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---
title: tabular.transform
keywords: fastai
sidebar: home_sidebar
summary: "Transforms to clean and preprocess tabular data"
---
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<h2 id="Tabular-data-preprocessing">Tabular data preprocessing<a class="anchor-link" href="#Tabular-data-preprocessing">¶</a></h2>
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<h2 id="Overview">Overview<a class="anchor-link" href="#Overview">¶</a></h2>
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<p>This package contains the basic class to define a transformation for preprocessing dataframes of tabular data, as well as basic <a href="/tabular.transform.html#TabularProc"><code>TabularProc</code></a>. Preprocessing includes things like</p>
<ul>
<li>replacing non-numerical variables by categories, then their ids,</li>
<li>filling missing values,</li>
<li>normalizing continuous variables.</li>
</ul>
<p>In all those steps we have to be careful to use the correspondence we decide on our training set (which id we give to each category, what is the value we put for missing data, or how the mean/std we use to normalize) on our validation or test set. To deal with this, we use a special class called <a href="/tabular.transform.html#TabularProc"><code>TabularProc</code></a>.</p>
<p>The data used in this document page is a subset of the <a href="https://archive.ics.uci.edu/ml/datasets/adult">adult dataset</a>. It gives a certain amount of data on individuals to train a model to predict whether their salary is greater than \$50k or not.</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">path</span> <span class="o">=</span> <span class="n">untar_data</span><span class="p">(</span><span class="n">URLs</span><span class="o">.</span><span class="n">ADULT_SAMPLE</span><span class="p">)</span>
<span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="n">path</span><span class="o">/</span><span class="s1">'adult.csv'</span><span class="p">)</span>
<span class="n">train_df</span><span class="p">,</span> <span class="n">valid_df</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">iloc</span><span class="p">[:</span><span class="mi">800</span><span class="p">]</span><span class="o">.</span><span class="n">copy</span><span class="p">(),</span> <span class="n">df</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="mi">800</span><span class="p">:</span><span class="mi">1000</span><span class="p">]</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
<span class="n">train_df</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
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<style scoped>
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}
.dataframe tbody tr th {
vertical-align: top;
}
.dataframe thead th {
text-align: right;
}
</style>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>age</th>
<th>workclass</th>
<th>fnlwgt</th>
<th>education</th>
<th>education-num</th>
<th>marital-status</th>
<th>occupation</th>
<th>relationship</th>
<th>race</th>
<th>sex</th>
<th>capital-gain</th>
<th>capital-loss</th>
<th>hours-per-week</th>
<th>native-country</th>
<th>salary</th>
</tr>
</thead>
<tbody>
<tr>
<th>0</th>
<td>49</td>
<td>Private</td>
<td>101320</td>
<td>Assoc-acdm</td>
<td>12.0</td>
<td>Married-civ-spouse</td>
<td>NaN</td>
<td>Wife</td>
<td>White</td>
<td>Female</td>
<td>0</td>
<td>1902</td>
<td>40</td>
<td>United-States</td>
<td>>=50k</td>
</tr>
<tr>
<th>1</th>
<td>44</td>
<td>Private</td>
<td>236746</td>
<td>Masters</td>
<td>14.0</td>
<td>Divorced</td>
<td>Exec-managerial</td>
<td>Not-in-family</td>
<td>White</td>
<td>Male</td>
<td>10520</td>
<td>0</td>
<td>45</td>
<td>United-States</td>
<td>>=50k</td>
</tr>
<tr>
<th>2</th>
<td>38</td>
<td>Private</td>
<td>96185</td>
<td>HS-grad</td>
<td>NaN</td>
<td>Divorced</td>
<td>NaN</td>
<td>Unmarried</td>
<td>Black</td>
<td>Female</td>
<td>0</td>
<td>0</td>
<td>32</td>
<td>United-States</td>
<td><50k</td>
</tr>
<tr>
<th>3</th>
<td>38</td>
<td>Self-emp-inc</td>
<td>112847</td>
<td>Prof-school</td>
<td>15.0</td>
<td>Married-civ-spouse</td>
<td>Prof-specialty</td>
<td>Husband</td>
<td>Asian-Pac-Islander</td>
<td>Male</td>
<td>0</td>
<td>0</td>
<td>40</td>
<td>United-States</td>
<td>>=50k</td>
</tr>
<tr>
<th>4</th>
<td>42</td>
<td>Self-emp-not-inc</td>
<td>82297</td>
<td>7th-8th</td>
<td>NaN</td>
<td>Married-civ-spouse</td>
<td>Other-service</td>
<td>Wife</td>
<td>Black</td>
<td>Female</td>
<td>0</td>
<td>0</td>
<td>50</td>
<td>United-States</td>
<td><50k</td>
</tr>
</tbody>
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<p>We see it contains numerical variables (like <code>age</code> or <code>education-num</code>) as well as categorical ones (like <code>workclass</code> or <code>relationship</code>). The original dataset is clean, but we removed a few values to give examples of dealing with missing variables.</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">cat_names</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'workclass'</span><span class="p">,</span> <span class="s1">'education'</span><span class="p">,</span> <span class="s1">'marital-status'</span><span class="p">,</span> <span class="s1">'occupation'</span><span class="p">,</span> <span class="s1">'relationship'</span><span class="p">,</span> <span class="s1">'race'</span><span class="p">,</span> <span class="s1">'sex'</span><span class="p">,</span> <span class="s1">'native-country'</span><span class="p">]</span>
<span class="n">cont_names</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'age'</span><span class="p">,</span> <span class="s1">'fnlwgt'</span><span class="p">,</span> <span class="s1">'education-num'</span><span class="p">,</span> <span class="s1">'capital-gain'</span><span class="p">,</span> <span class="s1">'capital-loss'</span><span class="p">,</span> <span class="s1">'hours-per-week'</span><span class="p">]</span>
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<h2 id="Transforms-for-tabular-data">Transforms for tabular data<a class="anchor-link" href="#Transforms-for-tabular-data">¶</a></h2>
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<h2 id="TabularProc" class="doc_header"><code>class</code> <code>TabularProc</code><a href="https://github.com/fastai/fastai/blob/master/fastai/tabular/transform.py#L116" class="source_link" style="float:right">[source]</a><a class="source_link" data-toggle="collapse" data-target="#TabularProc-pytest" style="float:right; padding-right:10px">[test]</a></h2><blockquote><p><code>TabularProc</code>(<strong><code>cat_names</code></strong>:<code>StrList</code>, <strong><code>cont_names</code></strong>:<code>StrList</code>)</p>
</blockquote>
<div class="collapse" id="TabularProc-pytest"><div class="card card-body pytest_card"><a type="button" data-toggle="collapse" data-target="#TabularProc-pytest" class="close" aria-label="Close"><span aria-hidden="true">×</span></a><p>No tests found for <code>TabularProc</code>. To contribute a test please refer to <a href="/dev/test.html">this guide</a> and <a href="https://forums.fast.ai/t/improving-expanding-functional-tests/32929">this discussion</a>.</p></div></div><p>A processor for tabular dataframes.</p>
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<p>Base class for creating transforms for dataframes with categorical variables <code>cat_names</code> and continuous variables <code>cont_names</code>. Note that any column not in one of those lists won't be touched.</p>
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<h4 id="TabularProc.__call__" class="doc_header"><code>__call__</code><a href="https://github.com/fastai/fastai/blob/master/fastai/tabular/transform.py#L121" class="source_link" style="float:right">[source]</a><a class="source_link" data-toggle="collapse" data-target="#TabularProc-__call__-pytest" style="float:right; padding-right:10px">[test]</a></h4><blockquote><p><code>__call__</code>(<strong><code>df</code></strong>:<code>DataFrame</code>, <strong><code>test</code></strong>:<code>bool</code>=<strong><em><code>False</code></em></strong>)</p>
</blockquote>
<div class="collapse" id="TabularProc-__call__-pytest"><div class="card card-body pytest_card"><a type="button" data-toggle="collapse" data-target="#TabularProc-__call__-pytest" class="close" aria-label="Close"><span aria-hidden="true">×</span></a><p>No tests found for <code>__call__</code>. To contribute a test please refer to <a href="/dev/test.html">this guide</a> and <a href="https://forums.fast.ai/t/improving-expanding-functional-tests/32929">this discussion</a>.</p></div></div><p>Apply the correct function to <code>df</code> depending on <code>test</code>.</p>
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<h4 id="TabularProc.apply_train" class="doc_header"><code>apply_train</code><a href="https://github.com/fastai/fastai/blob/master/fastai/tabular/transform.py#L126" class="source_link" style="float:right">[source]</a><a class="source_link" data-toggle="collapse" data-target="#TabularProc-apply_train-pytest" style="float:right; padding-right:10px">[test]</a></h4><blockquote><p><code>apply_train</code>(<strong><code>df</code></strong>:<code>DataFrame</code>)</p>
</blockquote>
<div class="collapse" id="TabularProc-apply_train-pytest"><div class="card card-body pytest_card"><a type="button" data-toggle="collapse" data-target="#TabularProc-apply_train-pytest" class="close" aria-label="Close"><span aria-hidden="true">×</span></a><p>Tests found for <code>apply_train</code>:</p><p>Some other tests where <code>apply_train</code> is used:</p><ul><li><code>pytest -sv tests/test_tabular_transform.py::test_categorify</code> <a href="https://github.com/fastai/fastai/blob/master/tests/test_tabular_transform.py#L6" class="source_link" style="float:right">[source]</a></li><li><code>pytest -sv tests/test_tabular_transform.py::test_fill_missing_leaves_no_na_values</code> <a href="https://github.com/fastai/fastai/blob/master/tests/test_tabular_transform.py#L36" class="source_link" style="float:right">[source]</a></li><li><code>pytest -sv tests/test_tabular_transform.py::test_fill_missing_returns_correct_medians</code> <a href="https://github.com/fastai/fastai/blob/master/tests/test_tabular_transform.py#L49" class="source_link" style="float:right">[source]</a></li></ul><p>To run tests please refer to this <a href="/dev/test.html#quick-guide">guide</a>.</p></div></div><p>Function applied to <code>df</code> if it's the train set.</p>
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<h4 id="TabularProc.apply_test" class="doc_header"><code>apply_test</code><a href="https://github.com/fastai/fastai/blob/master/fastai/tabular/transform.py#L129" class="source_link" style="float:right">[source]</a><a class="source_link" data-toggle="collapse" data-target="#TabularProc-apply_test-pytest" style="float:right; padding-right:10px">[test]</a></h4><blockquote><p><code>apply_test</code>(<strong><code>df</code></strong>:<code>DataFrame</code>)</p>
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<div class="collapse" id="TabularProc-apply_test-pytest"><div class="card card-body pytest_card"><a type="button" data-toggle="collapse" data-target="#TabularProc-apply_test-pytest" class="close" aria-label="Close"><span aria-hidden="true">×</span></a><p>Tests found for <code>apply_test</code>:</p><p>Some other tests where <code>apply_test</code> is used:</p><ul><li><code>pytest -sv tests/test_tabular_transform.py::test_categorify</code> <a href="https://github.com/fastai/fastai/blob/master/tests/test_tabular_transform.py#L6" class="source_link" style="float:right">[source]</a></li><li><code>pytest -sv tests/test_tabular_transform.py::test_fill_missing_leaves_no_na_values</code> <a href="https://github.com/fastai/fastai/blob/master/tests/test_tabular_transform.py#L36" class="source_link" style="float:right">[source]</a></li><li><code>pytest -sv tests/test_tabular_transform.py::test_fill_missing_returns_correct_medians</code> <a href="https://github.com/fastai/fastai/blob/master/tests/test_tabular_transform.py#L49" class="source_link" style="float:right">[source]</a></li></ul><p>To run tests please refer to this <a href="/dev/test.html#quick-guide">guide</a>.</p></div></div><p>Function applied to <code>df</code> if it's the test set.</p>
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<div markdown="span" class="alert alert-warning" role="alert"><i class="fa fa-warning-circle"></i> <b>Important: </b>Those two functions must be implemented in a subclass. `apply_test` defaults to `apply_train`.</div>
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<p>The following <a href="/tabular.transform.html#TabularProc"><code>TabularProc</code></a> are implemented in the fastai library. Note that the replacement from categories to codes as well as the normalization of continuous variables are automatically done in a <a href="/tabular.data.html#TabularDataBunch"><code>TabularDataBunch</code></a>.</p>
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<h2 id="Categorify" class="doc_header"><code>class</code> <code>Categorify</code><a href="https://github.com/fastai/fastai/blob/master/fastai/tabular/transform.py#L133" class="source_link" style="float:right">[source]</a><a class="source_link" data-toggle="collapse" data-target="#Categorify-pytest" style="float:right; padding-right:10px">[test]</a></h2><blockquote><p><code>Categorify</code>(<strong><code>cat_names</code></strong>:<code>StrList</code>, <strong><code>cont_names</code></strong>:<code>StrList</code>) :: <a href="/tabular.transform.html#TabularProc"><code>TabularProc</code></a></p>
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<div class="collapse" id="Categorify-pytest"><div class="card card-body pytest_card"><a type="button" data-toggle="collapse" data-target="#Categorify-pytest" class="close" aria-label="Close"><span aria-hidden="true">×</span></a><p>Tests found for <code>Categorify</code>:</p><ul><li><code>pytest -sv tests/test_tabular_transform.py::test_categorify</code> <a href="https://github.com/fastai/fastai/blob/master/tests/test_tabular_transform.py#L6" class="source_link" style="float:right">[source]</a></li></ul><p>To run tests please refer to this <a href="/dev/test.html#quick-guide">guide</a>.</p></div></div><p>Transform the categorical variables to that type.</p>
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<p>Variables in <code>cont_names</code> aren't affected.</p>
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<h4 id="Categorify.apply_train" class="doc_header"><code>apply_train</code><a href="https://github.com/fastai/fastai/blob/master/fastai/tabular/transform.py#L135" class="source_link" style="float:right">[source]</a><a class="source_link" data-toggle="collapse" data-target="#Categorify-apply_train-pytest" style="float:right; padding-right:10px">[test]</a></h4><blockquote><p><code>apply_train</code>(<strong><code>df</code></strong>:<code>DataFrame</code>)</p>
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<div class="collapse" id="Categorify-apply_train-pytest"><div class="card card-body pytest_card"><a type="button" data-toggle="collapse" data-target="#Categorify-apply_train-pytest" class="close" aria-label="Close"><span aria-hidden="true">×</span></a><p>Tests found for <code>apply_train</code>:</p><p>Some other tests where <code>apply_train</code> is used:</p><ul><li><code>pytest -sv tests/test_tabular_transform.py::test_categorify</code> <a href="https://github.com/fastai/fastai/blob/master/tests/test_tabular_transform.py#L6" class="source_link" style="float:right">[source]</a></li><li><code>pytest -sv tests/test_tabular_transform.py::test_fill_missing_leaves_no_na_values</code> <a href="https://github.com/fastai/fastai/blob/master/tests/test_tabular_transform.py#L36" class="source_link" style="float:right">[source]</a></li><li><code>pytest -sv tests/test_tabular_transform.py::test_fill_missing_returns_correct_medians</code> <a href="https://github.com/fastai/fastai/blob/master/tests/test_tabular_transform.py#L49" class="source_link" style="float:right">[source]</a></li></ul><p>To run tests please refer to this <a href="/dev/test.html#quick-guide">guide</a>.</p></div></div><p>Transform <code>self.cat_names</code> columns in categorical.</p>
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<h4 id="Categorify.apply_test" class="doc_header"><code>apply_test</code><a href="https://github.com/fastai/fastai/blob/master/fastai/tabular/transform.py#L142" class="source_link" style="float:right">[source]</a><a class="source_link" data-toggle="collapse" data-target="#Categorify-apply_test-pytest" style="float:right; padding-right:10px">[test]</a></h4><blockquote><p><code>apply_test</code>(<strong><code>df</code></strong>:<code>DataFrame</code>)</p>
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<div class="collapse" id="Categorify-apply_test-pytest"><div class="card card-body pytest_card"><a type="button" data-toggle="collapse" data-target="#Categorify-apply_test-pytest" class="close" aria-label="Close"><span aria-hidden="true">×</span></a><p>Tests found for <code>apply_test</code>:</p><p>Some other tests where <code>apply_test</code> is used:</p><ul><li><code>pytest -sv tests/test_tabular_transform.py::test_categorify</code> <a href="https://github.com/fastai/fastai/blob/master/tests/test_tabular_transform.py#L6" class="source_link" style="float:right">[source]</a></li><li><code>pytest -sv tests/test_tabular_transform.py::test_fill_missing_leaves_no_na_values</code> <a href="https://github.com/fastai/fastai/blob/master/tests/test_tabular_transform.py#L36" class="source_link" style="float:right">[source]</a></li><li><code>pytest -sv tests/test_tabular_transform.py::test_fill_missing_returns_correct_medians</code> <a href="https://github.com/fastai/fastai/blob/master/tests/test_tabular_transform.py#L49" class="source_link" style="float:right">[source]</a></li></ul><p>To run tests please refer to this <a href="/dev/test.html#quick-guide">guide</a>.</p></div></div><p>Transform <code>self.cat_names</code> columns in categorical using the codes decided in <code>apply_train</code>.</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">tfm</span> <span class="o">=</span> <span class="n">Categorify</span><span class="p">(</span><span class="n">cat_names</span><span class="p">,</span> <span class="n">cont_names</span><span class="p">)</span>
<span class="n">tfm</span><span class="p">(</span><span class="n">train_df</span><span class="p">)</span>
<span class="n">tfm</span><span class="p">(</span><span class="n">valid_df</span><span class="p">,</span> <span class="n">test</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
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<p>Since we haven't changed the categories by their codes, nothing visible has changed in the dataframe yet, but we can check that the variables are now categorical and view their corresponding codes.</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">train_df</span><span class="p">[</span><span class="s1">'workclass'</span><span class="p">]</span><span class="o">.</span><span class="n">cat</span><span class="o">.</span><span class="n">categories</span>
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<pre>Index([' ?', ' Federal-gov', ' Local-gov', ' Private', ' Self-emp-inc',
' Self-emp-not-inc', ' State-gov', ' Without-pay'],
dtype='object')</pre>
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<p>The test set will be given the same category codes as the training set.</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">valid_df</span><span class="p">[</span><span class="s1">'workclass'</span><span class="p">]</span><span class="o">.</span><span class="n">cat</span><span class="o">.</span><span class="n">categories</span>
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<pre>Index([' ?', ' Federal-gov', ' Local-gov', ' Private', ' Self-emp-inc',
' Self-emp-not-inc', ' State-gov', ' Without-pay'],
dtype='object')</pre>
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<h2 id="FillMissing" class="doc_header"><code>class</code> <code>FillMissing</code><a href="https://github.com/fastai/fastai/blob/master/fastai/tabular/transform.py#L150" class="source_link" style="float:right">[source]</a><a class="source_link" data-toggle="collapse" data-target="#FillMissing-pytest" style="float:right; padding-right:10px">[test]</a></h2><blockquote><p><code>FillMissing</code>(<strong><code>cat_names</code></strong>:<code>StrList</code>, <strong><code>cont_names</code></strong>:<code>StrList</code>, <strong><code>fill_strategy</code></strong>:<a href="/tabular.transform.html#FillStrategy"><code>FillStrategy</code></a>=<strong><em><code><FillStrategy.MEDIAN: 1></code></em></strong>, <strong><code>add_col</code></strong>:<code>bool</code>=<strong><em><code>True</code></em></strong>, <strong><code>fill_val</code></strong>:<code>float</code>=<strong><em><code>0.0</code></em></strong>) :: <a href="/tabular.transform.html#TabularProc"><code>TabularProc</code></a></p>
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<div class="collapse" id="FillMissing-pytest"><div class="card card-body pytest_card"><a type="button" data-toggle="collapse" data-target="#FillMissing-pytest" class="close" aria-label="Close"><span aria-hidden="true">×</span></a><p>Tests found for <code>FillMissing</code>:</p><ul><li><code>pytest -sv tests/test_tabular_transform.py::test_default_fill_strategy_is_median</code> <a href="https://github.com/fastai/fastai/blob/master/tests/test_tabular_transform.py#L30" class="source_link" style="float:right">[source]</a></li></ul><p>Some other tests where <code>FillMissing</code> is used:</p><ul><li><code>pytest -sv tests/test_tabular_transform.py::test_fill_missing_leaves_no_na_values</code> <a href="https://github.com/fastai/fastai/blob/master/tests/test_tabular_transform.py#L36" class="source_link" style="float:right">[source]</a></li><li><code>pytest -sv tests/test_tabular_transform.py::test_fill_missing_returns_correct_medians</code> <a href="https://github.com/fastai/fastai/blob/master/tests/test_tabular_transform.py#L49" class="source_link" style="float:right">[source]</a></li></ul><p>To run tests please refer to this <a href="/dev/test.html#quick-guide">guide</a>.</p></div></div><p>Fill the missing values in continuous columns.</p>
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<p><code>cat_names</code> variables are left untouched (their missing value will be replaced by code 0 in the <a href="/tabular.data.html#TabularDataBunch"><code>TabularDataBunch</code></a>). <a href="#FillStrategy"><code>fill_strategy</code></a> is adopted to replace those nans and if <code>add_col</code> is True, whenever a column <code>c</code> has missing values, a column named <code>c_nan</code> is added and flags the line where the value was missing.</p>
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<h4 id="FillMissing.apply_train" class="doc_header"><code>apply_train</code><a href="https://github.com/fastai/fastai/blob/master/fastai/tabular/transform.py#L155" class="source_link" style="float:right">[source]</a><a class="source_link" data-toggle="collapse" data-target="#FillMissing-apply_train-pytest" style="float:right; padding-right:10px">[test]</a></h4><blockquote><p><code>apply_train</code>(<strong><code>df</code></strong>:<code>DataFrame</code>)</p>
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<div class="collapse" id="FillMissing-apply_train-pytest"><div class="card card-body pytest_card"><a type="button" data-toggle="collapse" data-target="#FillMissing-apply_train-pytest" class="close" aria-label="Close"><span aria-hidden="true">×</span></a><p>Tests found for <code>apply_train</code>:</p><ul><li><code>pytest -sv tests/test_tabular_transform.py::test_fill_missing_leaves_no_na_values</code> <a href="https://github.com/fastai/fastai/blob/master/tests/test_tabular_transform.py#L36" class="source_link" style="float:right">[source]</a></li><li><code>pytest -sv tests/test_tabular_transform.py::test_fill_missing_returns_correct_medians</code> <a href="https://github.com/fastai/fastai/blob/master/tests/test_tabular_transform.py#L49" class="source_link" style="float:right">[source]</a></li></ul><p>Some other tests where <code>apply_train</code> is used:</p><ul><li><code>pytest -sv tests/test_tabular_transform.py::test_categorify</code> <a href="https://github.com/fastai/fastai/blob/master/tests/test_tabular_transform.py#L6" class="source_link" style="float:right">[source]</a></li></ul><p>To run tests please refer to this <a href="/dev/test.html#quick-guide">guide</a>.</p></div></div><p>Fill missing values in <code>self.cont_names</code> according to <code>self.fill_strategy</code>.</p>
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<h4 id="FillMissing.apply_test" class="doc_header"><code>apply_test</code><a href="https://github.com/fastai/fastai/blob/master/fastai/tabular/transform.py#L169" class="source_link" style="float:right">[source]</a><a class="source_link" data-toggle="collapse" data-target="#FillMissing-apply_test-pytest" style="float:right; padding-right:10px">[test]</a></h4><blockquote><p><code>apply_test</code>(<strong><code>df</code></strong>:<code>DataFrame</code>)</p>
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<div class="collapse" id="FillMissing-apply_test-pytest"><div class="card card-body pytest_card"><a type="button" data-toggle="collapse" data-target="#FillMissing-apply_test-pytest" class="close" aria-label="Close"><span aria-hidden="true">×</span></a><p>Tests found for <code>apply_test</code>:</p><ul><li><code>pytest -sv tests/test_tabular_transform.py::test_fill_missing_leaves_no_na_values</code> <a href="https://github.com/fastai/fastai/blob/master/tests/test_tabular_transform.py#L36" class="source_link" style="float:right">[source]</a></li><li><code>pytest -sv tests/test_tabular_transform.py::test_fill_missing_returns_correct_medians</code> <a href="https://github.com/fastai/fastai/blob/master/tests/test_tabular_transform.py#L49" class="source_link" style="float:right">[source]</a></li></ul><p>Some other tests where <code>apply_test</code> is used:</p><ul><li><code>pytest -sv tests/test_tabular_transform.py::test_categorify</code> <a href="https://github.com/fastai/fastai/blob/master/tests/test_tabular_transform.py#L6" class="source_link" style="float:right">[source]</a></li></ul><p>To run tests please refer to this <a href="/dev/test.html#quick-guide">guide</a>.</p></div></div><p>Fill missing values in <code>self.cont_names</code> like in <code>apply_train</code>.</p>
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<p>Fills the missing values in the <code>cont_names</code> columns with the ones picked during train.</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">train_df</span><span class="p">[</span><span class="n">cont_names</span><span class="p">]</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
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<td>42</td>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">tfm</span> <span class="o">=</span> <span class="n">FillMissing</span><span class="p">(</span><span class="n">cat_names</span><span class="p">,</span> <span class="n">cont_names</span><span class="p">)</span>
<span class="n">tfm</span><span class="p">(</span><span class="n">train_df</span><span class="p">)</span>
<span class="n">tfm</span><span class="p">(</span><span class="n">valid_df</span><span class="p">,</span> <span class="n">test</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="n">train_df</span><span class="p">[</span><span class="n">cont_names</span><span class="p">]</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
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<th>0</th>
<td>49</td>
<td>101320</td>
<td>12.0</td>
<td>0</td>
<td>1902</td>
<td>40</td>
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<th>1</th>
<td>44</td>
<td>236746</td>
<td>14.0</td>
<td>10520</td>
<td>0</td>
<td>45</td>
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<th>2</th>
<td>38</td>
<td>96185</td>
<td>10.0</td>
<td>0</td>
<td>0</td>
<td>32</td>
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<th>3</th>
<td>38</td>
<td>112847</td>
<td>15.0</td>
<td>0</td>
<td>0</td>
<td>40</td>
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<th>4</th>
<td>42</td>
<td>82297</td>
<td>10.0</td>
<td>0</td>
<td>0</td>
<td>50</td>
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<p>Values missing in the <code>education-num</code> column are replaced by 10, which is the median of the column in <code>train_df</code>. Categorical variables are not changed, since <code>nan</code> is simply used as another category.</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">valid_df</span><span class="p">[</span><span class="n">cont_names</span><span class="p">]</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
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<th>age</th>
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<th>capital-gain</th>
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<th>800</th>
<td>45</td>
<td>96975</td>
<td>10.0</td>
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<td>46</td>
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<td>15024</td>
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<td>36</td>
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<td>10.0</td>
<td>0</td>
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<td>38</td>
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<th>803</th>
<td>25</td>
<td>50053</td>
<td>10.0</td>
<td>0</td>
<td>0</td>
<td>45</td>
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<th>804</th>
<td>37</td>
<td>164526</td>
<td>10.0</td>
<td>0</td>
<td>0</td>
<td>40</td>
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<h2 id="FillStrategy" class="doc_header">`FillStrategy`<a class="source_link" data-toggle="collapse" data-target="#FillStrategy-pytest" style="float:right; padding-right:10px">[test]</a></h2><blockquote><p><code>Enum</code> = [MEDIAN, COMMON, CONSTANT]</p>
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<div class="collapse" id="FillStrategy-pytest"><div class="card card-body pytest_card"><a type="button" data-toggle="collapse" data-target="#FillStrategy-pytest" class="close" aria-label="Close"><span aria-hidden="true">×</span></a><p>Tests found for <code>FillStrategy</code>:</p><p>Some other tests where <code>FillStrategy</code> is used:</p><ul><li><code>pytest -sv tests/test_tabular_transform.py::test_default_fill_strategy_is_median</code> <a href="https://github.com/fastai/fastai/blob/master/tests/test_tabular_transform.py#L30" class="source_link" style="float:right">[source]</a></li></ul><p>To run tests please refer to this <a href="/dev/test.html#quick-guide">guide</a>.</p></div></div><p>Enum flag represents determines how <a href="/tabular.transform.html#FillMissing"><code>FillMissing</code></a> should handle missing/nan values</p>
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<li><em>MEDIAN</em>: nans are replaced by the median value of the column</li>
<li><em>COMMON</em>: nans are replaced by the most common value of the column</li>
<li><em>CONSTANT</em>: nans are replaced by <code>fill_val</code> </li>
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<h2 id="Normalize" class="doc_header"><code>class</code> <code>Normalize</code><a href="https://github.com/fastai/fastai/blob/master/fastai/tabular/transform.py#L181" class="source_link" style="float:right">[source]</a><a class="source_link" data-toggle="collapse" data-target="#Normalize-pytest" style="float:right; padding-right:10px">[test]</a></h2><blockquote><p><code>Normalize</code>(<strong><code>cat_names</code></strong>:<code>StrList</code>, <strong><code>cont_names</code></strong>:<code>StrList</code>) :: <a href="/tabular.transform.html#TabularProc"><code>TabularProc</code></a></p>
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<div class="collapse" id="Normalize-pytest"><div class="card card-body pytest_card"><a type="button" data-toggle="collapse" data-target="#Normalize-pytest" class="close" aria-label="Close"><span aria-hidden="true">×</span></a><p>No tests found for <code>Normalize</code>. To contribute a test please refer to <a href="/dev/test.html">this guide</a> and <a href="https://forums.fast.ai/t/improving-expanding-functional-tests/32929">this discussion</a>.</p></div></div><p>Normalize the continuous variables.</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">norm</span> <span class="o">=</span> <span class="n">Normalize</span><span class="p">(</span><span class="n">cat_names</span><span class="p">,</span> <span class="n">cont_names</span><span class="p">)</span>
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<h4 id="Normalize.apply_train" class="doc_header"><code>apply_train</code><a href="https://github.com/fastai/fastai/blob/master/fastai/tabular/transform.py#L183" class="source_link" style="float:right">[source]</a><a class="source_link" data-toggle="collapse" data-target="#Normalize-apply_train-pytest" style="float:right; padding-right:10px">[test]</a></h4><blockquote><p><code>apply_train</code>(<strong><code>df</code></strong>:<code>DataFrame</code>)</p>
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<div class="collapse" id="Normalize-apply_train-pytest"><div class="card card-body pytest_card"><a type="button" data-toggle="collapse" data-target="#Normalize-apply_train-pytest" class="close" aria-label="Close"><span aria-hidden="true">×</span></a><p>Tests found for <code>apply_train</code>:</p><p>Some other tests where <code>apply_train</code> is used:</p><ul><li><code>pytest -sv tests/test_tabular_transform.py::test_categorify</code> <a href="https://github.com/fastai/fastai/blob/master/tests/test_tabular_transform.py#L6" class="source_link" style="float:right">[source]</a></li><li><code>pytest -sv tests/test_tabular_transform.py::test_fill_missing_leaves_no_na_values</code> <a href="https://github.com/fastai/fastai/blob/master/tests/test_tabular_transform.py#L36" class="source_link" style="float:right">[source]</a></li><li><code>pytest -sv tests/test_tabular_transform.py::test_fill_missing_returns_correct_medians</code> <a href="https://github.com/fastai/fastai/blob/master/tests/test_tabular_transform.py#L49" class="source_link" style="float:right">[source]</a></li></ul><p>To run tests please refer to this <a href="/dev/test.html#quick-guide">guide</a>.</p></div></div><p>Compute the means and stds of <code>self.cont_names</code> columns to normalize them.</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">norm</span><span class="o">.</span><span class="n">apply_train</span><span class="p">(</span><span class="n">train_df</span><span class="p">)</span>
<span class="n">train_df</span><span class="p">[</span><span class="n">cont_names</span><span class="p">]</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
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