python pandas replacing strings in dataframe with numbers

Question

Is there anyway 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

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.

Answer 2

When no of features are not much :

mymap = {'a':1, 'b':2, 'c':3, 'd':4, 'e':5}
df.applymap(lambda s: mymap.get(s) if s in mymap else s)

When it's not possible manually :

temp_df2 = pd.DataFrame({'data': data.data.unique(), 'data_new':range(len(data.data.unique()))})# create a temporary dataframe 
data = data.merge(temp_df2, on='data', how='left')# Now merge it by assigning different values to different strings.

Answer 3

The simplest way to replace any value in the dataframe:

df=df.replace(to_replace="set",value="1")
df=df.replace(to_replace="test",value="2")

Hope this will help.