Decomposing trend, seasonal and residual time series elements

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.

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.resid
s.seasonal
s.trend