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<h1 id="working-with-large-data-sets"><span id="module-statsmodels.base.distributed_estimation"></span>Working with Large Data Sets<a class="headerlink" href="#working-with-large-data-sets" title="Link to this heading">¶</a></h1>
<p>Big data is something of a buzzword in the modern world. While statsmodels
works well with small and moderately-sized data sets that can be loaded in
memory–perhaps tens of thousands of observations–use cases exist with
millions of observations or more. Depending your use case, statsmodels may or
may not be a sufficient tool.</p>
<p>statsmodels and most of the software stack it is written on operates in
memory. Resultantly, building models on larger data sets can be challenging
or even impractical. With that said, there are 2 general strategies for
building models on larger data sets with statsmodels.</p>
<h2 id="divide-and-conquer-distributing-jobs">Divide and Conquer - Distributing Jobs<a class="headerlink" href="#divide-and-conquer-distributing-jobs" title="Link to this heading">¶</a></h2>
<p>If your system is capable of loading all the data, but the analysis you are
attempting to perform is slow, you might be able to build models on horizontal
slices of the data and then aggregate the individual models once fit.</p>
<p>A current limitation of this approach is that it generally does not support
<a class="reference external" href="https://patsy.readthedocs.io/en/latest/">patsy</a> so constructing your
design matrix (known as <cite>exog</cite>) in statsmodels, is a little challenging.</p>
<p>A detailed example is available
<a class="reference internal" href="examples/notebooks/generated/distributed_estimation.html"><span class="doc">here</span></a>.</p>
<table class="autosummary longtable docutils data align-default">
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/statsmodels.base.distributed_estimation.DistributedModel.html#statsmodels.base.distributed_estimation.DistributedModel" title="statsmodels.base.distributed_estimation.DistributedModel (Python class) — Distributed model class"><code class="xref py py-obj docutils literal notranslate"><span class="pre">DistributedModel</span></code></a>(partitions[, model_class, ...])</p></td>
<td><p>Distributed model class</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/statsmodels.base.distributed_estimation.DistributedResults.html#statsmodels.base.distributed_estimation.DistributedResults" title="statsmodels.base.distributed_estimation.DistributedResults (Python class) — Class to contain model results"><code class="xref py py-obj docutils literal notranslate"><span class="pre">DistributedResults</span></code></a>(model, params)</p></td>
<td><p>Class to contain model results</p></td>
</tr>
</tbody>
</table>
<h2 id="subsetting-your-data">Subsetting your data<a class="headerlink" href="#subsetting-your-data" title="Link to this heading">¶</a></h2>
<p>If your entire data set is too large to store in memory, you might try storing
it in a columnar container like <a class="reference external" href="https://parquet.apache.org/">Apache Parquet</a>
or <a class="reference external" href="http://bcolz.blosc.org/en/latest/">bcolz</a>. Using the patsy formula
interface, statsmodels will use the <cite>__getitem__</cite> function (i.e. data[‘Item’])
to pull only the specified columns.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><code><span class="kn">import</span><span class="w"> </span><span class="nn">pyarrow</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">pa</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">pyarrow.parquet</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">pq</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">statsmodels.formula.api</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">smf</span>
<span class="k">class</span><span class="w"> </span><span class="nc">DataSet</span><span class="p">(</span><span class="nb">dict</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">path</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">parquet</span> <span class="o">=</span> <span class="n">pq</span><span class="o">.</span><span class="n">ParquetFile</span><span class="p">(</span><span class="n">path</span><span class="p">)</span>
<span class="k">def</span><span class="w"> </span><span class="fm">__getitem__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">key</span><span class="p">):</span>
<span class="k">try</span><span class="p">:</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">parquet</span><span class="o">.</span><span class="n">read</span><span class="p">([</span><span class="n">key</span><span class="p">])</span><span class="o">.</span><span class="n">to_pandas</span><span class="p">()[</span><span class="n">key</span><span class="p">]</span>
<span class="k">except</span><span class="p">:</span>
<span class="k">raise</span> <span class="ne">KeyError</span>
<span class="n">LargeData</span> <span class="o">=</span> <span class="n">DataSet</span><span class="p">(</span><span class="s1">'LargeData.parquet'</span><span class="p">)</span>
<span class="n">res</span> <span class="o">=</span> <span class="n">smf</span><span class="o">.</span><span class="n">ols</span><span class="p">(</span><span class="s1">'Profit ~ Sugar + Power + Women'</span><span class="p">,</span> <span class="n">data</span><span class="o">=</span><span class="n">LargeData</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">()</span>
</code></pre></div>
</div>
<p>Additionally, you can add code to this example <cite>DataSet</cite> object to return only
a subset of the rows until you have built a good model. Then, you can refit
your final model on more data.</p>
<hr>
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Last update:
Feb 19, 2025
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