摘要：
Columns=['A'，df['20130102'：locprint（df.loc['20130102']）#选择具有行属性“20130102”（df.loc[：'B']）的数据打印#选择列属性“A”，“B”]]）#选择行属性“20130102”，“C”]）#选中前3行，
from __future__ import print_function
import pandas as pd
import numpy as np
np.random.seed(1)
dates = pd.date_range('20130101', periods=6)
df = pd.DataFrame(np.random.randn(6, 4), index=dates, columns=['A', 'B', 'C', 'D'])
print(df['A'], df.A) # 选取属性值为‘A’的列，两种方法效果相同
print(df[0:3], df['20130102':'20130104']) # 选取数据的前三行，选取行属性之间的数据（包括了右边属性的数据） 两种方法效果相同
select by label: loc 属性值
# select by label: loc
print(df.loc['20130102']) # 选取行属性为‘20130102’的数据
print(df.loc[:,['A','B']]) # 选取列属性为‘A’，‘B’的所有数据
print(df.loc['20130102', ['A','B']]) # 选取行属性为‘20130102’，列属性为‘A’，‘B’的数据
select by position: iloc 属性编号
# select by position: iloc
print(df.iloc[3]) # 选取行第4行的数据
print(df.iloc[3, 1]) # 选取第4行第2列的数据
print(df.iloc[3:5,0:2]) # 选取行编号为3,4 列编号为0,1的数据
print(df.iloc[[1,2,4],[0,2]]) # 选取第2,3,5行，第1，3列的数据
mixed selection: ix 标签值+标签标号
# mixed selection: ix
print(df.ix[:3, ['A', 'C']]) # 选取前3行，列属性为‘A’，‘C’的数据
Boolean indexing 布尔
# Boolean indexing
print(df[df.A > 0]) # 选取所有值大于0的数据
下面是所有的输出结果：
print(df)
> A B C D
> 2013-01-01 1.624345 -0.611756 -0.528172 -1.072969
> 2013-01-02 0.865408 -2.301539 1.744812 -0.761207
> 2013-01-03 0.319039 -0.249370 1.462108 -2.060141
> 2013-01-04 -0.322417 -0.384054 1.133769 -1.099891
> 2013-01-05 -0.172428 -0.877858 0.042214 0.582815
> 2013-01-06 -1.100619 1.144724 0.901591 0.502494
print(df['A'])
print('
')
print(df.A)
> 2013-01-01 1.624345
> 2013-01-02 0.865408
> 2013-01-03 0.319039
> 2013-01-04 -0.322417
> 2013-01-05 -0.172428
> 2013-01-06 -1.100619
> Freq: D, Name: A, dtype: float64
> 2013-01-01 1.624345
> 2013-01-02 0.865408
> 2013-01-03 0.319039
> 2013-01-04 -0.322417
> 2013-01-05 -0.172428
> 2013-01-06 -1.100619
> Freq: D, Name: A, dtype: float64
print(df[0:3])
print('
')
print(df['20130102':'20130104'])
> A B C D
> 2013-01-01 1.624345 -0.611756 -0.528172 -1.072969
> 2013-01-02 0.865408 -2.301539 1.744812 -0.761207
> 2013-01-03 0.319039 -0.249370 1.462108 -2.060141
> A B C D
> 2013-01-02 0.865408 -2.301539 1.744812 -0.761207
> 2013-01-03 0.319039 -0.249370 1.462108 -2.060141
> 2013-01-04 -0.322417 -0.384054 1.133769 -1.099891
# select by label: loc
print(df.loc['20130102'])
> A 0.865408
> B -2.301539
> C 1.744812
> D -0.761207
> Name: 2013-01-02 00:00:00, dtype: float64
print(df.loc[:, ['A', 'B']])
> A B
> 2013-01-01 1.624345 -0.611756
> 2013-01-02 0.865408 -2.301539
> 2013-01-03 0.319039 -0.249370
> 2013-01-04 -0.322417 -0.384054
> 2013-01-05 -0.172428 -0.877858
> 2013-01-06 -1.100619 1.144724
print(df.loc['20130102', ['A', 'B']])
> A 0.865408
> B -2.301539
> Name: 2013-01-02 00:00:00, dtype: float64
# select by position: iloc
print(df.iloc[3])
> A -0.322417
> B -0.384054
> C 1.133769
> D -1.099891
> Name: 2013-01-04 00:00:00, dtype: float64
print(df.iloc[3, 1])
> -0.38405435466841564
print(df.iloc[3:5, 0:2])
> A B
> 2013-01-04 -0.322417 -0.384054
> 2013-01-05 -0.172428 -0.877858
print(df.iloc[[1, 2, 4], [0, 2]])
A C
2013-01-02 0.865408 1.744812
2013-01-03 0.319039 1.462108
2013-01-05 -0.172428 0.042214
# mixed selection: ix
print(df.ix[:3, ['A', 'C']])
> A C
> 2013-01-01 1.624345 -0.528172
> 2013-01-02 0.865408 1.744812
> 2013-01-03 0.319039 1.462108
# Boolean indexing
print(df[df.A > 0])
> A B C D
> 2013-01-01 1.624345 -0.611756 -0.528172 -1.072969
> 2013-01-02 0.865408 -2.301539 1.744812 -0.761207
> 2013-01-03 0.319039 -0.249370 1.462108 -2.060141
END