在pandas中的groupby和在sql语句中的groupby有异曲同工之妙，不过也难怪，毕竟关系数据库中的存放数据的结构也是一张大表罢了，与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() 可以统计每个组 成员的 数量
'''
city
BJ 6
GZ 4
SH 8
SZ 2
dtype: int64
'''
print(g.get_group('BJ')) # 得到 某个 分组
'''
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()) # 对这个 分组 求平均
'''
temperature 10.000000
wind 2.833333
dtype: float64
'''
# 直接使用 g 对象，求平均值
print(g.mean()) # 对 每一个 分组， 都计算分组
'''
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 对象还可以使用 for 进行循环遍历
for name, group in g:
print(name)
print(group)
# g 可以转化为 list类型， dict类型
print(list(g)) # 元组第一个元素是 分组的label，第二个是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))) # 返回键值对，值的类型是 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}
'''到此这篇关于pandas中pd.groupby()的用法详解的文章就介绍到这了,更多相关pandas pd.groupby()内容请搜索编程网以前的文章或继续浏览下面的相关文章希望大家以后多多支持编程网！
