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

Thanks for the gist, variation:

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


def plot_seasonal_decompose(result: DecomposeResult, title="Seasonal Decomposition"):
    return (
        make_subplots(
            rows=4,
            cols=1,
            subplot_titles=["Observed", "Trend", "Seasonal", "Residuals"],
        )
        .add_trace(
            go.Scatter(x=result.seasonal.index, y=result.observed, mode="lines"),
            row=1,
            col=1,
        )
        .add_trace(
            go.Scatter(x=result.trend.index, y=result.trend, mode="lines"),
            row=2,
            col=1,
        )
        .add_trace(
            go.Scatter(x=result.seasonal.index, y=result.seasonal, mode="lines"),
            row=3,
            col=1,
        )
        .add_trace(
            go.Scatter(x=result.resid.index, y=result.resid, mode="lines"),
            row=4,
            col=1,
        )
        .update_layout(
            height=900, title=title, margin=dict(t=100), title_x=0.5, showlegend=False
        )
    )

Please be careful about using the code above. They both have bugs.

If you run it as you have presented above, it will throw:

1. Indentation Error:

   IndentationError: unexpected indent
     <[command-1424241324418648]()>, line 33
       fig = make_subplots(
       ^

2. Attribute Error:

   AttributeError: 'numpy.ndarray' object has no attribute 'index'
   <[command-1424241324418648]()> in plot_seasonal_decompose(result, title)
        12         )
        13         .add_trace(
   ---> 14             go.Scatter(x=result.seasonal.index, y=result.observed, mode="lines"),
        15             row=1,
        16             col=1,

---

I have now fixed it, please see below.

I've also added another optional parameter to include dates, which can be added to the x-axis for ease of reference.

from plotly.subplots import make_subplots
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'<b>{title}</b>', margin={'t':100}, title_x=0.5, showlegend=False
        )
    )

Now, it will work like this:

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()

@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()

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