title: Pandas group by filter based on conditions
body: <p>I have a dataset quite similar to mentioned here
  <a href="http://pandas.pydata.org/pandas-docs/stable/10min.html#grouping" rel="nofollow noreferrer">http://pandas.pydata.org/pandas-docs/stable/10min.html#grouping</a></p>
  
  <pre><code>&gt;&gt;&gt; df
       A      B         C
  0  foo    one -1.735400
  1  bar    one -0.148954
  2  foo    two  0.103798
  3  bar  three -0.576249
  4  foo    two  1.379046
  5  bar    two  0.802281
  6  foo    one -0.758771
  7  foo  three  1.270179
  </code></pre>
  
  <p>I have executed command as <code>df.groupby(['A','B'])['C'].sum()</code></p>
  
  <pre><code>&gt;&gt;&gt; grpd = df.groupby(['A','B'])['C'].sum()
  
  &gt;&gt;&gt; grpd
  A    B    
  bar  one     -0.148954
       three   -0.576249
       two      0.802281
  foo  one     -2.494171
       three    1.270179
       two      1.482844
  Name: C, dtype: float64
  </code></pre>
  
  <p>In my particular case, Column A has storeid, B is month (over year) and C is sales value</p>
  
  <p>Goal is to find All storeid who have sold beyond a certain value for each month. </p>
  
  <p>Example question - Which storied have sold beyond $1000 for any month?</p>
  
  <p>I can easily do this in SQL but not sure how to do it in Pandas data frame.</p>