title: How to specify the number of partitions reading parquet into a Dask dataframe?
body: <p>I read my parquet data as follows:</p>
  
  <pre><code>file_names = glob.glob('./events/*/*/*/*/*/part*.parquet')
  pf = fp.ParquetFile(file_names, root='./events')
  pf.cats = {'customer': pf.cats['customer']}
  dfs = (delayed(pf.read_row_group_file)(rg, pf.columns, pf.cats) for rg in pf.row_groups)
  df = dd.from_delayed(dfs)
  </code></pre>
  
  <p>I can't use <code>dd.read_parquet</code>, because my parquet is partitioned, and I want to avoid loading some of the categoricals.</p>
  
  <p>I have two questions here:</p>
  
  <ul>
  <li><p>How can I tell Dask what number of partitions I want my dataframe to have?</p></li>
  <li><p>How many partitions will Dask create by default?</p></li>
  </ul>