p08.py

import quandl
import pandas as pd
import pickle
import matplotlib.pyplot as plt
from matplotlib import style

style.use('fivethirtyeight')
api_key = "Cxe41MDVyJhXQYGuVGx7"


def state_list():
    fiddy_states = pd.read_html('https://simple.wikipedia.org/wiki/List_of_U.S._states')
    return fiddy_states[0][0][1:]


def grab_initial_state_dat():
    states = state_list()
    main_df = pd.DataFrame()
    for abbv in states:
        query = ("FMAC/HPI_" + str(abbv))
        df = quandl.get(query, authtoken=api_key)
        df.columns = [str(abbv)]
        df[abbv] = (df[abbv] - df[abbv][0]) / df[abbv][0] * 100.0

        if main_df.empty:
            main_df = df
        else:
            main_df = main_df.join(df)
    # print(main_df.head())

    pickle_out = open('fiddy_states.pickle', 'wb')
    pickle.dump(main_df, pickle_out)
    pickle_out.close()


def HPI_benchmark():
    df = quandl.get("FMAC/HPI_USA", authtoken=api_key)
    df['Value'] = (df['Value'] - df['Value'][0]) / df['Value'][0] * 100.0
    return df


# grab_initial_state_dat()

fig = plt.figure()
ax1 = plt.subplot2grid((1, 1), (0, 0))

HPI_data = pd.read_pickle('fiddy_states.pickle')

HPI_data['TX1yr'] = HPI_data['TX'].resample("A").mean()
print(HPI_data[['TX', 'TX1yr']])

HPI_data.dropna(inplace=True)
print(HPI_data[['TX', 'TX1yr']])

HPI_data[['TX', 'TX1yr']].plot(ax=ax1)

plt.legend()
plt.show()