Pandas现在支持三种类型的多轴索引;
编号 | 索引 | 描述 |
---|---|---|
1 | .loc() |
基于标签 |
2 | .iloc() |
基于整数 |
3 | .ix() |
基于标签和整数 |
.loc()
Pandas提供了各种方法来完成基于标签的索引。 切片时,也包括起始边界。整数是有效的标签,但它们是指标签而不是位置。
.loc()
具有多种访问方式,如 -
- 单个标量标签
- 标签列表
- 切片对象
- 一个布尔数组
loc
需要两个单/列表/范围运算符,用","
分隔。第一个表示行,第二个表示列。
示例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'])
输出结果:
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
示例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']])
输出结果:
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
示例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']])
输出结果:
A C
a -1.959731 0.720956
b 1.318976 0.199987
f -1.117735 -0.181116
h -0.147029 0.027369
示例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']) # 没有写列标签,就将所有的列全部输出
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
示例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
其他方法
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