title: Create different dataframes from dictionary
body: <p>I have created a dictionary by grouping some raingauges by their code with this coding</p>
  
  <pre><code>dict_of_gauges = {k: v for k, v in PE_14.groupby('gauge_code')}
  </code></pre>
  
  <p>which gave me some entries like the ones shown below</p>
  
  <pre><code> 11800  261070705A  PAULISTA    PE 2014-08-21 17:10:00      0.2
   11801  261070705A  PAULISTA    PE 2014-08-21 17:20:00      0.0
   11802  261070705A  PAULISTA    PE 2014-08-21 17:30:00      0.2
   11803  261070705A  PAULISTA    PE 2014-08-21 17:40:00      0.0
   11804  261070705A  PAULISTA    PE 2014-08-21 18:00:00      0.0
  [3966 rows x 5 columns],
   '261070704A':        gauge_code      city state            datetime  rain_mm
   11493  261070704A  PAULISTA    PE 2014-08-21 21:20:00      0.2
   11494  261070704A  PAULISTA    PE 2014-08-21 21:30:00      0.0
   11495  261070704A  PAULISTA    PE 2014-08-21 21:40:00      0.0
   11496  261070704A  PAULISTA    PE 2014-08-21 21:50:00      0.0
   11497  261070704A  PAULISTA    PE 2014-08-21 22:00:00      0.0
  [4180 rows x 5 columns],
  </code></pre>
  
  <p>now i really want to create dataframes for each one of them, and assign names like "df1", "df2" etc... but i dont seem to know how to do that inside a FOR. 
  The code i ended up using was</p>
  
  <pre><code>df1 = pd.DataFrame.from_records(dict_of_gauges['261070703A'])
  df2 = pd.DataFrame.from_records(dict_of_gauges['261070705A'])
  .
  .
  .
  </code></pre>
  
  <p>but its not very professional to do the same thing so many times, i don't know how to assign those names and the pseudocode that i tried to make (below) didnt really work as it was overwriting the "df" at every loop, as expected.</p>
  
  <pre><code>listdfs = ['df0','df1','df2','df3','df4','df5']
  for df, gauge in zip(listdfs, dict_of_gauges):
      df = pd.DataFrame.from_records(dict_of_gauges[gauge]) 
  </code></pre>
  
  <p>Could someone please give me some light in this?</p>