title: How to ignore rows where blank values excist Pandas Python
body: <p>What i'm trying to do is query a Panda DataFrame in order to give me a filtered version of the original one </p>
  
  <pre><code>self.waferInfo = pd.read_csv(fileName, index_col= None, na_values=['NA', ""] , usecols=[18,5,6,8,2])
  
  print(self.waferInfo.head(5))
  
  self.df2 = self.waferInfo[(self.waferInfo.FILE_FINISH_TS &gt;= dateBegin) &amp; (self.waferInfo.FILE_FINISH_TS &lt;= dateEnd) ]
  
  print(self.df2.head(5))
  </code></pre>
  
  <p>when the first print happens the expected rows print out but when the 2nd one is called, it appears empty. I figured out the reason that was happening was because the original DataFrame has some blanks 
  for example :</p>
  
  <pre><code>18 5 6 8 2
  A  B C   E
  D  E T Y P
  F  R B A L
  </code></pre>
  
  <p>I would want my Dataframe to return </p>
  
  <pre><code>18 5 6 8 2
  D  E T Y P
  F  R B A L
  </code></pre>
  
  <p>the fact that Column 8 has an empty cell it returns a complete empty DataFrame. I know this because I deleted all the rows that had empty cell's in excel and the DataFrame worked fine after that. 
  is there any way to ignore rows that have a missing value.</p>