如何在基于两个数据框之间的多个条件的数据框中获取新列？

Question

I have two dataframes and I am looking to get a column in DF1 that will have values of the "current date" column plus number of days relating the the relevant status and technology in DF2. For example in the below the first value in the "new date" column is 18/03/2022 + 1095 days as it is checking to see if technology = wind and status = construction.

DF 1

Current Date	Technology	Status	New Date DESIRED FROM CODE
18/03/2022	Wind	Construction	16/12/2022
15/02/2022	Solar	Construction	15/11/2022
24/01/2022	Battery	Application approved	24/10/2022
23/09/2020	Wind	Application approved	24/03/2023
18/11/2021	Solar	Application submitted	18/11/2023
25/06/2020	Solar	Application approved	25/03/2021
27/02/2020	Wind	Application submitted	25/02/2025
10/03/2022	Battery	Application submitted	09/03/2024

DF 2

Technology	Application submitted	Application approved	Construction
Battery	730	273.75	273.75
Solar Photovoltaics	730	273.75	273.75
Wind	1825	912.5	1095

Answer 1

Use DataFrame.melt with convert values to timedeltas by to_timedelta (if need better accuracy remove .astype(int) ):

df2 = (df2.melt('Technology', var_name='Status', value_name='New Date')
          .assign(**{'New Date': 
                    lambda x: pd.to_timedelta(x['New Date'].astype(int), unit='d')}))
print (df2)
            Technology                 Status  New Date
0              Battery  Application submitted  730 days
1  Solar Photovoltaics  Application submitted  730 days
2                 Wind  Application submitted 1825 days
3              Battery   Application approved  273 days
4  Solar Photovoltaics   Application approved  273 days
5                 Wind   Application approved  912 days
6              Battery           Construction  273 days
7  Solar Photovoltaics           Construction  273 days
8                 Wind           Construction 1095 days

And then use left join and add column Current Date :

df = df1.merge(df2, on=['Technology','Status'], how='left')
df['New Date'] += pd.to_datetime(df['Current Date'], dayfirst=True)
print (df)
  Current Date Technology                 Status   New Date
0   18/03/2022       Wind           Construction 2025-03-17
1   15/02/2022      Solar           Construction        NaT
2   24/01/2022    Battery   Application approved 2022-10-24
3   23/09/2020       Wind   Application approved 2023-03-24
4   18/11/2021      Solar  Application submitted        NaT
5   25/06/2020      Solar   Application approved        NaT
6   27/02/2020       Wind  Application submitted 2025-02-25
7   10/03/2022    Battery  Application submitted 2024-03-09

For match Solar Photovoltaics values is possible use split and select first values:

df2['Technology'] = df2['Technology'].str.split().str[0]

df2 = (df2.melt('Technology', var_name='Status', value_name='New Date')
          .assign(**{'New Date':  
                  lambda x: pd.to_timedelta(x['New Date'].astype(int), unit='d')}))
print (df2)
  Technology                 Status  New Date
0    Battery  Application submitted  730 days
1      Solar  Application submitted  730 days
2       Wind  Application submitted 1825 days
3    Battery   Application approved  273 days
4      Solar   Application approved  273 days
5       Wind   Application approved  912 days
6    Battery           Construction  273 days
7      Solar           Construction  273 days
8       Wind           Construction 1095 days


df = df1.merge(df2, on=['Technology','Status'], how='left')
df['New Date'] += pd.to_datetime(df['Current Date'], dayfirst=True)
print (df)
  Current Date Technology                 Status   New Date
0   18/03/2022       Wind           Construction 2025-03-17
1   15/02/2022      Solar           Construction 2022-11-15
2   24/01/2022    Battery   Application approved 2022-10-24
3   23/09/2020       Wind   Application approved 2023-03-24
4   18/11/2021      Solar  Application submitted 2023-11-18
5   25/06/2020      Solar   Application approved 2021-03-25
6   27/02/2020       Wind  Application submitted 2025-02-25
7   10/03/2022    Battery  Application submitted 2024-03-09