title: Pandas merge two dataframes by time column
body: <p>I have been struggling to merge data frames. I need to have the rows arranged by the time, with both sets of data's columns merged into a new data frame. I'm sorry if this is clearly documented somewhere, but it has been hard for me to find an appropriate method. I tried <code>append</code> and <code>merge</code> but I am struggling to find an appropriate solution.</p>
  
  <p>dataframe1:</p>
  
  <pre><code>     # Date Time, GMT-07:00  Crossflow (Cold) (Volts)  \
  0        1           8:51:00 AM                   1.13431   
  1        2           8:51:01 AM                   1.12821   
  2        3           8:51:02 AM                   1.12943   
  3        4           8:51:03 AM                   1.12759   
  4        5           8:51:04 AM                   1.13065   
  5        6           8:51:05 AM                   1.12821   
  6        7           8:51:06 AM                   1.12943   
  7        8           8:51:07 AM                   1.13065   
  8        9           8:51:08 AM                   1.13126   
  9       10           8:51:09 AM                   1.13126   
  10      11           8:51:10 AM                   1.12821   
  </code></pre>
  
  <p>dataframe2:</p>
  
  <pre><code>         # Date Time, GMT-07:00  \
  0        1           9:06:39 AM   
  1        2           9:06:40 AM   
  2        3           9:06:41 AM   
  3        4           9:06:42 AM   
  4        5           9:06:43 AM   
  5        6           9:06:44 AM   
  6        7           9:06:45 AM   
  7        8           9:06:46 AM   
  8        9           9:06:47 AM   
  9       10           9:06:48 AM   
  10      11           9:06:49 AM   
        K-Type, °C (LGR S/N: 10118625, SEN S/N: 10118625)  
  0                                                 43.96  
  1                                                 47.25  
  2                                                 48.90  
  3                                                 50.21  
  4                                                 43.63  
  5                                                 43.63  
  6                                                 42.97  
  7                                                 42.97  
  8                                                 42.30  
  9                                                 41.64  
  10                                                40.98  
  </code></pre>