SKlear中的LabelEncoding和OneHotEncoding

# In this exercise we'll load the titanic data (from Project 0)
# And then perform one-hot encoding on the feature names


import numpy as np
import pandas as pd


# Load the dataset
X = pd.read_csv('titanic_data.csv')
# Limit to categorical data
X = X.select_dtypes(include=[object])


from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import OneHotEncoder


# TODO: Create a LabelEncoder object, which will turn all labels present in
#       in each feature to numbers. For example, the labels ['cat', 'dog', 'fish']
#       might be transformed into [0, 1, 2]


le = LabelEncoder()


# TODO: For each feature in X, apply the LabelEncoder's fit_transform
#       function, which will first learn the labels for the feature (fit)
#       and then change the labels to numbers (transform). 


for feature in X:
    le.fit(X[feature])
    le.classes_
    X[feature] = le.transform(X[feature])


# TODO: Create a OneHotEncoder object, which will create a feature for each
#       label present in the data. For example, for a feature 'animal' that had
#       the labels ['cat','dog','fish'], the new features (instead of 'animal') 
#       could be ['animal_cat', 'animal_dog', 'animal_fish']


ohe = OneHotEncoder()


# TODO: Apply the OneHotEncoder's fit_transform function to all of X, which will
#       first learn of all the (now numerical) labels in the data (fit), and then
#       change the data to one-hot encoded entries (transform).
ohe.fit(X)
onehotlabels = ohe.transform(X)