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Python Pandas库按截断日期重新采样

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使用python3库pandas，我在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

我希望按此示例按星期和时间分组。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

在通过熊猫重新采样后，我得到以下结果：

        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

我可以从熊猫库的重采样方法获得此结果吗？

keke0328 回答：Python Pandas库按截断日期重新采样

我相信您需要Series.dt.strftime之前的日期时间的自定义格式，然后在GroupBy.agg之前进行汇总：

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
dataframepandaspython-3.xresampling
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