Title: Preprocessing Categorical Features
Slug: preprocessing_categorical_features
Summary: Preprocessing Categorical Features
Date: 2016-11-01 12:00
Category: Machine Learning
Tags: Preprocessing Structured Data
Authors: Chris Albon
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, Apples, Oranges, and Bananas, one for each category in the categorical feature.
There are common ways to preprocess categorical features: using pandas or scikit-learn.
In [1]:
from sklearn import preprocessing
from sklearn.pipeline import Pipeline
import pandas as pd
In [2]:
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'])
df
Out[2]:
In [3]:
# Create dummy variables for every unique category in df.city
pd.get_dummies(df["city"])
Out[3]:
In [4]:
# Convert strings categorical names to integers
integerized_data = preprocessing.LabelEncoder().fit_transform(df["city"])
# View data
integerized_data
Out[4]:
In [5]:
# Convert integer categorical representations to OneHot encodings
preprocessing.OneHotEncoder().fit_transform(integerized_data.reshape(-1,1)).toarray()
Out[5]:
Note that the output of pd.get_dummies() and the scikit methods produces the same output matrix.