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<li><a class="reference internal" href="#"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.compose</span></code>.make_column_transformer</a><ul>
<li><a class="reference internal" href="#sklearn.compose.make_column_transformer"><code class="docutils literal notranslate"><span class="pre">make_column_transformer</span></code></a></li>
<li><a class="reference internal" href="#examples-using-sklearn-compose-make-column-transformer">Examples using <code class="docutils literal notranslate"><span class="pre">sklearn.compose.make_column_transformer</span></code></a></li>
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<section id="sklearn-compose-make-column-transformer">
<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>.make_column_transformer<a class="headerlink" href="#sklearn-compose-make-column-transformer" title="Permalink to this heading">¶</a></h1>
<dl class="py function">
<dt class="sig sig-object py" id="sklearn.compose.make_column_transformer">
<span class="sig-prename descclassname"><span class="pre">sklearn.compose.</span></span><span class="sig-name descname"><span class="pre">make_column_transformer</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">transformers</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">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/3f89022fa/sklearn/compose/_column_transformer.py#L967"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.compose.make_column_transformer" title="Permalink to this definition">¶</a></dt>
<dd><p>Construct a ColumnTransformer from the given transformers.</p>
<p>This is a shorthand for the ColumnTransformer constructor; it does not
require, and does not permit, naming the transformers. Instead, they will
be given names automatically based on their types. It also does not allow
weighting with <code class="docutils literal notranslate"><span class="pre">transformer_weights</span></code>.</p>
<p>Read more in the <a class="reference internal" href="../compose.html#make-column-transformer"><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>*transformers</strong><span class="classifier">tuples</span></dt><dd><p>Tuples of the form (transformer, columns) specifying the
transformer objects to be applied to subsets of the data.</p>
<dl class="simple">
<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, slice, array-like of bool 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> will be automatically passed
through. This subset of columns is concatenated with the output of
the transformers.
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>.</p>
</dd>
<dt><strong>sparse_threshold</strong><span class="classifier">float, default=0.3</span></dt><dd><p>If the transformed output consists of a mix of sparse and dense data,
it will be stacked as a sparse matrix if the 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 sparse or all dense data,
the stacked result will be sparse or dense, respectively, 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 v1.4.dev0)"><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>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.html#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.html#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">Returns<span class="colon">:</span></dt>
<dd class="field-even"><dl class="simple">
<dt><strong>ct</strong><span class="classifier">ColumnTransformer</span></dt><dd><p>Returns a <a class="reference internal" href="sklearn.compose.ColumnTransformer.html#sklearn.compose.ColumnTransformer" title="sklearn.compose.ColumnTransformer"><code class="xref py py-class docutils literal notranslate"><span class="pre">ColumnTransformer</span></code></a> object.</p>
</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.ColumnTransformer.html#sklearn.compose.ColumnTransformer" title="sklearn.compose.ColumnTransformer"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ColumnTransformer</span></code></a></dt><dd><p>Class that allows combining the outputs of multiple transformer objects used on column subsets of the data into a single feature space.</p>
</dd>
</dl>
</div>
<p class="rubric">Examples</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.preprocessing</span> <span class="kn">import</span> <span class="n">StandardScaler</span><span class="p">,</span> <span class="n">OneHotEncoder</span>
<span class="gp">>>> </span><span class="kn">from</span> <span class="nn">sklearn.compose</span> <span class="kn">import</span> <span class="n">make_column_transformer</span>
<span class="gp">>>> </span><span class="n">make_column_transformer</span><span class="p">(</span>
<span class="gp">... </span> <span class="p">(</span><span class="n">StandardScaler</span><span class="p">(),</span> <span class="p">[</span><span class="s1">'numerical_column'</span><span class="p">]),</span>
<span class="gp">... </span> <span class="p">(</span><span class="n">OneHotEncoder</span><span class="p">(),</span> <span class="p">[</span><span class="s1">'categorical_column'</span><span class="p">]))</span>
<span class="go">ColumnTransformer(transformers=[('standardscaler', StandardScaler(...),</span>
<span class="go"> ['numerical_column']),</span>
<span class="go"> ('onehotencoder', OneHotEncoder(...),</span>
<span class="go"> ['categorical_column'])])</span>
</pre></div>
</div>
</dd></dl>
<section id="examples-using-sklearn-compose-make-column-transformer">
<h2>Examples using <code class="docutils literal notranslate"><span class="pre">sklearn.compose.make_column_transformer</span></code><a class="headerlink" href="#examples-using-sklearn-compose-make-column-transformer" 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 0.23! Many bug fixes and improvements we..."><img alt="" src="../../_images/sphx_glr_plot_release_highlights_0_23_0_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/release_highlights/plot_release_highlights_0_23_0.html#sphx-glr-auto-examples-release-highlights-plot-release-highlights-0-23-0-py"><span class="std std-ref">Release Highlights for scikit-learn 0.23</span></a></p>
<div class="sphx-glr-thumbnail-title">Release Highlights for scikit-learn 0.23</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="In this example, we will compare the training times and prediction performances of HistGradient..."><img alt="" src="../../_images/sphx_glr_plot_gradient_boosting_categorical_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/ensemble/plot_gradient_boosting_categorical.html#sphx-glr-auto-examples-ensemble-plot-gradient-boosting-categorical-py"><span class="std std-ref">Categorical Feature Support in Gradient Boosting</span></a></p>
<div class="sphx-glr-thumbnail-title">Categorical Feature Support in Gradient Boosting</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Stacking refers to a method to blend estimators. In this strategy, some estimators are individu..."><img alt="" src="../../_images/sphx_glr_plot_stack_predictors_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/ensemble/plot_stack_predictors.html#sphx-glr-auto-examples-ensemble-plot-stack-predictors-py"><span class="std std-ref">Combine predictors using stacking</span></a></p>
<div class="sphx-glr-thumbnail-title">Combine predictors using stacking</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="In linear models, the target value is modeled as a linear combination of the features (see the ..."><img alt="" src="../../_images/sphx_glr_plot_linear_model_coefficient_interpretation_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/inspection/plot_linear_model_coefficient_interpretation.html#sphx-glr-auto-examples-inspection-plot-linear-model-coefficient-interpretation-py"><span class="std std-ref">Common pitfalls in the interpretation of coefficients of linear models</span></a></p>
<div class="sphx-glr-thumbnail-title">Common pitfalls in the interpretation of coefficients of linear models</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example illustrates different ways estimators and pipelines can be displayed."><img alt="" src="../../_images/sphx_glr_plot_estimator_representation_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/miscellaneous/plot_estimator_representation.html#sphx-glr-auto-examples-miscellaneous-plot-estimator-representation-py"><span class="std std-ref">Displaying estimators and complex pipelines</span></a></p>
<div class="sphx-glr-thumbnail-title">Displaying estimators and complex pipelines</div>
</div></div><div class="clearer"></div></section>
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