MII_Yunusov_Niyaz/notebooks/lec1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Работа с NumPy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "matrix = \n",
      " [[4 5 0]\n",
      " [9 9 9]] \n",
      "\n",
      "tmatrix = \n",
      " [[4 9]\n",
      " [5 9]\n",
      " [0 9]] \n",
      "\n",
      "vector = \n",
      " [4 5 0 9 9 9] \n",
      "\n",
      "tvector = \n",
      " [[4]\n",
      " [5]\n",
      " [0]\n",
      " [9]\n",
      " [9]\n",
      " [9]] \n",
      "\n",
      "list_matrix = \n",
      " [array([4, 5, 0]), array([9, 9, 9])] \n",
      "\n",
      "matrix as str = \n",
      " [[4 5 0]\n",
      " [9 9 9]] \n",
      "\n",
      "matrix type is <class 'numpy.ndarray'> \n",
      "\n",
      "vector type is <class 'numpy.ndarray'> \n",
      "\n",
      "list_matrix type is <class 'list'> \n",
      "\n",
      "str_matrix type is <class 'str'> \n",
      "\n",
      "formatted_vector = \n",
      " 4; 5; 0; 9; 9; 9 \n",
      "\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "matrix = np.array([[4, 5, 0], [9, 9, 9]])\n",
    "print(\"matrix = \\n\", matrix, \"\\n\")\n",
    "\n",
    "tmatrix = matrix.T\n",
    "print(\"tmatrix = \\n\", tmatrix, \"\\n\")\n",
    "\n",
    "vector = np.ravel(matrix)\n",
    "print(\"vector = \\n\", vector, \"\\n\")\n",
    "\n",
    "tvector = np.reshape(vector, (6, 1))\n",
    "print(\"tvector = \\n\", tvector, \"\\n\")\n",
    "\n",
    "list_matrix = list(matrix)\n",
    "print(\"list_matrix = \\n\", list_matrix, \"\\n\")\n",
    "\n",
    "str_matrix = str(matrix)\n",
    "print(\"matrix as str = \\n\", str_matrix, \"\\n\")\n",
    "\n",
    "print(\"matrix type is\", type(matrix), \"\\n\")\n",
    "\n",
    "print(\"vector type is\", type(vector), \"\\n\")\n",
    "\n",
    "print(\"list_matrix type is\", type(list_matrix), \"\\n\")\n",
    "\n",
    "print(\"str_matrix type is\", type(str_matrix), \"\\n\")\n",
    "\n",
    "formatted_vector = \"; \".join(map(str, vector))\n",
    "print(\"formatted_vector = \\n\", formatted_vector, \"\\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Работа с Pandas DataFrame\n",
    "\n",
    "https://pandas.pydata.org/docs/user_guide/10min.html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Работа с данными - чтение и запись CSV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df = pd.read_csv(\"../data/car_price_prediction.csv\")\n",
    "\n",
    "df.to_csv(\"../data/test.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Работа с данными - основные команды"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 19237 entries, 0 to 19236\n",
      "Data columns (total 18 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   ID                19237 non-null  int64  \n",
      " 1   Price             19237 non-null  int64  \n",
      " 2   Levy              19237 non-null  object \n",
      " 3   Manufacturer      19237 non-null  object \n",
      " 4   Model             19237 non-null  object \n",
      " 5   Prod. year        19237 non-null  int64  \n",
      " 6   Category          19237 non-null  object \n",
      " 7   Leather interior  19237 non-null  object \n",
      " 8   Fuel type         19237 non-null  object \n",
      " 9   Engine volume     19237 non-null  object \n",
      " 10  Mileage           19237 non-null  object \n",
      " 11  Cylinders         19237 non-null  float64\n",
      " 12  Gear box type     19237 non-null  object \n",
      " 13  Drive wheels      19237 non-null  object \n",
      " 14  Doors             19237 non-null  object \n",
      " 15  Wheel             19237 non-null  object \n",
      " 16  Color             19237 non-null  object \n",
      " 17  Airbags           19237 non-null  int64  \n",
      "dtypes: float64(1), int64(4), object(13)\n",
      "memory usage: 2.6+ MB\n",
      "              count          mean            std         min         25%  \\\n",
      "ID          19237.0  4.557654e+07  936591.422799  20746880.0  45698374.0   \n",
      "Price       19237.0  1.855593e+04  190581.269684         1.0      5331.0   \n",
      "Prod. year  19237.0  2.010913e+03       5.668673      1939.0      2009.0   \n",
      "Cylinders   19237.0  4.582991e+00       1.199933         1.0         4.0   \n",
      "Airbags     19237.0  6.582627e+00       4.320168         0.0         4.0   \n",
      "\n",
      "                   50%         75%         max  \n",
      "ID          45772308.0  45802036.0  45816654.0  \n",
      "Price          13172.0     22075.0  26307500.0  \n",
      "Prod. year      2012.0      2015.0      2020.0  \n",
      "Cylinders          4.0         4.0        16.0  \n",
      "Airbags            6.0        12.0        16.0  \n",
      "         ID  Price  Levy Manufacturer    Model  Prod. year   Category  \\\n",
      "0  45654403  13328  1399        LEXUS   RX 450        2010       Jeep   \n",
      "1  44731507  16621  1018    CHEVROLET  Equinox        2011       Jeep   \n",
      "2  45774419   8467     -        HONDA      FIT        2006  Hatchback   \n",
      "3  45769185   3607   862         FORD   Escape        2011       Jeep   \n",
      "4  45809263  11726   446        HONDA      FIT        2014  Hatchback   \n",
      "\n",
      "  Fuel type Engine volume    Mileage  Cylinders Gear box type Drive wheels  \\\n",
      "0    Hybrid           3.5  186005 km        6.0     Automatic          4x4   \n",
      "1    Petrol             3  192000 km        6.0     Tiptronic          4x4   \n",
      "2    Petrol           1.3  200000 km        4.0      Variator        Front   \n",
      "3    Hybrid           2.5  168966 km        4.0     Automatic          4x4   \n",
      "4    Petrol           1.3   91901 km        4.0     Automatic        Front   \n",
      "\n",
      "    Doors             Wheel  Airbags  \n",
      "0  04-May        Left wheel       12  \n",
      "1  04-May        Left wheel        8  \n",
      "2  04-May  Right-hand drive        2  \n",
      "3  04-May        Left wheel        0  \n",
      "4  04-May        Left wheel        4  \n",
      "             ID  Price  Levy   Manufacturer    Model  Prod. year Category  \\\n",
      "19232  45798355   8467     -  MERCEDES-BENZ  CLK 200        1999    Coupe   \n",
      "19233  45778856  15681   831        HYUNDAI   Sonata        2011    Sedan   \n",
      "19234  45804997  26108   836        HYUNDAI   Tucson        2010     Jeep   \n",
      "19235  45793526   5331  1288      CHEVROLET  Captiva        2007     Jeep   \n",
      "19236  45813273    470   753        HYUNDAI   Sonata        2012    Sedan   \n",
      "\n",
      "      Fuel type Engine volume    Mileage  Cylinders Gear box type  \\\n",
      "19232       CNG     2.0 Turbo  300000 km        4.0        Manual   \n",
      "19233    Petrol           2.4  161600 km        4.0     Tiptronic   \n",
      "19234    Diesel             2  116365 km        4.0     Automatic   \n",
      "19235    Diesel             2   51258 km        4.0     Automatic   \n",
      "19236    Hybrid           2.4  186923 km        4.0     Automatic   \n",
      "\n",
      "      Drive wheels   Doors       Wheel  Airbags  \n",
      "19232         Rear  02-Mar  Left wheel        5  \n",
      "19233        Front  04-May  Left wheel        8  \n",
      "19234        Front  04-May  Left wheel        4  \n",
      "19235        Front  04-May  Left wheel        4  \n",
      "19236        Front  04-May  Left wheel       12  \n",
      "             ID  Price  Levy Manufacturer     Model  Prod. year   Category  \\\n",
      "7815   45765530      1     -         OPEL     Astra        1999      Sedan   \n",
      "16992  45772201      1  1202    CHEVROLET   Lacetti        2006  Hatchback   \n",
      "4776   45687380      3   810   VOLKSWAGEN     Jetta        2016      Sedan   \n",
      "13419  45816352      3   503       TOYOTA   Prius C        2012  Hatchback   \n",
      "14642  45816369      3    87      PORSCHE  Panamera        2011      Sedan   \n",
      "\n",
      "      Fuel type Engine volume    Mileage  Cylinders Gear box type  \\\n",
      "7815     Petrol           1.6  122231 km        4.0        Manual   \n",
      "16992    Petrol           1.6  200000 km        4.0        Manual   \n",
      "4776     Petrol     1.8 Turbo   41000 km        4.0     Automatic   \n",
      "13419    Petrol           1.5  172800 km        4.0     Automatic   \n",
      "14642    Petrol             0  196800 km        6.0     Automatic   \n",
      "\n",
      "      Drive wheels   Doors       Wheel  Airbags  \n",
      "7815         Front  04-May  Left wheel        4  \n",
      "16992        Front  04-May  Left wheel        2  \n",
      "4776         Front  04-May  Left wheel        8  \n",
      "13419        Front  04-May  Left wheel       12  \n",
      "14642         Rear  04-May  Left wheel       12  \n",
      "             ID     Price  Levy   Manufacturer              Model  Prod. year  \\\n",
      "14839  45792307    297930     -     LAND ROVER  Range Rover Vogue        2019   \n",
      "5008   45810285    308906  1694        PORSCHE                911        2016   \n",
      "1225   45795524    627220     -  MERCEDES-BENZ     G 65 AMG 63AMG        2020   \n",
      "8541   45761204    872946  2067    LAMBORGHINI               Urus        2019   \n",
      "16983  45812886  26307500     -           OPEL              Combo        1999   \n",
      "\n",
      "          Category Fuel type Engine volume   Mileage  Cylinders Gear box type  \\\n",
      "14839         Jeep    Diesel     3.0 Turbo   4500 km        8.0     Tiptronic   \n",
      "5008         Coupe    Petrol             4   8690 km        6.0     Automatic   \n",
      "1225          Jeep    Petrol     6.3 Turbo      0 km        8.0     Tiptronic   \n",
      "8541     Universal    Petrol             4   2531 km        8.0     Tiptronic   \n",
      "16983  Goods wagon    Diesel           1.7  99999 km        4.0        Manual   \n",
      "\n",
      "      Drive wheels   Doors       Wheel  Airbags  \n",
      "14839          4x4  04-May  Left wheel       12  \n",
      "5008          Rear  02-Mar  Left wheel       12  \n",
      "1225           4x4  04-May  Left wheel       12  \n",
      "8541           4x4  04-May  Left wheel        0  \n",
      "16983        Front  02-Mar  Left wheel        0  \n"
     ]
    }
   ],
   "source": [
    "df.info()\n",
    "\n",
    "print(df.describe().transpose())\n",
    "\n",
    "cleared_df = df.drop([\"Color\", \"Leather interior\"], axis=1)\n",
    "print(cleared_df.head())\n",
    "print(cleared_df.tail())\n",
    "\n",
    "sorted_df = cleared_df.sort_values(by=\"Price\")\n",
    "print(sorted_df.head())\n",
    "print(sorted_df.tail())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Работа с данными - работа с элементами"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0         RX 450\n",
      "1        Equinox\n",
      "2            FIT\n",
      "3         Escape\n",
      "4            FIT\n",
      "          ...   \n",
      "19232    CLK 200\n",
      "19233     Sonata\n",
      "19234     Tucson\n",
      "19235    Captiva\n",
      "19236     Sonata\n",
      "Name: Model, Length: 19237, dtype: object\n",
      "ID                    45809760\n",
      "Price                     2901\n",
      "Levy                       473\n",
      "Manufacturer         CHEVROLET\n",
      "Model                    Cruze\n",
      "Prod. year                2014\n",
      "Category                 Sedan\n",
      "Leather interior           Yes\n",
      "Fuel type               Petrol\n",
      "Engine volume              1.4\n",
      "Mileage               80827 km\n",
      "Cylinders                  4.0\n",
      "Gear box type        Automatic\n",
      "Drive wheels             Front\n",
      "Doors                   04-May\n",
      "Wheel               Left wheel\n",
      "Color                    Green\n",
      "Airbags                     12\n",
      "Name: 100, dtype: object\n",
      "2901\n",
      "        Model  Price\n",
      "100     Cruze   2901\n",
      "101       911    706\n",
      "102    Fusion    314\n",
      "103    GX 460  15681\n",
      "104  Sprinter  26657\n",
      "..        ...    ...\n",
      "196    Sonata    706\n",
      "197     E 350   5802\n",
      "198     Camry  25716\n",
      "199   Orlando  14426\n",
      "200     RAV 4  10820\n",
      "\n",
      "[101 rows x 2 columns]\n",
      "         ID  Price  Levy Manufacturer    Model  Prod. year   Category  \\\n",
      "0  45654403  13328  1399        LEXUS   RX 450        2010       Jeep   \n",
      "1  44731507  16621  1018    CHEVROLET  Equinox        2011       Jeep   \n",
      "2  45774419   8467     -        HONDA      FIT        2006  Hatchback   \n",
      "\n",
      "  Leather interior Fuel type Engine volume    Mileage  Cylinders  \\\n",
      "0              Yes    Hybrid           3.5  186005 km        6.0   \n",
      "1               No    Petrol             3  192000 km        6.0   \n",
      "2               No    Petrol           1.3  200000 km        4.0   \n",
      "\n",
      "  Gear box type Drive wheels   Doors             Wheel   Color  Airbags  \n",
      "0     Automatic          4x4  04-May        Left wheel  Silver       12  \n",
      "1     Tiptronic          4x4  04-May        Left wheel   Black        8  \n",
      "2      Variator        Front  04-May  Right-hand drive   Black        2  \n",
      "ID                    45654403\n",
      "Price                    13328\n",
      "Levy                      1399\n",
      "Manufacturer             LEXUS\n",
      "Model                   RX 450\n",
      "Prod. year                2010\n",
      "Category                  Jeep\n",
      "Leather interior           Yes\n",
      "Fuel type               Hybrid\n",
      "Engine volume              3.5\n",
      "Mileage              186005 km\n",
      "Cylinders                  6.0\n",
      "Gear box type        Automatic\n",
      "Drive wheels               4x4\n",
      "Doors                   04-May\n",
      "Wheel               Left wheel\n",
      "Color                   Silver\n",
      "Airbags                     12\n",
      "Name: 0, dtype: object\n",
      "         ID  Price\n",
      "3  45769185   3607\n",
      "4  45809263  11726\n",
      "         ID  Price\n",
      "3  45769185   3607\n",
      "4  45809263  11726\n"
     ]
    }
   ],
   "source": [
    "print(df[\"Model\"])\n",
    "\n",
    "print(df.loc[100])\n",
    "\n",
    "print(df.loc[100, \"Price\"])\n",
    "\n",
    "print(df.loc[100:200, [\"Model\", \"Price\"]])\n",
    "\n",
    "print(df[0:3])\n",
    "\n",
    "print(df.iloc[0])\n",
    "\n",
    "print(df.iloc[3:5, 0:2])\n",
    "\n",
    "print(df.iloc[[3, 4], [0, 1]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Работа с данными - отбор и группировка"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['LEXUS' 'CHEVROLET' 'HONDA' 'FORD' 'HYUNDAI' 'TOYOTA' 'MERCEDES-BENZ'\n",
      " 'OPEL' 'PORSCHE' 'BMW' 'JEEP' 'VOLKSWAGEN' 'AUDI' 'RENAULT' 'NISSAN'\n",
      " 'SUBARU' 'DAEWOO' 'KIA' 'MITSUBISHI' 'SSANGYONG' 'MAZDA' 'GMC' 'FIAT'\n",
      " 'INFINITI' 'ALFA ROMEO' 'SUZUKI' 'ACURA' 'LINCOLN' 'VAZ' 'GAZ' 'CITROEN'\n",
      " 'LAND ROVER' 'MINI' 'DODGE' 'CHRYSLER' 'JAGUAR' 'ISUZU' 'SKODA'\n",
      " 'DAIHATSU' 'BUICK' 'TESLA' 'CADILLAC' 'PEUGEOT' 'BENTLEY' 'VOLVO' 'სხვა'\n",
      " 'HAVAL' 'HUMMER' 'SCION' 'UAZ' 'MERCURY' 'ZAZ' 'ROVER' 'SEAT' 'LANCIA'\n",
      " 'MOSKVICH' 'MASERATI' 'FERRARI' 'SAAB' 'LAMBORGHINI' 'ROLLS-ROYCE'\n",
      " 'PONTIAC' 'SATURN' 'ASTON MARTIN' 'GREATWALL']\n",
      "LEXUS count = 982\n",
      "CHEVROLET count = 1069\n",
      "HONDA count = 977\n",
      "FORD count = 1111\n",
      "HYUNDAI count = 3769\n",
      "TOYOTA count = 3662\n",
      "MERCEDES-BENZ count = 2076\n",
      "OPEL count = 397\n",
      "PORSCHE count = 54\n",
      "BMW count = 1049\n",
      "JEEP count = 138\n",
      "VOLKSWAGEN count = 579\n",
      "AUDI count = 255\n",
      "RENAULT count = 37\n",
      "NISSAN count = 660\n",
      "SUBARU count = 275\n",
      "DAEWOO count = 91\n",
      "KIA count = 421\n",
      "MITSUBISHI count = 289\n",
      "SSANGYONG count = 441\n",
      "MAZDA count = 183\n",
      "GMC count = 15\n",
      "FIAT count = 78\n",
      "INFINITI count = 30\n",
      "ALFA ROMEO count = 4\n",
      "SUZUKI count = 76\n",
      "ACURA count = 15\n",
      "LINCOLN count = 15\n",
      "VAZ count = 48\n",
      "GAZ count = 12\n",
      "CITROEN count = 9\n",
      "LAND ROVER count = 49\n",
      "MINI count = 48\n",
      "DODGE count = 91\n",
      "CHRYSLER count = 26\n",
      "JAGUAR count = 42\n",
      "ISUZU count = 4\n",
      "SKODA count = 20\n",
      "DAIHATSU count = 13\n",
      "BUICK count = 16\n",
      "TESLA count = 1\n",
      "CADILLAC count = 14\n",
      "PEUGEOT count = 17\n",
      "BENTLEY count = 2\n",
      "VOLVO count = 19\n",
      "სხვა count = 2\n",
      "HAVAL count = 1\n",
      "HUMMER count = 5\n",
      "SCION count = 7\n",
      "UAZ count = 12\n",
      "MERCURY count = 4\n",
      "ZAZ count = 2\n",
      "ROVER count = 3\n",
      "SEAT count = 2\n",
      "LANCIA count = 1\n",
      "MOSKVICH count = 4\n",
      "MASERATI count = 4\n",
      "FERRARI count = 2\n",
      "SAAB count = 2\n",
      "LAMBORGHINI count = 1\n",
      "ROLLS-ROYCE count = 2\n",
      "PONTIAC count = 1\n",
      "SATURN count = 1\n",
      "ASTON MARTIN count = 1\n",
      "GREATWALL count = 1\n",
      "Total count =  19237\n",
      "        Model   Category  Count\n",
      "0      09-Mar      Sedan      2\n",
      "1         100       Jeep      2\n",
      "2      100 NX      Sedan      1\n",
      "3        1000       Jeep      6\n",
      "4        1000      Sedan     11\n",
      "...       ...        ...    ...\n",
      "2053    macan       Jeep      1\n",
      "2054    macan      Sedan      1\n",
      "2055  macan S       Jeep      1\n",
      "2056       tC      Coupe      3\n",
      "2057       xD  Hatchback      3\n",
      "\n",
      "[2058 rows x 3 columns]\n"
     ]
    }
   ],
   "source": [
    "s_values = df[\"Manufacturer\"].unique()\n",
    "print(s_values)\n",
    "\n",
    "s_total = 0\n",
    "for s_value in s_values:\n",
    "    count = df[df[\"Manufacturer\"] == s_value].shape[0]\n",
    "    s_total += count\n",
    "    print(s_value, \"count =\", count)\n",
    "print(\"Total count = \", s_total)\n",
    "\n",
    "print(df.groupby([\"Model\", \"Category\"]).size().reset_index(name=\"Count\"))  # type: ignore"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Визуализация - Исходные данные"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        Manufacturer    Model  Levy\n",
      "0              LEXUS   RX 450  1399\n",
      "1          CHEVROLET  Equinox  1018\n",
      "2              HONDA      FIT     -\n",
      "3               FORD   Escape   862\n",
      "4              HONDA      FIT   446\n",
      "...              ...      ...   ...\n",
      "19232  MERCEDES-BENZ  CLK 200     -\n",
      "19233        HYUNDAI   Sonata   831\n",
      "19234        HYUNDAI   Tucson   836\n",
      "19235      CHEVROLET  Captiva  1288\n",
      "19236        HYUNDAI   Sonata   753\n",
      "\n",
      "[19237 rows x 3 columns]\n"
     ]
    }
   ],
   "source": [
    "data = df[[\"Manufacturer\", \"Model\", \"Levy\"]].copy()\n",
    "data.dropna(subset=[\"Levy\"], inplace=True)\n",
    "print(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Визуализация - Сводка пяти чисел\n",
    "\n",
    "<img src=\"assets/quantile.png\" width=\"400\" style=\"background-color: white\">"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             Prod. year                                        \n",
      "                    min       q1      q2  median       q3   max\n",
      "Manufacturer                                                   \n",
      "ACURA              2001  2010.00  2012.0  2012.0  2014.00  2015\n",
      "ALFA ROMEO         2001  2001.00  2003.0  2003.0  2007.00  2013\n",
      "ASTON MARTIN       2007  2007.00  2007.0  2007.0  2007.00  2007\n",
      "AUDI               1995  2010.00  2013.0  2013.0  2014.00  2018\n",
      "BENTLEY            2012  2013.00  2014.0  2014.0  2015.00  2016\n",
      "...                 ...      ...     ...     ...      ...   ...\n",
      "VAZ                1974  1986.00  1995.5  1995.5  2004.25  2018\n",
      "VOLKSWAGEN         1980  2003.00  2012.0  2012.0  2014.00  2019\n",
      "VOLVO              1993  2000.50  2006.0  2006.0  2010.50  2014\n",
      "ZAZ                1989  1989.25  1989.5  1989.5  1989.75  1990\n",
      "სხვა               2005  2005.50  2006.0  2006.0  2006.50  2007\n",
      "\n",
      "[65 rows x 6 columns]\n",
      "             Prod. year                 \n",
      "                low_iqr    iqr  high_iqr\n",
      "Manufacturer                            \n",
      "ACURA          2004.000   4.00  2020.000\n",
      "ALFA ROMEO     1992.000   6.00  2016.000\n",
      "ASTON MARTIN   2007.000   0.00  2007.000\n",
      "AUDI           2004.000   4.00  2020.000\n",
      "BENTLEY        2010.000   2.00  2018.000\n",
      "...                 ...    ...       ...\n",
      "VAZ            1958.625  18.25  2031.625\n",
      "VOLKSWAGEN     1986.500  11.00  2030.500\n",
      "VOLVO          1985.500  10.00  2025.500\n",
      "ZAZ            1988.500   0.50  1990.500\n",
      "სხვა           2004.000   1.00  2008.000\n",
      "\n",
      "[65 rows x 3 columns]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: title={'center': 'Prod. year'}, xlabel='Manufacturer'>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def q1(x):\n",
    "    return x.quantile(0.25)\n",
    "\n",
    "\n",
    "# median = quantile(0.5)\n",
    "def q2(x):\n",
    "    return x.quantile(0.5)\n",
    "\n",
    "\n",
    "def q3(x):\n",
    "    return x.quantile(0.75)\n",
    "\n",
    "\n",
    "def iqr(x):\n",
    "    return q3(x) - q1(x)\n",
    "\n",
    "\n",
    "def low_iqr(x):\n",
    "    return max(0, q1(x) - 1.5 * iqr(x))\n",
    "\n",
    "\n",
    "def high_iqr(x):\n",
    "    return q3(x) + 1.5 * iqr(x)\n",
    "\n",
    "quantiles = df[[\"Manufacturer\", \"Prod. year\"]].groupby([\"Manufacturer\"]).aggregate([\"min\", q1, q2, \"median\", q3, \"max\"])\n",
    "print(quantiles)\n",
    "\n",
    "iqrs = (\n",
    "    df[[\"Manufacturer\", \"Prod. year\"]].groupby([\"Manufacturer\"]).aggregate([low_iqr, iqr, high_iqr])\n",
    ")\n",
    "print(iqrs)\n",
    "\n",
    "df.boxplot(column=\"Prod. year\", by=\"Manufacturer\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Визуализация - Гистограмма"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: ylabel='Frequency'>"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot.hist(column=[\"Prod. year\"], bins=80)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Визуализация - Точечная диаграмма"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Fuel type', ylabel='Category'>"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot.scatter(x=\"Fuel type\", y=\"Category\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Визуализация - Столбчатая диаграмма"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x173840b6240>"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot = df.groupby([\"Category\", \"Gear box type\"]).size().unstack().plot.bar(color=[\"pink\", \"green\"])\n",
    "plot.legend([\"Automatic\",\"Not automatic\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Визуализация - Временные ряды"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 19237 entries, 0 to 19236\n",
      "Data columns (total 4 columns):\n",
      " #   Column        Non-Null Count  Dtype         \n",
      "---  ------        --------------  -----         \n",
      " 0   Manufacturer  19237 non-null  object        \n",
      " 1   Price         19237 non-null  int64         \n",
      " 2   Prod. year    19237 non-null  int64         \n",
      " 3   date          19237 non-null  datetime64[ns]\n",
      "dtypes: datetime64[ns](1), int64(2), object(1)\n",
      "memory usage: 601.3+ KB\n",
      "        Manufacturer  Price  Prod. year       date\n",
      "0              LEXUS  13328        2010 2010-01-01\n",
      "1          CHEVROLET  16621        2011 2011-01-01\n",
      "2              HONDA   8467        2006 2006-01-01\n",
      "3               FORD   3607        2011 2011-01-01\n",
      "4              HONDA  11726        2014 2014-01-01\n",
      "...              ...    ...         ...        ...\n",
      "19232  MERCEDES-BENZ   8467        1999 1999-01-01\n",
      "19233        HYUNDAI  15681        2011 2011-01-01\n",
      "19234        HYUNDAI  26108        2010 2010-01-01\n",
      "19235      CHEVROLET   5331        2007 2007-01-01\n",
      "19236        HYUNDAI    470        2012 2012-01-01\n",
      "\n",
      "[19237 rows x 4 columns]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Locator attempting to generate 32544 ticks ([-12802.0, ..., 19741.0]), which exceeds Locator.MAXTICKS (1000).\n",
      "Locator attempting to generate 32544 ticks ([-12802.0, ..., 19741.0]), which exceeds Locator.MAXTICKS (1000).\n",
      "Locator attempting to generate 32544 ticks ([-12802.0, ..., 19741.0]), which exceeds Locator.MAXTICKS (1000).\n",
      "Locator attempting to generate 32544 ticks ([-12802.0, ..., 19741.0]), which exceeds Locator.MAXTICKS (1000).\n",
      "Locator attempting to generate 32544 ticks ([-12802.0, ..., 19741.0]), which exceeds Locator.MAXTICKS (1000).\n",
      "Locator attempting to generate 32544 ticks ([-12802.0, ..., 19741.0]), which exceeds Locator.MAXTICKS (1000).\n",
      "Locator attempting to generate 32544 ticks ([-12802.0, ..., 19741.0]), which exceeds Locator.MAXTICKS (1000).\n",
      "Locator attempting to generate 32544 ticks ([-12802.0, ..., 19741.0]), which exceeds Locator.MAXTICKS (1000).\n",
      "Locator attempting to generate 32544 ticks ([-12802.0, ..., 19741.0]), which exceeds Locator.MAXTICKS (1000).\n",
      "Locator attempting to generate 32544 ticks ([-12802.0, ..., 19741.0]), which exceeds Locator.MAXTICKS (1000).\n",
      "Locator attempting to generate 32544 ticks ([-12802.0, ..., 19741.0]), which exceeds Locator.MAXTICKS (1000).\n",
      "Locator attempting to generate 32544 ticks ([-12802.0, ..., 19741.0]), which exceeds Locator.MAXTICKS (1000).\n",
      "Locator attempting to generate 32544 ticks ([-12802.0, ..., 19741.0]), which exceeds Locator.MAXTICKS (1000).\n",
      "Locator attempting to generate 32544 ticks ([-12802.0, ..., 19741.0]), which exceeds Locator.MAXTICKS (1000).\n",
      "Locator attempting to generate 32544 ticks ([-12802.0, ..., 19741.0]), which exceeds Locator.MAXTICKS (1000).\n",
      "Locator attempting to generate 32544 ticks ([-12802.0, ..., 19741.0]), which exceeds Locator.MAXTICKS (1000).\n",
      "Locator attempting to generate 32544 ticks ([-12802.0, ..., 19741.0]), which exceeds Locator.MAXTICKS (1000).\n",
      "Locator attempting to generate 32544 ticks ([-12802.0, ..., 19741.0]), which exceeds Locator.MAXTICKS (1000).\n",
      "Locator attempting to generate 32544 ticks ([-12802.0, ..., 19741.0]), which exceeds Locator.MAXTICKS (1000).\n",
      "Locator attempting to generate 32544 ticks ([-12802.0, ..., 19741.0]), which exceeds Locator.MAXTICKS (1000).\n",
      "Locator attempting to generate 32544 ticks ([-12802.0, ..., 19741.0]), which exceeds Locator.MAXTICKS (1000).\n",
      "Locator attempting to generate 32544 ticks ([-12802.0, ..., 19741.0]), which exceeds Locator.MAXTICKS (1000).\n",
      "Locator attempting to generate 32544 ticks ([-12802.0, ..., 19741.0]), which exceeds Locator.MAXTICKS (1000).\n",
      "Locator attempting to generate 32544 ticks ([-12802.0, ..., 19741.0]), which exceeds Locator.MAXTICKS (1000).\n",
      "Locator attempting to generate 32544 ticks ([-12802.0, ..., 19741.0]), which exceeds Locator.MAXTICKS (1000).\n",
      "Locator attempting to generate 32544 ticks ([-12802.0, ..., 19741.0]), which exceeds Locator.MAXTICKS (1000).\n",
      "Locator attempting to generate 32544 ticks ([-12802.0, ..., 19741.0]), which exceeds Locator.MAXTICKS (1000).\n",
      "Locator attempting to generate 32544 ticks ([-12802.0, ..., 19741.0]), which exceeds Locator.MAXTICKS (1000).\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from datetime import datetime\n",
    "import matplotlib.dates as md\n",
    "\n",
    "dt = pd.read_csv(\"../data/car_price_prediction.csv\")\n",
    "ts = dt[[\"Manufacturer\",\"Price\",\"Prod. year\"]].copy()\n",
    "tf = ts[\"Manufacturer\"].unique()\n",
    "ts[\"date\"] = ts[\"Prod. year\"].astype(str)\n",
    "ts[\"date\"] = ts.apply(lambda row: datetime.strptime(row[\"date\"], \"%Y\"), axis=1)\n",
    "ts.info()\n",
    "\n",
    "print(ts)\n",
    "\n",
    "plot = ts.plot.line(x=\"date\", y=\"Price\")\n",
    "plot.xaxis.set_major_locator(md.DayLocator(interval=1))\n",
    "plot.xaxis.set_major_formatter(md.DateFormatter(\"%Y\"))\n",
    "plot.tick_params(axis=\"x\", labelrotation=90)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": ".venv",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}