在pandas中的groupby和在sql語句中的groupby有異曲同工之妙，不過也難怪，畢竟關係資料庫中的存放資料的結構也是一張大表罷了，與dataframe的形式相似。

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


df = pd.read_csv('./city_weather.csv')
print(df)
'''
          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
'''
 


g = df.groupby(df['city'])
# <pandas.core.groupby.groupby.DataFrameGroupBy object at 0x7f10450e12e8>

print(g.groups)

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


print(g.size()) # g.size() 可以統計每個組 成員的 數量
'''
city
BJ    6
GZ    4
SH    8
SZ    2
dtype: int64
'''

print(g.get_group('BJ')) # 得到 某個 分組
'''
         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
'''
 


df_bj = g.get_group('BJ')
print(df_bj.mean()) # 對這個 分組 求平均
'''
temperature    10.000000
wind            2.833333
dtype: float64
'''

# 直接使用 g 物件，求平均值
print(g.mean()) # 對 每一個 分組， 都計算分組
'''
      temperature      wind
city                       
BJ         10.000  2.833333
GZ          8.750  4.000000
SH          4.625  3.625000
SZ          5.000  2.500000
'''

print(g.max())
'''
            date  temperature  wind
city                               
BJ    31/01/2016           19     5
GZ    31/07/2016           25     5
SH    27/03/2016           20     5
SZ    25/09/2016           20     4
'''

print(g.min())
'''
            date  temperature  wind
city                               
BJ    03/01/2016           -3     2
GZ    14/08/2016           -1     2
SH    03/07/2016          -10     2
SZ    11/09/2016          -10     1
'''

# g 物件還可以使用 for 進行迴圈遍歷
for name, group in g:
    print(name)
    print(group)




# g 可以轉化為 list型別， dict型別
print(list(g)) # 元組第一個元素是 分組的label，第二個是dataframe
'''
[('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)]
'''
print(dict(list(g))) # 返回鍵值對，值的型別是 dataframe
'''
{'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, 
'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, 
'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}
'''