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<!DOCTYPE html>
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<li class="toctree-l2"><a class="reference internal" href="modules/data_ops/basics/what_are_data_ops.html">Basics of DataOps: the DataOps plan, variables, and learners</a></li>
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<li class="toctree-l2"><a class="reference internal" href="modules/data_ops/ml_pipeline/using_part_of_data_ops_plan.html">Using only a part of a DataOps plan</a></li>
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<li class="toctree-l2"><a class="reference internal" href="modules/data_ops/validation/tuning_validating_data_ops.html">Tuning and validating skrub DataOps plans</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/data_ops/validation/hyperparameter_tuning.html">Using the skrub <code class="docutils literal notranslate"><span class="pre">choose_*</span></code> functions to tune hyperparameters</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/data_ops/validation/nested_cross_validation.html">Validating hyperparameter search with nested cross-validation</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/data_ops/validation/nesting_choices_choosing_pipelines.html">Going beyond estimator hyperparameters: nesting choices and choosing pipelines</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/data_ops/validation/exporting_data_ops.html">Exporting the DataOps plan as a learner and reusing it</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/data_ops/validation/tuning_with_optuna.html">Tuning DataOps with Optuna</a></li>
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<li class="toctree-l2"><a class="reference internal" href="modules/configuration_and_utils/fetching_datasets.html">Working with the example datasets provided by skrub</a></li>
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<span id="id1"></span><h1>User Guide<a class="headerlink" href="#user-guide" title="Link to this heading">#</a></h1>
<p>Skrub is a Python library that facilitates machine learning with tabular data
(dataframes, such as pandas and polars) using a scikit-learn-compatible API.</p>
<p>Use the sections below to navigate the guide. For a quickstart example,
try <a class="reference internal" href="auto_examples/0000_getting_started.html#sphx-glr-auto-examples-0000-getting-started-py"><span class="std std-ref">Getting Started</span></a>.
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<li class="toctree-l1"><a class="reference internal" href="exploring_a_dataframe.html">Exploring a Dataframe</a><ul>
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<li class="toctree-l3"><a class="reference internal" href="modules/tablereport/exploring_dataframes_interactively.html#a-demo-of-the-tablereport">A demo of the <code class="xref py py-class docutils literal notranslate"><span class="pre">TableReport</span></code></a></li>
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<li class="toctree-l2"><a class="reference internal" href="modules/tablereport/finding_correlated_columns.html">Finding Correlated Columns in a DataFrame</a></li>
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<li class="toctree-l1"><a class="reference internal" href="default_wrangling.html">Wrangling data with good defaults</a><ul>
<li class="toctree-l2"><a class="reference internal" href="modules/default_wrangling/cleaning_dataframes.html"><code class="xref py py-class docutils literal notranslate"><span class="pre">Cleaner</span></code>: sanitizing a dataframe</a><ul>
<li class="toctree-l3"><a class="reference internal" href="modules/default_wrangling/cleaning_dataframes.html#converting-numeric-dtypes-to-float32-with-the-cleaner">Converting numeric dtypes to <code class="docutils literal notranslate"><span class="pre">float32</span></code> with the <code class="xref py py-class docutils literal notranslate"><span class="pre">Cleaner</span></code></a></li>
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<li class="toctree-l2"><a class="reference internal" href="modules/default_wrangling/table_vectorizer.html">Transforming a table into an array of numeric features: <code class="xref py py-class docutils literal notranslate"><span class="pre">TableVectorizer</span></code></a><ul>
<li class="toctree-l3"><a class="reference internal" href="modules/default_wrangling/table_vectorizer.html#numeric-strings-and-categorical-encoding">Numeric strings and categorical encoding</a></li>
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<li class="toctree-l3"><a class="reference internal" href="modules/column_level_featurizing/feature_engineering_categorical.html#comparing-the-categorical-encoders-included-in-skrub">Comparing the categorical encoders included in skrub</a></li>
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<li class="toctree-l3"><a class="reference internal" href="modules/column_level_featurizing/advanced_columnwise_operations.html#the-single-column-transformer">The single column transformer</a></li>
<li class="toctree-l3"><a class="reference internal" href="modules/column_level_featurizing/advanced_columnwise_operations.html#rejection-handling-with-applytocols-and-rejectcolumn">Rejection handling with <code class="xref py py-class docutils literal notranslate"><span class="pre">ApplyToCols</span></code> and <code class="xref py py-class docutils literal notranslate"><span class="pre">core.RejectColumn</span></code></a></li>
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<li class="toctree-l2"><a class="reference internal" href="modules/multi_column_operations/drop_uninformative.html">Removing unneeded columns with <code class="xref py py-class docutils literal notranslate"><span class="pre">DropUninformative</span></code> and <code class="xref py py-class docutils literal notranslate"><span class="pre">Cleaner</span></code></a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/multi_column_operations/drop_uninformative.html#dropping-columns-with-many-missing-values">Dropping columns with many missing values</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/multi_column_operations/drop_uninformative.html#applying-dropuninformative-only-to-a-subset-of-columns">Applying <code class="xref py py-class docutils literal notranslate"><span class="pre">DropUninformative</span></code> only to a subset of columns</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/multi_column_operations/selectors.html">Skrub Selectors, for selecting columns in a dataframe</a><ul>
<li class="toctree-l3"><a class="reference internal" href="modules/multi_column_operations/selectors.html#introduction-to-selectors">Introduction to selectors</a></li>
<li class="toctree-l3"><a class="reference internal" href="modules/multi_column_operations/selectors.html#type-of-selectors">Type of selectors</a></li>
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<li class="toctree-l2"><a class="reference internal" href="modules/multi_column_operations/advanced_selectors.html"><code class="xref py py-func docutils literal notranslate"><span class="pre">filter()</span></code> and <code class="xref py py-func docutils literal notranslate"><span class="pre">filter_names()</span></code> to select with user-defined criteria</a><ul>
<li class="toctree-l3"><a class="reference internal" href="modules/multi_column_operations/advanced_selectors.html#example-of-custom-criteria-in-filter-selecting-columns-with-outliers">Example of custom criteria in <code class="xref py py-func docutils literal notranslate"><span class="pre">filter()</span></code>: selecting columns with outliers</a></li>
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<li class="toctree-l2"><a class="reference internal" href="modules/multi_column_operations/advanced_selectors.html#select-columns-with-null-values">Select columns with null values</a><ul>
<li class="toctree-l3"><a class="reference internal" href="modules/multi_column_operations/advanced_selectors.html#example-selecting-columns-by-null-percentage-with-has-nulls">Example: Selecting columns by null percentage with <code class="xref py py-func docutils literal notranslate"><span class="pre">has_nulls()</span></code></a></li>
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<li class="toctree-l1"><a class="reference internal" href="data_ops.html">Complex multi-table pipelines with Data Ops</a><ul>
<li class="toctree-l2"><a class="reference internal" href="data_ops.html#data-ops-basic-concepts">Data Ops basic concepts</a><ul>
<li class="toctree-l3"><a class="reference internal" href="modules/data_ops/basics/what_are_data_ops.html">Basics of DataOps: the DataOps plan, variables, and learners</a></li>
<li class="toctree-l3"><a class="reference internal" href="modules/data_ops/basics/building_data_ops_plan.html">Building a simple DataOps plan</a></li>
<li class="toctree-l3"><a class="reference internal" href="modules/data_ops/basics/using_previews.html">Using previews for easier development and debugging</a></li>
<li class="toctree-l3"><a class="reference internal" href="modules/data_ops/basics/direct_access_methods.html">DataOps allow direct access to methods of the underlying data</a></li>
<li class="toctree-l3"><a class="reference internal" href="modules/data_ops/basics/control_flow.html">Control flow in DataOps: eager and deferred evaluation</a></li>
<li class="toctree-l3"><a class="reference internal" href="modules/data_ops/basics/data_ops_vs_alternatives.html">How do skrub Data Ops differ from the alternatives?</a></li>
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<li class="toctree-l2"><a class="reference internal" href="data_ops.html#building-a-complex-pipeline-with-the-skrub-data-ops">Building a complex pipeline with the skrub Data Ops</a><ul>
<li class="toctree-l3"><a class="reference internal" href="modules/data_ops/ml_pipeline/applying_ml_estimators.html">Applying machine-learning estimators</a></li>
<li class="toctree-l3"><a class="reference internal" href="modules/data_ops/ml_pipeline/applying_different_transformers.html">Applying different transformers using skrub selectors and DataOps</a></li>
<li class="toctree-l3"><a class="reference internal" href="modules/data_ops/ml_pipeline/documenting_data_ops_plan.html">Documenting the DataOps plan with node names and descriptions</a></li>
<li class="toctree-l3"><a class="reference internal" href="modules/data_ops/ml_pipeline/evaluating_debugging_data_ops.html">Evaluating and debugging the DataOps plan with <code class="xref py py-meth docutils literal notranslate"><span class="pre">.skb.full_report()</span></code></a></li>
<li class="toctree-l3"><a class="reference internal" href="modules/data_ops/ml_pipeline/using_part_of_data_ops_plan.html">Using only a part of a DataOps plan</a></li>
<li class="toctree-l3"><a class="reference internal" href="modules/data_ops/ml_pipeline/subsampling_data.html">Subsampling data for easier development and debugging</a></li>
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<li class="toctree-l2"><a class="reference internal" href="data_ops.html#tuning-and-validating-skrub-dataops-plans">Tuning and validating Skrub DataOps plans</a><ul>
<li class="toctree-l3"><a class="reference internal" href="modules/data_ops/validation/tuning_validating_data_ops.html">Tuning and validating skrub DataOps plans</a></li>
<li class="toctree-l3"><a class="reference internal" href="modules/data_ops/validation/tuning_validating_data_ops.html#improving-the-confidence-in-our-score-through-cross-validation">Improving the confidence in our score through cross-validation</a></li>
<li class="toctree-l3"><a class="reference internal" href="modules/data_ops/validation/tuning_validating_data_ops.html#splitting-the-data-in-train-and-test-sets">Splitting the data in train and test sets</a></li>
<li class="toctree-l3"><a class="reference internal" href="modules/data_ops/validation/tuning_validating_data_ops.html#passing-additional-arguments-to-the-splitter">Passing additional arguments to the splitter</a></li>
<li class="toctree-l3"><a class="reference internal" href="modules/data_ops/validation/hyperparameter_tuning.html">Using the skrub <code class="docutils literal notranslate"><span class="pre">choose_*</span></code> functions to tune hyperparameters</a></li>
<li class="toctree-l3"><a class="reference internal" href="modules/data_ops/validation/hyperparameter_tuning.html#feature-selection-with-skrub-selectcols-and-dropcols">Feature selection with skrub <code class="xref py py-class docutils literal notranslate"><span class="pre">SelectCols</span></code> and <code class="xref py py-class docutils literal notranslate"><span class="pre">DropCols</span></code></a></li>
<li class="toctree-l3"><a class="reference internal" href="modules/data_ops/validation/nested_cross_validation.html">Validating hyperparameter search with nested cross-validation</a></li>
<li class="toctree-l3"><a class="reference internal" href="modules/data_ops/validation/nesting_choices_choosing_pipelines.html">Going beyond estimator hyperparameters: nesting choices and choosing pipelines</a></li>
<li class="toctree-l3"><a class="reference internal" href="modules/data_ops/validation/nesting_choices_choosing_pipelines.html#linking-choices-depending-on-other-choices">Linking choices depending on other choices</a></li>
<li class="toctree-l3"><a class="reference internal" href="modules/data_ops/validation/exporting_data_ops.html">Exporting the DataOps plan as a learner and reusing it</a></li>
<li class="toctree-l3"><a class="reference internal" href="modules/data_ops/validation/tuning_with_optuna.html">Tuning DataOps with Optuna</a></li>
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<li class="toctree-l1"><a class="reference internal" href="configuration_and_utils.html">Configuration and dataset utilities</a><ul>
<li class="toctree-l2"><a class="reference internal" href="modules/configuration_and_utils/customizing_configuration.html">Customizing the global configuration</a><ul>
<li class="toctree-l3"><a class="reference internal" href="modules/configuration_and_utils/customizing_configuration.html#configuration-parameters">Configuration parameters</a></li>
</ul>
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<li class="toctree-l2"><a class="reference internal" href="modules/configuration_and_utils/deduplicate_categorical_data.html">Deduplicating categorical data with <code class="xref py py-func docutils literal notranslate"><span class="pre">deduplicate()</span></code></a><ul>
<li class="toctree-l3"><a class="reference internal" href="modules/configuration_and_utils/deduplicate_categorical_data.html#deduplicating-values-in-a-dataframe">Deduplicating values in a dataframe</a></li>
</ul>
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<li class="toctree-l2"><a class="reference internal" href="modules/configuration_and_utils/fetching_datasets.html">Working with the example datasets provided by skrub</a><ul>
<li class="toctree-l3"><a class="reference internal" href="modules/configuration_and_utils/fetching_datasets.html#modifying-the-download-location-of-skrub-datasets">Modifying the download location of <code class="docutils literal notranslate"><span class="pre">skrub</span></code> datasets</a></li>
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<li class="toctree-l1"><a class="reference internal" href="joining_dataframes.html">Joining Dataframes</a><ul>
<li class="toctree-l2"><a class="reference internal" href="modules/joining_tables/assembling.html">Assembling: joining multiple tables</a><ul>
<li class="toctree-l3"><a class="reference internal" href="modules/joining_tables/assembling.html#joining-external-tables-for-machine-learning">Joining external tables for machine learning</a></li>
<li class="toctree-l3"><a class="reference internal" href="modules/joining_tables/assembling.html#fuzzy-joining-tables">Fuzzy joining tables</a></li>
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