
        
        
            
                
                
                    import pandas as pd

df = pd.DataFrame({'name':['alex','bob','cathy','doge'], 'class':['a','b','c','a']})
df
                
            
               name  class
0  alex    a
1  bob     b
2  cathy   c
3  doge    a

Solution

To create a new column class_int that encodes the labels with numeric integers:


        
        
            
                
                
                    df['class_int'] = pd.Categorical(df['class']).codes
df
                
            
               name  class  class_int
0  alex    a        0
1  bob     b        1
2  cathy   c        2
3  doge    a        0

Explanation

Here, we are first converting the class column to Categorical type:


        
        
            
                
                
                    pd.Categorical(df['class'])
                
            
            ['a', 'b', 'c', 'a']
Categories (3, object): ['a', 'b', 'c']

Under the hood, pd.Categorical assigns numeric values starting from 0 to each unique label. These encoded numeric values can be accessed using the codes property:


        
        
            
                
                
                    pd.Categorical(df['class']).codes
                
            
            array([0, 1, 2, 0], dtype=int8)

Supplementary information

Converting numeric value back to string label

To convert numeric values back to string label, use the categories property:


        
        
            
                
                
                    int_label = 1
pd.Categorical(df['class']).categories[int_label]
                
            
            'b'

Here, categories returns an Index holding unique string categories:


        
        
            
                
                
                    pd.Categorical(df['class']).categories
                
            
            Index(['a', 'b', 'c'], dtype='object')

Published by Isshin Inada

Edited by 0 others

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