title: Filter out data in a large pandas dataframe
body: <p>I'm trying to filter out some years in my very large dataframe.<br>
  This is a very short and easy version of my data:</p>
  
  <pre><code>Year  Value  Symbol
  2001  0.1      ABC
  2002  0.1      ABC
  2003  0.2      ABC
  2002  0.4      ZZZ
  2003  0.8      ZZZ
  2000  0.9      ASD
  2001  0.1      ASD
  2002  0.3      ASD
  2003  0.2      ASD
  </code></pre>
  
  <p>As you can see different symbols have different starting year in my dataset. For example symbol ASD has its first value in 2000 while symbol ZZZ only in 2002. I in this specific I would like only to preserve the data for years where all symbols have their value.  The result should look like this:</p>
  
  <pre><code>Year  Value  Symbol  
  2002  0.1      ABC
  2003  0.2      ABC
  2002  0.4      ZZZ
  2003  0.8      ZZZ
  2002  0.3      ASD
  2003  0.2      ASD
  </code></pre>
  
  <p>The problem with my large dataframe is that I have a lot of dates and I dont know the starting date for every symbol(and I have a lot of symbols). So I should first find the starting date for each symbol(or the symbol with the latter starting date) and after that delete all the unnecessary data. Is there a way of efficiently doing this in pandas/python? </p>