def stationarity_check(df, col='BidClose', window=80, freq='BH'):
    """From learn.co lesson: use ADFuller Test for Stationary and Plot"""
    import matplotlib.pyplot as plt
    TS = df[col].copy()
    TS = TS.asfreq(freq)
    TS.fillna(method='ffill', inplace=True)
    TS.dropna(inplace=True)
    from statsmodels.tsa.stattools import adfuller
    import pandas as pd
    import numpy as np
    rolmean = TS.rolling(window=window, center=False).mean()
    rolstd = TS.rolling(window=window, center=False).std()
    dftest = adfuller(TS)
    fig, ax = plt.subplots(nrows=2, ncols=1, figsize=(8, 4))
    ax[0].set_title('Rolling Mean & Standard Deviation')
    ax[0].plot(TS, color='blue', label='Original')
    ax[0].plot(rolmean, color='red', label='Rolling Mean', alpha=0.6)
    ax[1].plot(rolstd, color='black', label='Rolling Std')
    ax[0].legend()
    ax[1].legend()
    plt.tight_layout()
    print('Results of Dickey-Fuller Test:')
    print('\tIf p<.05 then timeseries IS stationary.')
    dfoutput = pd.Series(dftest[0:4], index=['Test Statistic', 'p-value',
        '#Lags Used', 'Number of Observations Used'])
    for key, value in dftest[4].items():
        dfoutput['Critical Value (%s)' % key] = value
    print(dfoutput)
    return None
