title: Is there a faster way to export data from Dask DataFrame to CSV?
body: <p>I am reading CSV file (10 GB) using Dask. Then after performing some operations, I am Exporting file in CSV format using <code>to_csv</code>. But problem is that exporting this file is taking around <em>27 Minutes</em> (According to ProgressBar Diagnostics).</p>
  
  <p>CSV file includes 350 columns with one column of timestamp and other column's datatype are set to <code>float64</code>.</p>
  
  <ul>
  <li>Machine Specs: 
  
  <ul>
  <li>Intel i7-4610M @ 3.00 GHz</li>
  <li>8 GB DDR3 RAM</li>
  <li>500 GB SSD</li>
  <li>Windows 10 Pro</li>
  </ul></li>
  </ul>
  
  <p>I have tried exporting in separate files like <code>to_csv('filename-*.csv')</code> and also have tried without including <code>.csv</code>. So, Dask exports file with extension of <code>.part</code>. But doing this also takes same time as mentioned above.</p>
  
  <p>I think this should not be an issue with I/O operations as I am using SSD. But I am not sure about that.</p>
  
  <p>Here is my code (simplified):</p>
  
  <pre class="lang-py prettyprint-override"><code>df = dd.read_csv('path\\to\\csv')
  # Doing some operations using df.loc
  df.to_csv('export.csv', single_file=True)
  </code></pre>
  
  <p>I am using Dask v2.6.0.</p>
  
  <p>Expected output --> complete this process in less time without changing specs of machine.</p>
  
  <p>Is there anyway, I can export this file in less time?</p>