python pandas replacing strings in dataframe with numbers

Question

Is there any way to use the mapping function or something better to replace values in an entire dataframe?

I only know how to perform the mapping on series.

I would like to replace the strings in the 'tesst' and 'set' column with a number for example set = 1, test =2

Here is a example of my dataset: (Original dataset is very large)

ds_r   respondent  brand engine  country  aware  aware_2  aware_3  age tesst   set 0          a  volvo      p      swe      1        0        1   23   set   set 1          b  volvo   None      swe      0        0        1   45   set   set 2          c    bmw      p       us      0        0        1   56  test  test 3          d    bmw      p       us      0        1        1   43  test  test 4          e    bmw      d  germany      1        0        1   34   set   set 5          f   audi      d  germany      1        0        1   59   set   set 6          g  volvo      d      swe      1        0        0   65  test   set 7          h   audi      d      swe      1        0        0   78  test   set 8          i  volvo      d       us      1        1        1   32   set   set 

Final result should be

 ds_r   respondent  brand engine  country  aware  aware_2  aware_3  age  tesst  set 0          a  volvo      p      swe      1        0        1   23      1    1 1          b  volvo   None      swe      0        0        1   45      1    1 2          c    bmw      p       us      0        0        1   56      2    2 3          d    bmw      p       us      0        1        1   43      2    2 4          e    bmw      d  germany      1        0        1   34      1    1 5          f   audi      d  germany      1        0        1   59      1    1 6          g  volvo      d      swe      1        0        0   65      2    1 7          h   audi      d      swe      1        0        0   78      2    1 8          i  volvo      d       us      1        1        1   32      1    1 

Answer 1

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.