title: Drop columns that aren't common between two dataframes?
body: <p>I have two dataframes that have many columns in column but a few that do not exist in both.  I would like to create a dataframe that only has the columns that are in common between both dataframes.  So for example:</p>
  
  <pre><code>list(df1)
  ['Survived', 'Age', 'Title_Mr', 'Title_Mrs', 'Title_Captain']
  list(df2)
  ['Survived', 'Age', 'Title_Mr', 'Title_Mrs', 'Title_Countess']
  </code></pre>
  
  <p>And I would like to go to:</p>
  
  <pre><code>['Survived', 'Age', 'Title_Mr', 'Title_Mrs']
  </code></pre>
  
  <p>Since <code>Title_Mr</code> and <code>Title_Mrs</code> are in both <code>df1</code> and <code>df2</code>.  I've figured out how to do it by manually entering in the columns names like so:</p>
  
  <pre><code>df1 = df1.drop(['Title_Captain'], axis=1)
  </code></pre>
  
  <p>But I'd like to find a more robust solution where I don't have to manually enter the column names.  Suggestions?</p>