title: Fast method for removing duplicate columns in pandas.Dataframe
body: <p>so by using </p>
  
  <pre><code>df_ab = pd.concat([df_a, df_b], axis=1, join='inner')
  </code></pre>
  
  <p>I get a Dataframe looking like this:</p>
  
  <pre><code>    A    A    B    B
  0   5    5   10   10
  1   6    6   19   19
  </code></pre>
  
  <p>and I want to remove its multiple columns:</p>
  
  <pre><code>    A     B
  0   5    10
  1   6    19
  </code></pre>
  
  <p>Because df_a and df_b are subsets of the same Dataframe I know that all rows have the same values if the column name is the same. I have a working solution:</p>
  
  <pre><code>df_ab = df_ab.T.drop_duplicates().T
  </code></pre>
  
  <p>but I have a number of rows so this one is very slow. Does someone have a faster solution? I would prefer a solution where explicit knowledge of the column names isn't needed.</p>