Pandas_Select_Data_iloc
iloc[ ]是基于位置的，也可以使用布爾數(shù)組寝受。
可以輸入如下幾種類型：
- 單個標(biāo)簽坷牛，例如5或'a'；
- 列表或標(biāo)簽數(shù)組很澄。['a', 'b', 'c']
- 帶標(biāo)簽的切片對象'a':'f'京闰；
- 布爾數(shù)組
- 函數(shù)。
import pandas as pd
import numpy as np
import seaborn as sns
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iris = pd.read_csv('iris.csv',header=0)
iris.head()
out:
sepal_length sepal_width petal_length petal_width species
0 5.1 3.5 1.4 0.2 setosa
1 4.9 3.0 1.4 0.2 setosa
2 4.7 3.2 1.3 0.2 setosa
3 4.6 3.1 1.5 0.2 setosa
4 5.0 3.6 1.4 0.2 setosa
修改值
iris.sepal_length.iloc[:3] = 4
iris.head()
out:
sepal_length sepal_width petal_length petal_width species
0 4.0 3.5 1.4 0.2 setosa
1 4.0 3.0 1.4 0.2 setosa
2 4.0 3.2 1.3 0.2 setosa
3 4.6 3.1 1.5 0.2 setosa
4 5.0 3.6 1.4 0.2 setosa
使用切片索引
iris.iloc[2:4, 3:5]
out:
petal_width species
2 0.2 setosa
3 0.2 setosa
iris.iloc[:3]
out:
sepal_length sepal_width petal_length petal_width species
0 4.0 3.5 1.4 0.2 setosa
1 4.0 3.0 1.4 0.2 setosa
2 4.0 3.2 1.3 0.2 setosa
iris.iloc[:, 2:4].head()
out:
petal_length petal_width
0 1.4 0.2
1 1.4 0.2
2 1.3 0.2
3 1.5 0.2
4 1.4 0.2
iris.iloc[10:20, :]
out:
sepal_length sepal_width petal_length petal_width species
10 5.4 3.7 1.5 0.2 setosa
11 4.8 3.4 1.6 0.2 setosa
12 4.8 3.0 1.4 0.1 setosa
13 4.3 3.0 1.1 0.1 setosa
14 5.8 4.0 1.2 0.2 setosa
15 5.7 4.4 1.5 0.4 setosa
16 5.4 3.9 1.3 0.4 setosa
17 5.1 3.5 1.4 0.3 setosa
18 5.7 3.8 1.7 0.3 setosa
19 5.1 3.8 1.5 0.3 setosa
獲取單一值
iris.iloc[2,3]
out:
0.2
參數(shù)可以超出索引范圍痴怨，且不會報錯忙干。
下面這個例子，160超過了行數(shù)浪藻，行數(shù)為150.
iris.iloc[145:160]
out:
sepal_length sepal_width petal_length petal_width species
145 6.7 3.0 5.2 2.3 virginica
146 6.3 2.5 5.0 1.9 virginica
147 6.5 3.0 5.2 2.0 virginica
148 6.2 3.4 5.4 2.3 virginica
149 5.9 3.0 5.1 1.8 virginica
使用可調(diào)用函數(shù)進行選擇
df = pd.DataFrame(np.random.randn(6,4), index=list('abcdef'), columns=list('ABCD'))
df.iloc[lambda df: [1,3]]
out:
A B C D
b 0.801227 0.864968 1.398595 0.948333
d 0.424825 -0.411694 -0.012486 -0.442733
df.iloc[lambda df: df.index.isin(['a','c'])]
out:
A B C D
a -1.585934 0.841256 2.189439 -0.509777
c -1.831144 -0.946563 1.413636 -0.090958
df.iloc[:, lambda df: [2, 3]]
out:
C D
a 2.189439 -0.509777
b 1.398595 0.948333
c 1.413636 -0.090958
d -0.012486 -0.442733
e -2.336897 3.220324
f 1.023109 -1.247364
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