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pandas_7.py
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pandas_7.py
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import quandl
import pandas as pd
import pickle
import matplotlib.pyplot as plt
from matplotlib import style
style.use('fivethirtyeight')
api_key = open('poorly_delimited.txt', 'r').read()
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_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
print(df.head())
if main_df.empty:
main_df = df
else:
main_df = main_df.join(df)
pickle_out = open('fiddy_states3.pickle', 'wb')
pickle.dump(main_df, pickle_out)
pickle_out.close()
def HPI_Benchmark():
df = quandl.get("FMAC/HPI_USA", authtoken=api_key)
df.columns = ["United States"]
df["United States"] = (df["United States"] - df["United States"][0])
return df
#grab_initial_state_data()
fig = plt.figure()
ax1 = plt.subplot2grid((1,1), (0,0))
HPI_data = pd.read_pickle('fiddy_states3.pickle')
HPI_data['TX1yr'] = HPI_data['TX'].resample('A').mean()
HPI_data.fillna(method='bfill',inplace=True)
HPI_data.dropna(inplace=True)
print(HPI_data[['TX', 'TX1yr']])
HPI_data['TX'].plot(ax=ax1)
HPI_data['TX1yr'].plot(color='k',ax=ax1)
plt.legend().remove()
plt.show()
#HPI_State_Correlation = HPI_data.corr()
#print(HPI_State_Correlation.describe())
#benchmark = HPI_Benchmark()
#HPI_data.plot(ax=ax1)
#benchmark.plot(color='k',ax=ax1,linewidth=10)