A nicer seasonal decompose chart using plotly.

seasonal_decompose_plotly.py

from statsmodels.tsa.seasonal import seasonal_decompose
import plotly.tools as tls

def plotSeasonalDecompose(
    x,
    model='additive',
    filt=None,
    period=None,
    two_sided=True,
    extrapolate_trend=0,
    title="Seasonal Decomposition"):

    """
    Plot time series decomposition

    :param x: Time series.
    
    See documentation of the remaining models here - 
        https://www.statsmodels.org/stable/generated/statsmodels.tsa.seasonal.seasonal_decompose.html
     
    Example - 
    import pandas as pd
    from datetime import datetime
    import PlotTimeSeries

    s = pd.DataFrame(list(range(1, 11))*10,
                    index=pd.date_range(start=datetime(2010,  1, 1), periods=100))

    fig = PlotTimeSeries.plotSeasonalDecompose(s)
    fig.show()
    """

    result = seasonal_decompose(
            x, model=model, filt=filt, period=period,
            two_sided=two_sided, extrapolate_trend=extrapolate_trend)
        fig = make_subplots(
            rows=4, cols=1,
            subplot_titles=["Observed", "Trend", "Seasonal", "Residuals"])
        fig.add_trace(
            go.Scatter(x=result.seasonal.index, y=result.observed, mode='lines'),
                row=1, col=1,
            )

        fig.add_trace(
            go.Scatter(x=result.trend.index, y=result.trend, mode='lines'),
                row=2, col=1,
            )

        fig.add_trace(
            go.Scatter(x=result.seasonal.index, y=result.seasonal, mode='lines'),
                row=3, col=1,
            )

        fig.add_trace(
            go.Scatter(x=result.resid.index, y=result.resid, mode='lines'),
                row=4, col=1,
            )

        return fig

from plotly.subplots import make_subplots
import plotly.graph_objects as go
from statsmodels.tsa.seasonal import DecomposeResult, seasonal_decompose

def plot_seasonal_decompose(result:DecomposeResult, dates:pd.Series=None, title:str="Seasonal Decomposition"):
x_values = dates if dates is not None else np.arange(len(result.observed))
return (
make_subplots(
rows=4,
cols=1,
subplot_titles=["Observed", "Trend", "Seasonal", "Residuals"],
)
.add_trace(
go.Scatter(x=x_values, y=result.observed, mode="lines", name='Observed'),
row=1,
col=1,
)
.add_trace(
go.Scatter(x=x_values, y=result.trend, mode="lines", name='Trend'),
row=2,
col=1,
)
.add_trace(
go.Scatter(x=x_values, y=result.seasonal, mode="lines", name='Seasonal'),
row=3,
col=1,
)
.add_trace(
go.Scatter(x=x_values, y=result.resid, mode="lines", name='Residual'),
row=4,
col=1,
)
.update_layout(
height=900, title=f'{title}', margin={'t':100}, title_x=0.5, showlegend=False
)
)

import pandas as pd
from statsmodels.tsa.seasonal import seasonal_decompose
data = pd.read_csv("https://raw.githubusercontent.com/swilsonmfc/pandas/main/AirPassengers.csv")
decomposition = seasonal_decompose(data['#Passengers'], model='additive', period=12)
fig = plot_seasonal_decompose(decomposition, dates=data['Month'])
fig.show()

Updated code combining above comments