Question

我一直在寻找可以找到的每个线程，并且唯一与这种格式问题有关的线程在这里，但这是针对Java的...

How parse 2013-03-13T20:59:31+0000 date string to Date

我有一列包含诸如201604和201605的值，我需要将其转换为诸如2016-04-01和2016-05-01的日期值。为此，我已经完成了下面的工作。

#Create Number to build full date
df['DAY_NBR'] = '01'

#Convert Max and Min date to string to do date transformation
df['MAXDT'] = df['MAXDT'].astype(str)
df['MINDT'] = df['MINDT'].astype(str)

#Add the day number to the max date month and year
df['MAXDT'] = df['MAXDT'] + df['DAY_NBR']

#Add the day number to the min date month and year
df['MINDT'] = df['MINDT'] + df['DAY_NBR']

#Convert Max and Min date to integer values
df['MAXDT'] = df['MAXDT'].astype(int)
df['MINDT'] = df['MINDT'].astype(int)

#Convert Max date to datetime
df['MAXDT'] = pd.to_datetime(df['MAXDT'], format='%Y%m%d')

#Convert Min date to datetime
df['MINDT'] = pd.to_datetime(df['MINDT'], format='%Y%m%d')

说实话，我可以处理此输出，但这有点混乱，因为两列的唯一值是...

MAXDT Values
['2016-07-01T00:00:00.000000000' '2017-09-01T00:00:00.000000000'
 '2018-06-01T00:00:00.000000000' '2017-07-01T00:00:00.000000000'
 '2017-03-01T00:00:00.000000000' '2018-12-01T00:00:00.000000000'
 '2017-12-01T00:00:00.000000000' '2019-01-01T00:00:00.000000000'
 '2018-09-01T00:00:00.000000000' '2018-10-01T00:00:00.000000000'
 '2016-04-01T00:00:00.000000000' '2018-03-01T00:00:00.000000000'
 '2017-05-01T00:00:00.000000000' '2018-08-01T00:00:00.000000000'
 '2017-02-01T00:00:00.000000000' '2016-12-01T00:00:00.000000000'
 '2018-01-01T00:00:00.000000000' '2018-02-01T00:00:00.000000000'
 '2017-06-01T00:00:00.000000000' '2018-11-01T00:00:00.000000000'
 '2018-05-01T00:00:00.000000000' '2019-11-01T00:00:00.000000000'
 '2016-06-01T00:00:00.000000000' '2017-10-01T00:00:00.000000000'
 '2016-08-01T00:00:00.000000000' '2018-04-01T00:00:00.000000000'
 '2016-03-01T00:00:00.000000000' '2016-10-01T00:00:00.000000000'
 '2016-11-01T00:00:00.000000000' '2019-12-01T00:00:00.000000000'
 '2016-09-01T00:00:00.000000000' '2017-08-01T00:00:00.000000000'
 '2016-05-01T00:00:00.000000000' '2017-01-01T00:00:00.000000000'
 '2017-11-01T00:00:00.000000000' '2018-07-01T00:00:00.000000000'
 '2017-04-01T00:00:00.000000000' '2016-01-01T00:00:00.000000000'
 '2016-02-01T00:00:00.000000000' '2019-02-01T00:00:00.000000000'
 '2019-07-01T00:00:00.000000000' '2019-10-01T00:00:00.000000000'
 '2019-09-01T00:00:00.000000000' '2019-03-01T00:00:00.000000000'
 '2019-05-01T00:00:00.000000000' '2019-04-01T00:00:00.000000000'
 '2019-08-01T00:00:00.000000000' '2019-06-01T00:00:00.000000000'
 '2020-02-01T00:00:00.000000000' '2020-01-01T00:00:00.000000000']
MINDT Values
['2016-04-01T00:00:00.000000000' '2017-07-01T00:00:00.000000000'
 '2016-02-01T00:00:00.000000000' '2017-01-01T00:00:00.000000000'
 '2017-02-01T00:00:00.000000000' '2018-12-01T00:00:00.000000000'
 '2017-08-01T00:00:00.000000000' '2018-04-01T00:00:00.000000000'
 '2017-10-01T00:00:00.000000000' '2019-01-01T00:00:00.000000000'
 '2018-05-01T00:00:00.000000000' '2018-09-01T00:00:00.000000000'
 '2018-10-01T00:00:00.000000000' '2016-01-01T00:00:00.000000000'
 '2016-03-01T00:00:00.000000000' '2017-11-01T00:00:00.000000000'
 '2017-05-01T00:00:00.000000000' '2018-07-01T00:00:00.000000000'
 '2018-06-01T00:00:00.000000000' '2017-12-01T00:00:00.000000000'
 '2016-10-01T00:00:00.000000000' '2018-02-01T00:00:00.000000000'
 '2017-06-01T00:00:00.000000000' '2018-08-01T00:00:00.000000000'
 '2018-03-01T00:00:00.000000000' '2018-11-01T00:00:00.000000000'
 '2016-08-01T00:00:00.000000000' '2016-06-01T00:00:00.000000000'
 '2018-01-01T00:00:00.000000000' '2016-07-01T00:00:00.000000000'
 '2016-11-01T00:00:00.000000000' '2016-09-01T00:00:00.000000000'
 '2017-04-01T00:00:00.000000000' '2016-05-01T00:00:00.000000000'
 '2017-09-01T00:00:00.000000000' '2016-12-01T00:00:00.000000000'
 '2017-03-01T00:00:00.000000000']

我正在尝试构建一个遍历这些日期的循环，并且该循环有效，但是我不想拥有一个索引，其中包含所有这些不相关的零和一个T。如何将这些空的时间戳值转换为yyyy-mm-dd格式的日期？

谢谢！

Answer 1

不幸的是，我相信Pandas总是将日期时间对象存储为datetime64[ns]，这意味着精度必须是这样。即使您尝试另存为datetime64[D]，也会将其强制转换为datetime64[ns]。

可以将这些日期时间对象存储为字符串，但是最简单的解决方案可能是在遍历它们时（例如，使用df['MAXDT'].to_numpy().astype('datetime64[D]')并遍历格式化的numpy数组）去掉多余的零。），或仅使用日期时间重新格式化。

仅将时间戳转换为日期

1 个答案: