Resample a datetimeIndex start day wrong - pandas

Question

Source:
import pandas as pd
import numpy as np
cols = ['Date', 'Time', 'Load', 'Battery', 'Panel',
'Wind', 'Temp', 'Humidity', 'Volt']
data = pd.read_csv('test.csv',delimiter=';',header=0,names=cols,
decimal=',',parse_dates[[0,1]],
infer_datetime_format=True)
data.set_index('Date_Time',inplace=True)
I have this data frame:
In [126]: data.head()
Out[126]:
Load Battery Panel Wind Temp Humidity Volt
Date_Time
2018-07-31 13:07:15 13.3 326.3 353.1 0.98 33.93 21.92 3.89
2018-07-31 13:08:15 14.0 314.4 342.5 0.59 33.88 21.84 3.88
2018-07-31 13:09:16 13.4 309.6 335.5 0.39 33.84 22.14 3.88
2018-07-31 13:10:16 13.8 285.1 313.8 2.55 33.71 23.18 3.88
2018-07-31 13:11:16 13.6 292.9 314.7 2.03 33.62 23.25 3.88
......
with other 93000 rows. from 2018-07-31 to 2018-04-10. I'd like to resample by taking the sum of values for each 10minute frame. So I tried:
In [127]: data.resample('10min',closed='left',label='left').sum()
Out[127]:
Load Battery Panel Wind Temp Humidity Volt
Date_Time
2018-01-08 00:00:00 136.9 -140.6 -2.9 19.06 291.27 245.63 39.45
2018-01-08 00:10:00 137.3 -140.7 -3.1 15.14 290.62 244.88 39.42
2018-01-08 00:20:00 137.4 -140.4 -2.3 18.03 288.61 246.44 39.44
2018-01-08 00:30:00 137.5 -140.4 -2.2 12.61 286.97 246.83 39.43
That is close to what I expect, but the 'resample' remove all the data from the first day (I suspect maybe because the series do not start at midnight), what is the proper way to do the resampling? There are two issues:
The first day is missing in the result, i.e. all data removed and the resampled dataframe starts in the first of august and not on 07/31.
It is ok to consider intervals that starts at midnight and are so, perfectly multiple of 10min (so, ok for 00:00, 10:00, 20:00) but then I expect that the first grouping is:
2018-07-31 13:07:15 13.3 326.3 353.1 0.98 33.93 21.92 3.89
2018-07-31 13:08:15 14.0 314.4 342.5 0.59 33.88 21.84 3.88
2018-07-31 13:09:16 13.4 309.6 335.5 0.39 33.84 22.14 3.88
and then from 13:10:16, of course in the first day of the dataset and not on the second.
Ok. I solved it using:
x = data['2018-07-31'].resample('10min').sum()
y = data.resample('10min',closed='left',label='left').sum()
r = pd.concat([x,y])
but I think that this must be a form of bug in resample.

Answer 1

For output that starts at exactly 2018-07-31 13:07:15, you need to add in the argument base: "the origin of the aggregated intervals": documentation.
Example code:
start = pd.to_datetime('2018-07-31 13:07:15', format='%Y-%m-%d %H:%M:%S')
minutes = pd.date_range(start, start + timedelta(10), freq='min')
df = pd.DataFrame({'Date_Time': minutes, 'Load': np.random.randint(13, size=len(minutes))})
df.set_index('Date_Time', inplace=True)
df.resample('10min', closed='left', label='left', base=7.25).sum()
Result:
Date_Time Load
2018-07-31 13:07:15 11
2018-07-31 13:17:15 1
2018-07-31 13:27:15 6