Python 我想把熊猫数据转换成日期时间索引
试试这个：Python 我想把熊猫数据转换成日期时间索引,python,pandas,dataframe,Python,Pandas,Dataframe,试试这个： names=['Ticker','Date','Time','open','high','low','close','Volume'] havellsdata = pd.read_csv('C:\\Users\\nEW u\Desktop\havellsjuly2015.csv',names=names,index_col= ' Ticker']) k=havellsdata['high']-havellsdata['open'] havellsdata.insert(6,'ope
names=['Ticker','Date','Time','open','high','low','close','Volume']
havellsdata = pd.read_csv('C:\\Users\\nEW u\Desktop\havellsjuly2015.csv',names=names,index_col= '
Ticker'])
k=havellsdata['high']-havellsdata['open']
havellsdata.insert(6,'openhighcheck',k)
havellsdata
#havellsdata[15]
Date Time open high low close openhighcheck Volume
Ticker
HAVELLS 20150703 09:16:00 287.90 287.90 285.00 285.90 0.00 2978
HAVELLS 20150703 09:17:00 286.00 286.95 284.70 286.95 0.95 2684
HAVELLS 20150703 09:18:00 287.00 287.55 285.90 287.15 0.55 3717
HAVELLS 20150703 09:19:00 287.40 287.40 286.75 286.75 0.00 1451
HAVELLS 20150703 09:20:00 287.00 287.15 286.25 286.60 0.15 2721
HAVELLS 20150703 09:21:00 286.40 286.45 285.70 285.95 0.05 6084
HAVELLS 20150703 09:22:00 286.25 286.30 286.00 286.25 0.05 3466
HAVELLS 20150703 09:23:00 285.90 286.95 285.90 286.50 1.05 3831
HAVELLS 20150703 09:24:00 287.25 287.80 286.95 287.80 0.55 5686
HAVELLS 20150703 09:25:00 288.00 288.10 287.60 288.00 0.10 2844
HAVELLS 20150703 09:26:00 287.95 288.10 287.50 288.00 0.15 3149
HAVELLS 20150703 09:27:00 288.10 288.10 287.15 287.40 0.00 2216
HAVELLS 20150703 09:28:00 287.35 288.00 287.15 287.15 0.65 2511
HAVELLS 20150703 09:29:00 287.25 287.25 286.65 286.80 0.00 2744
HAVELLS 20150703 09:30:00 287.10 287.10 286.75 287.00 0.00 1588
HAVELLS 20150703 09:31:00 286.85 287.00 286.65 286.95 0.15 652
HAVELLS 20150703 09:32:00 286.85 286.90 286.75 286.75 0.05 481
HAVELLS 20150703 09:33:00 286.75 286.90 286.75 286.85 0.15 664
HAVELLS 20150703 09:34:00 286.90 287.00 286.75 287.00 0.10 608
HAVELLS 20150703 09:35:00 287.20 287.20 287.00 287.00 0.00 467
HAVELLS 20150703 09:36:00 287.00 287.15 286.70 286.70 0.15 2505
HAVELLS 20150703 09:37:00 286.50 287.40 286.00 287.40 0.90 4426
HAVELLS 20150703 09:38:00 287.40 287.65 286.45 286.75 0.25 5032
HAVELLS 20150703 09:39:00 286.65 288.25 286.65 288.05 1.60 5384
HAVELLS 20150703 09:40:00 288.05 288.50 287.80 287.80 0.45 8018
HAVELLS 20150703 09:41:00 287.70 287.70 286.45 287.40 0.00 9697
HAVELLS 20150703 09:42:00 287.55 287.55 287.05 287.10 0.00 1113
HAVELLS 20150703 09:43:00 287.30 287.45 287.00 287.45 0.15 2392
HAVELLS 20150703 09:44:00 287.65 287.65 287.05 287.10 0.00 857
HAVELLS 20150703 09:45:00 287.35 287.35 286.75 287.15 0.00 3159
... ... ... ... ... ... ... ... ...
HAVELLS 20150720 15:01:00 310.40 310.65 310.35 310.55 0.25 2222
HAVELLS 20150720 15:02:00 310.50 310.70 310.50 310.70 0.20 1250
HAVELLS 20150720 15:03:00 310.70 310.70 310.45 310.60 0.00 1667
HAVELLS 20150720 15:04:00 310.60 310.70 310.45 310.50 0.10 4395
HAVELLS 20150720 15:05:00 310.70 310.70 310.40 310.65 0.00 2580
HAVELLS 20150720 15:06:00 310.65 310.65 310.15 310.30 0.00 3864
HAVELLS 20150720 15:07:00 310.25 310.55 310.25 310.50 0.30 2275
HAVELLS 20150720 15:08:00 310.50 310.50 309.85 309.95 0.00 15803
HAVELLS 20150720 15:09:00 309.95 310.00 309.50 309.90 0.05 8086
HAVELLS 20150720 15:10:00 309.85 309.90 309.55 309.65 0.05 3743
HAVELLS 20150720 15:11:00 309.65 309.70 309.50 309.50 0.05 7241
HAVELLS 20150720 15:12:00 309.55 309.70 309.30 309.35 0.15 3823
HAVELLS 20150720 15:13:00 309.35 309.50 309.25 309.50 0.15 6280
HAVELLS 20150720 15:14:00 309.60 309.70 309.25 309.50 0.10 9540
HAVELLS 20150720 15:15:00 309.50 309.65 309.25 309.50 0.15 5264
HAVELLS 20150720 15:16:00 309.50 309.65 309.25 309.60 0.15 5783
HAVELLS 20150720 15:17:00 309.40 310.00 309.25 309.80 0.60 6529
HAVELLS 20150720 15:18:00 309.85 310.45 309.75 310.30 0.60 15943
HAVELLS 20150720 15:19:00 310.35 310.80 310.10 310.80 0.45 3616
HAVELLS 20150720 15:20:00 310.75 310.85 310.40 310.50 0.10 6405
HAVELLS 20150720 15:21:00 310.35 310.75 310.15 310.40 0.40 7800
HAVELLS 20150720 15:22:00 310.35 310.65 310.10 310.40 0.30 10439
HAVELLS 20150720 15:23:00 310.50 310.65 310.05 310.40 0.15 4467
HAVELLS 20150720 15:24:00 310.40 310.65 310.35 310.65 0.25 7938
HAVELLS 20150720 15:25:00 310.65 310.65 310.50 310.65 0.00 3290
HAVELLS 20150720 15:26:00 310.65 310.65 310.50 310.60 0.00 4851
HAVELLS 20150720 15:27:00 310.60 310.80 310.55 310.55 0.20 3662
HAVELLS 20150720 15:28:00 310.60 310.85 310.55 310.60 0.25 3874
HAVELLS 20150720 15:29:00 310.60 310.90 310.60 310.90 0.30 13271
HAVELLS 20150720 15:30:00 310.90 311.00 310.00 311.00 0.10 8751
havellsdata['Datetime'] = pd.to_datetime(havellsdata['Date'].apply(str) + ' ' + havellsdata['Time'])
havellsdata = havellsdata.set_index('Datetime')
您好，欢迎来到SO。你的问题似乎都是代码，而且格式不好。请您抽出一些时间来正确格式化所有代码好吗？它给出了以下错误类型错误：ufunc“add”不包含签名匹配类型为dtype的循环（“@KeshavChoudhary Fixed。请检查我的更新答案。谢谢您，这起作用了，我现在可以在15分钟内重新采样了？
In [1723]: df
Out[1723]:
Date Time
0 20150703 09:16:00
1 20150703 09:17:00
2 20150703 09:18:00
In [1725]: df['Datetime'] = pd.to_datetime(df['Date'].apply(str) + ' ' + df['Time'])
In [1727]: df = df.set_index('Datetime')
In [1728]: df
Out[1728]:
Date Time
Datetime
2015-07-03 09:16:00 20150703 09:16:00
2015-07-03 09:17:00 20150703 09:17:00
2015-07-03 09:18:00 20150703 09:18:00