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 smdta = sm.datasets.co2.load_pandas().data# deal with missing values. see issuedta.co2.interpolate(inplace=True)res = sm.tsa.seasonal_decompose(dta.co2)resplot = res.plot()

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

res.residres.seasonalres.trend

I hope this helps!

I already answered this question here, but below is a quick function on how to do this with rpy2. This enables you to use R's robust statistical decomposition with loess, but in python!

    import pandas as pd    from rpy2.robjects import r, pandas2ri    import numpy as np    from rpy2.robjects.packages import importrdef decompose(series, frequency, s_window = 'periodic', log = False,  **kwargs):    '''    Decompose a time series into seasonal, trend and irregular components using loess,     acronym STL.    https://www.rdocumentation.org/packages/stats/versions/3.4.3/topics/stl    params:        series: a time series        frequency: the number of observations per “cycle”                    (normally a year, but sometimes a week, a day or an hour)                   https://robjhyndman.com/hyndsight/seasonal-periods/        s_window: either the character string "periodic" or the span                  (in lags) of the loess window for seasonal extraction,                  which should be odd and at least 7, according to Cleveland                  et al.        log:    boolean.  take log of series        **kwargs:  See other params for stl at            https://www.rdocumentation.org/packages/stats/versions/3.4.3/topics/stl    '''    df = pd.DataFrame()    df['date'] = series.index    if log: series = series.pipe(np.log)    s = [x for x in series.values]    length = len(series)    s = r.ts(s, frequency=frequency)    decomposed = [x for x in r.stl(s, s_window).rx2('time.series')]    df['observed'] = series.values    df['trend'] = decomposed[length:2*length]    df['seasonal'] = decomposed[0:length]    df['residuals'] = decomposed[2*length:3*length]    return df

The above function assumes that your series has a datetime index. It returns a dataframe with the individual components that you can then graph with your favorite graphing library.

You can pass the parameters for stl seen here, but change any period to underscore, for example the positional argument in the above function is s_window, but in the above link it is s.window. Also, I found some of the above code on this repository.

Example data

Hopefully the below works, honestly haven't tried it since this is a request long after I answered the question.

import pandas as pdimport numpy as npobs_per_cycle = 52observations = obs_per_cycle * 3data = [v+2*i for i,v in enumerate(np.random.normal(5, 1, observations))]tidx = pd.date_range('2016-07-01', periods=observations, freq='w')ts = pd.Series(data=data, index=tidx)df = decompose(ts, frequency=obs_per_cycle, s_window = 'periodic')

You can call R functions from python using rpy2Install rpy2 using pip with: pip install rpy2Then use this wrapper: https://gist.github.com/andreas-h/7808564 to call the STL functionality provided by R