title: PCA on sklearn - how to interpret pca.components_
body: <p>I ran PCA on a data frame with 10 features using this simple code:</p>
  
  <pre><code>pca = PCA()
  fit = pca.fit(dfPca)
  </code></pre>
  
  <p>The result of <code>pca.explained_variance_ratio_</code> shows:</p>
  
  <pre><code>array([  5.01173322e-01,   2.98421951e-01,   1.00968655e-01,
           4.28813755e-02,   2.46887288e-02,   1.40976609e-02,
           1.24905823e-02,   3.43255532e-03,   1.84516942e-03,
           4.50314168e-16])
  </code></pre>
  
  <p>I believe that means that the first PC explains 52% of the variance, the second component explains 29% and so on...</p>
  
  <p>What I dont undestand is the output of <code>pca.components_</code>. If I do the following: </p>
  
  <pre><code>df = pd.DataFrame(pca.components_, columns=list(dfPca.columns))
  </code></pre>
  
  <p>I get the data frame bellow where each line is a principal component. 
  What I'd like to understand is how to interpret that table. I know that if I square all the features on each component and sum them I get 1, but what does the -0.56 on PC1 mean? Dos it tell something about "Feature E" since it is the highest magnitude on a component that explains 52% of the variance?</p>
  
  <p><a href="https://i.stack.imgur.com/XB2Jj.png" rel="noreferrer"><img src="https://i.stack.imgur.com/XB2Jj.png" alt="enter image description here"></a></p>
  
  <p>Thanks</p>