Pandas - 按时间增量和计数计算连续行数

Question

有以下DF：

    id           timestamp
0    1 2020-09-01 15:14:35
1    1 2020-09-01 15:15:40
2    1 2020-09-01 15:16:59
3    1 2020-09-01 15:24:42
4    1 2020-09-01 15:25:50
5    1 2020-09-01 15:26:40
6    2 2020-09-01 18:14:35
7    2 2020-09-01 18:17:39
8    2 2020-09-01 18:24:40
9    2 2020-09-01 18:24:42
10   2 2020-09-01 18:34:40
11   2 2020-09-01 18:35:40
12   2 2020-09-01 18:36:40

每个 id 是一个服务器端点，时间戳是单个请求的时间。绘制时间线图：

我想计算每台服务器的加载周期数，我这样定义加载周期：
至少 3 个请求，时间增量小于 5 分钟。强>

所以服务器 1 有 2 个负载，而服务器 2 只有 1 个负载。我希望输出如下：

    id      timestamp       loads_detected
0    1 2020-09-01 15:14:35  0
1    1 2020-09-01 15:15:40  0
2    1 2020-09-01 15:16:59  1 <-- 3 requests in a row with less than 5 minuets a part
3    1 2020-09-01 15:25:42  1 <-- next request is more than 5 minutes
4    1 2020-09-01 15:25:50  1
5    1 2020-09-01 15:26:40  2 <-- 3 requests in a row with less than 5 minuets a part
6    2 2020-09-01 18:14:35  0 
7    2 2020-09-01 18:17:39  0 <-- Only 2 requests with less than 5 minuets, not increasing counter
8    2 2020-09-01 18:24:40  0
9    2 2020-09-01 18:24:42  0
10   2 2020-09-01 18:34:40  0
11   2 2020-09-01 18:35:40  0
12   2 2020-09-01 18:36:40  1 <-- 3 requests in a row with less than 5 minuets a part

任何帮助将不胜感激:)

Answer 1

IIUC，您可以按 id 和 5 分钟的 频率 进行分组，计算 3 个连续请求出现的次数，然后对该结果进行 cumsum：

df['loads_detected'] = df.groupby(['id', pd.Grouper(key="timestamp", freq='5min', origin='start')]).cumcount().eq(2)
df['loads_detected'] = df.groupby('id').cumsum()
print(df)

输出

    id           timestamp  loads_detected
0    1 2020-09-01 15:14:35               0
1    1 2020-09-01 15:15:40               0
2    1 2020-09-01 15:16:59               1
3    1 2020-09-01 15:24:42               1
4    1 2020-09-01 15:25:50               1
5    1 2020-09-01 15:26:40               2
6    2 2020-09-01 18:14:35               0
7    2 2020-09-01 18:17:39               0
8    2 2020-09-01 18:24:40               0
9    2 2020-09-01 18:24:42               0
10   2 2020-09-01 18:34:40               0
11   2 2020-09-01 18:35:40               0
12   2 2020-09-01 18:36:40               1