Python Pandas : weekly columns(int) to Timestamp columns conversion (in weeks)

Question

我有一个每周列的 df，如下所示。我想将列索引更改为时间戳。这是我的 df.columns

df.columns:
Int64Index([201601, 201602, 201603, 201604, 201605, 201606, 201607,
        201608, 201609,
        ...],
       dtype='int64', name='timeline', length=104)

df.columns[0]:
201553

我想将我的 df.columns 更改为时间戳，如下所示

 df.columns:
 DatetimeIndex(['2016-01-04', '2016-01-11', '2016-01-18', '2016-01-25',
           '2016-02-01', '2016-02-08', '2016-02-15', '2016-02-22',
           '2016-02-29'.....],
           dtype='int64', name='timeline', length=104)
df.columns[0]:
Timestamp('2016-01-04 00:00:00')

底线是我的 df.columns 采用 int 格式，表示 yyyyww 值。从这个 int 开始，我想将其更改为显示每周星期一日期的时间戳。请告诉我一个改变这种情况的好方法。谢谢您

Answer 1

您可以使用to_datetime ，但首先为 Monday 添加 1 并将 %W 与 %w 一起使用:

Source - http://strftime.org/ :

%w Weekday as a decimal number, where 0 is Sunday and 6 is Saturday.
%W Week number of the year (Monday as the first day of the week) as a decimal number. All days in a new year preceding the first Monday are considered to be in week 0.

a = pd.Int64Index([201601, 201602, 201603, 201604, 201605, 201606, 201607,
        201608, 201609])

print (pd.to_datetime(a.astype(str) + '1', format='%Y%W%w'))
DatetimeIndex(['2016-01-04', '2016-01-11', '2016-01-18', '2016-01-25',
               '2016-02-01', '2016-02-08', '2016-02-15', '2016-02-22',
               '2016-02-29'],
              dtype='datetime64[ns]', freq=None)