title: Split data frame into multiple data frames based on unique column combinations
body: <p>I have the following data frame:</p>
  <pre><code>import pandas as pd
  
  units = [1, 1, 1, 5, 5, 5]
  locations = [30, 30, 30, 32, 32, 32]
  timestamps = [1, 2, 3, 1, 2, 3]
  quantities = [1, 5, 3, 10, 35, 39]
  data = {'units': units, 'locations': locations, 'timestamps': timestamps,
          'quantities': quantities}
  df = pd.DataFrame(data=data)
  </code></pre>
  <p>that looks like this:</p>
  <pre><code> &gt;&gt;&gt; df
     units  locations  timestamps  quantities
  0      1         30           1           1
  1      1         30           2           5
  2      1         30           3           3
  3      5         32           1          10
  4      5         32           2          35
  5      5         32           3          39
  </code></pre>
  <p>I need to get a list of data frames from all <strong>unique combinations</strong> of units and locations, i.e. something that uses <code>df.groupby(['units', 'locations'])</code>. The end result should look something like this:</p>
  <pre><code>(1, 30)
     timestamps  quantities
  0           1           1
  1           2           5
  2           3           3
  
  (5, 32)
     timestamps  quantities
  3           1          10
  4           2          35
  5           3          39
  </code></pre>
  <p>is this possible, please?</p>