title: How do I group hourly data by day and count only values greater than a set amount in Pandas?
body: <p>I am new to Pandas but have been working with python for a few years now.</p>
  
  <p>I have a large data set of hourly data with multiple columns.  I need to group the data by day then count how many times the value is above 85 for each day for each column.</p>
  
  <p>example data:</p>
  
  <pre><code>date                     KMRY  KSNS  PCEC1  KFAT                                          
  2014-06-06 13:00:00  56.000000  63.0     17    11
  2014-06-06 14:00:00  58.000000  61.0     17    11
  2014-06-06 15:00:00  63.000000  63.0     16    10
  2014-06-06 16:00:00  67.000000  65.0     12    11
  2014-06-06 17:00:00  67.000000  67.0     10    13
  2014-06-06 18:00:00  72.000000  75.0      9    14
  2014-06-06 19:00:00  77.000000  79.0      9    15
  2014-06-06 20:00:00  84.000000  81.0      9    23
  2014-06-06 21:00:00  81.000000  86.0     12    31
  2014-06-06 22:00:00  84.000000  84.0     13    28
  2014-06-06 23:00:00  83.000000  86.0     15    34
  2014-06-07 00:00:00  84.000000  86.0     16    36
  2014-06-07 01:00:00  86.000000  89.0     17    43
  2014-06-07 02:00:00  86.000000  89.0     20    44
  2014-06-07 03:00:00  89.000000  89.0     22    49
  2014-06-07 04:00:00  86.000000  86.0     22    51
  2014-06-07 05:00:00  86.000000  89.0     21    53
  </code></pre>
  
  <p>From the sample above my results should look like the following:</p>
  
  <pre><code> date           KMRY  KSNS  PCEC1  KFAT   
   2014-06-06     0     2     0      0    
   2014-06-07     5     6     0      0
  </code></pre>
  
  <p>Any help you be greatly appreciated.</p>
  
  <pre><code>(D_RH&gt;85).sum()
  </code></pre>
  
  <p>The above code gets me close but I need a daily break down also not just the column counts.</p>