Data-Analysis-Jupyter/sentdex_data_analysis/pandas_additionalEconomic.py

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

style.use("seaborn")

quandl.ApiConfig.api_key = "rFsSehe51RLzREtYhLfo"


def mortgage_30yr():
    df = quandl.get("FMAC/MORTG", trim_start="1975-01-01")
    df["Value"] = (df["Value"] - df["Value"][0]) / df["Value"][0] * 100
    df = df.resample("M").mean()
    df.rename(columns={"Value": "M30"}, inplace=True)
    df = df["M30"]
    return df


def sp500_data():
    df = quandl.get("YAHOO/INDEX_GSPC", trim_start="1975-01-01")
    df["Adjusted Close"] = (
        (df["Adjusted Close"] - df["Adjusted Close"][0])
        / df["Adjusted Close"][0]
        * 100.0
    )
    df = df.resample("M").mean()
    df.rename(columns={"Adjusted Close": "sp500"}, inplace=True)
    df = df["sp500"]
    return df


def gdp_data():
    df = quandl.get("BCB/4385", trim_start="1975-01-01")
    df["Value"] = (df["Value"] - df["Value"][0]) / df["Value"][0] * 100.0
    df = df.resample("M").mean()
    df.rename(columns={"Value": "GDP"}, inplace=True)
    df = df["GDP"]  # DataFrame to Series
    return df


def us_unemployment():
    df = quandl.get("ECPI/JOB_G", trim_start="1975-01-01")
    df["Unemployment Rate"] = (
        (df["Unemployment Rate"] - df["Unemployment Rate"][0])
        / df["Unemployment Rate"][0]
        * 100.0
    )
    df = df.resample("1D").mean()
    df = df.resample("M").mean()
    return df


# m30 = mortgage_30yr() # Series
# sp500 = sp500_data() # Series
# gdp = gdp_data() # Series
# unemployment = us_unemployment() # DataFrame
# HPI = HPI_data.join([m30, unemployment, gdp, sp500])

ax1 = plt.subplot(2, 1, 1)
ax2 = plt.subplot(2, 1, 2, sharex=ax1)

# initial_state_data()

pickle_in = open("fifty_states_pct.pickle", "rb")
HPI_data = pickle.load(pickle_in)

# HPI_Benchmark()

pickle_in = open("us_pct.pickle", "rb")
benchmark = pickle.load(pickle_in)

pickle_in = open("HPI_complete.pickle", "rb")
HPI = pickle.load(pickle_in)
HPI.dropna(inplace=True)
print(HPI.head())


state_HPI_M30 = HPI_data.join(HPI["M30"])


# print(state_HPI_M30.corr().describe()['M30'])