根据小时和星期几插入数据

Question

I have a set of hourly data taken from 07-Feb-19 to 17-Feb-19:

                             t     v_amm     v_alc     v_no2
0    2019-02-07 08:00:00+00:00  0.320000  0.344000  1.612000
1    2019-02-07 09:00:00+00:00  0.322889  0.391778  1.580889
2    2019-02-07 10:00:00+00:00  0.209375  0.325208  2.371250
...
251  2019-02-17 19:00:00+00:00  1.082041  0.652041  0.967143
252  2019-02-17 20:00:00+00:00  0.936923  0.598654  1.048077
253  2019-02-17 21:00:00+00:00  0.652553  0.499574  1.184894

and another similar set of hourly data taken from 01-Mar-19 to 11-Mar-19:

                            t     v_amm     v_alc     v_no2
0   2019-03-01 00:00:00+00:00  0.428222  0.384444  1.288222
1   2019-03-01 01:00:00+00:00  0.398600  0.359600  1.325800
2   2019-03-01 02:00:00+00:00  0.365682  0.352273  1.360000
...
244 2019-03-11 04:00:00+00:00  0.444048  0.415238  1.265000
245 2019-03-11 05:00:00+00:00  0.590698  0.591395  1.156977
246 2019-03-11 06:00:00+00:00  0.497872  0.465319  1.228298

However, there is no data available between 17-Feb-19 and 01-Mar-19 . Hence, I'd like to insert the following data (grouped by day of the week and the hour) into the missing date and times:

                     v_amm     v_alc     v_no2
day_of_week hour                              
0           0     0.432222  0.351111  1.258889
            1     0.371026  0.324359  1.323333
            2     0.371026  0.324359  1.323333
            3     0.250000  0.285000  1.510000
            4     0.220000  0.274500  1.616500
            5     0.195263  0.264211  1.666053
...
6           18    0.919158  0.557793  1.018703
            19    1.065220  0.599320  0.965771
            20    0.896227  0.543689  1.045634
            21    0.648488  0.469210  1.187928
            22    0.592200  0.417200  1.154400
            23    0.485918  0.366531  1.215918

Does anyone know how to obtain this in pandas?

Answer 1

First, generate the missing indexes then concatenate dataframes.

new_index = pd.date_range(start='2019-02-17', end='2019-03-01', freq='H')
new_df = pd.DataFrame([new_index], index=['t']).T
new_df['day_of_week'] = [z.weekday() for z in new_index]
new_df['hour'] = [z.hour for z in new_index]
new_df = new_df.merge(<your_df>, on=['day_of_week', 'hour']), how='left')
new_df = new_df.drop(['day_of_week', 'hour'], axis=1)
filled_df = pd.concat([<df1>, new_df, <df2>], axis=0)