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  <section id="load-linnerud">
<h1>load_linnerud<a class="headerlink" href="#load-linnerud" title="Link to this heading">#</a></h1>
<dl class="py function">
<dt class="sig sig-object py" id="sklearn.datasets.load_linnerud">
<span class="sig-prename descclassname"><span class="pre">sklearn.datasets.</span></span><span class="sig-name descname"><span class="pre">load_linnerud</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_X_y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">as_frame</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/86d099ec1/sklearn/datasets/_base.py#L1166"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.datasets.load_linnerud" title="Link to this definition">#</a></dt>
<dd><p>Load and return the physical exercise Linnerud dataset.</p>
<p>This dataset is suitable for multi-output regression tasks.</p>
<div class="pst-scrollable-table-container"><table class="table">
<tbody>
<tr class="row-odd"><td><p>Samples total</p></td>
<td><p>20</p></td>
</tr>
<tr class="row-even"><td><p>Dimensionality</p></td>
<td><p>3 (for both data and target)</p></td>
</tr>
<tr class="row-odd"><td><p>Features</p></td>
<td><p>integer</p></td>
</tr>
<tr class="row-even"><td><p>Targets</p></td>
<td><p>integer</p></td>
</tr>
</tbody>
</table>
</div>
<p>Read more in the <a class="reference internal" href="../../datasets/toy_dataset.html#linnerrud-dataset"><span class="std std-ref">User Guide</span></a>.</p>
<dl class="field-list">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><dl>
<dt><strong>return_X_y</strong><span class="classifier">bool, default=False</span></dt><dd><p>If True, returns <code class="docutils literal notranslate"><span class="pre">(data,</span> <span class="pre">target)</span></code> instead of a Bunch object.
See below for more information about the <code class="docutils literal notranslate"><span class="pre">data</span></code> and <code class="docutils literal notranslate"><span class="pre">target</span></code> object.</p>
<div class="versionadded">
<p><span class="versionmodified added">Added in version 0.18.</span></p>
</div>
</dd>
<dt><strong>as_frame</strong><span class="classifier">bool, default=False</span></dt><dd><p>If True, the data is a pandas DataFrame including columns with
appropriate dtypes (numeric, string or categorical). The target is
a pandas DataFrame or Series depending on the number of target columns.
If <code class="docutils literal notranslate"><span class="pre">return_X_y</span></code> is True, then (<code class="docutils literal notranslate"><span class="pre">data</span></code>, <code class="docutils literal notranslate"><span class="pre">target</span></code>) will be pandas
DataFrames or Series as described below.</p>
<div class="versionadded">
<p><span class="versionmodified added">Added in version 0.23.</span></p>
</div>
</dd>
</dl>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><dl>
<dt><strong>data</strong><span class="classifier"><a class="reference internal" href="sklearn.utils.Bunch.html#sklearn.utils.Bunch" title="sklearn.utils.Bunch"><code class="xref py py-class docutils literal notranslate"><span class="pre">Bunch</span></code></a></span></dt><dd><p>Dictionary-like object, with the following attributes.</p>
<dl>
<dt>data<span class="classifier">{ndarray, dataframe} of shape (20, 3)</span></dt><dd><p>The data matrix. If <code class="docutils literal notranslate"><span class="pre">as_frame=True</span></code>, <code class="docutils literal notranslate"><span class="pre">data</span></code> will be a pandas
DataFrame.</p>
</dd>
<dt>target: {ndarray, dataframe} of shape (20, 3)</dt><dd><p>The regression targets. If <code class="docutils literal notranslate"><span class="pre">as_frame=True</span></code>, <code class="docutils literal notranslate"><span class="pre">target</span></code> will be
a pandas DataFrame.</p>
</dd>
<dt>feature_names: list</dt><dd><p>The names of the dataset columns.</p>
</dd>
<dt>target_names: list</dt><dd><p>The names of the target columns.</p>
</dd>
<dt>frame: DataFrame of shape (20, 6)</dt><dd><p>Only present when <code class="docutils literal notranslate"><span class="pre">as_frame=True</span></code>. DataFrame with <code class="docutils literal notranslate"><span class="pre">data</span></code> and
<code class="docutils literal notranslate"><span class="pre">target</span></code>.</p>
<div class="versionadded">
<p><span class="versionmodified added">Added in version 0.23.</span></p>
</div>
</dd>
<dt>DESCR: str</dt><dd><p>The full description of the dataset.</p>
</dd>
<dt>data_filename: str</dt><dd><p>The path to the location of the data.</p>
</dd>
<dt>target_filename: str</dt><dd><p>The path to the location of the target.</p>
<div class="versionadded">
<p><span class="versionmodified added">Added in version 0.20.</span></p>
</div>
</dd>
</dl>
</dd>
<dt><strong>(data, target)</strong><span class="classifier">tuple if <code class="docutils literal notranslate"><span class="pre">return_X_y</span></code> is True</span></dt><dd><p>Returns a tuple of two ndarrays or dataframe of shape
<code class="docutils literal notranslate"><span class="pre">(20,</span> <span class="pre">3)</span></code>. Each row represents one sample and each column represents the
features in <code class="docutils literal notranslate"><span class="pre">X</span></code> and a target in <code class="docutils literal notranslate"><span class="pre">y</span></code> of a given sample.</p>
<div class="versionadded">
<p><span class="versionmodified added">Added in version 0.18.</span></p>
</div>
</dd>
</dl>
</dd>
</dl>
<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">from</span><span class="w"> </span><span class="nn">sklearn.datasets</span><span class="w"> </span><span class="kn">import</span> <span class="n">load_linnerud</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">linnerud</span> <span class="o">=</span> <span class="n">load_linnerud</span><span class="p">()</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">linnerud</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">shape</span>
<span class="go">(20, 3)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">linnerud</span><span class="o">.</span><span class="n">target</span><span class="o">.</span><span class="n">shape</span>
<span class="go">(20, 3)</span>
</pre></div>
</div>
</dd></dl>

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