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<ul>
<li><a class="reference internal" href="#"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.compose</span></code>.ColumnTransformer</a><ul>
<li><a class="reference internal" href="#sklearn.compose.ColumnTransformer"><code class="docutils literal notranslate"><span class="pre">ColumnTransformer</span></code></a><ul>
<li><a class="reference internal" href="#sklearn.compose.ColumnTransformer.fit"><code class="docutils literal notranslate"><span class="pre">ColumnTransformer.fit</span></code></a></li>
<li><a class="reference internal" href="#sklearn.compose.ColumnTransformer.fit_transform"><code class="docutils literal notranslate"><span class="pre">ColumnTransformer.fit_transform</span></code></a></li>
<li><a class="reference internal" href="#sklearn.compose.ColumnTransformer.get_feature_names_out"><code class="docutils literal notranslate"><span class="pre">ColumnTransformer.get_feature_names_out</span></code></a></li>
<li><a class="reference internal" href="#sklearn.compose.ColumnTransformer.get_metadata_routing"><code class="docutils literal notranslate"><span class="pre">ColumnTransformer.get_metadata_routing</span></code></a></li>
<li><a class="reference internal" href="#sklearn.compose.ColumnTransformer.get_params"><code class="docutils literal notranslate"><span class="pre">ColumnTransformer.get_params</span></code></a></li>
<li><a class="reference internal" href="#sklearn.compose.ColumnTransformer.named_transformers_"><code class="docutils literal notranslate"><span class="pre">ColumnTransformer.named_transformers_</span></code></a></li>
<li><a class="reference internal" href="#sklearn.compose.ColumnTransformer.set_output"><code class="docutils literal notranslate"><span class="pre">ColumnTransformer.set_output</span></code></a></li>
<li><a class="reference internal" href="#sklearn.compose.ColumnTransformer.set_params"><code class="docutils literal notranslate"><span class="pre">ColumnTransformer.set_params</span></code></a></li>
<li><a class="reference internal" href="#sklearn.compose.ColumnTransformer.transform"><code class="docutils literal notranslate"><span class="pre">ColumnTransformer.transform</span></code></a></li>
</ul>
</li>
<li><a class="reference internal" href="#examples-using-sklearn-compose-columntransformer">Examples using <code class="docutils literal notranslate"><span class="pre">sklearn.compose.ColumnTransformer</span></code></a></li>
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<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 notranslate"><span class="pre">sklearn.compose</span></code></a>.ColumnTransformer<a class="headerlink" href="#sklearn-compose-columntransformer" title="Permalink to this heading">¶</a></h1>
<dl class="py class">
<dt class="sig sig-object py" id="sklearn.compose.ColumnTransformer">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.compose.</span></span><span class="sig-name descname"><span class="pre">ColumnTransformer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">transformers</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">remainder</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'drop'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sparse_threshold</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</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">transformer_weights</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">verbose</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">verbose_feature_names_out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/702316c27/sklearn/compose/_column_transformer.py#L42"><span class="viewcode-link"><span class="pre">[source]</span></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>This estimator allows different columns or column subsets of the input
to be transformed separately and the features generated by each transformer
will be concatenated to form 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 added">New in version 0.20.</span></p>
</div>
<dl class="field-list">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><dl>
<dt><strong>transformers</strong><span class="classifier">list of tuples</span></dt><dd><p>List of (name, transformer, columns) tuples specifying the
transformer objects to be applied to subsets of the data.</p>
<dl class="simple">
<dt>name<span class="classifier">str</span></dt><dd><p>Like in Pipeline and FeatureUnion, this allows the transformer and
its parameters to be set using <code class="docutils literal notranslate"><span class="pre">set_params</span></code> and searched in grid
search.</p>
</dd>
<dt>transformer<span class="classifier">{‘drop’, ‘passthrough’} or estimator</span></dt><dd><p>Estimator must support <a class="reference internal" href="../../glossary.html#term-fit"><span class="xref std std-term">fit</span></a> and <a class="reference internal" href="../../glossary.html#term-transform"><span class="xref std std-term">transform</span></a>.
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>columns<span class="classifier">str, array-like of str, int, array-like of int, array-like of bool, slice or callable</span></dt><dd><p>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 notranslate"><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.
A callable is passed the input data <code class="docutils literal notranslate"><span class="pre">X</span></code> and can return any of the
above. To select multiple columns by name or dtype, you can use
<a class="reference internal" href="sklearn.compose.make_column_selector.html#sklearn.compose.make_column_selector" title="sklearn.compose.make_column_selector"><code class="xref py py-obj docutils literal notranslate"><span class="pre">make_column_selector</span></code></a>.</p>
</dd>
</dl>
</dd>
<dt><strong>remainder</strong><span class="classifier">{‘drop’, ‘passthrough’} or estimator, default=’drop’</span></dt><dd><p>By default, only the specified columns in <code class="docutils literal notranslate"><span class="pre">transformers</span></code> are
transformed and combined in the output, and the non-specified
columns are dropped. (default of <code class="docutils literal notranslate"><span class="pre">'drop'</span></code>).
By specifying <code class="docutils literal notranslate"><span class="pre">remainder='passthrough'</span></code>, all remaining columns that
were not specified in <code class="docutils literal notranslate"><span class="pre">transformers</span></code>, but present in the data passed
to <code class="docutils literal notranslate"><span class="pre">fit</span></code> will be automatically passed through. This subset of columns
is concatenated with the output of the transformers. For dataframes,
extra columns not seen during <code class="docutils literal notranslate"><span class="pre">fit</span></code> will be excluded from the output
of <code class="docutils literal notranslate"><span class="pre">transform</span></code>.
By setting <code class="docutils literal notranslate"><span class="pre">remainder</span></code> to be an estimator, the remaining
non-specified columns will use the <code class="docutils literal notranslate"><span class="pre">remainder</span></code> estimator. The
estimator must support <a class="reference internal" href="../../glossary.html#term-fit"><span class="xref std std-term">fit</span></a> and <a class="reference internal" href="../../glossary.html#term-transform"><span class="xref std std-term">transform</span></a>.
Note that using this feature requires that the DataFrame columns
input at <a class="reference internal" href="../../glossary.html#term-fit"><span class="xref std std-term">fit</span></a> and <a class="reference internal" href="../../glossary.html#term-transform"><span class="xref std std-term">transform</span></a> have identical order.</p>
</dd>
<dt><strong>sparse_threshold</strong><span class="classifier">float, default=0.3</span></dt><dd><p>If the output of the different transformers contains sparse matrices,
these will be stacked as a sparse matrix if the overall density is
lower than this value. Use <code class="docutils literal notranslate"><span class="pre">sparse_threshold=0</span></code> to always return
dense. When the transformed output consists of all dense data, the
stacked result will be dense, and this keyword will be ignored.</p>
</dd>
<dt><strong>n_jobs</strong><span class="classifier">int, default=None</span></dt><dd><p>Number of jobs to run in parallel.
<code class="docutils literal notranslate"><span class="pre">None</span></code> means 1 unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.3.1)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context.
<code class="docutils literal notranslate"><span class="pre">-1</span></code> means using all processors. See <a class="reference internal" href="../../glossary.html#term-n_jobs"><span class="xref std std-term">Glossary</span></a>
for more details.</p>
</dd>
<dt><strong>transformer_weights</strong><span class="classifier">dict, default=None</span></dt><dd><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>
</dd>
<dt><strong>verbose</strong><span class="classifier">bool, default=False</span></dt><dd><p>If True, the time elapsed while fitting each transformer will be
printed as it is completed.</p>
</dd>
<dt><strong>verbose_feature_names_out</strong><span class="classifier">bool, default=True</span></dt><dd><p>If True, <a class="reference internal" href="#sklearn.compose.ColumnTransformer.get_feature_names_out" title="sklearn.compose.ColumnTransformer.get_feature_names_out"><code class="xref py py-meth docutils literal notranslate"><span class="pre">ColumnTransformer.get_feature_names_out</span></code></a> will prefix
all feature names with the name of the transformer that generated that
feature.
If False, <a class="reference internal" href="#sklearn.compose.ColumnTransformer.get_feature_names_out" title="sklearn.compose.ColumnTransformer.get_feature_names_out"><code class="xref py py-meth docutils literal notranslate"><span class="pre">ColumnTransformer.get_feature_names_out</span></code></a> will not
prefix any feature names and will error if feature names are not
unique.</p>
<div class="versionadded">
<p><span class="versionmodified added">New in version 1.0.</span></p>
</div>
</dd>
</dl>
</dd>
<dt class="field-even">Attributes<span class="colon">:</span></dt>
<dd class="field-even"><dl>
<dt><strong>transformers_</strong><span class="classifier">list</span></dt><dd><p>The collection of fitted transformers as tuples of
(name, fitted_transformer, column). <code class="docutils literal notranslate"><span class="pre">fitted_transformer</span></code> can be an
estimator, ‘drop’, or ‘passthrough’. In case there were no columns
selected, this will be the unfitted transformer.
If there are remaining columns, the final element is a tuple of the
form:
(‘remainder’, transformer, remaining_columns) corresponding to the
<code class="docutils literal notranslate"><span class="pre">remainder</span></code> parameter. If there are remaining columns, then
<code class="docutils literal notranslate"><span class="pre">len(transformers_)==len(transformers)+1</span></code>, otherwise
<code class="docutils literal notranslate"><span class="pre">len(transformers_)==len(transformers)</span></code>.</p>
</dd>
<dt><a class="reference internal" href="#sklearn.compose.ColumnTransformer.named_transformers_" title="sklearn.compose.ColumnTransformer.named_transformers_"><code class="xref py py-obj docutils literal notranslate"><span class="pre">named_transformers_</span></code></a><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>Access the fitted transformer by name.</p>
</dd>
<dt><strong>sparse_output_</strong><span class="classifier">bool</span></dt><dd><p>Boolean flag indicating whether the output of <code class="docutils literal notranslate"><span class="pre">transform</span></code> is a
sparse matrix or a dense numpy array, which depends on the output
of the individual transformers and the <code class="docutils literal notranslate"><span class="pre">sparse_threshold</span></code> keyword.</p>
</dd>
<dt><strong>output_indices_</strong><span class="classifier">dict</span></dt><dd><p>A dictionary from each transformer name to a slice, where the slice
corresponds to indices in the transformed output. This is useful to
inspect which transformer is responsible for which transformed
feature(s).</p>
<div class="versionadded">
<p><span class="versionmodified added">New in version 1.0.</span></p>
</div>
</dd>
<dt><strong>n_features_in_</strong><span class="classifier">int</span></dt><dd><p>Number of features seen during <a class="reference internal" href="../../glossary.html#term-fit"><span class="xref std std-term">fit</span></a>. Only defined if the
underlying transformers expose such an attribute when fit.</p>
<div class="versionadded">
<p><span class="versionmodified added">New in version 0.24.</span></p>
</div>
</dd>
</dl>
</dd>
</dl>
<div class="admonition seealso">
<p class="admonition-title">See also</p>
<dl class="simple">
<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 notranslate"><span class="pre">make_column_transformer</span></code></a></dt><dd><p>Convenience function for combining the outputs of multiple transformer objects applied to column subsets of the original feature space.</p>
</dd>
<dt><a class="reference internal" href="sklearn.compose.make_column_selector.html#sklearn.compose.make_column_selector" title="sklearn.compose.make_column_selector"><code class="xref py py-obj docutils literal notranslate"><span class="pre">make_column_selector</span></code></a></dt><dd><p>Convenience function for selecting columns based on datatype or the columns name with a regex pattern.</p>
</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 <code class="docutils literal notranslate"><span class="pre">transformers</span></code> list.
Columns of the original feature matrix that are not specified are
dropped from the resulting transformed feature matrix, unless specified
in the <code class="docutils literal notranslate"><span class="pre">passthrough</span></code> keyword. Those columns specified with <code class="docutils literal notranslate"><span class="pre">passthrough</span></code>
are added at the right to the output of the transformers.</p>
<p class="rubric">Examples</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="gp">>>> </span><span class="kn">from</span> <span class="nn">sklearn.compose</span> <span class="kn">import</span> <span class="n">ColumnTransformer</span>
<span class="gp">>>> </span><span class="kn">from</span> <span class="nn">sklearn.preprocessing</span> <span class="kn">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><a class="reference internal" href="#sklearn.compose.ColumnTransformer" title="sklearn.compose.ColumnTransformer"><code class="xref py py-class docutils literal notranslate"><span class="pre">ColumnTransformer</span></code></a> can be configured with a transformer that requires
a 1d array by setting the column to a string:</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">from</span> <span class="nn">sklearn.feature_extraction</span> <span class="kn">import</span> <span class="n">FeatureHasher</span>
<span class="gp">>>> </span><span class="kn">from</span> <span class="nn">sklearn.preprocessing</span> <span class="kn">import</span> <span class="n">MinMaxScaler</span>
<span class="gp">>>> </span><span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
<span class="gp">>>> </span><span class="n">X</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span>
<span class="gp">... </span> <span class="s2">"documents"</span><span class="p">:</span> <span class="p">[</span><span class="s2">"First item"</span><span class="p">,</span> <span class="s2">"second one here"</span><span class="p">,</span> <span class="s2">"Is this the last?"</span><span class="p">],</span>
<span class="gp">... </span> <span class="s2">"width"</span><span class="p">:</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">],</span>
<span class="gp">... </span><span class="p">})</span>
<span class="gp">>>> </span><span class="c1"># "documents" is a string which configures ColumnTransformer to</span>
<span class="gp">>>> </span><span class="c1"># pass the documents column as a 1d array to the FeatureHasher</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">"text_preprocess"</span><span class="p">,</span> <span class="n">FeatureHasher</span><span class="p">(</span><span class="n">input_type</span><span class="o">=</span><span class="s2">"string"</span><span class="p">),</span> <span class="s2">"documents"</span><span class="p">),</span>
<span class="gp">... </span> <span class="p">(</span><span class="s2">"num_preprocess"</span><span class="p">,</span> <span class="n">MinMaxScaler</span><span class="p">(),</span> <span class="p">[</span><span class="s2">"width"</span><span class="p">])])</span>
<span class="gp">>>> </span><span class="n">X_trans</span> <span class="o">=</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>
</pre></div>
</div>
<p class="rubric">Methods</p>
<table class="autosummary longtable docutils align-default">
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="#sklearn.compose.ColumnTransformer.fit" title="sklearn.compose.ColumnTransformer.fit"><code class="xref py py-obj docutils literal notranslate"><span class="pre">fit</span></code></a>(X[, y])</p></td>
<td><p>Fit all transformers using X.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="#sklearn.compose.ColumnTransformer.fit_transform" title="sklearn.compose.ColumnTransformer.fit_transform"><code class="xref py py-obj docutils literal notranslate"><span class="pre">fit_transform</span></code></a>(X[, y])</p></td>
<td><p>Fit all transformers, transform the data and concatenate results.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="#sklearn.compose.ColumnTransformer.get_feature_names_out" title="sklearn.compose.ColumnTransformer.get_feature_names_out"><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_feature_names_out</span></code></a>([input_features])</p></td>
<td><p>Get output feature names for transformation.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="#sklearn.compose.ColumnTransformer.get_metadata_routing" title="sklearn.compose.ColumnTransformer.get_metadata_routing"><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_metadata_routing</span></code></a>()</p></td>
<td><p>Get metadata routing of this object.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="#sklearn.compose.ColumnTransformer.get_params" title="sklearn.compose.ColumnTransformer.get_params"><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_params</span></code></a>([deep])</p></td>
<td><p>Get parameters for this estimator.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="#sklearn.compose.ColumnTransformer.set_output" title="sklearn.compose.ColumnTransformer.set_output"><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_output</span></code></a>(*[, transform])</p></td>
<td><p>Set the output container when <code class="docutils literal notranslate"><span class="pre">"transform"</span></code> and <code class="docutils literal notranslate"><span class="pre">"fit_transform"</span></code> are called.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="#sklearn.compose.ColumnTransformer.set_params" title="sklearn.compose.ColumnTransformer.set_params"><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_params</span></code></a>(**kwargs)</p></td>
<td><p>Set the parameters of this estimator.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="#sklearn.compose.ColumnTransformer.transform" title="sklearn.compose.ColumnTransformer.transform"><code class="xref py py-obj docutils literal notranslate"><span class="pre">transform</span></code></a>(X)</p></td>
<td><p>Transform X separately by each transformer, concatenate results.</p></td>
</tr>
</tbody>
</table>
<dl class="py method">
<dt class="sig sig-object py" id="sklearn.compose.ColumnTransformer.fit">
<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</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/scikit-learn/scikit-learn/blob/702316c27/sklearn/compose/_column_transformer.py#L689"><span class="viewcode-link"><span class="pre">[source]</span></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>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>X</strong><span class="classifier">{array-like, dataframe} of shape (n_samples, n_features)</span></dt><dd><p>Input data, of which specified subsets are used to fit the
transformers.</p>
</dd>
<dt><strong>y</strong><span class="classifier">array-like of shape (n_samples,…), default=None</span></dt><dd><p>Targets for supervised learning.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><dl class="simple">
<dt><strong>self</strong><span class="classifier">ColumnTransformer</span></dt><dd><p>This estimator.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="sklearn.compose.ColumnTransformer.fit_transform">
<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</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/scikit-learn/scikit-learn/blob/702316c27/sklearn/compose/_column_transformer.py#L711"><span class="viewcode-link"><span class="pre">[source]</span></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>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>X</strong><span class="classifier">{array-like, dataframe} of shape (n_samples, n_features)</span></dt><dd><p>Input data, of which specified subsets are used to fit the
transformers.</p>
</dd>
<dt><strong>y</strong><span class="classifier">array-like of shape (n_samples,), default=None</span></dt><dd><p>Targets for supervised learning.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><dl class="simple">
<dt><strong>X_t</strong><span class="classifier">{array-like, sparse matrix} of shape (n_samples, sum_n_components)</span></dt><dd><p>Horizontally stacked 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>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="sklearn.compose.ColumnTransformer.get_feature_names_out">
<span class="sig-name descname"><span class="pre">get_feature_names_out</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_features</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/scikit-learn/scikit-learn/blob/702316c27/sklearn/compose/_column_transformer.py#L499"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.compose.ColumnTransformer.get_feature_names_out" title="Permalink to this definition">¶</a></dt>
<dd><p>Get output feature names for transformation.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>input_features</strong><span class="classifier">array-like of str or None, default=None</span></dt><dd><p>Input features.</p>
<ul class="simple">
<li><p>If <code class="docutils literal notranslate"><span class="pre">input_features</span></code> is <code class="docutils literal notranslate"><span class="pre">None</span></code>, then <code class="docutils literal notranslate"><span class="pre">feature_names_in_</span></code> is
used as feature names in. If <code class="docutils literal notranslate"><span class="pre">feature_names_in_</span></code> is not defined,
then the following input feature names are generated:
<code class="docutils literal notranslate"><span class="pre">["x0",</span> <span class="pre">"x1",</span> <span class="pre">...,</span> <span class="pre">"x(n_features_in_</span> <span class="pre">-</span> <span class="pre">1)"]</span></code>.</p></li>
<li><p>If <code class="docutils literal notranslate"><span class="pre">input_features</span></code> is an array-like, then <code class="docutils literal notranslate"><span class="pre">input_features</span></code> must
match <code class="docutils literal notranslate"><span class="pre">feature_names_in_</span></code> if <code class="docutils literal notranslate"><span class="pre">feature_names_in_</span></code> is defined.</p></li>
</ul>
</dd>
</dl>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><dl class="simple">
<dt><strong>feature_names_out</strong><span class="classifier">ndarray of str objects</span></dt><dd><p>Transformed feature names.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="sklearn.compose.ColumnTransformer.get_metadata_routing">
<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/702316c27/sklearn/utils/_metadata_requests.py#L1287"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.compose.ColumnTransformer.get_metadata_routing" title="Permalink to this definition">¶</a></dt>
<dd><p>Get metadata routing of this object.</p>
<p>Please check <a class="reference internal" href="../../metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
mechanism works.</p>
<dl class="field-list simple">
<dt class="field-odd">Returns<span class="colon">:</span></dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>routing</strong><span class="classifier">MetadataRequest</span></dt><dd><p>A <a class="reference internal" href="sklearn.utils.metadata_routing.MetadataRequest.html#sklearn.utils.metadata_routing.MetadataRequest" title="sklearn.utils.metadata_routing.MetadataRequest"><code class="xref py py-class docutils literal notranslate"><span class="pre">MetadataRequest</span></code></a> encapsulating
routing information.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="sklearn.compose.ColumnTransformer.get_params">
<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/702316c27/sklearn/compose/_column_transformer.py#L314"><span class="viewcode-link"><span class="pre">[source]</span></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>
<p>Returns the parameters given in the constructor as well as the
estimators contained within the <code class="docutils literal notranslate"><span class="pre">transformers</span></code> of the
<code class="docutils literal notranslate"><span class="pre">ColumnTransformer</span></code>.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>deep</strong><span class="classifier">bool, default=True</span></dt><dd><p>If True, will return the parameters for this estimator and
contained subobjects that are estimators.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><dl class="simple">
<dt><strong>params</strong><span class="classifier">dict</span></dt><dd><p>Parameter names mapped to their values.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py property">
<dt class="sig sig-object py" id="sklearn.compose.ColumnTransformer.named_transformers_">
<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">named_transformers_</span></span><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="py method">
<dt class="sig sig-object py" id="sklearn.compose.ColumnTransformer.set_output">
<span class="sig-name descname"><span class="pre">set_output</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">transform</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/scikit-learn/scikit-learn/blob/702316c27/sklearn/compose/_column_transformer.py#L277"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.compose.ColumnTransformer.set_output" title="Permalink to this definition">¶</a></dt>
<dd><p>Set the output container when <code class="docutils literal notranslate"><span class="pre">"transform"</span></code> and <code class="docutils literal notranslate"><span class="pre">"fit_transform"</span></code> are called.</p>
<p>Calling <code class="docutils literal notranslate"><span class="pre">set_output</span></code> will set the output of all estimators in <code class="docutils literal notranslate"><span class="pre">transformers</span></code>
and <code class="docutils literal notranslate"><span class="pre">transformers_</span></code>.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>transform</strong><span class="classifier">{“default”, “pandas”}, default=None</span></dt><dd><p>Configure output of <code class="docutils literal notranslate"><span class="pre">transform</span></code> and <code class="docutils literal notranslate"><span class="pre">fit_transform</span></code>.</p>
<ul class="simple">
<li><p><code class="docutils literal notranslate"><span class="pre">"default"</span></code>: Default output format of a transformer</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">"pandas"</span></code>: DataFrame output</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">None</span></code>: Transform configuration is unchanged</p></li>
</ul>
</dd>
</dl>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><dl class="simple">
<dt><strong>self</strong><span class="classifier">estimator instance</span></dt><dd><p>Estimator instance.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="sklearn.compose.ColumnTransformer.set_params">
<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/702316c27/sklearn/compose/_column_transformer.py#L334"><span class="viewcode-link"><span class="pre">[source]</span></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 notranslate"><span class="pre">get_params()</span></code>. Note that you
can directly set the parameters of the estimators contained in
<code class="docutils literal notranslate"><span class="pre">transformers</span></code> of <code class="docutils literal notranslate"><span class="pre">ColumnTransformer</span></code>.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>**kwargs</strong><span class="classifier">dict</span></dt><dd><p>Estimator parameters.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><dl class="simple">
<dt><strong>self</strong><span class="classifier">ColumnTransformer</span></dt><dd><p>This estimator.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="sklearn.compose.ColumnTransformer.transform">
<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/702316c27/sklearn/compose/_column_transformer.py#L771"><span class="viewcode-link"><span class="pre">[source]</span></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>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>X</strong><span class="classifier">{array-like, dataframe} of shape (n_samples, n_features)</span></dt><dd><p>The data to be transformed by subset.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><dl class="simple">
<dt><strong>X_t</strong><span class="classifier">{array-like, sparse matrix} of shape (n_samples, sum_n_components)</span></dt><dd><p>Horizontally stacked 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>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
</dd></dl>
<section id="examples-using-sklearn-compose-columntransformer">
<h2>Examples using <code class="docutils literal notranslate"><span class="pre">sklearn.compose.ColumnTransformer</span></code><a class="headerlink" href="#examples-using-sklearn-compose-columntransformer" title="Permalink to this heading">¶</a></h2>
<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="We are pleased to announce the release of scikit-learn 1.2! Many bug fixes and improvements wer..."><img alt="" src="../../_images/sphx_glr_plot_release_highlights_1_2_0_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/release_highlights/plot_release_highlights_1_2_0.html#sphx-glr-auto-examples-release-highlights-plot-release-highlights-1-2-0-py"><span class="std std-ref">Release Highlights for scikit-learn 1.2</span></a></p>
<div class="sphx-glr-thumbnail-title">Release Highlights for scikit-learn 1.2</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="We are pleased to announce the release of scikit-learn 1.1! Many bug fixes and improvements wer..."><img alt="" src="../../_images/sphx_glr_plot_release_highlights_1_1_0_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/release_highlights/plot_release_highlights_1_1_0.html#sphx-glr-auto-examples-release-highlights-plot-release-highlights-1-1-0-py"><span class="std std-ref">Release Highlights for scikit-learn 1.1</span></a></p>
<div class="sphx-glr-thumbnail-title">Release Highlights for scikit-learn 1.1</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="We are very pleased to announce the release of scikit-learn 1.0! The library has been stable fo..."><img alt="" src="../../_images/sphx_glr_plot_release_highlights_1_0_0_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/release_highlights/plot_release_highlights_1_0_0.html#sphx-glr-auto-examples-release-highlights-plot-release-highlights-1-0-0-py"><span class="std std-ref">Release Highlights for scikit-learn 1.0</span></a></p>
<div class="sphx-glr-thumbnail-title">Release Highlights for scikit-learn 1.0</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This notebook introduces different strategies to leverage time-related features for a bike shar..."><img alt="" src="../../_images/sphx_glr_plot_cyclical_feature_engineering_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/applications/plot_cyclical_feature_engineering.html#sphx-glr-auto-examples-applications-plot-cyclical-feature-engineering-py"><span class="std std-ref">Time-related feature engineering</span></a></p>
<div class="sphx-glr-thumbnail-title">Time-related feature engineering</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example illustrates the use of log-linear Poisson regression on the French Motor Third-Par..."><img alt="" src="../../_images/sphx_glr_plot_poisson_regression_non_normal_loss_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/linear_model/plot_poisson_regression_non_normal_loss.html#sphx-glr-auto-examples-linear-model-plot-poisson-regression-non-normal-loss-py"><span class="std std-ref">Poisson regression and non-normal loss</span></a></p>
<div class="sphx-glr-thumbnail-title">Poisson regression and non-normal loss</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example illustrates the use of Poisson, Gamma and Tweedie regression on the French Motor T..."><img alt="" src="../../_images/sphx_glr_plot_tweedie_regression_insurance_claims_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/linear_model/plot_tweedie_regression_insurance_claims.html#sphx-glr-auto-examples-linear-model-plot-tweedie-regression-insurance-claims-py"><span class="std std-ref">Tweedie regression on insurance claims</span></a></p>
<div class="sphx-glr-thumbnail-title">Tweedie regression on insurance claims</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Partial dependence plots show the dependence between the target function [2]_ and a set of feat..."><img alt="" src="../../_images/sphx_glr_plot_partial_dependence_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/inspection/plot_partial_dependence.html#sphx-glr-auto-examples-inspection-plot-partial-dependence-py"><span class="std std-ref">Partial Dependence and Individual Conditional Expectation Plots</span></a></p>
<div class="sphx-glr-thumbnail-title">Partial Dependence and Individual Conditional Expectation Plots</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="In this example, we will compare the impurity-based feature importance of RandomForestClassifie..."><img alt="" src="../../_images/sphx_glr_plot_permutation_importance_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/inspection/plot_permutation_importance.html#sphx-glr-auto-examples-inspection-plot-permutation-importance-py"><span class="std std-ref">Permutation Importance vs Random Forest Feature Importance (MDI)</span></a></p>
<div class="sphx-glr-thumbnail-title">Permutation Importance vs Random Forest Feature Importance (MDI)</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="The default configuration for displaying a pipeline in a Jupyter Notebook is 'diagram' where se..."><img alt="" src="../../_images/sphx_glr_plot_pipeline_display_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/miscellaneous/plot_pipeline_display.html#sphx-glr-auto-examples-miscellaneous-plot-pipeline-display-py"><span class="std std-ref">Displaying Pipelines</span></a></p>
<div class="sphx-glr-thumbnail-title">Displaying Pipelines</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example will demonstrate the set_output API to configure transformers to output pandas Dat..."><img alt="" src="../../_images/sphx_glr_plot_set_output_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/miscellaneous/plot_set_output.html#sphx-glr-auto-examples-miscellaneous-plot-set-output-py"><span class="std std-ref">Introducing the set_output API</span></a></p>
<div class="sphx-glr-thumbnail-title">Introducing the set_output API</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Datasets can often contain components that require different feature extraction and processing ..."><img alt="" src="../../_images/sphx_glr_plot_column_transformer_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/compose/plot_column_transformer.html#sphx-glr-auto-examples-compose-plot-column-transformer-py"><span class="std std-ref">Column Transformer with Heterogeneous Data Sources</span></a></p>
<div class="sphx-glr-thumbnail-title">Column Transformer with Heterogeneous Data Sources</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example illustrates how to apply different preprocessing and feature extraction pipelines ..."><img alt="" src="../../_images/sphx_glr_plot_column_transformer_mixed_types_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/compose/plot_column_transformer_mixed_types.html#sphx-glr-auto-examples-compose-plot-column-transformer-mixed-types-py"><span class="std std-ref">Column Transformer with Mixed Types</span></a></p>
<div class="sphx-glr-thumbnail-title">Column Transformer with Mixed Types</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="The TargetEncoder uses the value of the target to encode each categorical feature. In this exam..."><img alt="" src="../../_images/sphx_glr_plot_target_encoder_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/preprocessing/plot_target_encoder.html#sphx-glr-auto-examples-preprocessing-plot-target-encoder-py"><span class="std std-ref">Comparing Target Encoder with Other Encoders</span></a></p>
<div class="sphx-glr-thumbnail-title">Comparing Target Encoder with Other Encoders</div>
</div></div><div class="clearer"></div></section>
</section>
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