我有以下数据框。每天我都有9分。 日期，时间是多索引索引。 我想在其他时间（00：00：00~2：00：00）每天更换时间指数
Date Time a b c
2018-01-09 6:00:00 20.31 0 -2.95
2018-01-09 6:15:00 20.76 26738 -2.88
2018-01-09 6:30:00 21.4 22462 -2.77
2018-01-09 6:45:00 21.84 20033 -3
2018-01-09 7:00:00 22.17 20010 -3.28
2018-01-09 7:15:00 22.38 18133 -2.82
2018-01-09 7:30:00 22.75 18254 -3.14
2018-01-09 7:45:00 22.93 17039 -3.22
2018-01-09 8:00:00 23.13 15934 -3.27
2018-01-10 6:00:00 20.31 0 -2.95
2018-01-10 6:15:00 20.76 26738 -2.88
2018-01-10 6:30:00 21.4 22462 -2.77
2018-01-10 6:45:00 21.84 20033 -3
2018-01-10 7:00:00 22.17 20010 -3.28
2018-01-10 7:15:00 22.38 18133 -2.82
2018-01-10 7:30:00 22.75 18254 -3.14
2018-01-10 7:45:00 22.93 17039 -3.22
2018-01-10 8:00:00 23.13 15934 -3.27
所以结果应该低于
Date Time a b c
2018-01-09 0:00:00 20.31 0 -2.95
2018-01-09 0:15:00 20.76 26738 -2.88
2018-01-09 0:30:00 21.4 22462 -2.77
2018-01-09 0:45:00 21.84 20033 -3
2018-01-09 1:00:00 22.17 20010 -3.28
2018-01-09 1:15:00 22.38 18133 -2.82
2018-01-09 1:30:00 22.75 18254 -3.14
2018-01-09 1:45:00 22.93 17039 -3.22
2018-01-09 2:00:00 23.13 15934 -3.27
2018-01-10 0:00:00 20.31 0 -2.95
2018-01-10 0:15:00 20.76 26738 -2.88
2018-01-10 0:30:00 21.4 22462 -2.77
2018-01-10 0:45:00 21.84 20033 -3
2018-01-10 1:00:00 22.17 20010 -3.28
2018-01-10 1:15:00 22.38 18133 -2.82
2018-01-10 1:30:00 22.75 18254 -3.14
2018-01-10 1:45:00 22.93 17039 -3.22
2018-01-10 2:00:00 23.13 15934 -3.27
我该怎么做？
答案 0 :(得分：1)
如果要将所有值按15min间隔时间替换天数，您可以先创建dictinary用于映射：
d = dict(enumerate(pd.date_range(start='2018-01-01', end='2018-01-02', freq='15T').strftime('%H:%M:%S')))
print (d)
{0: '00:00:00', 1: '00:15:00', 2: '00:30:00', 3: '00:45:00', 4: '01:00:00', 5: '01:15:00', 6: '01:30:00', 7: '01:45:00', 8: '02:00:00', 9: '02:15:00', 10: '02:30:00', 11: '02:45:00', 12: '03:00:00', 13: '03:15:00', 14: '03:30:00', 15: '03:45:00', 16: '04:00:00', 17: '04:15:00', 18: '04:30:00', 19: '04:45:00', 20: '05:00:00', 21: '05:15:00', 22: '05:30:00', 23: '05:45:00', 24: '06:00:00', 25: '06:15:00', 26: '06:30:00', 27: '06:45:00', 28: '07:00:00', 29: '07:15:00', 30: '07:30:00', 31: '07:45:00', 32: '08:00:00', 33: '08:15:00', 34: '08:30:00', 35: '08:45:00', 36: '09:00:00', 37: '09:15:00', 38: '09:30:00', 39: '09:45:00', 40: '10:00:00', 41: '10:15:00', 42: '10:30:00', 43: '10:45:00', 44: '11:00:00', 45: '11:15:00', 46: '11:30:00', 47: '11:45:00', 48: '12:00:00', 49: '12:15:00', 50: '12:30:00', 51: '12:45:00', 52: '13:00:00', 53: '13:15:00', 54: '13:30:00', 55: '13:45:00', 56: '14:00:00', 57: '14:15:00', 58: '14:30:00', 59: '14:45:00', 60: '15:00:00', 61: '15:15:00', 62: '15:30:00', 63: '15:45:00', 64: '16:00:00', 65: '16:15:00', 66: '16:30:00', 67: '16:45:00', 68: '17:00:00', 69: '17:15:00', 70: '17:30:00', 71: '17:45:00', 72: '18:00:00', 73: '18:15:00', 74: '18:30:00', 75: '18:45:00', 76: '19:00:00', 77: '19:15:00', 78: '19:30:00', 79: '19:45:00', 80: '20:00:00', 81: '20:15:00', 82: '20:30:00', 83: '20:45:00', 84: '21:00:00', 85: '21:15:00', 86: '21:30:00', 87: '21:45:00', 88: '22:00:00', 89: '22:15:00', 90: '22:30:00', 91: '22:45:00', 92: '23:00:00', 93: '23:15:00', 94: '23:30:00', 95: '23:45:00', 96: '00:00:00'}
s = df.groupby(level=0).cumcount().map(d)
print (s)
Date Time
2018-01-09 6:00:00 00:00:00
6:15:00 00:15:00
6:30:00 00:30:00
6:45:00 00:45:00
7:00:00 01:00:00
7:15:00 01:15:00
7:30:00 01:30:00
7:45:00 01:45:00
8:00:00 02:00:00
2018-01-10 6:00:00 00:00:00
6:15:00 00:15:00
6:30:00 00:30:00
6:45:00 00:45:00
7:00:00 01:00:00
7:15:00 01:15:00
7:30:00 01:30:00
7:45:00 01:45:00
8:00:00 02:00:00
最后为set_index的get_level_values重新分配新索引，用于第一级索引：
df = df.set_index([df.index.get_level_values(0), s])
print (df)
a b c
Date
2018-01-09 00:00:00 20.31 0 -2.95
00:15:00 20.76 26738 -2.88
00:30:00 21.40 22462 -2.77
00:45:00 21.84 20033 -3.00
01:00:00 22.17 20010 -3.28
01:15:00 22.38 18133 -2.82
01:30:00 22.75 18254 -3.14
01:45:00 22.93 17039 -3.22
02:00:00 23.13 15934 -3.27
2018-01-10 00:00:00 20.31 0 -2.95
00:15:00 20.76 26738 -2.88
00:30:00 21.40 22462 -2.77
00:45:00 21.84 20033 -3.00
01:00:00 22.17 20010 -3.28
01:15:00 22.38 18133 -2.82
01:30:00 22.75 18254 -3.14
01:45:00 22.93 17039 -3.22
02:00:00 23.13 15934 -3.27