In [1]:

    

import pandas as pd
from sklearn.preprocessing import LabelEncoder


    

In [2]:

    

df = pd.DataFrame({'col_1': [1, 0, 1, None], 
                   '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_1
      
col_2
      
col_3
      
col_4
    
  
  

    

      
0
      
1.0
      
1.2
      
1
      
a
    
    

      
1
      
0.0
      
3.1
      
2
      
b
    
    

      
2
      
1.0
      
4.4
      
3
      
c
    
    

      
3
      
NaN
      
5.5
      
4
      
d
    
  

In [4]:

    

df.info()


    

    




<class 'pandas.core.frame.DataFrame'>
RangeIndex: 4 entries, 0 to 3
Data columns (total 4 columns):
col_1    3 non-null float64
col_2    4 non-null float64
col_3    4 non-null int64
col_4    4 non-null object
dtypes: float64(2), int64(1), object(1)
memory usage: 208.0+ bytes

In [5]:

    

bool_cols = [col for col in df if len(df[[col]].dropna()[col].unique()) == 2]


    

In [6]:

    

for col in bool_cols:
    label = LabelEncoder()
    label.fit(list(df[col].values.astype("str")))
    df[col] = label.transform(list(df[col].values.astype("str")))


    

In [7]:

    

df.info()


    

    




<class 'pandas.core.frame.DataFrame'>
RangeIndex: 4 entries, 0 to 3
Data columns (total 4 columns):
col_1    4 non-null int64
col_2    4 non-null float64
col_3    4 non-null int64
col_4    4 non-null object
dtypes: float64(1), int64(2), object(1)
memory usage: 208.0+ bytes

In [8]:

    

df


    

    
Out[8]:






  

    

      

      
col_1
      
col_2
      
col_3
      
col_4
    
  
  

    

      
0
      
1
      
1.2
      
1
      
a
    
    

      
1
      
0
      
3.1
      
2
      
b
    
    

      
2
      
1
      
4.4
      
3
      
c
    
    

      
3
      
2
      
5.5
      
4
      
d
    
  

In [ ]: