title: Pandas parallel apply with koalas (pyspark)
body: <p>I'm new to Koalas (pyspark), and I was trying to utilize Koalas for parallel apply, but it seemed like it was using a single core for the whole operation (correct me if I'm wrong) and ended up using dask for parallel apply (using map_partition) which worked pretty well. </p>
  
  <p>However, I would like to know if there's a way to utilize Koalas for parallel apply. </p>
  
  <p>I used basic codes for operation like below.</p>
  
  <pre><code>import pandas as pd
  import databricks.koalas as ks
  
  my_big_data = ks.read_parquet('my_big_file') # file is single partitioned parquet file
  
  my_big_data['new_column'] = my_big_data['string_column'].apply(my_prep) # my_prep does stirng operations
  
  
  my_big_data.to_parquet('my_big_file_modified') # for Koalas does lazy evaluation
  
  </code></pre>