title: Control row groups with pandas.DataFrame.to_parquet
body: <p>To read a parquet file into multiple partitions, it should be stored using row groups (see <em><a href="https://stackoverflow.com/q/58437182/974555">How to read a single large parquet file into multiple partitions using dask/dask-cudf?</a></em>).  The pandas documentation <a href="https://pandas.pydata.org/pandas-docs/version/1.0.0/user_guide/io.html#partitioning-parquet-files" rel="noreferrer">describes partitioning of columns</a>, the <a href="https://arrow.apache.org/docs/python/parquet.html#finer-grained-reading-and-writing" rel="noreferrer">pyarrow documentation describes how to write multiple row groups</a>.  Using the pandas DataFrame <code>.to_parquet</code> method, can I interface the ability to write multiple row groups, or will it always write to a single partition?  If yes, how?</p>
  
  <p>Although the dataset is small (currently only 3 GB), I want to <a href="https://stackoverflow.com/q/58437182/974555">read into multiple partitions</a> such that subsequent processing using dask will use multiple cores (I can repartition, but this creates additional overhead) (and I might work with datasets of some 10s of GB later, still small but too large for RAM).</p>