import numpy as np
import pandas as pd

from sklearn import preprocessing
X = pd.read_csv('H:/My Documents/Data_Science/datasets/titanic_train.csv')
X.head(3)
X = X.select_dtypes(include=[object])
X.head(3)
X.shape

X.columns

le = preprocessing.LabelEncoder()

X['cabin'] = X['cabin'].factorize()[0]
X['embarked'] = X['embarked'].factorize()[0]

2/3. FIT AND TRANSFORM

use df.apply() to apply le.fit_transform to all columns

X_2 = X.apply(le.fit_transform)
X_2.head()

TODO: create a OneHotEncoder object, and fit it to all of X

1. INSTANTIATE

enc = preprocessing.OneHotEncoder()

2. FIT

enc.fit(X_2)

3. Transform

onehotlabels = enc.transform(X_2).toarray()
onehotlabels.shape

as you can see, you've the same number of rows 891

but now you've so many more columns due to how we changed all the categorical data into numerical data

onehotlabels

type(onehotlabels)