Encoding Categorical Data

Ordinal Encoding 

# define data
data = asarray([['red'], ['green'], ['blue']])
print(data)
# define ordinal encoding
encoder = OrdinalEncoder()
# transform data
result = encoder.fit_transform(data)
print(result)

[['red']
 ['green']
 ['blue']]
[[2.]
 [1.]
 [0.]]

One-Hot Encoding 

from numpy import asarray
from sklearn.preprocessing import OneHotEncoder
# define data
data = asarray([['red'], ['green'], ['blue']])
print(data)
# define one hot encoding
encoder = OneHotEncoder(sparse=False)
# transform data
onehot = encoder.fit_transform(data)
print(onehot)

[['red']
 ['green']
 ['blue']]
[[0. 0. 1.]
 [0. 1. 0.]
 [1. 0. 0.]]

Dummy Variable Encoding 

from numpy import asarray
from sklearn.preprocessing import OneHotEncoder
# define data
data = asarray([['red'], ['green'], ['blue']])
print(data)
# define one hot encoding
encoder = OneHotEncoder(drop='first', sparse=False)
# transform data
onehot = encoder.fit_transform(data)
print(onehot)

[['red']
 ['green']
 ['blue']]
[[0. 1.]
 [1. 0.]
 [0. 0.]]

Categorical Encoding example 

from pandas import read_csv
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import OrdinalEncoder
# define the location of the dataset
url = "https://raw.githubusercontent.com/jbrownlee/Datasets/master/breast-cancer.csv"
# load the dataset
dataset = read_csv(url, header=None)
# retrieve the array of data
data = dataset.values
# separate into input and output columns
X = data[:, :-1].astype(str)
y = data[:, -1].astype(str)
# summarize
print('Input', X.shape)
print('Output', y.shape)

Input (286, 9)
Output (286,)

dataset.head()
0 1 2 3 4 5 6 7 8 9
0 '40-49' 'premeno' '15-19' '0-2' 'yes' '3' 'right' 'left_up' 'no' 'recurrence-events'
1 '50-59' 'ge40' '15-19' '0-2' 'no' '1' 'right' 'central' 'no' 'no-recurrence-events'
2 '50-59' 'ge40' '35-39' '0-2' 'no' '2' 'left' 'left_low' 'no' 'recurrence-events'
3 '40-49' 'premeno' '35-39' '0-2' 'yes' '3' 'right' 'left_low' 'yes' 'no-recurrence-events'
4 '40-49' 'premeno' '30-34' '3-5' 'yes' '2' 'left' 'right_up' 'no' 'recurrence-events' 
type(dataset), type(data), type(X), type(y)

(pandas.core.frame.DataFrame, numpy.ndarray, numpy.ndarray, numpy.ndarray)
y.shape, X.shape, data.shape, dataset.shape

((286,), (286, 9), (286, 10), (286, 10))

OrdinalEncoder Transform 

ordinal_encoder = OrdinalEncoder()
X = ordinal_encoder.fit_transform(X)
# ordinal encode target variable
label_encoder = LabelEncoder()
y = label_encoder.fit_transform(y)
# summarize the transformed data
print('Input', X.shape)
print(X[:5, :])
print('Output', y.shape)
print(y[:5])

Input (286, 9)
[[2. 2. 2. 0. 1. 2. 1. 2. 0.]
 [3. 0. 2. 0. 0. 0. 1. 0. 0.]
 [3. 0. 6. 0. 0. 1. 0. 1. 0.]
 [2. 2. 6. 0. 1. 2. 1. 1. 1.]
 [2. 2. 5. 4. 1. 1. 0. 4. 0.]]
Output (286,)
[1 0 1 0 1]

from numpy import mean
from numpy import std
from pandas import read_csv
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import OrdinalEncoder
from sklearn.metrics import accuracy_score

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=1)
# ordinal encode input variables
ordinal_encoder = OrdinalEncoder()
ordinal_encoder.fit(X_train)
X_train = ordinal_encoder.transform(X_train)
X_test = ordinal_encoder.transform(X_test)

# ordinal encode target variable
label_encoder = LabelEncoder()
label_encoder.fit(y_train)
y_train = label_encoder.transform(y_train)
y_test = label_encoder.transform(y_test)

# define the model
model = LogisticRegression()
# fit on the training set
model.fit(X_train, y_train)
# predict on test set
yhat = model.predict(X_test)
# evaluate predictions
accuracy = accuracy_score(y_test, yhat)
print('Accuracy: %.2f' % (accuracy*100))

Accuracy: 75.79

OneHotEncoder Transform 

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=1)
# one-hot encode input variables
onehot_encoder = OneHotEncoder()
onehot_encoder.fit(X_train)
X_train = onehot_encoder.transform(X_train)
X_test = onehot_encoder.transform(X_test)
# ordinal encode target variable
label_encoder = LabelEncoder()
label_encoder.fit(y_train)
y_train = label_encoder.transform(y_train)
y_test = label_encoder.transform(y_test)
# define the model
model = LogisticRegression()
# fit on the training set
model.fit(X_train, y_train)
# predict on test set
yhat = model.predict(X_test)
# evaluate predictions
accuracy = accuracy_score(y_test, yhat)
print('Accuracy: %.2f' % (accuracy*100))

Accuracy: 70.53