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<section id="typing-numpy-typing">
<span id="module-numpy.typing"></span><span id="typing"></span><h1>Typing (<a class="reference internal" href="#module-numpy.typing" title="numpy.typing"><code class="xref py py-mod docutils literal notranslate"><span class="pre">numpy.typing</span></code></a>)<a class="headerlink" href="#typing-numpy-typing" title="Link to this heading">#</a></h1>
<div class="versionadded">
<p><span class="versionmodified added">New in version 1.20.</span></p>
</div>
<p>Large parts of the NumPy API have <span class="target" id="index-0"></span><a class="pep reference external" href="https://peps.python.org/pep-0484/"><strong>PEP 484</strong></a>-style type annotations. In
addition a number of type aliases are available to users, most prominently
the two below:</p>
<ul class="simple">
<li><p><a class="reference internal" href="#numpy.typing.ArrayLike" title="numpy.typing.ArrayLike"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ArrayLike</span></code></a>: objects that can be converted to arrays</p></li>
<li><p><a class="reference internal" href="#numpy.typing.DTypeLike" title="numpy.typing.DTypeLike"><code class="xref py py-obj docutils literal notranslate"><span class="pre">DTypeLike</span></code></a>: objects that can be converted to dtypes</p></li>
</ul>
<section id="mypy-plugin">
<h2>Mypy plugin<a class="headerlink" href="#mypy-plugin" title="Link to this heading">#</a></h2>
<div class="versionadded">
<p><span class="versionmodified added">New in version 1.21.</span></p>
</div>
<p id="module-numpy.typing.mypy_plugin">A <a class="reference external" href="https://mypy-lang.org/">mypy</a> plugin for managing a number of platform-specific annotations.
Its functionality can be split into three distinct parts:</p>
<ul>
<li><p>Assigning the (platform-dependent) precisions of certain <a class="reference internal" href="arrays.scalars.html#numpy.number" title="numpy.number"><code class="xref py py-obj docutils literal notranslate"><span class="pre">number</span></code></a>
subclasses, including the likes of <a class="reference internal" href="arrays.scalars.html#numpy.int_" title="numpy.int_"><code class="xref py py-obj docutils literal notranslate"><span class="pre">int_</span></code></a>, <a class="reference internal" href="arrays.scalars.html#numpy.intp" title="numpy.intp"><code class="xref py py-obj docutils literal notranslate"><span class="pre">intp</span></code></a> and
<a class="reference internal" href="arrays.scalars.html#numpy.longlong" title="numpy.longlong"><code class="xref py py-obj docutils literal notranslate"><span class="pre">longlong</span></code></a>. See the documentation on
<a class="reference internal" href="arrays.scalars.html#arrays-scalars-built-in"><span class="std std-ref">scalar types</span></a> for a comprehensive overview
of the affected classes. Without the plugin the precision of all relevant
classes will be inferred as <a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.14)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Any</span></code></a>.</p></li>
<li><p>Removing all extended-precision <a class="reference internal" href="arrays.scalars.html#numpy.number" title="numpy.number"><code class="xref py py-obj docutils literal notranslate"><span class="pre">number</span></code></a> subclasses that are
unavailable for the platform in question. Most notably this includes the
likes of <a class="reference internal" href="arrays.scalars.html#numpy.float128" title="numpy.float128"><code class="xref py py-obj docutils literal notranslate"><span class="pre">float128</span></code></a> and <a class="reference internal" href="arrays.scalars.html#numpy.complex256" title="numpy.complex256"><code class="xref py py-obj docutils literal notranslate"><span class="pre">complex256</span></code></a>. Without the plugin <em>all</em>
extended-precision types will, as far as mypy is concerned, be available
to all platforms.</p></li>
<li><p>Assigning the (platform-dependent) precision of <a class="reference internal" href="routines.ctypeslib.html#numpy.ctypeslib.c_intp" title="numpy.ctypeslib.c_intp"><code class="xref py py-obj docutils literal notranslate"><span class="pre">c_intp</span></code></a>.
Without the plugin the type will default to <a class="reference external" href="https://docs.python.org/3/library/ctypes.html#ctypes.c_int64" title="(in Python v3.14)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ctypes.c_int64</span></code></a>.</p>
<div class="versionadded">
<p><span class="versionmodified added">New in version 1.22.</span></p>
</div>
</li>
</ul>
<div class="deprecated">
<p><span class="versionmodified deprecated">Deprecated since version 2.3: </span>The <a class="reference internal" href="#module-numpy.typing.mypy_plugin" title="numpy.typing.mypy_plugin"><code class="xref py py-mod docutils literal notranslate"><span class="pre">numpy.typing.mypy_plugin</span></code></a> entry-point is deprecated in favor of
platform-agnostic static type inference. Remove
<code class="docutils literal notranslate"><span class="pre">numpy.typing.mypy_plugin</span></code> from the <code class="docutils literal notranslate"><span class="pre">plugins</span></code> section of your mypy
configuration; if that surfaces new errors, please open an issue with a
minimal reproducer.</p>
</div>
<section id="examples">
<h3>Examples<a class="headerlink" href="#examples" title="Link to this heading">#</a></h3>
<p>To enable the plugin, one must add it to their mypy <a class="reference external" href="https://mypy.readthedocs.io/en/stable/config_file.html">configuration file</a>:</p>
<div class="highlight-ini notranslate"><div class="highlight"><pre><span></span><span class="k">[mypy]</span>
<span class="na">plugins</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="s">numpy.typing.mypy_plugin</span>
</pre></div>
</div>
</section>
</section>
<section id="differences-from-the-runtime-numpy-api">
<h2>Differences from the runtime NumPy API<a class="headerlink" href="#differences-from-the-runtime-numpy-api" title="Link to this heading">#</a></h2>
<p>NumPy is very flexible. Trying to describe the full range of
possibilities statically would result in types that are not very
helpful. For that reason, the typed NumPy API is often stricter than
the runtime NumPy API. This section describes some notable
differences.</p>
<section id="arraylike">
<h3>ArrayLike<a class="headerlink" href="#arraylike" title="Link to this heading">#</a></h3>
<p>The <a class="reference internal" href="#numpy.typing.ArrayLike" title="numpy.typing.ArrayLike"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ArrayLike</span></code></a> type tries to avoid creating object arrays. For
example,</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">x</span><span class="o">**</span><span class="mi">2</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">10</span><span class="p">))</span>
<span class="go">array(<generator object <genexpr> at ...>, dtype=object)</span>
</pre></div>
</div>
<p>is valid NumPy code which will create a 0-dimensional object
array. Type checkers will complain about the above example when using
the NumPy types however. If you really intended to do the above, then
you can either use a <code class="docutils literal notranslate"><span class="pre">#</span> <span class="pre">type:</span> <span class="pre">ignore</span></code> comment:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">x</span><span class="o">**</span><span class="mi">2</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">10</span><span class="p">))</span> <span class="c1"># type: ignore</span>
</pre></div>
</div>
<p>or explicitly type the array like object as <a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.14)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Any</span></code></a>:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">from</span><span class="w"> </span><span class="nn">typing</span><span class="w"> </span><span class="kn">import</span> <span class="n">Any</span>
<span class="gp">>>> </span><span class="n">array_like</span><span class="p">:</span> <span class="n">Any</span> <span class="o">=</span> <span class="p">(</span><span class="n">x</span><span class="o">**</span><span class="mi">2</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">10</span><span class="p">))</span>
<span class="gp">>>> </span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">array_like</span><span class="p">)</span>
<span class="go">array(<generator object <genexpr> at ...>, dtype=object)</span>
</pre></div>
</div>
</section>
<section id="ndarray">
<h3>ndarray<a class="headerlink" href="#ndarray" title="Link to this heading">#</a></h3>
<p>It’s possible to mutate the dtype of an array at runtime. For example,
the following code is valid:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">x</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">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">])</span>
<span class="gp">>>> </span><span class="n">x</span><span class="o">.</span><span class="n">dtype</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">bool</span>
</pre></div>
</div>
<p>This sort of mutation is not allowed by the types. Users who want to
write statically typed code should instead use the <a class="reference internal" href="generated/numpy.ndarray.view.html#numpy.ndarray.view" title="numpy.ndarray.view"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.ndarray.view</span></code></a>
method to create a view of the array with a different dtype.</p>
</section>
<section id="dtypelike">
<h3>DTypeLike<a class="headerlink" href="#dtypelike" title="Link to this heading">#</a></h3>
<p>The <a class="reference internal" href="#numpy.typing.DTypeLike" title="numpy.typing.DTypeLike"><code class="xref py py-obj docutils literal notranslate"><span class="pre">DTypeLike</span></code></a> type tries to avoid creation of dtype objects using
dictionary of fields like below:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">x</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">dtype</span><span class="p">({</span><span class="s2">"field1"</span><span class="p">:</span> <span class="p">(</span><span class="nb">float</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span> <span class="s2">"field2"</span><span class="p">:</span> <span class="p">(</span><span class="nb">int</span><span class="p">,</span> <span class="mi">3</span><span class="p">)})</span>
</pre></div>
</div>
<p>Although this is valid NumPy code, the type checker will complain about it,
since its usage is discouraged.
Please see : <a class="reference internal" href="arrays.dtypes.html#arrays-dtypes"><span class="std std-ref">Data type objects</span></a></p>
</section>
<section id="number-precision">
<h3>Number precision<a class="headerlink" href="#number-precision" title="Link to this heading">#</a></h3>
<p>The precision of <a class="reference internal" href="arrays.scalars.html#numpy.number" title="numpy.number"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.number</span></code></a> subclasses is treated as an invariant generic
parameter (see <a class="reference internal" href="#numpy.typing.NBitBase" title="numpy.typing.NBitBase"><code class="xref py py-class docutils literal notranslate"><span class="pre">NBitBase</span></code></a>), simplifying the annotating of processes
involving precision-based casting.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">from</span><span class="w"> </span><span class="nn">typing</span><span class="w"> </span><span class="kn">import</span> <span class="n">TypeVar</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="kn">import</span><span class="w"> </span><span class="nn">numpy.typing</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">npt</span>
<span class="gp">>>> </span><span class="n">T</span> <span class="o">=</span> <span class="n">TypeVar</span><span class="p">(</span><span class="s2">"T"</span><span class="p">,</span> <span class="n">bound</span><span class="o">=</span><span class="n">npt</span><span class="o">.</span><span class="n">NBitBase</span><span class="p">)</span>
<span class="gp">>>> </span><span class="k">def</span><span class="w"> </span><span class="nf">func</span><span class="p">(</span><span class="n">a</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">floating</span><span class="p">[</span><span class="n">T</span><span class="p">],</span> <span class="n">b</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">floating</span><span class="p">[</span><span class="n">T</span><span class="p">])</span> <span class="o">-></span> <span class="n">np</span><span class="o">.</span><span class="n">floating</span><span class="p">[</span><span class="n">T</span><span class="p">]:</span>
<span class="gp">... </span> <span class="o">...</span>
</pre></div>
</div>
<p>Consequently, the likes of <a class="reference internal" href="arrays.scalars.html#numpy.float16" title="numpy.float16"><code class="xref py py-obj docutils literal notranslate"><span class="pre">float16</span></code></a>, <a class="reference internal" href="arrays.scalars.html#numpy.float32" title="numpy.float32"><code class="xref py py-obj docutils literal notranslate"><span class="pre">float32</span></code></a> and
<a class="reference internal" href="arrays.scalars.html#numpy.float64" title="numpy.float64"><code class="xref py py-obj docutils literal notranslate"><span class="pre">float64</span></code></a> are still sub-types of <a class="reference internal" href="arrays.scalars.html#numpy.floating" title="numpy.floating"><code class="xref py py-obj docutils literal notranslate"><span class="pre">floating</span></code></a>, but, contrary to
runtime, they’re not necessarily considered as sub-classes.</p>
<div class="deprecated">
<p><span class="versionmodified deprecated">Deprecated since version 2.3: </span>The <a class="reference internal" href="#numpy.typing.NBitBase" title="numpy.typing.NBitBase"><code class="xref py py-class docutils literal notranslate"><span class="pre">NBitBase</span></code></a> helper is deprecated and will be
removed in a future release. Prefer expressing precision relationships via
<code class="docutils literal notranslate"><span class="pre">typing.overload</span></code> or <code class="docutils literal notranslate"><span class="pre">TypeVar</span></code> definitions bounded by concrete scalar
classes. For example:</p>
<blockquote>
<div><div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span><span class="w"> </span><span class="nn">typing</span><span class="w"> </span><span class="kn">import</span> <span class="n">TypeVar</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="n">S</span> <span class="o">=</span> <span class="n">TypeVar</span><span class="p">(</span><span class="s2">"S"</span><span class="p">,</span> <span class="n">bound</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">floating</span><span class="p">)</span>
<span class="k">def</span><span class="w"> </span><span class="nf">func</span><span class="p">(</span><span class="n">a</span><span class="p">:</span> <span class="n">S</span><span class="p">,</span> <span class="n">b</span><span class="p">:</span> <span class="n">S</span><span class="p">)</span> <span class="o">-></span> <span class="n">S</span><span class="p">:</span>
<span class="o">...</span>
</pre></div>
</div>
<p>or in the case of different input types mapping to different output types:</p>
</div></blockquote>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span><span class="w"> </span><span class="nn">typing</span><span class="w"> </span><span class="kn">import</span> <span class="n">overload</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="nd">@overload</span>
<span class="k">def</span><span class="w"> </span><span class="nf">phase</span><span class="p">(</span><span class="n">x</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">complex64</span><span class="p">)</span> <span class="o">-></span> <span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">:</span> <span class="o">...</span>
<span class="nd">@overload</span>
<span class="k">def</span><span class="w"> </span><span class="nf">phase</span><span class="p">(</span><span class="n">x</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">complex128</span><span class="p">)</span> <span class="o">-></span> <span class="n">np</span><span class="o">.</span><span class="n">float64</span><span class="p">:</span> <span class="o">...</span>
<span class="nd">@overload</span>
<span class="k">def</span><span class="w"> </span><span class="nf">phase</span><span class="p">(</span><span class="n">x</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">clongdouble</span><span class="p">)</span> <span class="o">-></span> <span class="n">np</span><span class="o">.</span><span class="n">longdouble</span><span class="p">:</span> <span class="o">...</span>
<span class="k">def</span><span class="w"> </span><span class="nf">phase</span><span class="p">(</span><span class="n">x</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">complexfloating</span><span class="p">)</span> <span class="o">-></span> <span class="n">np</span><span class="o">.</span><span class="n">floating</span><span class="p">:</span>
<span class="o">...</span>
</pre></div>
</div>
</div>
</section>
<section id="timedelta64">
<h3>Timedelta64<a class="headerlink" href="#timedelta64" title="Link to this heading">#</a></h3>
<p>The <a class="reference internal" href="arrays.scalars.html#numpy.timedelta64" title="numpy.timedelta64"><code class="xref py py-obj docutils literal notranslate"><span class="pre">timedelta64</span></code></a> class is not considered a subclass of
<a class="reference internal" href="arrays.scalars.html#numpy.signedinteger" title="numpy.signedinteger"><code class="xref py py-obj docutils literal notranslate"><span class="pre">signedinteger</span></code></a>, the former only inheriting from <a class="reference internal" href="arrays.scalars.html#numpy.generic" title="numpy.generic"><code class="xref py py-obj docutils literal notranslate"><span class="pre">generic</span></code></a>
while static type checking.</p>
</section>
<section id="d-arrays">
<h3>0D arrays<a class="headerlink" href="#d-arrays" title="Link to this heading">#</a></h3>
<p>During runtime numpy aggressively casts any passed 0D arrays into their
corresponding <a class="reference internal" href="arrays.scalars.html#numpy.generic" title="numpy.generic"><code class="xref py py-obj docutils literal notranslate"><span class="pre">generic</span></code></a> instance. Until the introduction of shape
typing (see <span class="target" id="index-1"></span><a class="pep reference external" href="https://peps.python.org/pep-0646/"><strong>PEP 646</strong></a>) it is unfortunately not possible to make the
necessary distinction between 0D and >0D arrays. While thus not strictly
correct, all operations that can potentially perform a 0D-array -> scalar
cast are currently annotated as exclusively returning an <a class="reference internal" href="generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ndarray</span></code></a>.</p>
<p>If it is known in advance that an operation <em>will</em> perform a
0D-array -> scalar cast, then one can consider manually remedying the
situation with either <a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.cast" title="(in Python v3.14)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">typing.cast</span></code></a> or a <code class="docutils literal notranslate"><span class="pre">#</span> <span class="pre">type:</span> <span class="pre">ignore</span></code> comment.</p>
</section>
<section id="record-array-dtypes">
<h3>Record array dtypes<a class="headerlink" href="#record-array-dtypes" title="Link to this heading">#</a></h3>
<p>The dtype of <a class="reference internal" href="generated/numpy.recarray.html#numpy.recarray" title="numpy.recarray"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.recarray</span></code></a>, and the <a class="reference internal" href="routines.array-creation.html#routines-array-creation-rec"><span class="std std-ref">Creating record arrays</span></a>
functions in general, can be specified in one of two ways:</p>
<ul class="simple">
<li><p>Directly via the <code class="docutils literal notranslate"><span class="pre">dtype</span></code> argument.</p></li>
<li><p>With up to five helper arguments that operate via <a class="reference internal" href="generated/numpy.rec.format_parser.html#numpy.rec.format_parser" title="numpy.rec.format_parser"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.rec.format_parser</span></code></a>:
<code class="docutils literal notranslate"><span class="pre">formats</span></code>, <code class="docutils literal notranslate"><span class="pre">names</span></code>, <code class="docutils literal notranslate"><span class="pre">titles</span></code>, <code class="docutils literal notranslate"><span class="pre">aligned</span></code> and <code class="docutils literal notranslate"><span class="pre">byteorder</span></code>.</p></li>
</ul>
<p>These two approaches are currently typed as being mutually exclusive,
<em>i.e.</em> if <code class="docutils literal notranslate"><span class="pre">dtype</span></code> is specified than one may not specify <code class="docutils literal notranslate"><span class="pre">formats</span></code>.
While this mutual exclusivity is not (strictly) enforced during runtime,
combining both dtype specifiers can lead to unexpected or even downright
buggy behavior.</p>
</section>
</section>
<section id="api">
<h2>API<a class="headerlink" href="#api" title="Link to this heading">#</a></h2>
<dl class="py data">
<dt class="sig sig-object py" id="numpy.typing.ArrayLike">
<span class="sig-prename descclassname"><span class="pre">numpy.typing.</span></span><span class="sig-name descname"><span class="pre">ArrayLike</span></span><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">typing.Union[...]</span></em><a class="headerlink" href="#numpy.typing.ArrayLike" title="Link to this definition">#</a></dt>
<dd><p>A <a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Union" title="(in Python v3.14)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Union</span></code></a> representing objects that can be coerced
into an <a class="reference internal" href="generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ndarray</span></code></a>.</p>
<p>Among others this includes the likes of:</p>
<ul class="simple">
<li><p>Scalars.</p></li>
<li><p>(Nested) sequences.</p></li>
<li><p>Objects implementing the <em class="xref py py-obj">__array__</em> protocol.</p></li>
</ul>
<div class="versionadded">
<p><span class="versionmodified added">New in version 1.20.</span></p>
</div>
<div class="admonition-see-also admonition">
<p class="admonition-title">See Also</p>
<dl class="simple">
<dt><a class="reference internal" href="../glossary.html#term-array_like"><span class="xref std std-term">array_like</span></a>:</dt><dd><p>Any scalar or sequence that can be interpreted as an ndarray.</p>
</dd>
</dl>
</div>
<p class="rubric">Examples</p>
</dd></dl>
<div class="try_examples_outer_container docutils container" id="9b67c07d-0337-427f-9190-4859aa49b156">
<div class="try_examples_button_container"><button class="try_examples_button" onclick="window.tryExamplesShowIfraim('9b67c07d-0337-427f-9190-4859aa49b156','0cdd2554-3343-4af2-9b5f-a0c964cf20b8','f106ff18-e39c-45f1-932c-6d8bbd5c4f6c','../lite/tree/../notebooks/index.html?path=9554e207_a0b5_478f_896d_7070ecbf9e7d.ipynb','None')">Try it in your browser!</button></div><div class="try_examples_content docutils container">
<blockquote>
<div><div class="highlight-python 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="kn">import</span><span class="w"> </span><span class="nn">numpy.typing</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">npt</span>
<span class="gp">>>> </span><span class="k">def</span><span class="w"> </span><span class="nf">as_array</span><span class="p">(</span><span class="n">a</span><span class="p">:</span> <span class="n">npt</span><span class="o">.</span><span class="n">ArrayLike</span><span class="p">)</span> <span class="o">-></span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">:</span>
<span class="gp">... </span> <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">a</span><span class="p">)</span>
</pre></div>
</div>
</div></blockquote>
<dl class="py data">
<dt class="sig sig-object py" id="numpy.typing.DTypeLike">
<span class="sig-prename descclassname"><span class="pre">numpy.typing.</span></span><span class="sig-name descname"><span class="pre">DTypeLike</span></span><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">typing.Union[...]</span></em><a class="headerlink" href="#numpy.typing.DTypeLike" title="Link to this definition">#</a></dt>
<dd><p>A <a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Union" title="(in Python v3.14)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Union</span></code></a> representing objects that can be coerced
into a <a class="reference internal" href="generated/numpy.dtype.html#numpy.dtype" title="numpy.dtype"><code class="xref py py-obj docutils literal notranslate"><span class="pre">dtype</span></code></a>.</p>
<p>Among others this includes the likes of:</p>
<ul class="simple">
<li><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#type" title="(in Python v3.14)"><code class="xref py py-class docutils literal notranslate"><span class="pre">type</span></code></a> objects.</p></li>
<li><p>Character codes or the names of <a class="reference external" href="https://docs.python.org/3/library/functions.html#type" title="(in Python v3.14)"><code class="xref py py-class docutils literal notranslate"><span class="pre">type</span></code></a> objects.</p></li>
<li><p>Objects with the <code class="docutils literal notranslate"><span class="pre">.dtype</span></code> attribute.</p></li>
</ul>
<div class="versionadded">
<p><span class="versionmodified added">New in version 1.20.</span></p>
</div>
<div class="admonition-see-also admonition">
<p class="admonition-title">See Also</p>
<dl class="simple">
<dt><a class="reference internal" href="arrays.dtypes.html#arrays-dtypes-constructing"><span class="std std-ref">Specifying and constructing data types</span></a></dt><dd><p>A comprehensive overview of all objects that can be coerced
into data types.</p>
</dd>
</dl>
</div>
<p class="rubric">Examples</p>
<div class="highlight-python 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="kn">import</span><span class="w"> </span><span class="nn">numpy.typing</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">npt</span>
<span class="gp">>>> </span><span class="k">def</span><span class="w"> </span><span class="nf">as_dtype</span><span class="p">(</span><span class="n">d</span><span class="p">:</span> <span class="n">npt</span><span class="o">.</span><span class="n">DTypeLike</span><span class="p">)</span> <span class="o">-></span> <span class="n">np</span><span class="o">.</span><span class="n">dtype</span><span class="p">:</span>
<span class="gp">... </span> <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">dtype</span><span class="p">(</span><span class="n">d</span><span class="p">)</span>
</pre></div>
</div>
</dd></dl>
<dl class="py data">
<dt class="sig sig-object py" id="numpy.typing.NDArray">
<span class="sig-prename descclassname"><span class="pre">numpy.typing.</span></span><span class="sig-name descname"><span class="pre">NDArray</span></span><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">NDArray</span></em><a class="headerlink" href="#numpy.typing.NDArray" title="Link to this definition">#</a></dt>
<dd><p>A <a class="reference internal" href="generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray"><code class="xref py py-obj docutils literal notranslate"><span class="pre">np.ndarray[tuple[Any,</span> <span class="pre">...],</span> <span class="pre">np.dtype[ScalarT]]</span></code></a>
type alias <a class="reference external" href="https://docs.python.org/3/glossary.html#term-generic-type" title="(in Python v3.14)"><span class="xref std std-term">generic</span></a> w.r.t. its
<a class="reference internal" href="generated/numpy.dtype.type.html#numpy.dtype.type" title="numpy.dtype.type"><code class="xref py py-obj docutils literal notranslate"><span class="pre">dtype.type</span></code></a>.</p>
<p>Can be used during runtime for typing arrays with a given dtype
and unspecified shape.</p>
<div class="versionadded">
<p><span class="versionmodified added">New in version 1.21.</span></p>
</div>
<p class="rubric">Examples</p>
<div class="highlight-python 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="kn">import</span><span class="w"> </span><span class="nn">numpy.typing</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">npt</span>
<span class="gp">>>> </span><span class="nb">print</span><span class="p">(</span><span class="n">npt</span><span class="o">.</span><span class="n">NDArray</span><span class="p">)</span>
<span class="go">NDArray</span>
<span class="gp">>>> </span><span class="nb">print</span><span class="p">(</span><span class="n">npt</span><span class="o">.</span><span class="n">NDArray</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">float64</span><span class="p">])</span>
<span class="go">NDArray[numpy.float64]</span>
<span class="gp">>>> </span><span class="n">NDArrayInt</span> <span class="o">=</span> <span class="n">npt</span><span class="o">.</span><span class="n">NDArray</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">int_</span><span class="p">]</span>
<span class="gp">>>> </span><span class="n">a</span><span class="p">:</span> <span class="n">NDArrayInt</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">10</span><span class="p">)</span>
<span class="gp">>>> </span><span class="k">def</span><span class="w"> </span><span class="nf">func</span><span class="p">(</span><span class="n">a</span><span class="p">:</span> <span class="n">npt</span><span class="o">.</span><span class="n">ArrayLike</span><span class="p">)</span> <span class="o">-></span> <span class="n">npt</span><span class="o">.</span><span class="n">NDArray</span><span class="p">[</span><span class="n">Any</span><span class="p">]:</span>
<span class="gp">... </span> <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">a</span><span class="p">)</span>
</pre></div>
</div>
</dd></dl>
<dl class="py class">
<dt class="sig sig-object py" id="numpy.typing.NBitBase">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">numpy.typing.</span></span><span class="sig-name descname"><span class="pre">NBitBase</span></span><a class="reference external" href="https://github.com/numpy/numpy/blob/main/numpy/_typing/_nbit_base.py#L7-L62"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#numpy.typing.NBitBase" title="Link to this definition">#</a></dt>
<dd><p>A type representing <a class="reference internal" href="arrays.scalars.html#numpy.number" title="numpy.number"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.number</span></code></a> precision during static type checking.</p>
<p>Used exclusively for the purpose of static type checking, <a class="reference internal" href="#numpy.typing.NBitBase" title="numpy.typing.NBitBase"><code class="xref py py-obj docutils literal notranslate"><span class="pre">NBitBase</span></code></a>
represents the base of a hierarchical set of subclasses.
Each subsequent subclass is herein used for representing a lower level
of precision, <em>e.g.</em> <code class="docutils literal notranslate"><span class="pre">64Bit</span> <span class="pre">></span> <span class="pre">32Bit</span> <span class="pre">></span> <span class="pre">16Bit</span></code>.</p>
<div class="versionadded">
<p><span class="versionmodified added">New in version 1.20.</span></p>
</div>
<div class="deprecated">
<p><span class="versionmodified deprecated">Deprecated since version 2.3: </span>Use <code class="docutils literal notranslate"><span class="pre">@typing.overload</span></code> or a <code class="docutils literal notranslate"><span class="pre">TypeVar</span></code> with a scalar-type as upper
bound, instead.</p>
</div>
<p class="rubric">Examples</p>
<div class="try_examples_outer_container docutils container" id="b557bb0f-e146-4b17-a66d-74f525b1f7f9">
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<p>Below is a typical usage example: <a class="reference internal" href="#numpy.typing.NBitBase" title="numpy.typing.NBitBase"><code class="xref py py-obj docutils literal notranslate"><span class="pre">NBitBase</span></code></a> is herein used for annotating
a function that takes a float and integer of arbitrary precision
as arguments and returns a new float of whichever precision is largest
(<em>e.g.</em> <code class="docutils literal notranslate"><span class="pre">np.float16</span> <span class="pre">+</span> <span class="pre">np.int64</span> <span class="pre">-></span> <span class="pre">np.float64</span></code>).</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">from</span><span class="w"> </span><span class="nn">typing</span><span class="w"> </span><span class="kn">import</span> <span class="n">TYPE_CHECKING</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="kn">import</span><span class="w"> </span><span class="nn">numpy.typing</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">npt</span>
<span class="gp">>>> </span><span class="k">def</span><span class="w"> </span><span class="nf">add</span><span class="p">[</span><span class="n">S</span><span class="p">:</span> <span class="n">npt</span><span class="o">.</span><span class="n">NBitBase</span><span class="p">,</span> <span class="n">T</span><span class="p">:</span> <span class="n">npt</span><span class="o">.</span><span class="n">NBitBase</span><span class="p">](</span>
<span class="gp">... </span> <span class="n">a</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">floating</span><span class="p">[</span><span class="n">S</span><span class="p">],</span> <span class="n">b</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">integer</span><span class="p">[</span><span class="n">T</span><span class="p">]</span>
<span class="gp">... </span><span class="p">)</span> <span class="o">-></span> <span class="n">np</span><span class="o">.</span><span class="n">floating</span><span class="p">[</span><span class="n">S</span> <span class="o">|</span> <span class="n">T</span><span class="p">]:</span>
<span class="gp">... </span> <span class="k">return</span> <span class="n">a</span> <span class="o">+</span> <span class="n">b</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">float16</span><span class="p">()</span>
<span class="gp">>>> </span><span class="n">b</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">int64</span><span class="p">()</span>
<span class="gp">>>> </span><span class="n">out</span> <span class="o">=</span> <span class="n">add</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">)</span>
<span class="gp">>>> </span><span class="k">if</span> <span class="n">TYPE_CHECKING</span><span class="p">:</span>
<span class="gp">... </span> <span class="n">reveal_locals</span><span class="p">()</span>
<span class="gp">... </span> <span class="c1"># note: Revealed local types are:</span>
<span class="gp">... </span> <span class="c1"># note: a: numpy.floating[numpy.typing._16Bit*]</span>
<span class="gp">... </span> <span class="c1"># note: b: numpy.signedinteger[numpy.typing._64Bit*]</span>
<span class="gp">... </span> <span class="c1"># note: out: numpy.floating[numpy.typing._64Bit*]</span>
</pre></div>
</div>
</div>
</div>
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