import psycopg2 import getpass import pprint import random as r import FOLL as F import pandas as pd import string import itertools #GRUNNUR AD MOVIES TÖFLU movie_table = F.movies_table('movies.dat') #GRUNNUR AD RATING TÖFLLU rating_table = F.ratings_table('ratings.dat') #GRUNNUR AÐ NOTENDATÖFLU user_table = F.username_table('users.dat') #GRUNNUR AÐ GENRES TÖFLU movieid_genresid = F.genres_table('movies.dat') #TENGJUMST GAGNAGRUNNI host = 'localhost' dbname = 'movies' username = '******' #input('User name for {}.{}: '.format(host,dbname)) pw = '1313' #getpass.getpass() conn_string = "host='{}' dbname='{}' user='******' password='******'".format(host, dbname, username, pw)
import csv import string import numpy as np import pandas as pd from collections import Counter import FOLL as F #Skilgreinum numpy fylkja breytur fyrir völdu launaflokkana okkar, allar breytur innihalda ár og svo laun. avg_year_income_overall = F.data_to_numpy_overall('SALLS.csv') avg_year_income_KK,count_years_KK = F.data_to_numpy('VKK.csv') avg_year_income_KVK,count_years_KVK = F.data_to_numpy('VKVK.csv') #Skilgreinum numpy fylkja breytur fyrir völdu launaflokkana okkar, nú miðað við vísitölu neysluverðs. income_basedOnIndex_KK = F.index_function('visitolur.CSV',avg_year_income_KK,count_years_KK) income_basedOnIndex_KVK = F.index_function('visitolur.CSV', avg_year_income_KVK,count_years_KK) income_basedOnIndex_overall = F.index_function('visitolur.CSV', avg_year_income_overall, count_years_KK) #Skilgreinum numpy fylkja breytur fyrir völdu launaflokkana okkar, en breyturnar innihalda raunverulega hækkun á launum milli ára. real_increase_overall =F.real_increase(avg_year_income_overall,income_basedOnIndex_overall,count_years_KK) real_increase_KK = F.real_increase(avg_year_income_KK, income_basedOnIndex_KK,count_years_KK) real_increase_KVK = F.real_increase(avg_year_income_KVK, income_basedOnIndex_KVK,count_years_KK) #Setjum raunhækkanir nú í pandas töflu sem sýnir niðurstöðurnar best. #Skilgreinum flokkana í dálkunum A = 'Raunhaekkun á launum stjórnenda alls(KK/KVK)' B = 'Raunhaekkun á launum KK í verkfr. stöðu' C = 'Raunhaekkun á launum KVK í verkfr. stöðu' columns = [A,B,C] index = [avg_year_income_KK[:,0]] #Náum í öll árin úr þessari numpy breytu, hefðum getað valið nánast hvaða numpy breytu
if BMI < 18.5: print("You have to gain a lot of weight to be obese!") elif 18.5 <= BMI <= 24: print("You are in the normal weight range, that is good!") elif 24 < BMI <= 25: print("You are in the normal weight range, but you have to be careful...") elif 25 < BMI < 30: print("You are overweight.") else: print("You are obese, get help!") print("\n") if sex == 'Female': ave_bmi_country = F.ave_bmi_kvk_country(country, cursor)[0][0] ave_bmi_country = float(format(ave_bmi_country, '.2f')) ave_bmi_world = F.ave_bmi_kvk_world(cursor)[0][0] ave_bmi_world = float(format(ave_bmi_world, '.2f')) elif sex == 'Male': ave_bmi_country = F.ave_bmi_kk_country(country, cursor)[0][0] ave_bmi_country = float(format(ave_bmi_country, '.2f')) ave_bmi_world = F.ave_bmi_kk_world(cursor)[0][0] ave_bmi_world = float(format(ave_bmi_world, '.2f')) print("The average BMI for {} in {} is {}".format(sex, country, ave_bmi_country)) print("The average BMI for {} in the world is {}".format(sex, ave_bmi_world)) if ave_bmi_world > BMI: prosenta = (BMI / ave_bmi_world)*100 prosenta = format(prosenta, '.2f')