index=pd date_rangeinput_df date_of_visit min()input_df date_of_visit max()freq=' 'deompose seasonal_decompose(ts_visits interpolate()freq= deompose plot(we first create pandas series object with additional care taken to set the frequency of the time series index it is important to note that statsmodels has number of time series modeling modules available and they rely on underlying data structures (such as pandasnumpyetc to specify the frequency of the time series in this casesince the data is at daily levelwe set the frequency of ts_visits object to 'ddenoting daily frequency we then simply use the seasonal_decompose(function from statsmodels to get the required constituents the decomposed series is shown in the plot in figure - figure - web site visit time series and its constituent signals it is apparent from figure - the time series at hand has both upward and downward trends in it it shows gradual increasing trend until octoberpost which it starts downward behavior the series certainly has monthly periodicity or seasonality to it the remaining signal is what is marked as residual in figure -