# Render our plots inline
%matplotlib inline

import pandas as pd
import matplotlib.pyplot as plt
import numpy as np

pd.set_option('display.mpl_style', 'default') # Make the graphs a bit prettier
plt.rcParams['figure.figsize'] = (15, 5)




#get our data--temporary home
!wget http://www.columbia.edu/~mj340/ml-100k.tar.gz

!wget http://www.columbia.edu/~mj340/HMXPC13_DI_v2_5-14-14.csv.gz

!gunzip HMXPC13_DI_v2_5-14-14.csv.gz

!tar -zxvf ml-100k.tar.gz

#check contents of directory!



CPI={"2010": 218.056, "2011": 224.939, "2012": 229.594, "2013": 232.957} #http://www.bls.gov/cpi/home.htm

















    films=pd.read_csv('./ml-100k/u.item', sep="|", names=["movie id", "movie_title", "release_date", "video_release_date", "IMDb_URL", "unknown", "Action","Adventure", "Animation", "Children's", "Comedy", "Crime", "Documentary", "Drama", "Fantasy", "Film-Noir", "Horror", "Musical", "Mystery", "Romance", "Sci-Fi", "Thriller", "War", "Western"])

users=pd.read_csv('./ml-100k/u.user', sep="|", names=["user_id", "age", "gender","occupation","zip_code"], index_col="user_id")