test_stationarity_in_timeseries.py

import pandas as pd
import statsmodels.api as sm
from statsmodels.tsa.stattools import adfuller

def test_stationarity(timeseries):
  '''
  timeseries has a date index and a single column of numeric values
  '''
    
    #Determing rolling statistics
    rolmean = timeseries.rolling(window=52,center=False).mean() 
    rolstd = timeseries.rolling(window=52,center=False).std()

    #Plot rolling statistics:
    orig = plt.plot(timeseries, color='blue',label='Original')
    mean = plt.plot(rolmean, color='red', label='Rolling Mean')
    std = plt.plot(rolstd, color='black', label = 'Rolling Std')
    plt.legend(loc='best')
    plt.title('Rolling Mean & Standard Deviation')
    plt.show(block=False)
    
    #Perform Dickey-Fuller test:
    print('Results of Dickey-Fuller Test:')
    dftest = adfuller(timeseries.iloc[:, 0].values, autolag='AIC')
    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)