Data-Analysis-Jupyter/sentdex_data_analysis/pandas_indexing.py

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

style.use("seaborn")

api_key = "rFsSehe51RLzREtYhLfo"


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


def initial_state_data():
    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)

    pickle_out = open("fifty_states_pct.pickle", "wb")
    pickle.dump(main_df, pickle_out)
    pickle_out.close()


# initial_state_data()


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


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

pickle_in = open("fifty_states_pct.pickle", "rb")
HPI_data = pickle.load(pickle_in)
benchmark = HPI_Benchmark()
# HPI_data = HPI_data.pct_change()

HPI_data.plot(ax=ax1)
benchmark["United States"].plot(ax=ax1, color="k", linewidth=10)
plt.legend().remove()

HPI_complete_data = HPI_data
HPI_complete_data["United States"] = benchmark["United States"]
# print(HPI_complete_data.head())
HPI_State_Correlation = HPI_data.corr()

HPI_complete_correlation = HPI_complete_data.corr()
HPI_US_correlation = HPI_complete_correlation["United States"]
HPI_US_correlation_sorted = HPI_US_correlation.sort_values(ascending=True)
print(
    HPI_US_correlation_sorted[
        HPI_US_correlation_sorted == HPI_US_correlation_sorted[-2]
    ].index
)
plt.show()
# print(HPI_data[['IL','WI']].corr())