问题
I have the following data:
MTU (CET) Day-ahead Price [EUR/MWh]
0 09.10.2017 00:00 - 09.10.2017 01:00 43.13
1 09.10.2017 01:00 - 09.10.2017 02:00 34.80
2 09.10.2017 02:00 - 09.10.2017 03:00 33.31
3 09.10.2017 03:00 - 09.10.2017 04:00 32.24
.......
22 09.10.2017 22:00 - 09.10.2017 23:00 49.06
23 09.10.2017 23:00 - 10.10.2017 00:00 38.46
From which I would like to have data for every 5 minutes. By using:
price = pd.read_csv(price_data)
price_x = price.set_index(pd.DatetimeIndex(price['MTU (CET)'].str[:-19]))
price2 = price_x.resample('300S').pad()
I get the following data:
2017-09-10 00:00:00 43.13
2017-09-10 00:05:00 43.13
2017-09-10 00:10:00 43.13
...
2017-09-10 22:45:00 49.06
2017-09-10 22:50:00 49.06
2017-09-10 22:55:00 49.06
2017-09-10 23:00:00 38.46
However, for the minutes between 23:00 and 00:00 the price should also be 38.46. Does anyone know how to help?
回答1:
You need manually add last row with next hour and with data from last row seelcted by iloc
:
price_x = price.set_index(pd.DatetimeIndex(price['MTU (CET)'].str[:-19]))
price_x.loc[price_x.index[-1] + pd.Timedelta(1, unit='h')] = price_x.iloc[-1]
print (price_x.tail(3))
Day-ahead Price [EUR/MWh]
MTU (CET)
2017-09-10 22:00:00 49.06
2017-09-10 23:00:00 38.46
2017-09-11 00:00:00 38.46
price2 = price_x.resample('300S').pad()
print (price2.tail(20))
Day-ahead Price [EUR/MWh]
MTU (CET)
2017-09-10 22:25:00 49.06
2017-09-10 22:30:00 49.06
2017-09-10 22:35:00 49.06
2017-09-10 22:40:00 49.06
2017-09-10 22:45:00 49.06
2017-09-10 22:50:00 49.06
2017-09-10 22:55:00 49.06
2017-09-10 23:00:00 38.46
2017-09-10 23:05:00 38.46
2017-09-10 23:10:00 38.46
2017-09-10 23:15:00 38.46
2017-09-10 23:20:00 38.46
2017-09-10 23:25:00 38.46
2017-09-10 23:30:00 38.46
2017-09-10 23:35:00 38.46
2017-09-10 23:40:00 38.46
2017-09-10 23:45:00 38.46
2017-09-10 23:50:00 38.46
2017-09-10 23:55:00 38.46
2017-09-11 00:00:00 38.46
来源:https://stackoverflow.com/questions/46666464/upsampling-hourly-data-to-5-minute-data-in-pandas