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<li class="toctree-l1 has-children"><a class="reference internal" href="exploring_a_dataframe.html">Exploring a Dataframe</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
<li class="toctree-l2"><a class="reference internal" href="modules/tablereport/exploring_dataframes_interactively.html">Exploring dataframes interactively with 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-l1 has-children"><a class="reference internal" href="default_wrangling.html">Wrangling data with good defaults</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><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></li>
<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></li>
<li class="toctree-l2"><a class="reference internal" href="modules/default_wrangling/tabular_pipeline.html">Building robust ML baselines with <code class="xref py py-func docutils literal notranslate"><span class="pre">tabular_pipeline()</span></code></a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/default_wrangling/apply_to_cols.html">Transforming selected columns with <code class="xref py py-class docutils literal notranslate"><span class="pre">ApplyToCols</span></code></a></li>
</ul>
</details></li>
<li class="toctree-l1 has-children"><a class="reference internal" href="column_level_featurizing.html">Column-level feature extraction</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
<li class="toctree-l2"><a class="reference internal" href="modules/column_level_featurizing/feature_engineering_categorical.html">Encoding string and text columns as numeric features</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/column_level_featurizing/feature_engineering_datetimes.html">Handling datetimes: parsing from strings and encoding as numbers</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/column_level_featurizing/robust_scaling.html">Robust scaling of numeric features using <code class="xref py py-class docutils literal notranslate"><span class="pre">SquashingScaler</span></code></a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/column_level_featurizing/advanced_columnwise_operations.html">Advanced columnwise operations</a></li>
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<li class="toctree-l1 has-children"><a class="reference internal" href="multi_column_operations.html">Multi-column operations</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
<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/selectors.html">Skrub Selectors, for selecting columns in a dataframe</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/multi_column_operations/type_of_selectors.html">Selecting based on dtype or data properties</a></li>
<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></li>
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<li class="toctree-l1 has-children"><a class="reference internal" href="data_ops.html">Complex multi-table pipelines with Data Ops</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
<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>
<li class="toctree-l2"><a class="reference internal" href="modules/data_ops/basics/building_data_ops_plan.html">Building a simple DataOps plan</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/data_ops/basics/using_previews.html">Using previews for easier development and debugging</a></li>
<li class="toctree-l2"><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-l2"><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-l2"><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>
<li class="toctree-l2"><a class="reference internal" href="modules/data_ops/ml_pipeline/applying_ml_estimators.html">Applying machine-learning estimators</a></li>
<li class="toctree-l2"><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-l2"><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-l2"><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-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>
<li class="toctree-l2"><a class="reference internal" href="modules/data_ops/ml_pipeline/subsampling_data.html">Subsampling data for easier development and debugging</a></li>
<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-l1 has-children"><a class="reference internal" href="configuration_and_utils.html">Configuration and dataset utilities</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
<li class="toctree-l2"><a class="reference internal" href="modules/configuration_and_utils/customizing_configuration.html">Customizing the global configuration</a></li>
<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></li>
<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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<li class="toctree-l1 current active has-children"><a class="current reference internal" href="#">Joining Dataframes</a><details open="open"><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
<li class="toctree-l2"><a class="reference internal" href="modules/joining_tables/assembling.html">Assembling: joining multiple tables</a></li>
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<span id="user-guide-joining-dataframes"></span><h1>Joining Dataframes<a class="headerlink" href="#joining-dataframes" title="Link to this heading">#</a></h1>
<p>This section covers the various methods provided by skrub to join dataframes.</p>
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