如何将 EST/EDT 转换为 GMT?
我在一列中有一些记录,代表 EST 或 EDT 时间。我需要将这些时间转换为 GMT 时间。时间的格式是:
10/1/2010 0:0:0
10/1/2010 0:6:0
...
10/1/2010 23:54:0
...
10/3/2010 0:0:0
...
有人可以帮我吗?谢谢
I have a few records inside a column which represent either EST or EDT Time. I need to convert these times to GMT time. The format of the time are:
10/1/2010 0:0:0
10/1/2010 0:6:0
...
10/1/2010 23:54:0
...
10/3/2010 0:0:0
...
Can someone help me out here? thanks
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我知道在时区之间转换的最简单、最可靠的方法是使用第三方 pytz 模块:
产量:
但请注意,您的数据未指定日期时间是在 EST 还是 EDT 时区。
有时,当您不指定 EST 或 EDT 时,会产生歧义。例如,“10/27/2002 1:30:00”将是不明确的:
因为由于夏令时,该时间发生了两次。
还有一些日期时间,例如 2002-04-07 02:30:00,
不存在。请参阅此链接
讨论这些以及在处理当地时间时出现的更奇怪的问题。
如果您愿意忽略这些棘手的极端情况,并且您的机器设置在当地时区(例如 EST/EDT),
有一种方法可以在本地时区和 UTC 时区之间进行转换
不需要安装
pytz
。这个想法是转换日期时间 -->时间元组 -->时间戳 --> UTC 日期时间。转换链是通过例如:
产量
上面测试的最后两个日期显示,即使时间接近 EST 和 EDT 之间的切换,转换也能正常工作。
综上所述,使用替代方法(不使用 pytz),以下是如何将表示本地时间的日期时间对象转换为表示 GMT 的日期时间对象
时间,反之亦然:
The easiest, most reliable way I know to convert between timezones is to use the third-party pytz module:
yields:
Note however, your data does not specify if the datetime is in the EST or EDT timezone.
There are some times which are ambiguous when you don't specify EST or EDT. For example, '10/27/2002 1:30:00' would be ambiguous:
since this time happened twice due to Daylight Savings Time.
Also some datetimes, like 2002-04-07 02:30:00,
are nonexistent. See this link
for a discussion of these and even more bizarre issues that arise when dealing with localtimes.
If you are willing to overlook these knotty corner cases, and if your machine is setup in the local timezone (e.g. EST/EDT),
there is a way to convert between the local and UTC timezones which does
not require the installation of
pytz
. The idea is to convert the datetime --> timetuple --> timestamp --> UTC datetime. The chain of conversions is done withFor example:
yields
The last two dates tested above show the conversion works correctly even with times close to the switch between EST and EDT.
In summary, using the alternate method (without pytz), here is how to convert datetime objects representing local time to datetime objects representing GMT
time, and vice versa:
每条记录的伪代码:
制作一个时间戳字符串: field[0].strip() + " " + field[1].strip()
使用 datetime.datetime.strptime() 将其转换为 datetime.datetime 实例
添加一个 timedelta例如 timedelta(hours=-4) 到您的时间戳,
使用 timestamp.strftime() 生成您想要的输出的任何字符串表示形式。
对于时间字段为空的情况:如果这意味着 0:0:0,请修改以上内容以适应。如果这意味着“时间未知”,那么您需要做其他事情......
Pseudocode for each record:
make a timestamp string: field[0].strip() + " " + field[1].strip()
use datetime.datetime.strptime() to convert that into a datetime.datetime instance
add a timedelta e.g. timedelta(hours=-4) to your timestamp
use timestamp.strftime() to produce whatever string representation you want for the output.
For the case where the time field is empty: If that means 0:0:0, modify the above to suit. If it means "time unknown", you'll need to do something else ...
假设我们有一个美国/东部时间的日期时间字符串“2019-04-09T23:59:55ET”。
下面是将字符串转换为 UTC 的函数:
Assume that we have a datetime string as "2019-04-09T23:59:55ET" in US/Eastern time.
Here is the function to convert string to UTC:
如果没有关联的时间,时区就无关紧要……日期也不能转换为不同的时区。另一栏里有相关时间吗?
编辑:好吧,现在有时间了,我会让 python 大师接手。 ;]
Without an associated time, the time zone doesn't matter ... nor can the date be translated to a different time zone. Is there a related time in another column?
EDIT: Alright, now that there IS a time, I'll let the python guru's take over. ;]
您可以使用
pandas.DataFrame。 tz_convert()
像这样:最后一行的作用是从日期时间对象中删除时区信息,这样您就可以只操作日期和时间(不用担心,这不会改变再次输入时区,它只是将其从时间戳字符串中删除)。
You can use
pandas.DataFrame.tz_convert()
like this:What the last row does is removing the timezone info from the datetime object, so you can operate with the date and time only (don't worry, that doesn't change the timezone again, it just strips it from the timestamp string).
我必须在 Python 中创建一个自定义函数来将 EST 转换为 GMT,这是我编写的代码:
我还没有添加对 EDT 的支持。目前是二月,正在遵循美国东部时间。如有任何更改或更正,欢迎!
I had to create a custom function in Python to convert EST to GMT, here's the code I've written:
I haven't added support for EDT. It is February currently and EST is being followed. Any changes or corrections are welcome!