Pandas玩转数据（十二) -- 数据聚合技术Aggregation
数据分析汇总学习
https://blog.csdn.net/weixin_39778570/article/details/81157884
import pandas as pd
import numpy as np
from pandas import Series, DataFrame
# 打开csv文件
f = open('city_weather.csv')
df = pd.read_csv(f)
df
Out[12]:
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
# 对df进行分组
g = df.groupby('city')
# 实验聚合函数min
g.agg('min')
Out[7]:
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
# 自定义函数，目的查看传如函数的数据类型
def foo (attr):
print(type(attr))
return np.nan
#传入一个Sreies对象
g.agg(foo)
<class 'pandas.core.series.Series'>
<class 'pandas.core.series.Series'>
<class 'pandas.core.series.Series'>
<class 'pandas.core.series.Series'>
<class 'pandas.core.series.Series'>
<class 'pandas.core.series.Series'>
<class 'pandas.core.series.Series'>
<class 'pandas.core.series.Series'>
<class 'pandas.core.series.Series'>
<class 'pandas.core.series.Series'>
<class 'pandas.core.series.Series'>
<class 'pandas.core.series.Series'>
Out[9]:
date temperature wind
city
BJ NaN NaN NaN
GZ NaN NaN NaN
SH NaN NaN NaN
SZ NaN NaN NaN
# 自定义函数进行聚合
def foo (attr):
return attr.max() - attr.min()
g.agg(foo)
Out[11]:
temperature wind
city
BJ 22 3
GZ 26 3
SH 30 3
SZ 30 3
# 可以对两列进行分组
g_new = df.groupby(['city', 'wind'])
g_new
Out[14]: 0x000001C7B3DCDE10>
g_new.groups
Out[15]:
{('BJ', 2): Int64Index([1, 2, 4], dtype='int64'),
('BJ', 3): Int64Index([3, 5], dtype='int64'),
('BJ', 5): Int64Index([0], dtype='int64'),
('GZ', 2): Int64Index([14], dtype='int64'),
('GZ', 4): Int64Index([17], dtype='int64'),
('GZ', 5): Int64Index([15, 16], dtype='int64'),
('SH', 2): Int64Index([10], dtype='int64'),
('SH', 3): Int64Index([7, 8, 9], dtype='int64'),
('SH', 4): Int64Index([6, 11], dtype='int64'),
('SH', 5): Int64Index([12, 13], dtype='int64'),
('SZ', 1): Int64Index([18], dtype='int64'),
('SZ', 4): Int64Index([19], dtype='int64')}
# 获取某一组
g_new.get_group(('BJ',2))
Out[18]:
date city temperature wind
1 17/01/2016 BJ 12 2
2 31/01/2016 BJ 19 2
4 28/02/2016 BJ 19 2
# 打印每个组
n [19]: for (name_1,name_2), group in g_new:
print(name_1,name_2)
print(group)
BJ 2
date city temperature wind
1 17/01/2016 BJ 12 2
2 31/01/2016 BJ 19 2
4 28/02/2016 BJ 19 2
BJ 3
date city temperature wind
3 14/02/2016 BJ -3 3
5 13/03/2016 BJ 5 3
BJ 5
date city temperature wind
0 03/01/2016 BJ 8 5
GZ 2
date city temperature wind
14 17/07/2016 GZ 10 2
GZ 4
date city temperature wind
17 28/08/2016 GZ 25 4
GZ 5
date city temperature wind
15 31/07/2016 GZ -1 5
16 14/08/2016 GZ 1 5
SH 2
date city temperature wind
10 22/05/2016 SH 4 2
SH 3
date city temperature wind
7 10/04/2016 SH 19 3
8 24/04/2016 SH 20 3
9 08/05/2016 SH 17 3
SH 4
date city temperature wind
6 27/03/2016 SH -4 4
11 05/06/2016 SH -10 4
SH 5
date city temperature wind
12 19/06/2016 SH 0 5
13 03/07/2016 SH -9 5
SZ 1
date city temperature wind
18 11/09/2016 SZ 20 1
SZ 4
date city temperature wind
19 25/09/2016 SZ -10 4