{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Необходимо определить, в какой год были максимальные и минимальные значения показателей"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Годы', 'Единиц', 'На 1000 человек населения']\n",
      "['1950', '1222971', '12,0']\n",
      "['1960', '1499581', '12,5']\n",
      "['1970', '1319227', '10,1']\n",
      "['1980', '1464579', '10,6']\n",
      "['1990', '1319928', '8,9']\n",
      "['1995', '1075219', '7,3']\n",
      "['2000', '897327', '6,2']\n",
      "['2001', '1001589', '6,9']\n",
      "['2002', '1019762', '7,1']\n",
      "['2003', '1091778', '7,5']\n",
      "['2004', '979667', '6,8']\n",
      "['2005', '1066366', '7,4']\n",
      "['2006', '1113562', '7,8']\n",
      "['2007', '1262500', '8,8']\n",
      "['2008', '1179007', '8,3']\n",
      "['2009', '1199446', '8,4']\n",
      "['2010', '1215066', '8,5']\n",
      "['2011', '1316011', '9,2']\n",
      "['2012', '1213598', '8,5']\n",
      "['2013', '1225501', '8,5']\n",
      "['2014', '1225985', '8,4']\n",
      "['2015', '1161068', '7,9']\n",
      "['2016', '985836', '6,7']\n",
      "['2017', '1049735', '7,1']\n",
      "['2018', '893039', '6,1']\n"
     ]
    }
   ],
   "source": [
    "import csv\n",
    "\n",
    "with open(\"data/braki.stat\") as csvfile: # csvfile - переменная, через которую будем работать с файлом\n",
    "    stat = csv.reader(csvfile) # функция reader читает содержимое файла braki.stat, разбивая строку на отдельные элементы \n",
    "    # теперь в переменной stat содержится файл, с которым будем работать в цикле, запрашивая по одной строке:\n",
    "    for row in stat: \n",
    "        print(row)   # у нас небольшой файл в качестве примера, поэтому можем вывести его на экран,\n",
    "                     # но так обычно не следует делать при миллионах записей ;)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['1950', '1222971', '12,0']\n",
      "['1960', '1499581', '12,5']\n",
      "['1970', '1319227', '10,1']\n",
      "['1980', '1464579', '10,6']\n",
      "['1990', '1319928', '8,9']\n",
      "['1995', '1075219', '7,3']\n",
      "['2000', '897327', '6,2']\n",
      "['2001', '1001589', '6,9']\n",
      "['2002', '1019762', '7,1']\n",
      "['2003', '1091778', '7,5']\n",
      "['2004', '979667', '6,8']\n",
      "['2005', '1066366', '7,4']\n",
      "['2006', '1113562', '7,8']\n",
      "['2007', '1262500', '8,8']\n",
      "['2008', '1179007', '8,3']\n",
      "['2009', '1199446', '8,4']\n",
      "['2010', '1215066', '8,5']\n",
      "['2011', '1316011', '9,2']\n",
      "['2012', '1213598', '8,5']\n",
      "['2013', '1225501', '8,5']\n",
      "['2014', '1225985', '8,4']\n",
      "['2015', '1161068', '7,9']\n",
      "['2016', '985836', '6,7']\n",
      "['2017', '1049735', '7,1']\n",
      "['2018', '893039', '6,1']\n"
     ]
    }
   ],
   "source": [
    "import csv\n",
    "\n",
    "with open(\"data/braki.stat\") as csvfile: \n",
    "    stat = csv.reader(csvfile)    # delimiter=','\n",
    "    for row in stat: \n",
    "        # пропустим первую строку, которая содержит названия полей:\n",
    "        if row[0] == 'Годы':\n",
    "            continue\n",
    "        else:\n",
    "            print(row)                "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Есть способ лучше:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['1950', '1222971', '12,0']\n",
      "['1960', '1499581', '12,5']\n",
      "['1970', '1319227', '10,1']\n",
      "['1980', '1464579', '10,6']\n",
      "['1990', '1319928', '8,9']\n",
      "['1995', '1075219', '7,3']\n",
      "['2000', '897327', '6,2']\n",
      "['2001', '1001589', '6,9']\n",
      "['2002', '1019762', '7,1']\n",
      "['2003', '1091778', '7,5']\n",
      "['2004', '979667', '6,8']\n",
      "['2005', '1066366', '7,4']\n",
      "['2006', '1113562', '7,8']\n",
      "['2007', '1262500', '8,8']\n",
      "['2008', '1179007', '8,3']\n",
      "['2009', '1199446', '8,4']\n",
      "['2010', '1215066', '8,5']\n",
      "['2011', '1316011', '9,2']\n",
      "['2012', '1213598', '8,5']\n",
      "['2013', '1225501', '8,5']\n",
      "['2014', '1225985', '8,4']\n",
      "['2015', '1161068', '7,9']\n",
      "['2016', '985836', '6,7']\n",
      "['2017', '1049735', '7,1']\n",
      "['2018', '893039', '6,1']\n"
     ]
    }
   ],
   "source": [
    "import csv\n",
    "\n",
    "with open(\"data/braki.stat\") as csvfile: \n",
    "    stat = csv.reader(csvfile)\n",
    "    # пропустим первую строку, которая содержит названия полей:\n",
    "    next(csvfile)\n",
    "    for row in stat: \n",
    "        print(row) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Для сохранения привязки года к показателям воспользуемся словарем (dict):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{1950: 1222971, 1960: 1499581, 1970: 1319227, 1980: 1464579, 1990: 1319928, 1995: 1075219, 2000: 897327, 2001: 1001589, 2002: 1019762, 2003: 1091778, 2004: 979667, 2005: 1066366, 2006: 1113562, 2007: 1262500, 2008: 1179007, 2009: 1199446, 2010: 1215066, 2011: 1316011, 2012: 1213598, 2013: 1225501, 2014: 1225985, 2015: 1161068, 2016: 985836, 2017: 1049735, 2018: 893039}\n"
     ]
    }
   ],
   "source": [
    "import csv\n",
    "\n",
    "years = dict() # создаем пустой словарь\n",
    "\n",
    "with open(\"data/braki.stat\") as csvfile: \n",
    "    stat = csv.reader(csvfile)\n",
    "    next(csvfile)\n",
    "    for row in stat: \n",
    "        years[int(row[0])] = int(row[1])\n",
    "\n",
    "print(years)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Сложно ориентироваться в значениях, поэтому построим график:"
   ]
  },
  {
   "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": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "x_coord = list(years.keys()) # получаем список ключей словаря years\n",
    "y_coord = list(years.values()) # получаем список значений словаря years\n",
    " \n",
    "# Построить график.\n",
    "plt.plot(x_coord, y_coord)\n",
    "\n",
    "# Показать график.\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Следующий шаг - определить максимум и минимум значений словаря years.\n",
    "\n",
    "Здесь требуется рассмотреть тему сортировки словарей."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1950, 1960, 1970, 1980, 1990, 1995, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018]\n"
     ]
    }
   ],
   "source": [
    "# обычный sorted сортирует по ключам:\n",
    "print(sorted(years)) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1222971"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# метод get возвращает значение по ключу\n",
    "years.get(1950)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2018, 2000, 2004, 2016, 2001, 2002, 2017, 2005, 1995, 2003, 2006, 2015, 2008, 2009, 2012, 2010, 1950, 2013, 2014, 2007, 2011, 1970, 1990, 1980, 1960]\n"
     ]
    }
   ],
   "source": [
    "# сортировка (по возрастанию) значений словаря и печать их ключей:\n",
    "print(sorted(years, key=years.get))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1960, 1980, 1990, 1970, 2011, 2007, 2014, 2013, 1950, 2010, 2012, 2009, 2008, 2015, 2006, 2003, 1995, 2005, 2017, 2002, 2001, 2016, 2004, 2000, 2018]\n"
     ]
    }
   ],
   "source": [
    "# сортировка (по убыванию) значений словаря и печать их ключей:\n",
    "print(sorted(years, key=years.get, reverse=True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}