Python Pandas 库按截断日期重新采样

Question

使用python3库pandas ，我的 Excel 文件中有这样的数据

 Id |          Date           |  count
----+-------------------------+-----------
 1  |   '2019/10/01 10:40'    |   1
----+-------------------------------------
 2  |   '2019/10/01 10:43'    |   2
----+-------------------------------------
 3  |   '2019/10/02 10:40'    |   3
----+-------------------------------------
 4  |   '2019/10/05 10:40'    |   4
----+-------------------------------------
 5  |   '2019/10/08 10:40'    |   5
----+-------------------------------------
 6  |   '2019/10/09 10:40'    |   6
----+-------------------------------------
 7  |   '2019/10/15 10:40'    |   7

我想按周和时间按此示例进行分组。例如我需要的结果是:

 Id |       Week Time         |  count
----+-------------------------+-----------
 1  |   'Tuesday 10:40'       |   1
----+-------------------------------------
 2  |   'Tuesday 10:43'       |   2
----+-------------------------------------
 3  |   'Wednesday 10:40'     |   3
----+-------------------------------------
 4  |   'Saturday 10:40'      |   4
----+-------------------------------------
 5  |   'Tuesday 10:40'       |   5
----+-------------------------------------
 6  |   'Wednesday 10:40'     |   6
----+-------------------------------------
 7  |   'Tuesday 10:40'       |   7

在 pandas 重新采样后，我得到这个结果:

        Week Time         |  sum  | count | avg 
 -------------------------+-------+-------+---------
    'Tuesday 10:40'       |   14  |   3   | 4.66
 -------------------------+-------+-------+---------
    'Tuesday 10:43'       |   2   |   1   | 2.00
 -------------------------+-------+-------+---------
    'Wednesday 10:40'     |   9   |   2   | 4.50
 ---------------------------------+-------+---------
    'Saturday 10:40'      |   4   |   1   | 4.00

我可以通过pandas库的重采样方法得到这个结果吗？

Answer 1

我相信您需要 Series.dt.strftime 的自定义日期时间格式然后按 GroupBy.agg 聚合:

df['Date'] = pd.to_datetime(df['Date']).dt.strftime('%A %H:%M')
#if necessary remove trailing '
#df['Date'] = pd.to_datetime(df['Date'].str.strip("'")).dt.strftime('%A %H:%M')

df = df.groupby('Date', sort=False)['count'].agg(['sum','count', 'mean'])
print (df)
                 sum  count      mean
Date                                 
Tuesday 10:40     13      3  4.333333
Tuesday 10:43      2      1  2.000000
Wednesday 10:40    9      2  4.500000
Saturday 10:40     4      1  4.000000