Machine Learning for Beginners in Python: How to Preprocess Categorical Features

Preprocessing Categorical Features

Often, machine learning methods (e.g. logistic regression, SVM with a linear kernel, etc) will require that categorical variables be converted into dummy variables (also called OneHot encoding). For example, a single feature Fruit would be converted into three features, ApplesOranges, and Bananas, one for each category in the categorical feature.

There are common ways to preprocess categorical features: using pandas or scikit-learn.



from sklearn import preprocessing
from sklearn.pipeline import Pipeline
import pandas as pd

Create Data

raw_data = {'first_name': ['Jason', 'Molly', 'Tina', 'Jake', 'Amy'], 
        'last_name': ['Miller', 'Jacobson', 'Ali', 'Milner', 'Cooze'], 
        'age': [42, 52, 36, 24, 73], 
        'city': ['San Francisco', 'Baltimore', 'Miami', 'Douglas', 'Boston']}
df = pd.DataFrame(raw_data, columns = ['first_name', 'last_name', 'age', 'city'])
first_name last_name age city
0 Jason Miller 42 San Francisco
1 Molly Jacobson 52 Baltimore
2 Tina Ali 36 Miami
3 Jake Milner 24 Douglas
4 Amy Cooze 73 Boston

Convert Nominal Categorical Feature Into Dummy Variables Using Pandas

Baltimore Boston Douglas Miami San Francisco
0 0.0 0.0 0.0 0.0 1.0
1 1.0 0.0 0.0 0.0 0.0
2 0.0 0.0 0.0 1.0 0.0
3 0.0 0.0 1.0 0.0 0.0
4 0.0 1.0 0.0 0.0 0.0

Convert Nominal Categorical Data Into Dummy (OneHot) Features Using Scikit

integerized_data = preprocessing.LabelEncoder().fit_transform(df["city"])

array([4, 0, 3, 2, 1])

array([[ 0.,  0.,  0.,  0.,  1.],
       [ 1.,  0.,  0.,  0.,  0.],
       [ 0.,  0.,  0.,  1.,  0.],
       [ 0.,  0.,  1.,  0.,  0.],
       [ 0.,  1.,  0.,  0.,  0.]])

Note that the output of pd.get_dummies() and the scikit methods produces the same output matrix.


Python Example for Beginners

Two Machine Learning Fields

There are two sides to machine learning:

  • Practical Machine Learning:This is about querying databases, cleaning data, writing scripts to transform data and gluing algorithm and libraries together and writing custom code to squeeze reliable answers from data to satisfy difficult and ill defined questions. It’s the mess of reality.
  • Theoretical Machine Learning: This is about math and abstraction and idealized scenarios and limits and beauty and informing what is possible. It is a whole lot neater and cleaner and removed from the mess of reality.

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