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<section id="numpy-char-array">
<h1>numpy.char.array<a class="headerlink" href="#numpy-char-array" title="Link to this heading">#</a></h1>
<dl class="py function">
<dt class="sig sig-object py" id="numpy.char.array">
<span class="sig-prename descclassname"><span class="pre">char.</span></span><span class="sig-name descname"><span class="pre">array</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">obj</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">itemsize</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">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">unicode</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><span class="sig-paren">)</span><a class="reference external" href="https://github.com/numpy/numpy/blob/main/numpy/_core/defchararray.py#L1213-L1353"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#numpy.char.array" title="Link to this definition">#</a></dt>
<dd><p>Create a <a class="reference internal" href="numpy.char.chararray.html#numpy.char.chararray" title="numpy.char.chararray"><code class="xref py py-obj docutils literal notranslate"><span class="pre">chararray</span></code></a>.</p>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>This class is provided for numarray backward-compatibility.
New code (not concerned with numarray compatibility) should use
arrays of type <a class="reference internal" href="../arrays.scalars.html#numpy.bytes_" title="numpy.bytes_"><code class="xref py py-obj docutils literal notranslate"><span class="pre">bytes_</span></code></a> or <a class="reference internal" href="../arrays.scalars.html#numpy.str_" title="numpy.str_"><code class="xref py py-obj docutils literal notranslate"><span class="pre">str_</span></code></a> and use the free functions
in <a class="reference internal" href="../routines.char.html#module-numpy.char" title="numpy.char"><code class="xref py py-mod docutils literal notranslate"><span class="pre">numpy.char</span></code></a> for fast vectorized string operations instead.</p>
</div>
<p>Versus a NumPy array of dtype <a class="reference internal" href="../arrays.scalars.html#numpy.bytes_" title="numpy.bytes_"><code class="xref py py-obj docutils literal notranslate"><span class="pre">bytes_</span></code></a> or <a class="reference internal" href="../arrays.scalars.html#numpy.str_" title="numpy.str_"><code class="xref py py-obj docutils literal notranslate"><span class="pre">str_</span></code></a>, this
class adds the following functionality:</p>
<ol class="arabic simple">
<li><p>values automatically have whitespace removed from the end
when indexed</p></li>
<li><p>comparison operators automatically remove whitespace from the
end when comparing values</p></li>
<li><p>vectorized string operations are provided as methods
(e.g. <a class="reference internal" href="numpy.char.chararray.endswith.html#numpy.char.chararray.endswith" title="numpy.char.chararray.endswith"><code class="xref py py-obj docutils literal notranslate"><span class="pre">chararray.endswith</span></code></a>)
and infix operators (e.g. <code class="docutils literal notranslate"><span class="pre">+,</span> <span class="pre">*,</span> <span class="pre">%</span></code>)</p></li>
</ol>
<dl class="field-list">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><dl>
<dt><strong>obj</strong><span class="classifier">array of str or unicode-like</span></dt><dd></dd>
<dt><strong>itemsize</strong><span class="classifier">int, optional</span></dt><dd><p><em class="xref py py-obj">itemsize</em> is the number of characters per scalar in the
resulting array. If <em class="xref py py-obj">itemsize</em> is None, and <em class="xref py py-obj">obj</em> is an
object array or a Python list, the <em class="xref py py-obj">itemsize</em> will be
automatically determined. If <em class="xref py py-obj">itemsize</em> is provided and <em class="xref py py-obj">obj</em>
is of type str or unicode, then the <em class="xref py py-obj">obj</em> string will be
chunked into <em class="xref py py-obj">itemsize</em> pieces.</p>
</dd>
<dt><strong>copy</strong><span class="classifier">bool, optional</span></dt><dd><p>If true (default), then the object is copied. Otherwise, a copy
will only be made 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 (<em class="xref py py-obj">itemsize</em>, unicode, <em class="xref py py-obj">order</em>, etc.).</p>
</dd>
<dt><strong>unicode</strong><span class="classifier">bool, optional</span></dt><dd><p>When true, the resulting <a class="reference internal" href="numpy.char.chararray.html#numpy.char.chararray" title="numpy.char.chararray"><code class="xref py py-obj docutils literal notranslate"><span class="pre">chararray</span></code></a> can contain Unicode
characters, when false only 8-bit characters. If unicode is
None and <em class="xref py py-obj">obj</em> is one of the following:</p>
<ul class="simple">
<li><p>a <a class="reference internal" href="numpy.char.chararray.html#numpy.char.chararray" title="numpy.char.chararray"><code class="xref py py-obj docutils literal notranslate"><span class="pre">chararray</span></code></a>,</p></li>
<li><p>an ndarray of type <a class="reference internal" href="../arrays.scalars.html#numpy.str_" title="numpy.str_"><code class="xref py py-class docutils literal notranslate"><span class="pre">str_</span></code></a> or <a class="reference internal" href="../arrays.scalars.html#numpy.bytes_" title="numpy.bytes_"><code class="xref py py-class docutils literal notranslate"><span class="pre">bytes_</span></code></a></p></li>
<li><p>a Python <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">str</span></code></a> or <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#bytes" title="(in Python v3.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">bytes</span></code></a> object,</p></li>
</ul>
<p>then the unicode setting of the output array will be
automatically determined.</p>
</dd>
<dt><strong>order</strong><span class="classifier">{‘C’, ‘F’, ‘A’}, optional</span></dt><dd><p>Specify the order of the array. If order is ‘C’ (default), then the
array will be in C-contiguous order (last-index varies the
fastest). If order is ‘F’, then the returned array
will be in Fortran-contiguous order (first-index varies the
fastest). If order is ‘A’, then the returned array may
be in any order (either C-, Fortran-contiguous, or even
discontiguous).</p>
</dd>
</dl>
</dd>
</dl>
<p class="rubric">Examples</p>
<div class="try_examples_outer_container docutils container" id="ac1c134c-b9c6-4794-a41b-fb78c3f234be">
<div class="try_examples_button_container"><button class="try_examples_button" onclick="window.tryExamplesShowIframe('ac1c134c-b9c6-4794-a41b-fb78c3f234be','368f18ec-8d34-48db-b806-1aaa294092ea','2cfc2a6a-6463-4c78-ab29-becae94a5d33','../../lite/tree/../notebooks/index.html?path=f42e4170_bd94_4f76_a750_94d6d33f5f52.ipynb','None')">Try it in your browser!</button></div><div class="try_examples_content docutils container">
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></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">char_array</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">char</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="s1">'hello'</span><span class="p">,</span> <span class="s1">'world'</span><span class="p">,</span> <span class="s1">'numpy'</span><span class="p">,</span><span class="s1">'array'</span><span class="p">])</span>
<span class="gp">>>> </span><span class="n">char_array</span>
<span class="go">chararray(['hello', 'world', 'numpy', 'array'], dtype='<U5')</span>
</pre></div>
</div>
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