In [1]:

    

import pandas as pd


    

In [2]:

    

df = pd.DataFrame({'sex': ['M', 'F', 'M', 'F'], 
                   'col_2': [1.2, 3.1, 4.4, 5.5], 
                   'col_3': [1, 2, 3, 4], 
                   'col_4': ['a', 'b', 'c', 'd']})


    

In [3]:

    

df


    

    
Out[3]:






  

    

      

      
col_2
      
col_3
      
col_4
      
sex
    
  
  

    

      
0
      
1.2
      
1
      
a
      
M
    
    

      
1
      
3.1
      
2
      
b
      
F
    
    

      
2
      
4.4
      
3
      
c
      
M
    
    

      
3
      
5.5
      
4
      
d
      
F
    
  

In [4]:

    

categorical_variables = ['sex']

for variable in categorical_variables:
    # Fill missing data with the word "Missing"
    df[variable].fillna("Missing", inplace=True)
    # Create array of dummies
    dummies = pd.get_dummies(df[variable], prefix=variable)
    # Update dataframe to include dummies and drop the main variable
    df = pd.concat([df, dummies], axis=1)
    df.drop([variable], axis=1, inplace=True)


    

In [5]:

    

df


    

    
Out[5]:






  

    

      

      
col_2
      
col_3
      
col_4
      
sex_F
      
sex_M
    
  
  

    

      
0
      
1.2
      
1
      
a
      
0.0
      
1.0
    
    

      
1
      
3.1
      
2
      
b
      
1.0
      
0.0
    
    

      
2
      
4.4
      
3
      
c
      
0.0
      
1.0
    
    

      
3
      
5.5
      
4
      
d
      
1.0
      
0.0
    
  

In [ ]: