python - 如何将公交车进/出站记录汇总到行程中，赋予唯一ID，并剔除时间差异小的重复记录？

Question

我每天有一条公交线路的数据集，有 32 辆公交车和两辆 route_direction(0,1)，在第一个方向上有 18 个车站，每个车站都有一个从 1 到 18 的序列，而其他方向有15个站，seq(1-15)，记录进/出每个站的时间。每条记录包含bus_id、route_direction、station_seq、in_time、out_time、station_id。 enter image description here

route_id    route_direction bus_id  station_seq schdeule_date   in_time out_time

0   59  1   1349508393  2   2021-01-01  05:04:31    05:04:58

1   59  1   1349508393  2   2021-01-01  05:04:27    05:04:58

2   59  1   1349508393  2   2021-01-01  05:04:31    05:06:31

3   59  1   1349508393  2   2021-01-01  05:04:27    05:06:31

4   59  1   1349508393  1   2021-01-01  05:00:35    05:00:56

首先，我尝试对某些列进行分组，以便为​​每次旅行提供索引:

grouped = df.groupby(['bus_id', 'route_direction'])

我在这张图片中得到了类似的东西 enter image description here :

index   route_id    route_direction bus_id  station_seq schdeule_date   in_time out_time

654 59  0   1349508329  1   2021-01-01  NaN 06:34:10

663 59  0   1349508329  2   2021-01-01  06:33:34    06:34:04

664 59  0   1349508329  2   2021-01-01  06:33:33    06:34:04

677 59  0   1349508329  2   2021-01-01  06:33:34    06:35:34

678 59  0   1349508329  2   2021-01-01  06:33:33    06:35:34

... ... ... ... ... ... ... ...

12133   59  0   1349508329  12  2021-01-01  NaN NaN

如您所见，在几乎相同的日期和时间，相同的 bus_id 在同一车站的 enter exit 也有重复:我试过删除重复项但运气不好:

df = df.drop_duplicates(subset=['bus_id', 'route_direction', 'station_seq', 'station_id', 'in_time'], keep='first').reset_index(drop=True)

在 in_time 或 out_time 中也有一些 NaN 值，所以如果我 dropna 那么我可能会错过公交线路沿线站点之一的记录。

对一次旅行中的每条公共(public)汽车记录进行分组以赋予其 ID 有何帮助？在这种情况下如何删除重复的记录(输入时间略有不同)？任何帮助将不胜感激。

Answer 1

1. 使用“bus_id”和“in_time”对值进行排序
2. groupby 'bus_id'，对于每个 bus_id，计算每条记录与其之前记录的时间差
3. 如果时间差小于60s，则标记为0，否则标记为1，以设置某些组忽略时间差<60s
4. 在标签上使用cumsum，创建组标签
5. groupby grouptag，对于每个grouptag保持最小(in_time)和最大(out_time)

# convert the in_time to dateTime first, then sorted the values
df['in_time_t'] = pd.to_datetime(df['schdeule_date'] + ' ' + df['in_time'])
df.sort_values(['bus_id', 'in_time_t'], inplace=True)

# calculate the time difference for every bus_id
df['t_diff'] = df.groupby('bus_id')['in_time_t'].diff()

# set group_tag
cond = df['t_diff'].dt.seconds < 60
df['tag'] = np.where(cond, 0, 1).cumsum()

# for every grouptag keep min(in_time) and max(out_time)
df_result = df.groupby(['route_id', 'route_direction', 'bus_id', 'station_seq', 'schdeule_date',
       'tag']).agg({'in_time':'min', 'out_time':'max'}).reset_index()

df
        route_id    route_direction bus_id  station_seq schdeule_date   in_time out_time
    0   59  1   1349508393  2   2021-01-01  05:04:31    05:04:58
    1   59  1   1349508393  2   2021-01-01  05:04:27    05:04:58
    2   59  1   1349508393  2   2021-01-01  05:04:31    05:06:31
    3   59  1   1349508393  2   2021-01-01  05:04:27    05:06:31
    4   59  1   1349508393  1   2021-01-01  05:00:35    05:00:56
    654 59  0   1349508329  1   2021-01-01  NaN 06:34:10
    663 59  0   1349508329  2   2021-01-01  06:33:34    06:34:04
    664 59  0   1349508329  2   2021-01-01  06:33:33    06:34:04
    677 59  0   1349508329  2   2021-01-01  06:33:34    06:35:34
    678 59  0   1349508329  2   2021-01-01  06:33:33    06:35:34
    12133   59  0   1349508329  12  2021-01-01  NaN NaN

df_result
        route_id    route_direction bus_id  station_seq schdeule_date   tag in_time out_time
    0   59  0   1349508329  1   2021-01-01  2   NaN 06:34:10
    1   59  0   1349508329  2   2021-01-01  1   06:33:33    06:35:34
    2   59  0   1349508329  12  2021-01-01  3   NaN NaN
    3   59  1   1349508393  1   2021-01-01  4   05:00:35    05:00:56
    4   59  1   1349508393  2   2021-01-01  5   05:04:27    05:06:31

df with tag
|       |   route_id |   route_direction |     bus_id |   station_seq | schdeule_date   | in_time   | out_time   | in_time_t           | t_diff          |   tag |
|------:|-----------:|------------------:|-----------:|--------------:|:----------------|:----------|:-----------|:--------------------|:----------------|------:|
|   664 |         59 |                 0 | 1349508329 |             2 | 2021-01-01      | 06:33:33  | 06:34:04   | 2021-01-01 06:33:33 | NaT             |     1 |
|   678 |         59 |                 0 | 1349508329 |             2 | 2021-01-01      | 06:33:33  | 06:35:34   | 2021-01-01 06:33:33 | 0 days 00:00:00 |     1 |
|   663 |         59 |                 0 | 1349508329 |             2 | 2021-01-01      | 06:33:34  | 06:34:04   | 2021-01-01 06:33:34 | 0 days 00:00:01 |     1 |
|   677 |         59 |                 0 | 1349508329 |             2 | 2021-01-01      | 06:33:34  | 06:35:34   | 2021-01-01 06:33:34 | 0 days 00:00:00 |     1 |
|   654 |         59 |                 0 | 1349508329 |             1 | 2021-01-01      | nan       | 06:34:10   | NaT                 | NaT             |     2 |
| 12133 |         59 |                 0 | 1349508329 |            12 | 2021-01-01      | nan       | nan        | NaT                 | NaT             |     3 |
|     4 |         59 |                 1 | 1349508393 |             1 | 2021-01-01      | 05:00:35  | 05:00:56   | 2021-01-01 05:00:35 | NaT             |     4 |
|     1 |         59 |                 1 | 1349508393 |             2 | 2021-01-01      | 05:04:27  | 05:04:58   | 2021-01-01 05:04:27 | 0 days 00:03:52 |     5 |
|     3 |         59 |                 1 | 1349508393 |             2 | 2021-01-01      | 05:04:27  | 05:06:31   | 2021-01-01 05:04:27 | 0 days 00:00:00 |     5 |
|     0 |         59 |                 1 | 1349508393 |             2 | 2021-01-01      | 05:04:31  | 05:04:58   | 2021-01-01 05:04:31 | 0 days 00:00:04 |     5 |
|     2 |         59 |                 1 | 1349508393 |             2 | 2021-01-01      | 05:04:31  | 05:06:31   | 2021-01-01 05:04:31 | 0 days 00:00:00 |     5 |