cars_notebook.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "adfacbc4",
   "metadata": {},
   "source": [
    "<h1><center>Аналіз реєстру ТЗ від ГСЦ МВС за 2022 рік</center></h1>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4bbde487",
   "metadata": {},
   "source": [
    "<h3><center>Набір даних \"Дані щодо транспортних засобів за 2022 рік\" завантажено з <a href=\"https://data.gov.ua/dataset/06779371-308f-42d7-895e-5a39833375f0\">data.gov.ua</a></center></h3>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "56865c79",
   "metadata": {},
   "source": [
    "#### Імпорти"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "607493e5",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d19c9a16",
   "metadata": {},
   "source": [
    "#### Константи"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "94b18c9c",
   "metadata": {},
   "outputs": [],
   "source": [
    "CURRENT_YEAR = 2023\n",
    "\n",
    "REESTR_PATH = \"datasets/tz_opendata_z01012022_po01012023.csv\"\n",
    "LP_OLD_PATH = \"datasets/old_license_plates.csv\"\n",
    "LP_NEW_PATH = \"datasets/new_license_plates.csv\"\n",
    "\n",
    "NEW_CARS_PATH = \"datasets/new_cars.csv\"\n",
    "USED_FOREIGN_CARS_PATH = \"datasets/used_foreign_cars.csv\"\n",
    "USED_CARS_PATH = \"datasets/used_cars.csv\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "50df8b9c",
   "metadata": {},
   "source": [
    "## <center>1. Підготовка даних</center>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6e94a15e",
   "metadata": {},
   "source": [
    "#### 1.1 Завантаження даних"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3100ab2a",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Читання значень лише з обраних стовпчиків\n",
    "cols = [\"BRAND\", \n",
    "        \"MODEL\",\n",
    "        \"OPER_NAME\",\n",
    "        \"OPER_CODE\",\n",
    "        \"VIN\", \n",
    "        \"MAKE_YEAR\",\n",
    "        \"COLOR\", \n",
    "        \"KIND\", \n",
    "        \"BODY\", \n",
    "        \"FUEL\", \n",
    "        \"CAPACITY\",\n",
    "        \"N_REG_NEW\"]\n",
    "\n",
    "df = pd.read_csv(REESTR_PATH, \n",
    "                 sep=\";\", \n",
    "                 low_memory=False, \n",
    "                 on_bad_lines=\"skip\",\n",
    "                 usecols=cols)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b3b4d06",
   "metadata": {},
   "source": [
    "#### 1.2 Очищення даних"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "16f76850",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Обрання записів про опреції, що пов'язані з купівлею-продажем ТЗ\n",
    "df = df[df[\"OPER_CODE\"].isin([315, 308, 71, 70, 100, 105])]\n",
    "\n",
    "# Обрання записів лише про легкові авто\n",
    "df = df[df[\"KIND\"] == \"ЛЕГКОВИЙ\"]\n",
    "\n",
    "# Видалення рядків з неповними даними\n",
    "df = df.dropna()\n",
    "\n",
    "# Видалення дублікатів\n",
    "df = df.drop_duplicates()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8360c1c",
   "metadata": {},
   "source": [
    "#### 1.3 Доповнення даних"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "af83445b",
   "metadata": {},
   "source": [
    "Визначення класу ТЗ:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "017a898d",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_class(row):\n",
    "    if row[\"CAPACITY\"] < 1200:\n",
    "        return \"A\"\n",
    "    elif 1200 <= row[\"CAPACITY\"] < 1800:\n",
    "        return \"B\"\n",
    "    elif 1800 <= row[\"CAPACITY\"] < 3500:\n",
    "        return \"C\"\n",
    "    else:\n",
    "        return \"D\"\n",
    "\n",
    "df[\"CLASS\"] = df.apply(get_class, axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5923328b",
   "metadata": {},
   "source": [
    "Визначення області, у якій зареєстровано ТЗ:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "445aa1ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Виокремлення коду з номерного знаку\n",
    "def get_code(row):\n",
    "    return row[\"N_REG_NEW\"][:2]\n",
    "\n",
    "df[\"CODE\"] = df.apply(get_code, axis=1)\n",
    "\n",
    "# Відповідність між кодом та областю України\n",
    "lp_old = pd.read_csv(LP_OLD_PATH)\n",
    "lp_new = pd.read_csv(LP_NEW_PATH)\n",
    "lp = pd.concat([lp_old, lp_new])\n",
    "\n",
    "# Об'єднання\n",
    "df = df.merge(lp, on=\"CODE\", how=\"left\")\n",
    "\n",
    "# Характеристика для авто з іноземними номерами\n",
    "df[\"DISTRICT\"] = df[\"DISTRICT\"].fillna(\"ІНША КРАЇНА\")\n",
    "\n",
    "# Видалення ствопчика з кодом\n",
    "df = df.drop(\"CODE\", axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d9fb119b",
   "metadata": {},
   "source": [
    "#### 1.4 Збереження обробленого набору даних у файл"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ceed6f1",
   "metadata": {},
   "source": [
    "Всі авто, над якими здійснювали операцію реєстрації (а отже і купівлі-продажу):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "87e373f8",
   "metadata": {},
   "outputs": [],
   "source": [
    "purchased_cars = df[df[\"OPER_CODE\"].isin([315, 308])].drop([\"OPER_CODE\", \"OPER_NAME\"], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c297db9",
   "metadata": {},
   "source": [
    "Нові авто, які завезли з-за кордону в автосалони:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "5c44cb11",
   "metadata": {},
   "outputs": [],
   "source": [
    "new_cars = df[df[\"OPER_CODE\"] == 105].drop([\"OPER_CODE\", \"OPER_NAME\"], axis=1)\n",
    "new_cars.to_csv(NEW_CARS_PATH, index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ede2684",
   "metadata": {},
   "source": [
    "Привезені з-за кордону вживані авто:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "53dc70ed",
   "metadata": {},
   "outputs": [],
   "source": [
    "used_foreign_cars = df[df[\"OPER_CODE\"].isin([70, 71, 100])].drop([\"OPER_CODE\", \"OPER_NAME\"], axis=1)\n",
    "used_foreign_cars.to_csv(USED_FOREIGN_CARS_PATH, index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ccfa0e78",
   "metadata": {},
   "source": [
    "Всі авто, які ввезено з-за кордону:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "29a0f1b4",
   "metadata": {},
   "outputs": [],
   "source": [
    "foreign_cars = pd.concat([new_cars, used_foreign_cars])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cf7d6963",
   "metadata": {},
   "source": [
    "Вживані авто в Україні:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "26c6cfd6",
   "metadata": {},
   "outputs": [],
   "source": [
    "used_cars = purchased_cars[~purchased_cars[\"VIN\"].isin(foreign_cars[\"VIN\"])]\n",
    "used_cars.to_csv(USED_CARS_PATH, index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "82cde28a",
   "metadata": {},
   "source": [
    "## <center>2. Аналіз даних</center>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4cc0925c",
   "metadata": {},
   "source": [
    "#### 2.1 Завантаження оброблених даних"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "8c41f766",
   "metadata": {},
   "outputs": [],
   "source": [
    "new_cars = pd.read_csv(NEW_CARS_PATH)\n",
    "used_cars = pd.read_csv(USED_CARS_PATH)\n",
    "used_foreign_cars = pd.read_csv(USED_FOREIGN_CARS_PATH)\n",
    "\n",
    "# Всі авто\n",
    "all_cars = pd.concat([new_cars, used_foreign_cars, used_cars])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0fcb7bdb",
   "metadata": {},
   "source": [
    "#### 2.2 Структура ринку автомобілів"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "564ca9fe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Дані для діаграми\n",
    "new_cars_quantity = new_cars.shape[0]\n",
    "used_cars_quantity = used_cars.shape[0]\n",
    "used_foreign_cars_quantity = used_foreign_cars.shape[0]\n",
    "\n",
    "data = [new_cars_quantity, used_cars_quantity, used_foreign_cars_quantity]\n",
    "\n",
    "# Підписи на діаграмі\n",
    "labels = [\"Нові авто\", \"Вживані авто\", \"Ввезені авто\"]\n",
    "\n",
    "# Кольори для діаграми\n",
    "colors = sns.color_palette('pastel')[0:5]\n",
    "\n",
    "# PIE CHART\n",
    "plt.pie(data, labels=labels, colors=colors, autopct='%.0f%%')\n",
    "plt.savefig(\"diagrams/market.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "52d8abdd",
   "metadata": {},
   "source": [
    "#### 2.3 Поширеність класів автомобілів"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "40686748",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = all_cars.groupby(\"CLASS\")[\"VIN\"].count().sort_values(ascending=False).reset_index().rename(columns={\"VIN\": \"COUNT\"})\n",
    "\n",
    "# Кольори діаграми\n",
    "colors = sns.color_palette('pastel')[0:5]\n",
    "\n",
    "# PIE CHART\n",
    "plt.pie(data[\"COUNT\"], labels=data[\"CLASS\"], colors=colors, autopct='%.0f%%')\n",
    "plt.savefig(\"diagrams/classes.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5d1ed80b",
   "metadata": {},
   "source": [
    "#### 2.4 ТОП-10 моделей авто, які купували в автосалонах"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "00de33cc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BRAND       MODEL                 \n",
       "RENAULT     DUSTER                    1857\n",
       "TOYOTA      RAV4                      1523\n",
       "            RAV-4 HYBRID               961\n",
       "            LAND CRUISER PRADO 150     903\n",
       "HYUNDAI     TUCSON                     834\n",
       "CHERY       TIGGO 4                    714\n",
       "MITSUBISHI  OUTLANDER                  687\n",
       "KIA         SPORTAGE                   614\n",
       "TOYOTA      CAMRY                      581\n",
       "BMW         X5                         576\n",
       "Name: VIN, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_cars.groupby([\"BRAND\", \"MODEL\"])[\"VIN\"].count().sort_values(ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9081ca38",
   "metadata": {},
   "source": [
    "#### 2.5 ТОП-10 моделей авто, які купували на вторинному ринку"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e513d012",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BRAND       MODEL  \n",
       "VOLKSWAGEN  PASSAT     19123\n",
       "DAEWOO      LANOS      12720\n",
       "VOLKSWAGEN  GOLF       11989\n",
       "SKODA       OCTAVIA    11120\n",
       "CHEVROLET   AVEO        8613\n",
       "RENAULT     MEGANE      8450\n",
       "FORD        FOCUS       8439\n",
       "TOYOTA      CAMRY       8177\n",
       "SKODA       FABIA       8063\n",
       "OPEL        ASTRA       6553\n",
       "Name: VIN, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "used_cars.groupby([\"BRAND\", \"MODEL\"])[\"VIN\"].count().sort_values(ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a65fb43",
   "metadata": {},
   "source": [
    "Середній вік придбаних на вторинному ринку ТЗ:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "840f38f9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Середній вік придбаних на вторинному ринку ТЗ:  15.41 років\n"
     ]
    }
   ],
   "source": [
    "mean_year = used_cars[\"MAKE_YEAR\"].mean()\n",
    "mean_age = CURRENT_YEAR - mean_year\n",
    "\n",
    "print(f\"Середній вік придбаних на вторинному ринку ТЗ: {mean_age: .2f} років\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "190b5960",
   "metadata": {},
   "source": [
    "#### 2.6 ТОП-10 моделей авто, які ввозили з-за кордону"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c4d9cb5e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BRAND       MODEL        \n",
       "VOLKSWAGEN  PASSAT           22395\n",
       "            GOLF             20695\n",
       "SKODA       OCTAVIA          15115\n",
       "RENAULT     MEGANE           12585\n",
       "FORD        FOCUS             9791\n",
       "RENAULT     MEGANE SCENIC     9081\n",
       "VOLKSWAGEN  TOURAN            9077\n",
       "OPEL        ZAFIRA            8425\n",
       "AUDI        A4                8304\n",
       "OPEL        ASTRA             7172\n",
       "Name: VIN, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "used_foreign_cars.groupby([\"BRAND\", \"MODEL\"])[\"VIN\"].count().sort_values(ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a356279",
   "metadata": {},
   "source": [
    "Середній вік ввезених авто:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "aca5f259",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Середній вік ввезених ТЗ:  13.89 років\n"
     ]
    }
   ],
   "source": [
    "mean_year = used_foreign_cars[\"MAKE_YEAR\"].mean()\n",
    "mean_age = CURRENT_YEAR - mean_year\n",
    "\n",
    "print(f\"Середній вік ввезених ТЗ: {mean_age: .2f} років\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39e1b110",
   "metadata": {},
   "source": [
    "#### 2.7 Топ-10 моделей пікапів"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "d13ec787",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BRAND       MODEL   \n",
       "MITSUBISHI  L 200       522\n",
       "NISSAN      NAVARA      258\n",
       "FORD        RANGER      120\n",
       "            F 150       120\n",
       "TOYOTA      HILUX        94\n",
       "ЗАЗ         110557       75\n",
       "TOYOTA      TUNDRA       54\n",
       "VOLKSWAGEN  AMAROK       34\n",
       "DODGE       RAM-1500     29\n",
       "RAM         1500         28\n",
       "Name: VIN, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pickup_trucks = all_cars[all_cars[\"BODY\"] == \"ПІКАП\"]\n",
    "pickup_trucks.groupby([\"BRAND\", \"MODEL\"])[\"VIN\"].count().sort_values(ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d940e5a0",
   "metadata": {},
   "source": [
    "#### 2.8 Топ-10 екологічних авто"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "33229eb1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BRAND      MODEL            \n",
       "TOYOTA     RAV-4 HYBRID         1580\n",
       "           PRIUS                1222\n",
       "FORD       FUSION               1064\n",
       "TOYOTA     CAMRY                1008\n",
       "CHEVROLET  VOLT                  780\n",
       "FORD       C-MAX                 646\n",
       "TOYOTA     RAV4                  601\n",
       "HYUNDAI    SONATA                433\n",
       "VOLVO      XC90                  419\n",
       "TOYOTA     HIGHLANDER HYBRID     408\n",
       "Name: VIN, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eco_cars = all_cars[all_cars[\"FUEL\"].isin([\"ЕЛЕКТРО АБО БЕНЗИН\", \n",
    "                                          \"ЕЛЕКТРО АБО ДИЗЕЛЬНЕ ПАЛИВО\", \n",
    "                                          \"БЕНЗИН, ГАЗ АБО ЕЛЕКТРО\", \n",
    "                                          \"ЕЛЕКТРО\", \n",
    "                                          \"ГАЗ ТА ЕЛЕКТРО\"])]\n",
    "\n",
    "eco_cars.groupby([\"BRAND\", \"MODEL\"])[\"VIN\"].count().sort_values(ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92695427",
   "metadata": {},
   "source": [
    "#### 2.9 ТОП-10 люксових моделей авто"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "337ef7ca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BRAND       MODEL                 \n",
       "TOYOTA      LAND CRUISER 200          1663\n",
       "            LAND CRUISER PRADO 150     989\n",
       "PORSCHE     CAYENNE                    953\n",
       "LAND ROVER  RANGE ROVER                950\n",
       "TOYOTA      LAND CRUISER               845\n",
       "DODGE       JOURNEY                    801\n",
       "JEEP        GRAND CHEROKEE             798\n",
       "VOLKSWAGEN  TOUAREG                    581\n",
       "BMW         X5                         574\n",
       "LEXUS       LX 570                     523\n",
       "Name: VIN, dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "luxury_cars = all_cars[all_cars[\"CLASS\"] == \"D\"]\n",
    "luxury_cars.groupby([\"BRAND\", \"MODEL\"])[\"VIN\"].count().sort_values(ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac8c3748",
   "metadata": {},
   "source": [
    "#### 2.10 Машини якого кольору найчастіше купували?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "79c2d9d3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "COLOR\n",
       "СІРИЙ    352091\n",
       "Name: VIN, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_cars.groupby(\"COLOR\")[\"VIN\"].count().sort_values(ascending=False).head(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "00c375ad",
   "metadata": {},
   "source": [
    "#### 2.11 Машини з яким типом кузова найчастіше купували?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "1b2ae656",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BODY\n",
       "УНІВЕРСАЛ    482815\n",
       "Name: VIN, dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_cars.groupby(\"BODY\")[\"VIN\"].count().sort_values(ascending=False).head(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "83f3f099",
   "metadata": {},
   "source": [
    "#### 2.12 Машини з яким типом пального найчастіше купували?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "2e7e5218",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "FUEL\n",
       "БЕНЗИН    434909\n",
       "Name: VIN, dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_cars.groupby(\"FUEL\")[\"VIN\"].count().sort_values(ascending=False).head(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0c2b7005",
   "metadata": {},
   "source": [
    "#### 2.13 Машини якого бренду купували найчастіше?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "1c75462e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BRAND\n",
       "VOLKSWAGEN    141622\n",
       "RENAULT        68945\n",
       "SKODA          56831\n",
       "Name: VIN, dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_cars.groupby(\"BRAND\")[\"VIN\"].count().sort_values(ascending=False).head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f9081f4",
   "metadata": {},
   "source": [
    "#### 2.14 В яких областях України найбільш активно реєстрували ТЗ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "2de11e2d",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DISTRICT\n",
       "КИЇВ                 138048\n",
       "ЛЬВІВСЬКА             98040\n",
       "ДНІПРОПЕТРОВСЬКА      79540\n",
       "ОДЕСЬКА               63567\n",
       "КИЇВСЬКА              56832\n",
       "ВІННИЦЬКА             48948\n",
       "РІВНЕНСЬКА            46070\n",
       "ІВАНО-ФРАНКІВСЬКА     44222\n",
       "ВОЛИНСЬКА             43158\n",
       "ЗАКАРПАТСЬКА          39636\n",
       "Name: VIN, dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_cars.groupby(\"DISTRICT\")[\"VIN\"].count().sort_values(ascending=False).head(10)"
   ]
  }
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