理解GroupBy

在这里插入图片描述
类似于数据库分组的

GroupBy操作和数据库类似

城市天气进行GroupBy操作

对group的单个列求平均值是Series
对group求平均值返回DataFrame

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

# 读入城市天气csv文件
df = pd.read_csv('/Users/bennyrhys/Desktop/数据分析可视化-数据集/homework/city_weather.csv')
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

# 按照城市来GroupBy
g = df.groupby(df['city'])
g

<pandas.core.groupby.generic.DataFrameGroupBy object at 0x11a393610>

# 展示处理完会返回哪几个Group
g.groups

{'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')}

# get_group相当于根据某列的分组过滤出来
df_bj = g.get_group('BJ')
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

# 过滤求平均值
df_bj.mean()

temperature    10.000000
wind            2.833333
dtype: float64

# 过滤的均值 Series
type(df_bj.mean())

pandas.core.series.Series

# 按照过滤一组求平均值的思想，将4组group求平均值组合
# 支持Group直接求平均值
g.mean()

	temperature	wind
city
BJ	10.000	2.833333
GZ	8.750	4.000000
SH	4.625	3.625000
SZ	5.000	2.500000

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

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

<pandas.core.groupby.generic.DataFrameGroupBy object at 0x11a393610>

# 将DataFrameGroupBy -> list 字典
list(g)

[('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)]

dict(list(g))

{'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}

dict(list(g))['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

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

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【数据分析可视化】数据分组技术GroupBy

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理解GroupBy

城市天气进行GroupBy操作

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