The code is supposed to group start and end time logs and provide log counts and unique ID counts. The grouping will be variable 1 hour, 6 hours, 12 hours, 24 hours, etc.What is the better way to rewrite the simpler version for this code? Input: No of day reamins 'x' in inputs. import pandas as pdfn = r'Dart.csv'df = pd.read_csv(fn)df['m'] = df.StopTime + df.StartTimedf['d'] = df.StopTime - df.StartTime#df.StartTime = pd.to_datetime(df.StartTime, unit='s')#df.StopTime = pd.to_datetime(df.StopTime, unit='s')# 'start' and 'end' for the reporting DF: `r`# which will contain equal intervals (1 hour in this case)start = pd.to_datetime(df.StartTime.min(), unit='s').date()end = pd.to_datetime(df.StopTime.max(), unit='s').date() + pd.Timedelta(days=1) #change this to 'n'# building reporting DF: `r`freq = '1H'idx = pd.date_range(start, end, freq=freq)r = pd.DataFrame(index=idx)r['start'] = (r.index - pd.datetime(1970,1,1)).total_seconds().astype(np.int64)interval = 60*60 - 1r['LogCount'] = 0r['UniqueIDCount'] = 0# counting ...for i, row in r.iterrows():    # intervals overlap test    # https://en.wikipedia.org/wiki/Interval_tree#Overlap_test    # i've slightly simplified the calculations of m and d    # by getting rid of division by 2,    # because it can be done eliminating common terms    mask = np.abs(df.m - 2*row.start - interval) < df.d + interval    r.ix[i, ['LogCount', 'UniqueIDCount']] = [len(df[mask]), df[mask].UserID.nunique()]r['Day'] = pd.to_datetime(r.start, unit='s').dt.dayofweekr['StartTime'] = pd.to_datetime(r.start, unit='s').dt.timer['EndTime'] = pd.to_datetime(r.start + interval + 1, unit='s').dt.timeprint(r[r.LogCount > 0])Logs:UserID, StartTime, StopTime, GPS1, GPS200022d9064bc,1073260801,1073260803,819251,44000600022d9064bc,1073260803,1073260810,819213,43995400904b4557d3,1073260803,1073261920,817526,43945800022de73863,1073260804,1073265410,817558,43952500904b14b494,1073260804,1073262625,817558,43952500022d1406df,1073260807,1073260809,820428,43873500022d9064bc,1073260801,1073260803,819251,44000600022dba8f51,1073260801,1073260803,819251,44000600022de1c6c1,1073260801,1073260803,819251,440006003065f30f37,1073260801,1073260803,819251,44000600904b48a3b6,1073260801,1073260803,819251,44000600904b83a0ea,1073260803,1073260810,819213,43995400904b85d3cf,1073260803,1073261920,817526,43945800904b14b494,1073260804,1073265410,817558,43952500904b99499c,1073260804,1073262625,817558,43952500904bb96e83,1073260804,1073265163,817558,43952500904bf91b75,1073260804,1073263786,817558,439525
Grouping logs and providing counts
python;datetime;csv;logging;pandas