Искусственный Интеллект 1 РТ.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "54207b6b",
   "metadata": {},
   "source": [
    "# Рабочая тетрадь 1. Основы Python\n",
    "## Кармазин Вадим Сергеевич"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed12cc5b",
   "metadata": {},
   "source": [
    "<img src=\"https://aniyuki.com/wp-content/uploads/2021/12/aniyuki-itachi-if-8.gif\">"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "231ff449",
   "metadata": {},
   "source": [
    "### Типы данных\n",
    "```\n",
    "Все типы данных в Python относятся к одной из 2-х категорий: \n",
    "изменяемые (mutable) и неизменяемые (immutable).\n",
    "Неизменяемые объекты:\n",
    "\tчисловые данные (int, float),\n",
    "\tbool,\n",
    "\tNone,\n",
    "\tсимвольные строки (class 'str'),\n",
    "\tкортежи (tuple).\n",
    "Изменяемые объекты:\n",
    "\tсписки (list),\n",
    "\tмножества (set),\n",
    "\tсловари (dict).\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f11fcb56",
   "metadata": {},
   "source": [
    "***Пример:*** Выведите на печать и определите тип переменной."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "09685ed9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "39.4 | <class 'float'> \n",
      " None | <class 'NoneType'> \n",
      " ('a', 5, 12.345, (2, 'b')) | <class 'tuple'> \n",
      " [['Антонова Антонина', 34, 'ж'], ['Борисов Борис', 26, 'м']] | <class 'list'> \n",
      " {1, 2, 'title', 'content'} | <class 'set'>\n"
     ]
    }
   ],
   "source": [
    "x = 3+5.2*7\n",
    "y = None\n",
    "z = 'a', 5, 12.345, (2, 'b')\n",
    "df = [['Антонова Антонина', 34, 'ж'],['Борисов Борис', 26, 'м']]\n",
    "A = {1, 'title', 2, 'content'}\n",
    "print(x, '|', type(x), '\\n', y, '|', type(y), '\\n', z, '|', type(z), '\\n', df, '|', type(df), '\\n', A, '|', type(A))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f7bceded",
   "metadata": {},
   "source": [
    "***Задане:*** Выведите на печать и определите тип переменной."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4d1e9068",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True | <class 'bool'> \n",
      " {8, 1, 3, 7} | <class 'set'> \n",
      " {2, 4, 5, 10, 'apple'} | <class 'set'> \n",
      " set() | <class 'set'> \n",
      " ('Антонова Антонина', 34, 'ж') | <class 'tuple'> \n",
      " type | <class 'str'> \n",
      " [1, 'title', 2, 'content'] | <class 'list'> \n",
      "\n"
     ]
    }
   ],
   "source": [
    "x = 5 >= 2\n",
    "A = {1, 3, 7, 8}\n",
    "B = {2, 4, 5, 10, 'apple'}\n",
    "C = A & B\n",
    "df = 'Антонова Антонина', 34, 'ж'\n",
    "z = 'type'\n",
    "D = [1, 'title', 2, 'content']\n",
    "print(x, '|', type(x), '\\n', A, '|', type(A), '\\n', B, '|', type(B), '\\n', C, '|', type(C), '\\n', df, '|', type(df), '\\n', z, '|', type(z), '\\n', D, '|', type(D), '\\n', )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d38f573",
   "metadata": {},
   "source": [
    "<img src=\"https://giffiles.alphacoders.com/350/35099.gif\">"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c4262d4",
   "metadata": {},
   "source": [
    "### If - Условный оператор\n",
    "```\n",
    "В коде часто приходится проверять выполнимость или невыполнимость каких-то условий. Синтаксис:\n",
    "if<условие1 (булевское выражение)> :\n",
    "    <код, который выполнится, если условие верно>\n",
    "elif<условие2 (булевское выражение)>:\n",
    "    <код, который выполнится, если условие1 было неверно, а условие2верно>\n",
    "else:\n",
    "    <код, который выполнится, если условие1 и условие2 были неверны>\n",
    "Обратите внимание, что код, который должен выполняться внутри каждого условия, записывается с отступом в 4 пробела от уровня if, elif и else: в питоне области видимости переменных обозначаются отступами.\n",
    "```\n",
    "***То есть, отступы позволяют понять, где начинается код, который должен выполняться при выполнении условия в if, и где заканчивается.***\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ef3b7eaa",
   "metadata": {},
   "source": [
    "***Пример:*** Вывести на экран является ли переменная х положительной, отрицательной или равна нулю."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "46288ed3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x положительный\n"
     ]
    }
   ],
   "source": [
    "x = 125\n",
    "if x<0:\n",
    "    print(\"x отрицательный\")\n",
    "elif x==0:\n",
    "    print(\"x равен нулю\")\n",
    "else:\n",
    "    print(\"x положительный\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "140b1907",
   "metadata": {},
   "source": [
    "***Задание:*** Напишите код. Задается х, напечатать какому из интервалов принадлежит: (-infinity, -5), [-5, 5] или от (5, +infinity)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "926516f5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x принадлежит промежутку (5; ∞)\n"
     ]
    }
   ],
   "source": [
    "x = 100\n",
    "if x<-5:\n",
    "    print(\"x принадлежит промежутку (-∞;-5)\")\n",
    "elif x<=5:\n",
    "    print(\"x принадлежит промежутку [-5; 5]\")\n",
    "else:\n",
    "    print(\"x принадлежит промежутку (5; ∞)\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ef7f545a",
   "metadata": {},
   "source": [
    "<img src=\"https://c.tenor.com/pLhhw8tQib0AAAAC/killua-hunter-x-hunter.gif\">"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "448c88a2",
   "metadata": {},
   "source": [
    "### Циклы - for и while\n",
    "```\n",
    "Цикл while проверяет истинность условия, и если условие истинно, то выполняет инструкции цикла.\n",
    "Синтаксис:\n",
    "while условие:\n",
    "    инструкции\n",
    "Инструкции, которые относятся к циклу while, располагаются на последующих строках и должны иметь отступ от начала ключевого слова while.\n",
    "Цикл for проходит по набору значений, помещает каждое значение в переменную с которой в цикле можно производить различные действия.\n",
    "Синтаксис:\n",
    "for переменная in набор_значений:\n",
    "    инструкции\n",
    "После ключевого слова for идет название переменной, в которую будут помещаться значения. После оператора in указывается набор значений и двоеточие. Инструкции, которые относятся к циклу, располагаются на последующих строках и должны иметь отступ.\n",
    "При создании цикла удобно пользоваться функцией range(a, b), которая создает последовательность чисел от a до b-1\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4a2b0e30",
   "metadata": {},
   "source": [
    "***Пример:*** Вывести все числа от 1 до 10 с шагом 3."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "47b02102",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "4\n",
      "7\n",
      "10\n"
     ]
    }
   ],
   "source": [
    "x = 1\n",
    "while x <= 10:\n",
    "    print(x)\n",
    "    x += 3"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "96959235",
   "metadata": {},
   "source": [
    "***Пример:*** Задан список. Напечатать все элементы списка."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "76715ed5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KNN\n",
      "decision tree\n",
      "linear model\n"
     ]
    }
   ],
   "source": [
    "models = ['KNN', 'decision tree', 'linear model']\n",
    "for model in models:\n",
    "    print(model)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c630e1b2",
   "metadata": {},
   "source": [
    "***Пример:*** Создать список от 1 до 99 с шагом 7."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "6114e0c4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 8, 15, 22, 29, 36, 43, 50, 57, 64, 71, 78, 85, 92, 99]\n"
     ]
    }
   ],
   "source": [
    "list_int = range(1, 100, 7)\n",
    "print(list(list_int))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d4cdc825",
   "metadata": {},
   "source": [
    "***Пример:*** Вывести на экран числа от 5 до 105 с шагом 25, используя функцию range()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "3f2e13e0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5\n",
      "30\n",
      "55\n",
      "80\n",
      "105\n"
     ]
    }
   ],
   "source": [
    "for i in range (5, 106, 25):\n",
    "    print(i)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "83178afb",
   "metadata": {},
   "source": [
    "***Пример:*** Срез.Необходимо получить каждый k-й элемент массива a, начиная с элемента с индексом i (включительно) и заканчивая элементом с индексом j (не включительно), c шагом 2. Переставить все элементы в обратном порядке."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "436ecbc1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2, 4, 6]\n",
      "[9, 8, 7, 6, 5, 4, 3, 2, 1]\n"
     ]
    }
   ],
   "source": [
    "a = [1, 2, 3, 4, 5, 6, 7, 8, 9]\n",
    "b = a[1:7:2]\n",
    "c = a[::-1]\n",
    "print(b)\n",
    "print(c)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "51e04b73",
   "metadata": {},
   "source": [
    "***Задание:*** Выведите числа из примера на while (3.2.1) в обратном порядке."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "65035b72",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10\n",
      "7\n",
      "4\n",
      "1\n"
     ]
    }
   ],
   "source": [
    "x = 10\n",
    "while x>=1:\n",
    "    print(x)\n",
    "    x -= 3"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d2d2e4d6",
   "metadata": {},
   "source": [
    "***Задание:*** При решении задач машинного обучения часто в качестве объектов исследования выступает человек. Создайте список значимых характеристик (признаков), идентифицирующих человека. Выведите список на экран."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "b0b68cf2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "пол\n",
      "рост\n",
      "вес\n",
      "год рождения\n",
      "цвет волос\n"
     ]
    }
   ],
   "source": [
    "characteristics = ['пол', 'рост', 'вес', 'год рождения', 'цвет волос']\n",
    "for i in characteristics:\n",
    "    print(i)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a2119faa",
   "metadata": {},
   "source": [
    "***Задание:*** Создать список чисел от 2 до 15 с шагом 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "0cdeef81",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]\n"
     ]
    }
   ],
   "source": [
    "list_int = range(2, 16, 1)\n",
    "print(list(list_int))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a67f2f7a",
   "metadata": {},
   "source": [
    "***Задание:*** Выведите числа из примера на for c функцией range() (3.2.4) в\n",
    "обратном порядке."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "79411a0c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "105\n",
      "80\n",
      "55\n",
      "30\n",
      "5\n"
     ]
    }
   ],
   "source": [
    "for i in range(105, 4, -25):\n",
    "    print(i)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2eda3f53",
   "metadata": {},
   "source": [
    "***Задание:*** Срез. Напишите код, который все элементы массива x с четными индексами переставит в обратном порядке. Т.е. если x = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], то код должен сформировать [8, 1, 6, 3, 4, 5, 2, 7, 0, 9]."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "f09aeb51",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[8, 1, 6, 3, 4, 5, 2, 7, 0, 9]\n"
     ]
    }
   ],
   "source": [
    "x = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]\n",
    "x[0::2] = reversed(x[0::2])\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4869e003",
   "metadata": {},
   "source": [
    "<img src=\"https://pa1.narvii.com/7228/82e4b50709f25fd274a823b2f64fb6ef0b893912r1-540-304_hq.gif\">"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6dadbc81",
   "metadata": {},
   "source": [
    "```\n",
    "    Машинное обучение опирается на фундаментальные понятия и законы\n",
    "математики и информатики. Библиотека математических функций (math)\n",
    "применяется при решении большинства прикладных задач анализа данных.\n",
    "Также, и некоторые другие библиотеки Python содержат математические\n",
    "функции (например, NumPy). Важную роль играет библиотека matplotlib,\n",
    "которая позволяет производить визуализацию расчетов в удобной для\n",
    "восприятия человеком форме: графики, диаграммы, гистограммы.\n",
    "    Подключение библиотек – синтаксис:\n",
    "import math as m\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "etc…\n",
    "Библиотеки в python содержат не только функции, позволяющие решать\n",
    "различные задачи, но и некоторые популярные константы, например число\n",
    "Эйлера.\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bc71282b",
   "metadata": {},
   "source": [
    "***Пример:*** Рассчитать синус от е радиан и вывести результат на экран"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "338ef367",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.41078129050290885\n"
     ]
    }
   ],
   "source": [
    "import math as m\n",
    "print(m.sin (m.e))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92b1e89b",
   "metadata": {},
   "source": [
    "***Пример:*** Для функции \\\\(sin(𝑥𝑒^{cos(𝑥)})\\\\) на интервале [0;10] с шагом 0,05 построить\n",
    "график функции, закрасить площадь под ним и вычислить значение\n",
    "этой площади.</br>\n",
    "Для вычисления площади воспользуйте функции trapz(), вычисляющей\n",
    "интеграл по правилу трапеции. Для ее корректной работы необходимо\n",
    "подключить следующие библиотеки:</br>\n",
    "from scipy.integrate import simps</br>\n",
    "from numpy import trapz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "c4b2ab60",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "73.50792907748662\n"
     ]
    },
    {
     "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 numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.integrate import simps\n",
    "from numpy import trapz\n",
    "x = np.arange(0.0, 10, 0.1)\n",
    "y = np.abs(np.sin(x*np.exp(np.cos(x))))\n",
    "plt.grid()\n",
    "plt.plot(x, y, c = \"r\")\n",
    "plt.fill_between(x, y)\n",
    "\n",
    "area = trapz(y)\n",
    "print (area)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "668365f4",
   "metadata": {},
   "source": [
    "***Пример:*** Дано некоторое распределение оценок в группе за экзамен. Нарисовать круговую и точечную диаграммы распределения. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "8e20853e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x200114ae850>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "from matplotlib import pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "marks = ['Неуд', 'Удовл', 'Хор', 'Отл']\n",
    "\n",
    "data = [3, 7, 8, 4]\n",
    "fig = plt.figure(figsize =(10, 7))\n",
    "plt. pie(data, labels = marks)\n",
    "plt.show()\n",
    "plt.grid()\n",
    "plt.scatter (marks,data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "45e485c8",
   "metadata": {},
   "source": [
    "***Задание:*** Задайте массив случайных значений из интервала(0; 1). Рассчитайте средние и медианные значения для массива, сравните результаты, какие выводы можно сделать о значениях? Постройте точечную диаграмму рассения полученного ряда."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "7957cc2b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x2dbf3b9ffd0>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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 numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "mas=np.random.rand(10)\n",
    "rd = pd.DataFrame(mas)\n",
    "df = pd.DataFrame({'mean': rd.mean(), 'median': rd.median()})\n",
    "x=rd.mean()\n",
    "y=rd.median()\n",
    "fig, ax = plt.subplots()\n",
    "plt.grid()\n",
    "ax.scatter(mas, mas)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "83a7cd98",
   "metadata": {},
   "source": [
    "***Задание:*** Дана функция \\\\(\\frac{\\sqrt{1+e^{\\sqrt{x}}+\\cos(x^2)}}{|1-\\sin^3(x)|}+ln|2x|\\\\); Создать массив из 10 значений функции (𝑥, например, изменяется от 1 до 10). Выделить срез первой половины массива и построить графики для основного массива – линейный и для среза – точечный"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "ced350a4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x200115b0b80>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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 numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from numpy import trapz\n",
    "x = np.arange(1, 11,1)\n",
    "func = ((np.sqrt(1+np.e**(np.sqrt(x))+np.cos(x**2)))/(np.abs(1-np.sin(x)**3)))+ np.log(2*x)\n",
    "plt.grid()\n",
    "x1=x[0:5]\n",
    "y1=func[0:5]\n",
    "plt.plot(x, func,c='y')\n",
    "plt.scatter(x1,y1,c='b')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "805f3014",
   "metadata": {},
   "source": [
    "***Задание:*** Дана функция \\\\(|cos(𝑥𝑒^{(cos(𝑥)+ln(𝑥+1)})|\\\\) Построить график на\n",
    "интервале (0,10) с шагом 1 с заливкой площади и найти эту площадь\n",
    "под ним. Для вычисления площади воспользуйте функции trapz(), вычисляющей интеграл по правилу трапеции.\n",
    "Для ее корректной работы необходимо подключить следующие библиотеки:<br>\n",
    "from scipy.integrate import simps<br>\n",
    "from numpy import trapz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "7fbe9847",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6.748183214657733\n"
     ]
    },
    {
     "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 numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.integrate import simps \n",
    "from numpy import trapz\n",
    "x = np.arange(0, 11, 1)\n",
    "y = np.abs(np.cos(x*np.exp((np.cos(x))+np.log(x+1))))\n",
    "plt.grid()\n",
    "plt.plot(x, y, c = \"r\")\n",
    "plt.fill_between(x, y)\n",
    "area = trapz(y)\n",
    "print (area)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d46a64a",
   "metadata": {},
   "source": [
    "***Задание:*** Откройте статистические данные по стоимости акций Apple, Microsoft, Google за 12 месяцев 2021 г. Постройте 3 графика на плоскости и оцените их динамику. Подумайте, как можно улучшить читаемость результатов.\n",
    "\n",
    "Для начальных данных допустимо использовать следующий ресурс: https://finance.yahoo.com"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "96b221a6",
   "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"
    },
    {
     "data": {
      "image/png": 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8M3Cuqs4AZgKLROR0YAkwTVWnA9uBuwFEZApwNTAVWAQ8JCIDf0+vMSYsNHicJJ/QxZC+oSQyQvjqgnGAM7l4XHRw0l+3k7w66t2n0e6Pquobquq/f3cZkOs+vhJYrKrNqloI7ATm9VG5jTEnmUZPKwCJsYOnrvipWbkMS40b8N5AgSRwOMzjbuzUxFcB44EHVfWudutfAv6lqo+LyAPAMlV93F33CPCaqj7dwXFvBm4GyMnJmbN48eJul6m+vp6kpKRubx+qwiUOv3CKJ5xigcEbz+t7Wnhiq4cHz0sgMfrI0MKhHs+2ylYaWpTZOcf/BnIisSxcuHCVqs49ZoWq9vgHSAOW4jTT+JfdAzzHkRPHg8D/C1j/CPCZ4x17zpw52hNLly7t0fahKlzi8AuneMIpFtXBG8/9b27X0Xe9rB5v61HLB2s8HTmRWICV2kFO7VXvGlWtBgpw2toRkRuBy4Dr3BcDKAJGBuyWCxzszesZY0yDx0tMVES3p/Izjp70rsny95wRkXjgfGCriCwC7gKuUNXGgF1eBK4WkVgRGQPkA8v7rOTGmJNKY3Nr0HrWDGY9+YsNAx512+UjgCdV9WUR2QnEAkvcKbiWqepXVHWTiDwJbAa8wC2q2trH5TfGnCQaPF4SYgbPRddQ0e0kr6rrgVkdLB/fxT73Avf2rmjGGHNEY3MriYOk+2QoscYtY8yg0ODxkjCIuk+GCkvyxphBoaHZazX5XrAkb4wZFBo9rdYm3wuW5I0xg0KDx0ui9a7pMUvyxphBobG5dVANaRAqLMkbY0KOt9XHs6uLaGn1tS1r8FibfG9YkjfGhJyX1h/k9ifX8dDSXYAzI1NTi2/QjEAZSizJG2NCzt4K5+b51zYWo6psL6kDYFhqXDCLNSjZadEYE1JUlS3FtQBsPVTHVX/8iBFp8YjAwknZQS7d4GNJ3hgTMuqbvXz6oQ/YXlLPxdOGck5+Fvct2caKPVXMHT2kbUJt032W5I0xIePXS7azvcSZmygvM5FrTxvFFTOH88TH+5g9ekiQSzc4WZI3xoSEjQdq+OsHhVw1N5cxmUl8atYIwJnT9T8/MTbIpRu8LMkbY4Ku1afc8/xGhiTEcM8lU0hN6P6E2aZr1rvGmDDS6lOKaw7T6uv+tJ5dKapq5PpHPuZXb2zrk+N15onl+1i3v5rvXTbZEnwfs5q8MWHgg53l/Pjlzewub8Dj9fHtCyfwX+fmn/Bx739zB+/tKGdLcS3funBiH5T0WOX1zfz831s5Y2wGn5w5ol9e42RmNXljwsAbmw6xu7yBL5yZx4ScJF7ZcOio9c3eVu5+dgM73P7m3eXvr97s9R1ny9579MM91DV7+fEnp+JOPGT6kCV5Y8JARYOHEWnxfPeSyXxuzki2FNeyv/LIbJxPrSziieX7+OO7u3t03APVhwGoa/JS19TSp2UG5+TzxPJ9nDcpm/HZyX1+fGNJ3piwUNngIT0xBoCLpg4lMkK47V9r8Xh9tLT6+MM7zvAAT68qYmO5t1vH9Lb6OFTbRF5GAgDFNU19Xu4dJfWU13u40ppp+k1PJvKOE5HlIrJORDaJyA/d5ekiskREdri/hwTsc7eI7BSRbSJyUX8EYIw5OsmPykjg/z41jZV7q1i2u4IX1x6kqOowX1swDoBfrmzm/17dwke7KvB1cYG2uKaJVp9yal46cKRW35fK6poBGJ4W3+fHNo6e1OSbgXNVdQYwE1gkIqcD3wHeUtV84C33OSIyBbgamAosAh5yJwE3xvSxigYPGW6SB7j4lGGIwJp91TxUsJNJQ5O546KJrLjnfObmRPLwu7u55k/LmP/LpfzurR3sLqunvvnoGr4/qc8b4yT54uq+r8mX1jnHzLY7WftNTybyVqDefRrt/ihwJbDAXf4oUADc5S5frKrNQKGI7ATmAR/1RcGNMQ5VparBw5CAJJ8SF824rCR+/eZ2AH53zSxEhKzkWG6ZGcvk2WfwcWEF/1qxn18t2c6vlmwnPTGGj797HtGRTt2vqMpJ8rNGDSEyQjhQ3XjU65bWNREpQkZS7xN0aa1Tk7fhCvpPj7pQujXxVcB44EFV/VhEclS1GEBVi0XEP4LQCGBZwO5F7rKOjnszcDNATk4OBQUF3S5TfX19j7YPVeESh184xRPqsTS0KF6fUlW8n4KCI71q0sSpJQ9PEhIrt1FQ4CT8hoYGtq5ZRipwcz58ckQ8j23ysLHCwz9fWcroFOcL99vbPEQKFG5cwfBEYcnaQk6NdY5f51G++34jdR742sxY5g3tXW/stduaSYiCZR+81+v4Q/396Yn+iKVH74yqtgIzRSQNeE5EpnWxeUd9oTpsAFTVh4GHAebOnasLFizodpkKCgroyfahKlzi8AuneEI9lt1l9fDWO8ybMZkFs3PblsfklnP/Wzv41VUzyB2S0La8o3hOO62B+b8oIDonnwWnjQLgoa0fcUqujwvOPYvNvh385q3tTJl9OtkpcXz/hY3UefYC8FFlPHdefVavyr54/yqGp9ezYMH8Xu3fWTyDVX/E0qveNapajdMsswgoEZFhAO7vUnezImBkwG65wMHeFtQY07HKBg9A24VXvzPHZ/KvL59xVILvzKj0BNISolm3vxqAllYf6w9UM2tUGgCXTh+KKry20anJf7y7knMnZXP3xZNYs6+aPeUNvSp7WX2ztcf3s570rslya/CISDxwPrAVeBG40d3sRuAF9/GLwNUiEisiY4B8YHkfldsY4/In+YzE3idLEWFGbhor9lSiqry1pYSmFh+zRzmd5cZnJzMxJ5lX1hfjbfWxu7ye/Jwkrpg5HBF4fu2BXr1uaV2Ttcf3s57U5IcBS0VkPbACWKKqLwM/BS4QkR3ABe5zVHUT8CSwGfg3cIvb3GOM6UMV/pp8UsxxtuzaJacMZXd5A199fDVfeXw1YzMT+UR+VsD6YazYW8nyPZW0tCr52ckMS43njLEZPL/mAE7fjO5TVcrqrCbf37qd5FV1varOUtXpqjpNVX/kLq9Q1fNUNd/9XRmwz72qOk5VJ6rqa/0RgDEnu0M1fdMN8fIZw0mOjeLfmw5xwxmjefkbZx81WJi/yeZ3b+0EID87CYBPzhrBnopG1rpNPd1VXNNEU4uPUenHb04yvWcDlBkzSL21pYTx2UmU1jWRmRTT1vWxtxJionj4hrlECJw2NuOY9f4mm492VwAwzk3yi6YN5b+f38jzaw4wa1THE3t4vD7+47GVlNc1c+a4DC6dPoyKeucbyORhKSdUbtM1S/LGDDKqym/e3MH9b+1gXl46SXFRZCf3zQTXZ4w7NrkHuuSUYWwrqePSU4aRFOukj5S4aM6fksNL64v5nys6HmSsYFsp724vY3hqHI9+tIc/v1/Ytm7CUBuzpj9ZkjdmEFFVfvzyFv7yQSGjMxJYvqeS6EjhnIC28/705fljmTo85ZgJtWfkpvLK+mIaPK1tyT/Q82sPkJkUw7t3LqTB08qDS3fysDtYWkqcjR/fn2yAMmMGkWdWH+AvHxTyxbPyeOarZxIZIbS0KjkpA3PxMi46kvOn5BAZcXRtPSnWSdQdjVSpqry3o5wLpuQQFRlBanw03zzfGet+bGZi/xf6JGc1eWMGkY92VZCZFMP3L5uCiDBlWAobDtT0WXNNbyXHOamkrsnLsNSj11U2eKhr8pIfMJRwQkwUb31rfoe1ftO3rCZvzCCyrqiamSPT2tq9J+Q4iTMxNrhj/x1J8sfW5PdUODdK5WUe3YtmXFYSOSnBPTmdDCzJGzNI1DW1sKusnum5aW3LFk0bCsCItOB2Q0x229Vrm44dq35PuTOwWV6GNc0Eg31XMmYANLW08q0n1zEyPYHLpg9j6vCUTqe6a2n18cDbO0mIieTqeaNIjXcS6IYDNajCjJFpbdteMCWHJbd9gvFud8ZgSQlormlvT0UDEUK3hlcwfc9q8sYMgHX7q3llQzF/eGcXl/3uff7zsZWdbruisJL739rBT17bynm/eofn1hShqqzbXwM4PVkC5eckB31uVH9Nvq6phbqmFs7+2du8t6MMgD0VjeQOSSAmytJNMNhf3ZgBsM2dQPvVb5zDNfNG8uaW0rYJM9pbV+Qk88dvOo0RQ+K57V/r+NHLm1m3v5rRGQmkJZzY8AX9IfDC677KRoqqDvPkyiIA9lc22l2tQWRJ3pgBsPVQHSlxUUwelsxVc53BWZcXVh6z3YHqw7y3o4xR6QmcnZ/Jc189k8umD+OplUWs2FPJjID2+FCSEBNJhDg1ef+drAVbS/F4fRyqaWJYql1gDRZL8sYMgG2H6pg01GmHnzYilcSYSF7fVMJTK/dT3egkRVXlgvve4cNdFW1t7BERwmfn5FLf7KWiwcPMgPb4UCIiJMVGUd/kpaLBme2prtnLBzvLKa2zJB9MluSN6WeqyvZDdUwY6iTu6MgILjllGC+tO8gdT6/nN2/uAJwmnUaPM1Drp2cfmUTtzHGZjEiLZ15eOp+bm3vsC4SI5Lho6pq8bTX5mMgI/vHxXnwKQ1Ntou5gsd41xvSzygYPdc1exmYe6QHzs89M54qZw/mfFzfx/NoDXDAlh4Jtznw7H37nXIanHUmKMVERLP32gpC/cJkcF0Vtk5fyeg8xkREsnJTFG5tLAKwmH0Sh/V9jTBjY706IPTLg4mNEhDPezI8/OY3qxhau+/PH/Om9QvKzk45K8H6hnuDBGYOmrqmF8vpmMpJiuHCKMzQxwFBL8kFjNXlj+tm+SudmoI56mJw5LpM3b/8EFfUeGj2tjMsKbn/3E9Hi87FmXzUfF1YybUQK507KJjJCaPWp1eSDyJK8Mf1sv5vkc4d03C49PjuZ8dkdrhpU5owawpp91QAkx0YzJDGGU/OGsHZ/ddsNXWbgWZI3po98c/Ea6pu9/PnGU49aXlTVSEZiDIlhPhjXdy+ZzKlj0vny31exs6wegDsumsjWQ3VBv1nrZNaTibxHishSEdkiIptE5FZ3+UwRWSYia0VkpYjMC9jnbhHZKSLbROSi/gjAmFBwoPowL647yJtbStl6qPaodfsqG49qjw9XERHC/AnOuPanuzNLzRmdznWnjQ5msU56Pbma4wW+paqTgdOBW0RkCvBz4IeqOhP4vvscd93VwFRgEfCQiAR3qDxj+skzq4pQnAukt/xjdVuib/Upmw/WBn1smYESFx3Jh985l59/Znqwi2JcPZnIu1hVV7uP64AtwAhAAf8kjanAQffxlcBiVW1W1UJgJzAPYwa5rYdqeXtryVHL1u2vZkJ2Mg9eO5uqxhb+58VNgDOoWFVjC+fkZwajqEExPC2e+Birz4UKUX8fp57sJJIHvAtMw0n0rwOCc9I4U1X3isgDwDJVfdzd5xHgNVV9uoPj3QzcDJCTkzNn8eLF3S5LfX09SUmDv5YULnH4hVM87WP5xYrD7Kz28eB5CUS5MyTd8U4jY1Ij+NrMOJbsaeEfWz18Z14c2ypbeX5nC789N4HkmNBolw6n9wbCK54TiWXhwoWrVHVu++U9vhIkIknAM8A3VbVWRP4XuE1VnxGRq4BHgPNxkn57HZ5RVPVh4GGAuXPn6oIFC7pdnoKCAnqyfagKlzj8wimewFiaWlrZ8eYbeFohY/xMZo0aQqPHS9m/X+f6s8exYEE+p7e08sbPl/JOeSJN3lZmjITLLzwruEEECKf3BsIrnv6IpUdJXkSicRL8P1T1WXfxjcCt7uOngD+7j4uAkQG753KkKceYQWNnVSu73y9EgQNVh/F4fQB86qEP+fSsEUwc6szO5J+lKS46kq/MH8ePX94MwK3n5Qel3MZAD5K8OH2gHgG2qOp9AasOAvOBAuBcYIe7/EXgnyJyHzAcyAeW90GZjRlQf9rQTEnj5rbnGYkxtKpS3djCW1tLeXbNAQAm5Bz5mn3daaP4fcEuyuubmT8xa8DLbIxfT2ryZwHXAxtEZK277LvAfwL3i0gU0ITbtq6qm0TkSWAzTs+cW1S1ta8KbsxAUFUqm5QbzxjN7RdMBIH46EgOVDtDFYxIi+fDXeUcqmliTOaR6e3ioiO5a9FEnli+L2SHBzYnh24neVV9n47b2QHmdLLPvcC9vSiXMSGh5nALLT5n3JnUhCN3bQYm9AUTO75d9XNzR/K5uSM7XGfMQAn9UY+MCaKSWmds9JwUG3vFDE6W5I3pQkmtM0WfjaJoBitL8sZ0wZ/kc5ItyZvByZK8MV0orXOaa7JTYoNcEmN6x5K8MV0oqW0iMdrpLWPMYBTeY58a00vri6r57nMbqDncQma81YXM4GVJ3pgOvLj2IBsPOCNJ3nmqtcebwcuqKMZ0YNW+KqaNSOG9OxcyJcOaaszgZUnemHaaWlrZeKCGs8dnnRSTfZjwZknemHaWbi2lpVWZN2ZIsItizAmzJG9MAJ9Puf+tHYzJTOQT+TawmBn87MKrMcDO0jp2lTXg8ylbD9Xx68/PICrS6kBm8LMkbwxw1zMbWLe/mpHpCYzNTOSKGSOCXSRj+oQleXNS21vRwCX3v0eDxxkFu7C8gd98fiaREaExVZ8xJ8qSvDmpvbWllAZPKyPS4kmNj8bT6uPyGcODXSxj+owleXNSW7u/mqEpcXzwnXOpbPCgqlaLN2HFkrw5qa3dX83MkWkApCfGBLcwxvQD6z5g+oSqBrsIPba/spF9lY3MHJUW7KIY02+6neRFZKSILBWRLSKySURuDVj3dRHZ5i7/ecDyu0Vkp7vuor4uvAkeVWV/ZSMvrD3AJfe/x5k/fZtW3+BK9D99bStx0RFcYW3wJoz1pLnGC3xLVVeLSDKwSkSWADnAlcB0VW0WkWwAEZkCXA1MBYYDb4rIBJvMe3DZXlIHwIScZAA8Xh/1zV6+9eRalm4rO2rbqkYPmUmhPe76jpI63thcwuxRQ3hlQzG3nT+B4WnxwS6WMf2mJxN5FwPF7uM6EdkCjAD+E/ipqja760rdXa4EFrvLC0VkJzAP+KgPy2/6UVNLK9f9+WMamr08/h+nMXvUEL7+xGpe31RCTGQEF03N4evn5rOrrJ5bF6+loj50k3xRVSNDU+K457mNLN9TSWZSLCPS4vny/LHBLpox/Up605YqInnAu8A09/cLwCKgCfi2qq4QkQeAZar6uLvPI8Brqvp0B8e7GbgZICcnZ87ixYu7XZb6+nqSkpJ6HEOoCcU4luxt4R9bPKTECK2q3D0vnu99cLht/ZemxfCJ3Gi2VLTysxVN3HVqHJPdERtDKZ4Vh7w8tLaZi8dEs7Wyld01PgC+NjOWeUOPX88JpVj6gsUTuk4kloULF65S1bntl/e4d42IJAHPAN9U1VoRiQKGAKcDpwJPishYoKN+aB2eUVT1YeBhgLlz5+qCBQu6XZ6CggJ6sn2oCqU4fD5l+Z5KSvbuZmxWA49+cR6f/cOHRyV4gEvPmcOc0ekML6njZyveJXf8ZBa47duhEs+728t4eMkKRODDEvB4nX/Lz87J5Y7PTkfk+N0lQyWWvmLxhK7+iKVHvWtEJBonwf9DVZ91FxcBz6pjOeADMt3lIwN2zwUOnniRTX97fdMhrn54GW9uKWViTjIj0xN4/KbTjtlubKZT48hwux5WNngGtJzHs2pvJV/++yrGZSXx0HWzqW5sodHTyg8un8IvPzejWwnemMGuJ71rBHgE2KKq9wWseh44191mAhADlAMvAleLSKyIjAHygeV9VG7Tjz7aXdH2eFyWk8jzc5JZcc/5/PmGuYzLSiQjMYYhbnJPS4hBBCrqm4NS3o7sKW/gi39dQU5KLH+/6TQumjqUrGTnesFE9yKyMSeDnjTXnAVcD2wQkbXusu8CfwH+IiIbAQ9wozoN/ZtE5ElgM07PnFusZ83gsLywsu3x+Owj7YNZybGcPyWHTQdrqWw4ktAjI4QhCTFUhFBN/tWNxdQ2eXn56+e0JfcX/+ss/vrBHubk2Tjx5uTRk94179NxOzvA/+tkn3uBe3tRLjPAnl1dxKMf7aW5pZWth+ralgcmeb9bz88/ZllGYgwV9aGT5LcfqmN4ahyjMo7M7DQsNZ7vXjI5iKUyZuDZsAYGgD+/V0hZfTMzR6YxZVgKqQnR/PWDPYzNSuzW/umJMSHVJr+9pJ58a5YxxpK8gdqmFrYcquUb5+Zz2wUTAOeO1u9fNqXbFyczk2LZcKCmP4vZba0+ZWdZPWfnZwa7KMYEnSV5w6q9VajCvDHpbct62vNk1qg0XtlQzP7KxqBNfu2/eWt8VhIer4/8DpqajDnZ2ABlhtV7q4iMEGadwEBd503OAeCtLSUdrq9taun3QcweWrqTVXur+NfK/URHCqePzejX1zNmMLAkb9h0sJZxWYkkxPT+i92YzEQm5iTzi9e38bV/rOKFnR4+2FkOQM3hFs78yds8+uGeXh27vtlLU0vXHbN2ldXz+3d2ccbYDGKjIvjqgvFB+0ZhTCix5hrDluJaTgtoqumtP90wl9+8tZ3Ve6vYW9HCczs/ZsU957PpYA31zV7+tbKIL5w1pkfHrGlsYdH971Lf7OVPN8ztsHauqvz38xuJi47kt9fMIiYygpR4+9c2Bqwmf9KravBQXNPE5GEpJ3ysURkJ3HfVTAruWMhtc5y+6TtL61m9twpwTib+US276xdvbKW4pom6Ji9//2hvh9u8sbmED3dVcOeiSWQlx5KaEG13sxrjsiR/kttSXAvQJ0k+0PAk519rd3k9q/dVkzskngiBF9d2f2SLZm8rz685yGdm5/K5Obm8trGYt7aU4Gs3bv2afdVERwrXzhvVpzEYEw4syZ+kVJUnlu/jqVVFRAhMz03t0+Onxwlx0RHsKm1g3f5qFkzM4qzxmby47mC3L8C+t72c+mYvl80YxoKJ2fgUbnp0Jd9+eh0trb627YqqGhmeFm9zsxrTAWu4PEltOljL3c9uAJyuk2kJfTu/aYQIeRmJFGwvpa7Zy7ThqczITeOOp9ezdn81s0Y5QwtsPljLpKHJRHSQoF/dUExKXBRnjcvEp8p/LRxPg8fLXz/YQ01jCw9eN5u46EgOVB9mhE38YUyHrCZ/knpmdVHb4/7qajguK4ndZQ0ATB2eyqJpQ4mJiuAFt8lmb0UDl/z2PX7z5vZj9m32trJkcwkXTnX2iYuO5NsXTeQHl0/lx5+cxtvbSrn+kY/ZVVbPgSpL8sZ0xpL8Sail1ceLaw9y7qRsvnl+Pl86K69fXues8UfuOM3PSSI5LprzJ2fz0rqD1DW1sLvcOQH8/p1d7Gh3QfaDneXUNXu59JRhxxz3+tNH89urZ7FmXzXn/eodSuuaGTHEkrwxHbEkfxJ6Z1sZFQ0erp03im+eP6HPm2r8Pj17RNvjuGhnxqibPzGOqkYPv3h9GweqnElIoiIi+O5zG/D5lLqmFub8eAk3PbrSaaoZ3/HQBJfPGM5b35rf9jx3iPWJN6YjluRPQs+uKSIjMYb5E7P69XXioiN57mtn8tzXzmxbNnNkGhdOGco728s4UH2YqAjh+5dPYcWeKp5atZ8NB2qoaPCgCudPziEmqvN/0dEZiXx2Ti4AmUn9c6IyZrCzJN9HdpXVc9Gv3+WqP3zEsoBJN0JNdaOHNzeXcsXM4URH9v/bP2vUkLaLrH7D0uKorPdwoOoww9LiuPrUkZyaN4RfvbGdzQdr27a7uIOmmvZ+dOVUfnTlVM7J798TljGDlfWuOUHeVh87Suv5zjPrKatvpvqwh6sfXsY5+ZlkJMZwqLaJn356OnmZ3Ruyt7+9vL4YT6uPz8zODVoZMpNiqWv2UljewIi0eESEa+aN4vYn1/HUyiIyk2JZfPNpjM8+/lDBCTFR3HBGXv8X2phBymryPdDU0squsvqjlt23ZDsX3/8e64pquGXheN65YyHfu3Qymw7W8sK6g2woquEHL24KUomP9czqIibmJDN1eN/e/NQT/jlhNxyoYUSa05a+YGI2IrCtpI7Jw5K7leCNMcdnNfkeeHDpTn739k4euXEukRFCYXnDUbfaXzlzOHHRkfzHOWO5et4oSmqbeOT9Ql5aexCfTzvsCz6QdpfVs2ZfNXdfPCmot/1nJMW2Pc51e8WkJ8Zw1rhM3t9ZzpnjbBx4Y/pKt5O8iIwEHgOGAj7gYVW9P2D9t4FfAFmqWu4uuxu4CWgFvqGqr/dh2Qfc+iJnUoybHl3ZtiwrOZb7r5lJhAiZAckrKTaKpKwkZo8awj8/3seusuDPVPTeDmdUyEunH7+tuz9lBFwkDZx56q9fPJX6Jm/bBOHGmBPXk5q8F/iWqq4WkWRglYgsUdXN7gngAmCff2MRmQJcDUwFhgNvisiEwTyZ94Hqw2QmxfIf54xhzugh5GUkkpkU02Wt2D9G+5p91UFP8qV1TURGCMNTg9unPDPxyMlwXNaRiT2iIyMswRvTx7rdJq+qxaq62n1cB2wB/B2hfw3cCQQOSnIlsFhVm1W1ENgJzOuTUgdBU0srheUNXDNvJF+ZP45T89LJSo49brPHmIxE0hNj+CgEetyU1TWTmRQT9Gaj9ICa/JgQuSBtTLjq1YVXEckDZgEfi8gVwAFVXddusxHA/oDnRRw5KQwqTS2tfOqhD2n1KZOG9uyCZUSEMH9CFu9sLztm9MSBVlbXTFZy7PE37GeJMZFHHsfaZSFj+lOPP2EikgQ8A3wTpwnnHuDCjjbtYFmHWU5EbgZuBsjJyaGgoKDb5amvr+/R9t21o6qVtaWtfG5iDOvKvGwpbgbAc3ALBZXbenSs7FYvlQ0e/vbi24xNi+xwm/6KI9Du4sOkxkq/vw50P56BKMuJGoj3ZiBZPKGrX2JR1W7/ANHA68Dt7vNTgFJgj/vjxWmXHwrcDdwdsO/rwBnHe405c+ZoTyxdurRH23fXfz+/QUff9bIe9nj1J69u0XF3v6KNzd5eHauyvlnzvvOy/nrJtk636WkcHm+r+ny+Hu0z794leudT63q0T28dL55nVu3X97aXDUhZTlR//Y8Fi8UTuk4kFmCldpBTu91cI07j8yPAFlW9zz1BbFDVbFXNU9U8nCaZ2ap6CHgRuFpEYkVkDJAPLD+hM9IAKqltAqCywcPywgqm56YSH9NxLfx4hiTGMGtkGku3lfW6PFuKa2kNaO65/Hfv8/mHl+HxHhlXvaXVx7V/WsYr64uP2d/nU8rrPSHRXAPw6dm5nJ1vXSWN6W89aZM/C7geOFdE1ro/l3S2sapuAp4ENgP/Bm7RQdSz5lCt0zxTVHWY9UU1zBtzYsPxLpiYzfqiairqm49Z98d3dvHd9xs7nUyjuOYwF9//Hnc+vR6A0tomth6qY3lhJU+tOnLZY+nWUj7cVcHXn1hNXVMLS7eVtl0HqGr00OrTkEnyxpiB0ZPeNe+rqqjqdFWd6f682m6bPHX7yLvP71XVcao6UVVf68uC97dStya/ZPMhvD494YmuF07MRhXe3XF0bd7j9fGn9wo5WK/srzzc4b6F7pjsz6wuoqaxhbX7q9vW7a1obHv85Eon4SvwwNKdfPGvK7jmT8vYW9FAaZ1zcrEkb8zJxYY16IDPp21J8dUNhxCBOXlDjrNX16YOTyEzKYalW49O8ks2l1Du1u7XH6jucN99lUcS+dk/f5sHlu4kKsK5+cp/MqppbKFgWxnTc1NRhcc+3EtybBSbD9ay6Dfv8a8VzgkgJ8WSvDEnE0vyHShvaG5r/z5QfZgpw1JIiYs+oWM6XSmzeWd7GQ8u3cljH+0B4PFlexmRFk+UOGO5dGR/VSNREcJL/3U2Z4zNYH1RDVOHpzA6I6HtZLRkSwlen3LPJZOJjBAOt7Qya/QQ3rj9EyTHRfG3D53XC7z5yBgT/izJd6C09uh283kn2FTjd/mMYdQcbuEXr2/j+y9sYmdpPR/truDa00YxMjmCjR0k+cc+2sODS3cxYkg8p+Sm8vANc3njtk/wwLWzyU6ODfjGUcyItHjmjUknP9tJ5GMzExmWGt92121mUky/TRBijAlNluQ7cLD66LbxE22P95s/IYsZualtz//58T6iI4Wr5o4kJ1E6bJP//gvOCJZp8Ue+SUzISWZkeoKT5GubqG1q4b0dZVw8bSgiQk5KHHDkbtLJw5wbuMZaLd6Yk44l+XaqGz384MVNpMRFtdWIT83rmyQvIvz2mlmcOc7pqfOPj/dy0dShZCXHkhYrlNY1ddrDxtN67PLslDhqm7y8sr6YllblEnfgsWz34mpagnNi8Cf58dmW5I052ViSb+fdHeUU1zTxh/83h1NyU5k6POWooXFP1OiMRD7tTtjR7PWxaNpQAFJjI2hq8VFS28wt/1xNYXkDDc1eAEakxXPfVTOOOZa/p8yjH+5hWGocM3PTALhz0SSumTeKC6c4x/aPHT8xyAOkGWMGng0c0s7qvVXERUcwb0w600em0RJws1FfGZYa1/bYX8tOi3VGgXhrawmvrC8mNjKCb5yXD8DtF0xo2y6Qv8a+9VAdXzwrr23gsazkWH7y6VPatssdksC/bj6dGSPT+jwWY0xosyTfzpr91UzPTSMqMoKkyAjohx6HgUk+L8NpN/cn+VV7qwB4af1Bzp2cDUB2J90ep4040r5/6XHmQz1t7IndzGWMGZwsyQeoaWxh88EavnT2mH59nWEB47lHurVvf5JfvbcKEfD6lN+8uQOA7OS4Yw+CM1fqinvOZ31RNXNGn1g/fmNMeLI2+QAPv7eLllblkzP7d0Rk/xg4UwKaYNLinCS/p6KRoSlxXDA5h52lznyy2V3cpZqVHMt5k3OCOp2fMSZ0WU3eVV7fzF8/2MNl04d12P7d1z78zrmkBHSLjIuE+OhIDre0kjsknpvOHsMbm0uIjpS2XjLGGNNTVpN3/b5gF00trdx2wYQBeb3hafEkBUyYISKMTHeacbKSY5k3Jp1pI1LISYmzWroxptcsyeOM8vj3ZXv5zOzcoN72f/clkwGnN4yI8LtrZnP/1bOCVh5jzOBnzTXAr97Yjqq2dVkMloUTs3n+lrMYl+X0uBmTmWhzoBpjTshJn+Tf3lrC06uK+OqCcYxMTwh2cZhpfdmNMX3opG6u2VfRyFcfX82UYSncGuRavDHG9IeTOslvOlhDs9fHTz9zCnHRvZvazxhjQtlJneQrGz1A5zcbGWPMYHdSJ/mqBifJWz90Y0y46naSF5GRIrJURLaIyCYRudVd/gsR2Soi60XkORFJC9jnbhHZKSLbROSifij/CalsaCExJtKaaowxYasnNXkv8C1VnQycDtwiIlOAJcA0VZ0ObAfuBnDXXQ1MBRYBD4lISGXTqkYPQxJtpiRjTPjqdpJX1WJVXe0+rgO2ACNU9Q1V9bqbLQNy3cdXAotVtVlVC4GdwLy+K3rn6pu9vLjuYNs8rZ2pbPCQYUneGBPGpLOZiLrcSSQPeBenBl8bsPwl4F+q+riIPAAsU9XH3XWPAK+p6tMdHO9m4GaAnJycOYsXL+52Werr60lKOvou1cc3N/PmPi8Xjo7i2smdD+71ww8Pkxwj3D43+BdeO4pjMAuneMIpFrB4QtmJxLJw4cJVqjq3/fIe3wwlIknAM8A32yX4e3CadP7hX9TB7h2eUVT1YeBhgLlz5+qCBQu6XZ6CggL82zc0e2lqaeX9t94mIzGGN/Z6OP2UCZ0OHfy9j99m/Mh0FiyY2e3X6y+BcYSDcIonnGIBiyeU9UcsPUryIhKNk+D/oarPBiy/EbgMOE+PfDUoAkYG7J4LHDyx4nbuG0+s4cV1B5k5Mo2mFh+P3DiLxz7aw49f2czojATOm5xzzD5VDdYmb4wJbz3pXSPAI8AWVb0vYPki4C7gClVtDNjlReBqEYkVkTFAPrC8b4p9LP/AYmv3VwMwckgCv/n8LPIyEvnTe7uP2b6ppZUGTyvpluSNMWGsJ71rzgKuB84VkbXuzyXAA0AysMRd9gcAVd0EPAlsBv4N3KKqrX1b/CNuPT+f+ROy2p5np8QSHxPJuZOyWba7kvx7XmX1vqq29RsP1ACQmWRJ3hgTvrrdXKOq79NxO/urXexzL3BvL8rVK/65U9MSotv6vs8bk84j7xfS0qq8sOYAs0cNweP1cc9zGxmaEsfFx5kb1RhjBrOwGoVyqJvkcwKGKTg1L73tcUltMwAPLt3JtpI6HrlxLilxdrerMSZ8hdWwBv6afE7qkSSfnhjDE/95OmeNz2DLoVq2FNfy4NKdfHLm8A4vxhpjTDgJsyTvTJ+X027i6zPGZXDG2Az2VjRy099WkJYQzQ8unxqMIhpjzIAKsyTv1uRTjr256Zx856LswZomfnjFNOs6aYw5KYRVm3zukASGp8Yxo4PZlWaMTOPVb5zDtpJaLjll6MAXzhhjgiCsknx8TCQf3n1ep+unDE9hyvCUASyRMcYEV1g11xhjjDmaJXljjAljluSNMSaMWZI3xpgwZkneGGPCmCV5Y4wJY5bkjTEmjPVq+r/+JCJlwN4e7JIJlPdTcQZSuMThF07xhFMsYPGEshOJZbSqZrVfGHJJvqdEZGVH8xoONuESh184xRNOsYDFE8r6IxZrrjHGmDBmSd4YY8JYOCT5h4NdgD4SLnH4hVM84RQLWDyhrM9jGfRt8sYYYzoXDjV5Y4wxnbAkb4wxYcySvDFhSEQk2GUwoWFQJPlw+YcVkfSAx4M6JhFZICLH3HgxWInIt0TkQvfxoH5vXMn+B4M9nsFe/vYGOp6QTvIicqWIPArMCHZZToSILBKRd4HfiMivAHSQXvEOiOU6oDnY5TlRInKhiLwO3AXcAIP3vQEQkQtE5H3glyJyJwzeeMLl8+8XrHhCbvo/ERFVVRFZCPwYaAHOEJG9qloV5OJ1m3u2jgBuAr4E/ARYAzwmIher6mvBLF9PuLEI8Hngj8BNqvpUcEvVe2480cD3gfk4700McKqIRAPewZgYRSQX+B/gp0ABsFhEMlT1Lv/nKpjl64nB/vn3C4V8FlI1+Xb/iIXARcAdwGnA9KAVrIf8cahqK/A+cLaqvgA0AaXAJhGJ8G8bxKIeV0AsPuAg8Biw0133ORHJdRNjyMcCR8XjAV5Q1XNU9VWgCrhaVVsGWTIM/JtPAjao6kuqWgc8CNwmIvluogn59ydAIXAhg/Dz7xcq+SxkkryI/BfwrIjcJiJDVXWPqhar6ttACTBfREYEuZjH1S6OYaq6WVW9IjIbeB7Iw2kauM+/S3BKenwBsdwuIpk4J6z1wO9FZCtwFfA74CH/LsEpafd08N6scJdHq+o7wG4RuTi4pey+dvGkANuBs0XkDHeTbGAT8L1glbG7RORrIvIZ97EA+1X10GD7/PuFVD5T1aD/AJ8CVgALgb8CDwAzA9ZPBx4HPt1uPwl22bsbBzAGGOU+TgSqgbnBLnMPYnkQmAgMx2nemOVulw6UAXOCXeZevDcz/P9Hbhx/Bi4Mdll7Gc/vgRyc5sG/AR8A/3T/79YBecEucydxJAN/AA4B9UCUuzzC//keLJ//4/yvzQxYP6DxhEpN/jTg96q6FKdNsRD4hn+lqq7H+aNNE5FzReQud3mofa3uKI5bAVS1UFX3uY8bgCeBlCCVszvax7IHuENVDwI/VNU1AKpaifMNJSk4xey2rt4bdeOIx/lg4m9OC2EdxfNDVX0E+E/gNlW9FtgHLAdqg1XQrqjTrPSOqg4FXsapTICT8NTdZrB8/v1CKp8F9R85oI1wN3AtgKruBV4BEkXkioDNnwD+A/gXzpjLIdMGfJw4EtrFgYh8D5gKbB7IcnZHF7G8BCSLyBWq2hSw/X/jxLJ1oMvaHT38H3scmCcicepcgwg5XcTzIjBERD6lznWF5e52P8b55lg34IU9joBYXnR/fxO4xr2G0CoiUQHbhNznv/3rh2o+G/AkLyKR/scBZ66ngUYRudJ9XozTO2CKOJKA+4ENwHRVvaPd/gOup3G4+1wsTve2CcBnVfXQwJW4c72M5RwRWYoTy2dUtWTgSty13vyPucvigcVA6wAVtVt6GM9Ed598EXkBmIZTq28ZuBJ3rqNYVLVBRCLcz8NDOM1mqKpXVVVEEoHfEkKff9dR+TNU89mAJHkROUNEfgSgTo8T/3L/h6sKeA74qntFugbn63+cG3gTcKuqXqqqxQNR5o6cQBzx7votwFdU9YZgxgF9Esse4BZVvT7YscAJxRMb8OF6QVX/FAoJ8UQ+M+76QzjvzxXBPgF3FUv7ZjFV/Q4wxt0nR0ROdZs3vxHsz7+fiMwTkceBn4jIKXKkp5z/BBZS+azfk7yI3Ag8CnxPRK5yl0XBUWeueOB1nDPewyIyHJiF06fUf0Yv7e+yduUE4/C42+1R1Y0DXfb2+iiW/aoaEs1NJxiP13+cwAQUTH30malT1aKBLnt7x4tFVX1uzTY1YLef4Vw4fg9IcLcN6ucfnOs0IvIDnG8ar+HcZ3QL7s1NAf8/oZXPtP+vNJ8P5OL0ed0XsDzS/f0/7h9kFk4Ph//F+WrzkH+bUPgJlzjCLRaLJ7Tj6UYsPwD+DZzjPr8Y5/rOL4HoYJe/g3huAma7jzNwkv3cgPUh9970xx/hk8B3gcv8b6b/zcLpZ/3jgG2zcbp5jWt3jIQQeDPDIo5wi8XiCe14TjQWnGs+I4MdRwfxXOr/O7sxxbrPnwQuD+X3pi//GFk4XeneBb6Cc2fnp9x1Me7vqUANkNPB/hHBfkPDKY5wi8XiCe14+iCWUPsG0mk8AdtEAx8CE0L6venDP8rpOP2o/c+vBz5s/ybitGf91X18cbD/AOEaR7jFYvGEdjzhFEt34nGXnQI85z5OBua5j0PqJq0T/UPcACzA+QoT7T9D43ydmQP80R904JkN8OFcgf5OKJzxwiWOcIvF4gnteMIplh7G478rdwHwD+ALON0hbyLEEryq9nwUSrcL11CcticfsAvnDrtbVbVERCLVuZFhMjAE2noEqIiMBn6Nc9X8Fg1iT5NwiQPCKxaweCB04wmnWKDX8fh7ZF0EXIMz5PZ16tzJGnp6eKbzf+WaADzuP6vhDFL1bLttHgOuch9nub/TcL/SBPMnXOIIt1gsntCOJ5xiOcF4/DX8s4DPBzuO4/10qybv9mv9ERApIq/ijLnSCs5ZTUS+ARwUkfnqjOYHzmBDhe5NEJ8WkUvUGbtleQcvMSDCJQ4Ir1jA4nF3C8l4wikW6LN4LlbVD4JR/p467s1QIjIfWIXzVWUnRwa+Xygi86Dt69iPcPqI+u/8+hLO7b0pwEL3DQ6acIkDwisWsHjcfUIynnCKBfo0nv0DXvje6sZXmnOA6wOePwR8Fediwyp3WQROu9aTwGhgHPAb3JsGQuEnXOIIt1gsntCOJ5xiCcd4uhVzN/4oCUAsR9qmrgN+4j5eC3zdfTwXWBzsgMI9jnCLxeIJ7XjCKZZwjKc7P8dtrlHVRlVt1iPjMlyAM0kEwBeBySLyMs7Qmasg+EOAdiRc4oDwigUsHgjdeMIpFgi/eLqj210o3XYpxZl9xj/+cx3OLb/TgEJVPQAhMQRop8IlDgivWMDiCUohuymcYoHwi6crPRmF0odzg0A5MN092/034FPV9/1/kEEgXOKA8IoFLJ5QFk6xQPjF07ketmedjvPHeR+4KdhtTb39CZc4wi0Wiye0f8IplnCMp7Mf/0S53SIiuThjONynqs29O60EX7jEAeEVC1g8oSycYoHwi6czPUryxhhjBpdQn5HeGGPMCbAkb4wxYcySvDHGhDFL8sYYE8YsyRtjTBizJG9OaiLSKiJrRWSTiKwTkdtFpMvPhYjkici1A1VGY06EJXlzsjusqjNVdSrOOCaXAD84zj55gCV5MyhYP3lzUhORelVNCng+FlgBZOIMM/t3INFd/V+q+qGILAMmA4XAo8BvgZ/izPkZCzyoqn8csCCM6YIleXNSa5/k3WVVwCScAat8qtokIvnAE6o6V0QWAN9W1cvc7W8GslX1f0UkFvgA+JyqFg5kLMZ0pMcTeRtzEvAPLRsNPCAiM3Gmh5vQyfYX4gxy9Vn3eSqQj1PTNyaoLMkbE8BtrmkFSnHa5kuAGTjXr5o62w1nsonXB6SQxvSAXXg1xiUiWcAfgAfUacdMBYpV1YczkFWku2kdkByw6+vAV0Uk2j3OBBFJxJgQYDV5c7KLF5G1OE0zXpwLrfe56x4CnhGRzwFLgQZ3+XrAKyLrgL8B9+P0uFntziJUBnxyYIpvTNfswqsxxoQxa64xxpgwZkneGGPCmCV5Y4wJY5bkjTEmjFmSN8aYMGZJ3hhjwpgleWOMCWP/H5BQJZ0n280GAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "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 pandas_datareader as pdr\n",
    "import datetime \n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import pylab\n",
    "def finance_check(names,titles):\n",
    "    teg = pdr.get_data_yahoo(names, \n",
    "                          start=datetime.datetime(2021, 1, 1), \n",
    "                          end=datetime.datetime(2022, 1, 1))\n",
    "\n",
    "    pylab.title (titles)\n",
    "    teg.index\n",
    "    teg.columns\n",
    "    monthly_aapl = teg.resample('M').mean()\n",
    "    teg['Close'].plot(grid=True)\n",
    "    plt.show()\n",
    "finance_check('AAPL','Apple')\n",
    "finance_check('MSFT','Microsoft')\n",
    "finance_check('GOOGL','Google')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e5fb38f",
   "metadata": {},
   "source": [
    "***Задание:*** Создайте простейший калькулятор, включающий основные действия для двух переменных ′+′,′−′,′∙′,′÷′, а также вычисление следующих функций: 𝑒𝑥+𝑦,sin(𝑥+𝑦),cos(𝑥+𝑦),𝑥𝑦."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "7ff1dc8e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x=5\n",
      "y=21\n",
      "x+y=26\n",
      "x-y=-16\n",
      "x*y=105\n",
      "x/y=0.23809523809523808\n",
      "𝑒𝑥+𝑦=34.59140914229523\n",
      "sin(𝑥+𝑦)=0.7625584504796027\n",
      "cos(𝑥+𝑦)=0.6469193223286404\n",
      "𝑥𝑦=105\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "x=np.random.randint(0,100)\n",
    "print(f'x={x}')\n",
    "y=np.random.randint(0,100)\n",
    "print(f'y={y}')\n",
    "print(f'x+y={x+y}')\n",
    "print(f'x-y={x-y}')\n",
    "print(f'x*y={x*y}')\n",
    "print(f'x/y={x/y}')\n",
    "print(f'𝑒𝑥+𝑦={np.e*x+y}')\n",
    "print(f'sin(𝑥+𝑦)={np.sin(x+y)}')\n",
    "print(f'cos(𝑥+𝑦)={np.cos(x+y)}')\n",
    "print(f'𝑥𝑦={x*y}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b3dfaf31",
   "metadata": {},
   "source": [
    "<img src = \"https://pa1.narvii.com/6917/dead80bbb256be4394b6799f36b282d687cac732r1-540-305_hq.gif\">"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}