Geolife数据集是用户移动时记录的GPS轨迹。感谢新浪大比里提供了预处理数据的存储库。我使用他的预处理数据，并为69个可用用户创建了GSP日志的数据框架。
在这篇文章中是一个非常小的数据摘录，供3个用户通过问题来描述。
import pandas as pd
data = {'user': [10,10,10,10,10,10,10,10,21,21,21,54,54,54,54,54,54,54,54,54],
'lat': [39.921683,39.921583,39.92156,39.13622,39.136233,39.136241,39.136246,39.136251,42.171678,42.172055,
42.172243,39.16008333,39.15823333,39.1569,39.156,39.15403333,39.15346667,39.15273333,39.14811667,39.14753333],
'lon': [116.472342,116.472315,116.47229,117.218033,117.218046,117.218066,117.218166,117.218186,123.676778,123.677365,
123.677657,117.1994167,117.2002333,117.2007667,117.2012167,117.202,117.20225,117.20255,117.2043167,117.2045833],
'date': ['2009-03-21 13:30:35','2009-03-21 13:33:38','2009-03-21 13:34:40','2009-03-21 15:30:12','2009-03-21 15:32:35',
'2009-03-21 15:38:36','2009-03-21 15:44:42','2009-03-21 15:48:43','2007-04-30 16:00:20', '2007-04-30 16:05:22',
'2007-04-30 16:08:23','2007-04-30 11:47:38','2007-04-30 11:48:07','2007-04-30 11:48:27','2007-04-30 12:04:39',
'2007-04-30 12:04:07','2007-04-30 12:04:32','2007-04-30 12:19:41','2007-04-30 12:20:08','2007-04-30 12:20:21']
}
和数据框架:
df = pd.DataFrame(data)
df
user lat lon date
0 10 39.921683 116.472342 2009-03-21 13:30:35
1 10 39.921583 116.472315 2009-03-21 13:33:38
2 10 39.921560 116.472290 2009-03-21 13:34:40
3 10 39.136220 117.218033 2009-03-21 15:30:12
4 10 39.136233 117.218046 2009-03-21 15:32:35
5 10 39.136241 117.218066 2009-03-21 15:38:36
6 10 39.136246 117.218166 2009-03-21 15:44:42
7 10 39.136251 117.218186 2009-03-21 15:48:43
8 21 42.171678 123.676778 2007-04-30 16:00:20
9 21 42.172055 123.677365 2007-04-30 16:05:22
10 21 42.172243 123.677657 2007-04-30 16:08:23
11 54 39.160083 117.199417 2007-04-30 11:47:38
12 54 39.158233 117.200233 2007-04-30 11:48:07
13 54 39.156900 117.200767 2007-04-30 11:48:27
14 54 39.156000 117.201217 2007-04-30 12:04:39
15 54 39.154033 117.202000 2007-04-30 12:04:07
16 54 39.153467 117.202250 2007-04-30 12:04:32
17 54 39.152733 117.202550 2007-04-30 12:19:41
18 54 39.148117 117.204317 2007-04-30 12:20:08
19 54 39.147533 117.204583 2007-04-30 12:20:21
我的问题:
我想计算用户在特定时期的旅行时间。
例如。
- 用户在
March-2009的总旅行时间:只有用户10在本月旅行。从13:30:35到2009-03-21。但是在13:34:40之后，有一个很长的跳跃到15:30:12。由于这次跳跃时间超过30分钟，我们认为这是另一次旅行。用户10这个月有两次出行记录。第一次约5分钟，第二次约19分钟。所以用户这个月的行程时长为5 + 19 = 24 minutes。 - 在
April 2007中，用户21和54在同一天记录了行程。用户21从16:00:20开始约8分钟。用户54从11:47:38开始，大约1分钟后，我们看到它跳转到12:04:39。时间间隔不超过30分钟，所以我们认为这是一次旅行。为此，54美元支付了大约33分钟的旅行费用。因此用户当月的出行时间为8 + 33 = 41minutes。 - 有时，我也想确定从
February 2008到March 2009的行程时间。
如何执行这种分析?
任何一点，使用上面提供的小数据将是感激的。
这段代码不是最有效的，无论如何你可以测试它是否做了你需要的:
df['date'] = pd.to_datetime(df['date'])
duration = (df.groupby(['user', df['date'].dt.month]).
apply(lambda x: (x['date']-x['date'].shift()).dt.seconds).
rename('duration').
to_frame())
res = (duration.mask(duration>1800,0). # 1800 - limit for a trip duration in seconds
groupby(level=[0,1]).
sum().
truediv(60). # converting seconds to minutes
rename_axis(index={'date':'month'}))
print(res)
'''
duration
user month
10 3 22.60
21 4 8.05
54 4 33.25