Python 基于ID列减去行-行

我有一个数据框，看起来像这样：

UserId    Date_watched    Days_not_watch
  1        2010-09-11         5
  1        2010-10-01         8
  1        2010-10-28         1
  2        2010-05-06         12
  2        2010-05-18         5
  3        2010-08-09         10
  3        2010-09-25         5

UserId    Date_watched    Days_not_watch      Gap(2nd watch_date - 1st watch_date - days_not_watch)
  1        2010-09-11         5                0   (First gap will be 0 for all users)
  1        2010-10-01         8                15 (11th Sept+5=16th Sept; 1st Oct - 16th Sept=15days)
  1        2010-10-28         1                9
  2        2010-05-06         12               0
  2        2010-05-18         5                0 (because 6th May+12 days=18th May)
  3        2010-08-09         10               0
  3        2010-09-25         4                36
  3        2010-10-01         2                2

我想找出用户作为间隔给出的天数，因此我想为每个用户的每行设置一列，我的数据框应该如下所示：

UserId    Date_watched    Days_not_watch
  1        2010-09-11         5
  1        2010-10-01         8
  1        2010-10-28         1
  2        2010-05-06         12
  2        2010-05-18         5
  3        2010-08-09         10
  3        2010-09-25         5

UserId    Date_watched    Days_not_watch      Gap(2nd watch_date - 1st watch_date - days_not_watch)
  1        2010-09-11         5                0   (First gap will be 0 for all users)
  1        2010-10-01         8                15 (11th Sept+5=16th Sept; 1st Oct - 16th Sept=15days)
  1        2010-10-28         1                9
  2        2010-05-06         12               0
  2        2010-05-18         5                0 (because 6th May+12 days=18th May)
  3        2010-08-09         10               0
  3        2010-09-25         4                36
  3        2010-10-01         2                2

---

我已经提到了计算数据帧列名旁边间隙的公式。

这里有一种使用

groupby

+

shift

的方法：

# sort by date first
df['Date_watched'] = pd.to_datetime(df['Date_watched'])
df = df.sort_values(['UserId', 'Date_watched'])

# calculate groupwise start dates, shifted
grp = df.groupby('UserId')
starts = grp['Date_watched'].shift() + \
         pd.to_timedelta(grp['Days_not_watch'].shift(), unit='d')

# calculate timedelta gaps
df['Gap'] = (df['Date_watched'] - starts).fillna(pd.Timedelta(0))

# convert to days and then integers
df['Gap'] = (df['Gap'] / pd.Timedelta('1 day')).astype(int)

print(df)

   UserId Date_watched  Days_not_watch  Gap
0       1   2010-09-11               5    0
1       1   2010-10-01               8   15
2       1   2010-10-28               1   19
3       2   2010-05-06              12    0
4       2   2010-05-18               5    0
5       3   2010-08-09              10    0
6       3   2010-09-25               5   37

---

有一个问题，我的日期没有排序，在这里我按升序发布它们，但实际上没有排序，当我对其排序时，最终的数据帧索引不匹配。如何修复此问题？@DebadriDutta，然后首先按用户和日期排序，请参阅更新。我的解决方案在任何地方都不使用数据帧索引。我已将其排序。谢谢你的回答，它工作得很好：）