从开始日期和时间开始的python / pandas，创建日期时间索引
[英]python/pandas from start date + time, create datetime index
问题描述
Currently I have a dataframe that looks like the following: 
目前，我有一个如下数据框：
df =
Open High Low Close TotalVolume
0 113.40 113.54 113.40 113.54 7237
1 113.54 113.58 113.52 113.57 10099
2 113.59 113.81 113.52 113.78 13827
3 113.76 113.94 113.75 113.92 16129
4 113.91 114.01 113.88 113.97 27052
5 113.97 114.11 113.92 114.01 24925
6 114.00 114.15 113.99 114.04 13461
7 114.06 114.14 113.94 113.94 10702
8 113.92 113.99 113.86 113.99 5538
9 113.96 113.96 113.85 113.86 14000
It does not necessarily have to be a datetime index but I felt like it would be the easiest. 它不一定必须是日期时间索引，但我觉得这将是最简单的。 From this I have a variable startDate that follows this format startDate = "03-20-2018t14:00" 从这个我有一个变量startDate遵循这种格式startDate = "03-20-2018t14:00"
From this, this is minute data and for it to run another program, the format has to follow this, but this is the end result I am hoping for: 由此，这是微小的数据，并且要运行其他程序，格式必须遵循此规则，但这是我希望得到的最终结果：
updated_df =
Date Time Open High Low Close TotalVolume
03/20/2018 14:00 113.40 113.54 113.40 113.54 7237
03/20/2018 14:01 113.54 113.58 113.52 113.57 10099
03/20/2018 14:02 113.59 113.81 113.52 113.78 13827
03/20/2018 14:03 113.76 113.94 113.75 113.92 16129
03/20/2018 14:04 113.91 114.01 113.88 113.97 27052
03/20/2018 14:05 113.97 114.11 113.92 114.01 24925
03/20/2018 14:06 114.00 114.15 113.99 114.04 13461
03/20/2018 14:07 114.06 114.14 113.94 113.94 10702
03/20/2018 14:08 113.92 113.99 113.86 113.99 5538
03/20/2018 14:09 113.96 113.96 113.85 113.86 14000
1 个解决方案
解决方案1
2 已采纳 2018-07-20 13:47:35
You need to use pandas.date_range() with start , periods and freq parameters. 您需要将pandas.date_range（）与start ， periods和freq参数一起使用。
df['datetime'] = pd.date_range(start='03-20-2018t14:00', periods=len(df), freq="1min")
Or if you want them separate you can extract date and time from the DatetimeIndex as below: 或者，如果希望它们分开，则可以从DatetimeIndex提取date和time ，如下所示：
datetime_col = pd.date_range(start='03-20-2018t14:00', periods=len(df), freq="1min")
df['Date'] = datetime_col.date
df['Time'] = datetime_col.time
Refer to docs for detailed information. 请参阅文档以获取详细信息。
Output: 输出：
Date Time Open High Low Close TotalVolume
0 2018-03-20 14:00:00 113.40 113.54 113.40 113.54 7237
1 2018-03-20 14:01:00 113.54 113.58 113.52 113.57 10099
2 2018-03-20 14:02:00 113.59 113.81 113.52 113.78 13827
3 2018-03-20 14:03:00 113.76 113.94 113.75 113.92 16129
4 2018-03-20 14:04:00 113.91 114.01 113.88 113.97 27052
5 2018-03-20 14:05:00 113.97 114.11 113.92 114.01 24925
6 2018-03-20 14:06:00 114.00 114.15 113.99 114.04 13461
7 2018-03-20 14:07:00 114.06 114.14 113.94 113.94 10702
8 2018-03-20 14:08:00 113.92 113.99 113.86 113.99 5538
9 2018-03-20 14:09:00 113.96 113.96 113.85 113.86 14000
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