我的数据框df如下所示。
df
DATA ELEMENT PORT LOCATION P_RISK S_RISK W_RISK Total_Risk
Event 1000 SGSIN 0 0 0 0
Event 2000 MYKUL 0 1 0 1
Event 3000 PARIS 0 0 1 1
Event 4000 LYON 1 0 0 1
Event 1000 USNYK 0 0 0 0
Event 2000 INCOK 0 0 1 1
Event 3000 MUMBAI 0 0 0 0
Event 4000 LAHORE 1 0 0 1
Event 2000 INCOK 0 0 0 0
Event 3000 PARIS 0 0 0 0
条件
*如果Data Element是1000或2000，并且Total Risk是1，则用具有“风险名称+端口”的新列comment填充
*如果Data Element是3000或4000并且Total Risk是1，则用With comment填充新列Risk Name + LOCATION
*如果Total Risk为0，则填充“无风险”
预期产量
df
DATA ELEMENT PORT LOCATION P_RISK S_RISK W_RISK Total_Risk Comment
Event 1000 SGSIN 0 0 0 0 No Risk Indentified
Event 2000 MYKUL 0 1 0 1 S_RISK predicted at MYKUL
Event 3000 PARIS 0 0 1 1 W_RISK predicted at PARIS
Event 4000 LYON 1 0 0 1 P_RISK predicted at LYON
Event 1000 USNYK 0 0 0 0 No Risk Indentified
Event 2000 INCOK 0 0 1 1 W_RISK predicted at INCOK
Event 3000 MUMBAI 0 0 0 0 No Risk Indentified
Event 4000 LAHORE 1 0 0 1 P_RISK predicted at LAHORE
Event 2000 INCOK 0 0 0 0 No Risk Indentified
Event 3000 PARIS 0 0 0 0 No Risk Indentified
这怎么办？
答案 0 :(得分：3)
我将dot与np.where一起使用
s2=df.filter(like='_RISK')
s2=s2.dot(s2.columns)
df['new']=np.where(s2=='' ,'No Risk Indentified',s2 + ' predict at ' +df.PORT.mask(df.PORT=='',df.LOCATION))
df
Out[35]:
DATA ELEMENT PORT ... W_RISK Total_Risk new
0 Event 1000 SGSIN ... 0 0 No Risk Indentified
1 Event 2000 MYKUL ... 0 1 S_RISKpredict atMYKUL
2 Event 3000 ... 1 1 W_RISKpredict atPARIS
3 Event 4000 ... 0 1 P_RISKpredict atLYON
4 Event 1000 USNYK ... 0 0 No Risk Indentified
5 Event 2000 INCOK ... 1 1 W_RISKpredict atINCOK
6 Event 3000 ... 0 0 No Risk Indentified
7 Event 4000 ... 0 1 P_RISKpredict atLAHORE
8 Event 2000 INCOK ... 0 0 No Risk Indentified
9 Event 3000 ... 0 0 No Risk Indentified
[10 rows x 9 columns]
方法2
s2=df.filter(like='_RISK').ne(0).stack()
s2=s2[s2].reset_index(level=1)
df['new']=(s2['level_1'] + ' predict at ' +df.PORT.mask(df.PORT=='',df.LOCATION)).fillna('No Risk Indentified')