问题
I asked this question and I got an answer which works for a general case with sequential and non missing data but not for my case specifically. I have a DF that looks as follows.
eventTime MeteredEnergy Demand RunningHoursLamps
6/7/2018 0:00 67.728 64 1037.82
6/7/2018 1:00 67.793 64 1038.82
6/7/2018 2:00 67.857 64 1039.82
6/7/2018 3:00 67.922 64 1040.82
6/7/2018 4:00 67.987 64 1041.82
6/7/2018 5:00 64 1042.82
6/7/2018 6:00 1043.43
6/7/2018 23:00 68.288
6/8/2018 0:00 67.728 64 1037.82
6/8/2018 23:00 67.793 64 1097.82
I need a DF that finds the difference between RunningHoursLamps values at hour 0 and hour 23 for each unique date in "eventTime" If data is missing for hour 0 or hour 23, the resultant DF can have NaN
Expected output
Date 00:00 23:00 Difference
6/7/2018 1037.82 NaN NaN
6/8/2018 1037.82 1097.82 60
回答1:
Update: For those that are interested: I found a way to do this. I parsed out a separate column with dates and hours from the eventTime column and looped through it and handled exceptions when I did not have data for the required DateTime. Thanks.
#for loop to build the bill dataframe
bill = pd.DataFrame()
for i in range(len(unique_dates)):
try :
if i == 0:
hour0 = np.nan
else:
hour0 = df.loc[((df['date'] == unique_dates[i]) & (df['hour'] == 0)),'RunningHoursLamp'].values[0]
except IndexError:
hour0 = np.nan
try :
hour24 = df.loc[((df['date'] == unique_dates[i+1]) & (df['hour'] == 0)),'RunningHoursLamp'].values[0]
except IndexError:
hour24 = np.nan
temp = pd.DataFrame([[unique_dates[i],hour0,hour24]],columns=['Date','Hour_0','Hour_24'])
bill = bill.append(temp,ignore_index=True)
bill
来源:https://stackoverflow.com/questions/60709492/find-values-from-a-column-in-a-df-at-very-specific-times-for-every-unique-date