title: read csv-data with missing values into python using pandas
body: <p>I have a CSV-file looking like this:</p>
  
  <pre><code>"row ID","label","val"
  "Row0","5",6
  "Row1","",6
  "Row2","",6
  "Row3","5",7
  "Row4","5",8
  "Row5",,9
  "Row6","nan",
  "Row7","nan",
  "Row8","nan",0
  "Row9","nan",3
  "Row10","nan",
  </code></pre>
  
  <p>All quoted entries are strings. Non-quoted entries are numerical. Empty fields are missing values (NaN), Quoted empty fields still should be considered as empty strings.
  I tried to read it in with pandas read_csv but I cannot get it working the way I would like to have it... It still consideres ,"", and ,, as NaN, while it's not true for the first one.</p>
  
  <pre><code>d = pd.read_csv(csv_filename, sep=',', keep_default_na=False, na_values=[''], quoting = csv.QUOTE_NONNUMERIC)
  </code></pre>
  
  <p>Can anybody help? Is it possible at all?</p>