Even integer from 0 to 100 inclusive
   >>> randrange(0, 101, 2)              # Even integer from 0 to 100 inclusive
   26

   >>> choice(['win', 'lose', 'draw'])# Single random element from a sequence
   >>> choice(['win', 'lose', 'draw'])   # Single random element from a sequence
   'draw'

   >>> deck = 'ace two three four'.split()
   >>> shuffle(deck)# Shuffle a list
   >>> shuffle(deck)                     # Shuffle a list
   >>> deck
   ['four', 'two', 'ace', 'three']

   >>> sample([10, 20, 30, 40, 50], k=4)# Four samples without replacement
   >>> sample([10, 20, 30, 40, 50], k=4) # Four samples without replacement
   [40, 10, 50, 30]

 Simulations::
   including simulation, sampling, shuffling, and cross-validation.

   `Economics Simulation
   <https://nbviewer.jupyter.org/url/norvig.com/ipython/Economics.ipynb>`_
   <https://nbviewer.org/url/norvig.com/ipython/Economics.ipynb>`_
   a simulation of a marketplace by
   `Peter Norvig <https://norvig.com/bio.html>`_ that shows effective
   use of many of the tools and distributions provided by this module
   (gauss, uniform, sample, betavariate, choice, triangular, and randrange).

   `A Concrete Introduction to Probability (using Python)
   <https://nbviewer.jupyter.org/url/norvig.com/ipython/Probability.ipynb>`_
   <https://nbviewer.org/url/norvig.com/ipython/Probability.ipynb>`_
   a tutorial by `Peter Norvig <https://norvig.com/bio.html>`_ covering
   the basics of probability theory, how to write simulations, and
   how to perform data analysis using Python.
diff --git a/Doc/tools/.nitignore b/Doc/tools/.nitignore
 Doc/library/pyclbr.rst
 Doc/library/pydoc.rst
 Doc/library/pyexpat.rst
 Doc/library/random.rst
 Doc/library/readline.rst
 Doc/library/resource.rst
 Doc/library/select.rst
Original file line number	Diff line number	Diff line change
Expand Up		@@ -34,10 +34,8 @@ instance of the :class:`random.Random` class. You can instantiate your own
		instances of :class:`Random` to get generators that don't share state.

		Class :class:`Random` can also be subclassed if you want to use a different
		basic generator of your own devising: in that case, override the :meth:`~Random.random`,
		:meth:`~Random.seed`, :meth:`~Random.getstate`, and :meth:`~Random.setstate` methods.
		Optionally, a new generator can supply a :meth:`~Random.getrandbits` method --- this
		allows :meth:`randrange` to produce selections over an arbitrarily large range.
		basic generator of your own devising: see the documentation on that class for
		more details.

		The :mod:`random` module also provides the :class:`SystemRandom` class which
		uses the system function :func:`os.urandom` to generate random numbers
Expand DownExpand Up		@@ -88,7 +86,7 @@ Bookkeeping functions

		.. versionchanged:: 3.11
		The seed must be one of the following types:
		NoneType, :class:`int`, :class:`float`, :class:`str`,
		``None``, :class:`int`, :class:`float`, :class:`str`,
		:class:`bytes`, or :class:`bytearray`.

		.. function:: getstate()
Expand DownExpand Up		@@ -412,6 +410,37 @@ Alternative Generator
		``None``, :class:`int`, :class:`float`, :class:`str`,
		:class:`bytes`, or :class:`bytearray`.

		Subclasses of :class:`!Random` should override the following methods if they
		wish to make use of a different basic generator:

		.. method:: Random.seed(a=None, version=2)

		Override this method in subclasses to customise the :meth:`~random.seed`
		behaviour of :class:`!Random` instances.

		.. method:: Random.getstate()

		Override this method in subclasses to customise the :meth:`~random.getstate`
		behaviour of :class:`!Random` instances.

		.. method:: Random.setstate(state)

		Override this method in subclasses to customise the :meth:`~random.setstate`
		behaviour of :class:`!Random` instances.

		.. method:: Random.random()

		Override this method in subclasses to customise the :meth:`~random.random`
		behaviour of :class:`!Random` instances.

		Optionally, a custom generator subclass can also supply the following method:

		.. method:: Random.getrandbits(k)

		Override this method in subclasses to customise the
		:meth:`~random.getrandbits` behaviour of :class:`!Random` instances.


		.. class:: SystemRandom([seed])

		Class that uses the :func:`os.urandom` function for generating random numbers
Expand DownExpand Up		@@ -445,30 +474,30 @@ Examples

		Basic examples::

		>>> random()# Random float: 0.0 <= x < 1.0
		>>> random() # Random float: 0.0 <= x < 1.0
		0.37444887175646646

		>>> uniform(2.5, 10.0)# Random float: 2.5 <= x <= 10.0
		>>> uniform(2.5, 10.0) # Random float: 2.5 <= x <= 10.0
		3.1800146073117523

		>>> expovariate(1 / 5)# Interval between arrivals averaging 5 seconds
		>>> expovariate(1 / 5) # Interval between arrivals averaging 5 seconds
		5.148957571865031

		>>> randrange(10)# Integer from 0 to 9 inclusive
		>>> randrange(10) # Integer from 0 to 9 inclusive
		7

		>>> randrange(0, 101, 2)# Even integer from 0 to 100 inclusive
		>>> randrange(0, 101, 2) # Even integer from 0 to 100 inclusive
		26

		>>> choice(['win', 'lose', 'draw'])# Single random element from a sequence
		>>> choice(['win', 'lose', 'draw']) # Single random element from a sequence
		'draw'

		>>> deck = 'ace two three four'.split()
		>>> shuffle(deck)# Shuffle a list
		>>> shuffle(deck) # Shuffle a list
		>>> deck
		['four', 'two', 'ace', 'three']

		>>> sample([10, 20, 30, 40, 50], k=4)# Four samples without replacement
		>>> sample([10, 20, 30, 40, 50], k=4) # Four samples without replacement
		[40, 10, 50, 30]

		Simulations::
Expand DownExpand Up		@@ -572,14 +601,14 @@ Simulation of arrival times and service deliveries for a multiserver queue::
		including simulation, sampling, shuffling, and cross-validation.

		`Economics Simulation
		<https://nbviewer.jupyter.org/url/norvig.com/ipython/Economics.ipynb>`_
		<https://nbviewer.org/url/norvig.com/ipython/Economics.ipynb>`_
		a simulation of a marketplace by
		`Peter Norvig <https://norvig.com/bio.html>`_ that shows effective
		use of many of the tools and distributions provided by this module
		(gauss, uniform, sample, betavariate, choice, triangular, and randrange).

		`A Concrete Introduction to Probability (using Python)
		<https://nbviewer.jupyter.org/url/norvig.com/ipython/Probability.ipynb>`_
		<https://nbviewer.org/url/norvig.com/ipython/Probability.ipynb>`_
		a tutorial by `Peter Norvig <https://norvig.com/bio.html>`_ covering
		the basics of probability theory, how to write simulations, and
		how to perform data analysis using Python.
Expand Down
Original file line number	Diff line number	Diff line change
Expand Up		@@ -82,7 +82,6 @@ Doc/library/profile.rst
		Doc/library/pyclbr.rst
		Doc/library/pydoc.rst
		Doc/library/pyexpat.rst
		Doc/library/random.rst
		Doc/library/readline.rst
		Doc/library/resource.rst
		Doc/library/select.rst
Expand Down