Why statsmodels seasonal_decompose gives residuals as a scatterplot?

Question

Why statsmodels seasonal_decompose gives residuals as a scatterplot?

def plot_decomposition(series):
    result = seasonal_decompose(series, model='multiplicative', period=30)
    print(result.trend)
    print(result.seasonal)
    print(result.resid)
    print(result.observed)
    figure = result.plot()
    figure.set_size_inches(14, 6)
    plt.savefig(save_path + 'FB_dec.svg')
    figure.suptitle("Decomposition of Microsoft corporations's adjusted close", y=0.99, x=0.52)
    plt.show()

df = pd.read_csv('MSFT.csv', header=0, index_col=0, parse_dates=True)
series = df['Adj Close']
plot_decomposition(series)

plot

Answer 1

      from pandas_datareader import data as pdr
 current_date=datetime.datetime.now()
 start_date=datetime.datetime(current_date.year,1,1)
 df = pdr.get_data_yahoo("MSFT",start_date,current_date).reset_index()

 decomposition=sm.tsa.seasonal_decompose(x=df['High'],model='additive',      extrapolate_trend='freq', period=30)
 decomposition.plot()
 plt.show()

 decomposition_trend=decomposition.trend
 ax= decomposition_trend.plot(figsize=(14,2))
 ax.set_xlabel('Date')
 ax.set_ylabel('Trend of time series')
 ax.set_title('Trend values of the time series')
 plt.show()

 decomposition_residual=decomposition.resid
 ax= decomposition_residual.plot(figsize=(14,2))
 ax.set_xlabel('Date')
 ax.set_ylabel('Residual of time series')
 ax.set_title('Residual values of the time series')
 plt.show()