title: Iterating over PySpark GroupedData
body: <p>Lets assume that original data is like:</p>
  
  <pre><code>Competitor  Region  ProductA  ProductB
  Comp1       A       £10       £15
  Comp1       B       £11       £16
  Comp1       C       £11       £15
  Comp2       A       £9        £16
  Comp2       B       £12       £14
  Comp2       C       £14       £17
  Comp3       A       £11       £16
  Comp3       B       £10       £15
  Comp3       C       £12       £15
  </code></pre>
  
  <p>(Ref: <a href="https://stackoverflow.com/questions/40498463/python-splitting-dataframe-into-multiple-dataframes-based-on-column-values-and">Python - splitting dataframe into multiple dataframes based on column values and naming them with those values</a>)</p>
  
  <p>I wish to get list of sub dataframes based on column values, say Region, like:</p>
  
  <pre><code>df_A :
  
  Competitor  Region  ProductA  ProductB
  Comp1       A       £10       £15
  Comp2       A       £9        £16
  Comp3       A       £11       £16
  </code></pre>
  
  <p>In Python I could do:</p>
  
  <pre><code>for region, df_region in df.groupby('Region'):
      print(df_region)
  </code></pre>
  
  <p>Can I do same iteration if the df is Pyspark df?</p>
  
  <p>In Pyspark, once I do df.groupBy("Region") I get GroupedData. I dont need any aggregation like count, mean, etc. I just need list of sub dataframes, each have same "Region" value. Possible?</p>