如何使用dict对DataFrame进行子集化？

Question

说,我已经给出了一个DataFrame,其中大多数列都是分类数据.

> data.head()
  age risk     sex smoking
0  28   no    male      no
1  58   no  female      no
2  27   no    male     yes
3  26   no    male      no
4  29  yes  female     yes

我想通过这些分类变量的键值对字典对这些数据进行子集化.

tmp = {'risk':'no', 'smoking':'yes', 'sex':'female'}

因此,我想拥有以下子集.

data[ (data.risk == 'no') & (data.smoking == 'yes') & (data.sex == 'female')]

我想做的是:

data[tmp]

这样做最蟒蛇/熊猫的方法是什么？

---

最小的例子:

import numpy as np
import pandas as pd
from pandas import Series, DataFrame

x = Series(random.randint(0,2,50), dtype='category')
x.cat.categories = ['no', 'yes']

y = Series(random.randint(0,2,50), dtype='category')
y.cat.categories = ['no', 'yes']

z = Series(random.randint(0,2,50), dtype='category')
z.cat.categories = ['male', 'female']

a = Series(random.randint(20,60,50), dtype='category')

data = DataFrame({'risk':x, 'smoking':y, 'sex':z, 'age':a})

tmp = {'risk':'no', 'smoking':'yes', 'sex':'female'}

Answer 1

我会使用.query()方法来完成这个任务：

qry = ' and '.join(["{} == '{}'".format(k,v) for k,v in tmp.items()])    

data.query(qry)

输出：

   age risk     sex smoking
7   24   no  female     yes
22  43   no  female     yes
23  42   no  female     yes
25  24   no  female     yes
32  29   no  female     yes
40  34   no  female     yes
43  35   no  female     yes

请求参数：

print(qry)
"sex == 'female' and risk == 'no' and smoking == 'yes'"