pandas 中选择数据的方法有很多种,一般我们会用到这几种.
1.简单的筛选
2.根据标签：loc
3.根据序列：iloc
4.根据两种的混合：ix
5.通过判断的筛选

Demo.py

import pandas as pd
import numpy as np
#建立一个 6X4 的矩阵数据
dates = pd.date_range('20130101', periods=6)
df = pd.DataFrame(np.arange(24).reshape((6,4)),index=dates, columns=['A','B','C','D'])
print df
#简单筛选
print df['A']
print df.A
#选择跨越多行或多列
print df[0:3]
print df['20130102':'20130104']

#使用loc标签来选择数据
print df.loc['20130102']
print df.loc[:,['A','B']]
print df.loc['20130102',['A','B']]

##使用iloc标签来选择数据
print df.iloc[3,1]
print df.iloc[3:5,1:3]
print df.iloc[[1,3,5],1:3]

#使用ix混合标签来选择数据
print df.ix[:3,['A','C']]

#使用判断选择数据
print df[df.A>8]

结果：

             A   B   C   D
2013-01-01   0   1   2   3
2013-01-02   4   5   6   7
2013-01-03   8   9  10  11
2013-01-04  12  13  14  15
2013-01-05  16  17  18  19
2013-01-06  20  21  22  23
2013-01-01     0
2013-01-02     4
2013-01-03     8
2013-01-04    12
2013-01-05    16
2013-01-06    20
Freq: D, Name: A, dtype: int32
2013-01-01     0
2013-01-02     4
2013-01-03     8
2013-01-04    12
2013-01-05    16
2013-01-06    20
Freq: D, Name: A, dtype: int32
            A  B   C   D
2013-01-01  0  1   2   3
2013-01-02  4  5   6   7
2013-01-03  8  9  10  11
             A   B   C   D
2013-01-02   4   5   6   7
2013-01-03   8   9  10  11
2013-01-04  12  13  14  15
A    4
B    5
C    6
D    7
Name: 2013-01-02 00:00:00, dtype: int32
             A   B
2013-01-01   0   1
2013-01-02   4   5
2013-01-03   8   9
2013-01-04  12  13
2013-01-05  16  17
2013-01-06  20  21
A    4
B    5
Name: 2013-01-02 00:00:00, dtype: int32
13
             B   C
2013-01-04  13  14
2013-01-05  17  18
             B   C
2013-01-02   5   6
2013-01-04  13  14
2013-01-06  21  22
            A   C
2013-01-01  0   2
2013-01-02  4   6
2013-01-03  8  10
             A   B   C   D
2013-01-04  12  13  14  15
2013-01-05  16  17  18  19
2013-01-06  20  21  22  23