title: Using mca package in Python
body: <p>I am trying to use the <a href="https://github.com/esafak/mca/blob/master/docs/usage.rst" rel="noreferrer">mca package</a> to do multiple correspondence analysis in Python. </p>
  
  <p>I am a bit confused as to how to use it. With <code>PCA</code> I would expect to <em>fit</em> some data (i.e. find principal components for those data) and then later I would be able to use the principal components that I found to <em>transform</em> unseen data.</p>
  
  <p>Based on the MCA documentation, I cannot work out how to do this last step. I also don't understand what any of the weirdly cryptically named properties and methods do (i.e. <code>.E</code>, <code>.L</code>, <code>.K</code>, <code>.k</code> etc).</p>
  
  <p>So far if I have a DataFrame with a column containing strings (assume this is the only column in the DF) I would do something like</p>
  
  <pre><code>import mca
  ca = mca.MCA(pd.get_dummies(df, drop_first=True))
  </code></pre>
  
  <p>from what I can gather</p>
  
  <pre><code>ca.fs_r(1)
  </code></pre>
  
  <p>is the transformation of the data in <code>df</code> and </p>
  
  <pre><code>ca.L
  </code></pre>
  
  <p>is supposed to be the eigenvalues (although I get a vector of <code>1</code>s that is one element fewer that my number of features?).</p>
  
  <p>now if I had some more data with the same features, let's say <code>df_new</code> and assuming I've already converted this correctly to dummy variables, how do I find the equivalent of <code>ca.fs_r(1)</code> for the new data</p>