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   <div class="section" id="pyarrow-array">
 <h1>pyarrow.array<a class="headerlink" href="#pyarrow-array" title="Permalink to this headline">¶</a></h1>
 <dl class="py function">
 <dt id="pyarrow.array">
 <code class="sig-prename descclassname">pyarrow.</code><code class="sig-name descname">array</code><span class="sig-paren">(</span><em class="sig-param">obj</em>, <em class="sig-param">type=None</em>, <em class="sig-param">mask=None</em>, <em class="sig-param">size=None</em>, <em class="sig-param">from_pandas=None</em>, <em class="sig-param">bool safe=True</em>, <em class="sig-param">MemoryPool memory_pool=None</em><span class="sig-paren">)</span><a class="headerlink" href="#pyarrow.array" title="Permalink to this definition">¶</a></dt>
 <dd><p>Create pyarrow.Array instance from a Python object.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>obj</strong> (<em>sequence</em><em>, </em><em>iterable</em><em>, </em><em>ndarray</em><em> or </em><em>Series</em>) – If both type and size are specified may be a single use iterable. If
 not strongly-typed, Arrow type will be inferred for resulting array.</p></li>
 <li><p><strong>type</strong> (<a class="reference internal" href="pyarrow.DataType.html#pyarrow.DataType" title="pyarrow.DataType"><em>pyarrow.DataType</em></a>) – Explicit type to attempt to coerce to, otherwise will be inferred from
 the data.</p></li>
 <li><p><strong>mask</strong> (<em>array</em><em>[</em><em>bool</em><em>]</em><em>, </em><em>optional</em>) – Indicate which values are null (True) or not null (False).</p></li>
 <li><p><strong>size</strong> (<em>int64</em><em>, </em><em>optional</em>) – Size of the elements. If the input is larger than size bail at this
 length. For iterators, if size is larger than the input iterator this
 will be treated as a “max size”, but will involve an initial allocation
 of size followed by a resize to the actual size (so if you know the
 exact size specifying it correctly will give you better performance).</p></li>
 <li><p><strong>from_pandas</strong> (<em>bool</em><em>, </em><em>default None</em>) – Use pandas’s semantics for inferring nulls from values in
 ndarray-like data. If passed, the mask tasks precedence, but
 if a value is unmasked (not-null), but still null according to
 pandas semantics, then it is null. Defaults to False if not
 passed explicitly by user, or True if a pandas object is
 passed in.</p></li>
 <li><p><strong>safe</strong> (<em>bool</em><em>, </em><em>default True</em>) – Check for overflows or other unsafe conversions.</p></li>
 <li><p><strong>memory_pool</strong> (<a class="reference internal" href="pyarrow.MemoryPool.html#pyarrow.MemoryPool" title="pyarrow.MemoryPool"><em>pyarrow.MemoryPool</em></a><em>, </em><em>optional</em>) – If not passed, will allocate memory from the currently-set default
 memory pool.</p></li>
 </ul>
 </dd>
 <dt class="field-even">Returns</dt>
 <dd class="field-even"><p><p><strong>array</strong> (<em>pyarrow.Array or pyarrow.ChunkedArray</em>) – A ChunkedArray instead of an Array is returned if:
 - the object data overflowed binary storage.
 - the object’s <code class="docutils literal notranslate"><span class="pre">__arrow_array__</span></code> protocol method returned a chunked</p>
 <blockquote>
 <div><p>array.</p>
 </div></blockquote>
 </p>
 </dd>
 </dl>
 <p class="rubric">Notes</p>
 <p>Localized timestamps will currently be returned as UTC (pandas’s native
 representation). Timezone-naive data will be implicitly interpreted as
 UTC.</p>
 <p>Converting to dictionary array will promote to a wider integer type for
 indices if the number of distinct values cannot be represented, even if
 the index type was explicitly set. This means that if there are more than
 127 values the returned dictionary array’s index type will be at least
 pa.int16() even if pa.int8() was passed to the function. Note that an
 explicit index type will not be demoted even if it is wider than required.</p>
 <p class="rubric">Examples</p>
 <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">pyarrow</span> <span class="k">as</span> <span class="nn">pa</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">pa</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">pd</span><span class="o">.</span><span class="n">Series</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="go">&lt;pyarrow.lib.Int64Array object at 0x7f674e4c0e10&gt;</span>
 <span class="go">[</span>
 <span class="go">  1,</span>
 <span class="go">  2</span>
 <span class="go">]</span>
 </pre></div>
 </div>
 <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">pa</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="s2">&quot;a&quot;</span><span class="p">,</span> <span class="s2">&quot;b&quot;</span><span class="p">,</span> <span class="s2">&quot;a&quot;</span><span class="p">],</span> <span class="nb">type</span><span class="o">=</span><span class="n">pa</span><span class="o">.</span><span class="n">dictionary</span><span class="p">(</span><span class="n">pa</span><span class="o">.</span><span class="n">int8</span><span class="p">(),</span> <span class="n">pa</span><span class="o">.</span><span class="n">string</span><span class="p">()))</span>
 <span class="go">&lt;pyarrow.lib.DictionaryArray object at 0x7feb288d9040&gt;</span>
 <span class="go">-- dictionary:</span>
 <span class="go">[</span>
 <span class="go">  &quot;a&quot;,</span>
 <span class="go">  &quot;b&quot;</span>
 <span class="go">]</span>
 <span class="go">-- indices:</span>
 <span class="go">[</span>
 <span class="go">  0,</span>
 <span class="go">  1,</span>
 <span class="go">  0</span>
 <span class="go">]</span>
 </pre></div>
 </div>
 <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">pa</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">pd</span><span class="o">.</span><span class="n">Series</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="n">mask</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="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">bool</span><span class="p">))</span>
 <span class="go">&lt;pyarrow.lib.Int64Array object at 0x7f9019e11208&gt;</span>
 <span class="go">[</span>
 <span class="go">  1,</span>
 <span class="go">  null</span>
 <span class="go">]</span>
 </pre></div>
 </div>
 <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">arr</span> <span class="o">=</span> <span class="n">pa</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="mi">1024</span><span class="p">),</span> <span class="nb">type</span><span class="o">=</span><span class="n">pa</span><span class="o">.</span><span class="n">dictionary</span><span class="p">(</span><span class="n">pa</span><span class="o">.</span><span class="n">int8</span><span class="p">(),</span> <span class="n">pa</span><span class="o">.</span><span class="n">int64</span><span class="p">()))</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">arr</span><span class="o">.</span><span class="n">type</span><span class="o">.</span><span class="n">index_type</span>
 <span class="go">DataType(int16)</span>
 </pre></div>
 </div>
 </dd></dl>

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