我的数据框输出如下所示。我想对field id
进行分组,以获取24的max
中的HourlyTempF
min
,HourlyTempF
sum
和HourlyPrecipIn
小时时段。我当前的解决方案给出了每一行的最大值,顺便说一句又是整个数据帧。我应该为每个字段ID设置三个值。
我认为问题在于数据框中的每一行都是“自己的数据框”,每一行都有标题和列,因此当我得到max
,min
,sum
时,获取每小时的每一行的当前值,我实际上只是想对整个数据框进行分组,以便为我提供如下所示的输出,并根据所需输出中的值创建变量,以便将它们放在另一个数据框中。>
我的代码:
`import pandas as pd
import pandas
hrly_df = pd.DataFrame({'dateTime' :[t], 'field id': [id_], 'HourlyPrecipIn': [aPreVJ],'HourlyRH' : [aHumidVJ], 'HourlyTempF' : [aTempVJ]})
hrly_df = hrly_df[['dateTime','field id','HourlyPrecipIn','HourlyRH', 'HourlyTempF']]
hrly_df.head()
hrlydfs = hrLylst.append(hrly_df)
#GETS EACH MAX ROW INSTEAD OF MAX FOR DF
tempMax= hrly_df.groupby('field id')['HourlyTempF'].agg(['max'])
tempMax2 = tempMax.max().max()
# print 'Data successfully collected - writing to csv...'
tempDf = pd.DataFrame({'date' :[config.dayVal ], 'field id': [id_], 'DailyHighF': ['SHOULD BE MAX FROM hrly_df'],'DailyLowF' : ['SHOULD BE MIN FROM HRLY DF'], 'DailyPrecipIn' : ['SHOULD BE TOTAL FROM HRLY DF']})
我打印tempMax时的当前输出
Starting import of field id: 40238
44.9
45.1
45.1
45.3
46.7
46.7
48.6
50.2
52.1
54.0
54.3
54.5
54.7
54.5
56.4
56.6
55.7
54.0
54.0
54.1
54.1
53.6
52.2
Starting import of field id: 3402
44.9
45.1
45.1
45.3
46.7
46.7
48.6
50.2
52.1
54.0
54.4
54.5
54.7
54.5
56.5
56.6
55.7
54.1
54.0
54.1
54.2
53.6
52.2
Starting import of field id: 45883
45.3
45.6
45.7
45.9
47.1
47.3
49.1
50.7
52.7
54.3
54.8
55.0
55.2
55.0
57.1
57.5
56.2
54.6
54.4
54.6
54.6
53.8
52.7
所需的输出:
field id | max temp | min temp | total precip
40238 56.4 44.9 0.06
3402 56.6 44.9 0.06
45883 57.7 45.3 0.06
当前数据框
dateTime field id HourlyPrecipIn HourlyRH HourlyTempF
0 2019-05-22 01:00:00 40238 0.0 98.8 44.9
dateTime field id HourlyPrecipIn HourlyRH HourlyTempF
0 2019-05-22 02:00:00 40238 0.0 98.9 45.1
dateTime field id HourlyPrecipIn HourlyRH HourlyTempF
0 2019-05-22 03:00:00 40238 0.0 98.7 45.1
dateTime field id HourlyPrecipIn HourlyRH HourlyTempF
0 2019-05-22 04:00:00 40238 0.02 99.6 45.3
dateTime field id HourlyPrecipIn HourlyRH HourlyTempF
0 2019-05-22 05:00:00 40238 0.0 95.0 46.7
dateTime field id HourlyPrecipIn HourlyRH HourlyTempF
0 2019-05-22 06:00:00 40238 0.0 99.8 46.7
dateTime field id HourlyPrecipIn HourlyRH HourlyTempF
0 2019-05-22 07:00:00 40238 0.02 95.6 48.6
dateTime field id HourlyPrecipIn HourlyRH HourlyTempF
0 2019-05-22 08:00:00 40238 0.0 94.4 50.2
dateTime field id HourlyPrecipIn HourlyRH HourlyTempF
0 2019-05-22 09:00:00 40238 0.01 93.6 52.1
dateTime field id HourlyPrecipIn HourlyRH HourlyTempF
0 2019-05-22 10:00:00 40238 0.0 93.6 54.0
dateTime field id HourlyPrecipIn HourlyRH HourlyTempF
0 2019-05-22 11:00:00 40238 0.01 93.5 54.3
dateTime field id HourlyPrecipIn HourlyRH HourlyTempF
0 2019-05-22 12:00:00 40238 0.0 87.3 54.5
dateTime field id HourlyPrecipIn HourlyRH HourlyTempF
0 2019-05-22 13:00:00 40238 0.0 86.1 54.7
dateTime field id HourlyPrecipIn HourlyRH HourlyTempF
0 2019-05-22 14:00:00 40238 0.0 88.0 54.5
dateTime field id HourlyPrecipIn HourlyRH HourlyTempF
0 2019-05-22 15:00:00 40238 0.0 82.1 56.4
dateTime field id HourlyPrecipIn HourlyRH HourlyTempF
0 2019-05-22 16:00:00 40238 0.0 85.5 56.6
dateTime field id HourlyPrecipIn HourlyRH HourlyTempF
0 2019-05-22 17:00:00 40238 0.0 82.9 55.7
dateTime field id HourlyPrecipIn HourlyRH HourlyTempF
0 2019-05-22 18:00:00 40238 0.0 82.6 54.0
dateTime field id HourlyPrecipIn HourlyRH HourlyTempF
0 2019-05-22 19:00:00 40238 0.0 79.1 54.0
dateTime field id HourlyPrecipIn HourlyRH HourlyTempF
0 2019-05-22 20:00:00 40238 0.0 83.8 54.1
dateTime field id HourlyPrecipIn HourlyRH HourlyTempF
0 2019-05-22 21:00:00 40238 0.0 87.9 54.1
dateTime field id HourlyPrecipIn HourlyRH HourlyTempF
0 2019-05-22 22:00:00 40238 0.0 88.6 53.6
dateTime field id HourlyPrecipIn HourlyRH HourlyTempF
0 2019-05-22 23:00:00 40238 0.0 87.5 52.2
Starting import of field id: 3402
dateTime field id HourlyPrecipIn HourlyRH HourlyTempF
0 2019-05-22 01:00:00 3402 0.0 98.7 44.9
dateTime field id HourlyPrecipIn HourlyRH HourlyTempF
0 2019-05-22 02:00:00 3402 0.0 98.8 45.1
dateTime field id HourlyPrecipIn HourlyRH HourlyTempF
0 2019-05-22 03:00:00 3402 0.0 98.7 45.1
dateTime field id HourlyPrecipIn HourlyRH HourlyTempF
0 2019-05-22 04:00:00 3402 0.01 99.5 45.3
dateTime field id HourlyPrecipIn HourlyRH HourlyTempF
0 2019-05-22 05:00:00 3402 0.0 95.0 46.7
dateTime field id HourlyPrecipIn HourlyRH HourlyTempF
0 2019-05-22 06:00:00 3402 0.0 99.7 46.7
dateTime field id HourlyPrecipIn HourlyRH HourlyTempF
0 2019-05-22 07:00:00 3402 0.02 95.6 48.6
dateTime field id HourlyPrecipIn HourlyRH HourlyTempF
0 2019-05-22 08:00:00 3402 0.0 94.5 50.2
dateTime field id HourlyPrecipIn HourlyRH HourlyTempF
0 2019-05-22 09:00:00 3402 0.01 93.6 52.1
dateTime field id HourlyPrecipIn HourlyRH HourlyTempF
0 2019-05-22 10:00:00 3402 0.0 93.6 54.0
dateTime field id HourlyPrecipIn HourlyRH HourlyTempF
0 2019-05-22 11:00:00 3402 0.01 93.5 54.4
dateTime field id HourlyPrecipIn HourlyRH HourlyTempF
0 2019-05-22 12:00:00 3402 0.0 87.3 54.5
dateTime field id HourlyPrecipIn HourlyRH HourlyTempF
0 2019-05-22 13:00:00 3402 0.0 86.0 54.7
dateTime field id HourlyPrecipIn HourlyRH HourlyTempF
0 2019-05-22 14:00:00 3402 0.0 87.9 54.5
dateTime field id HourlyPrecipIn HourlyRH HourlyTempF
0 2019-05-22 15:00:00 3402 0.0 82.0 56.5
dateTime field id HourlyPrecipIn HourlyRH HourlyTempF
0 2019-05-22 16:00:00 3402 0.0 85.4 56.6
dateTime field id HourlyPrecipIn HourlyRH HourlyTempF
0 2019-05-22 17:00:00 3402 0.0 82.9 55.7
dateTime field id HourlyPrecipIn HourlyRH HourlyTempF
0 2019-05-22 18:00:00 3402 0.0 82.6 54.1
dateTime field id HourlyPrecipIn HourlyRH HourlyTempF
0 2019-05-22 19:00:00 3402 0.0 79.2 54.0
dateTime field id HourlyPrecipIn HourlyRH HourlyTempF
0 2019-05-22 20:00:00 3402 0.0 83.8 54.1
dateTime field id HourlyPrecipIn HourlyRH HourlyTempF
0 2019-05-22 21:00:00 3402 0.0 87.9 54.2
dateTime field id HourlyPrecipIn HourlyRH HourlyTempF
0 2019-05-22 22:00:00 3402 0.0 88.6 53.6
dateTime field id HourlyPrecipIn HourlyRH HourlyTempF
0 2019-05-22 23:00:00 3402 0.0 87.5 52.2
Starting import of field id: 45883
dateTime field id HourlyPrecipIn HourlyRH HourlyTempF
0 2019-05-22 01:00:00 45883 0.0 97.9 45.3
dateTime field id HourlyPrecipIn HourlyRH HourlyTempF
0 2019-05-22 02:00:00 45883 0.0 97.9 45.6
dateTime field id HourlyPrecipIn HourlyRH HourlyTempF
0 2019-05-22 03:00:00 45883 0.0 97.7 45.7
dateTime field id HourlyPrecipIn HourlyRH HourlyTempF
0 2019-05-22 04:00:00 45883 0.0 99.0 45.9
dateTime field id HourlyPrecipIn HourlyRH HourlyTempF
0 2019-05-22 05:00:00 45883 0.0 95.5 47.1
dateTime field id HourlyPrecipIn HourlyRH HourlyTempF
0 2019-05-22 06:00:00 45883 0.0 99.0 47.3
dateTime field id HourlyPrecipIn HourlyRH HourlyTempF
0 2019-05-22 07:00:00 45883 0.03 95.3 49.1
dateTime field id HourlyPrecipIn HourlyRH HourlyTempF
0 2019-05-22 08:00:00 45883 0.0 95.2 50.7
dateTime field id HourlyPrecipIn HourlyRH HourlyTempF
0 2019-05-22 09:00:00 45883 0.01 94.0 52.7
dateTime field id HourlyPrecipIn HourlyRH HourlyTempF
0 2019-05-22 10:00:00 45883 0.02 93.3 54.3
dateTime field id HourlyPrecipIn HourlyRH HourlyTempF
0 2019-05-22 11:00:00 45883 0.04 92.9 54.8
dateTime field id HourlyPrecipIn HourlyRH HourlyTempF
0 2019-05-22 12:00:00 45883 0.0 86.9 55.0
dateTime field id HourlyPrecipIn HourlyRH HourlyTempF
0 2019-05-22 13:00:00 45883 0.0 84.7 55.2
dateTime field id HourlyPrecipIn HourlyRH HourlyTempF
0 2019-05-22 14:00:00 45883 0.0 87.3 55.0
dateTime field id HourlyPrecipIn HourlyRH HourlyTempF
0 2019-05-22 15:00:00 45883 0.0 81.9 57.1
dateTime field id HourlyPrecipIn HourlyRH HourlyTempF
0 2019-05-22 16:00:00 45883 0.0 83.4 57.5
dateTime field id HourlyPrecipIn HourlyRH HourlyTempF
0 2019-05-22 17:00:00 45883 0.0 82.5 56.2
dateTime field id HourlyPrecipIn HourlyRH HourlyTempF
0 2019-05-22 18:00:00 45883 0.0 82.1 54.6
dateTime field id HourlyPrecipIn HourlyRH HourlyTempF
0 2019-05-22 19:00:00 45883 0.0 80.1 54.4
dateTime field id HourlyPrecipIn HourlyRH HourlyTempF
0 2019-05-22 20:00:00 45883 0.0 83.9 54.6
dateTime field id HourlyPrecipIn HourlyRH HourlyTempF
0 2019-05-22 21:00:00 45883 0.0 87.4 54.6
dateTime field id HourlyPrecipIn HourlyRH HourlyTempF
0 2019-05-22 22:00:00 45883 0.0 88.4 53.8
dateTime field id HourlyPrecipIn HourlyRH HourlyTempF
0 2019-05-22 23:00:00 45883 0.0 87.5 52.7
答案 0 :(得分:1)
请注意,由于复制/粘贴数据中引入的错误,我看到的值可能会略有不同,但是最简单的获取方法(我假设)是您的输出是在列上使用SELECT country
FROM `user`
WHERE created > now() - interval 9 day
GROUP BY country
ORDER BY count(*) desc
LIMIT 1
groupby
函数和聚合字典。
这是我的测试DF中数据的外观:
agg()
如果您的数据是作为单独的行进入的,那么将其放在单个数据框中可能完全是另一个问题。
dateTime field id HourlyPrecipIn HourlyRH HourlyTempF
0 2019-05-22 01:00:00 40238 0.0 98.8 44.9
0 2019-05-22 02:00:00 40238 0.0 98.9 45.1
0 2019-05-22 03:00:00 40238 0.0 98.7 45.1
0 2019-05-22 04:00:00 40238 0.02 99.6 45.3
0 2019-05-22 05:00:00 40238 0.0 95.0 46.7
0 2019-05-22 06:00:00 40238 0.0 99.8 46.7