Early yesterday morning many people gained an extra hour by repeating the second hour of the day. Those people in time zones that were previously on daylight saving time switched back to standard time and thus may have observed their digital devices record the following sequence of times:

2012-11-04 01:59:58
2012-11-04 01:59:59
2012-11-04 01:00:00
2012-11-04 01:00:01

The written timestamps make it appear as if time went back one hour, when of course time continued to proceed linearly. This perceived repetition of an hour can be a problem for computer systems that output dates in localized formats. For instance, python’s logging package produces log entries using a human-readable time. However, by default, these human readable times are in localized formats. That means an event could be logged as happening before a preceding event when the localized time zone experiences a change in daylight saving time.

An obvious solution to this problem is to not take into consideration daylight saving time when working with time. For people in the pacific time zone, that could mean always using pacific standard time (PST). Likewise for people in the eastern time zone, they could always use eastern standard time (EST). Continuing with the log file example, this means all events in a log files will appear linearly. The trade-off is anyone looking at log entries during daylight saving time needs to keep in mind the one hour difference when determining how long ago a logged event actually occurred.

A separate issue has to do with keeping track of dates and times between machines in different time zones. For simplicity, we’ll ignore the problem of clock synchronization and assume all the machines have the correct time set. The issue is that even when using standard times (non daylight saving times) on respective machines, dates and times on different machines cannot be compared in a human readable format without including time zone specific information.

Fortunately computers need not use a human readable format to store dates and times. In fact, most computers already internally keep track of time using unix timestamps that store dates and times as the number of seconds since an agreed upon date and time in coordinated universal time (UTC). In spite of this solution to synchronize dates and times from different time zones, issues such as the logging problem still occur. In the remainder of this article we will look at a few causes of those problems and how to solve them in the context of python.

Within python there are two primary ways to store date and time information. The first, is the unix timestamp, which was previously discussed. Although unix timestamps are time zone agnostic, the major problem is that it is not simple for a human to determine properties about a unix timestamp without first converting to another format. For instance, what day of the month does the unix timestamp 1325448000 represent? And, how many years does the previous unix timestamp differ from 473414400? To help answer such questions, programming languages usually have a date and time representation that can be created from unix timestamps. In python, this representation is accomplished through the datetime object:

>>> from datetime import datetime
>>> print(datetime.fromtimestamp(1325448000))
2012-01-01 12:00:00
>>> print(datetime.fromtimestamp(473414400))
1985-01-01 00:00:00

Using the datetime representation you should be able to more easily answer the previous questions. If you execute the above code, some of you might wonder why print(datetime.fromtimestamp(1325448000)) does not produce the same result on your computer. The difference is due to the fact that datetime objects are created in the context of the local time zone by default. On my system, the local time zone is “America/Los_Angeles”, which corresponds to either PST or PDT depending on the time of year. With that in mind let’s look at instances of datetime around the end of daylight savings time 2012.

>>> a = datetime.fromtimestamp(1352019599)
>>> b = datetime.fromtimestamp(1352019600)
>>> print((b - a).total_seconds())
-3599.0

Although the timestamp for a is one second less than the timestamp for b, it appears as if b represents a time 3599 seconds (one second less than one hour) prior to a. The reason is that, in addition to datetime objects using the localized time zone by default, they do not maintain the time zone information by default. Thus, naïve datetime objects, that is, those without time zone information, are only compared by their values. Below are the values for a and b from the previous example:

>>> print(a)
2012-11-04 01:59:59
>>> print(b)
2012-11-04 01:00:00

While these values are correct, we should also be able to compare them correctly. Fortunately python’s datetime objects allow us to also associate time zone information. Unfortunately, the easiest way is through the non-standard package pytz. With pytz we can easily create time zone specific datetime objects. Doing so will solve the comparison problem:

>>> import pytz
>>> a = datetime.fromtimestamp(1352019599, pytz.timezone('America/Los_Angeles'))
>>> b = datetime.fromtimestamp(1352019600, pytz.timezone('America/Los_Angeles'))
>>> print((b - a).total_seconds())
1.0
>>> print(a)
2012-11-04 01:59:59-07:00
>>> print(b)
2012-11-04 01:00:00-08:00

In the previous example we get a tzinfo object by calling pytz.timezone('America/Los_Angeles') and use that in creating the datetime object. Note that the time zone selected only affects how the datetime object is represented. Observe that the following two datetime objects are the same despite their different representation:

>>> a = datetime.fromtimestamp(1352019599, pytz.timezone('America/Los_Angeles'))
>>> b = datetime.fromtimestamp(1352019599, pytz.timezone('America/New_York'))
>>> a == b
True
>>> print(a)
2012-11-04 01:59:59-07:00
>>> print(b)
2012-11-04 03:59:59-05:00

It should now be obvious that it is always a good idea to create datetime objects with time zone information included. Two other common ways of creating datetime objects with time zone information are datetime.now(pytz.timezone('America/Los_Angeles')) to create a datetime object of the current date and time and datetime(2012, 11, 4, 1, 0, tzinfo=pytz.timezone('America/Los_Angeles')) to create a datetime object for a specific date and time.

With time zone specific datetime objects, it’s trivial to get a localized representation for any time zone through the astimezone function:

>>> print(a)
2012-11-04 01:59:59-07:00
>>> print(a.astimezone(pytz.timezone('America/New_York')))
2012-11-04 03:59:59-05:00
>>> print(a.astimezone(pytz.UTC))
2012-11-04 08:59:59+00:00

Thus far we’ve shown that using unix timestamps is great for date and time consistency between machines at the cost of not being human readable. We then gave a brief overview of datetime objects, including how to create them from unix timestamps. Most importantly we showed that datetime objects are time zone agnostic by default. We then showed how to include time zone information in datetime objects and how to convert between various local datetime representations. We will now show how to correctly convert from datetime objects back to the correct unix timestamp.

In the python standard library there is nothing that directly converts from datetime objects to unix timestamps. However, there are two functions, time.mktime, and calendar.timegm that will create timestamps from struct_time objects. These functions, in combination with the datetime timetuple functions, allow for the conversion. The primary difference between time.mktime and calendar.timegm is that time.mktime expects the struct_time object to store the date and time information in the system localized time zone, whereas calendar.timegm expects the date and time information to be stored in UTC.

Making no assumptions about the time zone the datetime object is currently represented in, the following shows how to convert a datetime object to a unix timestamp using time.mktime:

>>> import time
>>> import pytz.reference
>>> a = datetime.fromtimestamp(1352019599, pytz.timezone('America/New_York'))
>>> time.mktime(a.astimezone(pytz.reference.LocalTimezone()).timetuple())
1352019599.0

Notice that we must first convert the datetime object, a, into its identical representation in our localized time zone before calling timetuple. The pytz.reference.LocalTimezone() code prevents us from needing to hard-code our localized time zone.

The second approach to convert from a datetime object to a unix timestamp is to first convert to UTC and then use calendar.timegm:

>>> import calendar
>>> calendar.timegm(a.utctimetuple())
1352019599

When using calendar.timegm we can conveniently use the datetime utctimetuple function rather than using the astimezone function.

At this point you should have a decent grasp on converting between unix timestamps and time zone sensitive datetime objects in the context of python applications. If you are anything like me, you will now always create datetime objects with time zone information and ponder why that is not the python library default.

In a future blog post (if I ever get around to it) I will look at storing and retrieving datetime objects in databases using sqlalchemy. A few questions I have are:

  • When the python application and the database are in different time zones, how are the datetime objects represented?
  • What happens when either the database or the python application changes time zones?

That’s all for now. Happy coding!

Update: I just discovered that python 3.3 now has a datetime.timestamp function which handles the conversion from datetime objects to unix timestamps (source).


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