[英]Converting days since epoch to date
How can one convert a serial date number, representing the number of days since epoch (1970), to the corresponding date string?如何将表示自纪元(1970)以来的天数的序列日期数字转换为相应的日期字符串? I have seen multiple posts showing how to go from string to date number, but I haven't been able to find any posts on how to do the reverse.
我已经看到多篇文章展示了如何从字符串到日期编号,但我找不到任何关于如何反向操作的文章。
For example, 15951
corresponds to "2013-09-02"
.例如,
15951
对应于"2013-09-02"
。
>>> import datetime
>>> (datetime.datetime(2013, 9, 2) - datetime.datetime(1970,1,1)).days + 1
15951
(The + 1
because whatever generated these date numbers followed the convention that Jan 1, 1970 = 1.) (
+ 1
因为生成这些日期数字的任何内容都遵循 1970 年 1 月 1 日 = 1 的约定。)
TL;DR: Looking for something to do the following: TL;DR:正在寻找可以执行以下操作的方法:
>>> serial_date_to_string(15951) # arg is number of days since 1970
"2013-09-02"
This is different from Python: Converting Epoch time into the datetime because I am starting with days since 1970. I not sure if you can just multiply by 86,400 due to leap seconds, etc.这与Python不同:Converting Epoch time into the datetime因为我从 1970 年以来的天开始。我不确定你是否可以因为闰秒等原因乘以 86,400。
Use the datetime
package as follows:使用
datetime
包如下:
import datetime
def serial_date_to_string(srl_no):
new_date = datetime.datetime(1970,1,1,0,0) + datetime.timedelta(srl_no - 1)
return new_date.strftime("%Y-%m-%d")
This is a function which returns the string as required.这是一个根据需要返回字符串的函数。
So:所以:
serial_date_to_string(15951)
Returns退货
>> "2013-09-02"
And for a Pandas Dataframe:对于 Pandas 数据框:
df["date"] = pd.to_datetime(df["date"], unit="d")
... assuming that the "date" column contains values like 18687
which is days from Unix Epoch of 1970-01-01
to 2021-03-01
. ...假设“日期”列包含像
18687
这样的值,它是从 Unix Epoch of 1970-01-01
到2021-03-01
天2021-03-01
。
Also handles seconds and milliseconds since Unix Epoch, use unit="s"
and unit="ms"
respectively.还处理自 Unix Epoch 以来的秒和毫秒,分别使用
unit="s"
和unit="ms"
。
Also see my other answer with the exact reverse .另请参阅我的其他完全相反的答案。
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