OneHotEncoding.py

from numpy import array
from numpy import argmax
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import OneHotEncoder
# 200 OK
# 201 CREATED
# 204 NO CONTENT
# 304 NOT MODIFIED
# 400 BAD REQUEST
# 401 UNAUTHORIZED
# 403 FORBIDDEN
# 404 NOT FOUND
# 409 CONFLICT
data = ['200', '201', '204', '304', '400', '401', '403', '404', '409', '500']
values = array(data)
print(values)
# integer encode
label_encoder = LabelEncoder()
integer_encoded = label_encoder.fit_transform(values)
print(integer_encoded)
# binary encode
onehot_encoder = OneHotEncoder(sparse=False)
integer_encoded = integer_encoded.reshape(len(integer_encoded), 1)
onehot_encoded = onehot_encoder.fit_transform(integer_encoded)
print(onehot_encoded)

#http://machinelearningmastery.com/how-to-one-hot-encode-sequence-data-in-python/