How do I convert string values or items from an object column into binary with pandas?

Question

I’m trying to convert a column into a data set where there is 'negative' and 'positive' for binaries or numeric items like 0 and 1, but I don’t know if I’m doing it right with the Pandas library.

pd.cut(data.Class, bins=['negative','positive'],labels=['0','1'])

but the following error appears:

ValueError: could not convert string to float: 'negative' 

I have tried by Boolean Mask as follows:

mask = data['Class'] == 'negative'

data.loc[mask, 'Class'] = 0
data.loc[~mask, 'Class'] = 1

But then he turns them all into one of the guys!

A sample of the database I’m using is:

          v8      v9      Class  
0    0.00000  0.1224   negative  
1    0.00000  0.0000   negative  
2    0.00000  0.0000   negative  
3    0.00000  0.0000   negative  
4    0.00000  0.0561   negative  
..       ...     ...        ...  
166  0.66150  0.0000   negative  
167  1.06155  0.0000   negative  
168  1.62855  0.0000   negative  
169  1.71045  0.0000   positive  
170  1.54980  0.0000   positive

I would like the result to be as follows:

          v8      v9   Class  
0    0.00000  0.1224   0  
1    0.00000  0.0000   0  
2    0.00000  0.0000   0  
3    0.00000  0.0000   0  
4    0.00000  0.0561   0  
..       ...     ...    ...  
166  0.66150  0.0000   0  
167  1.06155  0.0000   0  
168  1.62855  0.0000   0  
169  1.71045  0.0000   1  
170  1.54980  0.0000   1  

Answer 1

A solution using map:

data['Class'].map({'positive':1,'negative':0})

Note that this solution serves several categories. In the specific case of binary variables, there is the get_dummies. In your case, the command would look like this:

pd.get_dummies(data['Class'])

The output will be two columns. Each one using one of the classes as reference. Choose the one you prefer.