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<li><a class="reference internal" href="#"><code class="xref py py-mod docutils literal notranslate"><span class="pre">random</span></code> — Generate pseudo-random numbers</a><ul>
<li><a class="reference internal" href="#bookkeeping-functions">Bookkeeping functions</a></li>
<li><a class="reference internal" href="#functions-for-bytes">Functions for bytes</a></li>
<li><a class="reference internal" href="#functions-for-integers">Functions for integers</a></li>
<li><a class="reference internal" href="#functions-for-sequences">Functions for sequences</a></li>
<li><a class="reference internal" href="#discrete-distributions">Discrete distributions</a></li>
<li><a class="reference internal" href="#real-valued-distributions">Real-valued distributions</a></li>
<li><a class="reference internal" href="#alternative-generator">Alternative Generator</a></li>
<li><a class="reference internal" href="#notes-on-reproducibility">Notes on Reproducibility</a></li>
<li><a class="reference internal" href="#examples">Examples</a></li>
<li><a class="reference internal" href="#recipes">Recipes</a></li>
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<section id="module-random">
<span id="random-generate-pseudo-random-numbers"></span><h1><code class="xref py py-mod docutils literal notranslate"><span class="pre">random</span></code> — Generate pseudo-random numbers<a class="headerlink" href="#module-random" title="Link to this heading"></a></h1>
<p><strong>Source code:</strong> <a class="extlink-source reference external" href="https://github.com/python/cpython/tree/3.13/Lib/random.py">Lib/random.py</a></p>
<hr class="docutils" />
<p>This module implements pseudo-random number generators for various
distributions.</p>
<p>For integers, there is uniform selection from a range. For sequences, there is
uniform selection of a random element, a function to generate a random
permutation of a list in-place, and a function for random sampling without
replacement.</p>
<p>On the real line, there are functions to compute uniform, normal (Gaussian),
lognormal, negative exponential, gamma, and beta distributions. For generating
distributions of angles, the von Mises distribution is available.</p>
<p>Almost all module functions depend on the basic function <a class="reference internal" href="#random.random" title="random.random"><code class="xref py py-func docutils literal notranslate"><span class="pre">random()</span></code></a>, which
generates a random float uniformly in the half-open range <code class="docutils literal notranslate"><span class="pre">0.0</span> <span class="pre">&lt;=</span> <span class="pre">X</span> <span class="pre">&lt;</span> <span class="pre">1.0</span></code>.
Python uses the Mersenne Twister as the core generator. It produces 53-bit precision
floats and has a period of 2**19937-1. The underlying implementation in C is
both fast and threadsafe. The Mersenne Twister is one of the most extensively
tested random number generators in existence. However, being completely
deterministic, it is not suitable for all purposes, and is completely unsuitable
for cryptographic purposes.</p>
<p>The functions supplied by this module are actually bound methods of a hidden
instance of the <a class="reference internal" href="#random.Random" title="random.Random"><code class="xref py py-class docutils literal notranslate"><span class="pre">random.Random</span></code></a> class. You can instantiate your own
instances of <a class="reference internal" href="#random.Random" title="random.Random"><code class="xref py py-class docutils literal notranslate"><span class="pre">Random</span></code></a> to get generators that dont share state.</p>
<p>Class <a class="reference internal" href="#random.Random" title="random.Random"><code class="xref py py-class docutils literal notranslate"><span class="pre">Random</span></code></a> can also be subclassed if you want to use a different
basic generator of your own devising: see the documentation on that class for
more details.</p>
<p>The <a class="reference internal" href="#module-random" title="random: Generate pseudo-random numbers with various common distributions."><code class="xref py py-mod docutils literal notranslate"><span class="pre">random</span></code></a> module also provides the <a class="reference internal" href="#random.SystemRandom" title="random.SystemRandom"><code class="xref py py-class docutils literal notranslate"><span class="pre">SystemRandom</span></code></a> class which
uses the system function <a class="reference internal" href="os.html#os.urandom" title="os.urandom"><code class="xref py py-func docutils literal notranslate"><span class="pre">os.urandom()</span></code></a> to generate random numbers
from sources provided by the operating system.</p>
<div class="admonition warning">
<p class="admonition-title">Warning</p>
<p>The pseudo-random generators of this module should not be used for
security purposes. For security or cryptographic uses, see the
<a class="reference internal" href="secrets.html#module-secrets" title="secrets: Generate secure random numbers for managing secrets."><code class="xref py py-mod docutils literal notranslate"><span class="pre">secrets</span></code></a> module.</p>
</div>
<div class="admonition seealso">
<p class="admonition-title">See also</p>
<p>M. Matsumoto and T. Nishimura, “Mersenne Twister: A 623-dimensionally
equidistributed uniform pseudorandom number generator”, ACM Transactions on
Modeling and Computer Simulation Vol. 8, No. 1, January pp.330 1998.</p>
<p><a class="reference external" href="https://code.activestate.com/recipes/576707-long-period-random-number-generator/">Complementary-Multiply-with-Carry recipe</a> for a compatible alternative
random number generator with a long period and comparatively simple update
operations.</p>
</div>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>The global random number generator and instances of <a class="reference internal" href="#random.Random" title="random.Random"><code class="xref py py-class docutils literal notranslate"><span class="pre">Random</span></code></a> are thread-safe.
However, in the free-threaded build, concurrent calls to the global generator or
to the same instance of <a class="reference internal" href="#random.Random" title="random.Random"><code class="xref py py-class docutils literal notranslate"><span class="pre">Random</span></code></a> may encounter contention and poor performance.
Consider using separate instances of <a class="reference internal" href="#random.Random" title="random.Random"><code class="xref py py-class docutils literal notranslate"><span class="pre">Random</span></code></a> per thread instead.</p>
</div>
<section id="bookkeeping-functions">
<h2>Bookkeeping functions<a class="headerlink" href="#bookkeeping-functions" title="Link to this heading"></a></h2>
<dl class="py function">
<dt class="sig sig-object py" id="random.seed">
<span class="sig-prename descclassname"><span class="pre">random.</span></span><span class="sig-name descname"><span class="pre">seed</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">a</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">version</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#random.seed" title="Link to this definition"></a></dt>
<dd><p>Initialize the random number generator.</p>
<p>If <em>a</em> is omitted or <code class="docutils literal notranslate"><span class="pre">None</span></code>, the current system time is used. If
randomness sources are provided by the operating system, they are used
instead of the system time (see the <a class="reference internal" href="os.html#os.urandom" title="os.urandom"><code class="xref py py-func docutils literal notranslate"><span class="pre">os.urandom()</span></code></a> function for details
on availability).</p>
<p>If <em>a</em> is an int, it is used directly.</p>
<p>With version 2 (the default), a <a class="reference internal" href="stdtypes.html#str" title="str"><code class="xref py py-class docutils literal notranslate"><span class="pre">str</span></code></a>, <a class="reference internal" href="stdtypes.html#bytes" title="bytes"><code class="xref py py-class docutils literal notranslate"><span class="pre">bytes</span></code></a>, or <a class="reference internal" href="stdtypes.html#bytearray" title="bytearray"><code class="xref py py-class docutils literal notranslate"><span class="pre">bytearray</span></code></a>
object gets converted to an <a class="reference internal" href="functions.html#int" title="int"><code class="xref py py-class docutils literal notranslate"><span class="pre">int</span></code></a> and all of its bits are used.</p>
<p>With version 1 (provided for reproducing random sequences from older versions
of Python), the algorithm for <a class="reference internal" href="stdtypes.html#str" title="str"><code class="xref py py-class docutils literal notranslate"><span class="pre">str</span></code></a> and <a class="reference internal" href="stdtypes.html#bytes" title="bytes"><code class="xref py py-class docutils literal notranslate"><span class="pre">bytes</span></code></a> generates a
narrower range of seeds.</p>
<div class="versionchanged">
<p><span class="versionmodified changed">Changed in version 3.2: </span>Moved to the version 2 scheme which uses all of the bits in a string seed.</p>
</div>
<div class="versionchanged">
<p><span class="versionmodified changed">Changed in version 3.11: </span>The <em>seed</em> must be one of the following types:
<code class="docutils literal notranslate"><span class="pre">None</span></code>, <a class="reference internal" href="functions.html#int" title="int"><code class="xref py py-class docutils literal notranslate"><span class="pre">int</span></code></a>, <a class="reference internal" href="functions.html#float" title="float"><code class="xref py py-class docutils literal notranslate"><span class="pre">float</span></code></a>, <a class="reference internal" href="stdtypes.html#str" title="str"><code class="xref py py-class docutils literal notranslate"><span class="pre">str</span></code></a>,
<a class="reference internal" href="stdtypes.html#bytes" title="bytes"><code class="xref py py-class docutils literal notranslate"><span class="pre">bytes</span></code></a>, or <a class="reference internal" href="stdtypes.html#bytearray" title="bytearray"><code class="xref py py-class docutils literal notranslate"><span class="pre">bytearray</span></code></a>.</p>
</div>
</dd></dl>
<dl class="py function">
<dt class="sig sig-object py" id="random.getstate">
<span class="sig-prename descclassname"><span class="pre">random.</span></span><span class="sig-name descname"><span class="pre">getstate</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#random.getstate" title="Link to this definition"></a></dt>
<dd><p>Return an object capturing the current internal state of the generator. This
object can be passed to <a class="reference internal" href="#random.setstate" title="random.setstate"><code class="xref py py-func docutils literal notranslate"><span class="pre">setstate()</span></code></a> to restore the state.</p>
</dd></dl>
<dl class="py function">
<dt class="sig sig-object py" id="random.setstate">
<span class="sig-prename descclassname"><span class="pre">random.</span></span><span class="sig-name descname"><span class="pre">setstate</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#random.setstate" title="Link to this definition"></a></dt>
<dd><p><em>state</em> should have been obtained from a previous call to <a class="reference internal" href="#random.getstate" title="random.getstate"><code class="xref py py-func docutils literal notranslate"><span class="pre">getstate()</span></code></a>, and
<a class="reference internal" href="#random.setstate" title="random.setstate"><code class="xref py py-func docutils literal notranslate"><span class="pre">setstate()</span></code></a> restores the internal state of the generator to what it was at
the time <a class="reference internal" href="#random.getstate" title="random.getstate"><code class="xref py py-func docutils literal notranslate"><span class="pre">getstate()</span></code></a> was called.</p>
</dd></dl>
</section>
<section id="functions-for-bytes">
<h2>Functions for bytes<a class="headerlink" href="#functions-for-bytes" title="Link to this heading"></a></h2>
<dl class="py function">
<dt class="sig sig-object py" id="random.randbytes">
<span class="sig-prename descclassname"><span class="pre">random.</span></span><span class="sig-name descname"><span class="pre">randbytes</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#random.randbytes" title="Link to this definition"></a></dt>
<dd><p>Generate <em>n</em> random bytes.</p>
<p>This method should not be used for generating security tokens.
Use <a class="reference internal" href="secrets.html#secrets.token_bytes" title="secrets.token_bytes"><code class="xref py py-func docutils literal notranslate"><span class="pre">secrets.token_bytes()</span></code></a> instead.</p>
<div class="versionadded">
<p><span class="versionmodified added">Added in version 3.9.</span></p>
</div>
</dd></dl>
</section>
<section id="functions-for-integers">
<h2>Functions for integers<a class="headerlink" href="#functions-for-integers" title="Link to this heading"></a></h2>
<dl class="py function">
<dt class="sig sig-object py" id="random.randrange">
<span class="sig-prename descclassname"><span class="pre">random.</span></span><span class="sig-name descname"><span class="pre">randrange</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">stop</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#random.randrange" title="Link to this definition"></a></dt>
<dt class="sig sig-object py">
<span class="sig-prename descclassname"><span class="pre">random.</span></span><span class="sig-name descname"><span class="pre">randrange</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">start</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stop</span></span></em><span class="optional">[</span>, <em class="sig-param"><span class="n"><span class="pre">step</span></span></em><span class="optional">]</span><span class="sig-paren">)</span></dt>
<dd><p>Return a randomly selected element from <code class="docutils literal notranslate"><span class="pre">range(start,</span> <span class="pre">stop,</span> <span class="pre">step)</span></code>.</p>
<p>This is roughly equivalent to <code class="docutils literal notranslate"><span class="pre">choice(range(start,</span> <span class="pre">stop,</span> <span class="pre">step))</span></code> but
supports arbitrarily large ranges and is optimized for common cases.</p>
<p>The positional argument pattern matches the <a class="reference internal" href="stdtypes.html#range" title="range"><code class="xref py py-func docutils literal notranslate"><span class="pre">range()</span></code></a> function.</p>
<p>Keyword arguments should not be used because they can be interpreted
in unexpected ways. For example <code class="docutils literal notranslate"><span class="pre">randrange(start=100)</span></code> is interpreted
as <code class="docutils literal notranslate"><span class="pre">randrange(0,</span> <span class="pre">100,</span> <span class="pre">1)</span></code>.</p>
<div class="versionchanged">
<p><span class="versionmodified changed">Changed in version 3.2: </span><a class="reference internal" href="#random.randrange" title="random.randrange"><code class="xref py py-meth docutils literal notranslate"><span class="pre">randrange()</span></code></a> is more sophisticated about producing equally distributed
values. Formerly it used a style like <code class="docutils literal notranslate"><span class="pre">int(random()*n)</span></code> which could produce
slightly uneven distributions.</p>
</div>
<div class="versionchanged">
<p><span class="versionmodified changed">Changed in version 3.12: </span>Automatic conversion of non-integer types is no longer supported.
Calls such as <code class="docutils literal notranslate"><span class="pre">randrange(10.0)</span></code> and <code class="docutils literal notranslate"><span class="pre">randrange(Fraction(10,</span> <span class="pre">1))</span></code>
now raise a <a class="reference internal" href="exceptions.html#TypeError" title="TypeError"><code class="xref py py-exc docutils literal notranslate"><span class="pre">TypeError</span></code></a>.</p>
</div>
</dd></dl>
<dl class="py function">
<dt class="sig sig-object py" id="random.randint">
<span class="sig-prename descclassname"><span class="pre">random.</span></span><span class="sig-name descname"><span class="pre">randint</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">a</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">b</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#random.randint" title="Link to this definition"></a></dt>
<dd><p>Return a random integer <em>N</em> such that <code class="docutils literal notranslate"><span class="pre">a</span> <span class="pre">&lt;=</span> <span class="pre">N</span> <span class="pre">&lt;=</span> <span class="pre">b</span></code>. Alias for
<code class="docutils literal notranslate"><span class="pre">randrange(a,</span> <span class="pre">b+1)</span></code>.</p>
</dd></dl>
<dl class="py function">
<dt class="sig sig-object py" id="random.getrandbits">
<span class="sig-prename descclassname"><span class="pre">random.</span></span><span class="sig-name descname"><span class="pre">getrandbits</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">k</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#random.getrandbits" title="Link to this definition"></a></dt>
<dd><p>Returns a non-negative Python integer with <em>k</em> random bits. This method
is supplied with the Mersenne Twister generator and some other generators
may also provide it as an optional part of the API. When available,
<a class="reference internal" href="#random.getrandbits" title="random.getrandbits"><code class="xref py py-meth docutils literal notranslate"><span class="pre">getrandbits()</span></code></a> enables <a class="reference internal" href="#random.randrange" title="random.randrange"><code class="xref py py-meth docutils literal notranslate"><span class="pre">randrange()</span></code></a> to handle arbitrarily large
ranges.</p>
<div class="versionchanged">
<p><span class="versionmodified changed">Changed in version 3.9: </span>This method now accepts zero for <em>k</em>.</p>
</div>
</dd></dl>
</section>
<section id="functions-for-sequences">
<h2>Functions for sequences<a class="headerlink" href="#functions-for-sequences" title="Link to this heading"></a></h2>
<dl class="py function">
<dt class="sig sig-object py" id="random.choice">
<span class="sig-prename descclassname"><span class="pre">random.</span></span><span class="sig-name descname"><span class="pre">choice</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">seq</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#random.choice" title="Link to this definition"></a></dt>
<dd><p>Return a random element from the non-empty sequence <em>seq</em>. If <em>seq</em> is empty,
raises <a class="reference internal" href="exceptions.html#IndexError" title="IndexError"><code class="xref py py-exc docutils literal notranslate"><span class="pre">IndexError</span></code></a>.</p>
</dd></dl>
<dl class="py function">
<dt class="sig sig-object py" id="random.choices">
<span class="sig-prename descclassname"><span class="pre">random.</span></span><span class="sig-name descname"><span class="pre">choices</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">population</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weights</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="keyword-only-separator o"><abbr title="Keyword-only parameters separator (PEP 3102)"><span class="pre">*</span></abbr></span></em>, <em class="sig-param"><span class="n"><span class="pre">cum_weights</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">k</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#random.choices" title="Link to this definition"></a></dt>
<dd><p>Return a <em>k</em> sized list of elements chosen from the <em>population</em> with replacement.
If the <em>population</em> is empty, raises <a class="reference internal" href="exceptions.html#IndexError" title="IndexError"><code class="xref py py-exc docutils literal notranslate"><span class="pre">IndexError</span></code></a>.</p>
<p>If a <em>weights</em> sequence is specified, selections are made according to the
relative weights. Alternatively, if a <em>cum_weights</em> sequence is given, the
selections are made according to the cumulative weights (perhaps computed
using <a class="reference internal" href="itertools.html#itertools.accumulate" title="itertools.accumulate"><code class="xref py py-func docutils literal notranslate"><span class="pre">itertools.accumulate()</span></code></a>). For example, the relative weights
<code class="docutils literal notranslate"><span class="pre">[10,</span> <span class="pre">5,</span> <span class="pre">30,</span> <span class="pre">5]</span></code> are equivalent to the cumulative weights
<code class="docutils literal notranslate"><span class="pre">[10,</span> <span class="pre">15,</span> <span class="pre">45,</span> <span class="pre">50]</span></code>. Internally, the relative weights are converted to
cumulative weights before making selections, so supplying the cumulative
weights saves work.</p>
<p>If neither <em>weights</em> nor <em>cum_weights</em> are specified, selections are made
with equal probability. If a weights sequence is supplied, it must be
the same length as the <em>population</em> sequence. It is a <a class="reference internal" href="exceptions.html#TypeError" title="TypeError"><code class="xref py py-exc docutils literal notranslate"><span class="pre">TypeError</span></code></a>
to specify both <em>weights</em> and <em>cum_weights</em>.</p>
<p>The <em>weights</em> or <em>cum_weights</em> can use any numeric type that interoperates
with the <a class="reference internal" href="functions.html#float" title="float"><code class="xref py py-class docutils literal notranslate"><span class="pre">float</span></code></a> values returned by <a class="reference internal" href="#module-random" title="random: Generate pseudo-random numbers with various common distributions."><code class="xref py py-func docutils literal notranslate"><span class="pre">random()</span></code></a> (that includes
integers, floats, and fractions but excludes decimals). Weights are assumed
to be non-negative and finite. A <a class="reference internal" href="exceptions.html#ValueError" title="ValueError"><code class="xref py py-exc docutils literal notranslate"><span class="pre">ValueError</span></code></a> is raised if all
weights are zero.</p>
<p>For a given seed, the <a class="reference internal" href="#random.choices" title="random.choices"><code class="xref py py-func docutils literal notranslate"><span class="pre">choices()</span></code></a> function with equal weighting
typically produces a different sequence than repeated calls to
<a class="reference internal" href="#random.choice" title="random.choice"><code class="xref py py-func docutils literal notranslate"><span class="pre">choice()</span></code></a>. The algorithm used by <a class="reference internal" href="#random.choices" title="random.choices"><code class="xref py py-func docutils literal notranslate"><span class="pre">choices()</span></code></a> uses floating-point
arithmetic for internal consistency and speed. The algorithm used
by <a class="reference internal" href="#random.choice" title="random.choice"><code class="xref py py-func docutils literal notranslate"><span class="pre">choice()</span></code></a> defaults to integer arithmetic with repeated selections
to avoid small biases from round-off error.</p>
<div class="versionadded">
<p><span class="versionmodified added">Added in version 3.6.</span></p>
</div>
<div class="versionchanged">
<p><span class="versionmodified changed">Changed in version 3.9: </span>Raises a <a class="reference internal" href="exceptions.html#ValueError" title="ValueError"><code class="xref py py-exc docutils literal notranslate"><span class="pre">ValueError</span></code></a> if all weights are zero.</p>
</div>
</dd></dl>
<dl class="py function">
<dt class="sig sig-object py" id="random.shuffle">
<span class="sig-prename descclassname"><span class="pre">random.</span></span><span class="sig-name descname"><span class="pre">shuffle</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#random.shuffle" title="Link to this definition"></a></dt>
<dd><p>Shuffle the sequence <em>x</em> in place.</p>
<p>To shuffle an immutable sequence and return a new shuffled list, use
<code class="docutils literal notranslate"><span class="pre">sample(x,</span> <span class="pre">k=len(x))</span></code> instead.</p>
<p>Note that even for small <code class="docutils literal notranslate"><span class="pre">len(x)</span></code>, the total number of permutations of <em>x</em>
can quickly grow larger than the period of most random number generators.
This implies that most permutations of a long sequence can never be
generated. For example, a sequence of length 2080 is the largest that
can fit within the period of the Mersenne Twister random number generator.</p>
<div class="versionchanged">
<p><span class="versionmodified changed">Changed in version 3.11: </span>Removed the optional parameter <em>random</em>.</p>
</div>
</dd></dl>
<dl class="py function">
<dt class="sig sig-object py" id="random.sample">
<span class="sig-prename descclassname"><span class="pre">random.</span></span><span class="sig-name descname"><span class="pre">sample</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">population</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">k</span></span></em>, <em class="sig-param"><span class="keyword-only-separator o"><abbr title="Keyword-only parameters separator (PEP 3102)"><span class="pre">*</span></abbr></span></em>, <em class="sig-param"><span class="n"><span class="pre">counts</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#random.sample" title="Link to this definition"></a></dt>
<dd><p>Return a <em>k</em> length list of unique elements chosen from the population
sequence. Used for random sampling without replacement.</p>
<p>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).</p>
<p>Members of the population need not be <a class="reference internal" href="../glossary.html#term-hashable"><span class="xref std std-term">hashable</span></a> or unique. If the population
contains repeats, then each occurrence is a possible selection in the sample.</p>
<p>Repeated elements can be specified one at a time or with the optional
keyword-only <em>counts</em> parameter. For example, <code class="docutils literal notranslate"><span class="pre">sample(['red',</span> <span class="pre">'blue'],</span>
<span class="pre">counts=[4,</span> <span class="pre">2],</span> <span class="pre">k=5)</span></code> is equivalent to <code class="docutils literal notranslate"><span class="pre">sample(['red',</span> <span class="pre">'red',</span> <span class="pre">'red',</span> <span class="pre">'red',</span>
<span class="pre">'blue',</span> <span class="pre">'blue'],</span> <span class="pre">k=5)</span></code>.</p>
<p>To choose a sample from a range of integers, use a <a class="reference internal" href="stdtypes.html#range" title="range"><code class="xref py py-func docutils literal notranslate"><span class="pre">range()</span></code></a> object as an
argument. This is especially fast and space efficient for sampling from a large
population: <code class="docutils literal notranslate"><span class="pre">sample(range(10000000),</span> <span class="pre">k=60)</span></code>.</p>
<p>If the sample size is larger than the population size, a <a class="reference internal" href="exceptions.html#ValueError" title="ValueError"><code class="xref py py-exc docutils literal notranslate"><span class="pre">ValueError</span></code></a>
is raised.</p>
<div class="versionchanged">
<p><span class="versionmodified changed">Changed in version 3.9: </span>Added the <em>counts</em> parameter.</p>
</div>
<div class="versionchanged">
<p><span class="versionmodified changed">Changed in version 3.11: </span>The <em>population</em> must be a sequence. Automatic conversion of sets
to lists is no longer supported.</p>
</div>
</dd></dl>
</section>
<section id="discrete-distributions">
<h2>Discrete distributions<a class="headerlink" href="#discrete-distributions" title="Link to this heading"></a></h2>
<p>The following function generates a discrete distribution.</p>
<dl class="py function">
<dt class="sig sig-object py" id="random.binomialvariate">
<span class="sig-prename descclassname"><span class="pre">random.</span></span><span class="sig-name descname"><span class="pre">binomialvariate</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">p</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#random.binomialvariate" title="Link to this definition"></a></dt>
<dd><p><a class="reference external" href="https://mathworld.wolfram.com/BinomialDistribution.html">Binomial distribution</a>.
Return the number of successes for <em>n</em> independent trials with the
probability of success in each trial being <em>p</em>:</p>
<p>Mathematically equivalent to:</p>
<div class="highlight-python3 notranslate"><div class="highlight"><pre><span></span><span class="nb">sum</span><span class="p">(</span><span class="n">random</span><span class="p">()</span> <span class="o">&lt;</span> <span class="n">p</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n</span><span class="p">))</span>
</pre></div>
</div>
<p>The number of trials <em>n</em> should be a non-negative integer.
The probability of success <em>p</em> should be between <code class="docutils literal notranslate"><span class="pre">0.0</span> <span class="pre">&lt;=</span> <span class="pre">p</span> <span class="pre">&lt;=</span> <span class="pre">1.0</span></code>.
The result is an integer in the range <code class="docutils literal notranslate"><span class="pre">0</span> <span class="pre">&lt;=</span> <span class="pre">X</span> <span class="pre">&lt;=</span> <span class="pre">n</span></code>.</p>
<div class="versionadded">
<p><span class="versionmodified added">Added in version 3.12.</span></p>
</div>
</dd></dl>
</section>
<section id="real-valued-distributions">
<span id="id1"></span><h2>Real-valued distributions<a class="headerlink" href="#real-valued-distributions" title="Link to this heading"></a></h2>
<p>The following functions generate specific real-valued distributions. Function
parameters are named after the corresponding variables in the distributions
equation, as used in common mathematical practice; most of these equations can
be found in any statistics text.</p>
<dl class="py function">
<dt class="sig sig-object py" id="random.random">
<span class="sig-prename descclassname"><span class="pre">random.</span></span><span class="sig-name descname"><span class="pre">random</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#random.random" title="Link to this definition"></a></dt>
<dd><p>Return the next random floating-point number in the range <code class="docutils literal notranslate"><span class="pre">0.0</span> <span class="pre">&lt;=</span> <span class="pre">X</span> <span class="pre">&lt;</span> <span class="pre">1.0</span></code></p>
</dd></dl>
<dl class="py function">
<dt class="sig sig-object py" id="random.uniform">
<span class="sig-prename descclassname"><span class="pre">random.</span></span><span class="sig-name descname"><span class="pre">uniform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">a</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">b</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#random.uniform" title="Link to this definition"></a></dt>
<dd><p>Return a random floating-point number <em>N</em> such that <code class="docutils literal notranslate"><span class="pre">a</span> <span class="pre">&lt;=</span> <span class="pre">N</span> <span class="pre">&lt;=</span> <span class="pre">b</span></code> for
<code class="docutils literal notranslate"><span class="pre">a</span> <span class="pre">&lt;=</span> <span class="pre">b</span></code> and <code class="docutils literal notranslate"><span class="pre">b</span> <span class="pre">&lt;=</span> <span class="pre">N</span> <span class="pre">&lt;=</span> <span class="pre">a</span></code> for <code class="docutils literal notranslate"><span class="pre">b</span> <span class="pre">&lt;</span> <span class="pre">a</span></code>.</p>
<p>The end-point value <code class="docutils literal notranslate"><span class="pre">b</span></code> may or may not be included in the range
depending on floating-point rounding in the expression
<code class="docutils literal notranslate"><span class="pre">a</span> <span class="pre">+</span> <span class="pre">(b-a)</span> <span class="pre">*</span> <span class="pre">random()</span></code>.</p>
</dd></dl>
<dl class="py function">
<dt class="sig sig-object py" id="random.triangular">
<span class="sig-prename descclassname"><span class="pre">random.</span></span><span class="sig-name descname"><span class="pre">triangular</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">low</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">high</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mode</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#random.triangular" title="Link to this definition"></a></dt>
<dd><p>Return a random floating-point number <em>N</em> such that <code class="docutils literal notranslate"><span class="pre">low</span> <span class="pre">&lt;=</span> <span class="pre">N</span> <span class="pre">&lt;=</span> <span class="pre">high</span></code> and
with the specified <em>mode</em> between those bounds. The <em>low</em> and <em>high</em> bounds
default to zero and one. The <em>mode</em> argument defaults to the midpoint
between the bounds, giving a symmetric distribution.</p>
</dd></dl>
<dl class="py function">
<dt class="sig sig-object py" id="random.betavariate">
<span class="sig-prename descclassname"><span class="pre">random.</span></span><span class="sig-name descname"><span class="pre">betavariate</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">alpha</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">beta</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#random.betavariate" title="Link to this definition"></a></dt>
<dd><p>Beta distribution. Conditions on the parameters are <code class="docutils literal notranslate"><span class="pre">alpha</span> <span class="pre">&gt;</span> <span class="pre">0</span></code> and
<code class="docutils literal notranslate"><span class="pre">beta</span> <span class="pre">&gt;</span> <span class="pre">0</span></code>. Returned values range between 0 and 1.</p>
</dd></dl>
<dl class="py function">
<dt class="sig sig-object py" id="random.expovariate">
<span class="sig-prename descclassname"><span class="pre">random.</span></span><span class="sig-name descname"><span class="pre">expovariate</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">lambd</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#random.expovariate" title="Link to this definition"></a></dt>
<dd><p>Exponential distribution. <em>lambd</em> 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 <em>lambd</em> is positive, and from
negative infinity to 0 if <em>lambd</em> is negative.</p>
<div class="versionchanged">
<p><span class="versionmodified changed">Changed in version 3.12: </span>Added the default value for <code class="docutils literal notranslate"><span class="pre">lambd</span></code>.</p>
</div>
</dd></dl>
<dl class="py function">
<dt class="sig sig-object py" id="random.gammavariate">
<span class="sig-prename descclassname"><span class="pre">random.</span></span><span class="sig-name descname"><span class="pre">gammavariate</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">alpha</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">beta</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#random.gammavariate" title="Link to this definition"></a></dt>
<dd><p>Gamma distribution. (<em>Not</em> the gamma function!) The shape and
scale parameters, <em>alpha</em> and <em>beta</em>, must have positive values.
(Calling conventions vary and some sources define beta
as the inverse of the scale).</p>
<p>The probability distribution function is:</p>
<div class="highlight-python3 notranslate"><div class="highlight"><pre><span></span> <span class="n">x</span> <span class="o">**</span> <span class="p">(</span><span class="n">alpha</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)</span> <span class="o">*</span> <span class="n">math</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="n">x</span> <span class="o">/</span> <span class="n">beta</span><span class="p">)</span>
<span class="n">pdf</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="o">=</span> <span class="o">--------------------------------------</span>
<span class="n">math</span><span class="o">.</span><span class="n">gamma</span><span class="p">(</span><span class="n">alpha</span><span class="p">)</span> <span class="o">*</span> <span class="n">beta</span> <span class="o">**</span> <span class="n">alpha</span>
</pre></div>
</div>
</dd></dl>
<dl class="py function">
<dt class="sig sig-object py" id="random.gauss">
<span class="sig-prename descclassname"><span class="pre">random.</span></span><span class="sig-name descname"><span class="pre">gauss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mu</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sigma</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#random.gauss" title="Link to this definition"></a></dt>
<dd><p>Normal distribution, also called the Gaussian distribution.
<em>mu</em> is the mean,
and <em>sigma</em> is the standard deviation. This is slightly faster than
the <a class="reference internal" href="#random.normalvariate" title="random.normalvariate"><code class="xref py py-func docutils literal notranslate"><span class="pre">normalvariate()</span></code></a> function defined below.</p>
<p>Multithreading note: When two threads call this function
simultaneously, it is possible that they will receive the
same return value. This can be avoided in three ways.
1) Have each thread use a different instance of the random
number generator. 2) Put locks around all calls. 3) Use the
slower, but thread-safe <a class="reference internal" href="#random.normalvariate" title="random.normalvariate"><code class="xref py py-func docutils literal notranslate"><span class="pre">normalvariate()</span></code></a> function instead.</p>
<div class="versionchanged">
<p><span class="versionmodified changed">Changed in version 3.11: </span><em>mu</em> and <em>sigma</em> now have default arguments.</p>
</div>
</dd></dl>
<dl class="py function">
<dt class="sig sig-object py" id="random.lognormvariate">
<span class="sig-prename descclassname"><span class="pre">random.</span></span><span class="sig-name descname"><span class="pre">lognormvariate</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mu</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sigma</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#random.lognormvariate" title="Link to this definition"></a></dt>
<dd><p>Log normal distribution. If you take the natural logarithm of this
distribution, youll get a normal distribution with mean <em>mu</em> and standard
deviation <em>sigma</em>. <em>mu</em> can have any value, and <em>sigma</em> must be greater than
zero.</p>
</dd></dl>
<dl class="py function">
<dt class="sig sig-object py" id="random.normalvariate">
<span class="sig-prename descclassname"><span class="pre">random.</span></span><span class="sig-name descname"><span class="pre">normalvariate</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mu</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sigma</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#random.normalvariate" title="Link to this definition"></a></dt>
<dd><p>Normal distribution. <em>mu</em> is the mean, and <em>sigma</em> is the standard deviation.</p>
<div class="versionchanged">
<p><span class="versionmodified changed">Changed in version 3.11: </span><em>mu</em> and <em>sigma</em> now have default arguments.</p>
</div>
</dd></dl>
<dl class="py function">
<dt class="sig sig-object py" id="random.vonmisesvariate">
<span class="sig-prename descclassname"><span class="pre">random.</span></span><span class="sig-name descname"><span class="pre">vonmisesvariate</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mu</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kappa</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#random.vonmisesvariate" title="Link to this definition"></a></dt>
<dd><p><em>mu</em> is the mean angle, expressed in radians between 0 and 2*<em>pi</em>, and <em>kappa</em>
is the concentration parameter, which must be greater than or equal to zero. If
<em>kappa</em> is equal to zero, this distribution reduces to a uniform random angle
over the range 0 to 2*<em>pi</em>.</p>
</dd></dl>
<dl class="py function">
<dt class="sig sig-object py" id="random.paretovariate">
<span class="sig-prename descclassname"><span class="pre">random.</span></span><span class="sig-name descname"><span class="pre">paretovariate</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">alpha</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#random.paretovariate" title="Link to this definition"></a></dt>
<dd><p>Pareto distribution. <em>alpha</em> is the shape parameter.</p>
</dd></dl>
<dl class="py function">
<dt class="sig sig-object py" id="random.weibullvariate">
<span class="sig-prename descclassname"><span class="pre">random.</span></span><span class="sig-name descname"><span class="pre">weibullvariate</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">alpha</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">beta</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#random.weibullvariate" title="Link to this definition"></a></dt>
<dd><p>Weibull distribution. <em>alpha</em> is the scale parameter and <em>beta</em> is the shape
parameter.</p>
</dd></dl>
</section>
<section id="alternative-generator">
<h2>Alternative Generator<a class="headerlink" href="#alternative-generator" title="Link to this heading"></a></h2>
<dl class="py class">
<dt class="sig sig-object py" id="random.Random">
<em class="property"><span class="k"><span class="pre">class</span></span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">random.</span></span><span class="sig-name descname"><span class="pre">Random</span></span><span class="sig-paren">(</span><span class="optional">[</span><em class="sig-param"><span class="n"><span class="pre">seed</span></span></em><span class="optional">]</span><span class="sig-paren">)</span><a class="headerlink" href="#random.Random" title="Link to this definition"></a></dt>
<dd><p>Class that implements the default pseudo-random number generator used by the
<a class="reference internal" href="#module-random" title="random: Generate pseudo-random numbers with various common distributions."><code class="xref py py-mod docutils literal notranslate"><span class="pre">random</span></code></a> module.</p>
<div class="versionchanged">
<p><span class="versionmodified changed">Changed in version 3.11: </span>Formerly the <em>seed</em> could be any hashable object. Now it is limited to:
<code class="docutils literal notranslate"><span class="pre">None</span></code>, <a class="reference internal" href="functions.html#int" title="int"><code class="xref py py-class docutils literal notranslate"><span class="pre">int</span></code></a>, <a class="reference internal" href="functions.html#float" title="float"><code class="xref py py-class docutils literal notranslate"><span class="pre">float</span></code></a>, <a class="reference internal" href="stdtypes.html#str" title="str"><code class="xref py py-class docutils literal notranslate"><span class="pre">str</span></code></a>,
<a class="reference internal" href="stdtypes.html#bytes" title="bytes"><code class="xref py py-class docutils literal notranslate"><span class="pre">bytes</span></code></a>, or <a class="reference internal" href="stdtypes.html#bytearray" title="bytearray"><code class="xref py py-class docutils literal notranslate"><span class="pre">bytearray</span></code></a>.</p>
</div>
<p>Subclasses of <code class="xref py py-class docutils literal notranslate"><span class="pre">Random</span></code> should override the following methods if they
wish to make use of a different basic generator:</p>
<dl class="py method">
<dt class="sig sig-object py" id="random.Random.seed">
<span class="sig-name descname"><span class="pre">seed</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">a</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">version</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#random.Random.seed" title="Link to this definition"></a></dt>
<dd><p>Override this method in subclasses to customise the <a class="reference internal" href="#random.seed" title="random.seed"><code class="xref py py-meth docutils literal notranslate"><span class="pre">seed()</span></code></a>
behaviour of <code class="xref py py-class docutils literal notranslate"><span class="pre">Random</span></code> instances.</p>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="random.Random.getstate">
<span class="sig-name descname"><span class="pre">getstate</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#random.Random.getstate" title="Link to this definition"></a></dt>
<dd><p>Override this method in subclasses to customise the <a class="reference internal" href="#random.getstate" title="random.getstate"><code class="xref py py-meth docutils literal notranslate"><span class="pre">getstate()</span></code></a>
behaviour of <code class="xref py py-class docutils literal notranslate"><span class="pre">Random</span></code> instances.</p>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="random.Random.setstate">
<span class="sig-name descname"><span class="pre">setstate</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#random.Random.setstate" title="Link to this definition"></a></dt>
<dd><p>Override this method in subclasses to customise the <a class="reference internal" href="#random.setstate" title="random.setstate"><code class="xref py py-meth docutils literal notranslate"><span class="pre">setstate()</span></code></a>
behaviour of <code class="xref py py-class docutils literal notranslate"><span class="pre">Random</span></code> instances.</p>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="random.Random.random">
<span class="sig-name descname"><span class="pre">random</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#random.Random.random" title="Link to this definition"></a></dt>
<dd><p>Override this method in subclasses to customise the <a class="reference internal" href="#random.random" title="random.random"><code class="xref py py-meth docutils literal notranslate"><span class="pre">random()</span></code></a>
behaviour of <code class="xref py py-class docutils literal notranslate"><span class="pre">Random</span></code> instances.</p>
</dd></dl>
<p>Optionally, a custom generator subclass can also supply the following method:</p>
<dl class="py method">
<dt class="sig sig-object py" id="random.Random.getrandbits">
<span class="sig-name descname"><span class="pre">getrandbits</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">k</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#random.Random.getrandbits" title="Link to this definition"></a></dt>
<dd><p>Override this method in subclasses to customise the
<a class="reference internal" href="#random.getrandbits" title="random.getrandbits"><code class="xref py py-meth docutils literal notranslate"><span class="pre">getrandbits()</span></code></a> behaviour of <code class="xref py py-class docutils literal notranslate"><span class="pre">Random</span></code> instances.</p>
</dd></dl>
</dd></dl>
<dl class="py class">
<dt class="sig sig-object py" id="random.SystemRandom">
<em class="property"><span class="k"><span class="pre">class</span></span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">random.</span></span><span class="sig-name descname"><span class="pre">SystemRandom</span></span><span class="sig-paren">(</span><span class="optional">[</span><em class="sig-param"><span class="n"><span class="pre">seed</span></span></em><span class="optional">]</span><span class="sig-paren">)</span><a class="headerlink" href="#random.SystemRandom" title="Link to this definition"></a></dt>
<dd><p>Class that uses the <a class="reference internal" href="os.html#os.urandom" title="os.urandom"><code class="xref py py-func docutils literal notranslate"><span class="pre">os.urandom()</span></code></a> function for generating random numbers
from sources provided by the operating system. Not available on all systems.
Does not rely on software state, and sequences are not reproducible. Accordingly,
the <a class="reference internal" href="#random.seed" title="random.seed"><code class="xref py py-meth docutils literal notranslate"><span class="pre">seed()</span></code></a> method has no effect and is ignored.
The <a class="reference internal" href="#random.getstate" title="random.getstate"><code class="xref py py-meth docutils literal notranslate"><span class="pre">getstate()</span></code></a> and <a class="reference internal" href="#random.setstate" title="random.setstate"><code class="xref py py-meth docutils literal notranslate"><span class="pre">setstate()</span></code></a> methods raise
<a class="reference internal" href="exceptions.html#NotImplementedError" title="NotImplementedError"><code class="xref py py-exc docutils literal notranslate"><span class="pre">NotImplementedError</span></code></a> if called.</p>
</dd></dl>
</section>
<section id="notes-on-reproducibility">
<h2>Notes on Reproducibility<a class="headerlink" href="#notes-on-reproducibility" title="Link to this heading"></a></h2>
<p>Sometimes it is useful to be able to reproduce the sequences given by a
pseudo-random number generator. By reusing a seed value, the same sequence should be
reproducible from run to run as long as multiple threads are not running.</p>
<p>Most of the random modules algorithms and seeding functions are subject to
change across Python versions, but two aspects are guaranteed not to change:</p>
<ul class="simple">
<li><p>If a new seeding method is added, then a backward compatible seeder will be
offered.</p></li>
<li><p>The generators <a class="reference internal" href="#random.Random.random" title="random.Random.random"><code class="xref py py-meth docutils literal notranslate"><span class="pre">random()</span></code></a> method will continue to produce the same
sequence when the compatible seeder is given the same seed.</p></li>
</ul>
</section>
<section id="examples">
<span id="random-examples"></span><h2>Examples<a class="headerlink" href="#examples" title="Link to this heading"></a></h2>
<p>Basic examples:</p>
<div class="highlight-python3 notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">random</span><span class="p">()</span> <span class="c1"># Random float: 0.0 &lt;= x &lt; 1.0</span>
<span class="go">0.37444887175646646</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">uniform</span><span class="p">(</span><span class="mf">2.5</span><span class="p">,</span> <span class="mf">10.0</span><span class="p">)</span> <span class="c1"># Random float: 2.5 &lt;= x &lt;= 10.0</span>
<span class="go">3.1800146073117523</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">expovariate</span><span class="p">(</span><span class="mi">1</span> <span class="o">/</span> <span class="mi">5</span><span class="p">)</span> <span class="c1"># Interval between arrivals averaging 5 seconds</span>
<span class="go">5.148957571865031</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">randrange</span><span class="p">(</span><span class="mi">10</span><span class="p">)</span> <span class="c1"># Integer from 0 to 9 inclusive</span>
<span class="go">7</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">randrange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">101</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span> <span class="c1"># Even integer from 0 to 100 inclusive</span>
<span class="go">26</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">choice</span><span class="p">([</span><span class="s1">&#39;win&#39;</span><span class="p">,</span> <span class="s1">&#39;lose&#39;</span><span class="p">,</span> <span class="s1">&#39;draw&#39;</span><span class="p">])</span> <span class="c1"># Single random element from a sequence</span>
<span class="go">&#39;draw&#39;</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">deck</span> <span class="o">=</span> <span class="s1">&#39;ace two three four&#39;</span><span class="o">.</span><span class="n">split</span><span class="p">()</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">shuffle</span><span class="p">(</span><span class="n">deck</span><span class="p">)</span> <span class="c1"># Shuffle a list</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">deck</span>
<span class="go">[&#39;four&#39;, &#39;two&#39;, &#39;ace&#39;, &#39;three&#39;]</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">sample</span><span class="p">([</span><span class="mi">10</span><span class="p">,</span> <span class="mi">20</span><span class="p">,</span> <span class="mi">30</span><span class="p">,</span> <span class="mi">40</span><span class="p">,</span> <span class="mi">50</span><span class="p">],</span> <span class="n">k</span><span class="o">=</span><span class="mi">4</span><span class="p">)</span> <span class="c1"># Four samples without replacement</span>
<span class="go">[40, 10, 50, 30]</span>
</pre></div>
</div>
<p>Simulations:</p>
<div class="highlight-python3 notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="c1"># Six roulette wheel spins (weighted sampling with replacement)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">choices</span><span class="p">([</span><span class="s1">&#39;red&#39;</span><span class="p">,</span> <span class="s1">&#39;black&#39;</span><span class="p">,</span> <span class="s1">&#39;green&#39;</span><span class="p">],</span> <span class="p">[</span><span class="mi">18</span><span class="p">,</span> <span class="mi">18</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span> <span class="n">k</span><span class="o">=</span><span class="mi">6</span><span class="p">)</span>
<span class="go">[&#39;red&#39;, &#39;green&#39;, &#39;black&#39;, &#39;black&#39;, &#39;red&#39;, &#39;black&#39;]</span>
<span class="gp">&gt;&gt;&gt; </span><span class="c1"># Deal 20 cards without replacement from a deck</span>
<span class="gp">&gt;&gt;&gt; </span><span class="c1"># of 52 playing cards, and determine the proportion of cards</span>
<span class="gp">&gt;&gt;&gt; </span><span class="c1"># with a ten-value: ten, jack, queen, or king.</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">deal</span> <span class="o">=</span> <span class="n">sample</span><span class="p">([</span><span class="s1">&#39;tens&#39;</span><span class="p">,</span> <span class="s1">&#39;low cards&#39;</span><span class="p">],</span> <span class="n">counts</span><span class="o">=</span><span class="p">[</span><span class="mi">16</span><span class="p">,</span> <span class="mi">36</span><span class="p">],</span> <span class="n">k</span><span class="o">=</span><span class="mi">20</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">deal</span><span class="o">.</span><span class="n">count</span><span class="p">(</span><span class="s1">&#39;tens&#39;</span><span class="p">)</span> <span class="o">/</span> <span class="mi">20</span>
<span class="go">0.15</span>
<span class="gp">&gt;&gt;&gt; </span><span class="c1"># Estimate the probability of getting 5 or more heads from 7 spins</span>
<span class="gp">&gt;&gt;&gt; </span><span class="c1"># of a biased coin that settles on heads 60% of the time.</span>
<span class="gp">&gt;&gt;&gt; </span><span class="nb">sum</span><span class="p">(</span><span class="n">binomialvariate</span><span class="p">(</span><span class="n">n</span><span class="o">=</span><span class="mi">7</span><span class="p">,</span> <span class="n">p</span><span class="o">=</span><span class="mf">0.6</span><span class="p">)</span> <span class="o">&gt;=</span> <span class="mi">5</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">10_000</span><span class="p">))</span> <span class="o">/</span> <span class="mi">10_000</span>
<span class="go">0.4169</span>
<span class="gp">&gt;&gt;&gt; </span><span class="c1"># Probability of the median of 5 samples being in middle two quartiles</span>
<span class="gp">&gt;&gt;&gt; </span><span class="k">def</span><span class="w"> </span><span class="nf">trial</span><span class="p">():</span>
<span class="gp">... </span> <span class="k">return</span> <span class="mi">2_500</span> <span class="o">&lt;=</span> <span class="nb">sorted</span><span class="p">(</span><span class="n">choices</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="mi">10_000</span><span class="p">),</span> <span class="n">k</span><span class="o">=</span><span class="mi">5</span><span class="p">))[</span><span class="mi">2</span><span class="p">]</span> <span class="o">&lt;</span> <span class="mi">7_500</span>
<span class="gp">...</span>
<span class="gp">&gt;&gt;&gt; </span><span class="nb">sum</span><span class="p">(</span><span class="n">trial</span><span class="p">()</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">10_000</span><span class="p">))</span> <span class="o">/</span> <span class="mi">10_000</span>
<span class="go">0.7958</span>
</pre></div>
</div>
<p>Example of <a class="reference external" href="https://en.wikipedia.org/wiki/Bootstrapping_(statistics)">statistical bootstrapping</a> using resampling
with replacement to estimate a confidence interval for the mean of a sample:</p>
<div class="highlight-python3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># https://www.thoughtco.com/example-of-bootstrapping-3126155</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">statistics</span><span class="w"> </span><span class="kn">import</span> <span class="n">fmean</span> <span class="k">as</span> <span class="n">mean</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">random</span><span class="w"> </span><span class="kn">import</span> <span class="n">choices</span>
<span class="n">data</span> <span class="o">=</span> <span class="p">[</span><span class="mi">41</span><span class="p">,</span> <span class="mi">50</span><span class="p">,</span> <span class="mi">29</span><span class="p">,</span> <span class="mi">37</span><span class="p">,</span> <span class="mi">81</span><span class="p">,</span> <span class="mi">30</span><span class="p">,</span> <span class="mi">73</span><span class="p">,</span> <span class="mi">63</span><span class="p">,</span> <span class="mi">20</span><span class="p">,</span> <span class="mi">35</span><span class="p">,</span> <span class="mi">68</span><span class="p">,</span> <span class="mi">22</span><span class="p">,</span> <span class="mi">60</span><span class="p">,</span> <span class="mi">31</span><span class="p">,</span> <span class="mi">95</span><span class="p">]</span>
<span class="n">means</span> <span class="o">=</span> <span class="nb">sorted</span><span class="p">(</span><span class="n">mean</span><span class="p">(</span><span class="n">choices</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">k</span><span class="o">=</span><span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="p">)))</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">100</span><span class="p">))</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;The sample mean of </span><span class="si">{</span><span class="n">mean</span><span class="p">(</span><span class="n">data</span><span class="p">)</span><span class="si">:</span><span class="s1">.1f</span><span class="si">}</span><span class="s1"> has a 90% confidence &#39;</span>
<span class="sa">f</span><span class="s1">&#39;interval from </span><span class="si">{</span><span class="n">means</span><span class="p">[</span><span class="mi">5</span><span class="p">]</span><span class="si">:</span><span class="s1">.1f</span><span class="si">}</span><span class="s1"> to </span><span class="si">{</span><span class="n">means</span><span class="p">[</span><span class="mi">94</span><span class="p">]</span><span class="si">:</span><span class="s1">.1f</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
</pre></div>
</div>
<p>Example of a <a class="reference external" href="https://en.wikipedia.org/wiki/Resampling_(statistics)#Permutation_tests">resampling permutation test</a>
to determine the statistical significance or <a class="reference external" href="https://en.wikipedia.org/wiki/P-value">p-value</a> of an observed difference
between the effects of a drug versus a placebo:</p>
<div class="highlight-python3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Example from &quot;Statistics is Easy&quot; by Dennis Shasha and Manda Wilson</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">statistics</span><span class="w"> </span><span class="kn">import</span> <span class="n">fmean</span> <span class="k">as</span> <span class="n">mean</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">random</span><span class="w"> </span><span class="kn">import</span> <span class="n">shuffle</span>
<span class="n">drug</span> <span class="o">=</span> <span class="p">[</span><span class="mi">54</span><span class="p">,</span> <span class="mi">73</span><span class="p">,</span> <span class="mi">53</span><span class="p">,</span> <span class="mi">70</span><span class="p">,</span> <span class="mi">73</span><span class="p">,</span> <span class="mi">68</span><span class="p">,</span> <span class="mi">52</span><span class="p">,</span> <span class="mi">65</span><span class="p">,</span> <span class="mi">65</span><span class="p">]</span>
<span class="n">placebo</span> <span class="o">=</span> <span class="p">[</span><span class="mi">54</span><span class="p">,</span> <span class="mi">51</span><span class="p">,</span> <span class="mi">58</span><span class="p">,</span> <span class="mi">44</span><span class="p">,</span> <span class="mi">55</span><span class="p">,</span> <span class="mi">52</span><span class="p">,</span> <span class="mi">42</span><span class="p">,</span> <span class="mi">47</span><span class="p">,</span> <span class="mi">58</span><span class="p">,</span> <span class="mi">46</span><span class="p">]</span>
<span class="n">observed_diff</span> <span class="o">=</span> <span class="n">mean</span><span class="p">(</span><span class="n">drug</span><span class="p">)</span> <span class="o">-</span> <span class="n">mean</span><span class="p">(</span><span class="n">placebo</span><span class="p">)</span>
<span class="n">n</span> <span class="o">=</span> <span class="mi">10_000</span>
<span class="n">count</span> <span class="o">=</span> <span class="mi">0</span>
<span class="n">combined</span> <span class="o">=</span> <span class="n">drug</span> <span class="o">+</span> <span class="n">placebo</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n</span><span class="p">):</span>
<span class="n">shuffle</span><span class="p">(</span><span class="n">combined</span><span class="p">)</span>
<span class="n">new_diff</span> <span class="o">=</span> <span class="n">mean</span><span class="p">(</span><span class="n">combined</span><span class="p">[:</span><span class="nb">len</span><span class="p">(</span><span class="n">drug</span><span class="p">)])</span> <span class="o">-</span> <span class="n">mean</span><span class="p">(</span><span class="n">combined</span><span class="p">[</span><span class="nb">len</span><span class="p">(</span><span class="n">drug</span><span class="p">):])</span>
<span class="n">count</span> <span class="o">+=</span> <span class="p">(</span><span class="n">new_diff</span> <span class="o">&gt;=</span> <span class="n">observed_diff</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;</span><span class="si">{</span><span class="n">n</span><span class="si">}</span><span class="s1"> label reshufflings produced only </span><span class="si">{</span><span class="n">count</span><span class="si">}</span><span class="s1"> instances with a difference&#39;</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;at least as extreme as the observed difference of </span><span class="si">{</span><span class="n">observed_diff</span><span class="si">:</span><span class="s1">.1f</span><span class="si">}</span><span class="s1">.&#39;</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;The one-sided p-value of </span><span class="si">{</span><span class="n">count</span><span class="w"> </span><span class="o">/</span><span class="w"> </span><span class="n">n</span><span class="si">:</span><span class="s1">.4f</span><span class="si">}</span><span class="s1"> leads us to reject the null&#39;</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;hypothesis that there is no difference between the drug and the placebo.&#39;</span><span class="p">)</span>
</pre></div>
</div>
<p>Simulation of arrival times and service deliveries for a multiserver queue:</p>
<div class="highlight-python3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span><span class="w"> </span><span class="nn">heapq</span><span class="w"> </span><span class="kn">import</span> <span class="n">heapify</span><span class="p">,</span> <span class="n">heapreplace</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">random</span><span class="w"> </span><span class="kn">import</span> <span class="n">expovariate</span><span class="p">,</span> <span class="n">gauss</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">statistics</span><span class="w"> </span><span class="kn">import</span> <span class="n">mean</span><span class="p">,</span> <span class="n">quantiles</span>
<span class="n">average_arrival_interval</span> <span class="o">=</span> <span class="mf">5.6</span>
<span class="n">average_service_time</span> <span class="o">=</span> <span class="mf">15.0</span>
<span class="n">stdev_service_time</span> <span class="o">=</span> <span class="mf">3.5</span>
<span class="n">num_servers</span> <span class="o">=</span> <span class="mi">3</span>
<span class="n">waits</span> <span class="o">=</span> <span class="p">[]</span>
<span class="n">arrival_time</span> <span class="o">=</span> <span class="mf">0.0</span>
<span class="n">servers</span> <span class="o">=</span> <span class="p">[</span><span class="mf">0.0</span><span class="p">]</span> <span class="o">*</span> <span class="n">num_servers</span> <span class="c1"># time when each server becomes available</span>
<span class="n">heapify</span><span class="p">(</span><span class="n">servers</span><span class="p">)</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1_000_000</span><span class="p">):</span>
<span class="n">arrival_time</span> <span class="o">+=</span> <span class="n">expovariate</span><span class="p">(</span><span class="mf">1.0</span> <span class="o">/</span> <span class="n">average_arrival_interval</span><span class="p">)</span>
<span class="n">next_server_available</span> <span class="o">=</span> <span class="n">servers</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
<span class="n">wait</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="mf">0.0</span><span class="p">,</span> <span class="n">next_server_available</span> <span class="o">-</span> <span class="n">arrival_time</span><span class="p">)</span>
<span class="n">waits</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">wait</span><span class="p">)</span>
<span class="n">service_duration</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="mf">0.0</span><span class="p">,</span> <span class="n">gauss</span><span class="p">(</span><span class="n">average_service_time</span><span class="p">,</span> <span class="n">stdev_service_time</span><span class="p">))</span>
<span class="n">service_completed</span> <span class="o">=</span> <span class="n">arrival_time</span> <span class="o">+</span> <span class="n">wait</span> <span class="o">+</span> <span class="n">service_duration</span>
<span class="n">heapreplace</span><span class="p">(</span><span class="n">servers</span><span class="p">,</span> <span class="n">service_completed</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;Mean wait: </span><span class="si">{</span><span class="n">mean</span><span class="p">(</span><span class="n">waits</span><span class="p">)</span><span class="si">:</span><span class="s1">.1f</span><span class="si">}</span><span class="s1"> Max wait: </span><span class="si">{</span><span class="nb">max</span><span class="p">(</span><span class="n">waits</span><span class="p">)</span><span class="si">:</span><span class="s1">.1f</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Quartiles:&#39;</span><span class="p">,</span> <span class="p">[</span><span class="nb">round</span><span class="p">(</span><span class="n">q</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span> <span class="k">for</span> <span class="n">q</span> <span class="ow">in</span> <span class="n">quantiles</span><span class="p">(</span><span class="n">waits</span><span class="p">)])</span>
</pre></div>
</div>
<div class="admonition seealso">
<p class="admonition-title">See also</p>
<p><a class="reference external" href="https://www.youtube.com/watch?v=Iq9DzN6mvYA">Statistics for Hackers</a>
a video tutorial by
<a class="reference external" href="https://us.pycon.org/2016/speaker/profile/295/">Jake Vanderplas</a>
on statistical analysis using just a few fundamental concepts
including simulation, sampling, shuffling, and cross-validation.</p>
<p><a class="reference external" href="https://nbviewer.org/url/norvig.com/ipython/Economics.ipynb">Economics Simulation</a>
a simulation of a marketplace by
<a class="reference external" href="https://norvig.com/bio.html">Peter Norvig</a> that shows effective
use of many of the tools and distributions provided by this module
(gauss, uniform, sample, betavariate, choice, triangular, and randrange).</p>
<p><a class="reference external" href="https://nbviewer.org/url/norvig.com/ipython/Probability.ipynb">A Concrete Introduction to Probability (using Python)</a>
a tutorial by <a class="reference external" href="https://norvig.com/bio.html">Peter Norvig</a> covering
the basics of probability theory, how to write simulations, and
how to perform data analysis using Python.</p>
</div>
</section>
<section id="recipes">
<h2>Recipes<a class="headerlink" href="#recipes" title="Link to this heading"></a></h2>
<p>These recipes show how to efficiently make random selections
from the combinatoric iterators in the <a class="reference internal" href="itertools.html#module-itertools" title="itertools: Functions creating iterators for efficient looping."><code class="xref py py-mod docutils literal notranslate"><span class="pre">itertools</span></code></a> module:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="k">def</span><span class="w"> </span><span class="nf">random_product</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="n">repeat</span><span class="o">=</span><span class="mi">1</span><span class="p">):</span>
<span class="s2">&quot;Random selection from itertools.product(*args, **kwds)&quot;</span>
<span class="n">pools</span> <span class="o">=</span> <span class="p">[</span><span class="nb">tuple</span><span class="p">(</span><span class="n">pool</span><span class="p">)</span> <span class="k">for</span> <span class="n">pool</span> <span class="ow">in</span> <span class="n">args</span><span class="p">]</span> <span class="o">*</span> <span class="n">repeat</span>
<span class="k">return</span> <span class="nb">tuple</span><span class="p">(</span><span class="nb">map</span><span class="p">(</span><span class="n">random</span><span class="o">.</span><span class="n">choice</span><span class="p">,</span> <span class="n">pools</span><span class="p">))</span>
<span class="k">def</span><span class="w"> </span><span class="nf">random_permutation</span><span class="p">(</span><span class="n">iterable</span><span class="p">,</span> <span class="n">r</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="s2">&quot;Random selection from itertools.permutations(iterable, r)&quot;</span>
<span class="n">pool</span> <span class="o">=</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">iterable</span><span class="p">)</span>
<span class="n">r</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">pool</span><span class="p">)</span> <span class="k">if</span> <span class="n">r</span> <span class="ow">is</span> <span class="kc">None</span> <span class="k">else</span> <span class="n">r</span>
<span class="k">return</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">random</span><span class="o">.</span><span class="n">sample</span><span class="p">(</span><span class="n">pool</span><span class="p">,</span> <span class="n">r</span><span class="p">))</span>
<span class="k">def</span><span class="w"> </span><span class="nf">random_combination</span><span class="p">(</span><span class="n">iterable</span><span class="p">,</span> <span class="n">r</span><span class="p">):</span>
<span class="s2">&quot;Random selection from itertools.combinations(iterable, r)&quot;</span>
<span class="n">pool</span> <span class="o">=</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">iterable</span><span class="p">)</span>
<span class="n">n</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">pool</span><span class="p">)</span>
<span class="n">indices</span> <span class="o">=</span> <span class="nb">sorted</span><span class="p">(</span><span class="n">random</span><span class="o">.</span><span class="n">sample</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="n">n</span><span class="p">),</span> <span class="n">r</span><span class="p">))</span>
<span class="k">return</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">pool</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">indices</span><span class="p">)</span>
<span class="k">def</span><span class="w"> </span><span class="nf">random_combination_with_replacement</span><span class="p">(</span><span class="n">iterable</span><span class="p">,</span> <span class="n">r</span><span class="p">):</span>
<span class="s2">&quot;Choose r elements with replacement. Order the result to match the iterable.&quot;</span>
<span class="c1"># Result will be in set(itertools.combinations_with_replacement(iterable, r)).</span>
<span class="n">pool</span> <span class="o">=</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">iterable</span><span class="p">)</span>
<span class="n">n</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">pool</span><span class="p">)</span>
<span class="n">indices</span> <span class="o">=</span> <span class="nb">sorted</span><span class="p">(</span><span class="n">random</span><span class="o">.</span><span class="n">choices</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="n">n</span><span class="p">),</span> <span class="n">k</span><span class="o">=</span><span class="n">r</span><span class="p">))</span>
<span class="k">return</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">pool</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">indices</span><span class="p">)</span>
</pre></div>
</div>
<p>The default <a class="reference internal" href="#random.random" title="random.random"><code class="xref py py-func docutils literal notranslate"><span class="pre">random()</span></code></a> returns multiples of 2⁻⁵³ in the range
<em>0.0 ≤ x &lt; 1.0</em>. All such numbers are evenly spaced and are exactly
representable as Python floats. However, many other representable
floats in that interval are not possible selections. For example,
<code class="docutils literal notranslate"><span class="pre">0.05954861408025609</span></code> isnt an integer multiple of 2⁻⁵³.</p>
<p>The following recipe takes a different approach. All floats in the
interval are possible selections. The mantissa comes from a uniform
distribution of integers in the range <em>2⁵² ≤ mantissa &lt; 2⁵³</em>. The
exponent comes from a geometric distribution where exponents smaller
than <em>-53</em> occur half as often as the next larger exponent.</p>
<div class="highlight-python3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span><span class="w"> </span><span class="nn">random</span><span class="w"> </span><span class="kn">import</span> <span class="n">Random</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">math</span><span class="w"> </span><span class="kn">import</span> <span class="n">ldexp</span>
<span class="k">class</span><span class="w"> </span><span class="nc">FullRandom</span><span class="p">(</span><span class="n">Random</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">random</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="n">mantissa</span> <span class="o">=</span> <span class="mh">0x10_0000_0000_0000</span> <span class="o">|</span> <span class="bp">self</span><span class="o">.</span><span class="n">getrandbits</span><span class="p">(</span><span class="mi">52</span><span class="p">)</span>
<span class="n">exponent</span> <span class="o">=</span> <span class="o">-</span><span class="mi">53</span>
<span class="n">x</span> <span class="o">=</span> <span class="mi">0</span>
<span class="k">while</span> <span class="ow">not</span> <span class="n">x</span><span class="p">:</span>
<span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">getrandbits</span><span class="p">(</span><span class="mi">32</span><span class="p">)</span>
<span class="n">exponent</span> <span class="o">+=</span> <span class="n">x</span><span class="o">.</span><span class="n">bit_length</span><span class="p">()</span> <span class="o">-</span> <span class="mi">32</span>
<span class="k">return</span> <span class="n">ldexp</span><span class="p">(</span><span class="n">mantissa</span><span class="p">,</span> <span class="n">exponent</span><span class="p">)</span>
</pre></div>
</div>
<p>All <a class="reference internal" href="#real-valued-distributions"><span class="std std-ref">real valued distributions</span></a>
in the class will use the new method:</p>
<div class="highlight-python3 notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">fr</span> <span class="o">=</span> <span class="n">FullRandom</span><span class="p">()</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">fr</span><span class="o">.</span><span class="n">random</span><span class="p">()</span>
<span class="go">0.05954861408025609</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">fr</span><span class="o">.</span><span class="n">expovariate</span><span class="p">(</span><span class="mf">0.25</span><span class="p">)</span>
<span class="go">8.87925541791544</span>
</pre></div>
</div>
<p>The recipe is conceptually equivalent to an algorithm that chooses from
all the multiples of 2⁻¹⁰⁷⁴ in the range <em>0.0 ≤ x &lt; 1.0</em>. All such
numbers are evenly spaced, but most have to be rounded down to the
nearest representable Python float. (The value 2⁻¹⁰⁷⁴ is the smallest
positive unnormalized float and is equal to <code class="docutils literal notranslate"><span class="pre">math.ulp(0.0)</span></code>.)</p>
<div class="admonition seealso">
<p class="admonition-title">See also</p>
<p><a class="reference external" href="https://allendowney.com/research/rand/downey07randfloat.pdf">Generating Pseudo-random Floating-Point Values</a> a
paper by Allen B. Downey describing ways to generate more
fine-grained floats than normally generated by <a class="reference internal" href="#random.random" title="random.random"><code class="xref py py-func docutils literal notranslate"><span class="pre">random()</span></code></a>.</p>
</div>
</section>
<section id="command-line-usage">
<span id="random-cli"></span><h2>Command-line usage<a class="headerlink" href="#command-line-usage" title="Link to this heading"></a></h2>
<div class="versionadded">
<p><span class="versionmodified added">Added in version 3.13.</span></p>
</div>
<p>The <code class="xref py py-mod docutils literal notranslate"><span class="pre">random</span></code> module can be executed from the command line.</p>
<div class="highlight-sh notranslate"><div class="highlight"><pre><span></span>python<span class="w"> </span>-m<span class="w"> </span>random<span class="w"> </span><span class="o">[</span>-h<span class="o">]</span><span class="w"> </span><span class="o">[</span>-c<span class="w"> </span>CHOICE<span class="w"> </span><span class="o">[</span>CHOICE<span class="w"> </span>...<span class="o">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span>-i<span class="w"> </span>N<span class="w"> </span><span class="p">|</span><span class="w"> </span>-f<span class="w"> </span>N<span class="o">]</span><span class="w"> </span><span class="o">[</span>input<span class="w"> </span>...<span class="o">]</span>
</pre></div>
</div>
<p>The following options are accepted:</p>
<dl class="std option">
<dt class="sig sig-object std" id="cmdoption-random-h">
<span id="cmdoption-random-help"></span><span class="sig-name descname"><span class="pre">-h</span></span><span class="sig-prename descclassname"></span><span class="sig-prename descclassname"><span class="pre">,</span> </span><span class="sig-name descname"><span class="pre">--help</span></span><span class="sig-prename descclassname"></span><a class="headerlink" href="#cmdoption-random-h" title="Link to this definition"></a></dt>
<dd><p>Show the help message and exit.</p>
</dd></dl>
<dl class="std option">
<dt class="sig sig-object std" id="cmdoption-random-c">
<span class="sig-name descname"><span class="pre">-c</span></span><span class="sig-prename descclassname"> <span class="pre">CHOICE</span> <span class="pre">[CHOICE</span> <span class="pre">...]</span></span><a class="headerlink" href="#cmdoption-random-c" title="Link to this definition"></a></dt>
<dt class="sig sig-object std" id="cmdoption-random-choice">
<span class="sig-name descname"><span class="pre">--choice</span></span><span class="sig-prename descclassname"> <span class="pre">CHOICE</span> <span class="pre">[CHOICE</span> <span class="pre">...]</span></span><a class="headerlink" href="#cmdoption-random-choice" title="Link to this definition"></a></dt>
<dd><p>Print a random choice, using <a class="reference internal" href="#random.choice" title="random.choice"><code class="xref py py-meth docutils literal notranslate"><span class="pre">choice()</span></code></a>.</p>
</dd></dl>
<dl class="std option">
<dt class="sig sig-object std" id="cmdoption-random-i">
<span class="sig-name descname"><span class="pre">-i</span></span><span class="sig-prename descclassname"> <span class="pre">&lt;N&gt;</span></span><a class="headerlink" href="#cmdoption-random-i" title="Link to this definition"></a></dt>
<dt class="sig sig-object std" id="cmdoption-random-integer">
<span class="sig-name descname"><span class="pre">--integer</span></span><span class="sig-prename descclassname"> <span class="pre">&lt;N&gt;</span></span><a class="headerlink" href="#cmdoption-random-integer" title="Link to this definition"></a></dt>
<dd><p>Print a random integer between 1 and N inclusive, using <a class="reference internal" href="#random.randint" title="random.randint"><code class="xref py py-meth docutils literal notranslate"><span class="pre">randint()</span></code></a>.</p>
</dd></dl>
<dl class="std option">
<dt class="sig sig-object std" id="cmdoption-random-f">
<span class="sig-name descname"><span class="pre">-f</span></span><span class="sig-prename descclassname"> <span class="pre">&lt;N&gt;</span></span><a class="headerlink" href="#cmdoption-random-f" title="Link to this definition"></a></dt>
<dt class="sig sig-object std" id="cmdoption-random-float">
<span class="sig-name descname"><span class="pre">--float</span></span><span class="sig-prename descclassname"> <span class="pre">&lt;N&gt;</span></span><a class="headerlink" href="#cmdoption-random-float" title="Link to this definition"></a></dt>
<dd><p>Print a random floating-point number between 0 and N inclusive,
using <a class="reference internal" href="#random.uniform" title="random.uniform"><code class="xref py py-meth docutils literal notranslate"><span class="pre">uniform()</span></code></a>.</p>
</dd></dl>
<p>If no options are given, the output depends on the input:</p>
<ul class="simple">
<li><p>String or multiple: same as <a class="reference internal" href="#cmdoption-random-choice"><code class="xref std std-option docutils literal notranslate"><span class="pre">--choice</span></code></a>.</p></li>
<li><p>Integer: same as <a class="reference internal" href="#cmdoption-random-integer"><code class="xref std std-option docutils literal notranslate"><span class="pre">--integer</span></code></a>.</p></li>
<li><p>Float: same as <a class="reference internal" href="#cmdoption-random-float"><code class="xref std std-option docutils literal notranslate"><span class="pre">--float</span></code></a>.</p></li>
</ul>
</section>
<section id="command-line-example">
<span id="random-cli-example"></span><h2>Command-line example<a class="headerlink" href="#command-line-example" title="Link to this heading"></a></h2>
<p>Here are some examples of the <code class="xref py py-mod docutils literal notranslate"><span class="pre">random</span></code> command-line interface:</p>
<div class="highlight-console notranslate"><div class="highlight"><pre><span></span><span class="gp">$ </span><span class="c1"># Choose one at random</span>
<span class="gp">$ </span>python<span class="w"> </span>-m<span class="w"> </span>random<span class="w"> </span>egg<span class="w"> </span>bacon<span class="w"> </span>sausage<span class="w"> </span>spam<span class="w"> </span><span class="s2">&quot;Lobster Thermidor aux crevettes with a Mornay sauce&quot;</span>
<span class="go">Lobster Thermidor aux crevettes with a Mornay sauce</span>
<span class="gp">$ </span><span class="c1"># Random integer</span>
<span class="gp">$ </span>python<span class="w"> </span>-m<span class="w"> </span>random<span class="w"> </span><span class="m">6</span>
<span class="go">6</span>
<span class="gp">$ </span><span class="c1"># Random floating-point number</span>
<span class="gp">$ </span>python<span class="w"> </span>-m<span class="w"> </span>random<span class="w"> </span><span class="m">1</span>.8
<span class="go">1.7080016272295635</span>
<span class="gp">$ </span><span class="c1"># With explicit arguments</span>
<span class="gp">$ </span>python<span class="w"> </span>-m<span class="w"> </span>random<span class="w"> </span>--choice<span class="w"> </span>egg<span class="w"> </span>bacon<span class="w"> </span>sausage<span class="w"> </span>spam<span class="w"> </span><span class="s2">&quot;Lobster Thermidor aux crevettes with a Mornay sauce&quot;</span>
<span class="go">egg</span>
<span class="gp">$ </span>python<span class="w"> </span>-m<span class="w"> </span>random<span class="w"> </span>--integer<span class="w"> </span><span class="m">6</span>
<span class="go">3</span>
<span class="gp">$ </span>python<span class="w"> </span>-m<span class="w"> </span>random<span class="w"> </span>--float<span class="w"> </span><span class="m">1</span>.8
<span class="go">1.5666339105010318</span>
<span class="gp">$ </span>python<span class="w"> </span>-m<span class="w"> </span>random<span class="w"> </span>--integer<span class="w"> </span><span class="m">6</span>
<span class="go">5</span>
<span class="gp">$ </span>python<span class="w"> </span>-m<span class="w"> </span>random<span class="w"> </span>--float<span class="w"> </span><span class="m">6</span>
<span class="go">3.1942323316565915</span>
</pre></div>
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<ul>
<li><a class="reference internal" href="#"><code class="xref py py-mod docutils literal notranslate"><span class="pre">random</span></code> — Generate pseudo-random numbers</a><ul>
<li><a class="reference internal" href="#bookkeeping-functions">Bookkeeping functions</a></li>
<li><a class="reference internal" href="#functions-for-bytes">Functions for bytes</a></li>
<li><a class="reference internal" href="#functions-for-integers">Functions for integers</a></li>
<li><a class="reference internal" href="#functions-for-sequences">Functions for sequences</a></li>
<li><a class="reference internal" href="#discrete-distributions">Discrete distributions</a></li>
<li><a class="reference internal" href="#real-valued-distributions">Real-valued distributions</a></li>
<li><a class="reference internal" href="#alternative-generator">Alternative Generator</a></li>
<li><a class="reference internal" href="#notes-on-reproducibility">Notes on Reproducibility</a></li>
<li><a class="reference internal" href="#examples">Examples</a></li>
<li><a class="reference internal" href="#recipes">Recipes</a></li>
<li><a class="reference internal" href="#command-line-usage">Command-line usage</a></li>
<li><a class="reference internal" href="#command-line-example">Command-line example</a></li>
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