title: Skip rows with missing values in read_csv
body: <p>I have a very large csv which I need to read in. To make this fast and save RAM usage I am using read_csv and set the dtype of some columns to np.uint32.  The problem is that some rows have missing values and pandas uses a float to represent those.  </p>
  
  <ol>
  <li>Is it possible to simply skip rows with missing values? I know I could do this after reading in the whole file but this means I couldn't set the dtype until then and so would use too much RAM.</li>
  <li>Is it possible to convert missing values to some other I choose during the reading of the data? </li>
  </ol>