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<ul>
<li><a class="reference internal" href="#"><code class="docutils literal"><span class="pre">sklearn.compose</span></code>.ColumnTransformer</a><ul>
<li><a class="reference internal" href="#examples-using-sklearn-compose-columntransformer">Examples using <code class="docutils literal"><span class="pre">sklearn.compose.ColumnTransformer</span></code></a></li>
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<div class="section" id="sklearn-compose-columntransformer">
<h1><a class="reference internal" href="../classes.html#module-sklearn.compose" title="sklearn.compose"><code class="xref py py-mod docutils literal"><span class="pre">sklearn.compose</span></code></a>.ColumnTransformer<a class="headerlink" href="#sklearn-compose-columntransformer" title="Permalink to this headline">¶</a></h1>
<dl class="class">
<dt id="sklearn.compose.ColumnTransformer">
<em class="property">class </em><code class="descclassname">sklearn.compose.</code><code class="descname">ColumnTransformer</code><span class="sig-paren">(</span><em>transformers</em>, <em>remainder='passthrough'</em>, <em>n_jobs=1</em>, <em>transformer_weights=None</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/2d7cd0c/sklearn/compose/_column_transformer.py#L33"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.compose.ColumnTransformer" title="Permalink to this definition">¶</a></dt>
<dd><p>Applies transformers to columns of an array or pandas DataFrame.</p>
<p>EXPERIMENTAL: some behaviors may change between releases without
deprecation.</p>
<p>This estimator allows different columns or column subsets of the input
to be transformed separately and the results combined into a single
feature space.
This is useful for heterogeneous or columnar data, to combine several
feature extraction mechanisms or transformations into a single transformer.</p>
<p>Read more in the <a class="reference internal" href="../compose.html#column-transformer"><span class="std std-ref">User Guide</span></a>.</p>
<div class="versionadded">
<p><span class="versionmodified">New in version 0.20.</span></p>
</div>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><p class="first"><strong>transformers</strong> : list of tuples</p>
<blockquote>
<div><p>List of (name, transformer, column(s)) tuples specifying the
transformer objects to be applied to subsets of the data.</p>
<dl class="docutils">
<dt>name <span class="classifier-delimiter">:</span> <span class="classifier">string</span></dt>
<dd><p class="first last">Like in Pipeline and FeatureUnion, this allows the transformer and
its parameters to be set using <code class="docutils literal"><span class="pre">set_params</span></code> and searched in grid
search.</p>
</dd>
<dt>transformer <span class="classifier-delimiter">:</span> <span class="classifier">estimator or {‘passthrough’, ‘drop’}</span></dt>
<dd><p class="first last">Estimator must support <cite>fit</cite> and <cite>transform</cite>. Special-cased
strings ‘drop’ and ‘passthrough’ are accepted as well, to
indicate to drop the columns or to pass them through untransformed,
respectively.</p>
</dd>
<dt>column(s) <span class="classifier-delimiter">:</span> <span class="classifier">string or int, array-like of string or int, slice or boolean mask array</span></dt>
<dd><p class="first last">Indexes the data on its second axis. Integers are interpreted as
positional columns, while strings can reference DataFrame columns
by name. A scalar string or int should be used where
<code class="docutils literal"><span class="pre">transformer</span></code> expects X to be a 1d array-like (vector),
otherwise a 2d array will be passed to the transformer.</p>
</dd>
</dl>
</div></blockquote>
<p><strong>remainder</strong> : {‘passthrough’, ‘drop’}, default ‘passthrough’</p>
<blockquote>
<div><p>By default, all remaining columns that were not specified in
<cite>transformers</cite> will be automatically passed through (default of
<code class="docutils literal"><span class="pre">'passthrough'</span></code>). This subset of columns is concatenated with the
output of the transformers.
By using <code class="docutils literal"><span class="pre">remainder='drop'</span></code>, only the specified columns in
<cite>transformers</cite> are transformed and combined in the output, and the
non-specified columns are dropped.</p>
</div></blockquote>
<p><strong>n_jobs</strong> : int, optional</p>
<blockquote>
<div><p>Number of jobs to run in parallel (default 1).</p>
</div></blockquote>
<p><strong>transformer_weights</strong> : dict, optional</p>
<blockquote class="last">
<div><p>Multiplicative weights for features per transformer. The output of the
transformer is multiplied by these weights. Keys are transformer names,
values the weights.</p>
</div></blockquote>
</td>
</tr>
</tbody>
</table>
<p class="rubric">Attributes</p>
<table border="1" class="longtable docutils">
<colgroup>
<col width="10%" />
<col width="90%" />
</colgroup>
<tbody valign="top">
<tr class="row-odd"><td><a class="reference internal" href="#sklearn.compose.ColumnTransformer.named_transformers_" title="sklearn.compose.ColumnTransformer.named_transformers_"><code class="xref py py-obj docutils literal"><span class="pre">named_transformers_</span></code></a></td>
<td>Access the fitted transformer by name.</td>
</tr>
</tbody>
</table>
<table border="1" class="docutils">
<colgroup>
<col width="15%" />
<col width="85%" />
</colgroup>
<tbody valign="top">
<tr class="row-odd"><td><strong>transformers_</strong></td>
<td>(list) The collection of fitted transformers as tuples of (name, fitted_transformer, column).</td>
</tr>
</tbody>
</table>
<div class="admonition seealso">
<p class="first admonition-title">See also</p>
<dl class="last docutils">
<dt><a class="reference internal" href="sklearn.compose.make_column_transformer.html#sklearn.compose.make_column_transformer" title="sklearn.compose.make_column_transformer"><code class="xref py py-obj docutils literal"><span class="pre">sklearn.compose.make_column_transformer</span></code></a></dt>
<dd>convenience function for combining the outputs of multiple transformer objects applied to column subsets of the original feature space.</dd>
</dl>
</div>
<p class="rubric">Notes</p>
<p>The order of the columns in the transformed feature matrix follows the
order of how the columns are specified in the <cite>transformers</cite> list.
Columns of the original feature matrix that are not specified are
dropped from the resulting transformed feature matrix, unless specified
in the <cite>passthrough</cite> keyword. Those columns specified with <cite>passthrough</cite>
are added at the right to the output of the transformers.</p>
<p class="rubric">Examples</p>
<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">from</span> <span class="nn">sklearn.compose</span> <span class="k">import</span> <span class="n">ColumnTransformer</span>
<span class="gp">>>> </span><span class="kn">from</span> <span class="nn">sklearn.preprocessing</span> <span class="k">import</span> <span class="n">Normalizer</span>
<span class="gp">>>> </span><span class="n">ct</span> <span class="o">=</span> <span class="n">ColumnTransformer</span><span class="p">(</span>
<span class="gp">... </span> <span class="p">[(</span><span class="s2">"norm1"</span><span class="p">,</span> <span class="n">Normalizer</span><span class="p">(</span><span class="n">norm</span><span class="o">=</span><span class="s1">'l1'</span><span class="p">),</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="gp">... </span> <span class="p">(</span><span class="s2">"norm2"</span><span class="p">,</span> <span class="n">Normalizer</span><span class="p">(</span><span class="n">norm</span><span class="o">=</span><span class="s1">'l1'</span><span class="p">),</span> <span class="nb">slice</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">4</span><span class="p">))])</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="mf">0.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">2.</span><span class="p">,</span> <span class="mf">2.</span><span class="p">],</span>
<span class="gp">... </span> <span class="p">[</span><span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">0.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">]])</span>
<span class="gp">>>> </span><span class="c1"># Normalizer scales each row of X to unit norm. A separate scaling</span>
<span class="gp">>>> </span><span class="c1"># is applied for the two first and two last elements of each</span>
<span class="gp">>>> </span><span class="c1"># row independently.</span>
<span class="gp">>>> </span><span class="n">ct</span><span class="o">.</span><span class="n">fit_transform</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
<span class="go">array([[0. , 1. , 0.5, 0.5],</span>
<span class="go"> [0.5, 0.5, 0. , 1. ]])</span>
</pre></div>
</div>
<p class="rubric">Methods</p>
<table border="1" class="longtable docutils">
<colgroup>
<col width="10%" />
<col width="90%" />
</colgroup>
<tbody valign="top">
<tr class="row-odd"><td><a class="reference internal" href="#sklearn.compose.ColumnTransformer.fit" title="sklearn.compose.ColumnTransformer.fit"><code class="xref py py-obj docutils literal"><span class="pre">fit</span></code></a>(X[, y])</td>
<td>Fit all transformers using X.</td>
</tr>
<tr class="row-even"><td><a class="reference internal" href="#sklearn.compose.ColumnTransformer.fit_transform" title="sklearn.compose.ColumnTransformer.fit_transform"><code class="xref py py-obj docutils literal"><span class="pre">fit_transform</span></code></a>(X[, y])</td>
<td>Fit all transformers, transform the data and concatenate results.</td>
</tr>
<tr class="row-odd"><td><a class="reference internal" href="#sklearn.compose.ColumnTransformer.get_feature_names" title="sklearn.compose.ColumnTransformer.get_feature_names"><code class="xref py py-obj docutils literal"><span class="pre">get_feature_names</span></code></a>()</td>
<td>Get feature names from all transformers.</td>
</tr>
<tr class="row-even"><td><a class="reference internal" href="#sklearn.compose.ColumnTransformer.get_params" title="sklearn.compose.ColumnTransformer.get_params"><code class="xref py py-obj docutils literal"><span class="pre">get_params</span></code></a>([deep])</td>
<td>Get parameters for this estimator.</td>
</tr>
<tr class="row-odd"><td><a class="reference internal" href="#sklearn.compose.ColumnTransformer.set_params" title="sklearn.compose.ColumnTransformer.set_params"><code class="xref py py-obj docutils literal"><span class="pre">set_params</span></code></a>(**kwargs)</td>
<td>Set the parameters of this estimator.</td>
</tr>
<tr class="row-even"><td><a class="reference internal" href="#sklearn.compose.ColumnTransformer.transform" title="sklearn.compose.ColumnTransformer.transform"><code class="xref py py-obj docutils literal"><span class="pre">transform</span></code></a>(X)</td>
<td>Transform X separately by each transformer, concatenate results.</td>
</tr>
</tbody>
</table>
<dl class="method">
<dt id="sklearn.compose.ColumnTransformer.__init__">
<code class="descname">__init__</code><span class="sig-paren">(</span><em>transformers</em>, <em>remainder='passthrough'</em>, <em>n_jobs=1</em>, <em>transformer_weights=None</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/2d7cd0c/sklearn/compose/_column_transformer.py#L133"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.compose.ColumnTransformer.__init__" title="Permalink to this definition">¶</a></dt>
<dd></dd></dl>
<dl class="method">
<dt id="sklearn.compose.ColumnTransformer.fit">
<code class="descname">fit</code><span class="sig-paren">(</span><em>X</em>, <em>y=None</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/2d7cd0c/sklearn/compose/_column_transformer.py#L343"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.compose.ColumnTransformer.fit" title="Permalink to this definition">¶</a></dt>
<dd><p>Fit all transformers using X.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><p class="first"><strong>X</strong> : array-like or DataFrame of shape [n_samples, n_features]</p>
<blockquote>
<div><p>Input data, of which specified subsets are used to fit the
transformers.</p>
</div></blockquote>
<p><strong>y</strong> : array-like, shape (n_samples, …), optional</p>
<blockquote>
<div><p>Targets for supervised learning.</p>
</div></blockquote>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>self</strong> : ColumnTransformer</p>
<blockquote class="last">
<div><p>This estimator</p>
</div></blockquote>
</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="method">
<dt id="sklearn.compose.ColumnTransformer.fit_transform">
<code class="descname">fit_transform</code><span class="sig-paren">(</span><em>X</em>, <em>y=None</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/2d7cd0c/sklearn/compose/_column_transformer.py#L369"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.compose.ColumnTransformer.fit_transform" title="Permalink to this definition">¶</a></dt>
<dd><p>Fit all transformers, transform the data and concatenate results.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><p class="first"><strong>X</strong> : array-like or DataFrame of shape [n_samples, n_features]</p>
<blockquote>
<div><p>Input data, of which specified subsets are used to fit the
transformers.</p>
</div></blockquote>
<p><strong>y</strong> : array-like, shape (n_samples, …), optional</p>
<blockquote>
<div><p>Targets for supervised learning.</p>
</div></blockquote>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>X_t</strong> : array-like or sparse matrix, shape (n_samples, sum_n_components)</p>
<blockquote class="last">
<div><p>hstack of results of transformers. sum_n_components is the
sum of n_components (output dimension) over transformers. If
any result is a sparse matrix, everything will be converted to
sparse matrices.</p>
</div></blockquote>
</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="method">
<dt id="sklearn.compose.ColumnTransformer.get_feature_names">
<code class="descname">get_feature_names</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/2d7cd0c/sklearn/compose/_column_transformer.py#L261"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.compose.ColumnTransformer.get_feature_names" title="Permalink to this definition">¶</a></dt>
<dd><p>Get feature names from all transformers.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>feature_names</strong> : list of strings</p>
<blockquote class="last">
<div><p>Names of the features produced by transform.</p>
</div></blockquote>
</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="method">
<dt id="sklearn.compose.ColumnTransformer.get_params">
<code class="descname">get_params</code><span class="sig-paren">(</span><em>deep=True</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/2d7cd0c/sklearn/compose/_column_transformer.py#L156"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.compose.ColumnTransformer.get_params" title="Permalink to this definition">¶</a></dt>
<dd><p>Get parameters for this estimator.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><p class="first"><strong>deep</strong> : boolean, optional</p>
<blockquote>
<div><p>If True, will return the parameters for this estimator and
contained subobjects that are estimators.</p>
</div></blockquote>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>params</strong> : mapping of string to any</p>
<blockquote class="last">
<div><p>Parameter names mapped to their values.</p>
</div></blockquote>
</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="attribute">
<dt id="sklearn.compose.ColumnTransformer.named_transformers_">
<code class="descname">named_transformers_</code><a class="headerlink" href="#sklearn.compose.ColumnTransformer.named_transformers_" title="Permalink to this definition">¶</a></dt>
<dd><p>Access the fitted transformer by name.</p>
<p>Read-only attribute to access any transformer by given name.
Keys are transformer names and values are the fitted transformer
objects.</p>
</dd></dl>
<dl class="method">
<dt id="sklearn.compose.ColumnTransformer.set_params">
<code class="descname">set_params</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/2d7cd0c/sklearn/compose/_column_transformer.py#L172"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.compose.ColumnTransformer.set_params" title="Permalink to this definition">¶</a></dt>
<dd><p>Set the parameters of this estimator.</p>
<p>Valid parameter keys can be listed with <code class="docutils literal"><span class="pre">get_params()</span></code>.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Returns:</th><td class="field-body">self</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="method">
<dt id="sklearn.compose.ColumnTransformer.transform">
<code class="descname">transform</code><span class="sig-paren">(</span><em>X</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/2d7cd0c/sklearn/compose/_column_transformer.py#L416"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.compose.ColumnTransformer.transform" title="Permalink to this definition">¶</a></dt>
<dd><p>Transform X separately by each transformer, concatenate results.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><p class="first"><strong>X</strong> : array-like or DataFrame of shape [n_samples, n_features]</p>
<blockquote>
<div><p>The data to be transformed by subset.</p>
</div></blockquote>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>X_t</strong> : array-like or sparse matrix, shape (n_samples, sum_n_components)</p>
<blockquote class="last">
<div><p>hstack of results of transformers. sum_n_components is the
sum of n_components (output dimension) over transformers. If
any result is a sparse matrix, everything will be converted to
sparse matrices.</p>
</div></blockquote>
</td>
</tr>
</tbody>
</table>
</dd></dl>
</dd></dl>
<div class="section" id="examples-using-sklearn-compose-columntransformer">
<h2>Examples using <code class="docutils literal"><span class="pre">sklearn.compose.ColumnTransformer</span></code><a class="headerlink" href="#examples-using-sklearn-compose-columntransformer" title="Permalink to this headline">¶</a></h2>
<div class="sphx-glr-thumbcontainer" tooltip="Datasets can often contain components of that require different feature extraction and processi..."><div class="figure" id="id1">
<img alt="../../_images/sphx_glr_column_transformer_thumb.png" src="../../_images/sphx_glr_column_transformer_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/column_transformer.html#sphx-glr-auto-examples-column-transformer-py"><span class="std std-ref">Column Transformer with Heterogeneous Data Sources</span></a></span></p>
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