title: Which is the most efficient way of flattening down a pandas dataframe?
body: <p>I have a large pandas dataframe with 8 columns and several <code>NaN</code> values:</p>
  
  <pre><code>0   1   2   3   4   5   6   7   8
  1   Google, Inc. (Date 11/07/2016)  NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN
  2   Apple Inc. (Date 07/01/2016)    Amazon (Date 11/01/2016)    NaN     NaN     NaN     NaN     NaN     NaN     NaN
  3   IBM, Inc. (Date 11/08/2016)     NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN
  4   Microsoft (Date 11/10/2016)     Google, Inc. (Date 11/10/1990)  Google, Inc. (Date 11/07/2016)  Samsung (Date 05/02/2016)   NaN     NaN     NaN     NaN     NaN
  </code></pre>
  
  <p>How can I flatten down it like this:</p>
  
  <pre><code>0   companies
  1   Google, Inc. (Date 11/07/2016)
  2   Apple Inc. (Date 07/01/2016)
  3   Amazon (Date 11/01/2016)
  4   IBM, Inc. (Date 11/08/2016)
  5   Microsoft (Date 11/10/2016)
  6   Google, Inc. (Date 11/10/1990)
  7   Google, Inc. (Date 11/07/2016)
  8   Samsung (Date 05/02/2016)
  </code></pre>
  
  <p>I read the <a href="http://pandas.pydata.org/pandas-docs/stable/reshaping.html" rel="nofollow noreferrer">docs</a> and tried:</p>
  
  <pre><code>df.iloc[:,0]
  </code></pre>
  
  <p>The problem is that I lost information and order over the other columns. I idea of how to flat without lost data in the other cells and order?.</p>