Pandas玩转数据（十一) -- 数据分组技术Groupby

数据分析汇总学习

https://blog.csdn.net/weixin_39778570/article/details/81157884

import numpy as np
import pandas as pd
from pandas import Series, DataFrame

# 打开文件
f = open('city_weather.csv')
df = pd.read_csv(f)
df
Out[7]: 
          date city  temperature  wind
0   03/01/2016   BJ            8     5
1   17/01/2016   BJ           12     2
2   31/01/2016   BJ           19     2
3   14/02/2016   BJ           -3     3
4   28/02/2016   BJ           19     2
5   13/03/2016   BJ            5     3
6   27/03/2016   SH           -4     4
7   10/04/2016   SH           19     3
8   24/04/2016   SH           20     3
9   08/05/2016   SH           17     3
10  22/05/2016   SH            4     2
11  05/06/2016   SH          -10     4
12  19/06/2016   SH            0     5
13  03/07/2016   SH           -9     5
14  17/07/2016   GZ           10     2
15  31/07/2016   GZ           -1     5
16  14/08/2016   GZ            1     5
17  28/08/2016   GZ           25     4
18  11/09/2016   SZ           20     1
19  25/09/2016   SZ          -10     4

# 进行分组
g = df.groupby(df['city'])
g
Out[9]: 0x0000026486F22BA8>

g.groups
Out[10]: 
{'BJ': Int64Index([0, 1, 2, 3, 4, 5], dtype='int64'),
 'GZ': Int64Index([14, 15, 16, 17], dtype='int64'),
 'SH': Int64Index([6, 7, 8, 9, 10, 11, 12, 13], dtype='int64'),
 'SZ': Int64Index([18, 19], dtype='int64')}

# 获取某一个组
g.get_group('BJ')
Out[11]: 
         date city  temperature  wind
0  03/01/2016   BJ            8     5
1  17/01/2016   BJ           12     2
2  31/01/2016   BJ           19     2
3  14/02/2016   BJ           -3     3
4  28/02/2016   BJ           19     2
5  13/03/2016   BJ            5     3

# 对某一个组进行处理
df_bj = g.get_group('BJ')

df_bj.mean()
Out[14]: 
temperature    10.000000
wind            2.833333
dtype: float64

df_bj.describe()
Out[15]: 
       temperature      wind
count     6.000000  6.000000
mean     10.000000  2.833333
std       8.532292  1.169045
min      -3.000000  2.000000
25%       5.750000  2.000000
50%      10.000000  2.500000
75%      17.250000  3.000000
max      19.000000  5.000000

# 查看整个groupby
g.mean()
Out[16]: 
      temperature      wind
city                       
BJ         10.000  2.833333
GZ          8.750  4.000000
SH          4.625  3.625000
SZ          5.000  2.500000

g.describe()
Out[17]: 
     temperature                                                    wind  \
           count    mean        std   min   25%   50%    75%   max count   
city                                                                       
BJ           6.0  10.000   8.532292  -3.0  5.75  10.0  17.25  19.0   6.0   
GZ           4.0   8.750  11.842719  -1.0  0.50   5.5  13.75  25.0   4.0   
SH           8.0   4.625  12.489281 -10.0 -5.25   2.0  17.50  20.0   8.0   
SZ           2.0   5.000  21.213203 -10.0 -2.50   5.0  12.50  20.0   2.0   


          mean       std  min   25%  50%   75%  max  
city                                                 
BJ    2.833333  1.169045  2.0  2.00  2.5  3.00  5.0  
GZ    4.000000  1.414214  2.0  3.50  4.5  5.00  5.0  
SH    3.625000  1.060660  2.0  3.00  3.5  4.25  5.0  
SZ    2.500000  2.121320  1.0  1.75  2.5  3.25  4.0  

# groupby可转换为列表，列表中为元组，元组中第一个值为分组名，第二个值为dataframe
list(g)
Out[18]: 
[('BJ',          date city  temperature  wind
  0  03/01/2016   BJ            8     5
  1  17/01/2016   BJ           12     2
  2  31/01/2016   BJ           19     2
  3  14/02/2016   BJ           -3     3
  4  28/02/2016   BJ           19     2
  5  13/03/2016   BJ            5     3),
 ('GZ',           date city  temperature  wind
  14  17/07/2016   GZ           10     2
  15  31/07/2016   GZ           -1     5
  16  14/08/2016   GZ            1     5
  17  28/08/2016   GZ           25     4),
 ('SH',           date city  temperature  wind
  6   27/03/2016   SH           -4     4
  7   10/04/2016   SH           19     3
  8   24/04/2016   SH           20     3
  9   08/05/2016   SH           17     3
  10  22/05/2016   SH            4     2
  11  05/06/2016   SH          -10     4
  12  19/06/2016   SH            0     5
  13  03/07/2016   SH           -9     5),
 ('SZ',           date city  temperature  wind
  18  11/09/2016   SZ           20     1
  19  25/09/2016   SZ          -10     4)]

# 可以装换为字典
dict(list(g))
Out[19]: 
{'BJ':          date city  temperature  wind
 0  03/01/2016   BJ            8     5
 1  17/01/2016   BJ           12     2
 2  31/01/2016   BJ           19     2
 3  14/02/2016   BJ           -3     3
 4  28/02/2016   BJ           19     2
 5  13/03/2016   BJ            5     3,
 'GZ':           date city  temperature  wind
 14  17/07/2016   GZ           10     2
 15  31/07/2016   GZ           -1     5
 16  14/08/2016   GZ            1     5
 17  28/08/2016   GZ           25     4,
 'SH':           date city  temperature  wind
 6   27/03/2016   SH           -4     4
 7   10/04/2016   SH           19     3
 8   24/04/2016   SH           20     3
 9   08/05/2016   SH           17     3
 10  22/05/2016   SH            4     2
 11  05/06/2016   SH          -10     4
 12  19/06/2016   SH            0     5
 13  03/07/2016   SH           -9     5,
 'SZ':           date city  temperature  wind
 18  11/09/2016   SZ           20     1
 19  25/09/2016   SZ          -10     4}

# 打印groupby的中每个组的两个参数，name和dataframe
for name, group_df in g:
    print(name)
    print(group_df)

BJ
         date city  temperature  wind
0  03/01/2016   BJ            8     5
1  17/01/2016   BJ           12     2
2  31/01/2016   BJ           19     2
3  14/02/2016   BJ           -3     3
4  28/02/2016   BJ           19     2
5  13/03/2016   BJ            5     3
GZ
          date city  temperature  wind
14  17/07/2016   GZ           10     2
15  31/07/2016   GZ           -1     5
16  14/08/2016   GZ            1     5
17  28/08/2016   GZ           25     4
SH
          date city  temperature  wind
6   27/03/2016   SH           -4     4
7   10/04/2016   SH           19     3
8   24/04/2016   SH           20     3
9   08/05/2016   SH           17     3
10  22/05/2016   SH            4     2
11  05/06/2016   SH          -10     4
12  19/06/2016   SH            0     5
13  03/07/2016   SH           -9     5
SZ
          date city  temperature  wind
18  11/09/2016   SZ           20     1
19  25/09/2016   SZ          -10     4