This Code Snippet is used to decompose Time Series data into its respective components.

timeseries_decomp.py

from pandas_datareader import data
import statsmodels.api as sm
from pylab import rcParams
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns


df = data.DataReader("RELIANCE.NS",
                     data_source = "yahoo",
                     start = "2010-01-28",
                     end = "2020-12-28")

df['Date'] = pd.to_datetime(df.index)
df['Year'] = df['Date'].dt.year
df['Month'] = df['Date'].dt.month

plt.style.use("fivethirtyeight")
y = df[['Date','Open']].copy()
y.set_index('Date', inplace=True)
y.index = pd.to_datetime(y.index)
y = y.resample("1M").mean()

rcParams['figure.figsize'] = 15, 12
rcParams['axes.labelsize'] = 20
rcParams['ytick.labelsize'] = 16
rcParams['xtick.labelsize'] = 16

decomposition = sm.tsa.seasonal_decompose(y, model='multiplicative', freq = 12)
decomp = decomposition.plot()
decomp.suptitle('Open decomposition', fontsize=22)
print(decomp.get_size_inches)