提问人：Gopinathan 提问时间：6/13/2023 最后编辑：Gopinathan 更新时间：6/13/2023 访问量：60
Pandas：在具有条件的数据框中查找峰值元素
Pandas: Find the peak element in a Data frame with condition
问：
我有一组值为“A”、“B”的数据框,..在“ID”列中。 需要从“值”列中找到峰值元素值，其中“类型”等于低。匹配的条件值需要填充在不同的列（“Low_Peak_Value”和“Peak_Time”列）中，示例输出如下所示，任何人都可以建议如何做到这一点。 列“ID”具有各种值集;因此，此条件应适用于“ID”列中的每个组。
data = {'ID': ['A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A',
'A'],
'Date': ['2023-05-12 16:00:00', '2023-05-15 15:00:00', '2023-05-16 14:00:00', '2023-05-17 07:00:00',
'2023-05-17 20:00:00', '2023-05-18 02:00:00', '2023-05-18 16:00:00', '2023-05-19 07:00:00',
'2023-05-25 05:00:00',
'2023-05-26 01:00:00', '2023-05-26 16:00:00',
'2023-05-29 09:00:00', '2023-05-31 17:00:00', '2023-06-01 22:00:00', '2023-06-05 13:00:00',
'2023-06-06 03:00:00', '2023-06-06 15:00:00',
'2023-06-06 22:00:00', '2023-06-07 18:00:00', '2023-06-08 05:00:00', '2023-06-08 22:00:00',
'2023-06-09 11:00:00'],
'Value': [89845, 90065, 89462, 89437, 89455, 89248, 89013, 89405, 88687, 88521, 88543, 88682, 87747, 88208,
88406, 88731, 89118, 89178, 88824, 88865, 89459, 89363],
'Type': ['Low', '', '', 'Low', '', '', 'Low', '', '', 'Low', '', '', 'Low', '', 'Low', '', '', '', 'Low', '',
'', ''],
'Low_Peak_Value': ['', 89845, 89845, 89845, 89845, 89845, 89845, 89845, 89845, 89845, 89845,
89845, 89845, 87747, 87747, 87747, 87747, 87747, 87747, 87747, 87747, 87747],
'Peak_Time': ['', '2023-05-12 16:00:00', '2023-05-12 16:00:00', '2023-05-12 16:00:00', '2023-05-12 16:00:00',
'2023-05-12 16:00:00', '2023-05-12 16:00:00', '2023-05-12 16:00:00', '2023-05-12 16:00:00',
'2023-05-12 16:00:00', '2023-05-12 16:00:00', '2023-05-12 16:00:00', '2023-05-12 16:00:00',
'2023-05-31 17:00:00', '2023-05-31 17:00:00', '2023-05-31 17:00:00', '2023-05-31 17:00:00',
'2023-05-31 17:00:00', '2023-05-31 17:00:00', '2023-05-31 17:00:00', '2023-05-31 17:00:00',
'2023-05-31 17:00:00']
}
df = pd.DataFrame(data)
print(df)
Available Data:
**************
ID Date Value Type
A 12-05-2023 16:00:00 89845 Low
A 15-05-2023 15:00:00 90065
A 16-05-2023 14:00:00 89462
A 17-05-2023 07:00:00 89437 Low
A 17-05-2023 20:00:00 89455
A 18-05-2023 02:00:00 89248
A 18-05-2023 16:00:00 89013 Low
A 19-05-2023 07:00:00 89405
A 25-05-2023 05:00:00 88687
A 26-05-2023 01:00:00 88521 Low
A 26-05-2023 16:00:00 88543
A 29-05-2023 09:00:00 88682
A 31-05-2023 17:00:00 87747 Low
A 01-06-2023 22:00:00 88208
A 05-06-2023 13:00:00 88406 Low
A 06-06-2023 03:00:00 88731
A 06-06-2023 15:00:00 89118
A 06-06-2023 22:00:00 89178
A 07-06-2023 18:00:00 88824 Low
A 08-06-2023 05:00:00 88865
A 08-06-2023 22:00:00 89459
A 09-06-2023 11:00:00 89363
Required Output:
***************
ID Date Value Type Low_Peak_Value Peak_Date
A 12-05-2023 16:00:00 89845 Low
A 15-05-2023 15:00:00 90065 89845 12-05-2023 16:00:00
A 16-05-2023 14:00:00 89462 89845 12-05-2023 16:00:00
A 17-05-2023 07:00:00 89437 Low 89845 12-05-2023 16:00:00
A 17-05-2023 20:00:00 89455 89845 12-05-2023 16:00:00
A 18-05-2023 02:00:00 89248 89845 12-05-2023 16:00:00
A 18-05-2023 16:00:00 89013 Low 89845 12-05-2023 16:00:00
A 19-05-2023 07:00:00 89405 89845 12-05-2023 16:00:00
A 25-05-2023 05:00:00 88687 89845 12-05-2023 16:00:00
A 26-05-2023 01:00:00 88521 Low 89845 12-05-2023 16:00:00
A 26-05-2023 16:00:00 88543 89845 12-05-2023 16:00:00
A 29-05-2023 09:00:00 88682 89845 12-05-2023 16:00:00
A 31-05-2023 17:00:00 87747 Low 89845 12-05-2023 16:00:00
A 01-06-2023 22:00:00 88208 87747 31-05-2023 17:00:00
A 05-06-2023 13:00:00 88406 Low 87747 31-05-2023 17:00:00
A 06-06-2023 03:00:00 88731 87747 31-05-2023 17:00:00
A 06-06-2023 15:00:00 89118 87747 31-05-2023 17:00:00
A 06-06-2023 22:00:00 89178 87747 31-05-2023 17:00:00
A 07-06-2023 18:00:00 88824 Low 87747 31-05-2023 17:00:00
A 08-06-2023 05:00:00 88865 87747 31-05-2023 17:00:00
A 08-06-2023 22:00:00 89459 87747 31-05-2023 17:00:00
A 09-06-2023 11:00:00 89363 87747 31-05-2023 17:00:00
答： 暂无答案
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