From 5d676e58aafd6ed44429d13914ffb850a8e978be Mon Sep 17 00:00:00 2001 From: Mekhty <98921340+Mekhty111@users.noreply.github.com> Date: Fri, 15 Aug 2025 09:24:04 +0400 Subject: [PATCH] Add files via upload --- hh_ru_main.ipynb | 278835 ++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 278835 insertions(+) create mode 100644 hh_ru_main.ipynb diff --git a/hh_ru_main.ipynb b/hh_ru_main.ipynb new file mode 100644 index 00000000..6561b231 --- /dev/null +++ b/hh_ru_main.ipynb @@ -0,0 +1,278835 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "42289889-bd57-434e-9637-d3e354756316", + "metadata": {}, + "source": [ + "
\"drawing\"" + ] + }, + { + "cell_type": "markdown", + "id": "829cb75b-f14e-4ba8-9195-7ba929a7d656", + "metadata": { + "tags": [] + }, + "source": [ + "#
Проект: Анализ резюме из HeadHunter\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "id": "dccfde33-2452-43fb-a8a5-5c6ebc02a71d", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "import plotly.express as px" + ] + }, + { + "cell_type": "markdown", + "id": "0dc6476b-7a00-44f1-ac93-6e3baed33a29", + "metadata": {}, + "source": [ + "# Исследование структуры данных" + ] + }, + { + "cell_type": "markdown", + "id": "889782b6-8d1c-410e-8920-e57f27917c64", + "metadata": {}, + "source": [ + "1. Прочитайте данные с помощью библиотеки Pandas. Совет: перед чтением обратите внимание на разделитель внутри файла. " + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "id": "bc39e68e-ed02-4ee2-9235-7258a2f867a8", + "metadata": {}, + "outputs": [], + "source": [ + "df = pd.read_csv('data/dst-3.0_16_1_hh_database.csv', sep=';')" + ] + }, + { + "cell_type": "markdown", + "id": "b583485d-1f9e-494e-ada4-80ffff7c53d6", + "metadata": {}, + "source": [ + "2. Выведите несколько первых (последних) строк таблицы, чтобы убедиться в том, что ваши данные не повреждены. Ознакомьтесь с признаками и их структурой." + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "id": "5408c52c-3407-4a63-b25b-1aafa2322d28", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Пол, возрастЗПИщет работу на должность:Город, переезд, командировкиЗанятостьГрафикОпыт работыПоследнее/нынешнее место работыПоследняя/нынешняя должностьОбразование и ВУЗОбновление резюмеАвто
0Мужчина , 39 лет , родился 27 ноября 197929000 руб.Системный администраторСоветск (Калининградская область) , не готов к...частичная занятость, проектная работа, полная ...гибкий график, полный день, сменный график, ва...Опыт работы 16 лет 10 месяцев Август 2010 — п...МАОУ \"СОШ № 1 г.Немана\"Системный администраторНеоконченное высшее образование 2000 Балтийск...16.04.2019 15:59Имеется собственный автомобиль
1Мужчина , 60 лет , родился 20 марта 195940000 руб.Технический писательКоролев , не готов к переезду , готов к редким...частичная занятость, проектная работа, полная ...гибкий график, полный день, сменный график, уд...Опыт работы 19 лет 5 месяцев Январь 2000 — по...Временный трудовой коллективМенеджер проекта, Аналитик, Технический писательВысшее образование 1981 Военно-космическая ак...12.04.2019 08:42Не указано
2Женщина , 36 лет , родилась 12 августа 198220000 руб.ОператорТверь , не готова к переезду , не готова к ком...полная занятостьполный деньОпыт работы 10 лет 3 месяца Октябрь 2004 — Де...ПАО СбербанкКассир-операционистСреднее специальное образование 2002 Профессио...16.04.2019 08:35Не указано
3Мужчина , 38 лет , родился 25 июня 1980100000 руб.Веб-разработчик (HTML / CSS / JS / PHP / базы ...Саратов , не готов к переезду , готов к редким...частичная занятость, проектная работа, полная ...гибкий график, удаленная работаОпыт работы 18 лет 9 месяцев Август 2017 — Ап...OpenSoftИнженер-программистВысшее образование 2002 Саратовский государст...08.04.2019 14:23Не указано
4Женщина , 26 лет , родилась 3 марта 1993140000 руб.Региональный менеджер по продажамМосква , не готова к переезду , готова к коман...полная занятостьполный деньОпыт работы 5 лет 7 месяцев Региональный мене...МармеладМенеджер по продажамВысшее образование 2015 Кгу Психологии и педаг...22.04.2019 10:32Не указано
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Начнем с простого - с признака **\"Образование и ВУЗ\"**. Его текущий формат это: **<Уровень образования год выпуска ВУЗ специальность...>**. Например:\n", + "* Высшее образование 2016 Московский авиационный институт (национальный исследовательский университет)...\n", + "* Неоконченное высшее образование 2000 Балтийская государственная академия рыбопромыслового флота…\n", + "Нас будет интересовать только уровень образования.\n", + "\n", + "Создайте с помощью функции-преобразования новый признак **\"Образование\"**, который должен иметь 4 категории: \"высшее\", \"неоконченное высшее\", \"среднее специальное\" и \"среднее\".\n", + "\n", + "Выполните преобразование, ответьте на контрольные вопросы и удалите признак \"Образование и ВУЗ\".\n", + "\n", + "Совет: обратите внимание на структуру текста в столбце **\"Образование и ВУЗ\"**. Гарантируется, что текущий уровень образования соискателя всегда находится в первых 2ух слов и начинается с заглавной буквы. Воспользуйтесь этим.\n", + "\n", + "*Совет: проверяйте полученные категории, например, с помощью метода unique()*\n" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "id": "3f069da6-fdc9-4ae7-b514-fafe2675efd3", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Образование\n", + "Высшее 33863\n", + "Среднее специальное 5765\n", + "Неоконченное высшее 4557\n", + "Среднее 559\n", + "Name: count, dtype: int64" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "\n", + "\n", + "# Ловим первые 3 слова из признака, и сохраняем слова в новую строку пока не встретим слово \"образование\"\n", + "def splitter(col):\n", + " col_splitted = col.split()[:3]\n", + " final_str = ''\n", + " for word in col_splitted:\n", + " if word != 'образование':\n", + " final_str += f' {word}'\n", + " else:\n", + " break\n", + " return final_str\n", + " \n", + "\n", + "df['Образование'] = df['Образование и ВУЗ'].apply(splitter)\n", + "display(df['Образование'].value_counts())\n", + "df = df.drop(columns='Образование и ВУЗ', axis=1)\n" + ] + }, + { + "cell_type": "markdown", + "id": "4266f0b6-1646-492a-88b4-2f6a46502135", + "metadata": {}, + "source": [ + "2. Теперь нас интересует столбец **\"Пол, возраст\"**. Сейчас он представлен в формате **<Пол , возраст , дата рождения >**. Например:\n", + "* Мужчина , 39 лет , родился 27 ноября 1979 \n", + "* Женщина , 21 год , родилась 13 января 2000\n", + "Как вы понимаете, нам необходимо выделить каждый параметр в отдельный столбец.\n", + "\n", + "Создайте два новых признака **\"Пол\"** и **\"Возраст\"**. При этом важно учесть:\n", + "* Признак пола должен иметь 2 уникальных строковых значения: 'М' - мужчина, 'Ж' - женщина. \n", + "* Признак возраста должен быть представлен целыми числами.\n", + "\n", + "Выполните преобразование, ответьте на контрольные вопросы и удалите признак **\"Пол, возраст\"** из таблицы.\n", + "\n", + "*Совет: обратите внимание на структуру текста в столбце, в части на то, как разделены параметры пола, возраста и даты рождения между собой - символом ' , '. \n", + "Гарантируется, что структура одинакова для всех строк в таблице. Вы можете воспользоваться этим.*\n" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "id": "0c44b350-72eb-4d65-8b54-a1fc449ba9fd", + "metadata": {}, + "outputs": [], + "source": [ + "def age_sex_splitter(col):\n", + " col_splitted = col.split(', ')\n", + " if col_splitted[0].startswith('Мужчина'):\n", + " sex = 'М'\n", + " else:\n", + " sex = 'Ж'\n", + " return sex, int(col_splitted[1].split()[0])\n", + "\n", + "df['Пол'] = df['Пол, возраст'].apply(lambda x: age_sex_splitter(x)[0]) \n", + "df['Возраст'] = df['Пол, возраст'].apply(lambda x: age_sex_splitter(x)[1]) \n", + "\n", + "df = df.drop('Пол, возраст', axis=1)" + ] + }, + { + "cell_type": "markdown", + "id": "635c4d5d-f323-4c66-b428-50a088120aa0", + "metadata": {}, + "source": [ + "3. Следующим этапом преобразуем признак **\"Опыт работы\"**. Его текущий формат - это: **<Опыт работы: n лет m месяцев, периоды работы в различных компаниях…>**. \n", + "\n", + "Из столбца нам необходимо выделить общий опыт работы соискателя в месяцах, новый признак назовем \"Опыт работы (месяц)\"\n", + "\n", + "Для начала обсудим условия решения задачи:\n", + "* Во-первых, в данном признаке есть пропуски. Условимся, что если мы встречаем пропуск, оставляем его как есть (функция-преобразование возвращает NaN)\n", + "* Во-вторых, в данном признаке есть скрытые пропуски. Для некоторых соискателей в столбце стоит значения \"Не указано\". Их тоже обозначим как NaN (функция-преобразование возвращает NaN)\n", + "* В-третьих, нас не интересует информация, которая описывается после указания опыта работы (периоды работы в различных компаниях)\n", + "* В-четвертых, у нас есть проблема: опыт работы может быть представлен только в годах или только месяцах. Например, можно встретить следующие варианты:\n", + " * Опыт работы 3 года 2 месяца…\n", + " * Опыт работы 4 года…\n", + " * Опыт работы 11 месяцев…\n", + " * Учитывайте эту особенность в вашем коде\n", + "\n", + "Учитывайте эту особенность в вашем коде\n", + "\n", + "В результате преобразования у вас должен получиться столбец, содержащий информацию о том, сколько месяцев проработал соискатель.\n", + "Выполните преобразование, ответьте на контрольные вопросы и удалите столбец **\"Опыт работы\"** из таблицы.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "id": "603cdbe4-0ec3-4106-8eaf-c73fcdd29165", + "metadata": {}, + "outputs": [], + "source": [ + "def experience_transform(col):\n", + " # функция для безопасной проверки на nan, ведь если напрямую проверять np.isnan() и передать col, то \n", + " # numpy не может проверять строки, а при вызове функции float() без проверки можно наткнуться на строку со словами, \n", + " # что тоже вызовет ошибку, поэтому \"ловим\" эти ошибки и \"отпускаем\" такие строки\n", + " def is_nan_safe(val):\n", + " try:\n", + " return np.isnan(float(val))\n", + " except (ValueError, TypeError):\n", + " return False\n", + " if is_nan_safe(col) or col == 'Не указано':\n", + " return np.nan\n", + " col_splitted = str(col).split()[2:6]\n", + " just_year,just_month = False, False\n", + " for word in col_splitted:\n", + " if word.startswith('год') or word.startswith('лет'):\n", + " just_year = True\n", + " if word.startswith('месяц'):\n", + " just_month = True\n", + " if just_year and just_month:\n", + " return (int(col_splitted[0]) * 12) + int(col_splitted[2])\n", + " elif just_year and not just_month:\n", + " return int(col_splitted[0]) * 12\n", + " return int(col_splitted[0])\n", + "\n", + "df['Опыт работы (месяц)'] = df['Опыт работы'].apply(experience_transform)\n", + "\n", + "df = df.drop('Опыт работы', axis=1)" + ] + }, + { + "cell_type": "markdown", + "id": "f3fdaec9-5fd3-4260-9cf5-1d7406b628a5", + "metadata": {}, + "source": [ + "4. Хорошо идем! Следующий на очереди признак \"Город, переезд, командировки\". Информация в нем представлена в следующем виде: **<Город , (метро) , готовность к переезду (города для переезда) , готовность к командировкам>**. В скобках указаны необязательные параметры строки. Например, можно встретить следующие варианты:\n", + "\n", + "* Москва , не готов к переезду , готов к командировкам\n", + "* Москва , м. Беломорская , не готов к переезду, не готов к командировкам\n", + "* Воронеж , готов к переезду (Сочи, Москва, Санкт-Петербург) , готов к командировкам\n", + "\n", + "Создадим отдельные признаки **\"Город\"**, **\"Готовность к переезду\"**, **\"Готовность к командировкам\"**. При этом важно учесть:\n", + "\n", + "* Признак **\"Город\"** должен содержать только 4 категории: \"Москва\", \"Санкт-Петербург\" и \"город-миллионник\" (их список ниже), остальные обозначьте как \"другие\".\n", + "\n", + " Список городов-миллионников:\n", + " \n", + " million_cities = ['Новосибирск', 'Екатеринбург','Нижний Новгород','Казань', 'Челябинск','Омск', 'Самара', 'Ростов-на-Дону', 'Уфа', 'Красноярск', 'Пермь', 'Воронеж','Волгоград']\n", + " \n", + " Инфорация о метро, рядом с которым проживает соискатель нас не интересует.\n", + "* Признак **\"Готовность к переезду\"** должен иметь два возможных варианта: True или False. Обратите внимание, что возможны несколько вариантов описания готовности к переезду в признаке \"Город, переезд, командировки\". Например:\n", + " * … , готов к переезду , …\n", + " * … , не готова к переезду , …\n", + " * … , готова к переезду (Москва, Санкт-Петербург, Ростов-на-Дону)\n", + " * … , хочу переехать (США) , …\n", + " \n", + " Нас интересует только сам факт возможности или желания переезда.\n", + "* Признак **\"Готовность к командировкам\"** должен иметь два возможных варианта: True или False. Обратите внимание, что возможны несколько вариантов описания готовности к командировкам в признаке \"Город, переезд, командировки\". Например:\n", + " * … , готов к командировкам , … \n", + " * … , готова к редким командировкам , …\n", + " * … , не готов к командировкам , …\n", + " \n", + " Нас интересует только сам факт готовности к командировке.\n", + " \n", + " Еще один важный факт: при выгрузки данных у некоторых соискателей \"потерялась\" информация о готовности к командировкам. Давайте по умолчанию будем считать, что такие соискатели не готовы к командировкам.\n", + " \n", + "Выполните преобразования и удалите столбец **\"Город, переезд, командировки\"** из таблицы.\n", + "\n", + "*Совет: обратите внимание на то, что структура текста может меняться в зависимости от указания ближайшего метро. Учите это, если будете использовать порядок слов в своей программе.*\n" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "id": "1ea710ec-871b-431e-9462-c7d0fd87011b", + "metadata": {}, + "outputs": [], + "source": [ + "def city_rem_trip(col):\n", + " million_cities = ['Новосибирск', 'Екатеринбург','Нижний Новгород','Казань', 'Челябинск','Омск', 'Самара', 'Ростов-на-Дону', 'Уфа', 'Красноярск', 'Пермь', 'Воронеж','Волгоград']\n", + " \n", + " # такая конструкция в связи с тем, что простой split(\" , \") выдает ошибку\n", + " col_str = [word.strip() for word in col.split(', ', maxsplit=2)]\n", + " if col_str[0] == 'Москва':\n", + " city = 'Москва'\n", + " elif col_str[0] == 'Санкт-Петербург':\n", + " city = 'Санкт-Петербург'\n", + " elif col_str[0] in million_cities:\n", + " city = 'город-миллионник'\n", + " else:\n", + " city = 'другие'\n", + " \n", + " if ('не готов к переезду' in col) or ('не готова к переезду' in col):\n", + " removel = False\n", + " elif 'хочу' in col:\n", + " removel = True\n", + " else:\n", + " removel = True \n", + " \n", + " if ('командировка' in col):\n", + " if ('не готов к командировкам' in col) or('не готова к командировкам' in col):\n", + " trip = False\n", + " else: \n", + " trip = True\n", + " else:\n", + " trip = True\n", + "\n", + " return city, removel, trip\n", + "\n", + "df['Город'] = df['Город, переезд, командировки'].apply(lambda x: city_rem_trip(x)[0])\n", + "df['Готовность к переезду'] = df['Город, переезд, командировки'].apply(lambda x: city_rem_trip(x)[1])\n", + "df['Готовность к командировкам'] = df['Город, переезд, командировки'].apply(lambda x: city_rem_trip(x)[2])\n", + "\n", + "df = df.drop('Город, переезд, командировки', axis=1)" + ] + }, + { + "cell_type": "markdown", + "id": "0136ffee-0ead-4a6b-b608-61d1c753a438", + "metadata": {}, + "source": [ + "5. Рассмотрим поближе признаки **\"Занятость\"** и **\"График\"**. Сейчас признаки представляют собой набор категорий желаемой занятости (полная занятость, частичная занятость, проектная работа, волонтерство, стажировка) и желаемого графика работы (полный день, сменный график, гибкий график, удаленная работа, вахтовый метод).\n", + "На сайте hh.ru соискатель может указывать различные комбинации данных категорий, например:\n", + "* полная занятость, частичная занятость\n", + "* частичная занятость, проектная работа, волонтерство\n", + "* полный день, удаленная работа\n", + "* вахтовый метод, гибкий график, удаленная работа, полная занятость\n", + "\n", + "Такой вариант признаков имеет множество различных комбинаций, а значит множество уникальных значений, что мешает анализу. Нужно это исправить!\n", + "\n", + "Давайте создадим признаки-мигалки для каждой категории: если категория присутствует в списке желаемых соискателем, то в столбце на месте строки рассматриваемого соискателя ставится True, иначе - False.\n", + "\n", + "Такой метод преобразования категориальных признаков называется One Hot Encoding и его схема представлена на рисунке ниже:\n", + "\n", + "Выполните данное преобразование для признаков \"Занятость\" и \"График\", ответьте на контрольные вопросы, после чего удалите их из таблицы" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "id": "5b3a8e79-f7ab-4008-b767-12aea3a205ea", + "metadata": {}, + "outputs": [], + "source": [ + "# вначале найдем все возможные варианты\n", + "busyness_values = list()\n", + "schedule_values = list()\n", + "\n", + "# upd: к сожалению такой вариант не сработал по итогу, хотя ошибок не выдает, но и не работает,\n", + "# а хотелось оба признака одной функцией пропустить \n", + "\n", + "# def adding_values_to_list(col, lst):\n", + "# global busyness_values, schedule_values\n", + "# col_str = [word.strip() for word in col.split(',')]\n", + "# for i in col_str:\n", + "# lst.append(i)\n", + "# lst = list(set(lst))\n", + "# df['Занятость'].apply(adding_values_to_list, args=(busyness_values, ))\n", + "# df['График'].apply(adding_values_to_list, args=(schedule_values, ))\n", + "\n", + "def busyness_values_func(col):\n", + " global busyness_values\n", + " col_str = [word.strip() for word in col.split(',')]\n", + " for i in col_str:\n", + " busyness_values.append(i)\n", + " busyness_values = list(set(busyness_values))\n", + "df['Занятость'].apply(busyness_values_func)\n", + "\n", + "\n", + "def schedule_values_func(col):\n", + " global schedule_values\n", + " col_str = [word.strip() for word in col.split(',')]\n", + " for i in col_str:\n", + " schedule_values.append(i)\n", + " schedule_values = list(set(schedule_values))\n", + "df['График'].apply(schedule_values_func)\n", + "\n", + "def busyness_migalka(col):\n", + " col_str = [word.strip() for word in col.split(',')]\n", + " value_dic = {word: word in col_str for word in busyness_values}\n", + " return value_dic\n", + "for col in busyness_values:\n", + " df[col] = df['Занятость'].apply(lambda x: busyness_migalka(x)[col])\n", + " \n", + "def schedule_migalka(col):\n", + " col_str = [word.strip() for word in col.split(',')]\n", + " value_dic = {word: word in col_str for word in schedule_values}\n", + " return value_dic\n", + "for col in schedule_values:\n", + " df[col] = df['График'].apply(lambda x: schedule_migalka(x)[col])\n", + " \n", + "# display(df[['График', *schedule_values]])\n", + "\n", + "df = df.drop(['График', 'Занятость'], axis=1)" + ] + }, + { + "cell_type": "markdown", + "id": "344f123c-b63b-43dd-a8a4-809c1f1bb443", + "metadata": {}, + "source": [ + "6. (2 балла) Наконец, мы добрались до самого главного и самого важного - признака заработной платы **\"ЗП\"**. \n", + "В чем наша беда? В том, что помимо желаемой заработной платы соискатель указывает валюту, в которой он бы хотел ее получать, например:\n", + "* 30000 руб.\n", + "* 50000 грн.\n", + "* 550 USD\n", + "\n", + "Нам бы хотелось видеть заработную плату в единой валюте, например, в рублях. Возникает вопрос, а где взять курс валют по отношению к рублю?\n", + "\n", + "На самом деле язык Python имеет в арсенале огромное количество возможностей получения данной информации, от обращения к API Центробанка, до использования специальных библиотек, например pycbrf. Однако, это не тема нашего проекта.\n", + "\n", + "Поэтому мы пойдем в лоб: обратимся к специальным интернет-ресурсам для получения данных о курсе в виде текстовых файлов. Например, MDF.RU, данный ресурс позволяет удобно экспортировать данные о курсах различных валют и акций за указанные периоды в виде csv файлов. Мы уже сделали выгрузку курсов валют, которые встречаются в наших данных за период с 29.12.2017 по 05.12.2019. Скачать ее вы можете **на платформе**\n", + "\n", + "Создайте новый DataFrame из полученного файла. В полученной таблице нас будут интересовать столбцы:\n", + "* \"currency\" - наименование валюты в ISO кодировке,\n", + "* \"date\" - дата, \n", + "* \"proportion\" - пропорция, \n", + "* \"close\" - цена закрытия (последний зафиксированный курс валюты на указанный день).\n", + "\n", + "\n", + "Перед вами таблица соответствия наименований иностранных валют в наших данных и их общепринятых сокращений, которые представлены в нашем файле с курсами валют. Пропорция - это число, за сколько единиц валюты указан курс в таблице с курсами. Например, для казахстанского тенге курс на 20.08.2019 составляет 17.197 руб. за 100 тенге, тогда итоговый курс равен - 17.197 / 100 = 0.17197 руб за 1 тенге.\n", + "Воспользуйтесь этой информацией в ваших преобразованиях.\n", + "\n", + "\n", + "\n", + "\n", + "Осталось только понять, откуда брать дату, по которой определяется курс? А вот же она - в признаке **\"Обновление резюме\"**, в нем содержится дата и время, когда соискатель выложил текущий вариант своего резюме. Нас интересует только дата, по ней бы и будем сопоставлять курсы валют.\n", + "\n", + "Теперь у нас есть вся необходимая информация для того, чтобы создать признак \"ЗП (руб)\" - заработная плата в рублях.\n", + "\n", + "После ответа на контрольные вопросы удалите исходный столбец заработной платы \"ЗП\" и все промежуточные столбцы, если вы их создавали." + ] + }, + { + "cell_type": "markdown", + "id": "6d794257-61f5-4124-956b-67b92b9391a7", + "metadata": { + "tags": [] + }, + "source": [ + "Итак, давайте обсудим возможный алгоритм преобразования: \n", + "1. Перевести признак \"Обновление резюме\" из таблицы с резюме в формат datetime и достать из него дату. В тот же формат привести признак \"date\" из таблицы с валютами.\n", + "2. Выделить из столбца \"ЗП\" сумму желаемой заработной платы и наименование валюты, в которой она исчисляется. Наименование валюты перевести в стандарт ISO согласно с таблицей выше.\n", + "3. Присоединить к таблице с резюме таблицу с курсами по столбцам с датой и названием валюты (подумайте, какой тип объединения надо выбрать, чтобы в таблице с резюме сохранились данные о заработной плате, изначально представленной в рублях). Значение close для рубля заполнить единицей 1 (курс рубля самого к себе)\n", + "4. Умножить сумму желаемой заработной платы на присоединенный курс валюты (close) и разделить на пропорцию (обратите внимание на пропуски после объединения в этих столбцах), результат занести в новый столбец \"ЗП (руб)\".\n" + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "id": "e91f87ed-e25c-4f96-9f92-b642e8c0b4fa", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/vc/zgch74_n23sckf5dm03_fq380000gn/T/ipykernel_1042/1308697190.py:3: UserWarning:\n", + "\n", + "Parsing dates in %d.%m.%Y %H:%M format when dayfirst=False (the default) was specified. Pass `dayfirst=True` or specify a format to silence this warning.\n", + "\n", + "/var/folders/vc/zgch74_n23sckf5dm03_fq380000gn/T/ipykernel_1042/1308697190.py:4: UserWarning:\n", + "\n", + "Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", + "\n" + ] + } + ], + "source": [ + "currency_df = pd.read_csv('data/ExchangeRates.csv')\n", + "\n", + "df['Обновление резюме'] = pd.to_datetime(pd.to_datetime(df['Обновление резюме']).dt.date)\n", + "currency_df['date'] = pd.to_datetime(currency_df['date'])\n", + "\n", + "df['значение'] = df['ЗП'].apply(lambda x: x.split()[0])\n", + "df['валюта'] = df['ЗП'].apply(lambda x: x.split()[1])\n", + "\n", + "\n", + "df.loc[df['валюта'] == 'бел.руб.', 'валюта'] = 'BYN'\n", + "df.loc[df['валюта'] == 'грн.', 'валюта'] = 'UAH'\n", + "df.loc[df['валюта'] == 'сум', 'валюта'] = 'UZS'\n", + "\n", + "df = df.merge(currency_df[['currency', 'close', 'date', 'proportion']], left_on=['валюта', 'Обновление резюме'], right_on=['currency', 'date'], how='left')\n", + "\n", + "df.loc[df['валюта'] == 'руб.' , ['close', 'proportion']] = 1\n", + "df['значение'] = df['значение'].astype('float')\n", + "df['close'] = df['close'].astype('float')\n", + "df['proportion'] = df['proportion'].astype('int')\n", + "\n", + "df['ЗП (руб)'] = df['значение'] * (df['close']) / df['proportion']\n", + "\n", + "df = df.drop(['close', 'proportion', 'ЗП', 'валюта', 'значение', 'currency', 'date'], axis=1)\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "7ea20ab6-04a7-40a0-adec-443a0e5a42c3", + "metadata": {}, + "source": [ + "# Исследование зависимостей в данных" + ] + }, + { + "cell_type": "markdown", + "id": "968aed80-74ef-4ffb-b5bf-c65b84e9caea", + "metadata": {}, + "source": [ + "1. Постройте распределение признака **\"Возраст\"**. Опишите распределение, отвечая на следующие вопросы: чему равна мода распределения, каковы предельные значения признака, в каком примерном интервале находится возраст большинства соискателей? Есть ли аномалии для признака возраста, какие значения вы бы причислили к их числу?\n", + "*Совет: постройте гистограмму и коробчатую диаграмму рядом.*" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f2d62796-d400-4ec1-a675-88112739a50e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 30\n", + "Name: Возраст, dtype: int64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "5597 77\n", + "15419 76\n", + "10907 73\n", + "19628 73\n", + "Name: Возраст, dtype: int64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "alignmentgroup": "True", + "bingroup": "x", + "hovertemplate": "Возраст=%{x}
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+ } + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "display(df['Возраст'].mode(), df['Возраст'].sort_values(ascending=False).head(4))\n", + "\n", + "fig = px.histogram(\n", + " df, \n", + " x='Возраст',\n", + " title='Гистограмма распределения возраста'\n", + ")\n", + "fig.show()\n", + "\n", + "fig2 = px.box(\n", + " df,\n", + " x = 'Возраст',\n", + " title = 'Коробчатая диаграмма возраста'\n", + ")\n", + "fig2.show()\n", + "\n", + "# fig.write_html(\"Plotly_charts/1-1.html\")\n", + "# fig2.write_html(\"Plotly_charts/1-2.html\")" + ] + }, + { + "cell_type": "markdown", + "id": "88967466-5070-4bea-9e71-b3efefdc6e4a", + "metadata": {}, + "source": [ + "По коробчатой диаграме, да и в целом сразу выделяется значение возраста - 100 лет. Остальные же значения которые коробчатая диаграма считает выбросами лично я пока выбросами не считаю, 50+ лет для программиста - это еще не конец!\n", + "\n", + "Что касается гистограммы же, распределение похоже на нормальное, что есть хорошо! Медианное значение возраста же составляет 30 лет, что я думаю полностью соответствует реалиям на данный момент." + ] + }, + { + "cell_type": "markdown", + "id": "68377cfc-b378-43dc-ab7d-1195abf2c515", + "metadata": {}, + "source": [ + "2. Постройте распределение признака **\"Опыт работы (месяц)\"**. Опишите данное распределение, отвечая на следующие вопросы: чему равна мода распределения, каковы предельные значения признака, в каком примерном интервале находится опыт работы большинства соискателей? Есть ли аномалии для признака опыта работы, какие значения вы бы причислили к их числу?\n", + "*Совет: постройте гистограмму и коробчатую диаграмму рядом.*" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "dd7df27a-e3e8-412e-a21a-5d389b71e891", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 100.0\n", + "Name: Опыт работы (месяц), dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "3293 663.0\n", + "10044 653.0\n", + "44363 622.0\n", + "25135 586.0\n", + "Name: Опыт работы (месяц), dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "alignmentgroup": "True", + "bingroup": "x", + "hovertemplate": "Опыт работы (месяц)=%{x}
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распределения опыта работы (месяц)" + }, + "xaxis": { + "anchor": "y", + "domain": [ + 0, + 1 + ], + "title": { + "text": "Опыт работы (месяц)" + } + }, + "yaxis": { + "anchor": "x", + "domain": [ + 0, + 1 + ] + } + } + } + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "display(df['Опыт работы (месяц)'].mode(), df['Опыт работы (месяц)'].sort_values(ascending=False).head(4))\n", + "\n", + "fig = px.histogram(\n", + " df, \n", + " x='Опыт работы (месяц)',\n", + " title = 'График распределения опыта работы (месяц)'\n", + ")\n", + "fig.show()\n", + "\n", + "fig2 = px.box(\n", + " df,\n", + " x = 'Опыт работы (месяц)',\n", + " title='Коробчатая диаграмма распределения опыта работы (месяц)'\n", + ")\n", + "fig2.show()\n", + "\n", + "# fig.write_html(\"Plotly_charts/2-1.html\")\n", + "# fig2.write_html(\"Plotly_charts/2-2.html\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "7305c34b-089d-4986-be9f-5fa1c0ee281c", + "metadata": {}, + "source": [ + "Мода признака опыта работы равна 100 месяцев. Предельное значение же 1188, что несомненно является аномалией. Но как уже было выяснено, распределения похожие на логнормальные после правой границы зачастую имеют большое количество выбросов(потенциальных), на графике это и демонстрируется, хотя очевидно что не все выбросу на самом деле ими являются." + ] + }, + { + "cell_type": "markdown", + "id": "f2fe0acb-ab1a-4e15-9326-2560e22d73c1", + "metadata": {}, + "source": [ + "3. Постройте распределение признака **\"ЗП (руб)\"**. Опишите данное распределение, отвечая на следующие вопросы: каковы предельные значения признака, в каком примерном интервале находится заработная плата большинства соискателей? Есть ли аномалии для признака возраста? Обратите внимание на гигантские размеры желаемой заработной платы.\n", + "*Совет: постройте гистограмму и коробчатую диаграмму рядом.*\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6271f8ad-bd2e-4196-ae4a-5b39c4c1010e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "np.float64(59831.0)" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "32325 1000000.0\n", + "33449 923983.0\n", + "15836 900000.0\n", + "12725 800000.0\n", + "Name: ЗП (руб), dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "alignmentgroup": "True", + "bingroup": "x", + "hovertemplate": "ЗП (руб)=%{x}
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| (x > right_bound)]\n", + " cleaned = x[(x > left_bound) & (x < left_bound)]\n", + " \n", + " return outliers, cleaned\n", + "\n", + "outliers, cleaned = z_otklon(df, 'ЗП (руб)') \n", + "display(outliers.nlargest(3))\n", + "\n", + "# fig.write_html(\"Plotly_charts/3-1.html\")\n", + "# fig2.write_html(\"Plotly_charts/3-2.html\")" + ] + }, + { + "cell_type": "markdown", + "id": "6c39ca13-225e-48f7-a85c-ab07a6cc852a", + "metadata": {}, + "source": [ + "Из-за серьезных аномалий оба графика стали нечитаемы. Для себя отмечу, что ЗП > 1M - выбросы. Но главные проблемы тут - это зп в 7.5M и 24M, от них придется избавится далее.\n", + "\n", + "Дополнительно методом z-отклонений нашел подозрительные на выброс строки" + ] + }, + { + "cell_type": "markdown", + "id": "eab2206d-d95b-468b-8e5f-34381abf0ce7", + "metadata": {}, + "source": [ + "4. Постройте диаграмму, которая показывает зависимость **медианной** желаемой заработной платы (**\"ЗП (руб)\"**) от уровня образования (**\"Образование\"**). Используйте для диаграммы данные о резюме, где желаемая заработная плата меньше 1 млн рублей.\n", + "*Сделайте выводы по представленной диаграмме: для каких уровней образования наблюдаются наибольшие и наименьшие уровни желаемой заработной платы? Как вы считаете, важен ли признак уровня образования при прогнозировании заработной платы?*" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "80defb23-bfb1-4a18-8be6-bf36aa7165d5", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "alignmentgroup": "True", + "hovertemplate": "variable=ЗП (руб)
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"markdown", + "id": "dc463319-3f85-47dd-b66e-aecb0be0c76f", + "metadata": {}, + "source": [ + "Что и следовало ожидать, люди, имеющее высшее образование требует ЗП больше остальных, наименьшую ЗП делят люди со средним и средним-специальным образованием. Насчет признака образования - считаю признак полезным для прогнозов, так как признак сам по себе верен, и логичен " + ] + }, + { + "cell_type": "markdown", + "id": "0112af31-f6b0-4e3b-82ab-71296e752e5e", + "metadata": {}, + "source": [ + "5. Постройте диаграмму, которая показывает распределение желаемой заработной платы (**\"ЗП (руб)\"**) в зависимости от города (**\"Город\"**). Используйте для диаграммы данные о резюме, где желая заработная плата меньше 1 млн рублей.\n", + "*Сделайте выводы по полученной диаграмме: как соотносятся медианные уровни желаемой заработной платы и их размах в городах? Как вы считаете, важен ли признак города при прогнозировании заработной платы?*" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "973b0cfc-a972-470a-8b4a-68744ef220c4", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "alignmentgroup": "True", + "hovertemplate": "variable=ЗП (руб)
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"", + "zerolinecolor": "white" + }, + "yaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + }, + "zaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + } + }, + "shapedefaults": { + "line": { + "color": "#2a3f5f" + } + }, + "ternary": { + "aaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "baxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "bgcolor": "#E5ECF6", + "caxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + } + }, + "title": { + "x": 0.05 + }, + "xaxis": { + "automargin": true, + "gridcolor": "white", + "linecolor": "white", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "white", + "zerolinewidth": 2 + }, + "yaxis": { + "automargin": 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"2710ce73-2e81-4e38-b52f-833cd14ba47a", + "metadata": { + "tags": [] + }, + "source": [ + "Тут так-же ожидаемо впереди всех выступает - Москва, причем разница медианной ЗП в Москве и других городах кроме Санкт-Петербурга 40.000 или же в 2 раза.\n", + "\n", + "Считаю признак полезным и информативным, избавляться от него не стоит :)\n" + ] + }, + { + "cell_type": "markdown", + "id": "3bfcfdaf-755d-4d2b-8ed8-326201dc07ab", + "metadata": {}, + "source": [ + "6. Постройте **многоуровневую столбчатую диаграмму**, которая показывает зависимость медианной заработной платы (**\"ЗП (руб)\"**) от признаков **\"Готовность к переезду\"** и **\"Готовность к командировкам\"**. Проанализируйте график, сравнив уровень заработной платы в категориях." + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "id": "ad3f270f-2991-47eb-929b-e8f88a55b0d1", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'Диаграмма зависимости желаемой заработной платы от готовности к переезду и командировкам')" + ] + }, + "execution_count": 77, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "figure = plt.figure(figsize=(10,5))\n", + "\n", + "barplot = sns.barplot(\n", + " data=df,\n", + " x = 'Готовность к переезду',\n", + " y = 'ЗП (руб)',\n", + " hue = 'Готовность к командировкам',\n", + " estimator='median'\n", + " \n", + ")\n", + "barplot.set_title('Диаграмма зависимости желаемой заработной платы от готовности к переезду и командировкам')" + ] + }, + { + "cell_type": "markdown", + "id": "efcf512a-4918-4c3f-9357-b3035f9e5f5f", + "metadata": {}, + "source": [ + "На графике наглядно видно, что готовые к командировкам и переезду ожидают большую зарплату. Разница в ожиданиях зарплаты состовляет 20%-40%." + ] + }, + { + "cell_type": "markdown", + "id": "7b02bb86-0546-40fb-94a4-242f050a5c57", + "metadata": {}, + "source": [ + "7. Постройте сводную таблицу, иллюстрирующую зависимость **медианной** желаемой заработной платы от возраста (**\"Возраст\"**) и образования (**\"Образование\"**). На полученной сводной таблице постройте **тепловую карту**. Проанализируйте тепловую карту, сравнив показатели внутри групп." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5a0e32a4-e68a-4365-8a39-2fe24c542c36", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "coloraxis": "coloraxis", + "hovertemplate": "Возраст: %{x}
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"white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + } + }, + "shapedefaults": { + "line": { + "color": "#2a3f5f" + } + }, + "ternary": { + "aaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "baxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "bgcolor": "#E5ECF6", + "caxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + } + }, + "title": { + "x": 0.05 + }, + "xaxis": { + "automargin": true, + "gridcolor": "white", + "linecolor": "white", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "white", + "zerolinewidth": 2 + }, + "yaxis": { + "automargin": true, + "gridcolor": "white", + "linecolor": "white", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "white", + "zerolinewidth": 2 + } + } + }, + "title": { + "text": "Тепловая карта распределения возраста по категориям образования" + }, + "xaxis": { + "anchor": "y", + "constrain": "domain", + "domain": [ + 0, + 1 + ], + "scaleanchor": "y", + "title": { + "text": "Возраст" + } + }, + "yaxis": { + "anchor": "x", + "autorange": "reversed", + "constrain": "domain", + "domain": [ + 0, + 1 + ], + "title": { + "text": "Образование" + } + } + } + } + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "heat_data = df.pivot_table(aggfunc='median', index='Образование', columns='Возраст', values='ЗП (руб)')\n", + "\n", + "fig = px.imshow(heat_data, title='Тепловая карта распределения возраста по категориям образования')\n", + "fig.show()\n", + "\n", + "# fig.write_html(\"Plotly_charts/6.html\")" + ] + }, + { + "cell_type": "markdown", + "id": "7aade1c2-16a0-418c-8427-ad93927c7705", + "metadata": {}, + "source": [ + "Сразу бросается в глаза то, что люди с высшим образование почти имеют большие требования к зарплате.После них же идут люди с неоконченным высшим, а самые темные участки карты достались людям со средним специальным образованием, хотя и у них присутствуют яркие значения, целое одно - в среднем в возрасте 67 лет. ну и выброс в 100 лет так же наблюдается в средне специальном секторе" + ] + }, + { + "cell_type": "markdown", + "id": "d0247088-9003-455b-a591-a44650f487c3", + "metadata": {}, + "source": [ + "8. Постройте **диаграмму рассеяния**, показывающую зависимость опыта работы (**\"Опыт работы (месяц)\"**) от возраста (**\"Возраст\"**). Опыт работы переведите из месяцев в года, чтобы признаки были в едином масштабе. Постройте на графике дополнительно прямую, проходящую через точки (0, 0) и (100, 100). Данная прямая соответствует значениям, когда опыт работы равен возрасту человека. Точки, лежащие на этой прямой и выше нее - аномалии в наших данных (опыт работы больше либо равен возрасту соискателя)" + ] + }, + { + "cell_type": "code", + "execution_count": 79, + "id": "a4143e50-b900-4ec8-9ae9-fc3ec3347287", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/vc/zgch74_n23sckf5dm03_fq380000gn/T/ipykernel_1042/1477697000.py:2: SettingWithCopyWarning:\n", + "\n", + "\n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + "\n" + ] + }, + { + "data": { + "image/png": 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}, + "zaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + } + }, + "shapedefaults": { + "line": { + "color": "#2a3f5f" + } + }, + "ternary": { + "aaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "baxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "bgcolor": "#E5ECF6", + "caxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + } + }, + "title": { + "x": 0.05 + }, + "xaxis": { + "automargin": true, + "gridcolor": "white", + "linecolor": "white", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "white", + "zerolinewidth": 2 + }, + "yaxis": { + "automargin": true, + "gridcolor": "white", + "linecolor": "white", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "white", + "zerolinewidth": 2 + } + } + }, + "title": { + "text": "Топ 5 самых тяжелых должностей по признаку порога вхождения: " + }, + "width": 900, + "xaxis": { + "anchor": "y", + "domain": [ + 0, + 1 + ], + "title": { + "text": "Ищет работу на должность:" + } + }, + "yaxis": { + "anchor": "x", + "domain": [ + 0, + 1 + ], + "title": { + "text": "Опыт работы (месяц)" + } + } + } + } + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "top_5_data = df.groupby(by='Ищет работу на должность:')['ЗП (руб)'].mean().nlargest(5)\n", + "fig = px.bar(\n", + " top_5_data, \n", + " width=900,\n", + " height=800,\n", + " title='Топ 5 самых высокооплачиваемых должностей'\n", + " \n", + ")\n", + "fig.update_layout(\n", + " yaxis_title='ЗП (руб)'\n", + ")\n", + "fig.show()\n", + "\n", + "hard_jobs_df = df.groupby(by='Ищет работу на должность:')['Опыт работы (месяц)'].median().nlargest(5)\n", + "\n", + "\n", + "fig2 = px.bar(\n", + " hard_jobs_df,\n", + " width=900,\n", + " height=800,\n", + " title='Топ 5 самых тяжелых должностей по признаку порога вхождения: ',\n", + " color_discrete_sequence=px.colors.qualitative.Set2\n", + " \n", + ")\n", + "fig2.update_layout(yaxis_title='Опыт работы (месяц)')\n", + "fig2.show()\n", + "\n", + "# fig.write_html(\"Plotly_charts/Dop:8-1.html\")\n", + "# fig2.write_html(\"Plotly_charts/Dop:8-2.html\")" + ] + }, + { + "cell_type": "markdown", + "id": "04966e40-3bc3-4dc8-9ba2-e80580ce3abc", + "metadata": {}, + "source": [ + "1 график показывает топ 5 самых высокооплачиваемых должностей по мнению ищущих работу. Как видно на графике первые 2 места занимают менеджеры - что впринципе логично, у менеджеров большие требования :) \n", + "\n", + "UPD: для правильного отображения первого графика требуется дополнительно запустить данную ячейку кода, если до этого были запущенны все ячейки разом, так как изначально в датафрейме присутствовали выбросы, которые исказили данный график, а выбросы эти были удалены ячейками ниже, так что запустите данную ячейку еще раз!\n", + "\n", + "2 график же отображает топ 5 самых тяжелых должностей по мерке требования к опыту работы. Так, судя по графику самая требовательная к опыту работа - Инженер слаботочных систем, чуть меньше требует должность Эксперта по ИнфоБезу." + ] + }, + { + "cell_type": "markdown", + "id": "b1ef156a-efcb-49ce-bb23-90763e7f35b2", + "metadata": { + "tags": [] + }, + "source": [ + "# Очистка данных" + ] + }, + { + "cell_type": "markdown", + "id": "b9cd50a0-992d-4988-8463-2b2b1c4629b4", + "metadata": {}, + "source": [ + "1. Начнем с дубликатов в наших данных. Найдите **полные дубликаты** в таблице с резюме и удалите их. " + ] + }, + { + "cell_type": "code", + "execution_count": 81, + "id": "54ab0e50-c0f1-47cc-a54b-bc5c8bc363f9", + "metadata": {}, + "outputs": [], + "source": [ + "df = df.drop_duplicates(subset=df.columns)" + ] + }, + { + "cell_type": "markdown", + "id": "4dea20cb-a36b-474a-a0b9-e08e8e3857a5", + "metadata": {}, + "source": [ + "2. Займемся пропусками. Выведите информацию **о числе пропусков** в столбцах. " + ] + }, + { + "cell_type": "code", + "execution_count": 82, + "id": "9c61b3c0-bb66-46c9-ae1e-81d3f5752b4a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Index: 44583 entries, 0 to 44743\n", + "Data columns (total 23 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 Ищет работу на должность: 44583 non-null object \n", + " 1 Последнее/нынешнее место работы 44582 non-null object \n", + " 2 Последняя/нынешняя должность 44581 non-null object \n", + " 3 Обновление резюме 44583 non-null datetime64[ns]\n", + " 4 Авто 44583 non-null object \n", + " 5 Образование 44583 non-null object \n", + " 6 Пол 44583 non-null object \n", + " 7 Возраст 44583 non-null int64 \n", + " 8 Опыт работы (месяц) 44415 non-null float64 \n", + " 9 Город 44583 non-null object \n", + " 10 Готовность к переезду 44583 non-null bool \n", + " 11 Готовность к командировкам 44583 non-null bool \n", + " 12 стажировка 44583 non-null bool \n", + " 13 проектная работа 44583 non-null bool \n", + " 14 волонтерство 44583 non-null bool \n", + " 15 частичная занятость 44583 non-null bool \n", + " 16 полная занятость 44583 non-null bool \n", + " 17 вахтовый метод 44583 non-null bool \n", + " 18 гибкий график 44583 non-null bool \n", + " 19 полный день 44583 non-null bool \n", + " 20 сменный график 44583 non-null bool \n", + " 21 удаленная работа 44583 non-null bool \n", + " 22 ЗП (руб) 44583 non-null float64 \n", + "dtypes: bool(12), datetime64[ns](1), float64(2), int64(1), object(7)\n", + "memory usage: 4.6+ MB\n" + ] + } + ], + "source": [ + "df.info()" + ] + }, + { + "cell_type": "markdown", + "id": "eedceb80-c5ca-480e-b27a-278dfb8b4438", + "metadata": {}, + "source": [ + "3. Итак, у нас есть пропуски в 3ех столбцах: **\"Опыт работы (месяц)\"**, **\"Последнее/нынешнее место работы\"**, **\"Последняя/нынешняя должность\"**. Поступим следующим образом: удалите строки, где есть пропуск в столбцах с местом работы и должностью. Пропуски в столбце с опытом работы заполните **медианным** значением." + ] + }, + { + "cell_type": "code", + "execution_count": 83, + "id": "69621713-0cc5-4f1b-a1d7-bb144fa6148c", + "metadata": {}, + "outputs": [], + "source": [ + "df = df.fillna({'Опыт работы (месяц)' : df['Опыт работы (месяц)'].median()})\n", + "df = df.dropna(how='any')" + ] + }, + { + "cell_type": "markdown", + "id": "1be1e9c6-37cf-4a5f-bfeb-04cab799dc80", + "metadata": {}, + "source": [ + "4. Мы добрались до ликвидации выбросов. Сначала очистим данные вручную. Удалите резюме, в которых указана заработная плата либо выше 1 млн. рублей, либо ниже 1 тыс. рублей." + ] + }, + { + "cell_type": "code", + "execution_count": 84, + "id": "0036a355-9ccf-4564-8b01-73d8194297e7", + "metadata": {}, + "outputs": [], + "source": [ + "outliers = df[(df['ЗП (руб)'] > 1000000) | (df['ЗП (руб)'] < 1000)]\n", + "df = df.drop(outliers.index, axis=0)" + ] + }, + { + "cell_type": "markdown", + "id": "ec3c8db7-fa21-4a41-a994-1af63742a642", + "metadata": {}, + "source": [ + "5. В процессе разведывательного анализа мы обнаружили резюме, в которых **опыт работы в годах превышал возраст соискателя**. Найдите такие резюме и удалите их из данных\n" + ] + }, + { + "cell_type": "code", + "execution_count": 85, + "id": "949132bc-d5b6-4611-8218-0b9a504d74f4", + "metadata": {}, + "outputs": [], + "source": [ + "outliers = df[df['Возраст'] < (df['Опыт работы (месяц)']/12)]\n", + "df = df.drop(outliers.index, axis=0)" + ] + }, + { + "cell_type": "markdown", + "id": "1041c17a-ca8f-4543-a0ca-32778914fb00", + "metadata": {}, + "source": [ + "6. В результате анализа мы обнаружили потенциальные выбросы в признаке **\"Возраст\"**. Это оказались резюме людей чересчур преклонного возраста для поиска работы. Попробуйте построить распределение признака в **логарифмическом масштабе**. Добавьте к графику линии, отображающие **среднее и границы интервала метода трех сигм**. Напомним, сделать это можно с помощью метода axvline. Например, для построение линии среднего будет иметь вид:\n", + "\n", + "`histplot.axvline(log_age.mean(), color='k', lw=2)`\n", + "\n", + "В какую сторону асимметрично логарифмическое распределение? Напишите об этом в комментарии к графику.\n", + "Найдите выбросы с помощью метода z-отклонения и удалите их из данных, используйте логарифмический масштаб. Давайте сделаем послабление на **1 сигму** (возьмите 4 сигмы) в **правую сторону**.\n", + "\n", + "Выведите таблицу с полученными выбросами и оцените, с каким возрастом соискатели попадают под категорию выбросов?" + ] + }, + { + "cell_type": "code", + "execution_count": 86, + "id": "6a743d39-5204-48d3-8271-b9f88e68d40e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "np.float64(0.4523422276053838)" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " Ищет работу на должность: Последнее/нынешнее место работы \\\n", + "31137 Менеджер по работе с клиентами ООО \"ФёрстКэшКомпани\" \n", + "32950 Тестировщик игр OOO ЖМЫХ \n", + "33654 Frontend-разработчик Freelance \n", + "\n", + " Последняя/нынешняя должность Обновление резюме Авто \\\n", + "31137 Менеджер по работе с клиентами 2019-04-06 Не указано \n", + "32950 Тестировщик ПО 2019-04-09 Не указано \n", + "33654 Frontend-разработчик 2019-04-19 Не указано \n", + "\n", + " Образование Пол Возраст Опыт работы (месяц) \\\n", + "31137 Среднее М 15 2.0 \n", + "32950 Среднее специальное М 15 3.0 \n", + "33654 Среднее специальное М 100 30.0 \n", + "\n", + " Город ... проектная работа волонтерство \\\n", + "31137 Санкт-Петербург ... False False \n", + "32950 другие ... False False \n", + "33654 Санкт-Петербург ... True False \n", + "\n", + " частичная занятость полная занятость вахтовый метод гибкий график \\\n", + "31137 True False False True \n", + "32950 False True False False \n", + "33654 True True False True \n", + "\n", + " полный день сменный график удаленная работа ЗП (руб) \n", + "31137 False True True 10000.0 \n", + "32950 True False False 2000.0 \n", + "33654 True False True 60000.0 \n", + "\n", + "[3 rows x 23 columns]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def z_otklon(df, feature, log_scale=False, left=3, right=3):\n", + " if log_scale:\n", + " x = np.log(df[feature])\n", + " else:\n", + " x = df[feature]\n", + " sigma = x.mean()\n", + " mu = x.std()\n", + " \n", + " left_bound = sigma - left*mu\n", + " right_bound = sigma + right*mu\n", + " \n", + " outliers = x[(x < left_bound) | (x > right_bound)]\n", + " cleaned = x[(x > left_bound) & (x < right_bound)]\n", + " \n", + " return outliers, cleaned, (left_bound, right_bound)\n", + " \n", + "hist_data = np.log(df['Возраст'])\n", + "fig = plt.figure(figsize=(10,5))\n", + "hist = sns.histplot(\n", + " df,\n", + " x = np.log(df['Возраст']),\n", + ")\n", + "hist.set_title('Логарифмическое распределение возраста методом трех сигм')\n", + "hist.axvline(hist_data.mean(), color='k', lw=2)\n", + "hist.axvline(z_otklon(df,'Возраст', log_scale=True)[2][0], color='k', lw=2)\n", + "hist.axvline(z_otklon(df,'Возраст', log_scale=True)[2][1], color='k', lw=2)\n", + "\n", + "\n", + "outliers, _ , _= z_otklon(df, 'Возраст', log_scale=True, right=4)\n", + "display(hist_data.skew())\n", + "display(df.loc[outliers.index])\n", + "\n", + "df = df.drop(outliers.index, axis=0)" + ] + }, + { + "cell_type": "markdown", + "id": "4fb5c2b7", + "metadata": {}, + "source": [ + "На данном графике видно что распределение смещено в правую сторону, это видно на графике, и можно проверить методом .skew() результат 0.45 \n", + "\n", + "по методу z-отклонений под категорию выбросов попали люди с возрастом 15 и 100 лет. " + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.0" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +}