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<section id="numpy-asanyarray">
<h1>numpy.asanyarray<a class="headerlink" href="#numpy-asanyarray" title="Link to this heading">#</a></h1>
<dl class="py function">
<dt class="sig sig-object py" id="numpy.asanyarray">
<span class="sig-prename descclassname"><span class="pre">numpy.</span></span><span class="sig-name descname"><span class="pre">asanyarray</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">dtype</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">order</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">device</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">copy</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">like</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="#numpy.asanyarray" title="Link to this definition">#</a></dt>
<dd><p>Convert the input to an ndarray, but pass ndarray subclasses through.</p>
<dl class="field-list">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><dl>
<dt><strong>a</strong><span class="classifier">array_like</span></dt><dd><p>Input data, in any form that can be converted to an array. This
includes scalars, lists, lists of tuples, tuples, tuples of tuples,
tuples of lists, and ndarrays.</p>
</dd>
<dt><strong>dtype</strong><span class="classifier">data-type, optional</span></dt><dd><p>By default, the data-type is inferred from the input data.</p>
</dd>
<dt><strong>order</strong><span class="classifier">{‘C’, ‘F’, ‘A’, ‘K’}, optional</span></dt><dd><p>The memory layout of the output.
‘C’ gives a row-major layout (C-style),
‘F’ gives a column-major layout (Fortran-style).
‘C’ and ‘F’ will copy if needed to ensure the output format.
‘A’ (any) is equivalent to ‘F’ if input a is non-contiguous or Fortran-contiguous, otherwise, it is equivalent to ‘C’.
Unlike ‘C’ or ‘F’, ‘A’ does not ensure that the result is contiguous.
‘K’ (keep) preserves the input order for the output.
‘C’ is the default.</p>
</dd>
<dt><strong>device</strong><span class="classifier">str, optional</span></dt><dd><p>The device on which to place the created array. Default: <code class="docutils literal notranslate"><span class="pre">None</span></code>.
For Array-API interoperability only, so must be <code class="docutils literal notranslate"><span class="pre">"cpu"</span></code> if passed.</p>
<div class="versionadded">
<p><span class="versionmodified added">Added in version 2.1.0.</span></p>
</div>
</dd>
<dt><strong>copy</strong><span class="classifier">bool, optional</span></dt><dd><p>If <code class="docutils literal notranslate"><span class="pre">True</span></code>, then the object is copied. If <code class="docutils literal notranslate"><span class="pre">None</span></code> then the object is
copied only if needed, i.e. if <code class="docutils literal notranslate"><span class="pre">__array__</span></code> returns a copy, if obj
is a nested sequence, or if a copy is needed to satisfy any of
the other requirements (<code class="docutils literal notranslate"><span class="pre">dtype</span></code>, <code class="docutils literal notranslate"><span class="pre">order</span></code>, etc.).
For <code class="docutils literal notranslate"><span class="pre">False</span></code> it raises a <code class="docutils literal notranslate"><span class="pre">ValueError</span></code> if a copy cannot be avoided.
Default: <code class="docutils literal notranslate"><span class="pre">None</span></code>.</p>
<div class="versionadded">
<p><span class="versionmodified added">Added in version 2.1.0.</span></p>
</div>
</dd>
<dt><strong>like</strong><span class="classifier">array_like, optional</span></dt><dd><p>Reference object to allow the creation of arrays which are not
NumPy arrays. If an array-like passed in as <code class="docutils literal notranslate"><span class="pre">like</span></code> supports
the <code class="docutils literal notranslate"><span class="pre">__array_function__</span></code> protocol, the result will be defined
by it. In this case, it ensures the creation of an array object
compatible with that passed in via this argument.</p>
<div class="versionadded">
<p><span class="versionmodified added">Added in version 1.20.0.</span></p>
</div>
</dd>
</dl>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><dl class="simple">
<dt><strong>out</strong><span class="classifier">ndarray or an ndarray subclass</span></dt><dd><p>Array interpretation of <em class="xref py py-obj">a</em>. If <em class="xref py py-obj">a</em> is an ndarray or a subclass
of ndarray, it is returned as-is and no copy is performed.</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.asarray.html#numpy.asarray" title="numpy.asarray"><code class="xref py py-obj docutils literal notranslate"><span class="pre">asarray</span></code></a></dt><dd><p>Similar function which always returns ndarrays.</p>
</dd>
<dt><a class="reference internal" href="numpy.ascontiguousarray.html#numpy.ascontiguousarray" title="numpy.ascontiguousarray"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ascontiguousarray</span></code></a></dt><dd><p>Convert input to a contiguous array.</p>
</dd>
<dt><a class="reference internal" href="numpy.asfortranarray.html#numpy.asfortranarray" title="numpy.asfortranarray"><code class="xref py py-obj docutils literal notranslate"><span class="pre">asfortranarray</span></code></a></dt><dd><p>Convert input to an ndarray with column-major memory order.</p>
</dd>
<dt><a class="reference internal" href="numpy.asarray_chkfinite.html#numpy.asarray_chkfinite" title="numpy.asarray_chkfinite"><code class="xref py py-obj docutils literal notranslate"><span class="pre">asarray_chkfinite</span></code></a></dt><dd><p>Similar function which checks input for NaNs and Infs.</p>
</dd>
<dt><a class="reference internal" href="numpy.fromiter.html#numpy.fromiter" title="numpy.fromiter"><code class="xref py py-obj docutils literal notranslate"><span class="pre">fromiter</span></code></a></dt><dd><p>Create an array from an iterator.</p>
</dd>
<dt><a class="reference internal" href="numpy.fromfunction.html#numpy.fromfunction" title="numpy.fromfunction"><code class="xref py py-obj docutils literal notranslate"><span class="pre">fromfunction</span></code></a></dt><dd><p>Construct an array by executing a function on grid positions.</p>
</dd>
</dl>
</div>
<p class="rubric">Examples</p>
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<p>Convert a list into an array:</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">a</span> <span class="o">=</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="gp">>>> </span><span class="kn">import</span><span class="w"> </span><span class="nn">numpy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">np</span>
<span class="gp">>>> </span><span class="n">np</span><span class="o">.</span><span class="n">asanyarray</span><span class="p">(</span><span class="n">a</span><span class="p">)</span>
<span class="go">array([1, 2])</span>
</pre></div>
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<p>Instances of <a class="reference internal" href="numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ndarray</span></code></a> subclasses are passed through as-is:</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">a</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([(</span><span class="mf">1.</span><span class="p">,</span> <span class="mi">2</span><span class="p">),</span> <span class="p">(</span><span class="mf">3.</span><span class="p">,</span> <span class="mi">4</span><span class="p">)],</span> <span class="n">dtype</span><span class="o">=</span><span class="s1">'f4,i4'</span><span class="p">)</span><span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">recarray</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">np</span><span class="o">.</span><span class="n">asanyarray</span><span class="p">(</span><span class="n">a</span><span class="p">)</span> <span class="ow">is</span> <span class="n">a</span>
<span class="go">True</span>
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