What about DataFrame.replace?

In [9]: mapping = {'set': 1, 'test': 2}

In [10]: df.replace({'set': mapping, 'tesst': mapping})
Out[10]: 
   Unnamed: 0 respondent  brand engine  country  aware  aware_2  aware_3  age  \
0           0          a  volvo      p      swe      1        0        1   23   
1           1          b  volvo   None      swe      0        0        1   45   
2           2          c    bmw      p       us      0        0        1   56   
3           3          d    bmw      p       us      0        1        1   43   
4           4          e    bmw      d  germany      1        0        1   34   
5           5          f   audi      d  germany      1        0        1   59   
6           6          g  volvo      d      swe      1        0        0   65   
7           7          h   audi      d      swe      1        0        0   78   
8           8          i  volvo      d       us      1        1        1   32   

  tesst set  
0     2   1  
1     1   2  
2     2   1  
3     1   2  
4     2   1  
5     1   2  
6     2   1  
7     1   2  
8     2   1  

As @Jeff pointed out in the comments, in pandas versions < 0.11.1, manually tack .convert_objects() onto the end to properly convert tesst and set to int64 columns, in case that matters in subsequent operations.

You could also try to use a "map" function.

map_dict = {1: "pos"}
data["test"] = data["A"].map(map_dict)
data
-----------------------
|   | A  | B   | test |
-----------------------
| 0 | 1  | abc | pos  |
| 1 | 1  | def | pos  |
| 2 | 1  | ghi | pos  |
| 3 | -1 | jkl | NaN  |
| 4 | 1  | mno | pos  |
| 5 | 1  | pqr | pos  |
-----------------------

Read more: http://pandas.pydata.org/pandas-docs/version/0.17.1/generated/pandas.Series.map.html

This will work:

new_vals = {'Electronic': 1, 'Computers' : 2, 'Mobile Phone' : 3, 'Router' : 4 , 'Food' : 5}
df = df.replace({'categories': new_vals})