I have a pandas dataframe:

我有一个熊猫数据框：

    lat         lng         alt days              date        time
0   40.003834   116.321462  211 39745.175405      2008-10-24  04:12:35
1   40.003783   116.321431  201 39745.175463  2008-10-24      04:12:40
2   40.003690   116.321429  203 39745.175521      2008-10-24      04:12:45
3   40.003589   116.321427  194 39745.175579      2008-10-24      04:12:50
4   40.003522   116.321412  190 39745.175637      2008-10-24      04:12:55
5   40.003509   116.321484  188 39745.175694      2008-10-24      04:13:00

For which I am trying to convert the df['date'] and df['time'] columns into a datetime. I can do:

为此，我尝试将df[‘date’]和df[‘time’]列转换为日期时间。我可以做到：

df['Datetime'] = pd.to_datetime(df['date']+df['time'])
df = df.set_index(['Datetime'])
del df['date']
del df['time']

And I get:

我得到了：

                    lat         lng         alt days
Datetime                            
2008-10-2404:12:35  40.003834   116.321462  211 39745.175405    
2008-10-2404:12:40  40.003783   116.321431  201 39745.175463
2008-10-2404:12:45  40.003690   116.321429  203 39745.175521    
2008-10-2404:12:50  40.003589   116.321427  194 39745.175579    
2008-10-2404:12:55  40.003522   116.321412  190 39745.175637

But then if I try:

但如果我试一试：

df.between_time(time(1),time(22,59,59))['lng'].std()

I get an error - 'TypeError: Index must be DatetimeIndex'

我收到错误-‘TypeError：Index必须为DatetimeIndex’

So, I've also tried setting the DatetimeIndex:

因此，我还尝试设置了DatetimeIndex：

df['Datetime'] = pd.to_datetime(df['date']+df['time'])
#df = df.set_index(['Datetime'])
df = df.set_index(pd.DatetimeIndex(df['Datetime']))
del df['date']
del df['time']

And this throws an error also - 'DateParseError: unknown string format'

这也抛出了一个错误--‘DateParseError：未知的字符串格式’

How do I create the datetime column and DatetimeIndex correctly so that df.between_time() works right?

如何正确地创建datetime列和DatetimeIndex，以便df.between_time（）正常工作？

To simplify Kirubaharan's answer a bit:

将Kirubaharan的回答简单化一点：

df['Datetime'] = pd.to_datetime(df['date'] + ' ' + df['time'])
df = df.set_index('Datetime')

And to get rid of unwanted columns (as OP did but did not specify per se in the question):

并删除不需要的列(就像OP所做的那样，但没有在问题中具体说明其本身)：

df = df.drop(['date','time'], axis=1)

You are not creating datetime index properly,

您没有正确创建日期时间索引，

format = '%Y-%m-%d %H:%M:%S'
df['Datetime'] = pd.to_datetime(df['date'] + ' ' + df['time'], format=format)
df = df.set_index(pd.DatetimeIndex(df['Datetime']))

You may also want to set inplace=True. This way it returns the same df

您可能还希望设置inplace=True。通过这种方式，它返回相同的df

df["datetime"] = pd.to_datetime(df["date"] + " " + df["time"], format = "%Y-%m-%d %H:%M:%S")
df.set_index(["datetime"], inplace=True)

This worked best for me:

这对我来说效果最好：

format = '%Y-%m-%d%H:%M:%S'
df['Datetime'] = pd.to_datetime(df['date'] + df['time'].astype("string"), format=format)

In some cases Python treats df['date'] as column of integers.

在某些情况下，Python将df[‘date’]视为整数列。

I had trouble with setting a column formatted as YYYY-MM-DD as a date time index column in a data frame I needed for time series forecasting. This is how I solved it for a dateframe where I wanted "dateCol" to be the datetime index:

我在将YYYY-MM-DD格式的列设置为时间序列预测所需的数据框中的日期时间索引列时遇到了麻烦。这就是我如何解决日期框问题的方法，在该日期框中，我希望将“date Col”作为日期时间索引：

idx = pd.DatetimeIndex(self.df[dateCol])
self.df = self.df.set_index(idx)

Then to drop the column so it's not duplicated in the dataframe

然后删除该列，这样它就不会在数据帧中重复

self.df = self.df.drop(dateCol, axis=1)