how to create dummies for certain columns by pandas get_dummies() method?

Question

df = pd.DataFrame({\'A\': [\'x\', \'y\', \'x\'], \'B\': [\'z\', \'u\', \'z\'],
                  \'C\': [\'1\', \'2\', \'3\'],
                  \'D\':[\'j\', \'l\',         


        

Answer 1

Adding to the above perfect answers, in case you have a big dataset with lots of attributes, if you don't want to specify by hand all of the dummies you want, you can do set differences:

len(df.columns) = 50
non_dummy_cols = ['A','B','C'] 
# Takes all 47 other columns
dummy_cols = list(set(df.columns) - set(non_dummy_cols))
df = pd.get_dummies(df, columns=dummy_cols)

Answer 2

Just select the two columns you want to .get_dummies() for - column names indicate source column and variable label represented as binary variable, and pd.concat() the original columns you want unchanged:

pd.concat([pd.get_dummies(df[['A', 'D']]), df[['B', 'C']]], axis=1)

   A_x  A_y  D_j  D_l  B  C
0  1.0  0.0  1.0  0.0  z  1
1  0.0  1.0  0.0  1.0  u  2
2  1.0  0.0  1.0  0.0  z  3

Answer 3

It can be done without concatenation, using get_dummies() with required parameters

In [294]: pd.get_dummies(df, prefix=['A', 'D'], columns=['A', 'D'])
Out[294]: 
   B  C  A_x  A_y  D_j  D_l
0  z  1  1.0  0.0  1.0  0.0
1  u  2  0.0  1.0  0.0  1.0
2  z  3  1.0  0.0  1.0  0.0