Pandas rank by multiple columns

Question

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'?

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)

current output:

    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

I'm trying to generate an output like this:

    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

Answer 1

pd.factorize will generate unique values for each unique element of a iterable. We only need to sort in the order we'd like, then factorize. In order to do multiple columns, we convert the sorted result to tuples.

cols = ['SaleCount', 'TotalRevenue']
tups = df[cols].sort_values(cols, ascending=False).apply(tuple, 1)
f, i = pd.factorize(tups)
factorized = pd.Series(f + 1, tups.index)

df.assign(Rank=factorized)

         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
4  2016-12-02         20           500    S4     6
9  2016-12-02         20           500   S10     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