How to evaluate multiple columns on pandas?-CodePudding

I have the following pandas dataframe:

col1 col2 col3 .... colN
5    2    4    .... 9
1    2    3    .... 9
7    1    4    .... 0
1    4    7    .... 8

What I need is a way to determinate the order between several columns:

col1 col2 col3 .... colN
5    2    4    .... 9    ----> colN >= ... >= col5 >= col2 >= col3
1    2    3    .... 9    ----> colN >= ... >= col3 >= col2 >= col1
7    1    4    .... 0    ----> col1 >= ... >= col3 >= col2 >= colN
1    4    7    .... 8    ----> colN >= ... >= col3 >= col2 >= col1

And give them a numeric alias. For example:

colN >= ... >= col5 >= col2 >= col3 = X
colN >= ... >= col3 >= col2 >= col1 = Y
col1 >= ... >= col3 >= col2 >= colN = Z
:
:
col1 col2 col3 .... colN order
5    2    4    .... 9    X 
1    2    3    .... 9    Y
7    1    4    .... 0    Z
1    4    7    .... 8    Y
:
:

The number of columns may change and the alias has to follow a patron. Example with 3 columns:

col1 >= col2 >= col3 = 1
col1 >= col3 >= col2 = 2
col2 >= col1 >= col3 = 3
col2 >= col3 >= col2 = 4
col3 >= col1 >= col2 = 5
col3 >= col2 >= col1 = 6

Thanks and regards

CodePudding user response：

You can use:

df['order'] = df.apply(lambda x: '>='.join(x.sort_values(ascending=False).index), axis=1)
df['alias'] = df.groupby('order').ngroup()   1

Input

   col1  col2  col3
0     5     2     4
1     1     2     3
2     7     1     4
3     1     4     7

Output:

   col1  col2  col3             order  alias
0     5     2     4  col1>=col3>=col2      1
1     1     2     3  col3>=col2>=col1      2
2     7     1     4  col1>=col3>=col2      1
3     1     4     7  col3>=col2>=col1      2

Or for specific pattern:

alias_pattern = {'col1>=col3>=col2' : 2, 'col3>=col2>=col1' : 5}
df['alias'] = df['order'].map(alias_pattern)

Output:

   col1  col2  col3             order  alias
0     5     2     4  col1>=col3>=col2      2
1     1     2     3  col3>=col2>=col1      5
2     7     1     4  col1>=col3>=col2      2
3     1     4     7  col3>=col2>=col1      5