title: GroupBy and concat array columns pyspark
body: <p>I have this data frame</p>
  <pre><code>df = sc.parallelize([(1, [1, 2, 3]), (1, [4, 5, 6]) , (2,[2]),(2,[3])]).toDF([&quot;store&quot;, &quot;values&quot;])
  
  +-----+---------+
  |store|   values|
  +-----+---------+
  |    1|[1, 2, 3]|
  |    1|[4, 5, 6]|
  |    2|      [2]|
  |    2|      [3]|
  +-----+---------+
  </code></pre>
  <p>and I would like to convert into the follwing df:</p>
  <pre><code>+-----+------------------+
  |store|      values      |
  +-----+------------------+
  |    1|[1, 2, 3, 4, 5, 6]|
  |    2|            [2, 3]|
  +-----+------------------+
  </code></pre>
  <p>I did this:</p>
  <pre><code>from  pyspark.sql import functions as F
  df.groupBy(&quot;store&quot;).agg(F.collect_list(&quot;values&quot;))
  </code></pre>
  <p>but the solution has this <code>WrappedArrays</code></p>
  <pre><code>+-----+----------------------------------------------+
  |store|collect_list(values)                          |
  +-----+----------------------------------------------+
  |1    |[WrappedArray(1, 2, 3), WrappedArray(4, 5, 6)]|
  |2    |[WrappedArray(2), WrappedArray(3)]            |
  +-----+----------------------------------------------+
  </code></pre>
  <p>Is there any way to transform the <code>WrappedArrays</code> into concatenated arrays? Or can I do it differently?</p>