I would like to do the sum of the column duration group by date but the column begin and end are datetime in this piece of df:
begin end duration
2020-10-14 19:17:52.724020 2020-10-14 19:21:40.179003 227.45
2020-10-14 19:21:40.179003 2020-10-14 19:21:44.037103 3.86
2020-10-14 19:59:27.183161 2020-10-14 20:00:43.847816 76.66
2020-10-14 20:00:43.847816 2020-10-14 20:00:43.847822 0
2020-10-14 20:02:14.341240 2020-10-14 23:59:59.900000 14265.56
2020-10-15 00:00:00.000000 2020-10-15 05:25:32.935971 19532.94
2020-10-15 05:25:32.935971 2020-10-15 05:25:33.068959 0.13
df.info()
begin 41763 non-null datetime64[ns]
end 41763 non-null datetime64[ns]
duration 41763 non-null float64
The result must be:
begin duration
2020-10-14 14,573.53
2020-10-15 19,533.07
So I tried on my all df, this but its works for certain date and no for other. Because I do the same with excel and for a date I have a different result.
import pandas as pd
import datetime
df = df.groupby(df['begin_'].dt.date)['duration_'].sum()/3600
You can use the method date
of the datetime object. Apply it to the column and you get the date. Afterwards grouping is fine.
def reduce_to_date(value):
return value.date()
df['begin'] = df['begin'].apply(reduce_to_date)
df.groupby('begin')['duration'].sum()/3600
The first step is to separate Time and Date in the timestamp you have. I give below and example where the dates are defined the same way they are defined in your dataframe.
0 2018-07-02 10:54:00 227.45
1 2018-07-02 10:54:00 3.86
2 2018-07-02 10:54:00 76.66
3 2018-07-02 10:54:00 14265.56
4 2018-07-02 10:54:00 19532.94
d ={'DATA':['2018-07-02 10:54:00','2018-07-02 10:54:00' , '2018-07-02 10:54:00' , '2018-07-02 10:54:00' ,'2018-07-02 10:54:00'],'duration': [227.45,3.86,76.66,14265.56,19532.94]}
DF = df.assign(Date=df.Date.dt.date, Time=df.Date.dt.time, Duration = df.duration)
The next step is to groupby
the way you did it, but by simple give information about which variable you group by:
DF.groupby(['Date']).sum()
which give
Date Duration duration
2018-07-02 34106.47 34106.47
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