date = [] data = pd.read_csv('dataexport_20200925T050912.csv') #data -> Raining & Snow & Basel Temperature [2 m elevation corrected] mean & cloud_cover_low for i, d in enumerate((data["location"])): if i >= 9: date.append([ d[:-5], float(data["Basel.9"][i]), float(data["Basel.2"][i]), float(data["Basel.14"][i]) ]) # to remove T0000 things [:-5] ##### AI file AI = AI.AI_parsing(date) AI_done = AI.loop_parsing() temp_KNN = AI_done[0] rain_KNN = AI_done[1] claud_KNN = AI_done[2] ##### input file protect1 = int(date[0][0]) protect2 = int(date[-1][0]) day_input = input.dates(protect1, protect2) day_output = day_input.daysBefore() #print(day_output) ### class_predic file AX = class_predic.Weather_prediction(temp_KNN, rain_KNN, claud_KNN, date) predic_dato = AX.predict_weather_for(day_output)