.loc[]
.loc主要是基于標簽的伤柄，但也可以與布爾數組一起使用象泵。
可以輸入如下幾種類型：
- 單個標簽，例如5或'a'瑰枫；
- 列表或標簽數組踱葛。['a', 'b', 'c']
- 帶標簽的切片對象'a':'f'；
- 布爾數組
- 函數光坝。
import pandas as pd
import numpy as np
import seaborn as sns
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iris = pd.read_csv('iris.csv',header=0).sample(10)
iris
out:
sepal_length sepal_width petal_length petal_width species
11 4.8 3.4 1.6 0.2 setosa
106 4.9 2.5 4.5 1.7 virginica
14 5.8 4.0 1.2 0.2 setosa
61 5.9 3.0 4.2 1.5 versicolor
138 6.0 3.0 4.8 1.8 virginica
132 6.4 2.8 5.6 2.2 virginica
97 6.2 2.9 4.3 1.3 versicolor
119 6.0 2.2 5.0 1.5 virginica
31 5.4 3.4 1.5 0.4 setosa
19 5.1 3.8 1.5 0.3 setosa
iris.index = list('abcdefghij')
iris
out:
sepal_length sepal_width petal_length petal_width species
a 5.6 2.5 3.9 1.1 versicolor
b 6.0 3.0 4.8 1.8 virginica
c 7.2 3.6 6.1 2.5 virginica
d 5.4 3.7 1.5 0.2 setosa
e 6.6 3.0 4.4 1.4 versicolor
f 6.4 2.8 5.6 2.1 virginica
g 4.8 3.4 1.9 0.2 setosa
h 5.7 2.9 4.2 1.3 versicolor
i 6.1 3.0 4.9 1.8 virginica
j 6.5 3.2 5.1 2.0 virginica
Series
species = iris.species.copy()
species.loc['b']
out:
'virginica'
species.loc['c':'e']
out:
c virginica
d setosa
e versicolor
Name: species, dtype: object
species.loc['h':]
h versicolor
i virginica
j virginica
Name: species, dtype: object
DataFrame
直接通過標簽訪問
iris.loc[['a','c','d'], :]
out:
sepal_length sepal_width petal_length petal_width species
a 5.6 2.5 3.9 1.1 versicolor
c 7.2 3.6 6.1 2.5 virginica
d 5.4 3.7 1.5 0.2 setosa
通過標簽切片訪問
iris.loc['b':'f', 'sepal_length':'petal_length']
out:
sepal_length sepal_width petal_length
b 6.0 3.0 4.8
c 7.2 3.6 6.1
d 5.4 3.7 1.5
e 6.6 3.0 4.4
f 6.4 2.8 5.6
使用單個標簽
iris.loc['d']
out:
sepal_length 5.4
sepal_width 3.7
petal_length 1.5
petal_width 0.2
species setosa
Name: d, dtype: object
使用布爾數組
iris.loc[iris.sepal_length > iris.sepal_length.mean()]
out:
sepal_length sepal_width petal_length petal_width species
c 7.2 3.6 6.1 2.5 virginica
e 6.6 3.0 4.4 1.4 versicolor
f 6.4 2.8 5.6 2.1 virginica
i 6.1 3.0 4.9 1.8 virginica
j 6.5 3.2 5.1 2.0 virginica
iris.index = np.random.randint(0,10,10)
iris
out:
sepal_length sepal_width petal_length petal_width species
8 5.6 2.5 3.9 1.1 versicolor
5 6.0 3.0 4.8 1.8 virginica
9 7.2 3.6 6.1 2.5 virginica
4 5.4 3.7 1.5 0.2 setosa
2 6.6 3.0 4.4 1.4 versicolor
0 6.4 2.8 5.6 2.1 virginica
3 4.8 3.4 1.9 0.2 setosa
7 5.7 2.9 4.2 1.3 versicolor
3 6.1 3.0 4.9 1.8 virginica
5 6.5 3.2 5.1 2.0 virginica
使用.loc切片時尸诽，如果索引中存在開始和停止標簽，則返回位于兩者之間的元素（包括它們）：
iris.loc[9:2]
sepal_length sepal_width petal_length petal_width species
9 7.2 3.6 6.1 2.5 virginica
4 5.4 3.7 1.5 0.2 setosa
2 6.6 3.0 4.4 1.4 versicolor
如果兩個中至少有一個不存在盯另，但索引已排序性含，并且可以與開始和停止標簽進行比較，那么通過選擇在兩者之間排名的標簽鸳惯，切片仍將按預期工作：
iris.sort_index()
sepal_length sepal_width petal_length petal_width species
0 6.4 2.8 5.6 2.1 virginica
2 6.6 3.0 4.4 1.4 versicolor
3 4.8 3.4 1.9 0.2 setosa
3 6.1 3.0 4.9 1.8 virginica
4 5.4 3.7 1.5 0.2 setosa
5 6.0 3.0 4.8 1.8 virginica
5 6.5 3.2 5.1 2.0 virginica
7 5.7 2.9 4.2 1.3 versicolor
8 5.6 2.5 3.9 1.1 versicolor
9 7.2 3.6 6.1 2.5 virginica
iris.sort_index().loc[3:7]
out:
sepal_length sepal_width petal_length petal_width species
3 4.8 3.4 1.9 0.2 setosa
3 6.1 3.0 4.9 1.8 virginica
4 5.4 3.7 1.5 0.2 setosa
5 6.0 3.0 4.8 1.8 virginica
5 6.5 3.2 5.1 2.0 virginica
7 5.7 2.9 4.2 1.3 versicolor
使用可調用函數進行選擇
df = pd.DataFrame(np.random.randn(6,4), index=list('abcdef'), columns=list('ABCD'))
df
out:
A B C D
a 0.737161 -0.514738 -1.457052 0.353337
b 0.801916 0.266375 -0.968714 -0.087611
c -0.799433 -1.250238 -0.598625 1.259859
d -0.780325 1.910598 -0.522512 -0.680966
e -1.167703 -0.234484 0.243291 -1.931064
f -0.147435 0.145292 -0.256636 -0.110757
df.loc[lambda df: df.index > 'c']
out:
A B C D
d -0.780325 1.910598 -0.522512 -0.680966
e -1.167703 -0.234484 0.243291 -1.931064
f -0.147435 0.145292 -0.256636 -0.110757
df.loc[lambda df: df.A<0]
out:
A B C D
c -0.799433 -1.250238 -0.598625 1.259859
d -0.780325 1.910598 -0.522512 -0.680966
e -1.167703 -0.234484 0.243291 -1.931064
f -0.147435 0.145292 -0.256636 -0.110757
df.loc[lambda df: df.A<0, lambda df: ['A', 'B']]
out:
A B
c -0.799433 -1.250238
d -0.780325 1.910598
e -1.167703 -0.234484
f -0.147435 0.145292
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