2.3

from csv import reader
from re import sub


def get_salary_list(current_data: list, counter=10) -> list:
    salary_list = list()
    for element in current_data:
        salary_list.append(element)
        if len(salary_list) == counter:
            break

    return salary_list


def get_clean_string(current_string: str) -> str:
    current_string = sub(r'<[^>]*>', '', current_string)
    return ' '.join(current_string.split(' '))


def get_cities_top(current_data: list) -> tuple:
    cities_and_their_salary, cities_top_list = dict(), list()
    for element in current_data:
        city_key, salary_value = element['area_name'], \
            calculate_average(element['salary_from'],
                              element['salary_to'])
        if city_key in cities_and_their_salary.keys():
            cities_and_their_salary[city_key][0] += salary_value
            cities_and_their_salary[city_key][1] += 1
        else:
            cities_and_their_salary[city_key] = [salary_value, 1]

    for city, salary in cities_and_their_salary.items():
        if round(salary[1] / len(current_data), 3) >= 0.01:
            average_salary = salary[0] // salary[1]
            cities_top_list.append((city, average_salary, salary[1]))

    cities_top_list.sort(key=lambda skill: skill[1], reverse=True)

    return cities_top_list, len(cities_and_their_salary)


def get_popular_skills_list(current_data: list) -> list:
    skills_list, skills_and_their_count = list(), dict()
    for string in current_data:
        for current_skill in string['key_skills'].split('\n'):
            if current_skill not in skills_and_their_count.keys():
                skills_and_their_count[current_skill] = 1
            else:
                skills_and_their_count[current_skill] += 1

    for skill, count in skills_and_their_count.items():
        skills_list.append((skill, count))
    skills_list.sort(key=lambda el: el[1], reverse=True)

    return skills_list


def calculate_average(salary_from: int, salary_to: int) -> int:
    return int(float(salary_from) + float(salary_to)) // 2


def define_suffix_right_form(number: int, word: list) -> str:
    if 11 <= number % 100 <= 19:
        return f'{number} {word[2]}'
    if number % 10 == 1:
        return f'{number} {word[0]}'
    elif 2 <= number % 10 <= 4:
        return f'{number} {word[1]}'
    else:
        return f'{number} {word[2]}'


def print_top_skills(top_skills_list: list) -> None:
    skills_suffixes = ['скилла', 'скиллов', 'скиллов']
    print(f'Из {define_suffix_right_form(len(top_skills_list), skills_suffixes)}, '
          f'самыми популярными являются:')
    count_suffixes = ['раз', 'раза', 'раз']

    for i in range(0, min(len(top_skills_list), 10)):
        print(f'    {i + 1}) {top_skills_list[i][0]} - '
              f'упоминается {define_suffix_right_form(top_skills_list[i][1], count_suffixes)}')
    print()


def print_top_cities(top_cities_list: list, needed_count: int) -> None:
    cities_suffixes = ['города', 'городов', 'городов']
    print(f'Из {define_suffix_right_form(needed_count, cities_suffixes)}, самые высокие средние ЗП:')
    rubles_suffixes = ['рубль', 'рубля', 'рублей']
    vacancies_suffixes = ['вакансия', 'вакансии', 'вакансий']

    for i in range(0, min(len(top_cities_list), 10)):
        print(f'    {i + 1}) {top_cities_list[i][0]} - средняя зарплата'
              f' {define_suffix_right_form(top_cities_list[i][1], rubles_suffixes)} ('
              f'{define_suffix_right_form(top_cities_list[i][2], vacancies_suffixes)})')


def print_top_salaries(top_salaries: list, caption: str) -> None:
    rubles_suffixes = ['рубль', 'рубля', 'рублей']
    print(caption)
    for i in range(len(top_salaries)):
        name = top_salaries[i]["name"]
        employer_name = top_salaries[i]["employer_name"]
        avg_salary = calculate_average(top_salaries[i]['salary_from'], top_salaries[i]['salary_to'])
        city = top_salaries[i]['area_name']
        print(f'    {i + 1}) {name} в компании "{employer_name}"'
              f' - {avg_salary} {define_suffix_right_form(avg_salary, rubles_suffixes)} (г. {city})')
    print()


def csv_reader(file_naming: str) -> tuple:
    csv_file = reader(open(file_naming, encoding='utf_8_sig'))
    all_needed_data = [current_data for current_data in csv_file]

    return all_needed_data[0], \
        [x for x in all_needed_data[1:]
         if x.count('') == 0 and len(x) == len(all_needed_data[0])]


def csv_filer(current_reader: tuple, headline: list) -> list:
    vacancies_list = list()

    for vacancy in current_reader:
        vacancies_list.append({headline[i]: get_clean_string(vacancy[i]) for i in range(len(headline))})

    return vacancies_list


headlines, vacancies = csv_reader(input())
all_start_data, data_filtered = csv_filer(vacancies, headlines), list()
for cur_data in all_start_data:
    if 'RUR' == cur_data['salary_currency']:
        data_filtered.append(cur_data)

key_for_sorting = lambda d: calculate_average(d['salary_from'], d['salary_to'])

top_high_salaries = get_salary_list(sorted(data_filtered, key=key_for_sorting, reverse=True), True)
top_low_salaries = get_salary_list(sorted(data_filtered, key=key_for_sorting, reverse=False), False)
cities_top, cities_count = get_cities_top(data_filtered)

print_top_salaries(top_high_salaries, 'Самые высокие зарплаты:')
print_top_salaries(top_low_salaries, 'Самые низкие зарплаты:')

print_top_skills(get_popular_skills_list(data_filtered))
print_top_cities(cities_top, cities_count)