Dealing with Categorical Variables

Feature Engineering for Machine Learning in Python

Robert O'Callaghan

Director of Data Science, Ordergroove

Encoding categorical features

One-hot encoding
Dummy encoding

One-hot encoding

pd.get_dummies(df, columns=['Country'], 
               prefix='C')

    C_France    C_India    C_UK    C_USA
0          0          1       0        0
1          0          0       0        1
2          0          0       1        0
3          0          0       1        0
4          1          0       0        0

Dummy encoding

pd.get_dummies(df, columns=['Country'],
               drop_first=True, prefix='C')

     C_India    C_UK    C_USA
0          1       0        0
1          0       0        1
2          0       1        0
3          0       1        0
4          0       0        0

One-hot vs. dummies

One-hot encoding: Explainable features
Dummy encoding: Necessary information without duplication

Index	Sex
0	Male
1	Female
2	Male

Index	Male	Female
0	1	0
1	0	1
2	1	0

Index	Male
0	1
1	0
2	1

Limiting your columns

counts = df['Country'].value_counts()
print(counts)

'USA'      8
'UK'       6
'India'    2
'France'   1
Name: Country, dtype: object

Limiting your columns

mask = df['Country'].isin(counts[counts < 5].index)

df['Country'][mask] = 'Other'

print(pd.value_counts(colors))

'USA'      8
'UK'       6
'Other'    3
Name: Country, dtype: object

Now you deal with categorical variables

Feature Engineering for Machine Learning in Python