title: How to stack several 1 x 1 dataframes to get a 1 x n dataframe?
body: <p>I have a huge dataframe containing millions of rows. From these rows I derive new <code>k</code> dataframes which have only 1 row and 1 column. 
  What's a good way to concatenate these k dataframes together so as to now get a a dataframe <code>1 x k</code> that has 1 row and k columns.</p>
  
  <ol>
  <li><p>In the past I started with using a crossJoin among all the <code>k</code> dataframes, such as 
  <code>df1.crossJoin(df2).crossJoin(df3).crossJoin(dfk)</code></p>
  
  <p>This resulted in a broadcast timeout error, </p></li>
  <li><p>Later I moved to what I thought is a smarter solutions. </p>
  
  <p><code>df1.withColumn("temp_id", lit(0)).join(df2.withColumn("temp_id", lit(0)), "temp_id").drop("temp_id")</code>.</p>
  
  <p>This resulted in a weirder yet similar error of broadcast timeout.</p></li>
  </ol>
  
  <p>The result that I really want is a new DataFrame with 1 row and k columns which in numpy/pandas language could be</p>
  
  <p><code>pandas.concat(..., axis=1)</code>
  OR
  <code>np.vstack(.....)</code></p>