title: convert float to hours and minutes in pandas/numpy
body: <p>I have several columns in a pd.DataFrame in which decimal separates hours and minutes (e.g., 3.15 = 3 hours, 15 minutes). Is there a quick way to convert this so that the data are recognized as h.m ? The pandas Time Series documentation doesn't seem to apply to my case. I don't have or want to attach any dates.</p>
  
  <p>I tried:</p>
  
  <pre><code># create df
  hour_min = pd.DataFrame({'a': [4.5, 2.3, 3.17],
               'b': [2.12, 1.13, 9.13],
               'c': [8.23, 9.14, 7.45]})
  # convert to hours   
  hour_min.astype('timedelta64[h]') 
  </code></pre>
  
  <p>which gives</p>
  
  <pre><code>         a        b        c
  0 04:00:00 02:00:00 08:00:00
  1 02:00:00 01:00:00 09:00:00
  2 03:00:00 09:00:00 07:00:00
  </code></pre>
  
  <p>but I want</p>
  
  <pre><code>    a     b     c
  0 04:50 02:12 08:23
  1 02:30 01:13 09:14
  2 03:17 09:13 07:45
  </code></pre>
  
  <p>I also need the following type of result from adding/subtracting column values 1.32 + 1.32 = 3.04</p>