seasonal_time_series.py

import matplotlib.pyplot as plt
import pandas as pd
from statsmodels.graphics.tsaplots import plot_acf, plot_pacf
import seaborn as sns


df = pd.read_csv('boston_monthly_tmax_1998_2019.csv', header=0, infer_datetime_format=True, parse_dates=[0], index_col=[0])
df.plot(marker='.')
plt.show()

for i in range(1, 12+1):
	df['TMINUS' + str(i)] = df['Monthly Average Maximum'].shift(i)

for i in range(1, 12+1):
	ax = plt.subplot(4, 3, i)
	ax.set_title('LAG ' + str(i), fontdict={'fontsize': 10})
	plt.scatter(x=df['Monthly Average Maximum'].values, y=df['TMINUS' + str(i)].values, marker='.')
plt.show()

corr = df.corr()
sns.heatmap(corr, xticklabels=corr.columns,yticklabels=corr.columns)
plt.show()