How to use a dict to subset a DataFrame?

Question

Say, I have given a DataFrame with most of the columns being categorical data. ``` > 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 ``` And I would like to subset this data by a dict of key-value pairs for those categorical variables. ``` tmp = {'risk':'no', 'smoking':'yes', 'sex':'female'} ``` Hence, I would like to have the following subset. ``` data[ (data.risk == 'no') & (data.smoking == 'yes') & (data.sex == 'female')] ``` What I want to do is: ``` data[tmp] ``` What is the most python / pandas way of doing this? Minimal example: ``` 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…

Accepted Answer

I would use .query() method for this task:

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

data.query(qry)
```

output:

```
   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
```

Query string:

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

How to use a dict to subset a DataFrame?

Solution

Problem