Python Pandas：仅基于其中一列重新采样

Question

我有以下数据，我正在重新采样我的数据，以找出每15分钟到达每一个车站的自行车。 However, my code is aggregating my stations too, and I only want to aggregate the variable "dtm_end_trip"

Sample data:

id_trip	dtm_start_trip	dtm_end_trip	start_station	end_station
1	2018-10-01 10:15:00	2018-10-01 10:17:00	A	B
2	10:17:00	10:18:00	B	A
...	​	2018-10-01	​	​
2018-10-01	​00	2022-01-01 00:22:00	C	A
1000000	23:59:00	2022-01-01-01 00:29:00	A	D

试用代码：

df2 =  df(['end_station', 'dtm_end_trip']).size().to_frame(name = 'count').reset_index()
df2 = df2.sort_values(by='count', ascending=False)

df2= df2.set_index('dtm_end_trip')

df2 = df2.resample('15T').count()

输出I GET：

DTM_END_END_TRIP_TRIP	END_STATION_STATION_STATION_STATION_STATION_STATION	INC 2018-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10-10--10-
2021-12-31 01 00:15:00	2	2
2018-10-01 00:30:00	0 0	0
2018-10-01-01 00:45:00	1	1
2018-10-01 01:00	2	01
2018-10-10-01 01 01 01 ：15：00	1	1

所需的输出：

DTM_END_TRIP	END_STATION	COUNT
2018-10-01 00:15:00	A	2
2018-10-01 00:15:00	B	0
2018-10-10-01-01 00:15:00	C	1
2018--- 10-01 00:15:00	D	2
2018-10-01 00:30:00	A	3
2018-10-01 00:30:00	B	2

上表的计数列在这种情况下是用随机数构建，其唯一目的是示例所需输出的体系结构。

Answer 1

您可以使用 >这样：

out = df.groupby([
    pd.Grouper(freq='15min', key='dtm_end_trip'),
    'end_station',
]).size()

>>> out
dtm_end_trip         end_station
2018-10-01 10:15:00  A              1
                     B              1
2022-01-01 00:15:00  A              1
                     D              1
dtype: int64

结果是系列，但是您可以根据所需的输出轻松地将其转换为dataFrame，其标题：

>>> out.to_frame('count').reset_index()
         dtm_end_trip end_station  count
0 2018-10-01 10:15:00           A      1
1 2018-10-01 10:15:00           B      1
2 2022-01-01 00:15:00           A      1
3 2022-01-01 00:15:00           D      1

注意：这是从样本输入数据中的四行。