Decomposing trend, seasonal and residual time series elements

Works fine when you convert your index to DateTimeIndex:

df.reset_index(inplace=True)df['Date'] = pd.to_datetime(df['Date'])df = df.set_index('Date')s=sm.tsa.seasonal_decompose(df.divida)<statsmodels.tsa.seasonal.DecomposeResult object at 0x110ec3710>

Access the components via:

s.resids.seasonals.trend

Statsmodel will decompose the series only if you provide frequency. Usually all time series index will contain frequency eg: Daywise, Business days, weekly So it shows error. You can remove this error by two ways:

1. What Stefan did is he gave the index column to pandas DateTime function. It uses internal function infer_freq to find the frequency and return the index with frequency.
2. Else you can set the frequency to your index column as df.index.asfreq(freq='m'). Here m represents month. You can set the frequency if you have domain knowledge or by d.

It depends on the index format. You can have DateTimeIndex or you can have PeriodIndex. Stefan presented the example for DateTimeIndex. Here is my example for PeriodIndex. My original DataFrame has a MultiIndex index with year in first level and month in second level. Here is how I convert it to PeriodIndex:

df["date"] = pd.PeriodIndex (df.index.map(lambda x: "{0}{1:02d}".format(*x)),freq="M")df = df.set_index("date")

Now it is ready to be used by seasonal_decompose.