from sklearn.feature_extraction import DictVectorizer

X_dict = [{'interest': 'tech', 'occupation': 'professional'},

{'interest': 'fashion', 'occupation': 'student'},

{'interest': 'fashion', 'occupation': 'professional'},

{'interest': 'sports', 'occupation': 'student'},

{'interest': 'tech', 'occupation': 'student'},

{'interest': 'tech', 'occupation': 'retired'},

{'interest': 'sports', 'occupation': 'professional'}]

dict_one_hot_encoder = DictVectorizer(sparse=False)

X_encoded = dict_one_hot_encoder.fit_transform(X_dict)

print(X_encoded)

print(dict_one_hot_encoder.vocabulary_)

new_dict = [{'interest': 'sports', 'occupation': 'retired'}]

new_encoded = dict_one_hot_encoder.transform(new_dict)

print(new_encoded)

print(dict_one_hot_encoder.inverse_transform(new_encoded))

# new category not encountered before

new_dict = [{'interest': 'unknown_interest', 'occupation': 'retired'},

{'interest': 'tech', 'occupation': 'unseen_occupation'}]

new_encoded = dict_one_hot_encoder.transform(new_dict)

print(new_encoded)

import numpy as np

X_str = np.array([['tech', 'professional'],

['fashion', 'student'],

['fashion', 'professional'],

['sports', 'student'],

['tech', 'student'],

['tech', 'retired'],

['sports', 'professional']])

from sklearn.preprocessing import LabelEncoder, OneHotEncoder

label_encoder = LabelEncoder()

X_int = label_encoder.fit_transform(X_str.ravel()).reshape(*X_str.shape)

print(X_int)

one_hot_encoder = OneHotEncoder()

X_encoded = one_hot_encoder.fit_transform(X_int).toarray()

print(X_encoded)

# new category not encountered before

new_str = np.array([['unknown_interest', 'retired'],

['tech', 'unseen_occupation'],

['unknown_interest', 'unseen_occupation']])

def string_to_dict(columns, data_str):

data_dict = []

for sample_str in data_str:

data_dict.append({column: value for column, value in zip(columns, sample_str)})

return data_dict

columns = ['interest', 'occupation']

new_encoded = dict_one_hot_encoder.transform(string_to_dict(columns, new_str))

print(new_encoded)