{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Программирование и анализ данных\n",
    "\n",
    "*Алла Тамбовцева, НИУ ВШЭ*\n",
    "\n",
    "### Семинар 9 по теме: визуализация и использование виджетов"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Файл `Chile.txt` содержит результаты опроса перед плебисцитом 1988 года в Чили.\n",
    "\n",
    "**Показатели:**\n",
    "\n",
    "* `region`: регион, где живет респондент (`C`, Central; `M`, Metropolitan Santiago area; `N`, North; `S`, South; `SA`, City of Santiago);\n",
    "* `sex`: пол респондента;\n",
    "* `population`: численность населения в регионе респондента;\n",
    "* `age`: возраст респондента;\n",
    "* `education`: уровень образования респондента (`P`, Primary; `PS`, Post-secondary; `S`, Secondary);\n",
    "* `income`: доход респондента;\n",
    "* `statusquo`: индекс отношения к статус-кво (чем больше значение, тем больше человек склонен одобрять сохранение статус-кво – нахождение Пиночета у власти);\n",
    "* `vote`: как респондент собирается голосовать (`A`, will abstain; `N`, will vote no – against Pinochet; `U`, undecided; `Y`, will vote yes – for Pinochet)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Задание 1\n",
    "\n",
    "Загрузите файл `Chile.txt` и считайте строки из него в переменную `lines`, используя код ниже:\n",
    "    \n",
    "        f = open(\"Chile.txt\", encoding = \"UTF-8\")\n",
    "        lines = f.readlines()\n",
    "        \n",
    "**Пояснения к коду:** функция `open()` открывает файл, а метод `readlines()` разбивает текстовый файл на строки и возвращает список этих строк. \n",
    "\n",
    "Удобно ли работать с данными в `lines`? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "f = open(\"Chile.txt\", encoding = \"UTF-8\")\n",
    "lines = f.readlines()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\"\",\"region\",\"population\",\"sex\",\"age\",\"education\",\"income\",\"statusquo\",\"vote\"\\n'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lines[0]  # не очень удобно"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Задание 2\n",
    "\n",
    "Загрузите файл `Chile.txt`, используя функцию `read_table()` из библиотеки `pandas` и считая, что разделителем столбцов считается запятая. Сохраните результат в переменную `ch`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "ch = pd.read_table(\"Chile.txt\", sep = \",\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Задание 3\n",
    "\n",
    "Удалите из датафрейма `ch` строки с пропущенными значениями, сохраните изменения."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "ch.dropna(inplace = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Задание 4\n",
    "\n",
    "Создайте виджет типа `Dropdown`, выпадающее меню, где опциями меню являются названия столбцов датафрейма `ch`, кроме `Unnamed:0`, значением по умолчанию является `population`. Сохраните его в переменную `choose` и выведите на экран."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5c2c74806cc24a0fa72e8111412e12ea",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Dropdown(index=1, options=('region', 'population', 'sex', 'age', 'education', 'income', 'statusquo', 'vote'), …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import ipywidgets as widgets\n",
    "choose = widgets.Dropdown(options = ch.columns[1:], \n",
    "                          value = \"population\")\n",
    "display(choose)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Задание 5\n",
    "\n",
    "Напишите функцию `desc_plot()`, которая принимает на вход строку с названием столбца, которое выбрал пользователь в выпадающем меню, и строит соответствующий график:\n",
    "\n",
    "* если тип столбца `object`, то строит гистограмму;\n",
    "* если тип столбца не `object`, то строит столбиковую диаграмму."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "sns.set()\n",
    "\n",
    "def desc_plot(c):\n",
    "    if ch[c].dtype == \"object\":\n",
    "        tab = ch[c].value_counts()\n",
    "        x = tab.index\n",
    "        y = tab.values\n",
    "        sns.barplot(x, y)\n",
    "    else:\n",
    "        sns.distplot(ch[c])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "desc_plot(choose.value) "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}