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
