Getting the rolling.sum of row values with irregular time intervals

I am trying to get the rolling.sum of my time series. However, the rows have varying time intervals (see below my df_water_level_US1 dataframe):

    DATE    TIMEREAD    WATERLEVEL(M)   DateAndTime
0   01/01/2016  0:00:15 0.65    01/01/2016 0:00:15
1   01/01/2016  0:10:14 0.65    01/01/2016 0:10:14
2   01/01/2016  0:20:11 0.64    01/01/2016 0:20:11
3   01/01/2016  0:30:12 0.66    01/01/2016 0:30:12
4   01/01/2016  0:40:12 0.64    01/01/2016 0:40:12

and so on. I tried to use this to get the sum for each day and save it to final_1D:

final_1D = df_water_level_US1.set_index('DateAndTime').rolling('1D').sum()

but I get this error:

ValueError: window must be an integer 0 or greater

The expected output is:

DATETIMEREAD   WATERLEVEL(M) DateAndTime
01/01/2016     3.24         01/01/2016 

and so on (02/01/2016, 03/01/2016 etc)

Anyone who have idea how to fix this?

Solution 1:

Try:

df_water_level_US1['DateAndTime'] = pd.to_datetime(df_water_level_US1['DateAndTime'])
final_1D = df_water_level_US1.resample('D', on='DateAndTime')['WATERLEVEL(M)'].sum()

print(final_1D.reset_index())

# Output
  DateAndTime  WATERLEVEL(M)
0  2016-01-01           3.24

The first line is not mandatory if your column DateAndTime is already a DatetimeIndex.

Solution 2:

For daily sum of water level try grouping by date:

df.groupby('Date').sum('WATERLEVEL(M)')