Python datetime：tz内部方法与.replace之间的区别（tzinfo =）

Question

我最近修复了一个错误，但仍然不知道为什么会发生。我在Django模型中将以下时间戳（纪元）转换为datetimefield：

our_timezone = pytz.timezone("Asia/Jerusalem")
# e13 is used for true division
final_dict["published_date"] = datetime.datetime.fromtimestamp(
            float(article_as_dict["publish_date"]) / 1e3).replace(tzinfo=our_timezone)

上面的 返回的datetime对象始终 与真实的纪元时间（在我们的时区中）相距 39分钟 。

我设法通过移除来解决了这一问题replace，而是将时区放入了fromtimestamp方法中：

final_dict["published_date"] = datetime.datetime.fromtimestamp(
            float(article_as_dict["publish_date"]) / 1e3, tz=our_timezone)

那么，指定方法tz=our_timezone内部与创建对象之后进行操作有什么区别？为什么返回错误的时间？fromtimestamp``.replace(tzinfo=our_timezone)``.replace

Answer 1

>>> datetime.fromtimestamp(1000000000).replace(tzinfo=our_timezone)
datetime.datetime(2001, 9, 9, 3, 46, 40, tzinfo=<DstTzInfo 'Asia/Jerusalem' LMT+2:21:00 STD>)
>>> datetime.fromtimestamp(1000000000, tz=our_timezone)
datetime.datetime(2001, 9, 9, 4, 46, 40, tzinfo=<DstTzInfo 'Asia/Jerusalem' IDT+3:00:00 DST>)

请注意第一种情况下的奇数时区偏移量。

时区是多个 偏移量
的捆绑。时区偏移量随时间变化。它不仅会根据夏季和冬季的时间在一年内发生变化，而且在修订和更新时区数据时还会发生历史变化。您可以检查时区对象以获得一些有趣的历史数据，可以追溯到几十年前：

>>> our_timezone._utc_transition_times
[datetime.datetime(1, 1, 1, 0, 0), datetime.datetime(1901, 12, 13, 20, 45, 52), datetime.datetime(1917, 12, 31, 21, 39, 20), datetime.datetime(1940, 5, 31, 22, 0), datetime.datetime(1942, 10, 31, 21, 0), datetime.datetime(1943, 4, 1, 0, 0), datetime.datetime(1943, 10, 31, 21, 0), datetime.datetime(1944, 3, 31, 22, 0), datetime.datetime(1944, 10, 31, 21, 0), datetime.datetime(1945, 4, 15, 22, 0), datetime.datetime(1945, 10, 31, 23, 0), datetime.datetime(1946, 4, 16, 0, 0), datetime.datetime(1946, 10, 31, 21, 0), datetime.datetime(1948, 5, 22, 22, 0), datetime.datetime(1948, 8, 31, 20, 0), datetime.datetime(1948, 10, 31, 23, 0), datetime.datetime(1949, 4, 30, 22, 0), datetime.datetime(1949, 10, 31, 23, 0), datetime.datetime(1950, 4, 15, 22, 0), datetime.datetime(1950, 9, 15, 0, 0), datetime.datetime(1951, 3, 31, 22, 0), datetime.datetime(1951, 11, 11, 0, 0), datetime.datetime(1952, 4, 20, 0, 0), datetime.datetime(1952, 10, 19, 0, 0), datetime.datetime(1953, 4, 12, 0, 0), datetime.datetime(1953, 9, 13, 0, 0), datetime.datetime(1954, 6, 12, 22, 0), datetime.datetime(1954, 9, 11, 21, 0), datetime.datetime(1955, 6, 11, 0, 0), datetime.datetime(1955, 9, 10, 21, 0), datetime.datetime(1956, 6, 2, 22, 0), datetime.datetime(1956, 9, 30, 0, 0), datetime.datetime(1957, 4, 29, 0, 0), datetime.datetime(1957, 9, 21, 21, 0), datetime.datetime(1974, 7, 6, 22, 0), datetime.datetime(1974, 10, 12, 21, 0), datetime.datetime(1975, 4, 19, 22, 0), datetime.datetime(1975, 8, 30, 21, 0), datetime.datetime(1985, 4, 13, 22, 0), datetime.datetime(1985, 9, 14, 21, 0), datetime.datetime(1986, 5, 17, 22, 0), datetime.datetime(1986, 9, 6, 21, 0), datetime.datetime(1987, 4, 14, 22, 0), datetime.datetime(1987, 9, 12, 21, 0), datetime.datetime(1988, 4, 9, 22, 0), datetime.datetime(1988, 9, 3, 21, 0), datetime.datetime(1989, 4, 29, 22, 0), datetime.datetime(1989, 9, 2, 21, 0), datetime.datetime(1990, 3, 24, 22, 0), datetime.datetime(1990, 8, 25, 21, 0), datetime.datetime(1991, 3, 23, 22, 0), datetime.datetime(1991, 8, 31, 21, 0), datetime.datetime(1992, 3, 28, 22, 0), datetime.datetime(1992, 9, 5, 21, 0), datetime.datetime(1993, 4, 1, 22, 0), datetime.datetime(1993, 9, 4, 21, 0), datetime.datetime(1994, 3, 31, 22, 0), datetime.datetime(1994, 8, 27, 21, 0), datetime.datetime(1995, 3, 30, 22, 0), datetime.datetime(1995, 9, 2, 21, 0), datetime.datetime(1996, 3, 14, 22, 0), datetime.datetime(1996, 9, 15, 21, 0), datetime.datetime(1997, 3, 20, 22, 0), datetime.datetime(1997, 9, 13, 21, 0), datetime.datetime(1998, 3, 19, 22, 0), datetime.datetime(1998, 9, 5, 21, 0), datetime.datetime(1999, 4, 2, 0, 0), datetime.datetime(1999, 9, 2, 23, 0), datetime.datetime(2000, 4, 14, 0, 0), datetime.datetime(2000, 10, 5, 22, 0), datetime.datetime(2001, 4, 8, 23, 0), datetime.datetime(2001, 9, 23, 22, 0), datetime.datetime(2002, 3, 28, 23, 0), datetime.datetime(2002, 10, 6, 22, 0), datetime.datetime(2003, 3, 27, 23, 0), datetime.datetime(2003, 10, 2, 22, 0), datetime.datetime(2004, 4, 6, 23, 0), datetime.datetime(2004, 9, 21, 22, 0), datetime.datetime(2005, 4, 1, 0, 0), datetime.datetime(2005, 10, 8, 23, 0), datetime.datetime(2006, 3, 31, 0, 0), datetime.datetime(2006, 9, 30, 23, 0), datetime.datetime(2007, 3, 30, 0, 0), datetime.datetime(2007, 9, 15, 23, 0), datetime.datetime(2008, 3, 28, 0, 0), datetime.datetime(2008, 10, 4, 23, 0), datetime.datetime(2009, 3, 27, 0, 0), datetime.datetime(2009, 9, 26, 23, 0), datetime.datetime(2010, 3, 26, 0, 0), datetime.datetime(2010, 9, 11, 23, 0), datetime.datetime(2011, 4, 1, 0, 0), datetime.datetime(2011, 10, 1, 23, 0), datetime.datetime(2012, 3, 30, 0, 0), datetime.datetime(2012, 9, 22, 23, 0), datetime.datetime(2013, 3, 29, 0, 0), datetime.datetime(2013, 10, 26, 23, 0), datetime.datetime(2014, 3, 28, 0, 0), datetime.datetime(2014, 10, 25, 23, 0), datetime.datetime(2015, 3, 27, 0, 0), datetime.datetime(2015, 10, 24, 23, 0), datetime.datetime(2016, 3, 25, 0, 0), datetime.datetime(2016, 10, 29, 23, 0), datetime.datetime(2017, 3, 24, 0, 0), datetime.datetime(2017, 10, 28, 23, 0), datetime.datetime(2018, 3, 23, 0, 0), datetime.datetime(2018, 10, 27, 23, 0), datetime.datetime(2019, 3, 29, 0, 0), datetime.datetime(2019, 10, 26, 23, 0), datetime.datetime(2020, 3, 27, 0, 0), datetime.datetime(2020, 10, 24, 23, 0), datetime.datetime(2021, 3, 26, 0, 0), datetime.datetime(2021, 10, 30, 23, 0), datetime.datetime(2022, 3, 25, 0, 0), datetime.datetime(2022, 10, 29, 23, 0), datetime.datetime(2023, 3, 24, 0, 0), datetime.datetime(2023, 10, 28, 23, 0), datetime.datetime(2024, 3, 29, 0, 0), datetime.datetime(2024, 10, 26, 23, 0), datetime.datetime(2025, 3, 28, 0, 0), datetime.datetime(2025, 10, 25, 23, 0), datetime.datetime(2026, 3, 27, 0, 0), datetime.datetime(2026, 10, 24, 23, 0), datetime.datetime(2027, 3, 26, 0, 0), datetime.datetime(2027, 10, 30, 23, 0), datetime.datetime(2028, 3, 24, 0, 0), datetime.datetime(2028, 10, 28, 23, 0), datetime.datetime(2029, 3, 23, 0, 0), datetime.datetime(2029, 10, 27, 23, 0), datetime.datetime(2030, 3, 29, 0, 0), datetime.datetime(2030, 10, 26, 23, 0), datetime.datetime(2031, 3, 28, 0, 0), datetime.datetime(2031, 10, 25, 23, 0), datetime.datetime(2032, 3, 26, 0, 0), datetime.datetime(2032, 10, 30, 23, 0), datetime.datetime(2033, 3, 25, 0, 0), datetime.datetime(2033, 10, 29, 23, 0), datetime.datetime(2034, 3, 24, 0, 0), datetime.datetime(2034, 10, 28, 23, 0), datetime.datetime(2035, 3, 23, 0, 0), datetime.datetime(2035, 10, 27, 23, 0), datetime.datetime(2036, 3, 28, 0, 0), datetime.datetime(2036, 10, 25, 23, 0), datetime.datetime(2037, 3, 27, 0, 0), datetime.datetime(2037, 10, 24, 23, 0)]

随着datetime.fromtimestamp(1000000000)创建一个天真的时间戳，一个无时区的信息。然后，您只需使用将时区对象附加到该对象即可replace。这只会导致Python使用该时区对象中的许多偏移量对象中的第一个，这导致距100年前的偏移量很奇怪。

但是，将时区对象作为“上下文信息”fromtimestamp直接提供给该方法，可以选择适用于时间戳的正确偏移量，并正确生成一个连贯的时间戳对象。

您还可以在事实之后执行以下操作astimezone：

>>> datetime.fromtimestamp(1000000000).astimezone(our_timezone)
datetime.datetime(2001, 9, 9, 4, 46, 40, tzinfo=<DstTzInfo 'Asia/Jerusalem' IDT+3:00:00 DST>)

此方法还可以智能地选择适用的偏移量。

简而言之：replace==笨拙，只需覆盖原始数据fromtimestamp，astimezone== smart，就知道如何使用时区。