Data-Analysis-Jupyter/sentdex_data_analysis/pandas_comparisonOperators.py

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

style.use("seaborn")

bridge_height = {
    "meters": [10.26, 10.31, 10.27, 10.22, 10.23, 6212.42, 10.28, 10.25, 10.31]
}
df = pd.DataFrame(bridge_height)

df["std"] = df["meters"].rolling(window=2).std()

df_std = df.describe()["meters"]["std"]
df_mean = df.describe()["meters"]["mean"]

# df = df[df['std'] < df_std] # sentdex methods
df = df[df["meters"] < (df_mean + df_std)]  # my methods
print(df)

df["meters"].plot()
plt.show()

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)

# rolling statistics
HPI_data["TX12MA"] = HPI_data["TX"].rolling(window=12, center=False).mean()
HPI_data["TX12STD"] = HPI_data["TX"].rolling(window=12, center=False).std()
# standard deviation is a measure of the volatility of the price

HPI_data.dropna(inplace=True)

TK_AK_12corr = HPI_data["TX"].rolling(window=12).corr(HPI_data["AK"])

HPI_data["TX"].plot(ax=ax1, label="TX HPI")
HPI_data["AK"].plot(ax=ax1, label="AK HPI")
ax1.legend(loc=4)

TK_AK_12corr.plot(ax=ax2, label="TK AK 12 month correlation")
ax2.legend(loc=4)

# HPI_data[['TX12MA','TX']].plot(ax=ax1)
# HPI_data['TX12STD'].plot(ax=ax2)
# plt.show()