title: Pandas rank by multiple columns
body: <p>I am trying to rank a pandas data frame based on two columns.
  I can rank it based on one column, but how can to rank it based on two columns? 'SaleCount', then 'TotalRevenue'?</p>
  
  <pre><code>import pandas as pd
  
  df = pd.DataFrame({'TotalRevenue':[300,9000,1000,750,500,2000,0,600,50,500],
      'Date':['2016-12-02' for i in range(10)],
      'SaleCount':[10,100,30,35,20,100,0,30,2,20],
      'shops':['S3','S2','S1','S5','S4','S8','S6','S7','S9','S10']})
  
  df['Rank'] = df.SaleCount.rank(method='dense',ascending = False).astype(int)
  
  #df['Rank'] = df.TotalRevenue.rank(method='dense',ascending = False).astype(int)
  df.sort_values(['Rank'], inplace=True)
  
  print(df)
  </code></pre>
  
  <p>current output:</p>
  
  <pre><code>    Date        SaleCount   TotalRevenue    shops   Rank
  1   2016-12-02  100          9000            S2      1
  5   2016-12-06  100          2000            S8      1
  3   2016-12-04  35           750             S5      2
  2   2016-12-03  30           1000            S1      3
  7   2016-12-08  30           600             S7      3
  9   2016-12-10  20           500             S10     4
  4   2016-12-05  20           500             S4      4
  0   2016-12-01  10           300             S3      5
  8   2016-12-09  2            50              S9      6
  6   2016-12-07  0            0               S6      7
  </code></pre>
  
  <p>I'm trying to generate an output like this:</p>
  
  <pre><code>    Date        SaleCount   TotalRevenue    shops   Rank
  1   2016-12-02  100          9000            S2      1
  5   2016-12-02  100          2000            S8      2
  3   2016-12-02  35           750             S5      3
  2   2016-12-02  30           1000            S1      4
  7   2016-12-02  30           600             S7      5
  9   2016-12-02  20           500             S10     6
  4   2016-12-02  20           500             S4      6
  0   2016-12-02  10           300             S3      7
  8   2016-12-02  2            50              S9      8
  6   2016-12-02  0            0               S6      9
  </code></pre>