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StringTokenizer分隔一次(StringTokenizer delimit once)

 我想分割一个字符串（行）的第一个空白，但只有第一个。 
 

StringTokenizer linesplit = new StringTokenizer(line," ");

 

 以“这是一个测试”为例。 然后我想要这些字符串是“This”，“是一个测试”。 我怎么能使用StringTokenizer或有更好的选择？ 

I want to split a string (line) by the first whitespace, but only the first.
 

StringTokenizer linesplit = new StringTokenizer(line," ");

 

Take for example "This is a test". Then I want the strings to be "This" and "is a test". How could I use StringTokenizer or is there a better option?

原文：https://stackoverflow.com/questions/22015892
更新时间：2022-12-21 06:12

最满意答案

 这是在0.12中引入的。 它在cython中完成的速度要快得多。 单位是纪元秒数（如果你的日期时间是以纪元为单位的毫秒数，你也可以传递'ms'）。 这里的文档 
 

In [6]: df['end_time'] = pd.to_datetime(df['end_time'],unit='s')

In [7]: df['start_time'] = pd.to_datetime(df['end_time'],unit='s')

In [8]: df
Out[8]: 
             end_time          start_time
0 2013-10-03 13:04:41 2013-10-03 13:04:41
1 2013-10-03 13:04:42 2013-10-03 13:04:42
2 2013-10-03 13:04:43 2013-10-03 13:04:43
3 2013-10-03 13:04:44 2013-10-03 13:04:44
4 2013-10-03 13:04:45 2013-10-03 13:04:45
5 2013-10-03 13:04:46 2013-10-03 13:04:46
6 2013-10-03 13:04:47 2013-10-03 13:04:47
7 2013-10-03 13:04:48 2013-10-03 13:04:48
8 2013-10-03 13:04:49 2013-10-03 13:04:49
9 2013-10-03 13:04:50 2013-10-03 13:04:50

 

 请注意，这已经是GMT。 datetime.fromtimestamp转换为本地tz）。 如果你想要那个。 
 

In [21]: DatetimeIndex(pd.to_datetime(df['end_time'],unit='s'),tz='UTC').tz_convert('EST')
Out[21]: 
<class 'pandas.tseries.index.DatetimeIndex'>
[2013-10-03 08:04:41, ..., 2013-10-03 08:04:50]
Length: 10, Freq: None, Timezone: EST
In [32]: DataFrame(dict(end_time = DatetimeIndex(pd.to_datetime(df['end_time'],unit='s'),tz='UTC').tz_convert('Asia/Kolkata').asobject))

 

 要转换为亚洲/加尔各答的tz。 你必须将其表示为对象。 这将是有效的。 
 

                    end_time
 0  2013-10-03 18:34:41+05:30
 1  2013-10-03 18:34:42+05:30
 2  2013-10-03 18:34:43+05:30
 3  2013-10-03 18:34:44+05:30
 4  2013-10-03 18:34:45+05:30
 5  2013-10-03 18:34:46+05:30
 6  2013-10-03 18:34:47+05:30
 7  2013-10-03 18:34:48+05:30
 8  2013-10-03 18:34:49+05:30
 9  2013-10-03 18:34:50+05:30


This was introduced in 0.12. MUCH faster as its all done in cython. the unit is number of epoch seconds (you can also pass for example 'ms' if your datetimes in milliseconds since epoch). docs here
 

In [6]: df['end_time'] = pd.to_datetime(df['end_time'],unit='s')

In [7]: df['start_time'] = pd.to_datetime(df['end_time'],unit='s')

In [8]: df
Out[8]: 
             end_time          start_time
0 2013-10-03 13:04:41 2013-10-03 13:04:41
1 2013-10-03 13:04:42 2013-10-03 13:04:42
2 2013-10-03 13:04:43 2013-10-03 13:04:43
3 2013-10-03 13:04:44 2013-10-03 13:04:44
4 2013-10-03 13:04:45 2013-10-03 13:04:45
5 2013-10-03 13:04:46 2013-10-03 13:04:46
6 2013-10-03 13:04:47 2013-10-03 13:04:47
7 2013-10-03 13:04:48 2013-10-03 13:04:48
8 2013-10-03 13:04:49 2013-10-03 13:04:49
9 2013-10-03 13:04:50 2013-10-03 13:04:50

 

Note that this is in GMT already. datetime.fromtimestamp does a conversion to local tz). If you want that.
 

In [21]: DatetimeIndex(pd.to_datetime(df['end_time'],unit='s'),tz='UTC').tz_convert('EST')
Out[21]: 
<class 'pandas.tseries.index.DatetimeIndex'>
[2013-10-03 08:04:41, ..., 2013-10-03 08:04:50]
Length: 10, Freq: None, Timezone: EST
In [32]: DataFrame(dict(end_time = DatetimeIndex(pd.to_datetime(df['end_time'],unit='s'),tz='UTC').tz_convert('Asia/Kolkata').asobject))

 

To convert to the tz of Asia/Kolkata. You have to represent this as object. This is will work.
 

                    end_time
 0  2013-10-03 18:34:41+05:30
 1  2013-10-03 18:34:42+05:30
 2  2013-10-03 18:34:43+05:30
 3  2013-10-03 18:34:44+05:30
 4  2013-10-03 18:34:45+05:30
 5  2013-10-03 18:34:46+05:30
 6  2013-10-03 18:34:47+05:30
 7  2013-10-03 18:34:48+05:30
 8  2013-10-03 18:34:49+05:30
 9  2013-10-03 18:34:50+05:30

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