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)