如果你正在处理一个更大的数据集，其中一个方法就是引入numpy。

import numpy as np

df['F_Type'] = df[['1F_col', '2F_col', '3F_col']].any(axis=1).astype(int)
df['G_Type'] = df[['4G_col']].any(axis=1).astype(int)

df['Comm1'] = np.where(df['F_Type'] & df['G_Type'], 'Good', 
                        np.where(df['F_Type'], '4G', '1F'))

df['Comm2'] = np.where(np.logical_and(df['F_Type'] == 0, df['G_Type'] == 0), 
                        'Hard', np.where(df['Comm1'] == 'Good', 'Good', 'Soft'))

print(df)

ID  1F_col  2F_col  3F_col  4G_col  F_Type  G_Type Comm1 Comm2
0   1       0       1       1       1       1       1  Good  Good
1   2       0       1       0       0       1       0    4G  Soft
2   3       1       1       0       1       1       1  Good  Good
3   4       0       0       0       1       0       1    1F  Soft
4   5       0       0       0       0       0       0    1F  Hard
5   6       1       1       1       1       1       1  Good  Good

df = df.drop(columns=['1F_col', '2F_col', '3F_col', '4G_col'])
print(df)

ID  F_Type  G_Type Comm1 Comm2
0   1       1       1  Good  Good
1   2       1       0    4G  Soft
2   3       1       1  Good  Good
3   4       0       1    1F  Soft
4   5       0       0    1F  Hard
5   6       1       1  Good  Good

df_2 = pd.DataFrame(df.ID)
df_2["F_Type"] = (df.loc[:,df.columns.str.match("[0-9]*F_.*")]==1).any(1)
df_2["G_Type"] = (df.loc[:,df.columns.str.match("[0-9]*G_.*")]==1).any(1)

#Comm1
df_2.loc[df_2.F_Type & df_2.G_Type, "Comm1"] = "Good"
df_2.loc[df_2.F_Type & (~df_2.G_Type), "Comm1"] = "4G"
df_2.loc[~df_2.F_Type, "Comm1"] = "1F"

#Comm2
df_2["Comm2"] = df_2.Comm1
df_2.loc[(~df_2[["F_Type","G_Type"]]).all(1), "Comm2"] = "Hard"
df_2.loc[df_2[["F_Type","G_Type"]].sum(1)==1, "Comm2"] = "Soft"

#converting F_type and G_Type in int type
df_2[["F_Type","G_Type"]] = df_2[["F_Type","G_Type"]].astype(int)
df_2

下面是另一种方法：

d = {(1,1):['Good','Good'],
(1,0):['4G','soft'],
(0,1):['1F','soft'],
(0,0):['1F','hard']}

df1 = df.set_index('ID').groupby(lambda x: x[1],axis=1).any().astype(int)

df2 = pd.DataFrame(pd.MultiIndex.from_frame(df1).map(d).tolist(),columns = ['Comm1','Comm2'])

final_df = pd.concat([df1.reset_index(),df2],axis=1)

输出：

ID  F  G Comm1 Comm2
0   1  1  1  Good  Good
1   2  1  0    4G  soft
2   3  1  1  Good  Good
3   4  0  1    1F  soft
4   5  0  0    1F  hard
5   6  1  1  Good  Good