Skip rows with missing values in read_csv
<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>
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