First highest views based recommendation system in python part 1

df1 = pd.DataFrame(data1)
        df2 = pd.DataFrame(data2)
        view_data = df1.drop(["timestamp","user_email","user_name"],axis=1)
        movie_df = pd.merge(df2, view_data, on="movie_id")
        movie_grouped = movie_df.groupby(['movie_name','movie_id']).agg({'no_of_views': 'count'}).reset_index()
        grouped_sum = movie_grouped['no_of_views'].sum()
        movie_grouped['percentage']  = movie_grouped['no_of_views'].div(grouped_sum)*100
        movie_final = pd.DataFrame(movie_grouped)
        count = pd.DataFrame(movie_final)
        final_out = pd.merge(count, view_data, on="movie_id")
        final_up =final_out.drop_duplicates(subset='movie_id', keep='first', inplace=False)
        final = final_up.drop(["no_of_views_x","no_of_views_y","percentage"],axis=1)
        # converting it to csv with store id
        final_csv = final.to_csv('public/csv/'+str(studio_id)+'.csv')
        # opening the exported csv file to save to mongodb 
        f = open('public/csv/'+ str(studio_id) +'.csv','rU')
        #db URI
        col = connect_db(server).recomendations
        #reading csv
        reader = csv.DictReader(f)
        #askign desired header
        fieldnames=("movie_name","movie_id","user_id","customer_id","store_id")
        # loop to dismantle the dictionary as per the
        if(mode == 1):
            coly = connect_db(server).recomendations.delete_many({"customer_id":str(studio_id)})
        else:
            coly = connect_db(server).recomendations.delete_many({"store_id":str(studio_id)})
        # colx = connect_db(server).recomendations.delete_many({"store_id_x":"101"})
        for each in reader: 
            row={}
            for field in fieldnames:    
                row[field] = each[field]    
            col.insert(row)
        # cleaning the dataframes
        df1.iloc[0:0]
        df2.iloc[0:0]
        #deleting the csv after exporting to database
        # os.remove('public/csv/'+str(studio_id)+'.csv')
        print('Training Done !!')