Augmented Dickey–Fuller test
#import pandas as pd import numpy as np import matplotlib.pyplot as plt from statsmodels.tsa.stattools import adfuller # Generate synthetic stationary and non-stationary data np.random.seed(17) # Stationary data: White noise stationary_data = np.random.normal(size=82) # Non-stationary data: Random walk non_stationary_data = np.cumsum(np.random.normal(size=82)) # Plot the data plt.figure(figsize=(10,5)) plt.subplot(1, 2, 1) plt.plot(stationary_data) plt.title('Stationary Data') plt.subplot(1, 2, 2) plt.plot(non_stationary_data) plt.title('Non-Stationary Data') plt.tight_layout() plt.show() 
parts of the ADF test – tuple
result = adfuller(stationary_data) print(result) 
access by index
print('ADF Test Statistic:', result[0]) 
print('p-value:', result[1]) 
print('Number of Lags Used:', result[2]) 
print('Number of Observations used in the test:', result[3]) 
print('Critical Values:', result[4]) 
print('Information Criterion Best:', result[5]) 
adf_stat, p_value, lags, n_obs, crit_values, ic_best = result # Function to perform ADF test def adf_test(series): result = adfuller(series) print(f'p-value: {result[1]}') if result[1] <= 0.05: print("=> Strong evidence against the null hypothesis, reject the null hypothesis. Data is stationary.") else: print("=> Weak evidence against the null hypothesis, fail to reject the null hypothesis. Data is non-stationary.") adf_test(stationary_data) 
adf_test(non_stationary_data) 
