Time Series Decomposition function in Python

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I’ve been having a similar issue and am trying to find the best path forward. Try moving your data into a Pandas DataFrame and then call StatsModels tsa.seasonal_decompose. See the following example: 

import statsmodels.api as sm

dta = sm.datasets.co2.load_pandas().data
# deal with missing values. see issue
dta.co2.interpolate(inplace=True)

res = sm.tsa.seasonal_decompose(dta.co2)
resplot = res.plot()

Three plots produced from above input

You can then recover the individual components of the decomposition from:

res.resid
res.seasonal
res.trend

I hope this helps!

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