title: Why is Dask to_csv saving files in parts?
body: <h2>Preamble for context:</h2>
  <p>I have a sample csv file that has more columns than rows (~300 vs 190), I'm trying to learn how it all works before working with the whole 80 million records.
  I'm working on a google colab notebook.</p>
  <h2>What I'm trying to do:</h2>
  <p>Read a CSV file, execute a value_counts() for all the columns and save the results</p>
  <p>Here's the code, I left it as is :</p>
  <pre class="lang-py prettyprint-override"><code>import dask.dataframe as dd
  import pandas as pd
  
  # Here we're reading the csv
  dfd = dd.read_csv(
      'drive/MyDrive/csvs/sample.csv', 
      delimiter=';',
  
      # Down below we specify the types of the first columns
      dtype = {'ID': object, 'BSID': 'UInt32', 'CAM': 'UInt32',
                    'AGZ': 'UInt32', 'Zen': 'UInt16', 'taw': 'UInt16'
                    },
      blocksize=64000000 # = 64 Mb chunks
  )
  
  # Here we convert the rest of the ~300 columns to UInt8
  
  cols=[i for i in dfd.columns if i not in ['ID', 'BSID', 'CAM',
                    'AGZ', 'Zen', 'taw']]
  for col in cols:
      dfd[col]=dfd[col].astype('UInt8')
  
  # value_counts
  for col in dfd.columns:
    result = dfd[col].value_counts()
    result.to_csv('drive/MyDrive/csvs/Value_counts-' + col + '.csv')
  </code></pre>
  <h2>What's going wrong:</h2>
  <p>When the code is executed, the results are stored as files named 0.part in folders that are named following the <code>Value_counts-' + col + '.csv</code> schema. I expect it to be saved as <code>Value_counts-' + col + '.csv</code> files in <code>csvs</code> folder.</p>
  <p>Why is this happening?</p>
  <p><em><strong>Additional question:</strong></em></p>
  <p>Can I run value_counts() for all columns in a better way?</p>