考虑由 Timestamp 索引的数据帧“数据”如下:
Index Receiver Type Retry
1970-01-01 00:00:00.000000000 R1 Data 1
1970-01-01 00:00:00.800000000 R1 Non-Data 1
1970-01-01 00:00:01.000287000 R2 Data 0
1970-01-01 00:00:01.600896000 R2 Data 1
1970-01-01 00:00:02.001388000 R1 Data 1
1970-01-01 00:00:02.004698000 R1 Non-Data 1
1970-01-01 00:00:02.006706000 R2 Data 1
1970-01-01 00:00:02.501351000 R2 Data 1
1970-01-01 00:00:02.810382000 R1 Data 0
1970-01-01 00:00:03.001981000 R1 Data 0
1970-01-01 00:00:03.377116000 R1 Data 0
1970-01-01 00:00:03.701811000 R2 Data 1
1970-01-01 00:00:03.910326000 R2 Data 0
1970-01-01 00:00:03.951355000 R2 Non-Data 1
我想找到 Type 为“Data”且 Retry 等于 1 的所有行，然后以 1 秒的间隔对索引进行分组以查找每种 Receiver 类型的计数。
作为 df2 的期望输出:
Index R1 R2
1970-01-01 00:00:00 1 0
1970-01-01 00:00:01 0 1
1970-01-01 00:00:02 1 2
1970-01-01 00:00:03 0 1
请您参考如下方法:
这可以通过一个简单的pivot_table 来完成，即
ndf = df[df['Type'].eq('Data')].pivot_table(columns=['Receiver'],values='Retry',
index=pd.Grouper(key='Index',freq='s'), aggfunc='sum',fill_value=0)
Receiver R1 R2
Index
1970-01-01 00:00:00 1 0
1970-01-01 00:00:01 0 1
1970-01-01 00:00:02 1 2
1970-01-01 00:00:03 0 1
