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<section id="numpy-random-generator-choice">
<h1>numpy.random.Generator.choice<a class="headerlink" href="#numpy-random-generator-choice" title="Link to this heading">#</a></h1>
<p>method</p>
<dl class="py method">
<dt class="sig sig-object py" id="numpy.random.Generator.choice">
<span class="sig-prename descclassname"><span class="pre">random.Generator.</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">a</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">size</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">replace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</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">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">axis</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shuffle</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#numpy.random.Generator.choice" title="Link to this definition">#</a></dt>
<dd><p>Generates a random sample from a given array</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>a</strong><span class="classifier">{array_like, int}</span></dt><dd><p>If an ndarray, a random sample is generated from its elements.
If an int, the random sample is generated from np.arange(a).</p>
</dd>
<dt><strong>size</strong><span class="classifier">{int, tuple[int]}, optional</span></dt><dd><p>Output shape. If the given shape is, e.g., <code class="docutils literal notranslate"><span class="pre">(m,</span> <span class="pre">n,</span> <span class="pre">k)</span></code>, then
<code class="docutils literal notranslate"><span class="pre">m</span> <span class="pre">*</span> <span class="pre">n</span> <span class="pre">*</span> <span class="pre">k</span></code> samples are drawn from the 1-d <em class="xref py py-obj">a</em>. If <em class="xref py py-obj">a</em> has more
than one dimension, the <a class="reference internal" href="../../generated/numpy.size.html#numpy.size" title="numpy.size"><code class="xref py py-obj docutils literal notranslate"><span class="pre">size</span></code></a> shape will be inserted into the
<em class="xref py py-obj">axis</em> dimension, so the output <code class="docutils literal notranslate"><span class="pre">ndim</span></code> will be <code class="docutils literal notranslate"><span class="pre">a.ndim</span> <span class="pre">-</span> <span class="pre">1</span> <span class="pre">+</span>
<span class="pre">len(size)</span></code>. Default is None, in which case a single value is
returned.</p>
</dd>
<dt><strong>replace</strong><span class="classifier">bool, optional</span></dt><dd><p>Whether the sample is with or without replacement. Default is True,
meaning that a value of <code class="docutils literal notranslate"><span class="pre">a</span></code> can be selected multiple times.</p>
</dd>
<dt><strong>p</strong><span class="classifier">1-D array_like, optional</span></dt><dd><p>The probabilities associated with each entry in a.
If not given, the sample assumes a uniform distribution over all
entries in <code class="docutils literal notranslate"><span class="pre">a</span></code>.</p>
</dd>
<dt><strong>axis</strong><span class="classifier">int, optional</span></dt><dd><p>The axis along which the selection is performed. The default, 0,
selects by row.</p>
</dd>
<dt><strong>shuffle</strong><span class="classifier">bool, optional</span></dt><dd><p>Whether the sample is shuffled when sampling without replacement.
Default is True, False provides a speedup.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><dl class="simple">
<dt><strong>samples</strong><span class="classifier">single item or ndarray</span></dt><dd><p>The generated random samples</p>
</dd>
</dl>
</dd>
<dt class="field-odd">Raises<span class="colon">:</span></dt>
<dd class="field-odd"><dl class="simple">
<dt>ValueError</dt><dd><p>If a is an int and less than zero, if p is not 1-dimensional, if
a is array-like with a size 0, if p is not a vector of
probabilities, if a and p have different lengths, or if
replace=False and the sample size is greater than the population
size.</p>
</dd>
</dl>
</dd>
</dl>
<div class="admonition seealso">
<p class="admonition-title">See also</p>
<dl class="simple">
<dt><a class="reference internal" href="numpy.random.Generator.integers.html#numpy.random.Generator.integers" title="numpy.random.Generator.integers"><code class="xref py py-obj docutils literal notranslate"><span class="pre">integers</span></code></a>, <a class="reference internal" href="numpy.random.Generator.shuffle.html#numpy.random.Generator.shuffle" title="numpy.random.Generator.shuffle"><code class="xref py py-obj docutils literal notranslate"><span class="pre">shuffle</span></code></a>, <a class="reference internal" href="numpy.random.Generator.permutation.html#numpy.random.Generator.permutation" title="numpy.random.Generator.permutation"><code class="xref py py-obj docutils literal notranslate"><span class="pre">permutation</span></code></a></dt><dd></dd>
</dl>
</div>
<p class="rubric">Notes</p>
<p>Setting user-specified probabilities through <code class="docutils literal notranslate"><span class="pre">p</span></code> uses a more general but less
efficient sampler than the default. The general sampler produces a different sample
than the optimized sampler even if each element of <code class="docutils literal notranslate"><span class="pre">p</span></code> is 1 / len(a).</p>
<p><code class="docutils literal notranslate"><span class="pre">p</span></code> must sum to 1 when cast to <code class="docutils literal notranslate"><span class="pre">float64</span></code>. To ensure this, you may wish
to normalize using <code class="docutils literal notranslate"><span class="pre">p</span> <span class="pre">=</span> <span class="pre">p</span> <span class="pre">/</span> <span class="pre">np.sum(p,</span> <span class="pre">dtype=float)</span></code>.</p>
<p>When passing <code class="docutils literal notranslate"><span class="pre">a</span></code> as an integer type and <code class="docutils literal notranslate"><span class="pre">size</span></code> is not specified, the return
type is a native Python <code class="docutils literal notranslate"><span class="pre">int</span></code>.</p>
<p class="rubric">Examples</p>
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<p>Generate a uniform random sample from np.arange(5) of size 3:</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">rng</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">default_rng</span><span class="p">()</span>
<span class="gp">>>> </span><span class="n">rng</span><span class="o">.</span><span class="n">choice</span><span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>
<span class="go">array([0, 3, 4]) # random</span>
<span class="gp">>>> </span><span class="c1">#This is equivalent to rng.integers(0,5,3)</span>
</pre></div>
</div>
<p>Generate a non-uniform random sample from np.arange(5) of size 3:</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">rng</span><span class="o">.</span><span class="n">choice</span><span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="n">p</span><span class="o">=</span><span class="p">[</span><span class="mf">0.1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mf">0.3</span><span class="p">,</span> <span class="mf">0.6</span><span class="p">,</span> <span class="mi">0</span><span class="p">])</span>
<span class="go">array([3, 3, 0]) # random</span>
</pre></div>
</div>
<p>Generate a uniform random sample from np.arange(5) of size 3 without
replacement:</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">rng</span><span class="o">.</span><span class="n">choice</span><span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="n">replace</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
<span class="go">array([3,1,0]) # random</span>
<span class="gp">>>> </span><span class="c1">#This is equivalent to rng.permutation(np.arange(5))[:3]</span>
</pre></div>
</div>
<p>Generate a uniform random sample from a 2-D array along the first
axis (the default), without replacement:</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">rng</span><span class="o">.</span><span class="n">choice</span><span class="p">([[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">],</span> <span class="p">[</span><span class="mi">6</span><span class="p">,</span> <span class="mi">7</span><span class="p">,</span> <span class="mi">8</span><span class="p">]],</span> <span class="mi">2</span><span class="p">,</span> <span class="n">replace</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
<span class="go">array([[3, 4, 5], # random</span>
<span class="go"> [0, 1, 2]])</span>
</pre></div>
</div>
<p>Generate a non-uniform random sample from np.arange(5) of size
3 without replacement:</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">rng</span><span class="o">.</span><span class="n">choice</span><span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="n">replace</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">p</span><span class="o">=</span><span class="p">[</span><span class="mf">0.1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mf">0.3</span><span class="p">,</span> <span class="mf">0.6</span><span class="p">,</span> <span class="mi">0</span><span class="p">])</span>
<span class="go">array([2, 3, 0]) # random</span>
</pre></div>
</div>
<p>Any of the above can be repeated with an arbitrary array-like
instead of just integers. For instance:</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">aa_milne_arr</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'pooh'</span><span class="p">,</span> <span class="s1">'rabbit'</span><span class="p">,</span> <span class="s1">'piglet'</span><span class="p">,</span> <span class="s1">'Christopher'</span><span class="p">]</span>
<span class="gp">>>> </span><span class="n">rng</span><span class="o">.</span><span class="n">choice</span><span class="p">(</span><span class="n">aa_milne_arr</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="n">p</span><span class="o">=</span><span class="p">[</span><span class="mf">0.5</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">,</span> <span class="mf">0.3</span><span class="p">])</span>
<span class="go">array(['pooh', 'pooh', 'pooh', 'Christopher', 'piglet'], # random</span>
<span class="go"> dtype='<U11')</span>
</pre></div>
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