Question

使用给定的pandas数据帧，我想为连续的最高，第二高，第三高等值创建新列。然后为每个列的相应列名创建另一列。下面的代码对行的最大值执行此操作，但不执行以下操作。

改编自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)

这会产生：

    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

唯一需要注意的是，如果对性能有影响，可能会有非常多的列。谢谢你们。

Answer 1

使用关注性能的底层数组数据的一种方法是 -

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)

示例输入，输出 -

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

如果您需要按顺序排列值和ID，我们需要修改最后几步 -

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)

示例输出 -

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

在pandas数据框中对行进行排序并获取列ID

1 个答案: