title: in Pandas, when using read_csv(), how to assign a NaN to a value that's not the dtype intended?
body: <p><strong>Note:</strong> Please excuse my very low skilled English, feel free to modify the question's title, or the following text to be more understandable</p>
  
  <p>I have this line in my code:</p>
  
  <pre><code>moto = pd.read_csv('reporte.csv')
  </code></pre>
  
  <p>It sends a <strong><code>DtypeWarning: Columns (2,3,4,5,6,7,8,9,10,12,13) have mixed types.</code></strong> warning, so I change it to</p>
  
  <pre><code>moto = pd.read_csv('reporte.csv', dtype={'TP': np.float64})
  </code></pre>
  
  <p>Now it drops a <strong><code>ValueError: could not convert string to float: 'None'</code></strong>.</p>
  
  <p>I checked the file (around 200K lines) with Excel, and yes, I found some cells with "<strong>None</strong>" value.</p>
  
  <p>So my question is: <strong>Is there a way to ignore the error, or force python to fill the offending error with NaN or something else?</strong></p>
  
  <p>I tried the solution <a href="https://stackoverflow.com/questions/30314153/python-pandas-dtypewarning-specify-dtype-option-on-import-how">here</a> but it didn't work.</p>