title: Pandas, groupby where column value is greater than x
body: <p>I have a table like this</p>
  
  <pre><code>    timestamp   avg_hr  hr_quality  avg_rr  rr_quality  activity    sleep_summary_id
  
      1422404668  66      229             0       0           13              78
      1422404670  64      223             0       0           20              78
      1422404672  64      216             0       0           11              78
      1422404674  66      198             0       40          9               78
      1422404676  65      184             0       30          3               78
      1422404678  64      173             0       10          17              78
      1422404680  66      199             0       20          118             78
  </code></pre>
  
  <p>I'm trying to group the data by <code>timestamp</code>,<code>sleep id</code> and <code>rr_quality</code>, where <code>rr_quality</code> is <code>&gt; 0</code></p>
  
  <p>I've tried the following and none of them seems to work</p>
  
  <pre><code> df3 = df2.groupby([df2.index.hour,'sleep_summary_id',df2['rr_quality']&gt;0])
  
   df3 = df2.groupby([df2.index.hour,'sleep_summary_id','rr_quality'&gt;0])
  
   df3 = df2.groupby([df2.index.hour,'sleep_summary_id',['rr_quality']&gt;0])
  </code></pre>
  
  <p>All of them returns a keyerror.</p>
  
  <p><strong>EDIT:</strong></p>
  
  <p>Also can't seem to be able to pass more than one filter at a time.
  I tried the following: </p>
  
  <pre><code>df2[df2['rr_quality'] &gt;= 150, df2['hr_quality'] &gt; 200]
  df2[df2['rr_quality'] &gt;= 150, ['hr_quality'] &gt; 200]
  df2[[df2['rr_quality'] &gt;= 150, ['hr_quality'] &gt; 200]]
  </code></pre>
  
  <p>returns: <code>TypeError: 'Series' objects are mutable, thus they cannot be hashed</code></p>