python - 使用 pandas dataframe 按时间顺序转换日期

Question

我需要帮助来比较不同行和不同列中的日期并确保它们遵循时间顺序。

首先，我根据 Id 和 group 列对数据进行分组。接下来，每个日期值都应该发生在将来。

第一组 [1111 + A ] 包含错误，因为日期不遵循时间顺序:

1/1/2016  >  2/20/2016  > **2/19/2016** >  4/25/2016  >  **4/1/2016** >  5/1/2016

当前结果

      id       start         end group
0   1111  01/01/2016  02/20/2016     A
1   1111  02/19/2016  04/25/2016     A
2   1111  04/01/2016  05/01/2016     A
3   2345  05/01/2016  05/28/2016     B
4   2345  05/29/2016  06/28/2016     B
5   1234  08/01/2016  09/16/2016     F
6   9882  01/01/2016  08/29/2016     D
7   9992  03/01/2016  03/15/2016     C
8   9992  03/16/2016  08/03/2016     C
9   9992  05/16/2016  09/16/2016     C
10  9992  09/17/2016  10/16/2016     C
11  9992  10/17/2016  12/13/2016     C

答案应该是:

1/1/2016  >  2/20/2016  > **2/21/2016** >  4/25/2016  >  **4/26/2016** >     5/1/2016

所需输出

      id       start         end group
0   1111  01/01/2016  02/20/2016     A
1   1111  02/21/2016  04/25/2016     A
2   1111  04/26/2018  05/01/2016     A
3   2345  05/01/2016  05/28/2016     B
4   2345  05/29/2016  06/28/2016     B
5   1234  08/01/2016  09/16/2016     F
6   9882  01/01/2016  08/29/2016     C
7   9992  03/01/2016  03/15/2016     C
8   9992  03/16/2016  08/03/2016     C
9   9992  08/04/2016  09/16/2016     C
10  9992  09/17/2016  10/16/2016     C
11  9992  10/17/2016  12/13/2016     C

任何帮助将不胜感激。

Answer 1

一种方法是将您的逻辑应用于每个组，然后连接您的组。

# convert series to datetime
df['start'] = pd.to_datetime(df['start'])
df['end'] = pd.to_datetime(df['end'])

# iterate groups and add results to grps list
grps = []
for _, group in df.groupby(['id', 'group'], sort=False):
    end_shift = group['end'].shift()
    group.loc[group['start'] <= end_shift, 'start'] = end_shift + pd.DateOffset(1)
    grps.append(group)

# concatenate dataframes in grps to build a single dataframe
res = pd.concat(grps, ignore_index=True)

print(res)

      id      start        end group
0   1111 2016-01-01 2016-02-20     A
1   1111 2016-02-21 2016-04-25     A
2   1111 2016-04-26 2016-05-01     A
3   2345 2016-05-01 2016-05-28     B
4   2345 2016-05-29 2016-06-28     B
5   1234 2016-08-01 2016-09-16     F
6   9882 2016-01-01 2016-08-29     D
7   9992 2016-03-01 2016-03-15     C
8   9992 2016-03-16 2016-08-03     C
9   9992 2016-08-04 2016-09-16     C
10  9992 2016-09-17 2016-10-16     C
11  9992 2016-10-17 2016-12-13     C