我对一组数据的日期时间格式有疑问。造成此问题的原因是一天中的时间介于1到24之间，而第24小时设置为错误的一天（更确切地说是前一天）。我有下面的数据示例，
1/1/2019,14:00,0.2,0.1,0.0,0.2,3.0,36.7,3,153
1/1/2019,15:00,0.2,0.6,0.2,0.4,3.9,36.7,1,199
1/1/2019,16:00,1.8,2.4,0.8,1.6,1.1,33.0,0,307
1/1/2019,17:00,3.0,3.2,0.6,2.6,6.0,32.8,1,310
1/1/2019,18:00,1.6,2.2,0.5,1.7,7.9,33.1,4,293
1/1/2019,19:00,1.7,1.1,0.6,0.6,5.9,35.0,5,262
1/1/2019,20:00,1.0,0.5,0.2,0.2,2.9,32.6,5,201
1/1/2019,21:00,0.6,0.3,0.0,0.4,2.1,31.8,6,182
1/1/2019,22:00,0.4,0.3,0.0,0.4,5.1,31.4,6,187
1/1/2019,23:00,0.8,0.6,0.3,0.3,9.9,30.2,5,227
1/1/2019,24:00,1.0,0.7,0.3,0.4,6.9,27.9,4,225 --- Here the date should be 1/2/2019
1/2/2019,01:00,1.3,0.9,0.5,0.4,4.0,26.9,6,236
1/2/2019,02:00,0.4,0.4,0.2,0.2,5.0,27.3,6,168
1/2/2019,03:00,0.7,0.5,0.3,0.3,6.9,30.2,4,219
1/2/2019,04:00,1.3,0.8,0.5,0.3,5.9,32.3,4,242
1/2/2019,05:00,0.7,0.2,0.0,0.2,3.0,33.8,4,177
1/2/2019,06:00,0.5,0.2,0.2,0.1,5.1,36.1,4,195
1/2/2019,07:00,0.6,0.3,0.2,0.2,9.9,38.0,4,200
1/2/2019,08:00,0.5,0.6,0.4,0.3,6.8,38.9,4,179
1/2/2019,09:00,0.5,0.2,0.0,0.2,3.0,39.0,4,193
1/2/2019,10:00,0.3,0.3,0.2,0.1,4.0,38.7,5,198
1/2/2019,11:00,0.3,0.3,0.2,0.0,4.9,38.4,5,170
1/2/2019,12:00,0.6,0.3,0.3,0.0,2.0,38.4,4,172
1/2/2019,13:00,0.2,0.3,0.2,0.0,2.0,38.8,4,154
1/2/2019,14:00,0.3,0.1,0.0,0.2,1.9,39.3,4,145
这是一组相当大的数据，我需要对其进行时间序列图绘制，因此，我需要找到一种方法来解决此格式问题。我试图遍历行和熊猫数据框中的行以修复有问题的行，但这没有提供任何结果。谢谢您的帮助。
答案 0 :(得分：1)
您可以将to_datetime的date转换为日期时间，然后添加to_timedelta的time列转换为时间增量：
df['date'] = pd.to_datetime(df['date']) + pd.to_timedelta(df['time'] + ':00')
或者如果需要也删除time列：
print (df)
date time a b c d e f g h
0 1/1/2019 14:00 0.2 0.1 0.0 0.2 3.0 36.7 3 153
1 1/1/2019 15:00 0.2 0.6 0.2 0.4 3.9 36.7 1 199
2 1/1/2019 16:00 1.8 2.4 0.8 1.6 1.1 33.0 0 307
3 1/1/2019 17:00 3.0 3.2 0.6 2.6 6.0 32.8 1 310
4 1/1/2019 18:00 1.6 2.2 0.5 1.7 7.9 33.1 4 293
5 1/1/2019 19:00 1.7 1.1 0.6 0.6 5.9 35.0 5 262
6 1/1/2019 20:00 1.0 0.5 0.2 0.2 2.9 32.6 5 201
7 1/1/2019 21:00 0.6 0.3 0.0 0.4 2.1 31.8 6 182
8 1/1/2019 22:00 0.4 0.3 0.0 0.4 5.1 31.4 6 187
9 1/1/2019 23:00 0.8 0.6 0.3 0.3 9.9 30.2 5 227
10 1/1/2019 24:00 1.0 0.7 0.3 0.4 6.9 27.9 4 225
11 1/2/2019 01:00 1.3 0.9 0.5 0.4 4.0 26.9 6 236
12 1/2/2019 02:00 0.4 0.4 0.2 0.2 5.0 27.3 6 168
13 1/2/2019 03:00 0.7 0.5 0.3 0.3 6.9 30.2 4 219
14 1/2/2019 04:00 1.3 0.8 0.5 0.3 5.9 32.3 4 242
15 1/2/2019 05:00 0.7 0.2 0.0 0.2 3.0 33.8 4 177
16 1/2/2019 06:00 0.5 0.2 0.2 0.1 5.1 36.1 4 195
17 1/2/2019 07:00 0.6 0.3 0.2 0.2 9.9 38.0 4 200
18 1/2/2019 08:00 0.5 0.6 0.4 0.3 6.8 38.9 4 179
19 1/2/2019 09:00 0.5 0.2 0.0 0.2 3.0 39.0 4 193
20 1/2/2019 10:00 0.3 0.3 0.2 0.1 4.0 38.7 5 198
21 1/2/2019 11:00 0.3 0.3 0.2 0.0 4.9 38.4 5 170
22 1/2/2019 12:00 0.6 0.3 0.3 0.0 2.0 38.4 4 172
23 1/2/2019 13:00 0.2 0.3 0.2 0.0 2.0 38.8 4 154
24 1/2/2019 14:00 0.3 0.1 0.0 0.2 1.9 39.3 4 145
df['date'] = pd.to_datetime(df['date']) + pd.to_timedelta(df.pop('time') + ':00')
print (df)
date a b c d e f g h
0 2019-01-01 14:00:00 0.2 0.1 0.0 0.2 3.0 36.7 3 153
1 2019-01-01 15:00:00 0.2 0.6 0.2 0.4 3.9 36.7 1 199
2 2019-01-01 16:00:00 1.8 2.4 0.8 1.6 1.1 33.0 0 307
3 2019-01-01 17:00:00 3.0 3.2 0.6 2.6 6.0 32.8 1 310
4 2019-01-01 18:00:00 1.6 2.2 0.5 1.7 7.9 33.1 4 293
5 2019-01-01 19:00:00 1.7 1.1 0.6 0.6 5.9 35.0 5 262
6 2019-01-01 20:00:00 1.0 0.5 0.2 0.2 2.9 32.6 5 201
7 2019-01-01 21:00:00 0.6 0.3 0.0 0.4 2.1 31.8 6 182
8 2019-01-01 22:00:00 0.4 0.3 0.0 0.4 5.1 31.4 6 187
9 2019-01-01 23:00:00 0.8 0.6 0.3 0.3 9.9 30.2 5 227
10 2019-01-02 00:00:00 1.0 0.7 0.3 0.4 6.9 27.9 4 225
11 2019-01-02 01:00:00 1.3 0.9 0.5 0.4 4.0 26.9 6 236
12 2019-01-02 02:00:00 0.4 0.4 0.2 0.2 5.0 27.3 6 168
13 2019-01-02 03:00:00 0.7 0.5 0.3 0.3 6.9 30.2 4 219
14 2019-01-02 04:00:00 1.3 0.8 0.5 0.3 5.9 32.3 4 242
15 2019-01-02 05:00:00 0.7 0.2 0.0 0.2 3.0 33.8 4 177
16 2019-01-02 06:00:00 0.5 0.2 0.2 0.1 5.1 36.1 4 195
17 2019-01-02 07:00:00 0.6 0.3 0.2 0.2 9.9 38.0 4 200
18 2019-01-02 08:00:00 0.5 0.6 0.4 0.3 6.8 38.9 4 179
19 2019-01-02 09:00:00 0.5 0.2 0.0 0.2 3.0 39.0 4 193
20 2019-01-02 10:00:00 0.3 0.3 0.2 0.1 4.0 38.7 5 198
21 2019-01-02 11:00:00 0.3 0.3 0.2 0.0 4.9 38.4 5 170
22 2019-01-02 12:00:00 0.6 0.3 0.3 0.0 2.0 38.4 4 172
23 2019-01-02 13:00:00 0.2 0.3 0.2 0.0 2.0 38.8 4 154
24 2019-01-02 14:00:00 0.3 0.1 0.0 0.2 1.9 39.3 4 145