python - 检查最近n分钟内是否出现过一个值

Question

如果我们有以下数据:

timestamp = ['2016-01-09_14-49-18','2016-01-10_09-48-59','2016-01-10_09-50-29','2016-01-10_09-59-08','2016-01-10_10-33-01','2016-01-10_10-35-01','2016-01-10_10-39-05','2016-01-10_10-40-38','2016-01-10_10-50-55','2016-01-10_12-28-35','2016-01-10_15-13-34','2016-01-10_17-02-44','2016-01-10_17-05-48','2016-01-10_17-13-44','2016-01-10_17-15-52']
feature = ['A','A','B','C','B','C','C','A','A','A','B','A','C','C','A']
df = pd.DataFrame({'timestamp':timestamp, 'feature':feature})

如何为每个特征创建一个新列来表示该类别是否出现在过去 15 分钟内？

结果:

   feature    timestamp              A    B    C
0       A  2016-01-09_14-49-18       1    0    0
1       A  2016-01-10_09-48-59       1    0    0
2       B  2016-01-10_09-50-29       1    1    0
3       C  2016-01-10_09-59-08       1    1    1
4       B  2016-01-10_10-33-01       0    1    0
5       C  2016-01-10_10-35-01       0    1    1
6       C  2016-01-10_10-39-05       0    1    1
7       A  2016-01-10_10-40-38       1    1    1
8       A  2016-01-10_10-50-55       1    0    1
9       A  2016-01-10_12-28-35       1    0    0
10      B  2016-01-10_15-13-34       0    1    0
11      A  2016-01-10_17-02-44       1    0    0
12      C  2016-01-10_17-05-48       1    0    1
13      C  2016-01-10_17-13-44       1    0    1
14      A  2016-01-10_17-15-52       1    0    1

其中 1 = 该类(class)在过去 15 分钟内出现，0 = 该类(class)未出现。

Answer 1

您可以使用:

df['timestamp'] = pd.to_datetime(df['timestamp'], format='%Y-%m-%d_%H-%M-%S')

for col in df['feature'].unique():
    df[col] = df['timestamp'] - df['timestamp'].where(df['feature'] == col).ffill()
    df[col] = (df[col] < pd.to_timedelta('15min')).astype(int)
print (df)

   feature           timestamp  A  B  C
0        A 2016-01-09 14:49:18  1  0  0
1        A 2016-01-10 09:48:59  1  0  0
2        B 2016-01-10 09:50:29  1  1  0
3        C 2016-01-10 09:59:08  1  1  1
4        B 2016-01-10 10:33:01  0  1  0
5        C 2016-01-10 10:35:01  0  1  1
6        C 2016-01-10 10:39:05  0  1  1
7        A 2016-01-10 10:40:38  1  1  1
8        A 2016-01-10 10:50:55  1  0  1
9        A 2016-01-10 12:28:35  1  0  0
10       B 2016-01-10 15:13:34  0  1  0
11       A 2016-01-10 17:02:44  1  0  0
12       C 2016-01-10 17:05:48  1  0  1
13       C 2016-01-10 17:13:44  1  0  1
14       A 2016-01-10 17:15:52  1  0  1