title: PCA with several time series as features of one instance with sklearn
body: <p>I want to apply PCA on a data set where I have 20 time series as features for one instance. I have some 1000 instances of this kind and I am looking for a way to reduce dimensionality. For every instance I have a pandas Data Frame, like:</p>
  
  <pre><code>import pandas as pd
  import numpy as np
  df = pd.DataFrame(data=np.random.normal(0, 1, (300, 20)))
  </code></pre>
  
  <p>Is there a way to use <code>sklearn.fit</code> on <strong>all</strong> instances with each having a set of time series as feature space. I mean I could apply sklearn.fit on all instances separatly, but I want the same principal components for all.</p>
  
  <p>Is there a way? The only not satisfying idea I have by now is to append all those series of one instance to one, so that I have one time series for one instance. </p>