python - 如何对 Pandas 中的两个领域进行分组？

Question

给定以下输入，目标是使用 Avg 和 Sum 函数按小时对每个日期的值进行分组。按小时分组的解决方案是 here , 但它不考虑新的日子。

Date        Time    F1  F2  F3
21-01-16    8:11    5   2   4
21-01-16    9:25    9   8   2
21-01-16    9:39    7   3   2
21-01-16    9:53    6   5   1
21-01-16    10:07   4   6   7
21-01-16    10:21   7   3   1
21-01-16    10:35   5   6   7
21-01-16    11:49   1   2   1
21-01-16    12:03   3   3   1
22-01-16    9:45    6   5   1
22-01-16    9:20    4   6   7
22-01-16    12:10   7   3   1

预期输出:

Date,Time,SUM F1,SUM F2,SUM F3,AVG F1,AVG F2,AVG F3
21-01-16,8:00,5,2,4,5,2,4
21-01-16,9:00,22,16,5,7.3,5.3,1.6
21-01-16,10:00,16,15,15,5.3,5,5
21-01-16,11:00,1,2,1,1,2,1
21-01-16,12:00,3,3,1,3,3,1
22-01-16,9:00,10,11,8,5,5.5,4
22-01-16,12:00,7,3,1,7,3,1

Answer 1

您可以在读取 csv 文件期间解析日期:

from __future__ import print_function # make it work with Python 2 and 3

df = pd.read_csv('f123_dates.csv', index_col=0, parse_dates=[0, 1],
                 delim_whitespace=True)
print(df.groupby([df.index, df.Time.dt.hour]).agg(['mean','sum']))

输出:

                       F1            F2            F3    
                     mean sum      mean sum      mean sum
Date       Time                                          
2016-01-21 8     5.000000   5  2.000000   2  4.000000   4
           9     7.333333  22  5.333333  16  1.666667   5
           10    5.333333  16  5.000000  15  5.000000  15
           11    1.000000   1  2.000000   2  1.000000   1
           12    3.000000   3  3.000000   3  1.000000   1
2016-01-22 9     5.000000  10  5.500000  11  4.000000   8
           12    7.000000   7  3.000000   3  1.000000   1

一直到 csv:

from __future__ import print_function

df = pd.read_csv('f123_dates.csv', index_col=0, parse_dates=[0, 1],
                 delim_whitespace=True)
df2 = df.groupby([df.index, df.Time.dt.hour]).agg(['mean','sum'])
df3 = df2.reset_index()
df3.columns = [' '.join(col).strip() for col in df3.columns.values]
print(df3.to_csv(columns=df3.columns, index=False))

输出:

Date,Time,F1 mean,F1 sum,F2 mean,F2 sum,F3 mean,F3 sum
2016-01-21,8,5.0,5,2.0,2,4.0,4
2016-01-21,9,7.333333333333333,22,5.333333333333333,16,1.6666666666666667,5
2016-01-21,10,5.333333333333333,16,5.0,15,5.0,15
2016-01-21,11,1.0,1,2.0,2,1.0,1
2016-01-21,12,3.0,3,3.0,3,1.0,1
2016-01-22,9,5.0,10,5.5,11,4.0,8
2016-01-22,12,7.0,7,3.0,3,1.0,1