ch4-categoricalData.py

import pandas as pd
df = pd.DataFrame([
            ['green', 'M', 10.1, 'class1'], 
            ['red', 'L', 13.5, 'class2'], 
            ['blue', 'XL', 15.3, 'class1']])

df.columns = ['color', 'size', 'price', 'classlabel']
df

# convert ordinal data: sizes to integers
size_mapping = {
           'XL': 3,
           'L': 2,
           'M': 1}

df['size'] = df['size'].map(size_mapping)
df
inv_size_mapping = {v: k for k, v in size_mapping.items()}
df['size'].map(inv_size_mapping)

# class labels
import numpy as np
class_mapping = {label:idx for idx,label in enumerate(np.unique(df['classlabel']))}
class_mapping
df['classlabel'] = df['classlabel'].map(class_mapping)
df
inv_class_mapping = {v: k for k, v in class_mapping.items()}
df['classlabel'] = df['classlabel'].map(inv_class_mapping)
df


# alternative way for class labels
from sklearn.preprocessing import LabelEncoder
class_le = LabelEncoder()
y = class_le.fit_transform(df['classlabel'].values)
y
class_le.inverse_transform(y)



X = df[['color', 'size', 'price']].values
# Convert nominal data: color to numerical
color_le = LabelEncoder()
X[:, 0] = color_le.fit_transform(X[:, 0])
X


# One hot encoding to derive extra features from color so color integer values
# don't cause algorithm issues
from sklearn.preprocessing import OneHotEncoder
ohe = OneHotEncoder(categorical_features=[0])
ohe.fit_transform(X).toarray()

# Alternative: One hot encoding using pandas
pd.get_dummies(df[['price', 'color', 'size']])