desc_list = df_grouped['Medicare_Desc'].unique()
brand_list = df_grouped['RptBrand'].unique()

dfs={}
for b,d in [(b,d) for b in brand_list for d in desc_list]:
    series = df_grouped[(df_grouped['RptBrand'] == b) &
    (df_grouped['Medicare_Desc']== d)]
    series_trend = series.filter(items=['UtilPer1000'])

    try:
        hp_cycle, hp_trend = sm.tsa.filters.hpfilter(series_trend, lamb=100)
        hp_trend.rename(columns = {'UtilPer1000': 'Trend'}, inplace=True)

        rolling_avg = hp_trend.rolling(12).mean()
        rolling_avg.rename(columns = {'Trend': 'Rolling_avg'}, inplace=True)

    except:
        pass

    series = series.drop(['UtilPer1000', 'RptBrand'], axis=1)
    dfs[b] = pd.concat([series, hp_trend, rolling_avg], axis=1)

data=pd.concat(dfs, axis=0)