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

@chrimaho Thank you!

Nice !!

@chrimaho
i have modified your code that it takes a dataframe to plot multiple columns decompostion side by side

def plot_seasonal_decompose(title:str="Seasonal Decomposition", df:pd.DataFrame=None):
    
    
    fig = make_subplots(rows=4, cols=len(df.columns), subplot_titles=df.columns)
    
    for n, (column_name, Series) in enumerate(df.iteritems(), start=1):
        decomposition = seasonal_decompose(Series, model='additive', period=12)
        fig.add_trace(
            go.Scatter(x=Series.index, y=decomposition.observed, mode="lines", name='Observed'),
            row=1,
            col=n,)
        fig.add_trace(
            go.Scatter(x=Series.index, y=decomposition.trend, mode="lines", name='Observed'),
            row=2,
            col=n,)
        fig.add_trace(
            go.Scatter(x=Series.index, y=decomposition.seasonal, mode="lines", name='Seasonal'),
            row=3,
            col=n,)
        fig.add_trace(
            go.Scatter(x=Series.index, y=decomposition.resid, mode="lines", name='Residual'),
            row=4,
            col=n,
        )
    [fig.update_yaxes(title_text = x, row = n, col=1) for n,x in enumerate(["Observed", "Trend", "Seasonal", "Residuals"], start=1)]
    fig.update_layout(
        height=900, title=f'<b>{title}</b>', margin={'t':100}, title_x=0.5, showlegend=False
    )
    
    return fig
fig = plot_seasonal_decompose(df=df)
fig.show()