work2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Матрица Вандермонда (10 баллов)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Напишите функцию, генерирующую матрицу [Вандермонда](https://ru.wikipedia.org/wiki/Определитель_Вандермонда), принИмающую на вход вектор $(𝑥_1,...,𝑥_𝑛)$ .\n",
    "В этом задании запрещается пользоваться готовыми реализациями (например, numpy.vander), а также np.repeat и np.transpose.\n",
    "При решение задействуйте np.reshape и/или np.newaxis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  1,   9,  81, 729],\n",
       "       [  1,   7,  49, 343],\n",
       "       [  1,   4,  16,  64],\n",
       "       [  1,   5,  25, 125]])"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "z = np.array([9, 7, 4, 5])\n",
    "\n",
    "m = len(z) - 1\n",
    "a = np.outer(z, np.ones(m)).astype(int)\n",
    "b = np.ones((m + 1, 1)).astype(int)\n",
    "\n",
    "s = np.append(b, a, axis=1)\n",
    "np.cumprod(s, axis = 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Марафон (30 баллов)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### История\n",
    "Как-то раз Винни-Пух, Пятачок, Кролик и Иа-Иа решили пробежать марафон. Они договорились бежать $n$ часов. Побеждает тот, кто пробегает за эти $n$ часов больше всего километров. Каждый участник бежит по-разному, то сколько он пробежит за один час поддается одному из законов распределений. Например, если имеется равномерное распределение $U[1,3]$, то участник может пробежать за первый час 1.3км, за второй 2.4км и так далее.\n",
    "\n",
    "* Винни-Пух в целом может пробежать достаточно много, но с большой вероятностью останавливается покушать мёда и пробегает мало. Его закон распределения это показательное распределение с $\\lambda = 1$.\n",
    "* Пятачок бежит в одном темпе и старается пробегать приблизительно одинаковую дистанцию. Изредка, он может перепутать направление и немного пробежать не в ту сторону. Его закон распределения это нормальное распределение $N(1, 1)$.\n",
    "* Кролик - перфекционист. Он бежит целое количество киллометров. Если он понимает, что за оставшееся время он уже не пробежит еще один километр, то он останавливается и ждет следующий час. Его закон распределения это распределение Пуассона с $\\lambda = 1$.\n",
    "* Иа-Иа в течение часа может грустить с вероятностью 0.5 и пробжеать 0 километров или быть очень веселым (с вероятностью 0.5) и пробежать 2 километра. Его закон распределения это распределение Бернулли.\n",
    "\n",
    "#### Задание №1\n",
    "\n",
    "Вам нужно: \n",
    "* Создать трехмерную матрицу размера $10^4 \\times 4 \\times n$. Первая ось отвечает за номер марафона, вторая ось за каждого участника, а третья ось за каждый час марафона. Вторую и третью ось можно поменять, если вам так удобнее.\n",
    "* Выявить победителя в каждом из $10^4$ марафонов. \n",
    "* Нарисовать столбчатую диаграмму, на которой будет видно кто и сколько раз побеждал в марафоне.\n",
    "* Запрещено пользоваться циклами. Нужно использовать возможности numpy.\n",
    "\n",
    "Что можно сказать о результатах?\n",
    "\n",
    "#### Задание №2\n",
    " \n",
    "Участники решили объединиться в пары: Винни-Пух с Пятачком, Кролик с Иа-Иа. Они решили суммировать свои результаты.\n",
    "\n",
    "Вам нужно: \n",
    "* Сгенерировать новую трехмерную матрицу, но уже объединив участников в команды.\n",
    "* Выявить команду-победителя в каждом из $10^4$ марафонов. \n",
    "* Нарисовать столбчатую диаграмму, на которой будет видно кто и сколько раз побеждал в марафоне.\n",
    "\n",
    "Логичны ли полученные результаты?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Пункт 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from numpy import random\n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "n = int(input('Number of races: '))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Трехмерная матрица"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of races: 4\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[[ 0.10900438,  0.17930653,  1.68193029,  0.2985944 ],\n",
       "        [ 2.52006263,  1.03963843,  1.93631479,  0.30459797],\n",
       "        [ 0.        ,  2.        ,  0.        ,  2.        ],\n",
       "        [ 0.        ,  0.        ,  0.        ,  0.        ]],\n",
       "\n",
       "       [[ 0.4117575 ,  2.23516392,  0.68036802,  0.30061286],\n",
       "        [ 1.64909753,  1.16916162,  0.50636385,  1.79003401],\n",
       "        [ 0.        ,  0.        ,  0.        ,  0.        ],\n",
       "        [ 0.        ,  2.        ,  2.        ,  0.        ]],\n",
       "\n",
       "       [[ 0.90918116,  0.5069319 ,  1.65502208,  0.29902189],\n",
       "        [ 1.90421575,  1.47071875,  1.92002226,  0.8619511 ],\n",
       "        [ 2.        ,  2.        ,  0.        ,  1.        ],\n",
       "        [ 0.        ,  2.        ,  0.        ,  0.        ]],\n",
       "\n",
       "       ...,\n",
       "\n",
       "       [[ 1.08244249,  1.97720734,  0.10558048,  0.28224183],\n",
       "        [ 1.16056985,  1.28235226,  0.60442801,  0.16780253],\n",
       "        [ 2.        ,  0.        ,  1.        ,  0.        ],\n",
       "        [ 2.        ,  0.        ,  0.        ,  0.        ]],\n",
       "\n",
       "       [[ 0.90821144,  1.88319819,  0.58256542,  0.86485022],\n",
       "        [-1.17786266,  3.30676252,  0.15830281,  0.6752154 ],\n",
       "        [ 0.        ,  3.        ,  1.        ,  1.        ],\n",
       "        [ 0.        ,  0.        ,  2.        ,  0.        ]],\n",
       "\n",
       "       [[ 0.15074738,  0.59700846,  0.30154588,  0.42602518],\n",
       "        [-0.12105466, -1.41451687, -0.41835421,  2.51416875],\n",
       "        [ 2.        ,  4.        ,  1.        ,  1.        ],\n",
       "        [ 0.        ,  0.        ,  2.        ,  2.        ]]])"
      ]
     },
     "execution_count": 201,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "winnie = random.exponential(scale=1, size=(10 ** 4, 1, n))\n",
    "hog = random.normal(1, 1, size=(10 ** 4, 1, n))\n",
    "rabbit = random.poisson(lam = 1, size=(10 ** 4, 1, n))\n",
    "jackass = random.binomial(1, 0.5, size=(10 ** 4, 1, n)) * 2\n",
    "arr = np.concatenate((winnie, hog, rabbit, jackass),axis=1)\n",
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2.2688356 , 5.80061382, 4.        , 0.        ],\n",
       "       [3.62790231, 5.11465701, 0.        , 4.        ],\n",
       "       [3.37015703, 6.15690787, 5.        , 2.        ],\n",
       "       ...,\n",
       "       [3.44747214, 3.21515265, 3.        , 2.        ],\n",
       "       [4.23882527, 2.96241808, 5.        , 2.        ],\n",
       "       [1.47532691, 0.56024301, 8.        , 4.        ]])"
      ]
     },
     "execution_count": 202,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arrsum = arr.sum(axis = 2)\n",
    "arrsum"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Победитель в каждом из $10^4$ марафонов"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, ..., 0, 2, 2])"
      ]
     },
     "execution_count": 203,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result = arrsum.argmax(axis = 1)\n",
    "result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Столбчатая диаграмма, на которой видно кто и сколько раз побеждал в марафоне"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "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": [
    "a = ['Winnie', 'Hog', 'Rabbit', 'Jackass']\n",
    "ypos = np.arange(len(a))\n",
    "plt.hist(result, rwidth=1, histtype='bar', color='green', alpha=1, edgecolor='black', bins=np.arange(0,np.max(result)+2)-0.5);\n",
    "plt.xlabel('Paritcipants', fontsize=10);\n",
    "plt.xticks(ypos, a);\n",
    "plt.ylabel('Number of winnings', fontsize=10);\n",
    "plt.title('Race Winners Frequency', fontsize=16);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Вывод: На основе данных, можно сделать вывод, что каждый из игроков равновероятно побеждает в марафоне"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Пункт 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Winniehog's team"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 289,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[ 0.14106118,  2.79339487,  0.12173919,  0.0147933 ],\n",
       "        [ 0.61594219,  2.04312837,  2.18483994,  0.46194524]],\n",
       "\n",
       "       [[ 0.14300536,  0.02547355,  0.02187009,  0.0197329 ],\n",
       "        [ 1.39302596, -0.63292476,  0.93392787,  1.38781632]],\n",
       "\n",
       "       [[ 0.56697213,  0.72904612,  1.33290329,  2.41526341],\n",
       "        [ 0.63144348,  2.13188661,  1.17432843,  0.51656424]],\n",
       "\n",
       "       ...,\n",
       "\n",
       "       [[ 1.604507  ,  0.10002139,  0.90779369,  0.20468209],\n",
       "        [ 1.70370451,  1.39707243,  1.11364884,  1.04857561]],\n",
       "\n",
       "       [[ 0.09603059,  1.23193903,  0.1878015 ,  0.44521514],\n",
       "        [ 1.79245906,  0.86576605, -0.48198512,  0.72464528]],\n",
       "\n",
       "       [[ 0.70075855,  0.18687669,  0.29613611,  0.57432377],\n",
       "        [ 2.91445992,  1.46891569,  1.01557451,  2.30994207]]])"
      ]
     },
     "execution_count": 289,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "winnie = random.exponential(scale=1, size=(10 ** 4, 1, n))\n",
    "hog = random.normal(1, 1, size=(10 ** 4, 1, n))\n",
    "winniehog = np.concatenate((winnie, hog),axis=1)\n",
    "winniehog"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 290,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.75700338,  4.83652324,  2.30657913,  0.47673854],\n",
       "       [ 1.53603132, -0.60745121,  0.95579795,  1.40754922],\n",
       "       [ 1.19841561,  2.86093274,  2.50723172,  2.93182764],\n",
       "       ...,\n",
       "       [ 3.30821152,  1.49709381,  2.02144252,  1.2532577 ],\n",
       "       [ 1.88848965,  2.09770508, -0.29418362,  1.16986042],\n",
       "       [ 3.61521848,  1.65579238,  1.31171062,  2.88426584]])"
      ]
     },
     "execution_count": 290,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "winniehogsum = winniehog.sum(axis = 1)\n",
    "winniehogsum"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Jackaassrabbit's team"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 291,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 3, 0, 2],\n",
       "       [3, 0, 1, 2],\n",
       "       [0, 1, 0, 2],\n",
       "       ...,\n",
       "       [0, 0, 1, 1],\n",
       "       [4, 2, 1, 4],\n",
       "       [1, 2, 2, 2]])"
      ]
     },
     "execution_count": 291,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rabbit = random.poisson(lam = 1, size=(10 ** 4, 1, n))\n",
    "jackass = random.binomial(1, 0.5, size=(10 ** 4, 1, n)) * 2\n",
    "jackassrabbit = np.concatenate((rabbit, jackass),axis=1)\n",
    "jackassrabbitsum = jackassrabbit.sum(axis = 1)\n",
    "jackassrabbitsum"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Comparing teams"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 292,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[ 0.75700338,  4.83652324,  2.30657913,  0.47673854],\n",
       "        [ 0.        ,  3.        ,  0.        ,  2.        ]],\n",
       "\n",
       "       [[ 1.53603132, -0.60745121,  0.95579795,  1.40754922],\n",
       "        [ 3.        ,  0.        ,  1.        ,  2.        ]],\n",
       "\n",
       "       [[ 1.19841561,  2.86093274,  2.50723172,  2.93182764],\n",
       "        [ 0.        ,  1.        ,  0.        ,  2.        ]],\n",
       "\n",
       "       ...,\n",
       "\n",
       "       [[ 3.30821152,  1.49709381,  2.02144252,  1.2532577 ],\n",
       "        [ 0.        ,  0.        ,  1.        ,  1.        ]],\n",
       "\n",
       "       [[ 1.88848965,  2.09770508, -0.29418362,  1.16986042],\n",
       "        [ 4.        ,  2.        ,  1.        ,  4.        ]],\n",
       "\n",
       "       [[ 3.61521848,  1.65579238,  1.31171062,  2.88426584],\n",
       "        [ 1.        ,  2.        ,  2.        ,  2.        ]]])"
      ]
     },
     "execution_count": 292,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "teams = np.stack((winniehogsum, jackassrabbitsum), axis = 1)\n",
    "teams"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 293,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 8.37684428,  5.        ],\n",
       "       [ 3.29192728,  6.        ],\n",
       "       [ 9.49840772,  3.        ],\n",
       "       ...,\n",
       "       [ 8.08000555,  2.        ],\n",
       "       [ 4.86187153, 11.        ],\n",
       "       [ 9.46698732,  7.        ]])"
      ]
     },
     "execution_count": 293,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "teamssum = teams.sum(axis = 2)\n",
    "teamssum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 294,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 0, ..., 0, 1, 0])"
      ]
     },
     "execution_count": 294,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "teamsresult = teamssum.argmax(axis = 1)\n",
    "teamsresult"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Histogram for teams"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 295,
   "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": [
    "a = ['WinnieHog', 'JackassRabbit']\n",
    "ypos = np.arange(len(a))\n",
    "plt.hist(teamsresult, rwidth=1, histtype='bar', color='green', alpha=1, edgecolor='black', bins=np.arange(0,np.max(teamsresult)+2)-0.5);\n",
    "plt.xlabel('Teams', fontsize=10);\n",
    "plt.xticks(ypos, a);\n",
    "plt.ylabel('Number of winnings', fontsize=10);\n",
    "plt.title('Team Winners Frequency', fontsize=16);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Достаточно логичны, если каждый по отдельности выигрывает марафон равновероятно, то и команды скорее всего выиграют равновероятно"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Avito (30 баллов)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "В этом задании вам нужно будет написать функцию, которая парсит [Авито](https://www.avito.ru) (будем рассматривать только Москву). Эта функция принимает два параметра:\n",
    "* search - то, что мы ищем на avito\n",
    "* page - номер страницы, с которой парсим информацию\n",
    "\n",
    "```python\n",
    "def parse_page(search, page):\n",
    "    # тут ваш код\n",
    "df = parse_page('гречка', 4)\n",
    "```\n",
    "Нужно скачать следующую информацию:\n",
    "* название объявления\n",
    "* ссылка на объявление\n",
    "* цена\n",
    "* метро (если есть), вам нужно аккуратно обрабатывать None или использовать конструкцию try-except\n",
    "* сколько метров от метро (если есть)\n",
    "\n",
    "Эту информацию лучше сохранить в виде pandas df.\n",
    "\n",
    "В данном задании **не нужно** парсить все страницы (вас могут забанить). Нужно лишь сделать функцию, которая парсит одну страницу. Чтобы вас не забанили сделаейте request один раз и работайте с вашей страничкой без функции. После того как все будет готово, оберните весь ваш код в функцию."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 288,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data saved to CSV | avito_data.csv\n",
      "Browser closed\n"
     ]
    }
   ],
   "source": [
    "def parse_page(search, page):\n",
    "    import time\n",
    "    import csv\n",
    "    from selenium import webdriver\n",
    "    from selenium.webdriver.chrome.options import Options\n",
    "\n",
    "    while True:\n",
    "        try:\n",
    "            with open('avito_data.csv', 'w') as new_file:\n",
    "                # csv\n",
    "                fieldnames = ['Search key', 'Page', 'Price, RUB', 'Subway nearby']\n",
    "\n",
    "                csv_writer = csv.DictWriter(new_file, fieldnames=fieldnames, delimiter=',')\n",
    "\n",
    "                csv_writer.writeheader()\n",
    "\n",
    "\n",
    "                # prepare for parsing\n",
    "                chrome_options = Options()\n",
    "                chrome_options.add_argument(\"--headless\")  # we will not open selenium browser just not to slow your pc =D\n",
    "                website = 'https://www.avito.ru/moskva?q=' + str(search) + '&p=' + str(page)\n",
    "                driver = webdriver.Chrome('/usr/local/Caskroom/chromedriver/81.0.4044.69/chromedriver',\n",
    "                                          options=chrome_options)\n",
    "                driver.implicitly_wait(10)\n",
    "                driver.get(website)\n",
    "\n",
    "                # shit to find\n",
    "                name = \"h3[class='snippet-title']\"\n",
    "                url = \"a[class='snippet-link']\"\n",
    "                price = \"span[data-marker='item-price']\"\n",
    "                address = \"div[class='item-address']\"\n",
    "\n",
    "                # itemlines\n",
    "                try:\n",
    "                    driver.find_element_by_css_selector(\"div[class='item__line']\")  # check if selector exists\n",
    "                    itemlines = driver.find_elements_by_css_selector(\"div[class='item__line']\")\n",
    "                    # code to execute\n",
    "                    for el in itemlines:\n",
    "                        try:\n",
    "                            el.find_element_by_css_selector(address)\n",
    "                            new_file.write(str(el.find_element_by_css_selector(name).text).replace(',', '') + ',' +\n",
    "                                           el.find_element_by_css_selector(url).get_attribute('href') + ',' +\n",
    "                                           el.find_element_by_css_selector(price).text + ',' +\n",
    "                                           str(el.find_element_by_css_selector(address).text).replace(',', '.'))\n",
    "                            new_file.write('\\n')\n",
    "                        except:\n",
    "                            new_file.write(str(el.find_element_by_css_selector(name).text).replace(',', '') + ',' +\n",
    "                                           el.find_element_by_css_selector(url).get_attribute('href') + ',' +\n",
    "                                           el.find_element_by_css_selector(price).text + ',' +\n",
    "                                           'None')\n",
    "                            new_file.write('\\n')\n",
    "                    print('Data saved to CSV | avito_data.csv')\n",
    "                except:\n",
    "                    print('There are no items due to your request')\n",
    "                # global quit and log\n",
    "                print('Browser closed')\n",
    "                driver.quit()\n",
    "                break\n",
    "        except Exception as e:\n",
    "            driver.save_screenshot(\"screenshot.png\")\n",
    "            driver.quit()\n",
    "            print('Errot type: ' + str(e))\n",
    "            print('Trying again...')\n",
    "            pass\n",
    "\n",
    "df = parse_page('macbook', 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "btw while True: try...except в коде позволяет если упала страница(например сервер упал) или что-то случилось автоматически перезапустить код, да и вообще в коде много try..except в лучших традицих парсинга"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Я так понял надо еще и разделить метро и расстояние :). Элементы в веб странице, которые отвечают за \"Расстояние\" и \"Метро\" выглядят как очень неусточийвые \"item-address-georeferences-item__content\", поэтому не хочу их использовать, потому что если авито изменит элемент, то скрипт не будет работать. Неустойчивость всегда плохо. \n",
    "Лучше поработаю с таблицей."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "btw первел еще все значения расстояния в метры и убрал знак рубля, потому что единственный и официальной валютой средством платежа в России является российский рубль"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 289,
   "metadata": {
    "scrolled": true
   },
   "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>Search key</th>\n",
       "      <th>Page</th>\n",
       "      <th>Price, RUB</th>\n",
       "      <th>Subway name</th>\n",
       "      <th>Subway dist, m</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>MacBook Pro 13’’ touch pad</td>\n",
       "      <td>https://www.avito.ru/moskva/noutbuki/macbook_p...</td>\n",
       "      <td>85000</td>\n",
       "      <td>Речной вокзал</td>\n",
       "      <td>700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>MacBook AIR/PRO</td>\n",
       "      <td>https://www.avito.ru/moskva/noutbuki/macbook_a...</td>\n",
       "      <td>13900</td>\n",
       "      <td>Павелецкая</td>\n",
       "      <td>300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>MacBook Pro 15 2018 256Gb TouchBar SpaceGray</td>\n",
       "      <td>https://www.avito.ru/moskva/noutbuki/macbook_p...</td>\n",
       "      <td>114000</td>\n",
       "      <td>Октябрьское поле</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Macbook Pro Retina 13 2015</td>\n",
       "      <td>https://www.avito.ru/moskva/noutbuki/macbook_p...</td>\n",
       "      <td>48500</td>\n",
       "      <td>ВДНХ</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>MAC toledo palette / мак новая палетка теней</td>\n",
       "      <td>https://www.avito.ru/moskva/krasota_i_zdorove/...</td>\n",
       "      <td>1500</td>\n",
       "      <td>Киевская</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     Search key  \\\n",
       "0                    MacBook Pro 13’’ touch pad   \n",
       "1                               MacBook AIR/PRO   \n",
       "2  MacBook Pro 15 2018 256Gb TouchBar SpaceGray   \n",
       "3                    Macbook Pro Retina 13 2015   \n",
       "4  MAC toledo palette / мак новая палетка теней   \n",
       "\n",
       "                                                Page Price, RUB  \\\n",
       "0  https://www.avito.ru/moskva/noutbuki/macbook_p...      85000   \n",
       "1  https://www.avito.ru/moskva/noutbuki/macbook_a...      13900   \n",
       "2  https://www.avito.ru/moskva/noutbuki/macbook_p...     114000   \n",
       "3  https://www.avito.ru/moskva/noutbuki/macbook_p...      48500   \n",
       "4  https://www.avito.ru/moskva/krasota_i_zdorove/...       1500   \n",
       "\n",
       "        Subway name Subway dist, m  \n",
       "0     Речной вокзал           700   \n",
       "1        Павелецкая           300   \n",
       "2  Октябрьское поле           None  \n",
       "3              ВДНХ           100   \n",
       "4          Киевская           100   "
      ]
     },
     "execution_count": 289,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd \n",
    "   \n",
    "# reading csv file \n",
    "data = pd.read_csv(\"avito_data.csv\") \n",
    "data.dropna(inplace = True) \n",
    "new = data[\"Subway nearby\"].str.split(\".\", n = 1, expand = True) \n",
    "data[\"Subway name\"]= new[0] \n",
    "data[\"Subway dist, m\"]= new[1] \n",
    "data.drop(columns =[\"Subway nearby\"], inplace = True)\n",
    "data['Price, RUB'] = data['Price, RUB'].str.replace(\"₽\", \"\")\n",
    "for i in data['Subway dist, m']:\n",
    "    if type(i) == str:\n",
    "        if 'км' in i:\n",
    "            data['Subway dist, m'] = data['Subway dist, m'].str.replace(i, str(int(float(i.replace('км', '')) * 1000)))\n",
    "data['Subway dist, m'] = data['Subway dist, m'].str.replace(\"м\", \"\")\n",
    "data['Price, RUB'] = data['Price, RUB'].str.replace(' ', '')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Сохраним все в csv файл"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 290,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.to_csv('avito_data.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Walmart (30 баллов)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "В файле *walmart.csv* нам предоставлены исторические данные о продажах магазинов Walmart, расположенных в разных регионах.\n",
    "\n",
    "Нам доступны следующие переменные:\n",
    "\n",
    "* **Date** – дата;\n",
    "* **Store** – номер магазина;\n",
    "* **Dept** – номер отдела;\n",
    "* **Weekly_Sales** – объём продаж в данную неделю в данном отделе\n",
    "* **Type** – тип магазина;\n",
    "* **Size** – размер магазина;\n",
    "* **IsHoliday** – является ли неделя праздничной;\n",
    "* **Temperature** – средняя температура в регионе в градусах по Фаренгейту;\n",
    "* **Fuel_Price** – стоимость топлива в регионе;\n",
    "* **MarkDown1-5** – данные, связанные с рекламными уценками, которые запускает Walmart. Данные уценки доступны только после ноября 2011 года и доступны не для всех магазинов. Данные анонимизированы. Непонятно на какие именно товары производилась уценка и в каких количествах. Компании часто анонимизируют данные, когда выкладывают их в открытый доступ.\n",
    "* **CPI** – индекс потребительских цен;\n",
    "* **Unemployment** – уровень безработицы.\n",
    "\n",
    "\n",
    "Вам нужно выполнить следующие задания:\n",
    "1. Загрузить файл с данными. Отобразить первые и последние 5 наблюдений. Узнать сколько наблюдений и переменных есть в вашем наборе данных. Какой тип(формат) имеет каждая переменная? Привести переменную Date к формату даты.\n",
    "2. Разобраться с пропущенными значениями:\n",
    "    * вывести сколько пропущенных значений в процентах есть в каждой переменной;\n",
    "    * удалить переменные, которые имеют более 60% пропущенных значений.\n",
    "3. Сколько магазинов и отделов присутствует в выборке, за какой период времени у нас имеются данные? \n",
    "4. Посмотреть динамику продаж в магазинах walmart в целом. Для этого можно построить график, где по оси X будет дата, а по оси Y продажи всей сети Walmart.\n",
    "5. Найдите и нарисуйте матрицу корреляции для числовых показателей.\n",
    "6. Найдите топ-5 самых больших магазинов по суммарным продажам за все время и отобразите динамику их продаж на одном графике.\n",
    "7. Найдите топ-10 самых больших отделов по суммарным продажам за 2011 год среди магазинов с типом А и нарисуйте столбчатую диаграмму для них"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Пункт 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Первые 5 наблюдений"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('walmart.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Store</th>\n",
       "      <th>Date</th>\n",
       "      <th>Dept</th>\n",
       "      <th>Weekly_Sales</th>\n",
       "      <th>IsHoliday</th>\n",
       "      <th>Type</th>\n",
       "      <th>Size</th>\n",
       "      <th>Temperature</th>\n",
       "      <th>Fuel_Price</th>\n",
       "      <th>CPI</th>\n",
       "      <th>Unemployment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2010-02-05</td>\n",
       "      <td>1</td>\n",
       "      <td>24924.50</td>\n",
       "      <td>False</td>\n",
       "      <td>A</td>\n",
       "      <td>151315</td>\n",
       "      <td>42.31</td>\n",
       "      <td>2.572</td>\n",
       "      <td>211.096358</td>\n",
       "      <td>8.106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2010-02-12</td>\n",
       "      <td>1</td>\n",
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       "      <td>True</td>\n",
       "      <td>A</td>\n",
       "      <td>151315</td>\n",
       "      <td>38.51</td>\n",
       "      <td>2.548</td>\n",
       "      <td>211.242170</td>\n",
       "      <td>8.106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>2010-02-19</td>\n",
       "      <td>1</td>\n",
       "      <td>41595.55</td>\n",
       "      <td>False</td>\n",
       "      <td>A</td>\n",
       "      <td>151315</td>\n",
       "      <td>39.93</td>\n",
       "      <td>2.514</td>\n",
       "      <td>211.289143</td>\n",
       "      <td>8.106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>2010-02-26</td>\n",
       "      <td>1</td>\n",
       "      <td>19403.54</td>\n",
       "      <td>False</td>\n",
       "      <td>A</td>\n",
       "      <td>151315</td>\n",
       "      <td>46.63</td>\n",
       "      <td>2.561</td>\n",
       "      <td>211.319643</td>\n",
       "      <td>8.106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>2010-03-05</td>\n",
       "      <td>1</td>\n",
       "      <td>21827.90</td>\n",
       "      <td>False</td>\n",
       "      <td>A</td>\n",
       "      <td>151315</td>\n",
       "      <td>46.50</td>\n",
       "      <td>2.625</td>\n",
       "      <td>211.350143</td>\n",
       "      <td>8.106</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Store        Date  Dept  Weekly_Sales  IsHoliday Type    Size  Temperature  \\\n",
       "0      1  2010-02-05     1      24924.50      False    A  151315        42.31   \n",
       "1      1  2010-02-12     1      46039.49       True    A  151315        38.51   \n",
       "2      1  2010-02-19     1      41595.55      False    A  151315        39.93   \n",
       "3      1  2010-02-26     1      19403.54      False    A  151315        46.63   \n",
       "4      1  2010-03-05     1      21827.90      False    A  151315        46.50   \n",
       "\n",
       "   Fuel_Price         CPI  Unemployment  \n",
       "0       2.572  211.096358         8.106  \n",
       "1       2.548  211.242170         8.106  \n",
       "2       2.514  211.289143         8.106  \n",
       "3       2.561  211.319643         8.106  \n",
       "4       2.625  211.350143         8.106  "
      ]
     },
     "execution_count": 207,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Последние 5 наблюдений"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Store</th>\n",
       "      <th>Date</th>\n",
       "      <th>Dept</th>\n",
       "      <th>Weekly_Sales</th>\n",
       "      <th>IsHoliday</th>\n",
       "      <th>Type</th>\n",
       "      <th>Size</th>\n",
       "      <th>Temperature</th>\n",
       "      <th>Fuel_Price</th>\n",
       "      <th>MarkDown1</th>\n",
       "      <th>MarkDown2</th>\n",
       "      <th>MarkDown3</th>\n",
       "      <th>MarkDown4</th>\n",
       "      <th>MarkDown5</th>\n",
       "      <th>CPI</th>\n",
       "      <th>Unemployment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>418234</th>\n",
       "      <td>45</td>\n",
       "      <td>2012-09-21</td>\n",
       "      <td>98</td>\n",
       "      <td>467.30</td>\n",
       "      <td>False</td>\n",
       "      <td>B</td>\n",
       "      <td>118221</td>\n",
       "      <td>65.32</td>\n",
       "      <td>4.038</td>\n",
       "      <td>8452.20</td>\n",
       "      <td>92.28</td>\n",
       "      <td>63.24</td>\n",
       "      <td>2376.38</td>\n",
       "      <td>8670.40</td>\n",
       "      <td>191.856704</td>\n",
       "      <td>8.684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>418235</th>\n",
       "      <td>45</td>\n",
       "      <td>2012-09-28</td>\n",
       "      <td>98</td>\n",
       "      <td>508.37</td>\n",
       "      <td>False</td>\n",
       "      <td>B</td>\n",
       "      <td>118221</td>\n",
       "      <td>64.88</td>\n",
       "      <td>3.997</td>\n",
       "      <td>4556.61</td>\n",
       "      <td>20.64</td>\n",
       "      <td>1.50</td>\n",
       "      <td>1601.01</td>\n",
       "      <td>3288.25</td>\n",
       "      <td>192.013558</td>\n",
       "      <td>8.684</td>\n",
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       "    <tr>\n",
       "      <th>418236</th>\n",
       "      <td>45</td>\n",
       "      <td>2012-10-05</td>\n",
       "      <td>98</td>\n",
       "      <td>628.10</td>\n",
       "      <td>False</td>\n",
       "      <td>B</td>\n",
       "      <td>118221</td>\n",
       "      <td>64.89</td>\n",
       "      <td>3.985</td>\n",
       "      <td>5046.74</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18.82</td>\n",
       "      <td>2253.43</td>\n",
       "      <td>2340.01</td>\n",
       "      <td>192.170412</td>\n",
       "      <td>8.667</td>\n",
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       "    <tr>\n",
       "      <th>418237</th>\n",
       "      <td>45</td>\n",
       "      <td>2012-10-12</td>\n",
       "      <td>98</td>\n",
       "      <td>1061.02</td>\n",
       "      <td>False</td>\n",
       "      <td>B</td>\n",
       "      <td>118221</td>\n",
       "      <td>54.47</td>\n",
       "      <td>4.000</td>\n",
       "      <td>1956.28</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.89</td>\n",
       "      <td>599.32</td>\n",
       "      <td>3990.54</td>\n",
       "      <td>192.327265</td>\n",
       "      <td>8.667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>418238</th>\n",
       "      <td>45</td>\n",
       "      <td>2012-10-19</td>\n",
       "      <td>98</td>\n",
       "      <td>760.01</td>\n",
       "      <td>False</td>\n",
       "      <td>B</td>\n",
       "      <td>118221</td>\n",
       "      <td>56.47</td>\n",
       "      <td>3.969</td>\n",
       "      <td>2004.02</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.18</td>\n",
       "      <td>437.73</td>\n",
       "      <td>1537.49</td>\n",
       "      <td>192.330854</td>\n",
       "      <td>8.667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Store        Date  Dept  Weekly_Sales  IsHoliday Type    Size  \\\n",
       "418234     45  2012-09-21    98        467.30      False    B  118221   \n",
       "418235     45  2012-09-28    98        508.37      False    B  118221   \n",
       "418236     45  2012-10-05    98        628.10      False    B  118221   \n",
       "418237     45  2012-10-12    98       1061.02      False    B  118221   \n",
       "418238     45  2012-10-19    98        760.01      False    B  118221   \n",
       "\n",
       "        Temperature  Fuel_Price  MarkDown1  MarkDown2  MarkDown3  MarkDown4  \\\n",
       "418234        65.32       4.038    8452.20      92.28      63.24    2376.38   \n",
       "418235        64.88       3.997    4556.61      20.64       1.50    1601.01   \n",
       "418236        64.89       3.985    5046.74        NaN      18.82    2253.43   \n",
       "418237        54.47       4.000    1956.28        NaN       7.89     599.32   \n",
       "418238        56.47       3.969    2004.02        NaN       3.18     437.73   \n",
       "\n",
       "        MarkDown5         CPI  Unemployment  \n",
       "418234    8670.40  191.856704         8.684  \n",
       "418235    3288.25  192.013558         8.684  \n",
       "418236    2340.01  192.170412         8.667  \n",
       "418237    3990.54  192.327265         8.667  \n",
       "418238    1537.49  192.330854         8.667  "
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Количество наблюдений"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "418239"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df.index)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Количество переменных"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape[1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Тип переменных"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Store             int64\n",
       "Date             object\n",
       "Dept              int64\n",
       "Weekly_Sales    float64\n",
       "IsHoliday          bool\n",
       "Type             object\n",
       "Size              int64\n",
       "Temperature     float64\n",
       "Fuel_Price      float64\n",
       "MarkDown1       float64\n",
       "MarkDown2       float64\n",
       "MarkDown3       float64\n",
       "MarkDown4       float64\n",
       "MarkDown5       float64\n",
       "CPI             float64\n",
       "Unemployment    float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Приведение даты к нормальному виду"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Date'] = pd.to_datetime(df['Date'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Проверим, все ли ок. Должно быть \"datetime64[ns]\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Store                    int64\n",
       "Date            datetime64[ns]\n",
       "Dept                     int64\n",
       "Weekly_Sales           float64\n",
       "IsHoliday                 bool\n",
       "Type                    object\n",
       "Size                     int64\n",
       "Temperature            float64\n",
       "Fuel_Price             float64\n",
       "MarkDown1              float64\n",
       "MarkDown2              float64\n",
       "MarkDown3              float64\n",
       "MarkDown4              float64\n",
       "MarkDown5              float64\n",
       "CPI                    float64\n",
       "Unemployment           float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Все ок. Можно приступать ко второму пункту"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Пункт 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Пропущенные значения в каждой переменной"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Store           0.000000\n",
       "Date            0.000000\n",
       "Dept            0.000000\n",
       "Weekly_Sales    0.000000\n",
       "IsHoliday       0.000000\n",
       "Type            0.000000\n",
       "Size            0.000000\n",
       "Temperature     0.000000\n",
       "Fuel_Price      0.000000\n",
       "MarkDown1       0.647369\n",
       "MarkDown2       0.741430\n",
       "MarkDown3       0.678251\n",
       "MarkDown4       0.683638\n",
       "MarkDown5       0.645700\n",
       "CPI             0.000000\n",
       "Unemployment    0.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 211,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum() / len(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Удалим те, у которых более 60%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop(columns=df.columns[(df.isna().sum() / len(df) > 0.6)],axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Проверим, что все удалилось"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Store           0.0\n",
       "Date            0.0\n",
       "Dept            0.0\n",
       "Weekly_Sales    0.0\n",
       "IsHoliday       0.0\n",
       "Type            0.0\n",
       "Size            0.0\n",
       "Temperature     0.0\n",
       "Fuel_Price      0.0\n",
       "CPI             0.0\n",
       "Unemployment    0.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 217,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum() / len(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Пункт 3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Посчитаем количество магазинов"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "45"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Store'].nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Посчитаем количество отделов"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "81"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Dept'].nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Посчитаем период времени"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timedelta('987 days 00:00:00')"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.to_datetime(max(df['Date'])) - pd.to_datetime(min(df['Date']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Выходит ровно 987 дней"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Пункт 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = df[['Weekly_Sales', 'Date']].groupby('Date').sum().plot(figsize=(25,10), fontsize=15)\n",
    "ax.set_title('Sales',fontsize= 30)\n",
    "ax.set_xlabel(\"Dates\", fontsize= 15)\n",
    "ax.set_ylabel(\"Sales\", fontsize= 15);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Как и ожидалось - наибольшее число покупок совершаются перед рождеством. p.s. формат даты yyyy-mm-dd, так как wallmart американская сеть и логично предположить что данные в реальной жизни придется адаптировать под особенности страны, поэтому я не стал изменять на dd-mm-yyyy, что является  форматом в Европе и в России."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Пункт 5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Для начала дропну некоторы категориальные и бинарные признаки"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {
    "scrolled": true
   },
   "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>Date</th>\n",
       "      <th>Weekly_Sales</th>\n",
       "      <th>Type</th>\n",
       "      <th>Size</th>\n",
       "      <th>Temperature</th>\n",
       "      <th>Fuel_Price</th>\n",
       "      <th>CPI</th>\n",
       "      <th>Unemployment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2010-02-05</td>\n",
       "      <td>24924.50</td>\n",
       "      <td>A</td>\n",
       "      <td>151315</td>\n",
       "      <td>42.31</td>\n",
       "      <td>2.572</td>\n",
       "      <td>211.096358</td>\n",
       "      <td>8.106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2010-02-12</td>\n",
       "      <td>46039.49</td>\n",
       "      <td>A</td>\n",
       "      <td>151315</td>\n",
       "      <td>38.51</td>\n",
       "      <td>2.548</td>\n",
       "      <td>211.242170</td>\n",
       "      <td>8.106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2010-02-19</td>\n",
       "      <td>41595.55</td>\n",
       "      <td>A</td>\n",
       "      <td>151315</td>\n",
       "      <td>39.93</td>\n",
       "      <td>2.514</td>\n",
       "      <td>211.289143</td>\n",
       "      <td>8.106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2010-02-26</td>\n",
       "      <td>19403.54</td>\n",
       "      <td>A</td>\n",
       "      <td>151315</td>\n",
       "      <td>46.63</td>\n",
       "      <td>2.561</td>\n",
       "      <td>211.319643</td>\n",
       "      <td>8.106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2010-03-05</td>\n",
       "      <td>21827.90</td>\n",
       "      <td>A</td>\n",
       "      <td>151315</td>\n",
       "      <td>46.50</td>\n",
       "      <td>2.625</td>\n",
       "      <td>211.350143</td>\n",
       "      <td>8.106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>418234</th>\n",
       "      <td>2012-09-21</td>\n",
       "      <td>467.30</td>\n",
       "      <td>B</td>\n",
       "      <td>118221</td>\n",
       "      <td>65.32</td>\n",
       "      <td>4.038</td>\n",
       "      <td>191.856704</td>\n",
       "      <td>8.684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>418235</th>\n",
       "      <td>2012-09-28</td>\n",
       "      <td>508.37</td>\n",
       "      <td>B</td>\n",
       "      <td>118221</td>\n",
       "      <td>64.88</td>\n",
       "      <td>3.997</td>\n",
       "      <td>192.013558</td>\n",
       "      <td>8.684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>418236</th>\n",
       "      <td>2012-10-05</td>\n",
       "      <td>628.10</td>\n",
       "      <td>B</td>\n",
       "      <td>118221</td>\n",
       "      <td>64.89</td>\n",
       "      <td>3.985</td>\n",
       "      <td>192.170412</td>\n",
       "      <td>8.667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>418237</th>\n",
       "      <td>2012-10-12</td>\n",
       "      <td>1061.02</td>\n",
       "      <td>B</td>\n",
       "      <td>118221</td>\n",
       "      <td>54.47</td>\n",
       "      <td>4.000</td>\n",
       "      <td>192.327265</td>\n",
       "      <td>8.667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>418238</th>\n",
       "      <td>2012-10-19</td>\n",
       "      <td>760.01</td>\n",
       "      <td>B</td>\n",
       "      <td>118221</td>\n",
       "      <td>56.47</td>\n",
       "      <td>3.969</td>\n",
       "      <td>192.330854</td>\n",
       "      <td>8.667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>418239 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              Date  Weekly_Sales Type    Size  Temperature  Fuel_Price  \\\n",
       "0       2010-02-05      24924.50    A  151315        42.31       2.572   \n",
       "1       2010-02-12      46039.49    A  151315        38.51       2.548   \n",
       "2       2010-02-19      41595.55    A  151315        39.93       2.514   \n",
       "3       2010-02-26      19403.54    A  151315        46.63       2.561   \n",
       "4       2010-03-05      21827.90    A  151315        46.50       2.625   \n",
       "...            ...           ...  ...     ...          ...         ...   \n",
       "418234  2012-09-21        467.30    B  118221        65.32       4.038   \n",
       "418235  2012-09-28        508.37    B  118221        64.88       3.997   \n",
       "418236  2012-10-05        628.10    B  118221        64.89       3.985   \n",
       "418237  2012-10-12       1061.02    B  118221        54.47       4.000   \n",
       "418238  2012-10-19        760.01    B  118221        56.47       3.969   \n",
       "\n",
       "               CPI  Unemployment  \n",
       "0       211.096358         8.106  \n",
       "1       211.242170         8.106  \n",
       "2       211.289143         8.106  \n",
       "3       211.319643         8.106  \n",
       "4       211.350143         8.106  \n",
       "...            ...           ...  \n",
       "418234  191.856704         8.684  \n",
       "418235  192.013558         8.684  \n",
       "418236  192.170412         8.667  \n",
       "418237  192.327265         8.667  \n",
       "418238  192.330854         8.667  \n",
       "\n",
       "[418239 rows x 8 columns]"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop(['Store', 'Dept', 'IsHoliday'], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Воспользуемся seaborn для решения этой задачи"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(df.corr(), annot=True, fmt='.2f', cmap='RdBu').set_title('Сorrelation matrix');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Пункт 6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [],
   "source": [
    "import plotly_express as px\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Сгрупирую и проссумирую по Store и посортирую по Weekly_Sales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {
    "scrolled": true
   },
   "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>Dept</th>\n",
       "      <th>Weekly_Sales</th>\n",
       "      <th>IsHoliday</th>\n",
       "      <th>Size</th>\n",
       "      <th>Temperature</th>\n",
       "      <th>Fuel_Price</th>\n",
       "      <th>CPI</th>\n",
       "      <th>Unemployment</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Store</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>446829</td>\n",
       "      <td>2.993660e+08</td>\n",
       "      <td>723.0</td>\n",
       "      <td>2065128912</td>\n",
       "      <td>560723.35</td>\n",
       "      <td>34616.513</td>\n",
       "      <td>2.118369e+06</td>\n",
       "      <td>74717.345</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>453767</td>\n",
       "      <td>2.973940e+08</td>\n",
       "      <td>727.0</td>\n",
       "      <td>2098567422</td>\n",
       "      <td>633722.02</td>\n",
       "      <td>32764.265</td>\n",
       "      <td>1.311579e+06</td>\n",
       "      <td>60970.545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>438511</td>\n",
       "      <td>2.872952e+08</td>\n",
       "      <td>701.0</td>\n",
       "      <td>2001546774</td>\n",
       "      <td>575782.53</td>\n",
       "      <td>34016.298</td>\n",
       "      <td>1.855545e+06</td>\n",
       "      <td>86170.930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>463765</td>\n",
       "      <td>2.844811e+08</td>\n",
       "      <td>738.0</td>\n",
       "      <td>2282970690</td>\n",
       "      <td>557680.60</td>\n",
       "      <td>34107.434</td>\n",
       "      <td>1.337375e+06</td>\n",
       "      <td>72921.892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>451852</td>\n",
       "      <td>2.735476e+08</td>\n",
       "      <td>722.0</td>\n",
       "      <td>2055439120</td>\n",
       "      <td>691853.55</td>\n",
       "      <td>32693.814</td>\n",
       "      <td>2.190477e+06</td>\n",
       "      <td>77559.623</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Dept  Weekly_Sales  IsHoliday        Size  Temperature  Fuel_Price  \\\n",
       "Store                                                                         \n",
       "20     446829  2.993660e+08      723.0  2065128912    560723.35   34616.513   \n",
       "4      453767  2.973940e+08      727.0  2098567422    633722.02   32764.265   \n",
       "14     438511  2.872952e+08      701.0  2001546774    575782.53   34016.298   \n",
       "13     463765  2.844811e+08      738.0  2282970690    557680.60   34107.434   \n",
       "2      451852  2.735476e+08      722.0  2055439120    691853.55   32693.814   \n",
       "\n",
       "                CPI  Unemployment  \n",
       "Store                              \n",
       "20     2.118369e+06     74717.345  \n",
       "4      1.311579e+06     60970.545  \n",
       "14     1.855545e+06     86170.930  \n",
       "13     1.337375e+06     72921.892  \n",
       "2      2.190477e+06     77559.623  "
      ]
     },
     "execution_count": 205,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['Store']).sum().sort_values(by=['Weekly_Sales'], ascending=False).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Перейду к графику"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 376,
   "metadata": {},
   "outputs": [],
   "source": [
    "task6df = df.groupby(['Store'], as_index=False).sum().sort_values(by=['Weekly_Sales'], ascending=False).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 380,
   "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": [
    "height = task6df['Weekly_Sales']\n",
    "bars = task6df['Store']\n",
    "y_pos = [i for i in range(5)]\n",
    "plt.bar(y_pos, height, color='orange', edgecolor='black')\n",
    " \n",
    "# Create names on the x-axis\n",
    "plt.xticks(y_pos, bars)\n",
    "plt.title('Best seller stores', fontsize=16)\n",
    "plt.xlabel('Stores', fontsize=12)\n",
    "plt.ylabel('Sales, $*10^8$', fontsize=12)\n",
    "# Show graphic\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Пункт 7"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Новый DF в котором только департаменты типа А"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "        Store        Date  Dept  Weekly_Sales  IsHoliday Type    Size  \\\n",
       "0           1  2010-02-05     1      24924.50      False    A  151315   \n",
       "1           1  2010-02-12     1      46039.49       True    A  151315   \n",
       "2           1  2010-02-19     1      41595.55      False    A  151315   \n",
       "3           1  2010-02-26     1      19403.54      False    A  151315   \n",
       "4           1  2010-03-05     1      21827.90      False    A  151315   \n",
       "...       ...         ...   ...           ...        ...  ...     ...   \n",
       "387983     41  2012-09-07    99         20.12       True    A  196321   \n",
       "387984     41  2012-09-14    99          0.10      False    A  196321   \n",
       "387985     41  2012-09-21    99          0.08      False    A  196321   \n",
       "387986     41  2012-10-05    99        934.88      False    A  196321   \n",
       "387987     41  2012-10-12    99        230.03      False    A  196321   \n",
       "\n",
       "        Temperature  Fuel_Price         CPI  Unemployment  \n",
       "0             42.31       2.572  211.096358         8.106  \n",
       "1             38.51       2.548  211.242170         8.106  \n",
       "2             39.93       2.514  211.289143         8.106  \n",
       "3             46.63       2.561  211.319643         8.106  \n",
       "4             46.50       2.625  211.350143         8.106  \n",
       "...             ...         ...         ...           ...  \n",
       "387983        67.41       3.596  198.095048         6.432  \n",
       "387984        59.39       3.659  198.126718         6.432  \n",
       "387985        59.81       3.765  198.358523         6.432  \n",
       "387986        50.14       3.779  198.822132         6.195  \n",
       "387987        39.38       3.760  199.053937         6.195  \n",
       "\n",
       "[213807 rows x 11 columns]"
      ]
     },
     "execution_count": 224,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newdf = df.groupby('Type').get_group('A')\n",
    "newdf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Выделим групы департаментов и отсортируем по Weekly_Sales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "      <td>553721900</td>\n",
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       "      <td>3.034455e+08</td>\n",
       "      <td>220.0</td>\n",
       "      <td>553721900</td>\n",
       "      <td>190644.62</td>\n",
       "      <td>10439.512</td>\n",
       "      <td>544961.156541</td>\n",
       "      <td>24406.315</td>\n",
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       "      <th>90</th>\n",
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       "      <td>2.203874e+08</td>\n",
       "      <td>220.0</td>\n",
       "      <td>553721900</td>\n",
       "      <td>190644.62</td>\n",
       "      <td>10439.512</td>\n",
       "      <td>544961.156541</td>\n",
       "      <td>24406.315</td>\n",
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       "      <th>38</th>\n",
       "      <td>69438</td>\n",
       "      <td>2.031445e+08</td>\n",
       "      <td>220.0</td>\n",
       "      <td>553721900</td>\n",
       "      <td>190644.62</td>\n",
       "      <td>10439.512</td>\n",
       "      <td>544961.156541</td>\n",
       "      <td>24406.315</td>\n",
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       "      <th>72</th>\n",
       "      <td>60165</td>\n",
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       "      <td>201.0</td>\n",
       "      <td>543016250</td>\n",
       "      <td>170806.55</td>\n",
       "      <td>9530.754</td>\n",
       "      <td>499039.165949</td>\n",
       "      <td>22189.284</td>\n",
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       "      <th>91</th>\n",
       "      <td>69438</td>\n",
       "      <td>1.679659e+08</td>\n",
       "      <td>220.0</td>\n",
       "      <td>553721900</td>\n",
       "      <td>190644.62</td>\n",
       "      <td>10439.512</td>\n",
       "      <td>544961.156541</td>\n",
       "      <td>24406.315</td>\n",
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       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>69438</td>\n",
       "      <td>1.675846e+08</td>\n",
       "      <td>220.0</td>\n",
       "      <td>553721900</td>\n",
       "      <td>190644.62</td>\n",
       "      <td>10439.512</td>\n",
       "      <td>544961.156541</td>\n",
       "      <td>24406.315</td>\n",
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       "      <th>2</th>\n",
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       "      <td>1.624953e+08</td>\n",
       "      <td>220.0</td>\n",
       "      <td>553721900</td>\n",
       "      <td>190644.62</td>\n",
       "      <td>10439.512</td>\n",
       "      <td>544961.156541</td>\n",
       "      <td>24406.315</td>\n",
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       "      <th>94</th>\n",
       "      <td>69438</td>\n",
       "      <td>1.595790e+08</td>\n",
       "      <td>220.0</td>\n",
       "      <td>553721900</td>\n",
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       "      <td>544961.156541</td>\n",
       "      <td>24406.315</td>\n",
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       "      <th>93</th>\n",
       "      <td>69438</td>\n",
       "      <td>1.353205e+08</td>\n",
       "      <td>220.0</td>\n",
       "      <td>553721900</td>\n",
       "      <td>190644.62</td>\n",
       "      <td>10439.512</td>\n",
       "      <td>544961.156541</td>\n",
       "      <td>24406.315</td>\n",
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      "text/plain": [
       "      Store  Weekly_Sales  IsHoliday       Size  Temperature  Fuel_Price  \\\n",
       "Dept                                                                       \n",
       "92    69438  3.503512e+08      220.0  553721900    190644.62   10439.512   \n",
       "95    69438  3.034455e+08      220.0  553721900    190644.62   10439.512   \n",
       "90    69438  2.203874e+08      220.0  553721900    190644.62   10439.512   \n",
       "38    69438  2.031445e+08      220.0  553721900    190644.62   10439.512   \n",
       "72    60165  1.774588e+08      201.0  543016250    170806.55    9530.754   \n",
       "91    69438  1.679659e+08      220.0  553721900    190644.62   10439.512   \n",
       "40    69438  1.675846e+08      220.0  553721900    190644.62   10439.512   \n",
       "2     69438  1.624953e+08      220.0  553721900    190644.62   10439.512   \n",
       "94    69438  1.595790e+08      220.0  553721900    190644.62   10439.512   \n",
       "93    69438  1.353205e+08      220.0  553721900    190644.62   10439.512   \n",
       "\n",
       "                CPI  Unemployment  \n",
       "Dept                               \n",
       "92    544961.156541     24406.315  \n",
       "95    544961.156541     24406.315  \n",
       "90    544961.156541     24406.315  \n",
       "38    544961.156541     24406.315  \n",
       "72    499039.165949     22189.284  \n",
       "91    544961.156541     24406.315  \n",
       "40    544961.156541     24406.315  \n",
       "2     544961.156541     24406.315  \n",
       "94    544961.156541     24406.315  \n",
       "93    544961.156541     24406.315  "
      ]
     },
     "execution_count": 232,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newdf.groupby(['Dept']).sum().sort_values(by=['Weekly_Sales'], ascending=False).head(n=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Перейду к графику"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 343,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_depts = newdf.groupby(['Dept'], as_index=False).sum().sort_values(by=['Weekly_Sales'], ascending=False).head(n=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 382,
   "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",
    "height = df_depts['Weekly_Sales']\n",
    "bars = df_depts['Dept']\n",
    "y_pos = [i for i in range(10)]\n",
    "plt.bar(y_pos, height, color='orange', edgecolor='black')\n",
    " \n",
    "# Create names on the x-axis\n",
    "plt.xticks(y_pos, bars)\n",
    "plt.title('Best seller departments', fontsize=16)\n",
    "plt.xlabel('Departments', fontsize=12)\n",
    "plt.ylabel('Sales, $*10^8$', fontsize=12)\n",
    "# Show graphic\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Парадокс дней рождений (30 баллов)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Прочитайте на википедии про [парадокс дней рождений](https://ru.wikipedia.org/wiki/Парадокс_дней_рождения). В этой задаче нам предстоит его проверить. Итак, в произвольной группе из  $50$ человек вероятность того, что хотя бы у двоих людей дни рождения совпадут, равна  $0.97$. Но это всё только в теории. При решении задачи мы предполагаем, что рождения людей равномерно распределены в течение года. Однако на практике это может быть не так. В связи с этим реальная вероятность может отличаться от теоретической.\n",
    "\n",
    "В этой задачке вам предстоит проверить парадокс дней рождений на эмпирических данных. От вас требуется:\n",
    "1. Выбрать какое-то большое сообщество в Vk. С помощью VkAPI выкачать инфорацию по ее подписчикам (самое главное это дата рождения, а также id пользователя, который будет его характеризовать. Не оставляйте свой токен \n",
    "2. Удалите из данных всех, кто не указал когда он родился. Приведите колонку с датами рождений к удобному, однообразному виду.\n",
    "3. Постройте гистограмму, на которой было бы понятно, в какой месяц родилось какое количество людей. Правда ли, что рождаемость людей в течение года распределена равномерно?\n",
    "4. Эмпирически оцените вероятность того, что в группе из 50 произвольных людей найдутся хотя бы двое с одинаковым днём рождения. Для этого напишите цикл, в ходе которого из таблички будет делаться подвыборка из 50 строк. Для этих 50 строк внутри условия вы должны проверить совпадение дней рождений. Если совпало, то нужно запомнить это в переменную счётчик, которую вы впоследствии, чтобы получить вероятность, поделите на длину цикла.\n",
    "\n",
    "**Обратите внимание, что часто придётся прогонять один и тот же код. Не надо его копировать. Оформите его в виде функции.** "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Решение пунктов 1, 2 и 3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Прежде чем запустить скрипт, посмотрите гистограмму снизу. Потому иначе у вас будет fake credentials и/или captcha needed и вам придется вводить свои данные от вк :):):):). BTW, приятная таблица находится в пункте 4 в pd dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "All data saved to csv | vk_data.csv\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 vk_api\n",
    "import random\n",
    "import csv\n",
    "import time\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline \n",
    "\n",
    "import pandas as pd\n",
    "# csv first\n",
    "with open('vk_data.csv', 'w') as new_file:\n",
    "                # csv\n",
    "                fieldnames = ['id', 'bdate', 'bmonth', 'byear', 'dandm']\n",
    "\n",
    "                csv_writer = csv.DictWriter(new_file, fieldnames=fieldnames, delimiter=',')\n",
    "\n",
    "                csv_writer.writeheader()\n",
    "\n",
    "                newDict = dict()\n",
    "                # parsing\n",
    "                vk_session = vk_api.VkApi('username', 'password')\n",
    "                vk_session.auth()\n",
    "\n",
    "                vk = vk_session.get_api()\n",
    "                \n",
    "                vk_group = vk.groups.getMembers(group_id = 'mudakoff', fields = 'bdate')\n",
    "                \n",
    "                \n",
    "                for i in range(0, 20):\n",
    "                    vk_group = vk.groups.getMembers(group_id = 'mudakoff', offset = 1000 * i,  fields = 'bdate')\n",
    "                    for k in range(0, 1000):\n",
    "                        try:\n",
    "                            new_file.write(str(vk_group['items'][k][\"id\"]) + ',' + str(vk_group['items'][k][\"bdate\"]).replace('.', ','))\n",
    "                            new_file.write('\\n')\n",
    "                        except:\n",
    "                            pass\n",
    "                \n",
    "                print('All data saved to csv | vk_data.csv')\n",
    "                \n",
    "                # task 3\n",
    "                my_csv = pd.read_csv('vk_data.csv')\n",
    "                column = my_csv.bmonth\n",
    "                a = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']\n",
    "                ypos = np.arange(len(a))\n",
    "                # histogram\n",
    "                plt.hist(my_csv.bmonth, rwidth=2, histtype='bar', color='green', alpha=1, edgecolor='black', bins=np.arange(1,np.max(my_csv.bmonth)+2)-0.5)\n",
    "                plt.xlabel('Months')\n",
    "                plt.xticks(ypos + 1, a)\n",
    "                plt.ylabel('Frequency')\n",
    "                plt.title('Birthday problem')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ответ на вопрос про равновероятность: Дни рождения по месяцам не являются равновероятнотными событиями, что в целом дотстаточно логично - это всего лишь предпосылка для решения проблемы дней рождений. Очевидно, что будут наблюдаться различные сезонные являения, для различных локаций. Почему-то июль наиболее популярный месяц для дня рождения подписчиков МДК."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Пункт 4"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Его делаю отдельно не просто так. За большое количество запросов ВК может начать выдавать Каптчу из-за чего выполнение скрипта будет невозможно."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "metadata": {},
   "outputs": [],
   "source": [
    "import random\n",
    "import csv\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline \n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "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>id</th>\n",
       "      <th>bdate</th>\n",
       "      <th>bmonth</th>\n",
       "      <th>dandm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>177</td>\n",
       "      <td>23</td>\n",
       "      <td>4</td>\n",
       "      <td>23,4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>345</td>\n",
       "      <td>19</td>\n",
       "      <td>10</td>\n",
       "      <td>19,10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1080</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>4,7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1232</td>\n",
       "      <td>16</td>\n",
       "      <td>6</td>\n",
       "      <td>16,6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1295</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6,6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     id  bdate  bmonth  dandm\n",
       "0   177     23       4   23,4\n",
       "1   345     19      10  19,10\n",
       "2  1080      4       7    4,7\n",
       "3  1232     16       6   16,6\n",
       "4  1295      6       6    6,6"
      ]
     },
     "execution_count": 277,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "counter = 0\n",
    "# csv via pandas\n",
    "df = pd.read_csv('vk_data.csv')\n",
    "# firstly i made head with date and month('dandm'), now let's put data to this column\n",
    "df[\"dandm\"] = df['bdate'].astype(str) + ',' + df['bmonth'].astype(str)\n",
    "df = df.drop(['byear'], axis=1)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 278,
   "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>id</th>\n",
       "      <th>bdate</th>\n",
       "      <th>bmonth</th>\n",
       "      <th>dandm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>14584</th>\n",
       "      <td>733361</td>\n",
       "      <td>24</td>\n",
       "      <td>11</td>\n",
       "      <td>24,11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14585</th>\n",
       "      <td>733595</td>\n",
       "      <td>25</td>\n",
       "      <td>1</td>\n",
       "      <td>25,1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14586</th>\n",
       "      <td>733653</td>\n",
       "      <td>18</td>\n",
       "      <td>5</td>\n",
       "      <td>18,5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14587</th>\n",
       "      <td>733732</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>6,8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14588</th>\n",
       "      <td>733733</td>\n",
       "      <td>21</td>\n",
       "      <td>12</td>\n",
       "      <td>21,12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           id  bdate  bmonth  dandm\n",
       "14584  733361     24      11  24,11\n",
       "14585  733595     25       1   25,1\n",
       "14586  733653     18       5   18,5\n",
       "14587  733732      6       8    6,8\n",
       "14588  733733     21      12  21,12"
      ]
     },
     "execution_count": 278,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 281,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv('vk_data.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Проверим парадокс"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 279,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Вероятность: 0.972\n"
     ]
    }
   ],
   "source": [
    "# choose 50 rows with dandm data\n",
    "fifty = df[\"dandm\"].sample(n = 50)\n",
    "for i in range(0, 1000):\n",
    "    fifty = df[\"dandm\"].sample(n = 50)\n",
    "    for j in fifty.duplicated():\n",
    "        if j == True:\n",
    "            counter = counter + 1\n",
    "            break\n",
    "print('Вероятность:', counter / 1000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Вероятность совпадает с той, которая в парадоксе дне рождений. (В районе $97%$)"
   ]
  }
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