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   "source": [
    "# 1. Обзор библиотеки pandas, важность подготовки данных\n",
    "\n",
    "## 1.1. [Pandas](http://pandas.pydata.org/) - библиотека Python для анализа данных\n",
    "\n",
    "* Быстрая и эффективная работа с объектами таблиц (DataFrame) с индексацией; \n",
    "* **Чтение и запись данных между данными в памяти компьютера и файлами в разных форматах: CSV, текстовые файлы, Microsoft Excel, SQL-базы данных, HDF5;**\n",
    "* **Обработка пропущенных и неформатированных данных;**\n",
    "* Изменение формы, расположения, структуры данных;\n",
    "* Срезы на основе индексов и названий колонок, создание подмножеств для больших объёмов данных;\n",
    "\n",
    "Основными структурами данных в pandas являются классы *Series* и *DataFrame*.\n",
    "\n",
    "**Series** - это одномерный индексированный массив данных одного типа.\n",
    "\n",
    "**DataFrame** – двухмерная структура данных, таблица, каждый столбец которой содержит данные одного типа.\n",
    "\n",
    "Часто загружаемые с помощью pandas данные хранятся как таблицы. Популярные форматы таблиц:\n",
    "    - .csv = Comma Separated Valuse, .tsv = Tab Separated Values,\n",
    "    - Microsoft Excel: .xls, .xlsx,\n",
    "    - HDF5,\n",
    "    - базы данных SQL.\n",
    "    \n",
    "Сегодня мы познакомимся с чтением данных из формата CSV, фильтрацией и базовой подготовкой данных для анализа."
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "## 1.2. Признаки (features) в анализе данных. Категориальные и числовые признаки\n",
    "\n",
    "Наиболее распространённая задача анализа данных - обучение с учителем. Имеется множество объектов и множество возможных ответов. Существует некоторая зависимость между ответами и объектами, но она неизвестна. Известна только конечная совокупность прецедентов — пар «объект, ответ», называемая обучающей выборкой. На основе этих данных требуется восстановить зависимость, то есть построить алгоритм, способный для любого объекта выдать достаточно точный ответ. (По материалу  [machinelearning.ru](http://www.machinelearning.ru/wiki/index.php?title=%D0%9E%D0%B1%D1%83%D1%87%D0%B5%D0%BD%D0%B8%D0%B5_%D1%81_%D1%83%D1%87%D0%B8%D1%82%D0%B5%D0%BB%D0%B5%D0%BC).)\n",
    "\n",
    "Часто встречающимся типом входных данных является **признаковое описание** (матрица объекты-признаки). Каждый объект описывается набором своих характеристик, называемых признаками. Признаки могут быть числовыми или нечисловыми.\n",
    "\n",
    "**Числовые признаки** - характеристики данных, выраженные в числах: возраст, количество, размер детали, цена и т. д. Чаще всего такие признаки не требуют особой обработки.\n",
    "\n",
    "**Категориальные признаки** - определяют принадлежность к какой-то категории. Примеры: пол, город, время года, номер группы, категория товаров и т.п. Для автоматической обработки таких признаков вместо исходного значения значения категории (например, \"М\", \"Ж\" или \"Москва\", \"Лондон\") часто необходим перевод в числовую форму.\n",
    "\n",
    "Подробнее о кодировке категориальных признаков: \n",
    "    https://habr.com/ru/company/ods/blog/326418/\n",
    "    http://datareview.info/article/universalnyj-podxod-pochti-k-lyuboj-zadache-mashinnogo-obucheniya/\n",
    "    \n",
    "Иногда выделяют **текстовые признаки**. Примеры: полное имя, комментарий, описание. Такие признаки могут оказаться незначимыми в задаче, тогда их можно исключить, иначе к ним тоже применяется обработка и кодировка."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Подготовка данных к анализу** включает в себя много этапов: очистка и исправление неверных данных, отбор и кодировка признаков, трансформация данных, удаление и создание новых признаков и т. д.\n",
    "\n",
    "Подробная классификация процедур подготовки данных: http://www.machinelearning.ru/wiki/images/9/91/PZAD2017_07_datapreprocessing.pdf"
   ]
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Title</th>\n",
       "      <th>Year</th>\n",
       "      <th>Genres</th>\n",
       "      <th>Language</th>\n",
       "      <th>Country</th>\n",
       "      <th>Content Rating</th>\n",
       "      <th>Duration</th>\n",
       "      <th>Aspect Ratio</th>\n",
       "      <th>Budget</th>\n",
       "      <th>Gross Earnings</th>\n",
       "      <th>...</th>\n",
       "      <th>Facebook Likes - Actor 1</th>\n",
       "      <th>Facebook Likes - Actor 2</th>\n",
       "      <th>Facebook Likes - Actor 3</th>\n",
       "      <th>Facebook Likes - cast Total</th>\n",
       "      <th>Facebook likes - Movie</th>\n",
       "      <th>Facenumber in posters</th>\n",
       "      <th>User Votes</th>\n",
       "      <th>Reviews by Users</th>\n",
       "      <th>Reviews by Crtiics</th>\n",
       "      <th>IMDB Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>127 Hours</td>\n",
       "      <td>2010</td>\n",
       "      <td>Adventure|Biography|Drama|Thriller</td>\n",
       "      <td>English</td>\n",
       "      <td>USA</td>\n",
       "      <td>R</td>\n",
       "      <td>94.0</td>\n",
       "      <td>1.85</td>\n",
       "      <td>18000000.0</td>\n",
       "      <td>18329466.0</td>\n",
       "      <td>...</td>\n",
       "      <td>11000</td>\n",
       "      <td>642</td>\n",
       "      <td>223</td>\n",
       "      <td>11984</td>\n",
       "      <td>63000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>279179</td>\n",
       "      <td>440.0</td>\n",
       "      <td>450.0</td>\n",
       "      <td>7.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3 Backyards</td>\n",
       "      <td>2010</td>\n",
       "      <td>Drama</td>\n",
       "      <td>English</td>\n",
       "      <td>USA</td>\n",
       "      <td>R</td>\n",
       "      <td>88.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>300000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>795</td>\n",
       "      <td>659</td>\n",
       "      <td>301</td>\n",
       "      <td>1884</td>\n",
       "      <td>92</td>\n",
       "      <td>0.0</td>\n",
       "      <td>554</td>\n",
       "      <td>23.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>5.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2010</td>\n",
       "      <td>Comedy|Drama|Romance</td>\n",
       "      <td>German</td>\n",
       "      <td>Germany</td>\n",
       "      <td>Unrated</td>\n",
       "      <td>119.0</td>\n",
       "      <td>2.35</td>\n",
       "      <td>NaN</td>\n",
       "      <td>59774.0</td>\n",
       "      <td>...</td>\n",
       "      <td>24</td>\n",
       "      <td>20</td>\n",
       "      <td>9</td>\n",
       "      <td>69</td>\n",
       "      <td>2000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4212</td>\n",
       "      <td>18.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>6.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8: The Mormon Proposition</td>\n",
       "      <td>2010</td>\n",
       "      <td>Documentary</td>\n",
       "      <td>English</td>\n",
       "      <td>USA</td>\n",
       "      <td>R</td>\n",
       "      <td>80.0</td>\n",
       "      <td>1.78</td>\n",
       "      <td>2500000.0</td>\n",
       "      <td>99851.0</td>\n",
       "      <td>...</td>\n",
       "      <td>191</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>210</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1138</td>\n",
       "      <td>30.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>7.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A Turtle's Tale: Sammy's Adventures</td>\n",
       "      <td>2010</td>\n",
       "      <td>Adventure|Animation|Family</td>\n",
       "      <td>English</td>\n",
       "      <td>France</td>\n",
       "      <td>PG</td>\n",
       "      <td>88.0</td>\n",
       "      <td>2.35</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>783</td>\n",
       "      <td>749</td>\n",
       "      <td>602</td>\n",
       "      <td>3874</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5385</td>\n",
       "      <td>22.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>6.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 25 columns</p>\n",
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       "                                  Title  Year  \\\n",
       "0                            127 Hours   2010   \n",
       "1                          3 Backyards   2010   \n",
       "2                                    3   2010   \n",
       "3            8: The Mormon Proposition   2010   \n",
       "4  A Turtle's Tale: Sammy's Adventures   2010   \n",
       "\n",
       "                               Genres Language  Country Content Rating  \\\n",
       "0  Adventure|Biography|Drama|Thriller  English      USA              R   \n",
       "1                               Drama  English      USA              R   \n",
       "2                Comedy|Drama|Romance   German  Germany        Unrated   \n",
       "3                         Documentary  English      USA              R   \n",
       "4          Adventure|Animation|Family  English   France             PG   \n",
       "\n",
       "   Duration  Aspect Ratio      Budget  Gross Earnings     ...      \\\n",
       "0      94.0          1.85  18000000.0      18329466.0     ...       \n",
       "1      88.0           NaN    300000.0             NaN     ...       \n",
       "2     119.0          2.35         NaN         59774.0     ...       \n",
       "3      80.0          1.78   2500000.0         99851.0     ...       \n",
       "4      88.0          2.35         NaN             NaN     ...       \n",
       "\n",
       "  Facebook Likes - Actor 1 Facebook Likes - Actor 2 Facebook Likes - Actor 3  \\\n",
       "0                    11000                      642                      223   \n",
       "1                      795                      659                      301   \n",
       "2                       24                       20                        9   \n",
       "3                      191                       12                        5   \n",
       "4                      783                      749                      602   \n",
       "\n",
       "  Facebook Likes - cast Total  Facebook likes - Movie  Facenumber in posters  \\\n",
       "0                       11984                   63000                    0.0   \n",
       "1                        1884                      92                    0.0   \n",
       "2                          69                    2000                    0.0   \n",
       "3                         210                       0                    0.0   \n",
       "4                        3874                       0                    2.0   \n",
       "\n",
       "   User Votes  Reviews by Users  Reviews by Crtiics  IMDB Score  \n",
       "0      279179             440.0               450.0         7.6  \n",
       "1         554              23.0                20.0         5.2  \n",
       "2        4212              18.0                76.0         6.8  \n",
       "3        1138              30.0                28.0         7.1  \n",
       "4        5385              22.0                56.0         6.1  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
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   ],
   "source": [
    "# Loading (importing) pandas module with often used alias \"pd\"\n",
    "# Загрузка модуля pandas с коротким именем pd\n",
    "import pandas as pd\n",
    "\n",
    "# load Excel file with pandas as an example\n",
    "# Зазгрузка таблицы Excel \n",
    "excel_file = 'mov.xls'\n",
    "df = pd.read_excel(excel_file)\n",
    "\n",
    "# Review top rows of data\n",
    "# Просмотр верхней части таблицы\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Аккуратные данные (Tidy Data)\n",
    "\n",
    "## 2.1. Принципы аккуратных данных\n",
    "\n",
    "**“Data cleaning is one of the most frequent task in data science. No matter what kind of data you are dealing with or what kind of analysis you are performing, you will have to clean the data at some point.”** -- Jean-Nicholas Hould http://www.jeannicholashould.com/tidy-data-in-python.html\n",
    "\n",
    "\"**Аккуратные данные**\" - термин и набор правил для организации данных при статистических исследованиях из статьи Hadley Wickham, **[Tidy Data](http://www.jstatsoft.org/v59/i10)**, Vol. 59, Issue 10, Sep 2014, Journal of Statistical Software.\n",
    "\n",
    "* **Переменная (variable)** - оценка какого-то атрибута. Рост, вес, пол, день недели, размер... Переменная содержит все значения, которые отображают один и тот же атрибут всех объектов.\n",
    "* **Значение (value)**: конкретный результат измерения атрибута. 152 см, 80 кг, М/Ж, XXL. Каждое значение принадлежит переменной и наблюдению.\n",
    "* **Наблюдение (observation)** содержит все значения атрибутов, измеренные для одного и того же объекта (такого как человек, день, кинофильм).\n",
    "\n",
    "#### Три принципа аккуратных данных (Tidy Data) [http://vita.had.co.nz/papers/tidy-data.pdf]\n",
    "1. Каждая переменная формирует колонку и имеет значения\n",
    "2. Каждое наблюдение формирует строку (ряд)\n",
    "3. Каждый вид эксперимента формирует таблицу. То есть каждый факт выражается только в одном месте. Примеры: баллы студента по разным предметам и данные самого студента должны быть в разных таблицах, не должно быть дубликатов и разночтений.\n",
    "4. (При наличии многих таблиц, нужно включать колонки, которые связывают их https://en.wikipedia.org/wiki/Tidy_data)\n",
    "\n",
    "Иллюстрация принципов аккуратных данных из статьи https://r4ds.had.co.nz/tidy-data.html\n",
    "![Вид CSV файла](http://antakova.ru/wp-content/uploads/2019/01/tidy1.png \"Принципы аккуратных данных\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.2.  Распространённые проблемы неаккуратности данных\n",
    "\n",
    "Связанные с организацией данных в одной таблице:\n",
    "* Заголовки колонок - это значения, а не имена переменных\n",
    "* Несколько переменных хранятся в одной колонке\n",
    "* Переменные хранятся в колонках и строках\n",
    "\n",
    "Связанные с организацией группы таблиц:\n",
    "* Много типов наблюдений хранится в одной таблице\n",
    "* Один и тот же тип наблюдений хранится во многих таблицах\n",
    "\n",
    "Наш фокус в этом мастер-классе - организация данных в **_одной таблице_**.\n",
    "\n",
    "### Пример: заголовки колонок - это не имена переменных\n",
    "\n",
    "Из статьи [Tidy Data](http://vita.had.co.nz/papers/tidy-data.pdf)\n",
    "\n",
    "В этой таблице заголовок показывает доход в нескольких колонках.\n",
    "\n",
    "| religion     |    10k-20k    |  20k-30k  |\n",
    "| -------------|:-------------:|----------:|\n",
    "| Agnostic     | 27            | 34        |\n",
    "| Atheist      | 12            | 25        |\n",
    "| Buddhist     | 20            | 27        |\n",
    "\n",
    "Переменные в этом наборе - вид религии, доход (категориальный признак) и количество (или частота) людей такого дохода и религии. Для исправления этой таблицы по принципу аккуратных данных нужно из доход сделать переменной-колонкой, а её значения из бывших колонок сделать строками.\n",
    "\n",
    "| religion     |    income     | frequency |\n",
    "| -------------|:-------------:|----------:|\n",
    "| Agnostic     | 10k-20k       | 27        |\n",
    "| Agnostic     | 20k-30k       | 34        |\n",
    "| Atheist      | 10k-20k       | 12        |\n",
    "| Atheist      | 20k-30k       | 25        |\n",
    "| Buddhist     | 10k-20k       | 20        |\n",
    "| Buddhist     | 20k-30k       | 27        |\n",
    "\n",
    "### Несколько переменных хранятся в одной колонке\n",
    "\n",
    "Источник примера - [tutorial](http://garrettgman.github.io/tidying/)\n",
    "\n",
    "Колонка \"ключ\" (key) меняет тип переменной, представленной в соседней колонке, и колонка \"значение\" value имеет либо количество случаев cases, либо общую численность населения в зависимости от ключа. Это небоходимо разделить по колонкам.\n",
    "\n",
    "| country      |    year    | key        | value      |\n",
    "| -------------|:----------:|-----------:|-----------:|\n",
    "| Afghanistan  | 1999       | cases      | 745        |\n",
    "| Afghanistan  | 1999       | population | 19987071   |\n",
    "| Afghanistan  | 2000       | cases      | 2666       |\n",
    "| Afghanistan  | 2000       | population | 20595360   |\n",
    "| Brazil       | 1999       | cases      | 37737      |\n",
    "| Brazil       | 1999       | population | 172006362  |\n",
    "| Brazil       | 2000       | cases      | 80488      |\n",
    "| Brazil       | 2000       | population | 174504898  |\n",
    "\n",
    "В такой таблице неудобно считать средние, максимумы или минимумы по значениям cases, population.\n",
    "Аккуратное и более удобное расположение данных - создать колонки cases, population.\n",
    "\n",
    "| country      |    year    | cases      | population |\n",
    "| -------------|:----------:|-----------:|-----------:|\n",
    "| Afghanistan  | 1999       | 745        | 19987071   |\n",
    "| Afghanistan  | 2000       | 2666       | 20595360   |\n",
    "| Brazil       | 1999       | 37737      | 172006362  |\n",
    "| Brazil       | 2000       | 80488      | 174504898  |\n"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Переменные хранятся в колонках и строках\n",
    "\n",
    "**Пример 1.** Иллюстрация не полностью аккуратных данных из статьи http://garrettgman.github.io/tidying/\n",
    "![Переменные в колонках и строка](tidy7.png \"Не самые аккуратные данные\")\n",
    "\n",
    "**Пример 2.** По материалу https://habr.com/ru/post/248741/ \n",
    "\n",
    "Несколько студентов учились по разным предметам (classes) в течение семестра (term) и сдавали тесты в середине семестра (midterm) и в конце (final). Имеется таблица оценок студентов по всем тестам, которые они сдали. \n",
    "\n",
    "| name      | test         | class1     | class2 | class3 |\n",
    "| ----------|:------------:|-----------:|-------:|-------:|\n",
    "| Sally     | midterm      | A          | NA     |  NA    |\n",
    "| Sally     | final        | C          | NA     |  NA    |\n",
    "| Jeff      | midterm      | NA         | D      | A      |\n",
    "| Jeff      | final        | NA         | A      | C      |\n",
    "| Roger     | midterm      | NA         | NA     | A      |\n",
    "| Roger     | final        | NA         | NA     | C      |\n",
    "\n",
    "Столбцы class1, class2, class3 содержат значения одной переменной class. А значения столбца test (midterm, final) должны быть переменными и содержать результат теста для каждого студента.\n",
    "\n",
    "Создадим новую переменную class со значениями 1-3 и раскроем значения столбца test в переменные final-score и midterm-score.\n",
    "\n",
    "| name      | class        | final-score | midterm-score |\n",
    "| ----------|:------------:|------------:|--------------:|\n",
    "| Sally     | 1            | C           | A             |\n",
    "| Jeff      | 2            | A           | D             |\n",
    "| Jeff      | 3            | C           | A             |\n",
    "| Roger     | 3            | C           | A             |"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. Загрузка файлов CSV через Excel и pandas\n",
    "\n",
    "## 3.1. Просмотр \"сырого\" файла CSV\n",
    "\n",
    "![Вид CSV файла](http://antakova.ru/wp-content/uploads/2019/01/csv.png \"CSV файл\")\n",
    "\n",
    "## 3.2. Удобный просмотр файла CSV с помощью Excel\n",
    "\n",
    "Метод получения Excel таблицы из CSV - это команда в меню **Data > Text to Columns**. В диалоге перевода данных в Excel много параметров (тип разделения, разделитель, что делать с дополнительными пробельными символами и т. д.). Результат - читаемая таблица.\n",
    "\n",
    "![Обработанный CSV в Excel](http://antakova.ru/wp-content/uploads/2019/01/excel.jpg \"Обработанный CSV в Excel\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "## 3.3. Загрузка CSV через методы pandas. Настройка параметров загрузки"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>This is a dataset of red wine quality with about 10 parameters</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>fixed acidity;\"volatile acidity\";\"citric acid\"...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7.4;0.7;0;1.9;0.076;11;34;0.9978;3.51;0.56;9.4;5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.8;0.88;0;2.6;0.098;25;67;0.9968;3.2;0.68;9.8;5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7.8;0.76;0.04;2.3;0.092;15;54;0.997;3.26;0.65;...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11.2;0.28;0.56;1.9;0.075;17;60;0.998;3.16;0.58...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  This is a dataset of red wine quality with about 10 parameters\n",
       "\n",
       "0  fixed acidity;\"volatile acidity\";\"citric acid\"...             \n",
       "1   7.4;0.7;0;1.9;0.076;11;34;0.9978;3.51;0.56;9.4;5             \n",
       "2   7.8;0.88;0;2.6;0.098;25;67;0.9968;3.2;0.68;9.8;5             \n",
       "3  7.8;0.76;0.04;2.3;0.092;15;54;0.997;3.26;0.65;...             \n",
       "4  11.2;0.28;0.56;1.9;0.075;17;60;0.998;3.16;0.58...             "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read data from file 'winequality-red.csv' in the same directory that your python script\n",
    "# Простое чтение файла без параметров\n",
    "wine_filename = \"winequality-red.csv\"\n",
    "data = pd.read_csv(wine_filename) \n",
    "\n",
    "# Check top/head lines of loaded data, but the table seems strange\n",
    "# Убедимся, что данные присутствуют, однако таблица не выглядит верной\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>speed</th>\n",
       "      <th>period</th>\n",
       "      <th>warning</th>\n",
       "      <th>pair</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>26</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>26</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>26</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>26</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  speed  period  warning  pair\n",
       "0           1     26       1        1     1\n",
       "1           2     26       1        1     1\n",
       "2           3     26       1        1     1\n",
       "3           4     26       1        1     1\n",
       "4           5     27       1        1     1"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Loading CSV file from WEB (URL)\n",
    "# Загрузка файла по WEB-адресу\n",
    "# May not work undef proxy\n",
    "url_csv = 'https://vincentarelbundock.github.io/Rdatasets/csv/boot/amis.csv'\n",
    "df2 = pd.read_csv(url_csv)\n",
    "\n",
    "df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on function read_csv in module pandas.io.parsers:\n",
      "\n",
      "read_csv(filepath_or_buffer, sep=',', delimiter=None, header='infer', names=None, index_col=None, usecols=None, squeeze=False, prefix=None, mangle_dupe_cols=True, dtype=None, engine=None, converters=None, true_values=None, false_values=None, skipinitialspace=False, skiprows=None, nrows=None, na_values=None, keep_default_na=True, na_filter=True, verbose=False, skip_blank_lines=True, parse_dates=False, infer_datetime_format=False, keep_date_col=False, date_parser=None, dayfirst=False, iterator=False, chunksize=None, compression='infer', thousands=None, decimal=b'.', lineterminator=None, quotechar='\"', quoting=0, escapechar=None, comment=None, encoding=None, dialect=None, tupleize_cols=None, error_bad_lines=True, warn_bad_lines=True, skipfooter=0, doublequote=True, delim_whitespace=False, low_memory=True, memory_map=False, float_precision=None)\n",
      "    Read CSV (comma-separated) file into DataFrame\n",
      "    \n",
      "    Also supports optionally iterating or breaking of the file\n",
      "    into chunks.\n",
      "    \n",
      "    Additional help can be found in the `online docs for IO Tools\n",
      "    <http://pandas.pydata.org/pandas-docs/stable/io.html>`_.\n",
      "    \n",
      "    Parameters\n",
      "    ----------\n",
      "    filepath_or_buffer : str, pathlib.Path, py._path.local.LocalPath or any \\\n",
      "    object with a read() method (such as a file handle or StringIO)\n",
      "        The string could be a URL. Valid URL schemes include http, ftp, s3, and\n",
      "        file. For file URLs, a host is expected. For instance, a local file could\n",
      "        be file://localhost/path/to/table.csv\n",
      "    sep : str, default ','\n",
      "        Delimiter to use. If sep is None, the C engine cannot automatically detect\n",
      "        the separator, but the Python parsing engine can, meaning the latter will\n",
      "        be used and automatically detect the separator by Python's builtin sniffer\n",
      "        tool, ``csv.Sniffer``. In addition, separators longer than 1 character and\n",
      "        different from ``'\\s+'`` will be interpreted as regular expressions and\n",
      "        will also force the use of the Python parsing engine. Note that regex\n",
      "        delimiters are prone to ignoring quoted data. Regex example: ``'\\r\\t'``\n",
      "    delimiter : str, default ``None``\n",
      "        Alternative argument name for sep.\n",
      "    delim_whitespace : boolean, default False\n",
      "        Specifies whether or not whitespace (e.g. ``' '`` or ``'\\t'``) will be\n",
      "        used as the sep. Equivalent to setting ``sep='\\s+'``. If this option\n",
      "        is set to True, nothing should be passed in for the ``delimiter``\n",
      "        parameter.\n",
      "    \n",
      "        .. versionadded:: 0.18.1 support for the Python parser.\n",
      "    \n",
      "    header : int or list of ints, default 'infer'\n",
      "        Row number(s) to use as the column names, and the start of the\n",
      "        data.  Default behavior is to infer the column names: if no names\n",
      "        are passed the behavior is identical to ``header=0`` and column\n",
      "        names are inferred from the first line of the file, if column\n",
      "        names are passed explicitly then the behavior is identical to\n",
      "        ``header=None``. Explicitly pass ``header=0`` to be able to\n",
      "        replace existing names. The header can be a list of integers that\n",
      "        specify row locations for a multi-index on the columns\n",
      "        e.g. [0,1,3]. Intervening rows that are not specified will be\n",
      "        skipped (e.g. 2 in this example is skipped). Note that this\n",
      "        parameter ignores commented lines and empty lines if\n",
      "        ``skip_blank_lines=True``, so header=0 denotes the first line of\n",
      "        data rather than the first line of the file.\n",
      "    names : array-like, default None\n",
      "        List of column names to use. If file contains no header row, then you\n",
      "        should explicitly pass header=None. Duplicates in this list will cause\n",
      "        a ``UserWarning`` to be issued.\n",
      "    index_col : int or sequence or False, default None\n",
      "        Column to use as the row labels of the DataFrame. If a sequence is given, a\n",
      "        MultiIndex is used. If you have a malformed file with delimiters at the end\n",
      "        of each line, you might consider index_col=False to force pandas to _not_\n",
      "        use the first column as the index (row names)\n",
      "    usecols : list-like or callable, default None\n",
      "        Return a subset of the columns. If list-like, all elements must either\n",
      "        be positional (i.e. integer indices into the document columns) or strings\n",
      "        that correspond to column names provided either by the user in `names` or\n",
      "        inferred from the document header row(s). For example, a valid list-like\n",
      "        `usecols` parameter would be [0, 1, 2] or ['foo', 'bar', 'baz']. Element\n",
      "        order is ignored, so ``usecols=[0, 1]`` is the same as ``[1, 0]``.\n",
      "        To instantiate a DataFrame from ``data`` with element order preserved use\n",
      "        ``pd.read_csv(data, usecols=['foo', 'bar'])[['foo', 'bar']]`` for columns\n",
      "        in ``['foo', 'bar']`` order or\n",
      "        ``pd.read_csv(data, usecols=['foo', 'bar'])[['bar', 'foo']]``\n",
      "        for ``['bar', 'foo']`` order.\n",
      "    \n",
      "        If callable, the callable function will be evaluated against the column\n",
      "        names, returning names where the callable function evaluates to True. An\n",
      "        example of a valid callable argument would be ``lambda x: x.upper() in\n",
      "        ['AAA', 'BBB', 'DDD']``. Using this parameter results in much faster\n",
      "        parsing time and lower memory usage.\n",
      "    squeeze : boolean, default False\n",
      "        If the parsed data only contains one column then return a Series\n",
      "    prefix : str, default None\n",
      "        Prefix to add to column numbers when no header, e.g. 'X' for X0, X1, ...\n",
      "    mangle_dupe_cols : boolean, default True\n",
      "        Duplicate columns will be specified as 'X', 'X.1', ...'X.N', rather than\n",
      "        'X'...'X'. Passing in False will cause data to be overwritten if there\n",
      "        are duplicate names in the columns.\n",
      "    dtype : Type name or dict of column -> type, default None\n",
      "        Data type for data or columns. E.g. {'a': np.float64, 'b': np.int32}\n",
      "        Use `str` or `object` together with suitable `na_values` settings\n",
      "        to preserve and not interpret dtype.\n",
      "        If converters are specified, they will be applied INSTEAD\n",
      "        of dtype conversion.\n",
      "    engine : {'c', 'python'}, optional\n",
      "        Parser engine to use. The C engine is faster while the python engine is\n",
      "        currently more feature-complete.\n",
      "    converters : dict, default None\n",
      "        Dict of functions for converting values in certain columns. Keys can either\n",
      "        be integers or column labels\n",
      "    true_values : list, default None\n",
      "        Values to consider as True\n",
      "    false_values : list, default None\n",
      "        Values to consider as False\n",
      "    skipinitialspace : boolean, default False\n",
      "        Skip spaces after delimiter.\n",
      "    skiprows : list-like or integer or callable, default None\n",
      "        Line numbers to skip (0-indexed) or number of lines to skip (int)\n",
      "        at the start of the file.\n",
      "    \n",
      "        If callable, the callable function will be evaluated against the row\n",
      "        indices, returning True if the row should be skipped and False otherwise.\n",
      "        An example of a valid callable argument would be ``lambda x: x in [0, 2]``.\n",
      "    skipfooter : int, default 0\n",
      "        Number of lines at bottom of file to skip (Unsupported with engine='c')\n",
      "    nrows : int, default None\n",
      "        Number of rows of file to read. Useful for reading pieces of large files\n",
      "    na_values : scalar, str, list-like, or dict, default None\n",
      "        Additional strings to recognize as NA/NaN. If dict passed, specific\n",
      "        per-column NA values.  By default the following values are interpreted as\n",
      "        NaN: '', '#N/A', '#N/A N/A', '#NA', '-1.#IND', '-1.#QNAN', '-NaN', '-nan',\n",
      "        '1.#IND', '1.#QNAN', 'N/A', 'NA', 'NULL', 'NaN', 'n/a', 'nan',\n",
      "        'null'.\n",
      "    keep_default_na : bool, default True\n",
      "        Whether or not to include the default NaN values when parsing the data.\n",
      "        Depending on whether `na_values` is passed in, the behavior is as follows:\n",
      "    \n",
      "        * If `keep_default_na` is True, and `na_values` are specified, `na_values`\n",
      "          is appended to the default NaN values used for parsing.\n",
      "        * If `keep_default_na` is True, and `na_values` are not specified, only\n",
      "          the default NaN values are used for parsing.\n",
      "        * If `keep_default_na` is False, and `na_values` are specified, only\n",
      "          the NaN values specified `na_values` are used for parsing.\n",
      "        * If `keep_default_na` is False, and `na_values` are not specified, no\n",
      "          strings will be parsed as NaN.\n",
      "    \n",
      "        Note that if `na_filter` is passed in as False, the `keep_default_na` and\n",
      "        `na_values` parameters will be ignored.\n",
      "    na_filter : boolean, default True\n",
      "        Detect missing value markers (empty strings and the value of na_values). In\n",
      "        data without any NAs, passing na_filter=False can improve the performance\n",
      "        of reading a large file\n",
      "    verbose : boolean, default False\n",
      "        Indicate number of NA values placed in non-numeric columns\n",
      "    skip_blank_lines : boolean, default True\n",
      "        If True, skip over blank lines rather than interpreting as NaN values\n",
      "    parse_dates : boolean or list of ints or names or list of lists or dict, default False\n",
      "    \n",
      "        * boolean. If True -> try parsing the index.\n",
      "        * list of ints or names. e.g. If [1, 2, 3] -> try parsing columns 1, 2, 3\n",
      "          each as a separate date column.\n",
      "        * list of lists. e.g.  If [[1, 3]] -> combine columns 1 and 3 and parse as\n",
      "          a single date column.\n",
      "        * dict, e.g. {'foo' : [1, 3]} -> parse columns 1, 3 as date and call result\n",
      "          'foo'\n",
      "    \n",
      "        If a column or index contains an unparseable date, the entire column or\n",
      "        index will be returned unaltered as an object data type. For non-standard\n",
      "        datetime parsing, use ``pd.to_datetime`` after ``pd.read_csv``\n",
      "    \n",
      "        Note: A fast-path exists for iso8601-formatted dates.\n",
      "    infer_datetime_format : boolean, default False\n",
      "        If True and `parse_dates` is enabled, pandas will attempt to infer the\n",
      "        format of the datetime strings in the columns, and if it can be inferred,\n",
      "        switch to a faster method of parsing them. In some cases this can increase\n",
      "        the parsing speed by 5-10x.\n",
      "    keep_date_col : boolean, default False\n",
      "        If True and `parse_dates` specifies combining multiple columns then\n",
      "        keep the original columns.\n",
      "    date_parser : function, default None\n",
      "        Function to use for converting a sequence of string columns to an array of\n",
      "        datetime instances. The default uses ``dateutil.parser.parser`` to do the\n",
      "        conversion. Pandas will try to call `date_parser` in three different ways,\n",
      "        advancing to the next if an exception occurs: 1) Pass one or more arrays\n",
      "        (as defined by `parse_dates`) as arguments; 2) concatenate (row-wise) the\n",
      "        string values from the columns defined by `parse_dates` into a single array\n",
      "        and pass that; and 3) call `date_parser` once for each row using one or\n",
      "        more strings (corresponding to the columns defined by `parse_dates`) as\n",
      "        arguments.\n",
      "    dayfirst : boolean, default False\n",
      "        DD/MM format dates, international and European format\n",
      "    iterator : boolean, default False\n",
      "        Return TextFileReader object for iteration or getting chunks with\n",
      "        ``get_chunk()``.\n",
      "    chunksize : int, default None\n",
      "        Return TextFileReader object for iteration.\n",
      "        See the `IO Tools docs\n",
      "        <http://pandas.pydata.org/pandas-docs/stable/io.html#io-chunking>`_\n",
      "        for more information on ``iterator`` and ``chunksize``.\n",
      "    compression : {'infer', 'gzip', 'bz2', 'zip', 'xz', None}, default 'infer'\n",
      "        For on-the-fly decompression of on-disk data. If 'infer' and\n",
      "        `filepath_or_buffer` is path-like, then detect compression from the\n",
      "        following extensions: '.gz', '.bz2', '.zip', or '.xz' (otherwise no\n",
      "        decompression). If using 'zip', the ZIP file must contain only one data\n",
      "        file to be read in. Set to None for no decompression.\n",
      "    \n",
      "        .. versionadded:: 0.18.1 support for 'zip' and 'xz' compression.\n",
      "    \n",
      "    thousands : str, default None\n",
      "        Thousands separator\n",
      "    decimal : str, default '.'\n",
      "        Character to recognize as decimal point (e.g. use ',' for European data).\n",
      "    float_precision : string, default None\n",
      "        Specifies which converter the C engine should use for floating-point\n",
      "        values. The options are `None` for the ordinary converter,\n",
      "        `high` for the high-precision converter, and `round_trip` for the\n",
      "        round-trip converter.\n",
      "    lineterminator : str (length 1), default None\n",
      "        Character to break file into lines. Only valid with C parser.\n",
      "    quotechar : str (length 1), optional\n",
      "        The character used to denote the start and end of a quoted item. Quoted\n",
      "        items can include the delimiter and it will be ignored.\n",
      "    quoting : int or csv.QUOTE_* instance, default 0\n",
      "        Control field quoting behavior per ``csv.QUOTE_*`` constants. Use one of\n",
      "        QUOTE_MINIMAL (0), QUOTE_ALL (1), QUOTE_NONNUMERIC (2) or QUOTE_NONE (3).\n",
      "    doublequote : boolean, default ``True``\n",
      "       When quotechar is specified and quoting is not ``QUOTE_NONE``, indicate\n",
      "       whether or not to interpret two consecutive quotechar elements INSIDE a\n",
      "       field as a single ``quotechar`` element.\n",
      "    escapechar : str (length 1), default None\n",
      "        One-character string used to escape delimiter when quoting is QUOTE_NONE.\n",
      "    comment : str, default None\n",
      "        Indicates remainder of line should not be parsed. If found at the beginning\n",
      "        of a line, the line will be ignored altogether. This parameter must be a\n",
      "        single character. Like empty lines (as long as ``skip_blank_lines=True``),\n",
      "        fully commented lines are ignored by the parameter `header` but not by\n",
      "        `skiprows`. For example, if ``comment='#'``, parsing\n",
      "        ``#empty\\na,b,c\\n1,2,3`` with ``header=0`` will result in 'a,b,c' being\n",
      "        treated as the header.\n",
      "    encoding : str, default None\n",
      "        Encoding to use for UTF when reading/writing (ex. 'utf-8'). `List of Python\n",
      "        standard encodings\n",
      "        <https://docs.python.org/3/library/codecs.html#standard-encodings>`_\n",
      "    dialect : str or csv.Dialect instance, default None\n",
      "        If provided, this parameter will override values (default or not) for the\n",
      "        following parameters: `delimiter`, `doublequote`, `escapechar`,\n",
      "        `skipinitialspace`, `quotechar`, and `quoting`. If it is necessary to\n",
      "        override values, a ParserWarning will be issued. See csv.Dialect\n",
      "        documentation for more details.\n",
      "    tupleize_cols : boolean, default False\n",
      "        .. deprecated:: 0.21.0\n",
      "           This argument will be removed and will always convert to MultiIndex\n",
      "    \n",
      "        Leave a list of tuples on columns as is (default is to convert to\n",
      "        a MultiIndex on the columns)\n",
      "    error_bad_lines : boolean, default True\n",
      "        Lines with too many fields (e.g. a csv line with too many commas) will by\n",
      "        default cause an exception to be raised, and no DataFrame will be returned.\n",
      "        If False, then these \"bad lines\" will dropped from the DataFrame that is\n",
      "        returned.\n",
      "    warn_bad_lines : boolean, default True\n",
      "        If error_bad_lines is False, and warn_bad_lines is True, a warning for each\n",
      "        \"bad line\" will be output.\n",
      "    low_memory : boolean, default True\n",
      "        Internally process the file in chunks, resulting in lower memory use\n",
      "        while parsing, but possibly mixed type inference.  To ensure no mixed\n",
      "        types either set False, or specify the type with the `dtype` parameter.\n",
      "        Note that the entire file is read into a single DataFrame regardless,\n",
      "        use the `chunksize` or `iterator` parameter to return the data in chunks.\n",
      "        (Only valid with C parser)\n",
      "    memory_map : boolean, default False\n",
      "        If a filepath is provided for `filepath_or_buffer`, map the file object\n",
      "        directly onto memory and access the data directly from there. Using this\n",
      "        option can improve performance because there is no longer any I/O overhead.\n",
      "    \n",
      "    Returns\n",
      "    -------\n",
      "    result : DataFrame or TextParser\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(pd.read_csv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>This is a dataset of red wine quality with about 10 parameters</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>fixed acidity</th>\n",
       "      <th>volatile acidity</th>\n",
       "      <th>citric acid</th>\n",
       "      <th>residual sugar</th>\n",
       "      <th>chlorides</th>\n",
       "      <th>free sulfur dioxide</th>\n",
       "      <th>total sulfur dioxide</th>\n",
       "      <th>density</th>\n",
       "      <th>pH</th>\n",
       "      <th>sulphates</th>\n",
       "      <th>alcohol</th>\n",
       "      <td>quality</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7.4</th>\n",
       "      <th>0.7</th>\n",
       "      <th>0</th>\n",
       "      <th>1.9</th>\n",
       "      <th>0.076</th>\n",
       "      <th>11</th>\n",
       "      <th>34</th>\n",
       "      <th>0.9978</th>\n",
       "      <th>3.51</th>\n",
       "      <th>0.56</th>\n",
       "      <th>9.4</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">7.8</th>\n",
       "      <th>0.88</th>\n",
       "      <th>0</th>\n",
       "      <th>2.6</th>\n",
       "      <th>0.098</th>\n",
       "      <th>25</th>\n",
       "      <th>67</th>\n",
       "      <th>0.9968</th>\n",
       "      <th>3.2</th>\n",
       "      <th>0.68</th>\n",
       "      <th>9.8</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.76</th>\n",
       "      <th>0.04</th>\n",
       "      <th>2.3</th>\n",
       "      <th>0.092</th>\n",
       "      <th>15</th>\n",
       "      <th>54</th>\n",
       "      <th>0.997</th>\n",
       "      <th>3.26</th>\n",
       "      <th>0.65</th>\n",
       "      <th>9.8</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11.2</th>\n",
       "      <th>0.28</th>\n",
       "      <th>0.56</th>\n",
       "      <th>1.9</th>\n",
       "      <th>0.075</th>\n",
       "      <th>17</th>\n",
       "      <th>60</th>\n",
       "      <th>0.998</th>\n",
       "      <th>3.16</th>\n",
       "      <th>0.58</th>\n",
       "      <th>9.8</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                                                                                            This is a dataset of red wine quality with about 10 parameters\n",
       "\n",
       "fixed acidity volatile acidity citric acid residual sugar chlorides free sulfur dioxide total sulfur dioxide density pH   sulphates alcohol                                            quality             \n",
       "7.4           0.7              0           1.9            0.076     11                  34                   0.9978  3.51 0.56      9.4                                                      5             \n",
       "7.8           0.88             0           2.6            0.098     25                  67                   0.9968  3.2  0.68      9.8                                                      5             \n",
       "              0.76             0.04        2.3            0.092     15                  54                   0.997   3.26 0.65      9.8                                                      5             \n",
       "11.2          0.28             0.56        1.9            0.075     17                  60                   0.998   3.16 0.58      9.8                                                      6             "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Basic parameters for data loading from CSV\n",
    "# Простые параметры для настройки загрузки CSV\n",
    "\n",
    "# delimiter - разделитель значений в CSV-файле\n",
    "data = pd.read_csv(wine_filename, delimiter=';') \n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fixed acidity</th>\n",
       "      <th>volatile acidity</th>\n",
       "      <th>citric acid</th>\n",
       "      <th>residual sugar</th>\n",
       "      <th>chlorides</th>\n",
       "      <th>free sulfur dioxide</th>\n",
       "      <th>total sulfur dioxide</th>\n",
       "      <th>density</th>\n",
       "      <th>pH</th>\n",
       "      <th>sulphates</th>\n",
       "      <th>alcohol</th>\n",
       "      <th>quality</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7.4</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.076</td>\n",
       "      <td>11.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0.9978</td>\n",
       "      <td>3.51</td>\n",
       "      <td>0.56</td>\n",
       "      <td>9.4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7.8</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.6</td>\n",
       "      <td>0.098</td>\n",
       "      <td>25.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>0.9968</td>\n",
       "      <td>3.20</td>\n",
       "      <td>0.68</td>\n",
       "      <td>9.8</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.8</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.04</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0.092</td>\n",
       "      <td>15.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0.9970</td>\n",
       "      <td>3.26</td>\n",
       "      <td>0.65</td>\n",
       "      <td>9.8</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11.2</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.56</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.075</td>\n",
       "      <td>17.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>0.9980</td>\n",
       "      <td>3.16</td>\n",
       "      <td>0.58</td>\n",
       "      <td>9.8</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7.4</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.076</td>\n",
       "      <td>11.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0.9978</td>\n",
       "      <td>3.51</td>\n",
       "      <td>0.56</td>\n",
       "      <td>9.4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   fixed acidity  volatile acidity  citric acid  residual sugar  chlorides  \\\n",
       "0            7.4              0.70         0.00             1.9      0.076   \n",
       "1            7.8              0.88         0.00             2.6      0.098   \n",
       "2            7.8              0.76         0.04             2.3      0.092   \n",
       "3           11.2              0.28         0.56             1.9      0.075   \n",
       "4            7.4              0.70         0.00             1.9      0.076   \n",
       "\n",
       "   free sulfur dioxide  total sulfur dioxide  density    pH  sulphates  \\\n",
       "0                 11.0                  34.0   0.9978  3.51       0.56   \n",
       "1                 25.0                  67.0   0.9968  3.20       0.68   \n",
       "2                 15.0                  54.0   0.9970  3.26       0.65   \n",
       "3                 17.0                  60.0   0.9980  3.16       0.58   \n",
       "4                 11.0                  34.0   0.9978  3.51       0.56   \n",
       "\n",
       "   alcohol  quality  \n",
       "0      9.4        5  \n",
       "1      9.8        5  \n",
       "2      9.8        5  \n",
       "3      9.8        6  \n",
       "4      9.4        5  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Dataset has description in first row and one empty row, we skip them with skiprows parameter\n",
    "# Файл содержит краткое описание данных в первой строке и пустую строку, пропустим их с помощью параметра skiprows\n",
    "data = pd.read_csv(wine_filename, delimiter=';', skiprows = 2) \n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>residual sugar</th>\n",
       "      <th>density</th>\n",
       "      <th>alcohol</th>\n",
       "      <th>quality</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.9</td>\n",
       "      <td>0.9978</td>\n",
       "      <td>9.4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.6</td>\n",
       "      <td>0.9968</td>\n",
       "      <td>9.8</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.3</td>\n",
       "      <td>0.9970</td>\n",
       "      <td>9.8</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.9</td>\n",
       "      <td>0.9980</td>\n",
       "      <td>9.8</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.9</td>\n",
       "      <td>0.9978</td>\n",
       "      <td>9.4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   residual sugar  density  alcohol  quality\n",
       "0             1.9   0.9978      9.4        5\n",
       "1             2.6   0.9968      9.8        5\n",
       "2             2.3   0.9970      9.8        5\n",
       "3             1.9   0.9980      9.8        6\n",
       "4             1.9   0.9978      9.4        5"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Choose columns of interest with usecols parameter\n",
    "# Выбор отдельных колонок для анализа с помощью параметра usecols по списку названий или номеров\n",
    "# usecols = ['col_name1', 'col_name2'] or usecols=[1, 2, 5]\n",
    "data = pd.read_csv(wine_filename, delimiter=';',\n",
    "                   skiprows = 2, usecols = ['residual sugar', 'density', 'alcohol', 'quality']) \n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.5. Обзор данных: head(), tail(), shape, columns, info(), describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data.tail()\n",
      "      residual sugar  density  alcohol  quality\n",
      "1594             2.0  0.99490     10.5        5\n",
      "1595             2.2  0.99512     11.2        6\n",
      "1596             2.3  0.99574     11.0        6\n",
      "1597             2.0  0.99547     10.2        5\n",
      "1598             3.6  0.99549     11.0        6\n",
      "\n",
      "data.shape\n",
      "(1599, 4)\n",
      "\n",
      "data.columns\n",
      "Index(['residual sugar', 'density', 'alcohol', 'quality'], dtype='object')\n",
      "\n",
      "data.info()\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1599 entries, 0 to 1598\n",
      "Data columns (total 4 columns):\n",
      "residual sugar    1599 non-null float64\n",
      "density           1599 non-null float64\n",
      "alcohol           1599 non-null float64\n",
      "quality           1599 non-null int64\n",
      "dtypes: float64(3), int64(1)\n",
      "memory usage: 50.0 KB\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "# Check the end of data\n",
    "# Просмотр последних строк данных\n",
    "print(\"data.tail()\")\n",
    "print(data.tail())\n",
    "\n",
    "# Get dimensions of data table\n",
    "# Узнаем размеры таблицы данных\n",
    "print()\n",
    "print(\"data.shape\")\n",
    "print(data.shape)\n",
    "\n",
    "# Print list of columns in data\n",
    "# Распечатаем список колонок в таблице данных\n",
    "print()\n",
    "print(\"data.columns\")\n",
    "print(data.columns)\n",
    "\n",
    "# Print basic information about dataset\n",
    "# Узнаем описание таблицы данных\n",
    "print()\n",
    "print(\"data.info()\")\n",
    "print(data.info())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Метод describe() показывает основные статистические характеристики данных по каждому числовому признаку (типы int64 и float64): число непропущенных значений, среднее, стандартное отклонение, диапазон, медиану, 0.25 и 0.75 квартили."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
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       "      <th>count</th>\n",
       "      <td>1599.000000</td>\n",
       "      <td>1599.000000</td>\n",
       "      <td>1599.000000</td>\n",
       "      <td>1599.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.538806</td>\n",
       "      <td>0.996747</td>\n",
       "      <td>10.422983</td>\n",
       "      <td>5.636023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.409928</td>\n",
       "      <td>0.001887</td>\n",
       "      <td>1.065668</td>\n",
       "      <td>0.807569</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.900000</td>\n",
       "      <td>0.990070</td>\n",
       "      <td>8.400000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.900000</td>\n",
       "      <td>0.995600</td>\n",
       "      <td>9.500000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2.200000</td>\n",
       "      <td>0.996750</td>\n",
       "      <td>10.200000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2.600000</td>\n",
       "      <td>0.997835</td>\n",
       "      <td>11.100000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>15.500000</td>\n",
       "      <td>1.003690</td>\n",
       "      <td>14.900000</td>\n",
       "      <td>8.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       residual sugar      density      alcohol      quality\n",
       "count     1599.000000  1599.000000  1599.000000  1599.000000\n",
       "mean         2.538806     0.996747    10.422983     5.636023\n",
       "std          1.409928     0.001887     1.065668     0.807569\n",
       "min          0.900000     0.990070     8.400000     3.000000\n",
       "25%          1.900000     0.995600     9.500000     5.000000\n",
       "50%          2.200000     0.996750    10.200000     6.000000\n",
       "75%          2.600000     0.997835    11.100000     6.000000\n",
       "max         15.500000     1.003690    14.900000     8.000000"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4. Поиск некорректных данных с pandas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.1 Просмотр данных, чтение строк и столбцов c методами loc, iloc\n",
    "\n",
    "По материалу https://www.shanelynn.ie/select-pandas-dataframe-rows-and-columns-using-iloc-loc-and-ix/\n",
    "![Выбор данных с помощью loc, iloc](http://antakova.ru/wp-content/uploads/2019/01/pandas_selection.png \"Выбор данных с помощью loc, iloc\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "row3 type\n",
      "<class 'pandas.core.series.Series'>\n",
      "\n",
      "row3 contents:\n",
      "Title                          8: The Mormon Proposition \n",
      "Year                                                 2010\n",
      "Genres                                        Documentary\n",
      "Language                                          English\n",
      "Country                                               USA\n",
      "Content Rating                                          R\n",
      "Duration                                               80\n",
      "Aspect Ratio                                         1.78\n",
      "Budget                                            2.5e+06\n",
      "Gross Earnings                                      99851\n",
      "Director                                       Reed Cowan\n",
      "Actor 1                                Dustin Lance Black\n",
      "Actor 2                                     Emily Pearson\n",
      "Actor 3                                      Gavin Newsom\n",
      "Facebook Likes - Director                               0\n",
      "Facebook Likes - Actor 1                              191\n",
      "Facebook Likes - Actor 2                               12\n",
      "Facebook Likes - Actor 3                                5\n",
      "Facebook Likes - cast Total                           210\n",
      "Facebook likes - Movie                                  0\n",
      "Facenumber in posters                                   0\n",
      "User Votes                                           1138\n",
      "Reviews by Users                                       30\n",
      "Reviews by Crtiics                                     28\n",
      "IMDB Score                                            7.1\n",
      "Name: 3, dtype: object\n",
      "\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "                                  Title  Year\n",
      "1                          3 Backyards   2010\n",
      "2                                    3   2010\n",
      "4  A Turtle's Tale: Sammy's Adventures   2010\n"
     ]
    }
   ],
   "source": [
    "# Selection of data by row or col position (iloc)\n",
    "# Выбор данных по строке и колонке - по нумерованной позиции iloc\n",
    "\n",
    "# Print row 3\n",
    "# Вывод строки с индексом 3\n",
    "row3 = df.iloc[3]\n",
    "print(\"row3 type\")\n",
    "print(type(row3))\n",
    "print()\n",
    "print(\"row3 contents:\")\n",
    "print(row3)\n",
    "\n",
    "# Create sub-dataFrame with list of row indexes and a slice of columns\n",
    "# Создание подтаблицы по индексам колонок и строк\n",
    "print()\n",
    "sub_df1 = df.iloc[[1,2,4],0:2]\n",
    "print(type(sub_df1))\n",
    "print(sub_df1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Language  Country\n",
      "0  English      USA\n",
      "1  English      USA\n",
      "2   German  Germany\n",
      "3  English      USA\n",
      "4  English   France\n",
      "\n",
      "  Language  Country Content Rating  Duration  Aspect Ratio      Budget\n",
      "0  English      USA              R      94.0          1.85  18000000.0\n",
      "1  English      USA              R      88.0           NaN    300000.0\n",
      "2   German  Germany        Unrated     119.0          2.35         NaN\n"
     ]
    }
   ],
   "source": [
    "# Selection of data by label with loc[rows, columns]\n",
    "# Выбор данных по названиям (label)\n",
    "\n",
    "# Example: all rows and 2 named columns\n",
    "# Пример: все строки (:) и две колонки списком имён\n",
    "sub_frame1 = df.loc[:, ['Language', 'Country']]\n",
    "print(sub_frame1.head())\n",
    "\n",
    "print()\n",
    "\n",
    "# In this data frame row index is just numbers, rows don't have names\n",
    "# Example: rows 0, 1, 2 and a range of columns\n",
    "sub_frame2 = df.loc[0:2, 'Language':'Budget']\n",
    "print(sub_frame2.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.2. Поиск пропущенных данных"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "В разных типах таблиц пропущенные данные могут выглядеть как:\n",
    "* *np.nan* - это специальное значение из библиотеки numpy (np),\n",
    "* пустые значения,\n",
    "* неподходящие значения: пробельные символы, мусор\n",
    "\n",
    "В библиотеке pandas почти все пропущенные данные по умолчанию выражаются как **np.nan** "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Задание: с помощью методов loc, iloc и здравого смысла исправить рейтинг фильма со значением \"Unrated\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Title</th>\n",
       "      <th>Year</th>\n",
       "      <th>Genres</th>\n",
       "      <th>Language</th>\n",
       "      <th>Country</th>\n",
       "      <th>Content Rating</th>\n",
       "      <th>Duration</th>\n",
       "      <th>Aspect Ratio</th>\n",
       "      <th>Budget</th>\n",
       "      <th>Gross Earnings</th>\n",
       "      <th>...</th>\n",
       "      <th>Facebook Likes - Actor 1</th>\n",
       "      <th>Facebook Likes - Actor 2</th>\n",
       "      <th>Facebook Likes - Actor 3</th>\n",
       "      <th>Facebook Likes - cast Total</th>\n",
       "      <th>Facebook likes - Movie</th>\n",
       "      <th>Facenumber in posters</th>\n",
       "      <th>User Votes</th>\n",
       "      <th>Reviews by Users</th>\n",
       "      <th>Reviews by Crtiics</th>\n",
       "      <th>IMDB Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>127 Hours</td>\n",
       "      <td>2010</td>\n",
       "      <td>Adventure|Biography|Drama|Thriller</td>\n",
       "      <td>English</td>\n",
       "      <td>USA</td>\n",
       "      <td>R</td>\n",
       "      <td>94.0</td>\n",
       "      <td>1.85</td>\n",
       "      <td>18000000.0</td>\n",
       "      <td>18329466.0</td>\n",
       "      <td>...</td>\n",
       "      <td>11000</td>\n",
       "      <td>642</td>\n",
       "      <td>223</td>\n",
       "      <td>11984</td>\n",
       "      <td>63000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>279179</td>\n",
       "      <td>440.0</td>\n",
       "      <td>450.0</td>\n",
       "      <td>7.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3 Backyards</td>\n",
       "      <td>2010</td>\n",
       "      <td>Drama</td>\n",
       "      <td>English</td>\n",
       "      <td>USA</td>\n",
       "      <td>R</td>\n",
       "      <td>88.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>300000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>795</td>\n",
       "      <td>659</td>\n",
       "      <td>301</td>\n",
       "      <td>1884</td>\n",
       "      <td>92</td>\n",
       "      <td>0.0</td>\n",
       "      <td>554</td>\n",
       "      <td>23.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>5.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2010</td>\n",
       "      <td>Comedy|Drama|Romance</td>\n",
       "      <td>German</td>\n",
       "      <td>Germany</td>\n",
       "      <td>Unrated</td>\n",
       "      <td>119.0</td>\n",
       "      <td>2.35</td>\n",
       "      <td>NaN</td>\n",
       "      <td>59774.0</td>\n",
       "      <td>...</td>\n",
       "      <td>24</td>\n",
       "      <td>20</td>\n",
       "      <td>9</td>\n",
       "      <td>69</td>\n",
       "      <td>2000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4212</td>\n",
       "      <td>18.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>6.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8: The Mormon Proposition</td>\n",
       "      <td>2010</td>\n",
       "      <td>Documentary</td>\n",
       "      <td>English</td>\n",
       "      <td>USA</td>\n",
       "      <td>R</td>\n",
       "      <td>80.0</td>\n",
       "      <td>1.78</td>\n",
       "      <td>2500000.0</td>\n",
       "      <td>99851.0</td>\n",
       "      <td>...</td>\n",
       "      <td>191</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>210</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1138</td>\n",
       "      <td>30.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>7.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A Turtle's Tale: Sammy's Adventures</td>\n",
       "      <td>2010</td>\n",
       "      <td>Adventure|Animation|Family</td>\n",
       "      <td>English</td>\n",
       "      <td>France</td>\n",
       "      <td>PG</td>\n",
       "      <td>88.0</td>\n",
       "      <td>2.35</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>783</td>\n",
       "      <td>749</td>\n",
       "      <td>602</td>\n",
       "      <td>3874</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5385</td>\n",
       "      <td>22.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>6.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  Title  Year  \\\n",
       "0                            127 Hours   2010   \n",
       "1                          3 Backyards   2010   \n",
       "2                                    3   2010   \n",
       "3            8: The Mormon Proposition   2010   \n",
       "4  A Turtle's Tale: Sammy's Adventures   2010   \n",
       "\n",
       "                               Genres Language  Country Content Rating  \\\n",
       "0  Adventure|Biography|Drama|Thriller  English      USA              R   \n",
       "1                               Drama  English      USA              R   \n",
       "2                Comedy|Drama|Romance   German  Germany        Unrated   \n",
       "3                         Documentary  English      USA              R   \n",
       "4          Adventure|Animation|Family  English   France             PG   \n",
       "\n",
       "   Duration  Aspect Ratio      Budget  Gross Earnings     ...      \\\n",
       "0      94.0          1.85  18000000.0      18329466.0     ...       \n",
       "1      88.0           NaN    300000.0             NaN     ...       \n",
       "2     119.0          2.35         NaN         59774.0     ...       \n",
       "3      80.0          1.78   2500000.0         99851.0     ...       \n",
       "4      88.0          2.35         NaN             NaN     ...       \n",
       "\n",
       "  Facebook Likes - Actor 1 Facebook Likes - Actor 2 Facebook Likes - Actor 3  \\\n",
       "0                    11000                      642                      223   \n",
       "1                      795                      659                      301   \n",
       "2                       24                       20                        9   \n",
       "3                      191                       12                        5   \n",
       "4                      783                      749                      602   \n",
       "\n",
       "  Facebook Likes - cast Total  Facebook likes - Movie  Facenumber in posters  \\\n",
       "0                       11984                   63000                    0.0   \n",
       "1                        1884                      92                    0.0   \n",
       "2                          69                    2000                    0.0   \n",
       "3                         210                       0                    0.0   \n",
       "4                        3874                       0                    2.0   \n",
       "\n",
       "   User Votes  Reviews by Users  Reviews by Crtiics  IMDB Score  \n",
       "0      279179             440.0               450.0         7.6  \n",
       "1         554              23.0                20.0         5.2  \n",
       "2        4212              18.0                76.0         6.8  \n",
       "3        1138              30.0                28.0         7.1  \n",
       "4        5385              22.0                56.0         6.1  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Example in our small dataset of movies\n",
    "# Пример в подтаблице фильмов рейтинг - не установлен и некоторые значения NaN.\n",
    "# Content Rating = Unrated и Aspect Ratio = NaN\n",
    "\n",
    "# How to fix \"Unrated\"\n",
    "# Content rating should be set up according to \n",
    "# https://en.wikipedia.org/wiki/Motion_Picture_Association_of_America_film_rating_system\n",
    "# Как исправить \"Unrated\" - специалист по данным должен принять решение.\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Language  Country  Content Rating  Duration  Aspect Ratio  Budget\n",
      "0     False    False           False     False         False   False\n",
      "1     False    False           False     False          True   False\n",
      "2     False    False           False     False         False    True\n",
      "\n",
      "Budget column in whole movies dataset has this count of NaN values:\n",
      "11\n"
     ]
    }
   ],
   "source": [
    "na_mask = sub_frame2.isna()\n",
    "print(na_mask)\n",
    "print()\n",
    "\n",
    "# Example how to use: count NA values in one column\n",
    "# Пример: вычисление количества пропущенных значений в одной колонке для полной таблицы\n",
    "budget_col = df.loc[:, 'Budget']\n",
    "budget_col_na = budget_col.isna()\n",
    "\n",
    "print('Budget column in whole movies dataset has this count of NaN values:')\n",
    "print(budget_col_na.sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.3. Некорректные данные\n",
    "\n",
    "Данные могут присутствовать в таблице, но иметь проблемы: \n",
    "* неверный формат, \n",
    "* не подходить как категория признака, \n",
    "* иметь дубликаты,\n",
    "* содержать неточную информацию или ошибки\n",
    "* и другие :)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
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       "      <th>Title</th>\n",
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       "      <th>...</th>\n",
       "      <th>Facebook Likes - Actor 1</th>\n",
       "      <th>Facebook Likes - Actor 2</th>\n",
       "      <th>Facebook Likes - Actor 3</th>\n",
       "      <th>Facebook Likes - cast Total</th>\n",
       "      <th>Facebook likes - Movie</th>\n",
       "      <th>Facenumber in posters</th>\n",
       "      <th>User Votes</th>\n",
       "      <th>Reviews by Users</th>\n",
       "      <th>Reviews by Crtiics</th>\n",
       "      <th>IMDB Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>127 Hours</td>\n",
       "      <td>2010</td>\n",
       "      <td>Adventure|Biography|Drama|Thriller</td>\n",
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       "      <td>1.85</td>\n",
       "      <td>18000000.0</td>\n",
       "      <td>18329466.0</td>\n",
       "      <td>...</td>\n",
       "      <td>11000</td>\n",
       "      <td>642</td>\n",
       "      <td>223</td>\n",
       "      <td>11984</td>\n",
       "      <td>63000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>279179</td>\n",
       "      <td>440.0</td>\n",
       "      <td>450.0</td>\n",
       "      <td>7.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3 Backyards</td>\n",
       "      <td>2010</td>\n",
       "      <td>Drama</td>\n",
       "      <td>English</td>\n",
       "      <td>USA</td>\n",
       "      <td>R</td>\n",
       "      <td>88.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>300000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>795</td>\n",
       "      <td>659</td>\n",
       "      <td>301</td>\n",
       "      <td>1884</td>\n",
       "      <td>92</td>\n",
       "      <td>0.0</td>\n",
       "      <td>554</td>\n",
       "      <td>23.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>5.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2010</td>\n",
       "      <td>Comedy|Drama|Romance</td>\n",
       "      <td>German</td>\n",
       "      <td>Germany</td>\n",
       "      <td>Unrated</td>\n",
       "      <td>119.0</td>\n",
       "      <td>2.35</td>\n",
       "      <td>NaN</td>\n",
       "      <td>59774.0</td>\n",
       "      <td>...</td>\n",
       "      <td>24</td>\n",
       "      <td>20</td>\n",
       "      <td>9</td>\n",
       "      <td>69</td>\n",
       "      <td>2000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4212</td>\n",
       "      <td>18.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>6.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8: The Mormon Proposition</td>\n",
       "      <td>2010</td>\n",
       "      <td>Documentary</td>\n",
       "      <td>English</td>\n",
       "      <td>USA</td>\n",
       "      <td>R</td>\n",
       "      <td>80.0</td>\n",
       "      <td>1.78</td>\n",
       "      <td>2500000.0</td>\n",
       "      <td>99851.0</td>\n",
       "      <td>...</td>\n",
       "      <td>191</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>210</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1138</td>\n",
       "      <td>30.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>7.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A Turtle's Tale: Sammy's Adventures</td>\n",
       "      <td>2010</td>\n",
       "      <td>Adventure|Animation|Family</td>\n",
       "      <td>English</td>\n",
       "      <td>France</td>\n",
       "      <td>PG</td>\n",
       "      <td>88.0</td>\n",
       "      <td>2.35</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>783</td>\n",
       "      <td>749</td>\n",
       "      <td>602</td>\n",
       "      <td>3874</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5385</td>\n",
       "      <td>22.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>6.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Alice in Wonderland</td>\n",
       "      <td>2010</td>\n",
       "      <td>Adventure|Family|Fantasy</td>\n",
       "      <td>English</td>\n",
       "      <td>USA</td>\n",
       "      <td>PG</td>\n",
       "      <td>108.0</td>\n",
       "      <td>1.85</td>\n",
       "      <td>200000000.0</td>\n",
       "      <td>334185206.0</td>\n",
       "      <td>...</td>\n",
       "      <td>40000</td>\n",
       "      <td>25000</td>\n",
       "      <td>11000</td>\n",
       "      <td>79957</td>\n",
       "      <td>24000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>306320</td>\n",
       "      <td>736.0</td>\n",
       "      <td>451.0</td>\n",
       "      <td>6.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Alice in Wonderland</td>\n",
       "      <td>2010</td>\n",
       "      <td>Adventure|Family|Fantasy</td>\n",
       "      <td>English</td>\n",
       "      <td>USA</td>\n",
       "      <td>PG</td>\n",
       "      <td>108.0</td>\n",
       "      <td>1.85</td>\n",
       "      <td>200000000.0</td>\n",
       "      <td>334185206.0</td>\n",
       "      <td>...</td>\n",
       "      <td>40000</td>\n",
       "      <td>25000</td>\n",
       "      <td>11000</td>\n",
       "      <td>79957</td>\n",
       "      <td>24000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>306336</td>\n",
       "      <td>736.0</td>\n",
       "      <td>451.0</td>\n",
       "      <td>6.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>All Good Things</td>\n",
       "      <td>2010-2011</td>\n",
       "      <td>Crime|Drama|Mystery|Romance|Thriller</td>\n",
       "      <td>English</td>\n",
       "      <td>USA</td>\n",
       "      <td>R</td>\n",
       "      <td>101.0</td>\n",
       "      <td>1.85</td>\n",
       "      <td>NaN</td>\n",
       "      <td>578382.0</td>\n",
       "      <td>...</td>\n",
       "      <td>33000</td>\n",
       "      <td>4000</td>\n",
       "      <td>902</td>\n",
       "      <td>39515</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>41249</td>\n",
       "      <td>67.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>6.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Alpha and Omega</td>\n",
       "      <td>2010</td>\n",
       "      <td>Adventure|Animation|Comedy|Family|Romance</td>\n",
       "      <td>English</td>\n",
       "      <td>usa</td>\n",
       "      <td>PG</td>\n",
       "      <td>90.0</td>\n",
       "      <td>1.85</td>\n",
       "      <td>20000000.0</td>\n",
       "      <td>25077977.0</td>\n",
       "      <td>...</td>\n",
       "      <td>681</td>\n",
       "      <td>611</td>\n",
       "      <td>518</td>\n",
       "      <td>2486</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10986</td>\n",
       "      <td>84.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>5.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Amigo</td>\n",
       "      <td>2010</td>\n",
       "      <td>Drama|War</td>\n",
       "      <td>English</td>\n",
       "      <td>usa</td>\n",
       "      <td>R</td>\n",
       "      <td>124.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1700000.0</td>\n",
       "      <td>183490.0</td>\n",
       "      <td>...</td>\n",
       "      <td>38</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>60</td>\n",
       "      <td>594</td>\n",
       "      <td>0.0</td>\n",
       "      <td>463</td>\n",
       "      <td>8.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>5.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  Title       Year  \\\n",
       "0                            127 Hours        2010   \n",
       "1                          3 Backyards        2010   \n",
       "2                                    3        2010   \n",
       "3            8: The Mormon Proposition        2010   \n",
       "4  A Turtle's Tale: Sammy's Adventures        2010   \n",
       "5                  Alice in Wonderland        2010   \n",
       "6                  Alice in Wonderland        2010   \n",
       "7                      All Good Things   2010-2011   \n",
       "8                      Alpha and Omega        2010   \n",
       "9                                Amigo        2010   \n",
       "\n",
       "                                      Genres Language  Country Content Rating  \\\n",
       "0         Adventure|Biography|Drama|Thriller  English      USA              R   \n",
       "1                                      Drama  English      USA              R   \n",
       "2                       Comedy|Drama|Romance   German  Germany        Unrated   \n",
       "3                                Documentary  English      USA              R   \n",
       "4                 Adventure|Animation|Family  English   France             PG   \n",
       "5                   Adventure|Family|Fantasy  English      USA             PG   \n",
       "6                   Adventure|Family|Fantasy  English      USA             PG   \n",
       "7       Crime|Drama|Mystery|Romance|Thriller  English      USA              R   \n",
       "8  Adventure|Animation|Comedy|Family|Romance  English      usa             PG   \n",
       "9                                  Drama|War  English      usa              R   \n",
       "\n",
       "   Duration  Aspect Ratio       Budget  Gross Earnings     ...      \\\n",
       "0      94.0          1.85   18000000.0      18329466.0     ...       \n",
       "1      88.0           NaN     300000.0             NaN     ...       \n",
       "2     119.0          2.35          NaN         59774.0     ...       \n",
       "3      80.0          1.78    2500000.0         99851.0     ...       \n",
       "4      88.0          2.35          NaN             NaN     ...       \n",
       "5     108.0          1.85  200000000.0     334185206.0     ...       \n",
       "6     108.0          1.85  200000000.0     334185206.0     ...       \n",
       "7     101.0          1.85          NaN        578382.0     ...       \n",
       "8      90.0          1.85   20000000.0      25077977.0     ...       \n",
       "9     124.0           NaN    1700000.0        183490.0     ...       \n",
       "\n",
       "  Facebook Likes - Actor 1 Facebook Likes - Actor 2 Facebook Likes - Actor 3  \\\n",
       "0                    11000                      642                      223   \n",
       "1                      795                      659                      301   \n",
       "2                       24                       20                        9   \n",
       "3                      191                       12                        5   \n",
       "4                      783                      749                      602   \n",
       "5                    40000                    25000                    11000   \n",
       "6                    40000                    25000                    11000   \n",
       "7                    33000                     4000                      902   \n",
       "8                      681                      611                      518   \n",
       "9                       38                        8                        7   \n",
       "\n",
       "  Facebook Likes - cast Total  Facebook likes - Movie  Facenumber in posters  \\\n",
       "0                       11984                   63000                    0.0   \n",
       "1                        1884                      92                    0.0   \n",
       "2                          69                    2000                    0.0   \n",
       "3                         210                       0                    0.0   \n",
       "4                        3874                       0                    2.0   \n",
       "5                       79957                   24000                    0.0   \n",
       "6                       79957                   24000                    0.0   \n",
       "7                       39515                       0                    2.0   \n",
       "8                        2486                       0                    0.0   \n",
       "9                          60                     594                    0.0   \n",
       "\n",
       "   User Votes  Reviews by Users  Reviews by Crtiics  IMDB Score  \n",
       "0      279179             440.0               450.0         7.6  \n",
       "1         554              23.0                20.0         5.2  \n",
       "2        4212              18.0                76.0         6.8  \n",
       "3        1138              30.0                28.0         7.1  \n",
       "4        5385              22.0                56.0         6.1  \n",
       "5      306320             736.0               451.0         6.5  \n",
       "6      306336             736.0               451.0         6.5  \n",
       "7       41249              67.0               140.0         6.3  \n",
       "8       10986              84.0                84.0         5.3  \n",
       "9         463               8.0                35.0         5.8  \n",
       "\n",
       "[10 rows x 25 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 1 example on mov.xls: see rows 8, 9 column Country = \"usa\", not \"USA\"\n",
    "# 2 example on mov.xls: see row 7, column Year = 2010-2011 seems incorrect\n",
    "# 4 example on mov.xls: see rows 5 and 6, this is the same movie twice\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5. Исправление некорректных данных\n",
    "\n",
    "## 5.1. Удаление или замена значениями данных N/A (NaN)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Title</th>\n",
       "      <th>Year</th>\n",
       "      <th>Genres</th>\n",
       "      <th>Language</th>\n",
       "      <th>Country</th>\n",
       "      <th>Content Rating</th>\n",
       "      <th>Duration</th>\n",
       "      <th>Aspect Ratio</th>\n",
       "      <th>Budget</th>\n",
       "      <th>Gross Earnings</th>\n",
       "      <th>...</th>\n",
       "      <th>Facebook Likes - Actor 1</th>\n",
       "      <th>Facebook Likes - Actor 2</th>\n",
       "      <th>Facebook Likes - Actor 3</th>\n",
       "      <th>Facebook Likes - cast Total</th>\n",
       "      <th>Facebook likes - Movie</th>\n",
       "      <th>Facenumber in posters</th>\n",
       "      <th>User Votes</th>\n",
       "      <th>Reviews by Users</th>\n",
       "      <th>Reviews by Crtiics</th>\n",
       "      <th>IMDB Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>127 Hours</td>\n",
       "      <td>2010</td>\n",
       "      <td>Adventure|Biography|Drama|Thriller</td>\n",
       "      <td>English</td>\n",
       "      <td>USA</td>\n",
       "      <td>R</td>\n",
       "      <td>94.0</td>\n",
       "      <td>1.85</td>\n",
       "      <td>18000000.0</td>\n",
       "      <td>18329466.0</td>\n",
       "      <td>...</td>\n",
       "      <td>11000</td>\n",
       "      <td>642</td>\n",
       "      <td>223</td>\n",
       "      <td>11984</td>\n",
       "      <td>63000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>279179</td>\n",
       "      <td>440.0</td>\n",
       "      <td>450.0</td>\n",
       "      <td>7.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8: The Mormon Proposition</td>\n",
       "      <td>2010</td>\n",
       "      <td>Documentary</td>\n",
       "      <td>English</td>\n",
       "      <td>USA</td>\n",
       "      <td>R</td>\n",
       "      <td>80.0</td>\n",
       "      <td>1.78</td>\n",
       "      <td>2500000.0</td>\n",
       "      <td>99851.0</td>\n",
       "      <td>...</td>\n",
       "      <td>191</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>210</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1138</td>\n",
       "      <td>30.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>7.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Alice in Wonderland</td>\n",
       "      <td>2010</td>\n",
       "      <td>Adventure|Family|Fantasy</td>\n",
       "      <td>English</td>\n",
       "      <td>USA</td>\n",
       "      <td>PG</td>\n",
       "      <td>108.0</td>\n",
       "      <td>1.85</td>\n",
       "      <td>200000000.0</td>\n",
       "      <td>334185206.0</td>\n",
       "      <td>...</td>\n",
       "      <td>40000</td>\n",
       "      <td>25000</td>\n",
       "      <td>11000</td>\n",
       "      <td>79957</td>\n",
       "      <td>24000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>306320</td>\n",
       "      <td>736.0</td>\n",
       "      <td>451.0</td>\n",
       "      <td>6.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Alice in Wonderland</td>\n",
       "      <td>2010</td>\n",
       "      <td>Adventure|Family|Fantasy</td>\n",
       "      <td>English</td>\n",
       "      <td>USA</td>\n",
       "      <td>PG</td>\n",
       "      <td>108.0</td>\n",
       "      <td>1.85</td>\n",
       "      <td>200000000.0</td>\n",
       "      <td>334185206.0</td>\n",
       "      <td>...</td>\n",
       "      <td>40000</td>\n",
       "      <td>25000</td>\n",
       "      <td>11000</td>\n",
       "      <td>79957</td>\n",
       "      <td>24000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>306336</td>\n",
       "      <td>736.0</td>\n",
       "      <td>451.0</td>\n",
       "      <td>6.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Alpha and Omega</td>\n",
       "      <td>2010</td>\n",
       "      <td>Adventure|Animation|Comedy|Family|Romance</td>\n",
       "      <td>English</td>\n",
       "      <td>usa</td>\n",
       "      <td>PG</td>\n",
       "      <td>90.0</td>\n",
       "      <td>1.85</td>\n",
       "      <td>20000000.0</td>\n",
       "      <td>25077977.0</td>\n",
       "      <td>...</td>\n",
       "      <td>681</td>\n",
       "      <td>611</td>\n",
       "      <td>518</td>\n",
       "      <td>2486</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10986</td>\n",
       "      <td>84.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>5.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                        Title  Year  \\\n",
       "0                  127 Hours   2010   \n",
       "3  8: The Mormon Proposition   2010   \n",
       "5        Alice in Wonderland   2010   \n",
       "6        Alice in Wonderland   2010   \n",
       "8            Alpha and Omega   2010   \n",
       "\n",
       "                                      Genres Language Country Content Rating  \\\n",
       "0         Adventure|Biography|Drama|Thriller  English     USA              R   \n",
       "3                                Documentary  English     USA              R   \n",
       "5                   Adventure|Family|Fantasy  English     USA             PG   \n",
       "6                   Adventure|Family|Fantasy  English     USA             PG   \n",
       "8  Adventure|Animation|Comedy|Family|Romance  English     usa             PG   \n",
       "\n",
       "   Duration  Aspect Ratio       Budget  Gross Earnings     ...      \\\n",
       "0      94.0          1.85   18000000.0      18329466.0     ...       \n",
       "3      80.0          1.78    2500000.0         99851.0     ...       \n",
       "5     108.0          1.85  200000000.0     334185206.0     ...       \n",
       "6     108.0          1.85  200000000.0     334185206.0     ...       \n",
       "8      90.0          1.85   20000000.0      25077977.0     ...       \n",
       "\n",
       "  Facebook Likes - Actor 1 Facebook Likes - Actor 2 Facebook Likes - Actor 3  \\\n",
       "0                    11000                      642                      223   \n",
       "3                      191                       12                        5   \n",
       "5                    40000                    25000                    11000   \n",
       "6                    40000                    25000                    11000   \n",
       "8                      681                      611                      518   \n",
       "\n",
       "  Facebook Likes - cast Total  Facebook likes - Movie  Facenumber in posters  \\\n",
       "0                       11984                   63000                    0.0   \n",
       "3                         210                       0                    0.0   \n",
       "5                       79957                   24000                    0.0   \n",
       "6                       79957                   24000                    0.0   \n",
       "8                        2486                       0                    0.0   \n",
       "\n",
       "   User Votes  Reviews by Users  Reviews by Crtiics  IMDB Score  \n",
       "0      279179             440.0               450.0         7.6  \n",
       "3        1138              30.0                28.0         7.1  \n",
       "5      306320             736.0               451.0         6.5  \n",
       "6      306336             736.0               451.0         6.5  \n",
       "8       10986              84.0                84.0         5.3  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# drop missing data\n",
    "# Удаление строк с отсутствующими данными\n",
    "df_without_na = df.dropna(how='any')\n",
    "df_without_na.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Title</th>\n",
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       "      <th>Genres</th>\n",
       "      <th>Language</th>\n",
       "      <th>Country</th>\n",
       "      <th>Content Rating</th>\n",
       "      <th>Duration</th>\n",
       "      <th>Aspect Ratio</th>\n",
       "      <th>Budget</th>\n",
       "      <th>Gross Earnings</th>\n",
       "      <th>...</th>\n",
       "      <th>Facebook Likes - Actor 1</th>\n",
       "      <th>Facebook Likes - Actor 2</th>\n",
       "      <th>Facebook Likes - Actor 3</th>\n",
       "      <th>Facebook Likes - cast Total</th>\n",
       "      <th>Facebook likes - Movie</th>\n",
       "      <th>Facenumber in posters</th>\n",
       "      <th>User Votes</th>\n",
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       "      <th>Reviews by Crtiics</th>\n",
       "      <th>IMDB Score</th>\n",
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       "      <td>94.0</td>\n",
       "      <td>1.85</td>\n",
       "      <td>18000000.0</td>\n",
       "      <td>18329466.0</td>\n",
       "      <td>...</td>\n",
       "      <td>11000</td>\n",
       "      <td>642</td>\n",
       "      <td>223</td>\n",
       "      <td>11984</td>\n",
       "      <td>63000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>279179</td>\n",
       "      <td>440.0</td>\n",
       "      <td>450.0</td>\n",
       "      <td>7.6</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3 Backyards</td>\n",
       "      <td>2010</td>\n",
       "      <td>Drama</td>\n",
       "      <td>English</td>\n",
       "      <td>USA</td>\n",
       "      <td>R</td>\n",
       "      <td>88.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>300000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>795</td>\n",
       "      <td>659</td>\n",
       "      <td>301</td>\n",
       "      <td>1884</td>\n",
       "      <td>92</td>\n",
       "      <td>0.0</td>\n",
       "      <td>554</td>\n",
       "      <td>23.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>5.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2010</td>\n",
       "      <td>Comedy|Drama|Romance</td>\n",
       "      <td>German</td>\n",
       "      <td>Germany</td>\n",
       "      <td>Unrated</td>\n",
       "      <td>119.0</td>\n",
       "      <td>2.35</td>\n",
       "      <td>0.0</td>\n",
       "      <td>59774.0</td>\n",
       "      <td>...</td>\n",
       "      <td>24</td>\n",
       "      <td>20</td>\n",
       "      <td>9</td>\n",
       "      <td>69</td>\n",
       "      <td>2000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4212</td>\n",
       "      <td>18.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>6.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8: The Mormon Proposition</td>\n",
       "      <td>2010</td>\n",
       "      <td>Documentary</td>\n",
       "      <td>English</td>\n",
       "      <td>USA</td>\n",
       "      <td>R</td>\n",
       "      <td>80.0</td>\n",
       "      <td>1.78</td>\n",
       "      <td>2500000.0</td>\n",
       "      <td>99851.0</td>\n",
       "      <td>...</td>\n",
       "      <td>191</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>210</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1138</td>\n",
       "      <td>30.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>7.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A Turtle's Tale: Sammy's Adventures</td>\n",
       "      <td>2010</td>\n",
       "      <td>Adventure|Animation|Family</td>\n",
       "      <td>English</td>\n",
       "      <td>France</td>\n",
       "      <td>PG</td>\n",
       "      <td>88.0</td>\n",
       "      <td>2.35</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>783</td>\n",
       "      <td>749</td>\n",
       "      <td>602</td>\n",
       "      <td>3874</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5385</td>\n",
       "      <td>22.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>6.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  Title  Year  \\\n",
       "0                            127 Hours   2010   \n",
       "1                          3 Backyards   2010   \n",
       "2                                    3   2010   \n",
       "3            8: The Mormon Proposition   2010   \n",
       "4  A Turtle's Tale: Sammy's Adventures   2010   \n",
       "\n",
       "                               Genres Language  Country Content Rating  \\\n",
       "0  Adventure|Biography|Drama|Thriller  English      USA              R   \n",
       "1                               Drama  English      USA              R   \n",
       "2                Comedy|Drama|Romance   German  Germany        Unrated   \n",
       "3                         Documentary  English      USA              R   \n",
       "4          Adventure|Animation|Family  English   France             PG   \n",
       "\n",
       "   Duration  Aspect Ratio      Budget  Gross Earnings     ...      \\\n",
       "0      94.0          1.85  18000000.0      18329466.0     ...       \n",
       "1      88.0          0.00    300000.0             0.0     ...       \n",
       "2     119.0          2.35         0.0         59774.0     ...       \n",
       "3      80.0          1.78   2500000.0         99851.0     ...       \n",
       "4      88.0          2.35         0.0             0.0     ...       \n",
       "\n",
       "  Facebook Likes - Actor 1 Facebook Likes - Actor 2 Facebook Likes - Actor 3  \\\n",
       "0                    11000                      642                      223   \n",
       "1                      795                      659                      301   \n",
       "2                       24                       20                        9   \n",
       "3                      191                       12                        5   \n",
       "4                      783                      749                      602   \n",
       "\n",
       "  Facebook Likes - cast Total  Facebook likes - Movie  Facenumber in posters  \\\n",
       "0                       11984                   63000                    0.0   \n",
       "1                        1884                      92                    0.0   \n",
       "2                          69                    2000                    0.0   \n",
       "3                         210                       0                    0.0   \n",
       "4                        3874                       0                    2.0   \n",
       "\n",
       "   User Votes  Reviews by Users  Reviews by Crtiics  IMDB Score  \n",
       "0      279179             440.0               450.0         7.6  \n",
       "1         554              23.0                20.0         5.2  \n",
       "2        4212              18.0                76.0         6.8  \n",
       "3        1138              30.0                28.0         7.1  \n",
       "4        5385              22.0                56.0         6.1  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Simple fill missing data with 0 value\n",
    "# Простая замена отсутствующих данных нулями\n",
    "# https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.fillna.html\n",
    "df_filled = df.fillna(value=0)\n",
    "df_filled.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean film budget is\n",
      "36309750.0\n",
      "\n"
     ]
    },
    {
     "data": {
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       "      <td>18000000.0</td>\n",
       "      <td>18329466.0</td>\n",
       "      <td>...</td>\n",
       "      <td>11000</td>\n",
       "      <td>642</td>\n",
       "      <td>223</td>\n",
       "      <td>11984</td>\n",
       "      <td>63000</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>440.0</td>\n",
       "      <td>450.0</td>\n",
       "      <td>7.6</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3 Backyards</td>\n",
       "      <td>2010</td>\n",
       "      <td>Drama</td>\n",
       "      <td>English</td>\n",
       "      <td>USA</td>\n",
       "      <td>R</td>\n",
       "      <td>88.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>300000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>795</td>\n",
       "      <td>659</td>\n",
       "      <td>301</td>\n",
       "      <td>1884</td>\n",
       "      <td>92</td>\n",
       "      <td>0.0</td>\n",
       "      <td>554</td>\n",
       "      <td>23.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>5.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2010</td>\n",
       "      <td>Comedy|Drama|Romance</td>\n",
       "      <td>German</td>\n",
       "      <td>Germany</td>\n",
       "      <td>Unrated</td>\n",
       "      <td>119.0</td>\n",
       "      <td>2.35</td>\n",
       "      <td>36309750.0</td>\n",
       "      <td>59774.0</td>\n",
       "      <td>...</td>\n",
       "      <td>24</td>\n",
       "      <td>20</td>\n",
       "      <td>9</td>\n",
       "      <td>69</td>\n",
       "      <td>2000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4212</td>\n",
       "      <td>18.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>6.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8: The Mormon Proposition</td>\n",
       "      <td>2010</td>\n",
       "      <td>Documentary</td>\n",
       "      <td>English</td>\n",
       "      <td>USA</td>\n",
       "      <td>R</td>\n",
       "      <td>80.0</td>\n",
       "      <td>1.78</td>\n",
       "      <td>2500000.0</td>\n",
       "      <td>99851.0</td>\n",
       "      <td>...</td>\n",
       "      <td>191</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>210</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1138</td>\n",
       "      <td>30.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>7.1</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A Turtle's Tale: Sammy's Adventures</td>\n",
       "      <td>2010</td>\n",
       "      <td>Adventure|Animation|Family</td>\n",
       "      <td>English</td>\n",
       "      <td>France</td>\n",
       "      <td>PG</td>\n",
       "      <td>88.0</td>\n",
       "      <td>2.35</td>\n",
       "      <td>36309750.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>783</td>\n",
       "      <td>749</td>\n",
       "      <td>602</td>\n",
       "      <td>3874</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5385</td>\n",
       "      <td>22.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>6.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  Title  Year  \\\n",
       "0                            127 Hours   2010   \n",
       "1                          3 Backyards   2010   \n",
       "2                                    3   2010   \n",
       "3            8: The Mormon Proposition   2010   \n",
       "4  A Turtle's Tale: Sammy's Adventures   2010   \n",
       "\n",
       "                               Genres Language  Country Content Rating  \\\n",
       "0  Adventure|Biography|Drama|Thriller  English      USA              R   \n",
       "1                               Drama  English      USA              R   \n",
       "2                Comedy|Drama|Romance   German  Germany        Unrated   \n",
       "3                         Documentary  English      USA              R   \n",
       "4          Adventure|Animation|Family  English   France             PG   \n",
       "\n",
       "   Duration  Aspect Ratio      Budget  Gross Earnings     ...      \\\n",
       "0      94.0          1.85  18000000.0      18329466.0     ...       \n",
       "1      88.0           NaN    300000.0             NaN     ...       \n",
       "2     119.0          2.35  36309750.0         59774.0     ...       \n",
       "3      80.0          1.78   2500000.0         99851.0     ...       \n",
       "4      88.0          2.35  36309750.0             NaN     ...       \n",
       "\n",
       "  Facebook Likes - Actor 1 Facebook Likes - Actor 2 Facebook Likes - Actor 3  \\\n",
       "0                    11000                      642                      223   \n",
       "1                      795                      659                      301   \n",
       "2                       24                       20                        9   \n",
       "3                      191                       12                        5   \n",
       "4                      783                      749                      602   \n",
       "\n",
       "  Facebook Likes - cast Total  Facebook likes - Movie  Facenumber in posters  \\\n",
       "0                       11984                   63000                    0.0   \n",
       "1                        1884                      92                    0.0   \n",
       "2                          69                    2000                    0.0   \n",
       "3                         210                       0                    0.0   \n",
       "4                        3874                       0                    2.0   \n",
       "\n",
       "   User Votes  Reviews by Users  Reviews by Crtiics  IMDB Score  \n",
       "0      279179             440.0               450.0         7.6  \n",
       "1         554              23.0                20.0         5.2  \n",
       "2        4212              18.0                76.0         6.8  \n",
       "3        1138              30.0                28.0         7.1  \n",
       "4        5385              22.0                56.0         6.1  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# fill NA budget with some mean budget for these films\n",
    "# заполним пустой бюджет фильма средним значением по этой колонке\n",
    "mean = df['Budget'].mean()\n",
    "print('mean film budget is')\n",
    "print(mean)\n",
    "print()\n",
    "\n",
    "# inplace - значит заполнять в исходном датаFrame, а не только в возвращаемом новом\n",
    "df['Budget'].fillna(mean, inplace=True)\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5.2. Обработка строк для однотипного форматирования данных"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe tbody tr th {\n",
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       "    .dataframe thead th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <th>Aspect Ratio</th>\n",
       "      <th>Budget</th>\n",
       "      <th>Gross Earnings</th>\n",
       "      <th>...</th>\n",
       "      <th>Facebook Likes - Actor 1</th>\n",
       "      <th>Facebook Likes - Actor 2</th>\n",
       "      <th>Facebook Likes - Actor 3</th>\n",
       "      <th>Facebook Likes - cast Total</th>\n",
       "      <th>Facebook likes - Movie</th>\n",
       "      <th>Facenumber in posters</th>\n",
       "      <th>User Votes</th>\n",
       "      <th>Reviews by Users</th>\n",
       "      <th>Reviews by Crtiics</th>\n",
       "      <th>IMDB Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>127 Hours</td>\n",
       "      <td>2010</td>\n",
       "      <td>Adventure|Biography|Drama|Thriller</td>\n",
       "      <td>English</td>\n",
       "      <td>USA</td>\n",
       "      <td>R</td>\n",
       "      <td>94.0</td>\n",
       "      <td>1,85</td>\n",
       "      <td>18000000.0</td>\n",
       "      <td>18329466.0</td>\n",
       "      <td>...</td>\n",
       "      <td>11000</td>\n",
       "      <td>642</td>\n",
       "      <td>223</td>\n",
       "      <td>11984</td>\n",
       "      <td>63000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>279179</td>\n",
       "      <td>440.0</td>\n",
       "      <td>450.0</td>\n",
       "      <td>7,6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3 Backyards</td>\n",
       "      <td>2010-2011</td>\n",
       "      <td>Drama</td>\n",
       "      <td>English</td>\n",
       "      <td>USA</td>\n",
       "      <td>R</td>\n",
       "      <td>88.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>300000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>795</td>\n",
       "      <td>659</td>\n",
       "      <td>301</td>\n",
       "      <td>1884</td>\n",
       "      <td>92</td>\n",
       "      <td>0.0</td>\n",
       "      <td>554</td>\n",
       "      <td>23.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>5,2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8: The Mormon Proposition</td>\n",
       "      <td>2010</td>\n",
       "      <td>Documentary</td>\n",
       "      <td>English</td>\n",
       "      <td>USA</td>\n",
       "      <td>R</td>\n",
       "      <td>80.0</td>\n",
       "      <td>1,78</td>\n",
       "      <td>2500000.0</td>\n",
       "      <td>99851.0</td>\n",
       "      <td>...</td>\n",
       "      <td>191</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>210</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1138</td>\n",
       "      <td>30.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>7,1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A Turtle's Tale: Sammy's Adventures</td>\n",
       "      <td>2010</td>\n",
       "      <td>Adventure|Animation|Family</td>\n",
       "      <td>English</td>\n",
       "      <td>France</td>\n",
       "      <td>PG</td>\n",
       "      <td>88.0</td>\n",
       "      <td>2,35</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>783</td>\n",
       "      <td>749</td>\n",
       "      <td>602</td>\n",
       "      <td>3874</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5385</td>\n",
       "      <td>22.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>6,1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Alice in Wonderland</td>\n",
       "      <td>2010</td>\n",
       "      <td>Adventure|Family|Fantasy</td>\n",
       "      <td>English</td>\n",
       "      <td>USA</td>\n",
       "      <td>PG</td>\n",
       "      <td>108.0</td>\n",
       "      <td>1,85</td>\n",
       "      <td>200000000.0</td>\n",
       "      <td>334185206.0</td>\n",
       "      <td>...</td>\n",
       "      <td>40000</td>\n",
       "      <td>25000</td>\n",
       "      <td>11000</td>\n",
       "      <td>79957</td>\n",
       "      <td>24000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>306320</td>\n",
       "      <td>736.0</td>\n",
       "      <td>451.0</td>\n",
       "      <td>6,5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 Title       Year  \\\n",
       "0                            127 Hours       2010   \n",
       "1                          3 Backyards  2010-2011   \n",
       "2            8: The Mormon Proposition       2010   \n",
       "3  A Turtle's Tale: Sammy's Adventures       2010   \n",
       "4                  Alice in Wonderland       2010   \n",
       "\n",
       "                               Genres Language Country Content Rating  \\\n",
       "0  Adventure|Biography|Drama|Thriller  English     USA              R   \n",
       "1                               Drama  English     USA              R   \n",
       "2                         Documentary  English     USA              R   \n",
       "3          Adventure|Animation|Family  English  France             PG   \n",
       "4            Adventure|Family|Fantasy  English     USA             PG   \n",
       "\n",
       "   Duration Aspect Ratio       Budget  Gross Earnings     ...      \\\n",
       "0      94.0         1,85   18000000.0      18329466.0     ...       \n",
       "1      88.0          NaN     300000.0             NaN     ...       \n",
       "2      80.0         1,78    2500000.0         99851.0     ...       \n",
       "3      88.0         2,35          NaN             NaN     ...       \n",
       "4     108.0         1,85  200000000.0     334185206.0     ...       \n",
       "\n",
       "  Facebook Likes - Actor 1 Facebook Likes - Actor 2 Facebook Likes - Actor 3  \\\n",
       "0                    11000                      642                      223   \n",
       "1                      795                      659                      301   \n",
       "2                      191                       12                        5   \n",
       "3                      783                      749                      602   \n",
       "4                    40000                    25000                    11000   \n",
       "\n",
       "  Facebook Likes - cast Total  Facebook likes - Movie  Facenumber in posters  \\\n",
       "0                       11984                   63000                    0.0   \n",
       "1                        1884                      92                    0.0   \n",
       "2                         210                       0                    0.0   \n",
       "3                        3874                       0                    2.0   \n",
       "4                       79957                   24000                    0.0   \n",
       "\n",
       "   User Votes  Reviews by Users  Reviews by Crtiics  IMDB Score  \n",
       "0      279179             440.0               450.0         7,6  \n",
       "1         554              23.0                20.0         5,2  \n",
       "2        1138              30.0                28.0         7,1  \n",
       "3        5385              22.0                56.0         6,1  \n",
       "4      306320             736.0               451.0         6,5  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load Excel file with pandas as an example\n",
    "# Зазгрузка таблицы Excel \n",
    "# Using encoding parameter because unicode defaiult encoder gives some errors on content\n",
    "file = 'mov.csv'\n",
    "df = pd.read_csv(file, encoding=\"ISO-8859-1\", delimiter=';');\n",
    "\n",
    "# Review top rows of data\n",
    "# Просмотр верхней части таблицы\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Title                           object\n",
       "Year                            object\n",
       "Genres                          object\n",
       "Language                        object\n",
       "Country                         object\n",
       "Content Rating                  object\n",
       "Duration                       float64\n",
       "Aspect Ratio                    object\n",
       "Budget                         float64\n",
       "Gross Earnings                 float64\n",
       "Director                        object\n",
       "Actor 1                         object\n",
       "Actor 2                         object\n",
       "Actor 3                         object\n",
       "Facebook Likes - Director        int64\n",
       "Facebook Likes - Actor 1         int64\n",
       "Facebook Likes - Actor 2         int64\n",
       "Facebook Likes - Actor 3         int64\n",
       "Facebook Likes - cast Total      int64\n",
       "Facebook likes - Movie           int64\n",
       "Facenumber in posters          float64\n",
       "User Votes                       int64\n",
       "Reviews by Users               float64\n",
       "Reviews by Crtiics             float64\n",
       "IMDB Score                      object\n",
       "dtype: object"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Pay attention that dtype of \"Year\" is object, not a value\n",
    "# Because there are some years like 2010-2011\n",
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0          2010\n",
       "1     2010-2011\n",
       "2          2010\n",
       "3          2010\n",
       "4          2010\n",
       "5          2010\n",
       "6          2010\n",
       "7          2010\n",
       "8          2010\n",
       "9          2010\n",
       "10         2010\n",
       "11         2010\n",
       "12         2010\n",
       "13    2009-2010\n",
       "14         2010\n",
       "15         2010\n",
       "16         2010\n",
       "17         2010\n",
       "18         2010\n",
       "19         2010\n",
       "20         2010\n",
       "21         2010\n",
       "22         2010\n",
       "23       2008 ?\n",
       "24         2010\n",
       "25         2010\n",
       "26         2010\n",
       "27         2010\n",
       "28         2010\n",
       "29         2010\n",
       "30         2010\n",
       "31         2010\n",
       "32         2010\n",
       "33         2010\n",
       "34         2010\n",
       "35         2010\n",
       "36         2010\n",
       "37         2010\n",
       "38         2010\n",
       "39         2010\n",
       "40         2010\n",
       "41         2010\n",
       "42         2010\n",
       "43         2010\n",
       "44         2010\n",
       "45         2010\n",
       "46         2010\n",
       "47         2010\n",
       "48         2010\n",
       "49         2010\n",
       "Name: Year, dtype: object"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0          2010\n",
       "1     2010-2011\n",
       "2          2010\n",
       "3          2010\n",
       "4          2010\n",
       "5          2010\n",
       "6          2010\n",
       "7          2010\n",
       "8          2010\n",
       "9          2010\n",
       "10         2010\n",
       "11         2010\n",
       "12         2010\n",
       "13    2009-2010\n",
       "14         2010\n",
       "15         2010\n",
       "16         2010\n",
       "17         2010\n",
       "18         2010\n",
       "19         2010\n",
       "20         2010\n",
       "21         2010\n",
       "22         2010\n",
       "23         2008\n",
       "24         2010\n",
       "25         2010\n",
       "26         2010\n",
       "27         2010\n",
       "28         2010\n",
       "29         2010\n",
       "30         2010\n",
       "31         2010\n",
       "32         2010\n",
       "33         2010\n",
       "34         2010\n",
       "35         2010\n",
       "36         2010\n",
       "37         2010\n",
       "38         2010\n",
       "39         2010\n",
       "40         2010\n",
       "41         2010\n",
       "42         2010\n",
       "43         2010\n",
       "44         2010\n",
       "45         2010\n",
       "46         2010\n",
       "47         2010\n",
       "48         2010\n",
       "49         2010\n",
       "Name: Year, dtype: object"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Replace \" ?\" first and then choose the ending year\n",
    "df.Year = df.Year.replace(\"2008 ?\", \"2008\")\n",
    "\n",
    "df.Year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     2010\n",
       "1     2011\n",
       "2     2010\n",
       "3     2010\n",
       "4     2010\n",
       "5     2010\n",
       "6     2010\n",
       "7     2010\n",
       "8     2010\n",
       "9     2010\n",
       "10    2010\n",
       "11    2010\n",
       "12    2010\n",
       "13    2010\n",
       "14    2010\n",
       "15    2010\n",
       "16    2010\n",
       "17    2010\n",
       "18    2010\n",
       "19    2010\n",
       "20    2010\n",
       "21    2010\n",
       "22    2010\n",
       "23    2008\n",
       "24    2010\n",
       "25    2010\n",
       "26    2010\n",
       "27    2010\n",
       "28    2010\n",
       "29    2010\n",
       "30    2010\n",
       "31    2010\n",
       "32    2010\n",
       "33    2010\n",
       "34    2010\n",
       "35    2010\n",
       "36    2010\n",
       "37    2010\n",
       "38    2010\n",
       "39    2010\n",
       "40    2010\n",
       "41    2010\n",
       "42    2010\n",
       "43    2010\n",
       "44    2010\n",
       "45    2010\n",
       "46    2010\n",
       "47    2010\n",
       "48    2010\n",
       "49    2010\n",
       "Name: Year, dtype: object"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Choose the ending year as film year\n",
    "# Выберем год окончания как год фильма\n",
    "def getEndingYear(str):\n",
    "    return str[-4:]\n",
    "\n",
    "df['Year'] = df['Year'].apply(getEndingYear)\n",
    "\n",
    "df.Year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Title                           object\n",
       "Year                             int64\n",
       "Genres                          object\n",
       "Language                        object\n",
       "Country                         object\n",
       "Content Rating                  object\n",
       "Duration                       float64\n",
       "Aspect Ratio                    object\n",
       "Budget                         float64\n",
       "Gross Earnings                 float64\n",
       "Director                        object\n",
       "Actor 1                         object\n",
       "Actor 2                         object\n",
       "Actor 3                         object\n",
       "Facebook Likes - Director        int64\n",
       "Facebook Likes - Actor 1         int64\n",
       "Facebook Likes - Actor 2         int64\n",
       "Facebook Likes - Actor 3         int64\n",
       "Facebook Likes - cast Total      int64\n",
       "Facebook likes - Movie           int64\n",
       "Facenumber in posters          float64\n",
       "User Votes                       int64\n",
       "Reviews by Users               float64\n",
       "Reviews by Crtiics             float64\n",
       "IMDB Score                      object\n",
       "dtype: object"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Now covert year to int type\n",
    "# Теперь колонка с годом преобразуется в число целого типа\n",
    "df['Year'] = pd.to_numeric(df['Year'])\n",
    "\n",
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5.3. Упражнение: исправить проблемы данных в колонке 'Country'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0               USA\n",
       "1               USA\n",
       "2               USA\n",
       "3            France\n",
       "4               USA\n",
       "5               USA\n",
       "6               USA\n",
       "7               usa\n",
       "8               usa\n",
       "9               usa\n",
       "10          Germany\n",
       "11               UK\n",
       "12           Canada\n",
       "13              USA\n",
       "14              USA\n",
       "15        Australia\n",
       "16           Canada\n",
       "17           Mexico\n",
       "18              USA\n",
       "19              USA\n",
       "20              USA\n",
       "21               UK\n",
       "22            Spain\n",
       "23              USA\n",
       "24              USA\n",
       "25           canada\n",
       "26              USA\n",
       "27               UK\n",
       "28              USA\n",
       "29              USA\n",
       "30              USA\n",
       "31              USA\n",
       "32              USA\n",
       "33    Official site\n",
       "34              USA\n",
       "35              USA\n",
       "36              USA\n",
       "37              USA\n",
       "38              USA\n",
       "39              USA\n",
       "40              USA\n",
       "41              USA\n",
       "42              USA\n",
       "43              USA\n",
       "44              USA\n",
       "45              USA\n",
       "46           Sweden\n",
       "47              USA\n",
       "48               UK\n",
       "49              USA\n",
       "Name: Country, dtype: object"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Hint 1: issues in this column: 'Official site' value, 'canada' and 'Canada', 'USA' and 'usa',\n",
    "# Совет: проблемы в этой колонке - строчные и прописные буквы, неверное название страны 'Official site'\n",
    "\n",
    "# We can use functions from pandas.str\n",
    "# Можно использовать функции pandas для строк\n",
    "# dataFrame.Column.str.lower()\n",
    "# Converts all characters to lowercase.\n",
    "# dataFrame.Column.str.upper()\n",
    "# Converts all characters to uppercase.\n",
    "\n",
    "# Look at column to see its data problems\n",
    "# Просмотр колонки для визуального определения проблем с данными\n",
    "df.Country"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Title                            Country Strong\n",
       "Year                                       2010\n",
       "Genres                              Drama|Music\n",
       "Language                                English\n",
       "Country                           Official site\n",
       "Content Rating                            PG-13\n",
       "Duration                                    117\n",
       "Aspect Ratio                               2,35\n",
       "Budget                                  1.5e+07\n",
       "Gross Earnings                      2.02189e+07\n",
       "Director                            Shana Feste\n",
       "Actor 1                        Leighton Meester\n",
       "Actor 2                            Cinda McCain\n",
       "Actor 3                              Tim McGraw\n",
       "Facebook Likes - Director                    19\n",
       "Facebook Likes - Actor 1                   3000\n",
       "Facebook Likes - Actor 2                    646\n",
       "Facebook Likes - Actor 3                    461\n",
       "Facebook Likes - cast Total                4204\n",
       "Facebook likes - Movie                        0\n",
       "Facenumber in posters                         4\n",
       "User Votes                                14814\n",
       "Reviews by Users                            114\n",
       "Reviews by Crtiics                          135\n",
       "IMDB Score                                  6,3\n",
       "Name: 33, dtype: object"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Hint 2\n",
    "# print what is in row 33 where Country == Official site\n",
    "# Распечатаем строку 33, чтобы понять, для какого фильма неверно указана страна\n",
    "# Hint 2 - find out the real country of issuing this film here: https://www.imdb.com/title/tt1555064/\n",
    "# Можно узнать, в какой стране выпущен этот фильм на сайте https://www.imdb.com/title/tt1555064/\n",
    "df.loc[33, :]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 6. Cтатьи для самостоятельного изучения\n",
    "\n",
    "### 6.1. Загрузка и обработка файлов CSV c Pandas \n",
    "\n",
    "* [Python Pandas read_csv – Load Data from CSV Files](https://www.shanelynn.ie/python-pandas-read_csv-load-data-from-csv-files/)\n",
    "* [Interactive online tutorial for reading CSV with pandas from DataCamp](https://www.datacamp.com/community/tutorials/pandas-read-csv)\n",
    "\n",
    "### 6.2. Подготовка данных с Pandas, работа с признаками, создание аккуратных данных\n",
    "\n",
    "* [Selecting Subsets of Data in Pandas: Part 1, by Ted Petrou](https://medium.com/dunder-data/selecting-subsets-of-data-in-pandas-6fcd0170be9c)\n",
    "* [Открытый курс машинного обучения. Тема 1. Первичный анализ данных с Pandas](https://habr.com/ru/company/ods/blog/322626/)\n",
    "* [Продвинутая подготовка данных. Открытый курс машинного обучения. Тема 6. Построение и отбор признаков](https://habr.com/ru/company/ods/blog/325422/)\n",
    "* [Подготовка данных к анализу на языке R. Data tidying: Подготовка наборов данных для анализа на конкретных примерах](https://habr.com/ru/post/248741/)"
   ]
  }
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