title: how to explode all columns of a dataframe in pyspark?
body: <p>I have got a DataFrame like this:</p>
  
  <pre><code>+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+
  |               brand|              diesel|                 e10|                  e5|         houseNumber|                  id|              isOpen|                 lat|                 lng|                name|               place|            postCode|              street|               Datum|
  +--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+
  |[TOTAL, ARAL, She...|[1.049, 1.029, 1....|[1.249, 1.209, 1....|[1.269, 1.229, 1....|[49, 12-14, ,  , ...|[4409a024-b190-4b...|[true, true, true...|[50.93128, 50.952...|[6.962356, 6.9616...|[TOTAL KOELN, Ara...|[KOELN, Köln, KOE...|[50676, 50668, 50...|[HOLZMARKT, Riehl...|2016-08-01 10:50:...|
  +--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+
  </code></pre>
  
  <p>Basically all columns are arrays. It is based on nested JSON data.
  I tried to explode it. But that is only possible with one column in a select statement. Do you know a why how I can unpack all values at once in <code>pyspark</code> so that the relations are kept?</p>