# -*- coding: utf-8 -*-
import pandas as pd
import numpy as np
df = pd.DataFrame({
key1:list(aabba),
key2: [one,two,one,two,one],
data1: [1,3,5,7,9],
data2: [2,4,6,8,10]})
print df
grouped = df.groupby([key1]).size() #按key1的值分组，并统计个数
print grouped
print ++++++++++++++
grouped1 = df[data1].astype(float).groupby(df[key1]).mean() #先将data1转换成浮点型，然后分组求均值
print grouped1
print type(grouped1) #series类型
print ++++++++++++++++++
df[add] = [AA,BB,"CC",DD,EE] #dataframe追加一列
print df
grouped2=df.groupby([key1,key2]).size() #按两列属性分组
#注意若groupby前面用df的形式则后面参数直接用[key1]的形式
print grouped2
print type(grouped2)
print ++++++++++++++++++
grouped3=df[data1].astype(float).groupby([df[key1],df[add]]).mean() #按key1与key2分组，求data1这一列均值
#注意若groupby前面用df[data1]的形式则后面参数必须用df[key1]的形式
print grouped3
print type(grouped3) #series
运行结果如下：
data1 data2 key1 key2
0 1 2 a one
1 3 4 a two
2 5 6 b one
3 7 8 b two
4 9 10 a one
key1
a 3
b 2
dtype: int64
++++++++++++++
key1
a 4.333333
b 6.000000
Name: data1, dtype: float64
<class pandas.core.series.Series>
++++++++++++++++++
data1 data2 key1 key2 add
0 1 2 a one AA
1 3 4 a two BB
2 5 6 b one CC
3 7 8 b two DD
4 9 10 a one EE
key1 key2
a one 2
two 1
b one 1
two 1
dtype: int64
<class pandas.core.series.Series>
++++++++++++++++++
key1 add
a AA 1.0
BB 3.0
EE 9.0
b CC 5.0
DD 7.0
Name: data1, dtype: float64
<class pandas.core.series.Series>
