.loc[]

.loc主要是基于標(biāo)簽的，但也可以與布爾數(shù)組一起使用。

可以輸入如下幾種類型：

• 單個標(biāo)簽，例如5或'a'；
• 列表或標(biāo)簽數(shù)組。['a', 'b', 'c']
• 帶標(biāo)簽的切片對象'a':'f'；
• 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).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(biāo)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(biāo)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(biāo)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(shù)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，如果索引中存在開始和停止標(biāo)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，但索引已排序，并且可以與開始和停止標(biāo)簽進(jìn)行比較，那么通過選擇在兩者之間排名的標(biāo)簽，切片仍將按預(yù)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(diào)用函數(shù)進(jìn)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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