我想根據(jù)位置和時(shí)間戳創(chuàng)建會(huì)話。如果位置是新的或時(shí)間超過(guò)15 分鐘的間隔，則將新會(huì)話分配給數(shù)據(jù)幀中的記錄。下面的例子Location | Time | Session A 2016-01-01 00:00:15 1 A 2016-01-01 00:05:00 1 A 2016-01-01 00:10:08 1 A 2016-01-01 00:14:08 1 A 2016-01-01 00:15:49 2 B 2016-01-01 00:15:55 3 C 2016-01-01 00:15:58 4 C 2016-01-01 00:26:55 4 C 2016-01-01 00:29:55 4 C 2016-01-01 00:31:08 5這是對(duì)給定問(wèn)題不起作用的代碼。from datetime import timedeltacond1 = df.DateTime-df.DateTime.shift(1) > pd.Timedelta(15, 'm')#OR#15_min = df.DateTime.diff() > pd.Timedelta(minutes=15)cond2 = df.location != df.location.shift(1)session_id = (cond1|cond2).cumsum()df['session_id'] = session_id.map(pd.Series(range(0,10000))) 如果找到新位置或當(dāng)前位置還有 15 分鐘，我想要一個(gè)新會(huì)話。
1 回答
GCT1015
TA貢獻(xiàn)1827條經(jīng)驗(yàn) 獲得超4個(gè)贊
您可以groupby同時(shí)Location使用 和 將pd.Grouper其分為 15 分鐘間隔和位置，然后使用ngroup對(duì)每個(gè)組進(jìn)行編號(hào)：
df['Session'] = (df.groupby(['Location',pd.Grouper(key='Time',freq='15min')])
.ngroup()+1)
>>> df
Location Time Session
0 A 2016-01-01 00:00:15 1
1 A 2016-01-01 00:05:00 1
2 A 2016-01-01 00:10:08 1
3 A 2016-01-01 00:14:08 1
4 A 2016-01-01 00:15:49 2
5 B 2016-01-01 00:15:55 3
6 C 2016-01-01 00:15:58 4
7 C 2016-01-01 00:26:55 4
8 C 2016-01-01 00:29:55 4
9 C 2016-01-01 00:31:08 5
添加回答
舉報(bào)
0/150
提交
取消