Python 如何根据时间列更改日期列的值？
我有一个Python 如何根据时间列更改日期列的值？,python,python-3.x,pandas,Python,Python 3.x,Pandas,我有一个df Time Samstag Sonntag Werktag 00:15:00 95.3 87.8 94.7 00:30:00 95.5 88.3 94.1 00:45:00 96.2 89.0 94.1 01:00:00 97.4 90.1 95.0 01:15:00 98.9 91.3 96.6 01:30:00 100.3 92.4 98.4 01:45:00 1
dfTime Samstag Sonntag Werktag
00:15:00 95.3 87.8 94.7
00:30:00 95.5 88.3 94.1
00:45:00 96.2 89.0 94.1
01:00:00 97.4 90.1 95.0
01:15:00 98.9 91.3 96.6
01:30:00 100.3 92.4 98.4
01:45:00 101.0 92.9 99.8
02:00:00 100.4 92.5 99.8
02:15:00 98.2 91.0 98.0
02:30:00 95.1 88.7 95.1
02:45:00 91.9 86.4 91.9
03:00:00 89.5 84.7 89.5
03:15:00 88.6 84.0 88.4
03:30:00 88.6 84.0 88.3
03:45:00 88.7 84.0 88.3
04:00:00 88.3 83.5 87.7
04:15:00 86.8 82.1 86.1
04:30:00 85.1 80.6 84.3
04:45:00 84.2 80.1 83.5
05:00:00 85.3 81.6 84.7
05:15:00 89.0 85.9 88.5
05:30:00 94.1 91.6 94.0
05:45:00 99.3 97.0 99.5
06:00:00 102.8 100.4 103.4
06:15:00 103.7 100.7 104.7
06:30:00 102.6 98.8 104.0
06:45:00 100.7 96.2 102.4
07:00:00 99.2 94.3 101.0
07:15:00 99.1 94.4 100.8
07:30:00 100.8 95.7 102.1
07:45:00 104.4 97.6 105.3
08:00:00 110.1 99.2 110.7
08:15:00 117.7 99.7 118.2
08:30:00 126.1 99.6 126.7
08:45:00 133.9 99.2 134.7
09:00:00 139.7 99.2 140.9
09:15:00 142.4 99.8 144.2
09:30:00 142.9 100.9 145.4
09:45:00 142.4 102.1 145.5
10:00:00 142.1 102.8 145.8
10:15:00 142.9 102.9 147.0
10:30:00 144.5 102.5 149.0
10:45:00 146.3 101.8 151.2
11:00:00 147.6 101.0 153.0
11:15:00 147.9 100.4 154.0
11:30:00 147.5 100.0 154.3
11:45:00 146.8 99.8 154.3
12:00:00 146.4 99.8 154.2
12:15:00 146.3 100.0 154.3
12:30:00 146.5 100.5 154.5
12:45:00 146.2 101.0 154.3
13:00:00 145.1 101.6 153.6
13:15:00 142.8 102.2 152.2
13:30:00 139.3 102.4 149.9
13:45:00 134.6 102.1 147.0
14:00:00 128.8 101.0 143.3
14:15:00 122.3 98.9 139.2
14:30:00 115.5 96.3 135.2
14:45:00 109.4 93.8 132.1
15:00:00 104.6 91.9 130.6
15:15:00 101.8 91.1 131.3
15:30:00 100.5 91.2 133.5
15:45:00 100.2 91.8 136.2
16:00:00 100.4 92.5 138.5
16:15:00 100.6 93.1 139.8
16:30:00 101.0 93.4 140.3
16:45:00 101.9 93.6 140.5
17:00:00 103.4 93.7 140.9
17:15:00 105.8 93.9 142.0
17:30:00 108.7 94.3 143.7
17:45:00 111.5 95.2 145.8
18:00:00 113.7 96.8 148.2
18:15:00 115.0 99.1 150.6
18:30:00 115.7 102.2 152.5
18:45:00 116.3 105.7 153.3
19:00:00 117.3 109.5 152.4
19:15:00 119.0 113.2 149.3
19:30:00 120.6 116.3 144.4
19:45:00 121.4 117.9 138.4
20:00:00 120.4 117.3 131.8
20:15:00 117.0 114.2 125.3
20:30:00 112.1 109.4 119.3
20:45:00 106.8 104.2 114.3
21:00:00 102.2 99.8 110.7
21:15:00 99.2 97.1 108.8
21:30:00 97.4 95.9 108.1
21:45:00 96.4 95.4 108.0
22:00:00 95.6 95.0 107.7
22:15:00 94.5 94.1 106.6
22:30:00 93.3 92.8 104.9
22:45:00 92.0 91.2 103.0
23:00:00 90.7 89.5 101.0
23:15:00 89.6 87.8 99.3
23:30:00 88.6 86.4 97.8
23:45:00 88.0 85.7 96.6
00:00:00 87.7 85.9 95.6
td = pd.to_timedelta(df['Time'].astype(str))
df1 = df.assign(Time=td.mask(td == pd.Timedelta(0),td + pd.Timedelta('1 days 00:00:00')), a=1)
df2 = pd.DataFrame({'dates': pd.date_range(
'01.01.2020', '31.12.2020'), 'a': 1})
df = df2.merge(df1, how='outer').drop('a', axis=1)
df['dates'] = df['dates'].add(df.pop('Time')).dt.strftime('%d.%m.%Y %H:%M')
df['dates'] = pd.to_datetime(df['dates'], dayfirst=True)
df['day'] = df['dates'].dt.day_name()
dates Samstag Sonntag Werktag day
2020-01-01 00:15:00 95.3 87.8 94.7 Wednesday
2020-01-01 00:30:00 95.5 88.3 94.1 Wednesday
2020-01-01 00:45:00 96.2 89.0 94.1 Wednesday
2020-01-01 01:00:00 97.4 90.1 95.0 Wednesday
2020-01-01 01:15:00 98.9 91.3 96.6 Wednesday
2020-01-01 01:30:00 100.3 92.4 98.4 Wednesday
2020-01-01 01:45:00 101.0 92.9 99.8 Wednesday
2020-01-01 02:00:00 100.4 92.5 99.8 Wednesday
2020-01-01 02:15:00 98.2 91.0 98.0 Wednesday
2020-01-01 02:30:00 95.1 88.7 95.1 Wednesday
2020-01-01 02:45:00 91.9 86.4 91.9 Wednesday
2020-01-01 03:00:00 89.5 84.7 89.5 Wednesday
2020-01-01 03:15:00 88.6 84.0 88.4 Wednesday
2020-01-01 03:30:00 88.6 84.0 88.3 Wednesday
2020-01-01 03:45:00 88.7 84.0 88.3 Wednesday
2020-01-01 04:00:00 88.3 83.5 87.7 Wednesday
2020-01-01 04:15:00 86.8 82.1 86.1 Wednesday
2020-01-01 04:30:00 85.1 80.6 84.3 Wednesday
2020-01-01 04:45:00 84.2 80.1 83.5 Wednesday
2020-01-01 05:00:00 85.3 81.6 84.7 Wednesday
2020-01-01 05:15:00 89.0 85.9 88.5 Wednesday
2020-01-01 05:30:00 94.1 91.6 94.0 Wednesday
2020-01-01 05:45:00 99.3 97.0 99.5 Wednesday
2020-01-01 06:00:00 102.8 100.4 103.4 Wednesday
2020-01-01 06:15:00 103.7 100.7 104.7 Wednesday
2020-01-01 06:30:00 102.6 98.8 104.0 Wednesday
2020-01-01 06:45:00 100.7 96.2 102.4 Wednesday
2020-01-01 07:00:00 99.2 94.3 101.0 Wednesday
2020-01-01 07:15:00 99.1 94.4 100.8 Wednesday
2020-01-01 07:30:00 100.8 95.7 102.1 Wednesday
2020-01-01 07:45:00 104.4 97.6 105.3 Wednesday
2020-01-01 08:00:00 110.1 99.2 110.7 Wednesday
2020-01-01 08:15:00 117.7 99.7 118.2 Wednesday
2020-01-01 08:30:00 126.1 99.6 126.7 Wednesday
2020-01-01 08:45:00 133.9 99.2 134.7 Wednesday
2020-01-01 09:00:00 139.7 99.2 140.9 Wednesday
2020-01-01 09:15:00 142.4 99.8 144.2 Wednesday
2020-01-01 09:30:00 142.9 100.9 145.4 Wednesday
2020-01-01 09:45:00 142.4 102.1 145.5 Wednesday
2020-01-01 10:00:00 142.1 102.8 145.8 Wednesday
2020-01-01 10:15:00 142.9 102.9 147.0 Wednesday
2020-01-01 10:30:00 144.5 102.5 149.0 Wednesday
2020-01-01 10:45:00 146.3 101.8 151.2 Wednesday
2020-01-01 11:00:00 147.6 101.0 153.0 Wednesday
2020-01-01 11:15:00 147.9 100.4 154.0 Wednesday
2020-01-01 11:30:00 147.5 100.0 154.3 Wednesday
2020-01-01 11:45:00 146.8 99.8 154.3 Wednesday
2020-01-01 12:00:00 146.4 99.8 154.2 Wednesday
2020-01-01 12:15:00 146.3 100.0 154.3 Wednesday
2020-01-01 12:30:00 146.5 100.5 154.5 Wednesday
2020-01-01 12:45:00 146.2 101.0 154.3 Wednesday
2020-01-01 13:00:00 145.1 101.6 153.6 Wednesday
2020-01-01 13:15:00 142.8 102.2 152.2 Wednesday
2020-01-01 13:30:00 139.3 102.4 149.9 Wednesday
2020-01-01 13:45:00 134.6 102.1 147.0 Wednesday
2020-01-01 14:00:00 128.8 101.0 143.3 Wednesday
2020-01-01 14:15:00 122.3 98.9 139.2 Wednesday
2020-01-01 14:30:00 115.5 96.3 135.2 Wednesday
2020-01-01 14:45:00 109.4 93.8 132.1 Wednesday
2020-01-01 15:00:00 104.6 91.9 130.6 Wednesday
2020-01-01 15:15:00 101.8 91.1 131.3 Wednesday
2020-01-01 15:30:00 100.5 91.2 133.5 Wednesday
2020-01-01 15:45:00 100.2 91.8 136.2 Wednesday
2020-01-01 16:00:00 100.4 92.5 138.5 Wednesday
2020-01-01 16:15:00 100.6 93.1 139.8 Wednesday
2020-01-01 16:30:00 101.0 93.4 140.3 Wednesday
2020-01-01 16:45:00 101.9 93.6 140.5 Wednesday
2020-01-01 17:00:00 103.4 93.7 140.9 Wednesday
2020-01-01 17:15:00 105.8 93.9 142.0 Wednesday
2020-01-01 17:30:00 108.7 94.3 143.7 Wednesday
2020-01-01 17:45:00 111.5 95.2 145.8 Wednesday
2020-01-01 18:00:00 113.7 96.8 148.2 Wednesday
2020-01-01 18:15:00 115.0 99.1 150.6 Wednesday
2020-01-01 18:30:00 115.7 102.2 152.5 Wednesday
2020-01-01 18:45:00 116.3 105.7 153.3 Wednesday
2020-01-01 19:00:00 117.3 109.5 152.4 Wednesday
2020-01-01 19:15:00 119.0 113.2 149.3 Wednesday
2020-01-01 19:30:00 120.6 116.3 144.4 Wednesday
2020-01-01 19:45:00 121.4 117.9 138.4 Wednesday
2020-01-01 20:00:00 120.4 117.3 131.8 Wednesday
2020-01-01 20:15:00 117.0 114.2 125.3 Wednesday
2020-01-01 20:30:00 112.1 109.4 119.3 Wednesday
2020-01-01 20:45:00 106.8 104.2 114.3 Wednesday
2020-01-01 21:00:00 102.2 99.8 110.7 Wednesday
2020-01-01 21:15:00 99.2 97.1 108.8 Wednesday
2020-01-01 21:30:00 97.4 95.9 108.1 Wednesday
2020-01-01 21:45:00 96.4 95.4 108.0 Wednesday
2020-01-01 22:00:00 95.6 95.0 107.7 Wednesday
2020-01-01 22:15:00 94.5 94.1 106.6 Wednesday
2020-01-01 22:30:00 93.3 92.8 104.9 Wednesday
2020-01-01 22:45:00 92.0 91.2 103.0 Wednesday
2020-01-01 23:00:00 90.7 89.5 101.0 Wednesday
2020-01-01 23:15:00 89.6 87.8 99.3 Wednesday
2020-01-01 23:30:00 88.6 86.4 97.8 Wednesday
2020-01-01 23:45:00 88.0 85.7 96.6 Wednesday
2020-01-02 00:00:00 87.7 85.9 95.6 Thursday
2020-01-02 00:15:00 95.3 87.8 94.7 Thursday
2020-01-02 00:30:00 95.5 88.3 94.1 Thursday
2020-01-02 00:45:00 96.2 89.0 94.1 Thursday
2020-01-02 01:00:00 97.4 90.1 95.0 Thursday
2020-01-02 01:15:00 98.9 91.3 96.6 Thursday
2020-01-02 01:30:00 100.3 92.4 98.4 Thursday
2020-01-02 01:45:00 101.0 92.9 99.8 Thursday
2020-01-02 02:00:00 100.4 92.5 99.8 Thursday
2020-01-02 02:15:00 98.2 91.0 98.0 Thursday
2020-01-02 02:30:00 95.1 88.7 95.1 Thursday
2020-01-02 02:45:00 91.9 86.4 91.9 Thursday
2020-01-02 03:00:00 89.5 84.7 89.5 Thursday
2020-01-02 03:15:00 88.6 84.0 88.4 Thursday
2020-01-02 03:30:00 88.6 84.0 88.3 Thursday
2020-01-02 03:45:00 88.7 84.0 88.3 Thursday
2020-01-02 04:00:00 88.3 83.5 87.7 Thursday
2020-01-02 04:15:00 86.8 82.1 86.1 Thursday
2020-01-02 04:30:00 85.1 80.6 84.3 Thursday
2020-01-02 04:45:00 84.2 80.1 83.5 Thursday
2020-01-02 05:00:00 85.3 81.6 84.7 Thursday
2020-01-02 05:15:00 89.0 85.9 88.5 Thursday
2020-01-02 05:30:00 94.1 91.6 94.0 Thursday
2020-01-02 05:45:00 99.3 97.0 99.5 Thursday
2020-01-02 06:00:00 102.8 100.4 103.4 Thursday
2020-01-02 06:15:00 103.7 100.7 104.7 Thursday
2020-01-02 06:30:00 102.6 98.8 104.0 Thursday
2020-01-02 06:45:00 100.7 96.2 102.4 Thursday
2020-01-02 07:00:00 99.2 94.3 101.0 Thursday
2020-01-02 07:15:00 99.1 94.4 100.8 Thursday
2020-01-02 07:30:00 100.8 95.7 102.1 Thursday
2020-01-02 07:45:00 104.4 97.6 105.3 Thursday
2020-01-02 08:00:00 110.1 99.2 110.7 Thursday
2020-01-02 08:15:00 117.7 99.7 118.2 Thursday
2020-01-02 08:30:00 126.1 99.6 126.7 Thursday
2020-01-02 08:45:00 133.9 99.2 134.7 Thursday
2020-01-02 09:00:00 139.7 99.2 140.9 Thursday
2020-01-02 09:15:00 142.4 99.8 144.2 Thursday
2020-01-02 09:30:00 142.9 100.9 145.4 Thursday
2020-01-02 09:45:00 142.4 102.1 145.5 Thursday
2020-01-02 10:00:00 142.1 102.8 145.8 Thursday
2020-01-02 10:15:00 142.9 102.9 147.0 Thursday
2020-01-02 10:30:00 144.5 102.5 149.0 Thursday
2020-01-02 10:45:00 146.3 101.8 151.2 Thursday
2020-01-02 11:00:00 147.6 101.0 153.0 Thursday
2020-01-02 11:15:00 147.9 100.4 154.0 Thursday
2020-01-02 11:30:00 147.5 100.0 154.3 Thursday
2020-01-02 11:45:00 146.8 99.8 154.3 Thursday
2020-01-02 12:00:00 146.4 99.8 154.2 Thursday
2020-01-02 12:15:00 146.3 100.0 154.3 Thursday
2020-01-02 12:30:00 146.5 100.5 154.5 Thursday
2020-01-02 12:45:00 146.2 101.0 154.3 Thursday
2020-01-02 13:00:00 145.1 101.6 153.6 Thursday
2020-01-02 13:15:00 142.8 102.2 152.2 Thursday
2020-01-02 13:30:00 139.3 102.4 149.9 Thursday
2020-01-02 13:45:00 134.6 102.1 147.0 Thursday
2020-01-02 14:00:00 128.8 101.0 143.3 Thursday
2020-01-02 14:15:00 122.3 98.9 139.2 Thursday
2020-01-02 14:30:00 115.5 96.3 135.2 Thursday
2020-01-02 14:45:00 109.4 93.8 132.1 Thursday
2020-01-02 15:00:00 104.6 91.9 130.6 Thursday
2020-01-02 15:15:00 101.8 91.1 131.3 Thursday
2020-01-02 15:30:00 100.5 91.2 133.5 Thursday
2020-01-02 15:45:00 100.2 91.8 136.2 Thursday
2020-01-02 16:00:00 100.4 92.5 138.5 Thursday
2020-01-02 16:15:00 100.6 93.1 139.8 Thursday
2020-01-02 16:30:00 101.0 93.4 140.3 Thursday
2020-01-02 16:45:00 101.9 93.6 140.5 Thursday
2020-01-02 17:00:00 103.4 93.7 140.9 Thursday
2020-01-02 17:15:00 105.8 93.9 142.0 Thursday
2020-01-02 17:30:00 108.7 94.3 143.7 Thursday
2020-01-02 17:45:00 111.5 95.2 145.8 Thursday
2020-01-02 18:00:00 113.7 96.8 148.2 Thursday
2020-01-02 18:15:00 115.0 99.1 150.6 Thursday
2020-01-02 18:30:00 115.7 102.2 152.5 Thursday
2020-01-02 18:45:00 116.3 105.7 153.3 Thursday
2020-01-02 19:00:00 117.3 109.5 152.4 Thursday
2020-01-02 19:15:00 119.0 113.2 149.3 Thursday
2020-01-02 19:30:00 120.6 116.3 144.4 Thursday
2020-01-02 19:45:00 121.4 117.9 138.4 Thursday
2020-01-02 20:00:00 120.4 117.3 131.8 Thursday
2020-01-02 20:15:00 117.0 114.2 125.3 Thursday
2020-01-02 20:30:00 112.1 109.4 119.3 Thursday
2020-01-02 20:45:00 106.8 104.2 114.3 Thursday
2020-01-02 21:00:00 102.2 99.8 110.7 Thursday
2020-01-02 21:15:00 99.2 97.1 108.8 Thursday
2020-01-02 21:30:00 97.4 95.9 108.1 Thursday
2020-01-02 21:45:00 96.4 95.4 108.0 Thursday
2020-01-02 22:00:00 95.6 95.0 107.7 Thursday
2020-01-02 22:15:00 94.5 94.1 106.6 Thursday
2020-01-02 22:30:00 93.3 92.8 104.9 Thursday
2020-01-02 22:45:00 92.0 91.2 103.0 Thursday
2020-01-02 23:00:00 90.7 89.5 101.0 Thursday
2020-01-02 23:15:00 89.6 87.8 99.3 Thursday
2020-01-02 23:30:00 88.6 86.4 97.8 Thursday
2020-01-02 23:45:00 88.0 85.7 96.6 Thursday
2020-01-03 00:00:00 87.7 85.9 95.6 Friday
2020-01-03 00:15:00 95.3 87.8 94.7 Friday
2020-01-03 00:30:00 95.5 88.3 94.1 Friday
2020-01-03 00:45:00 96.2 89.0 94.1 Friday
2020-01-02 00:00:00时的day的值从星期四更改为星期三
，同样地，将day
在2020-01-03 00:00时的值从星期五
更改为星期四
，依此类推
换句话说：第二天00:00:00
的day
值应该与前一天的值相似，从00:15:00
开始，新的一天应该开始
预期产出
dates Samstag Sonntag Werktag day
2020-01-01 00:15:00 95.3 87.8 94.7 Wednesday
2020-01-01 00:30:00 95.5 88.3 94.1 Wednesday
2020-01-01 00:45:00 96.2 89.0 94.1 Wednesday
2020-01-01 01:00:00 97.4 90.1 95.0 Wednesday
2020-01-01 01:15:00 98.9 91.3 96.6 Wednesday
2020-01-01 01:30:00 100.3 92.4 98.4 Wednesday
2020-01-01 01:45:00 101.0 92.9 99.8 Wednesday
2020-01-01 02:00:00 100.4 92.5 99.8 Wednesday
2020-01-01 02:15:00 98.2 91.0 98.0 Wednesday
2020-01-01 02:30:00 95.1 88.7 95.1 Wednesday
2020-01-01 02:45:00 91.9 86.4 91.9 Wednesday
2020-01-01 03:00:00 89.5 84.7 89.5 Wednesday
2020-01-01 03:15:00 88.6 84.0 88.4 Wednesday
2020-01-01 03:30:00 88.6 84.0 88.3 Wednesday
2020-01-01 03:45:00 88.7 84.0 88.3 Wednesday
2020-01-01 04:00:00 88.3 83.5 87.7 Wednesday
2020-01-01 04:15:00 86.8 82.1 86.1 Wednesday
2020-01-01 04:30:00 85.1 80.6 84.3 Wednesday
2020-01-01 04:45:00 84.2 80.1 83.5 Wednesday
2020-01-01 05:00:00 85.3 81.6 84.7 Wednesday
2020-01-01 05:15:00 89.0 85.9 88.5 Wednesday
2020-01-01 05:30:00 94.1 91.6 94.0 Wednesday
2020-01-01 05:45:00 99.3 97.0 99.5 Wednesday
2020-01-01 06:00:00 102.8 100.4 103.4 Wednesday
2020-01-01 06:15:00 103.7 100.7 104.7 Wednesday
2020-01-01 06:30:00 102.6 98.8 104.0 Wednesday
2020-01-01 06:45:00 100.7 96.2 102.4 Wednesday
2020-01-01 07:00:00 99.2 94.3 101.0 Wednesday
2020-01-01 07:15:00 99.1 94.4 100.8 Wednesday
2020-01-01 07:30:00 100.8 95.7 102.1 Wednesday
2020-01-01 07:45:00 104.4 97.6 105.3 Wednesday
2020-01-01 08:00:00 110.1 99.2 110.7 Wednesday
2020-01-01 08:15:00 117.7 99.7 118.2 Wednesday
2020-01-01 08:30:00 126.1 99.6 126.7 Wednesday
2020-01-01 08:45:00 133.9 99.2 134.7 Wednesday
2020-01-01 09:00:00 139.7 99.2 140.9 Wednesday
2020-01-01 09:15:00 142.4 99.8 144.2 Wednesday
2020-01-01 09:30:00 142.9 100.9 145.4 Wednesday
2020-01-01 09:45:00 142.4 102.1 145.5 Wednesday
2020-01-01 10:00:00 142.1 102.8 145.8 Wednesday
2020-01-01 10:15:00 142.9 102.9 147.0 Wednesday
2020-01-01 10:30:00 144.5 102.5 149.0 Wednesday
2020-01-01 10:45:00 146.3 101.8 151.2 Wednesday
2020-01-01 11:00:00 147.6 101.0 153.0 Wednesday
2020-01-01 11:15:00 147.9 100.4 154.0 Wednesday
2020-01-01 11:30:00 147.5 100.0 154.3 Wednesday
2020-01-01 11:45:00 146.8 99.8 154.3 Wednesday
2020-01-01 12:00:00 146.4 99.8 154.2 Wednesday
2020-01-01 12:15:00 146.3 100.0 154.3 Wednesday
2020-01-01 12:30:00 146.5 100.5 154.5 Wednesday
2020-01-01 12:45:00 146.2 101.0 154.3 Wednesday
2020-01-01 13:00:00 145.1 101.6 153.6 Wednesday
2020-01-01 13:15:00 142.8 102.2 152.2 Wednesday
2020-01-01 13:30:00 139.3 102.4 149.9 Wednesday
2020-01-01 13:45:00 134.6 102.1 147.0 Wednesday
2020-01-01 14:00:00 128.8 101.0 143.3 Wednesday
2020-01-01 14:15:00 122.3 98.9 139.2 Wednesday
2020-01-01 14:30:00 115.5 96.3 135.2 Wednesday
2020-01-01 14:45:00 109.4 93.8 132.1 Wednesday
2020-01-01 15:00:00 104.6 91.9 130.6 Wednesday
2020-01-01 15:15:00 101.8 91.1 131.3 Wednesday
2020-01-01 15:30:00 100.5 91.2 133.5 Wednesday
2020-01-01 15:45:00 100.2 91.8 136.2 Wednesday
2020-01-01 16:00:00 100.4 92.5 138.5 Wednesday
2020-01-01 16:15:00 100.6 93.1 139.8 Wednesday
2020-01-01 16:30:00 101.0 93.4 140.3 Wednesday
2020-01-01 16:45:00 101.9 93.6 140.5 Wednesday
2020-01-01 17:00:00 103.4 93.7 140.9 Wednesday
2020-01-01 17:15:00 105.8 93.9 142.0 Wednesday
2020-01-01 17:30:00 108.7 94.3 143.7 Wednesday
2020-01-01 17:45:00 111.5 95.2 145.8 Wednesday
2020-01-01 18:00:00 113.7 96.8 148.2 Wednesday
2020-01-01 18:15:00 115.0 99.1 150.6 Wednesday
2020-01-01 18:30:00 115.7 102.2 152.5 Wednesday
2020-01-01 18:45:00 116.3 105.7 153.3 Wednesday
2020-01-01 19:00:00 117.3 109.5 152.4 Wednesday
2020-01-01 19:15:00 119.0 113.2 149.3 Wednesday
2020-01-01 19:30:00 120.6 116.3 144.4 Wednesday
2020-01-01 19:45:00 121.4 117.9 138.4 Wednesday
2020-01-01 20:00:00 120.4 117.3 131.8 Wednesday
2020-01-01 20:15:00 117.0 114.2 125.3 Wednesday
2020-01-01 20:30:00 112.1 109.4 119.3 Wednesday
2020-01-01 20:45:00 106.8 104.2 114.3 Wednesday
2020-01-01 21:00:00 102.2 99.8 110.7 Wednesday
2020-01-01 21:15:00 99.2 97.1 108.8 Wednesday
2020-01-01 21:30:00 97.4 95.9 108.1 Wednesday
2020-01-01 21:45:00 96.4 95.4 108.0 Wednesday
2020-01-01 22:00:00 95.6 95.0 107.7 Wednesday
2020-01-01 22:15:00 94.5 94.1 106.6 Wednesday
2020-01-01 22:30:00 93.3 92.8 104.9 Wednesday
2020-01-01 22:45:00 92.0 91.2 103.0 Wednesday
2020-01-01 23:00:00 90.7 89.5 101.0 Wednesday
2020-01-01 23:15:00 89.6 87.8 99.3 Wednesday
2020-01-01 23:30:00 88.6 86.4 97.8 Wednesday
2020-01-01 23:45:00 88.0 85.7 96.6 Wednesday
2020-01-02 00:00:00 87.7 85.9 95.6 Wednesday
2020-01-02 00:15:00 95.3 87.8 94.7 Thursday
2020-01-02 00:30:00 95.5 88.3 94.1 Thursday
2020-01-02 00:45:00 96.2 89.0 94.1 Thursday
2020-01-02 01:00:00 97.4 90.1 95.0 Thursday
2020-01-02 01:15:00 98.9 91.3 96.6 Thursday
2020-01-02 01:30:00 100.3 92.4 98.4 Thursday
2020-01-02 01:45:00 101.0 92.9 99.8 Thursday
2020-01-02 02:00:00 100.4 92.5 99.8 Thursday
2020-01-02 02:15:00 98.2 91.0 98.0 Thursday
2020-01-02 02:30:00 95.1 88.7 95.1 Thursday
2020-01-02 02:45:00 91.9 86.4 91.9 Thursday
2020-01-02 03:00:00 89.5 84.7 89.5 Thursday
2020-01-02 03:15:00 88.6 84.0 88.4 Thursday
2020-01-02 03:30:00 88.6 84.0 88.3 Thursday
2020-01-02 03:45:00 88.7 84.0 88.3 Thursday
2020-01-02 04:00:00 88.3 83.5 87.7 Thursday
2020-01-02 04:15:00 86.8 82.1 86.1 Thursday
2020-01-02 04:30:00 85.1 80.6 84.3 Thursday
2020-01-02 04:45:00 84.2 80.1 83.5 Thursday
2020-01-02 05:00:00 85.3 81.6 84.7 Thursday
2020-01-02 05:15:00 89.0 85.9 88.5 Thursday
2020-01-02 05:30:00 94.1 91.6 94.0 Thursday
2020-01-02 05:45:00 99.3 97.0 99.5 Thursday
2020-01-02 06:00:00 102.8 100.4 103.4 Thursday
2020-01-02 06:15:00 103.7 100.7 104.7 Thursday
2020-01-02 06:30:00 102.6 98.8 104.0 Thursday
2020-01-02 06:45:00 100.7 96.2 102.4 Thursday
2020-01-02 07:00:00 99.2 94.3 101.0 Thursday
2020-01-02 07:15:00 99.1 94.4 100.8 Thursday
2020-01-02 07:30:00 100.8 95.7 102.1 Thursday
2020-01-02 07:45:00 104.4 97.6 105.3 Thursday
2020-01-02 08:00:00 110.1 99.2 110.7 Thursday
2020-01-02 08:15:00 117.7 99.7 118.2 Thursday
2020-01-02 08:30:00 126.1 99.6 126.7 Thursday
2020-01-02 08:45:00 133.9 99.2 134.7 Thursday
2020-01-02 09:00:00 139.7 99.2 140.9 Thursday
2020-01-02 09:15:00 142.4 99.8 144.2 Thursday
2020-01-02 09:30:00 142.9 100.9 145.4 Thursday
2020-01-02 09:45:00 142.4 102.1 145.5 Thursday
2020-01-02 10:00:00 142.1 102.8 145.8 Thursday
2020-01-02 10:15:00 142.9 102.9 147.0 Thursday
2020-01-02 10:30:00 144.5 102.5 149.0 Thursday
2020-01-02 10:45:00 146.3 101.8 151.2 Thursday
2020-01-02 11:00:00 147.6 101.0 153.0 Thursday
2020-01-02 11:15:00 147.9 100.4 154.0 Thursday
2020-01-02 11:30:00 147.5 100.0 154.3 Thursday
2020-01-02 11:45:00 146.8 99.8 154.3 Thursday
2020-01-02 12:00:00 146.4 99.8 154.2 Thursday
2020-01-02 12:15:00 146.3 100.0 154.3 Thursday
2020-01-02 12:30:00 146.5 100.5 154.5 Thursday
2020-01-02 12:45:00 146.2 101.0 154.3 Thursday
2020-01-02 13:00:00 145.1 101.6 153.6 Thursday
2020-01-02 13:15:00 142.8 102.2 152.2 Thursday
2020-01-02 13:30:00 139.3 102.4 149.9 Thursday
2020-01-02 13:45:00 134.6 102.1 147.0 Thursday
2020-01-02 14:00:00 128.8 101.0 143.3 Thursday
2020-01-02 14:15:00 122.3 98.9 139.2 Thursday
2020-01-02 14:30:00 115.5 96.3 135.2 Thursday
2020-01-02 14:45:00 109.4 93.8 132.1 Thursday
2020-01-02 15:00:00 104.6 91.9 130.6 Thursday
2020-01-02 15:15:00 101.8 91.1 131.3 Thursday
2020-01-02 15:30:00 100.5 91.2 133.5 Thursday
2020-01-02 15:45:00 100.2 91.8 136.2 Thursday
2020-01-02 16:00:00 100.4 92.5 138.5 Thursday
2020-01-02 16:15:00 100.6 93.1 139.8 Thursday
2020-01-02 16:30:00 101.0 93.4 140.3 Thursday
2020-01-02 16:45:00 101.9 93.6 140.5 Thursday
2020-01-02 17:00:00 103.4 93.7 140.9 Thursday
2020-01-02 17:15:00 105.8 93.9 142.0 Thursday
2020-01-02 17:30:00 108.7 94.3 143.7 Thursday
2020-01-02 17:45:00 111.5 95.2 145.8 Thursday
2020-01-02 18:00:00 113.7 96.8 148.2 Thursday
2020-01-02 18:15:00 115.0 99.1 150.6 Thursday
2020-01-02 18:30:00 115.7 102.2 152.5 Thursday
2020-01-02 18:45:00 116.3 105.7 153.3 Thursday
2020-01-02 19:00:00 117.3 109.5 152.4 Thursday
2020-01-02 19:15:00 119.0 113.2 149.3 Thursday
2020-01-02 19:30:00 120.6 116.3 144.4 Thursday
2020-01-02 19:45:00 121.4 117.9 138.4 Thursday
2020-01-02 20:00:00 120.4 117.3 131.8 Thursday
2020-01-02 20:15:00 117.0 114.2 125.3 Thursday
2020-01-02 20:30:00 112.1 109.4 119.3 Thursday
2020-01-02 20:45:00 106.8 104.2 114.3 Thursday
2020-01-02 21:00:00 102.2 99.8 110.7 Thursday
2020-01-02 21:15:00 99.2 97.1 108.8 Thursday
2020-01-02 21:30:00 97.4 95.9 108.1 Thursday
2020-01-02 21:45:00 96.4 95.4 108.0 Thursday
2020-01-02 22:00:00 95.6 95.0 107.7 Thursday
2020-01-02 22:15:00 94.5 94.1 106.6 Thursday
2020-01-02 22:30:00 93.3 92.8 104.9 Thursday
2020-01-02 22:45:00 92.0 91.2 103.0 Thursday
2020-01-02 23:00:00 90.7 89.5 101.0 Thursday
2020-01-02 23:15:00 89.6 87.8 99.3 Thursday
2020-01-02 23:30:00 88.6 86.4 97.8 Thursday
2020-01-02 23:45:00 88.0 85.7 96.6 Thursday
2020-01-03 00:00:00 87.7 85.9 95.6 Thursday
2020-01-03 00:15:00 95.3 87.8 94.7 Friday
2020-01-03 00:30:00 95.5 88.3 94.1 Friday
2020-01-03 00:45:00 96.2 89.0 94.1 Friday
如何做到这一点
编辑1
import pandas as pd
df = pd.DataFrame({ 'dates': ['2020-01-01 22:15:00',
'2020-01-01 22:35:00',
'2020-01-01 22:45:00',
'2020-01-01 23:00:00',
'2020-01-01 23:15:00',
'2020-01-01 23:30:00',
'2020-01-01 23:45:00',
'2020-01-02 00:00:00',
'2020-01-02 22:15:00',
'2020-01-02 22:35:00',
'2020-01-02 22:45:00',
'2020-01-02 23:00:00',
'2020-01-02 23:15:00',
'2020-01-02 23:30:00',
'2020-01-02 23:45:00',
'2020-01-03 00:00:00'],
'expected_output':['Wednesday',
'Wednesday',
'Wednesday',
'Wednesday',
'Wednesday',
'Wednesday',
'Wednesday',
'Wednesday',
'Thursday',
'Thursday',
'Thursday',
'Thursday',
'Thursday','Thursday','Thursday','Thursday']})
只需使用apply
检查时间戳的分钟数即可
# df = pd.DataFrame({'dates': ['2020-01-01 22:15:00', .....]}, )
# convert str date into Timestamp
df['dates'] = pd.to_datetime(df['dates'])
def calculate_day(x):
# get previous day
if x.hour == 0 and x.minute < 15:
return (x - pd.DateOffset(days=1)).day_name()
return x.day_name()
df['day'] = df['dates'].apply(calculate_day)
print(df)
# dates day
#0 2020-01-01 22:15:00 Wednesday
#...
#df=pd.DataFrame（{'dates'：['2020-01-01 22:15:00'，…]}）
#将str日期转换为时间戳
df['dates']=pd.to_datetime（df['dates']）
def计算日（x）：
#前一天
如果x.hour==0且x.minute<15：
返回（x-pd.DateOffset（days=1））.day_name（）
返回x.day_name（）
df['day']=df['dates']。应用（计算日）
打印（df）
#日期日
#0200-01-01 22:15:00星期三
#...
JFYI:已弃用。使用day\u name（）
。
希望这有帮助。嘿@Danila，我试过你的建议，但是它给出了一个不正确的输出。它给出：Samstag Sonntag Werktag day 2020-01-01 00:15:00 95.3 87.8 94.7星期三2020-01-01-01 00:30:00 95.5 88.3 94.1星期三2020-01-01-01 00:45:00 96.2 89.0 94.1星期三2020-01-01-01 01 01:00 97.4 90.1 95.0星期二2020-01-01-01-01 01 01 01:15:00 98.9 91.3 96.6星期三2020-01-01-01-01-01:01:00 100.4 98.4星期三2020-01-01 01 01:45:00 101.0 92.9 99.8周三2020-01-01 02:00 100.4 92.5 99.8周二2020-01-01 02:15:00 98.2 91.0 98.0周三2020-01-01 02:30:00 95.1 88.7 95.1周三2020-01-01 02:45:00 91.9 86.4 91.9周三2020-01-01-01 03:00 89.5 84.7 89.5周二
@Junkrat hi。您能在问题中添加一些记录（如我的回答）并简短输出吗？这是当许多不必要的记录和列。或者请分享一些我答错的记录。嘿，@Danila。我正在使用我在问题中发布的df
。如果您可以尝试运行我发布的代码，您将获得我发布的输出，然后您可以检查我的输出与预期输出之间的差异。您的答案是它影响每小时的值，而不是期望的值。如果你在我发布的df
上运行你的代码，你就会知道其中的区别