在pandas数据框中对行进行排序并获取列ID

Question

With a given pandas dataframe, I'd like to create new columns for the highest, second highest, third highest, etc... values in a row. And then create another column for the corresponding column name of each of those. The code below does this for the max value of the row, but not those that follow.

Adapted from Find the column name which has the maximum value for each row

import pandas as pd

df = pd.DataFrame({'A': (23, 24, 55, 77, 33, 66),
                   'B': (12, 33, 0.2, 44, 23.5, 66),
                   'C': (1, 33, 66, 44, 5, 62),
                   'D': (9, 343, 4, 64, 24, 63),
                   'E': (123, 33, 2.2, 42, 2, 99)})

# Determine the max value and column name and add as columns to df
df['Max1'] = df.max(axis=1)
df['Col_Max1'] = df.idxmax(axis=1)

# Determine the 2nd and 3rd max PR and threshold levels and add as columns
# ???????????

print(df)

This produces:

    A     B   C    D      E   Max1 Col_Max1
0  23  12.0   1    9  123.0  123.0        E
1  24  33.0  33  343   33.0  343.0        D
2  55   0.2  66    4    2.2   66.0        C
3  77  44.0  44   64   42.0   77.0        A
4  33  23.5   5   24    2.0   33.0        A
5  66  66.0  62   63   99.0   99.0        E

Process finished with exit code 0

Only caveat would be that it is possible to have a very large number of columns, if that matters for performance. Thanks guys.

Answer 1

One approach using the underlying array data with focus on performance would be -

a = df.values
c = df.columns
idx = a.argsort(1)[:,::-1]
vals = a[np.arange(idx.shape[0])[:,None], idx]
IDs = c[idx]

names_vals = ['Max'+str(i+1) for i in range(a.shape[1])]
names_IDs = ['Col_Max'+str(i+1) for i in range(a.shape[1])]

df_vals = pd.DataFrame(vals, columns=names_vals)
df_IDs = pd.DataFrame(IDs, columns=names_IDs)
df_out = pd.concat([df, df_vals, df_IDs], axis=1)

Sample input, output -

In [40]: df
Out[40]: 
    A     B   C    D      E
0  23  12.0   1    9  123.0
1  24  33.0  33  343   33.0
2  55   0.2  66    4    2.2
3  77  44.0  44   64   42.0
4  33  23.5   5   24    2.0
5  66  66.0  62   63   99.0

In [41]: df_out
Out[41]: 
    A     B   C    D      E   Max1  Max2  Max3  Max4  Max5 Col_Max1 Col_Max2  \
0  23  12.0   1    9  123.0  123.0  23.0  12.0   9.0   1.0        E        A   
1  24  33.0  33  343   33.0  343.0  33.0  33.0  33.0  24.0        D        E   
2  55   0.2  66    4    2.2   66.0  55.0   4.0   2.2   0.2        C        A   
3  77  44.0  44   64   42.0   77.0  64.0  44.0  44.0  42.0        A        D   
4  33  23.5   5   24    2.0   33.0  24.0  23.5   5.0   2.0        A        D   
5  66  66.0  62   63   99.0   99.0  66.0  66.0  63.0  62.0        E        B   

  Col_Max3 Col_Max4 Col_Max5  
0        B        D        C  
1        C        B        A  
2        D        E        B  
3        C        B        E  
4        B        C        E  
5        A        D        C  

If you need the values and IDs in sequence, we need to modify the last few steps there -

df0 = pd.DataFrame(np.dstack((vals, IDs)).reshape(a.shape[0],-1))
df0.columns = np.vstack((names_vals, names_IDs)).T.ravel()
df_out = pd.concat([df, df0], axis=1)

Sample output -

In [62]: df_out
Out[62]: 
    A     B   C    D      E Max1 Col_Max1 Max2 Col_Max2  Max3 Col_Max3 Max4  \
0  23  12.0   1    9  123.0  123        E   23        A    12        B    9   
1  24  33.0  33  343   33.0  343        D   33        E    33        C   33   
2  55   0.2  66    4    2.2   66        C   55        A     4        D  2.2   
3  77  44.0  44   64   42.0   77        A   64        D    44        C   44   
4  33  23.5   5   24    2.0   33        A   24        D  23.5        B    5   
5  66  66.0  62   63   99.0   99        E   66        B    66        A   63   

  Col_Max4 Max5 Col_Max5  
0        D    1        C  
1        B   24        A  
2        E  0.2        B  
3        B   42        E  
4        C    2        E  
5        D   62        C