如何使用 group by 从 Pandas 获取 MAX、MIN、SUM 值答案

【问题标题】：How to use group by to get MAX, MIN, SUM values from Pandas如何使用 group by 从 Pandas 获取 MAX、MIN、SUM 值
【发布时间】：2019-10-09 11:31:16
【问题描述】：

我在下面有一个数据框，我想获取 MAX 每小时温度、MIN 每小时温度和 SUM 每小时沉淀。这是我当前的代码。我想打印每个field id 的最大值。需要在我的代码中进行哪些更改才能完成此操作？目前代码不打印最大值。它打印23:00 的最后一个值。我希望我的输出看起来像这样

import pandas
import pd as pandas

hrly_df = pd.DataFrame({'dateTime' :[t], 'field id': [id_], 'HourlyPrecipIn': [aPreVJ],'HourlyRH' : [aHumidVJ], 'HourlyTempF' : [aTempVJ]})

tempMax = hrly_df.loc[hrly_df.groupby('field id')['HourlyTempF'].idxmax()]

dateTime field id HourlyPrecipIn HourlyRH HourlyTempF 0 2019-05-21 01:00:00 40238 0.0 73.8 48.4 dateTime field id HourlyPrecipIn HourlyRH HourlyTempF 0 2019-05-21 02:00:00 40238 0.0 77.0 46.8 dateTime field id HourlyPrecipIn HourlyRH HourlyTempF 0 2019-05-21 03:00:00 40238 0.0 79.9 47.0 dateTime field id HourlyPrecipIn HourlyRH HourlyTempF 0 2019-05-21 04:00:00 40238 0.0 80.6 46.8 dateTime field id HourlyPrecipIn HourlyRH HourlyTempF 0 2019-05-21 05:00:00 40238 0.0 82.6 45.2 dateTime field id HourlyPrecipIn HourlyRH HourlyTempF 0 2019-05-21 06:00:00 40238 0.0 85.8 45.3 dateTime field id HourlyPrecipIn HourlyRH HourlyTempF 0 2019-05-21 07:00:00 40238 0.0 80.2 46.9 dateTime field id HourlyPrecipIn HourlyRH HourlyTempF 0 2019-05-21 08:00:00 40238 0.0 71.3 50.1 dateTime field id HourlyPrecipIn HourlyRH HourlyTempF 0 2019-05-21 09:00:00 40238 0.0 69.8 50.8 dateTime field id HourlyPrecipIn HourlyRH HourlyTempF 0 2019-05-21 10:00:00 40238 0.0 62.7 53.6 dateTime field id HourlyPrecipIn HourlyRH HourlyTempF 0 2019-05-21 11:00:00 40238 0.0 62.2 54.2 dateTime field id HourlyPrecipIn HourlyRH HourlyTempF 0 2019-05-21 12:00:00 40238 0.0 52.4 55.6 dateTime field id HourlyPrecipIn HourlyRH HourlyTempF 0 2019-05-21 13:00:00 40238 0.0 50.9 57.2 dateTime field id HourlyPrecipIn HourlyRH HourlyTempF 0 2019-05-21 14:00:00 40238 0.0 46.7 58.8 dateTime field id HourlyPrecipIn HourlyRH HourlyTempF 0 2019-05-21 15:00:00 40238 0.0 47.8 57.6 dateTime field id HourlyPrecipIn HourlyRH HourlyTempF 0 2019-05-21 16:00:00 40238 0.01 47.1 55.9 dateTime field id HourlyPrecipIn HourlyRH HourlyTempF 0 2019-05-21 17:00:00 40238 0.04 61.1 52.4 dateTime field id HourlyPrecipIn HourlyRH HourlyTempF 0 2019-05-21 18:00:00 40238 0.03 80.0 48.7 dateTime field id HourlyPrecipIn HourlyRH HourlyTempF 0 2019-05-21 19:00:00 40238 0.04 88.1 46.9 dateTime field id HourlyPrecipIn HourlyRH HourlyTempF 0 2019-05-21 20:00:00 40238 0.08 93.8 45.3 dateTime field id HourlyPrecipIn HourlyRH HourlyTempF 0 2019-05-21 21:00:00 40238 0.07 93.0 45.2 dateTime field id HourlyPrecipIn HourlyRH HourlyTempF 0 2019-05-21 22:00:00 40238 0.08 93.0 45.2 dateTime field id HourlyPrecipIn HourlyRH HourlyTempF 0 2019-05-21 23:00:00 40238 0.09 92.7 45.1 Starting import of field id: 3402 dateTime field id HourlyPrecipIn HourlyRH HourlyTempF 0 2019-05-21 01:00:00 3402 0.0 73.9 48.4 dateTime field id HourlyPrecipIn HourlyRH HourlyTempF 0 2019-05-21 02:00:00 3402 0.0 77.1 46.8 dateTime field id HourlyPrecipIn HourlyRH HourlyTempF 0 2019-05-21 03:00:00 3402 0.0 79.9 47.0 dateTime field id HourlyPrecipIn HourlyRH HourlyTempF 0 2019-05-21 04:00:00 3402 0.0 80.6 46.8 dateTime field id HourlyPrecipIn HourlyRH HourlyTempF 0 2019-05-21 05:00:00 3402 0.0 82.6 45.3 dateTime field id HourlyPrecipIn HourlyRH HourlyTempF 0 2019-05-21 06:00:00 3402 0.0 85.6 45.4 dateTime field id HourlyPrecipIn HourlyRH HourlyTempF 0 2019-05-21 07:00:00 3402 0.0 80.2 47.0 dateTime field id HourlyPrecipIn HourlyRH HourlyTempF 0 2019-05-21 08:00:00 3402 0.0 71.3 50.1 dateTime field id HourlyPrecipIn HourlyRH HourlyTempF 0 2019-05-21 09:00:00 3402 0.0 69.7 50.8 dateTime field id HourlyPrecipIn HourlyRH HourlyTempF 0 2019-05-21 10:00:00 3402 0.0 62.8 53.6 dateTime field id HourlyPrecipIn HourlyRH HourlyTempF 0 2019-05-21 11:00:00 3402 0.0 62.2 54.2 dateTime field id HourlyPrecipIn HourlyRH HourlyTempF 0 2019-05-21 12:00:00 3402 0.0 52.6 55.6 dateTime field id HourlyPrecipIn HourlyRH HourlyTempF 0 2019-05-21 13:00:00 3402 0.0 50.9 57.2 dateTime field id HourlyPrecipIn HourlyRH HourlyTempF 0 2019-05-21 14:00:00 3402 0.0 46.8 58.8 dateTime field id HourlyPrecipIn HourlyRH HourlyTempF 0 2019-05-21 15:00:00 3402 0.0 47.8 57.6 dateTime field id HourlyPrecipIn HourlyRH HourlyTempF 0 2019-05-21 16:00:00 3402 0.01 47.2 55.9 dateTime field id HourlyPrecipIn HourlyRH HourlyTempF 0 2019-05-21 17:00:00 3402 0.03 61.1 52.5 dateTime field id HourlyPrecipIn HourlyRH HourlyTempF 0 2019-05-21 18:00:00 3402 0.03 79.9 48.8 dateTime field id HourlyPrecipIn HourlyRH HourlyTempF 0 2019-05-21 19:00:00 3402 0.04 88.2 46.9 dateTime field id HourlyPrecipIn HourlyRH HourlyTempF 0 2019-05-21 20:00:00 3402 0.08 93.7 45.3 dateTime field id HourlyPrecipIn HourlyRH HourlyTempF 0 2019-05-21 21:00:00 3402 0.07 93.0 45.2 dateTime field id HourlyPrecipIn HourlyRH HourlyTempF 0 2019-05-21 22:00:00 3402 0.07 93.0 45.2 dateTime field id HourlyPrecipIn HourlyRH HourlyTempF 0 2019-05-21 23:00:00 3402 0.08 92.7 45.1 Starting import of field id: 45883 dateTime field id HourlyPrecipIn HourlyRH HourlyTempF 0 2019-05-21 01:00:00 45883 0.0 75.4 48.4 dateTime field id HourlyPrecipIn HourlyRH HourlyTempF 0 2019-05-21 02:00:00 45883 0.0 77.6 47.1 dateTime field id HourlyPrecipIn HourlyRH HourlyTempF 0 2019-05-21 03:00:00 45883 0.0 79.6 47.3 dateTime field id HourlyPrecipIn HourlyRH HourlyTempF 0 2019-05-21 04:00:00 45883 0.0 80.2 47.0 dateTime field id HourlyPrecipIn HourlyRH HourlyTempF 0 2019-05-21 05:00:00 45883 0.0 82.5 45.7 dateTime field id HourlyPrecipIn HourlyRH HourlyTempF 0 2019-05-21 06:00:00 45883 0.0 84.7 45.8 dateTime field id HourlyPrecipIn HourlyRH HourlyTempF 0 2019-05-21 07:00:00 45883 0.0 79.2 47.2 dateTime field id HourlyPrecipIn HourlyRH HourlyTempF 0 2019-05-21 08:00:00 45883 0.0 71.9 50.0 dateTime field id HourlyPrecipIn HourlyRH HourlyTempF 0 2019-05-21 09:00:00 45883 0.0 68.9 51.4 dateTime field id HourlyPrecipIn HourlyRH HourlyTempF 0 2019-05-21 10:00:00 45883 0.0 63.0 53.7 dateTime field id HourlyPrecipIn HourlyRH HourlyTempF 0 2019-05-21 11:00:00 45883 0.0 61.8 54.6 dateTime field id HourlyPrecipIn HourlyRH HourlyTempF 0 2019-05-21 12:00:00 45883 0.0 52.9 56.1 dateTime field id HourlyPrecipIn HourlyRH HourlyTempF 0 2019-05-21 13:00:00 45883 0.0 50.9 57.4 dateTime field id HourlyPrecipIn HourlyRH HourlyTempF 0 2019-05-21 14:00:00 45883 0.0 48.4 58.7 dateTime field id HourlyPrecipIn HourlyRH HourlyTempF 0 2019-05-21 15:00:00 45883 0.0 48.3 57.8 dateTime field id HourlyPrecipIn HourlyRH HourlyTempF 0 2019-05-21 16:00:00 45883 0.02 48.2 55.9 dateTime field id HourlyPrecipIn HourlyRH HourlyTempF 0 2019-05-21 17:00:00 45883 0.07 63.0 52.4 dateTime field id HourlyPrecipIn HourlyRH HourlyTempF 0 2019-05-21 18:00:00 45883 0.02 79.8 48.9 dateTime field id HourlyPrecipIn HourlyRH HourlyTempF 0 2019-05-21 19:00:00 45883 0.05 89.4 47.1 dateTime field id HourlyPrecipIn HourlyRH HourlyTempF 0 2019-05-21 20:00:00 45883 0.08 93.4 45.8 dateTime field id HourlyPrecipIn HourlyRH HourlyTempF 0 2019-05-21 21:00:00 45883 0.07 93.2 45.6 dateTime field id HourlyPrecipIn HourlyRH HourlyTempF 0 2019-05-21 22:00:00 45883 0.04 92.8 45.7 dateTime field id HourlyPrecipIn HourlyRH HourlyTempF 0 2019-05-21 23:00:00 45883 0.1 92.3 45.6

【问题讨论】：

您好，请在此处关注stackoverflow.com/help/minimal-reproducible-example 这个示例，很难真正理解您要实现的目标。您的示例不是最小的或几乎不可读。更重要的是它是不可验证的。请举例说明您期望数据的样子。

标签： python pandas

【解决方案1】：

你可以用agg做函数列表

hrly_df.groupby('field id')['HourlyTempF'].agg(['min','max','sum'])
Out[313]: 
           min   max    sum
field id                   
3402      46.8  48.4   95.2
40238     46.8  48.4  142.2

【讨论】：

【解决方案2】：

我不完全确定您的要求，所以我会尽我所能，直到您可以提供验证：

hrly_df = {'dateTime': ['2019-05-21 01:00:00', '2019-05-21 02:00:00', '2019-05-21 03:00:00', '2019-05-21 01:00:00', '2019-05-21 02:00:00'],
           'field id': [40238, 40238, 40238, 3402, 3402],
           'HourlyPrecipIn': [0.0, 0.0, 0.0, 0.0, 0.0],
           'HourlyRH': [73.8, 77.0, 79.9, 73.9, 77.1],
           'HourlyTempF': [48.4, 46.8, 47.0, 48.4, 46.8]}

hrly_df = pd.DataFrame(hrly_df)
# Gets you max 'HourlyTempF' per 'field id'
temp_max = hrly_df[['field id', 'HourlyTempF']].groupby('field id').max()
# Gets you min 'HourlyTempF' per 'field id'
temp_min = hrly_df[['field id', 'HourlyTempF']].groupby('field id').min()
# Gets you sum of 'HourlyPrecipIn' per 'field id'
precip_sum = hrly_df[['field id', 'HourlyPrecipIn']].groupby('field id').sum()

print(temp_max)
print(temp_min)
print(precip_sum)

【讨论】：