Pandas | 13 index and select the data

Pandas now supports three types of multi-axis indexing;

Numbering	index	description
1	`.loc()`	Based on the label
2	`.iloc()`	Based on Integer
3	`.ix()`	Based on the label and integer

.place()

Pandas offers a variety of ways to do tag-based index. Slice, including starting boundary. It is an integer of valid label, but they refer to the position label instead.

.loc()With a variety of access methods, such as -

Single scalar tag
Tag List
Slice objects
A Boolean array

locIt requires two single / listing / range operator, with ","the partition. The first represents a row, the second row indicates.

Example 1

import pandas as pd
import numpy as np

df = pd.DataFrame(np.random.randn(8, 4),
index = ['a','b','c','d','e','f','g','h'], columns = ['A', 'B', 'C', 'D'])
print(df)
print('\n')

print (df.loc[:,'A'])

Output:

　　　A 　　　　B　　　　C 　　　　　D
a 0.128933 1.113168 -2.908401 0.825420
b -1.386837 0.757495 1.632173 0.293825
c -0.131808 -1.372547 -0.623156 -0.090892
d 0.849492 -0.065772 -1.255859 2.891958
e 0.515384 0.781924 -0.816875 0.476188
f 1.962588 1.220072 -0.112463 -1.108805
g -0.893393 -0.346143 -0.757856 -0.871637
h -1.307739 -0.263241 -1.898776 0.621455

a 　　 0.128933
b 　　 -1.386837
c 　　 -0.131808
d 　　 0.849492
e 　　 0.515384
f 　　 1.962588
g 　　 -0.893393
h 　　 -1.307739
Name: A, dtype: float64

Example 2

import pandas as pd
import numpy as np

df = pd.DataFrame(np.random.randn(8, 4),
index = ['a','b','c','d','e','f','g','h'], columns = ['A', 'B', 'C', 'D'])
print (df.loc[:,['A','C']])

Output results:

          A         C
a -0.529735 -1.067299
b -2.230089 -1.798575
c  0.685852  0.333387
d  1.061853  0.131853
e  0.990459  0.189966
f  0.057314 -0.370055
g  0.453960 -0.624419
h  0.666668 -0.433971

Example 3

import pandas as pd
import numpy as np

df = pd.DataFrame(np.random.randn(8, 4),
index = ['a','b','c','d','e','f','g','h'], columns = ['A', 'B', 'C', 'D'])

print (df.loc[['a','b','f','h'],['A','C']])

Output:

          A         C
a -1.959731  0.720956
b  1.318976  0.199987
f -1.117735 -0.181116
h -0.147029  0.027369

Example 4

import pandas as pd
import numpy as np

df = pd.DataFrame(np.random.randn(8, 4),
index = ['a','b','c','d','e','f','g','h'], columns = ['A', 'B', 'C', 'D'])
print (df.loc['a':'h' ])　　　　　　　　　　# No write column labels, all columns will be full output

输出结果：

          A         B         C         D
a  1.556186  1.765712  1.060657  0.810279
b  1.377965 -0.183283 -0.224379  0.963105
c -0.530016  0.167183 -0.066459  0.074198
d -1.515189 -1.453529 -1.559400  1.072148
e -0.487399  0.436143 -1.045622 -0.029507
f  0.552548  0.410745  0.570222 -0.628133
g  0.865293 -0.638388  0.388827 -0.469282
h -0.690596  1.765139 -0.492070 -0.176074

Example 5

import pandas as pd
import numpy as np

df = pd.DataFrame(np.random.randn(8, 4),index = ['a','b','c','d','e','f','g','h'], columns = ['A', 'B', 'C', 'D'])
print (df.loc['a']>0)　　　　　　# 逻辑判断

输出结果 -

A    False
B     True
C    False
D     True
Name: a, dtype: bool

.iloc()

Pandas提供了各种方法，以获得纯整数索引。像python和numpy一样，第一个位置是基于0的索引。

各种访问方式如下 -

整数
整数列表
系列值

示例1：默认按行取

import pandas as pd
import numpy as np

df = pd.DataFrame(np.random.randn(8, 4), columns = ['A', 'B', 'C', 'D'])
print (df.iloc[:4])

输出结果：

          A         B         C         D
0  0.277146  0.274234  0.860555 -1.312323
1 -1.064776  2.082030  0.695930  2.409340
2  0.033953 -1.155217  0.113045 -0.028330
3  0.241075 -2.156415  0.939586 -1.670171

示例2：带逗号，则是行列

import pandas as pd
import numpy as np

df = pd.DataFrame(np.random.randn(8, 4), columns = ['A', 'B', 'C', 'D'])
print (df.iloc[:4])
print (df.iloc[1:5, 2:4])

输出结果：

          A         B         C         D
0  1.346210  0.251839  0.975964  0.319049
1  0.459074  0.038155  0.893615  0.659946
2 -1.097043  0.017080  0.869331 -1.443731
3  1.008033 -0.189436 -0.483688 -1.167312

          C         D
1  0.893615  0.659946
2  0.869331 -1.443731
3 -0.483688 -1.167312
4  1.566395 -1.292206

示例3

import pandas as pd
import numpy as np

df = pd.DataFrame(np.random.randn(8, 4), columns = ['A', 'B', 'C', 'D'])

print (df.iloc[[1, 3, 5], [1, 3]])
print (df.iloc[1:3, :])
print (df.iloc[:,1:3])

输出结果：

          B         D
1  0.081257  0.009109
3  1.037680 -1.467327
5  1.106721  0.320468

          A         B         C         D
1 -0.133711  0.081257 -0.031869  0.009109
2  0.895576 -0.513450 -0.048573  0.698965

          B         C
0  0.442735 -0.949859
1  0.081257 -0.031869
2 -0.513450 -0.048573
3  1.037680 -0.801157
4 -0.547456 -0.255016
5  1.106721  0.688142
6 -0.466452  0.219914
7  1.583112  0.982030

.ix()

除了基于纯标签和整数之外，Pandas还提供了一种使用.ix()运算符进行选择和子集化对象的混合方法。

示例1

import pandas as pd
import numpy as np

df = pd.DataFrame(np.random.randn(8, 4), columns = ['A', 'B', 'C', 'D'])
print (df.ix[:4])

输出结果：

          A         B         C         D
0 -1.449975 -0.002573  1.349962  0.539765
1 -1.249462 -0.800467  0.483950  0.187853
2  1.361273 -1.893519  0.307613 -0.119003
3 -0.103433 -1.058175 -0.587307 -0.114262
4 -0.612298  0.873136 -0.607457  1.047772

示例2

import pandas as pd
import numpy as np

df = pd.DataFrame(np.random.randn(8, 4), columns = ['A', 'B', 'C', 'D'])
print (df.ix[:,'A'])

输出结果：

0    1.539915
1    1.359477
2    0.239694
3    0.563254
4    2.123950
5    0.341554
6   -0.075717
7   -0.606742
Name: A, dtype: float64

使用符号

使用多轴索引从Pandas对象获取值可使用以下符号 -

对象	索引	描述
Series	`s.loc[indexer]`	标量值
DataFrame	`df.loc[row_index,col_index]`	标量对象
Panel	`p.loc[item_index,major_index, minor_index]`	p.loc[item_index,major_index, minor_index]

注意 - .iloc()和.ix()应用相同的索引选项和返回值。

现在来看看如何在DataFrame对象上执行每个操作。这里使用基本索引运算符[] -

示例1

import pandas as pd
import numpy as np

df = pd.DataFrame(np.random.randn(8, 4), columns = ['A', 'B', 'C', 'D'])
print (df['A'])

输出结果：

0    0.028277
1   -1.037595
2   -0.563495
3   -1.196961
4   -0.805250
5   -0.911648
6   -0.355171
7   -0.232612
Name: A, dtype: float64

示例2

import pandas as pd
import numpy as np

df = pd.DataFrame(np.random.randn(8, 4), columns = ['A', 'B', 'C', 'D'])
print (df[['A','B']])

输出结果：

          A         B
0 -0.767339 -0.729411
1 -0.563540 -0.639142
2  0.873589 -2.166382
3  0.900330  0.253875
4 -0.520105  0.064438
5 -1.452176 -0.440864
6 -0.291556 -0.861924
7 -1.464235  0.313168

示例3

import pandas as pd
import numpy as np

df = pd.DataFrame(np.random.randn(8, 4), columns = ['A', 'B', 'C', 'D'])
print (df[2:2])

输出结果：

Empty DataFrame
Columns: [A, B, C, D]
Index: []

属性访问

可以使用属性运算符.来选择列。

示例

import pandas as pd
import numpy as np

df = pd.DataFrame(np.random.randn(8, 4), columns = ['A', 'B', 'C', 'D'])
print (df.A)

输出结果：

0    0.104820
1   -1.206600
2    0.469083
3   -0.821226
4   -1.238865
5    1.083185
6   -0.827833
7   -0.199558
Name: A, dtype: float64