clustring.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "48d63d12",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.cluster import KMeans\n",
    "import matplotlib.pyplot as plt\n",
    "import scipy.cluster.hierarchy as shc\n",
    "from sklearn.preprocessing import normalize, LabelEncoder\n",
    "from sklearn.neighbors import KernelDensity\n",
    "from scipy.signal import argrelextrema\n",
    "from sklearn.cluster import AgglomerativeClustering\n",
    "from sklearn.cluster import DBSCAN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "f11a498b",
   "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>Unnamed: 0</th>\n",
       "      <th>archive_by_user</th>\n",
       "      <th>brand</th>\n",
       "      <th>cat1</th>\n",
       "      <th>cat2</th>\n",
       "      <th>cat3</th>\n",
       "      <th>city</th>\n",
       "      <th>created_at</th>\n",
       "      <th>desc</th>\n",
       "      <th>id</th>\n",
       "      <th>image_count</th>\n",
       "      <th>mileage</th>\n",
       "      <th>platform</th>\n",
       "      <th>price</th>\n",
       "      <th>title</th>\n",
       "      <th>type</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>for-the-home</td>\n",
       "      <td>furniture-and-home-decore</td>\n",
       "      <td>sofa-armchair</td>\n",
       "      <td>Tehran</td>\n",
       "      <td>Tuesday 07PM</td>\n",
       "      <td>کلاسیک و شیک و استثنایی\\nچرم مالزی\\nچوب راش\\nف...</td>\n",
       "      <td>54761638662241</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>mobile</td>\n",
       "      <td>3850000</td>\n",
       "      <td>ست مبلمان و نهارخوری ٩ نفره</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>for-the-home</td>\n",
       "      <td>furniture-and-home-decore</td>\n",
       "      <td>antiques-and-art</td>\n",
       "      <td>Mashhad</td>\n",
       "      <td>Tuesday 07PM</td>\n",
       "      <td>سلام,یک عدد گلدون نخل سه طبقه ی سالم دارم با پ...</td>\n",
       "      <td>42727631379133</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>mobile</td>\n",
       "      <td>30000</td>\n",
       "      <td>گلدون مصنوعی نخل</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>vehicles</td>\n",
       "      <td>cars</td>\n",
       "      <td>heavy</td>\n",
       "      <td>Mashhad</td>\n",
       "      <td>Tuesday 07PM</td>\n",
       "      <td>سریال 43j$NUMبدون شکستگی سه حلقه لاستیک نو یک ...</td>\n",
       "      <td>63194439667302</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>mobile</td>\n",
       "      <td>-1</td>\n",
       "      <td>لودر کاتر پیلار 950</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>for-the-home</td>\n",
       "      <td>furniture-and-home-decore</td>\n",
       "      <td>sofa-armchair</td>\n",
       "      <td>Tehran</td>\n",
       "      <td>Tuesday 07PM</td>\n",
       "      <td>مبل راحتی هفت نفره شامل سه نفره یک عدد دونفره ...</td>\n",
       "      <td>19133025491169</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>mobile</td>\n",
       "      <td>600000</td>\n",
       "      <td>مبل راحتی هفت نفره بامیز جلو مبلی</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>personal</td>\n",
       "      <td>baby-and-toys</td>\n",
       "      <td>personal-toys</td>\n",
       "      <td>Karaj</td>\n",
       "      <td>Tuesday 08PM</td>\n",
       "      <td>شارژی کنترلی سویچ حمل تا 35 کیلو صندلی برای دو...</td>\n",
       "      <td>58998998335444</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>mobile</td>\n",
       "      <td>450000</td>\n",
       "      <td>ماشین شارژی</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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",
       "      <td>...</td>\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>947630</th>\n",
       "      <td>947630</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>for-the-home</td>\n",
       "      <td>building-and-garden</td>\n",
       "      <td>stove-and-heating</td>\n",
       "      <td>Shiraz</td>\n",
       "      <td>Friday 07AM</td>\n",
       "      <td>دوعدد یکی یک متری و دیگری 1.4 متری موجود است\\n...</td>\n",
       "      <td>16127180351093</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>web</td>\n",
       "      <td>180000</td>\n",
       "      <td>رادیاتور پنلی نو ترک</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>947631</th>\n",
       "      <td>947631</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>electronic-devices</td>\n",
       "      <td>audio-video</td>\n",
       "      <td>camera-camcoders</td>\n",
       "      <td>Tehran</td>\n",
       "      <td>Friday 02PM</td>\n",
       "      <td>دوربین هندی کم کنون مدل لگریا fs36\\n\\nعکاسی و ...</td>\n",
       "      <td>29805298491725</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>mobile</td>\n",
       "      <td>350000</td>\n",
       "      <td>دوربین هندی کم  کنون</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>947632</th>\n",
       "      <td>947632</td>\n",
       "      <td>True</td>\n",
       "      <td>پراید صندوق‌دار::Pride</td>\n",
       "      <td>vehicles</td>\n",
       "      <td>cars</td>\n",
       "      <td>light</td>\n",
       "      <td>Shiraz</td>\n",
       "      <td>Friday 04PM</td>\n",
       "      <td>پراید دوگانه کارخانه میباشد و کم کارکرد.روکش ....</td>\n",
       "      <td>55514317985619</td>\n",
       "      <td>3</td>\n",
       "      <td>43000.0</td>\n",
       "      <td>mobile</td>\n",
       "      <td>17400000</td>\n",
       "      <td>پراید دوگانه کارخانه مدل93</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>947633</th>\n",
       "      <td>947633</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>electronic-devices</td>\n",
       "      <td>mobile-tablet</td>\n",
       "      <td>mobile-tablet-accessories</td>\n",
       "      <td>Mashhad</td>\n",
       "      <td>Friday 05PM</td>\n",
       "      <td>مونو پد یانگ تد همراه با پایه و اینه عقبش\\nواس...</td>\n",
       "      <td>65938921533442</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>mobile</td>\n",
       "      <td>40000</td>\n",
       "      <td>مونوپد یانگ تد خیلی تمیز و نو</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>947634</th>\n",
       "      <td>947634</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>personal</td>\n",
       "      <td>jewelry-and-watches</td>\n",
       "      <td>watches</td>\n",
       "      <td>Shiraz</td>\n",
       "      <td>Friday 05PM</td>\n",
       "      <td>جواب فقط تماس و. وات ساپ</td>\n",
       "      <td>18312774360687</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>mobile</td>\n",
       "      <td>25000</td>\n",
       "      <td>ساعت مارک</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>947635 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Unnamed: 0  archive_by_user                   brand  \\\n",
       "0                0            False                     NaN   \n",
       "1                1            False                     NaN   \n",
       "2                2            False                     NaN   \n",
       "3                3            False                     NaN   \n",
       "4                4            False                     NaN   \n",
       "...            ...              ...                     ...   \n",
       "947630      947630             True                     NaN   \n",
       "947631      947631             True                     NaN   \n",
       "947632      947632             True  پراید صندوق‌دار::Pride   \n",
       "947633      947633             True                     NaN   \n",
       "947634      947634             True                     NaN   \n",
       "\n",
       "                      cat1                       cat2  \\\n",
       "0             for-the-home  furniture-and-home-decore   \n",
       "1             for-the-home  furniture-and-home-decore   \n",
       "2                 vehicles                       cars   \n",
       "3             for-the-home  furniture-and-home-decore   \n",
       "4                 personal              baby-and-toys   \n",
       "...                    ...                        ...   \n",
       "947630        for-the-home        building-and-garden   \n",
       "947631  electronic-devices                audio-video   \n",
       "947632            vehicles                       cars   \n",
       "947633  electronic-devices              mobile-tablet   \n",
       "947634            personal        jewelry-and-watches   \n",
       "\n",
       "                             cat3     city    created_at  \\\n",
       "0                   sofa-armchair   Tehran  Tuesday 07PM   \n",
       "1                antiques-and-art  Mashhad  Tuesday 07PM   \n",
       "2                           heavy  Mashhad  Tuesday 07PM   \n",
       "3                   sofa-armchair   Tehran  Tuesday 07PM   \n",
       "4                   personal-toys    Karaj  Tuesday 08PM   \n",
       "...                           ...      ...           ...   \n",
       "947630          stove-and-heating   Shiraz   Friday 07AM   \n",
       "947631           camera-camcoders   Tehran   Friday 02PM   \n",
       "947632                      light   Shiraz   Friday 04PM   \n",
       "947633  mobile-tablet-accessories  Mashhad   Friday 05PM   \n",
       "947634                    watches   Shiraz   Friday 05PM   \n",
       "\n",
       "                                                     desc              id  \\\n",
       "0       کلاسیک و شیک و استثنایی\\nچرم مالزی\\nچوب راش\\nف...  54761638662241   \n",
       "1       سلام,یک عدد گلدون نخل سه طبقه ی سالم دارم با پ...  42727631379133   \n",
       "2       سریال 43j$NUMبدون شکستگی سه حلقه لاستیک نو یک ...  63194439667302   \n",
       "3       مبل راحتی هفت نفره شامل سه نفره یک عدد دونفره ...  19133025491169   \n",
       "4       شارژی کنترلی سویچ حمل تا 35 کیلو صندلی برای دو...  58998998335444   \n",
       "...                                                   ...             ...   \n",
       "947630  دوعدد یکی یک متری و دیگری 1.4 متری موجود است\\n...  16127180351093   \n",
       "947631  دوربین هندی کم کنون مدل لگریا fs36\\n\\nعکاسی و ...  29805298491725   \n",
       "947632  پراید دوگانه کارخانه میباشد و کم کارکرد.روکش ....  55514317985619   \n",
       "947633  مونو پد یانگ تد همراه با پایه و اینه عقبش\\nواس...  65938921533442   \n",
       "947634                           جواب فقط تماس و. وات ساپ  18312774360687   \n",
       "\n",
       "        image_count  mileage platform     price  \\\n",
       "0                 5      NaN   mobile   3850000   \n",
       "1                 1      NaN   mobile     30000   \n",
       "2                 4      NaN   mobile        -1   \n",
       "3                 4      NaN   mobile    600000   \n",
       "4                 2      NaN   mobile    450000   \n",
       "...             ...      ...      ...       ...   \n",
       "947630            1      NaN      web    180000   \n",
       "947631            1      NaN   mobile    350000   \n",
       "947632            3  43000.0   mobile  17400000   \n",
       "947633            1      NaN   mobile     40000   \n",
       "947634            1      NaN   mobile     25000   \n",
       "\n",
       "                                    title type  year  \n",
       "0             ست مبلمان و نهارخوری ٩ نفره  NaN   NaN  \n",
       "1                        گلدون مصنوعی نخل  NaN   NaN  \n",
       "2                     لودر کاتر پیلار 950  NaN   NaN  \n",
       "3       مبل راحتی هفت نفره بامیز جلو مبلی  NaN   NaN  \n",
       "4                             ماشین شارژی  NaN   NaN  \n",
       "...                                   ...  ...   ...  \n",
       "947630               رادیاتور پنلی نو ترک  NaN   NaN  \n",
       "947631               دوربین هندی کم  کنون  NaN   NaN  \n",
       "947632         پراید دوگانه کارخانه مدل93  NaN  1393  \n",
       "947633      مونوپد یانگ تد خیلی تمیز و نو  NaN   NaN  \n",
       "947634                          ساعت مارک  NaN   NaN  \n",
       "\n",
       "[947635 rows x 17 columns]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv (\"/Users/mohadeseh/Documents/me/session8/data_mining/FDM_Project_dataset-99-2/divar_dataset/divar_posts_dataset.csv\") \n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6634771c",
   "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_Order</th>\n",
       "      <th>ID_Customer</th>\n",
       "      <th>ID_Item</th>\n",
       "      <th>DateTime_CartFinalize</th>\n",
       "      <th>Amount_Gross_Order</th>\n",
       "      <th>city_name_fa</th>\n",
       "      <th>Quantity_item</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2714054</td>\n",
       "      <td>469662</td>\n",
       "      <td>21386</td>\n",
       "      <td>2015-10-15 08:50:56.000</td>\n",
       "      <td>597982.0</td>\n",
       "      <td>محمود آباد</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>11104039</td>\n",
       "      <td>3063877</td>\n",
       "      <td>248497</td>\n",
       "      <td>2018-02-11 00:29:26.000</td>\n",
       "      <td>980000.0</td>\n",
       "      <td>خرمدره</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4228130</td>\n",
       "      <td>3184893</td>\n",
       "      <td>50144</td>\n",
       "      <td>2016-06-14 00:30:08.000</td>\n",
       "      <td>229358.0</td>\n",
       "      <td>قرچک</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>22225624</td>\n",
       "      <td>6888562</td>\n",
       "      <td>70208</td>\n",
       "      <td>2018-09-03 14:37:19.000</td>\n",
       "      <td>16514.0</td>\n",
       "      <td>قم</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4068771</td>\n",
       "      <td>2533490</td>\n",
       "      <td>67627</td>\n",
       "      <td>2016-05-21 11:51:02.000</td>\n",
       "      <td>133028.0</td>\n",
       "      <td>تهران</td>\n",
       "      <td>1.0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199995</th>\n",
       "      <td>12172771</td>\n",
       "      <td>4403268</td>\n",
       "      <td>445940</td>\n",
       "      <td>2018-04-03 12:07:01.000</td>\n",
       "      <td>344037.0</td>\n",
       "      <td>تهران</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199996</th>\n",
       "      <td>4425393</td>\n",
       "      <td>3745774</td>\n",
       "      <td>131645</td>\n",
       "      <td>2016-07-09 19:14:09.000</td>\n",
       "      <td>600000.0</td>\n",
       "      <td>محمدیه</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199997</th>\n",
       "      <td>6671889</td>\n",
       "      <td>4845514</td>\n",
       "      <td>215045</td>\n",
       "      <td>2017-03-08 23:47:06.000</td>\n",
       "      <td>450000.0</td>\n",
       "      <td>اصفهان</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199998</th>\n",
       "      <td>6315995</td>\n",
       "      <td>2541418</td>\n",
       "      <td>264021</td>\n",
       "      <td>2017-02-08 14:59:39.000</td>\n",
       "      <td>193486.0</td>\n",
       "      <td>همدان</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199999</th>\n",
       "      <td>7584127</td>\n",
       "      <td>1342585</td>\n",
       "      <td>89427</td>\n",
       "      <td>2017-06-17 10:08:06.000</td>\n",
       "      <td>55046.0</td>\n",
       "      <td>بندر انزلی</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>200000 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        ID_Order  ID_Customer  ID_Item    DateTime_CartFinalize  \\\n",
       "0        2714054       469662    21386  2015-10-15 08:50:56.000   \n",
       "1       11104039      3063877   248497  2018-02-11 00:29:26.000   \n",
       "2        4228130      3184893    50144  2016-06-14 00:30:08.000   \n",
       "3       22225624      6888562    70208  2018-09-03 14:37:19.000   \n",
       "4        4068771      2533490    67627  2016-05-21 11:51:02.000   \n",
       "...          ...          ...      ...                      ...   \n",
       "199995  12172771      4403268   445940  2018-04-03 12:07:01.000   \n",
       "199996   4425393      3745774   131645  2016-07-09 19:14:09.000   \n",
       "199997   6671889      4845514   215045  2017-03-08 23:47:06.000   \n",
       "199998   6315995      2541418   264021  2017-02-08 14:59:39.000   \n",
       "199999   7584127      1342585    89427  2017-06-17 10:08:06.000   \n",
       "\n",
       "        Amount_Gross_Order city_name_fa  Quantity_item  \n",
       "0                 597982.0   محمود آباد            1.0  \n",
       "1                 980000.0       خرمدره            1.0  \n",
       "2                 229358.0         قرچک            1.0  \n",
       "3                  16514.0           قم            1.0  \n",
       "4                 133028.0        تهران            1.0  \n",
       "...                    ...          ...            ...  \n",
       "199995            344037.0        تهران            2.0  \n",
       "199996            600000.0       محمدیه            1.0  \n",
       "199997            450000.0       اصفهان            1.0  \n",
       "199998            193486.0        همدان            1.0  \n",
       "199999             55046.0   بندر انزلی            1.0  \n",
       "\n",
       "[200000 rows x 7 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "orders = pd.read_csv(\"/Users/mohadeseh/Documents/me/session8/data_mining/FDM_Project_dataset-99-2/digikala_dataset/orders.csv\")\n",
    "orders"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ace25eec",
   "metadata": {},
   "source": [
    "# 1.clustring cities based on posting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "e18a979f",
   "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>Unnamed: 0</th>\n",
       "      <th>archive_by_user</th>\n",
       "      <th>brand</th>\n",
       "      <th>cat1</th>\n",
       "      <th>cat2</th>\n",
       "      <th>cat3</th>\n",
       "      <th>city</th>\n",
       "      <th>created_at</th>\n",
       "      <th>desc</th>\n",
       "      <th>id</th>\n",
       "      <th>image_count</th>\n",
       "      <th>mileage</th>\n",
       "      <th>platform</th>\n",
       "      <th>price</th>\n",
       "      <th>title</th>\n",
       "      <th>type</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>for-the-home</td>\n",
       "      <td>furniture-and-home-decore</td>\n",
       "      <td>sofa-armchair</td>\n",
       "      <td>Tehran</td>\n",
       "      <td>Tuesday 07PM</td>\n",
       "      <td>کلاسیک و شیک و استثنایی\\nچرم مالزی\\nچوب راش\\nف...</td>\n",
       "      <td>54761638662241</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>mobile</td>\n",
       "      <td>3850000</td>\n",
       "      <td>ست مبلمان و نهارخوری ٩ نفره</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>for-the-home</td>\n",
       "      <td>furniture-and-home-decore</td>\n",
       "      <td>antiques-and-art</td>\n",
       "      <td>Mashhad</td>\n",
       "      <td>Tuesday 07PM</td>\n",
       "      <td>سلام,یک عدد گلدون نخل سه طبقه ی سالم دارم با پ...</td>\n",
       "      <td>42727631379133</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>mobile</td>\n",
       "      <td>30000</td>\n",
       "      <td>گلدون مصنوعی نخل</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>vehicles</td>\n",
       "      <td>cars</td>\n",
       "      <td>heavy</td>\n",
       "      <td>Mashhad</td>\n",
       "      <td>Tuesday 07PM</td>\n",
       "      <td>سریال 43j$NUMبدون شکستگی سه حلقه لاستیک نو یک ...</td>\n",
       "      <td>63194439667302</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>mobile</td>\n",
       "      <td>-1</td>\n",
       "      <td>لودر کاتر پیلار 950</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>for-the-home</td>\n",
       "      <td>furniture-and-home-decore</td>\n",
       "      <td>sofa-armchair</td>\n",
       "      <td>Tehran</td>\n",
       "      <td>Tuesday 07PM</td>\n",
       "      <td>مبل راحتی هفت نفره شامل سه نفره یک عدد دونفره ...</td>\n",
       "      <td>19133025491169</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>mobile</td>\n",
       "      <td>600000</td>\n",
       "      <td>مبل راحتی هفت نفره بامیز جلو مبلی</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>personal</td>\n",
       "      <td>baby-and-toys</td>\n",
       "      <td>personal-toys</td>\n",
       "      <td>Karaj</td>\n",
       "      <td>Tuesday 08PM</td>\n",
       "      <td>شارژی کنترلی سویچ حمل تا 35 کیلو صندلی برای دو...</td>\n",
       "      <td>58998998335444</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>mobile</td>\n",
       "      <td>450000</td>\n",
       "      <td>ماشین شارژی</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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",
       "      <td>...</td>\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>947630</th>\n",
       "      <td>947630</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>for-the-home</td>\n",
       "      <td>building-and-garden</td>\n",
       "      <td>stove-and-heating</td>\n",
       "      <td>Shiraz</td>\n",
       "      <td>Friday 07AM</td>\n",
       "      <td>دوعدد یکی یک متری و دیگری 1.4 متری موجود است\\n...</td>\n",
       "      <td>16127180351093</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>web</td>\n",
       "      <td>180000</td>\n",
       "      <td>رادیاتور پنلی نو ترک</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>947631</th>\n",
       "      <td>947631</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>electronic-devices</td>\n",
       "      <td>audio-video</td>\n",
       "      <td>camera-camcoders</td>\n",
       "      <td>Tehran</td>\n",
       "      <td>Friday 02PM</td>\n",
       "      <td>دوربین هندی کم کنون مدل لگریا fs36\\n\\nعکاسی و ...</td>\n",
       "      <td>29805298491725</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>mobile</td>\n",
       "      <td>350000</td>\n",
       "      <td>دوربین هندی کم  کنون</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>947632</th>\n",
       "      <td>947632</td>\n",
       "      <td>True</td>\n",
       "      <td>پراید صندوق‌دار::Pride</td>\n",
       "      <td>vehicles</td>\n",
       "      <td>cars</td>\n",
       "      <td>light</td>\n",
       "      <td>Shiraz</td>\n",
       "      <td>Friday 04PM</td>\n",
       "      <td>پراید دوگانه کارخانه میباشد و کم کارکرد.روکش ....</td>\n",
       "      <td>55514317985619</td>\n",
       "      <td>3</td>\n",
       "      <td>43000.0</td>\n",
       "      <td>mobile</td>\n",
       "      <td>17400000</td>\n",
       "      <td>پراید دوگانه کارخانه مدل93</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>947633</th>\n",
       "      <td>947633</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>electronic-devices</td>\n",
       "      <td>mobile-tablet</td>\n",
       "      <td>mobile-tablet-accessories</td>\n",
       "      <td>Mashhad</td>\n",
       "      <td>Friday 05PM</td>\n",
       "      <td>مونو پد یانگ تد همراه با پایه و اینه عقبش\\nواس...</td>\n",
       "      <td>65938921533442</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>mobile</td>\n",
       "      <td>40000</td>\n",
       "      <td>مونوپد یانگ تد خیلی تمیز و نو</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>947634</th>\n",
       "      <td>947634</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>personal</td>\n",
       "      <td>jewelry-and-watches</td>\n",
       "      <td>watches</td>\n",
       "      <td>Shiraz</td>\n",
       "      <td>Friday 05PM</td>\n",
       "      <td>جواب فقط تماس و. وات ساپ</td>\n",
       "      <td>18312774360687</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>mobile</td>\n",
       "      <td>25000</td>\n",
       "      <td>ساعت مارک</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>945877 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Unnamed: 0  archive_by_user                   brand  \\\n",
       "0                0            False                     NaN   \n",
       "1                1            False                     NaN   \n",
       "2                2            False                     NaN   \n",
       "3                3            False                     NaN   \n",
       "4                4            False                     NaN   \n",
       "...            ...              ...                     ...   \n",
       "947630      947630             True                     NaN   \n",
       "947631      947631             True                     NaN   \n",
       "947632      947632             True  پراید صندوق‌دار::Pride   \n",
       "947633      947633             True                     NaN   \n",
       "947634      947634             True                     NaN   \n",
       "\n",
       "                      cat1                       cat2  \\\n",
       "0             for-the-home  furniture-and-home-decore   \n",
       "1             for-the-home  furniture-and-home-decore   \n",
       "2                 vehicles                       cars   \n",
       "3             for-the-home  furniture-and-home-decore   \n",
       "4                 personal              baby-and-toys   \n",
       "...                    ...                        ...   \n",
       "947630        for-the-home        building-and-garden   \n",
       "947631  electronic-devices                audio-video   \n",
       "947632            vehicles                       cars   \n",
       "947633  electronic-devices              mobile-tablet   \n",
       "947634            personal        jewelry-and-watches   \n",
       "\n",
       "                             cat3     city    created_at  \\\n",
       "0                   sofa-armchair   Tehran  Tuesday 07PM   \n",
       "1                antiques-and-art  Mashhad  Tuesday 07PM   \n",
       "2                           heavy  Mashhad  Tuesday 07PM   \n",
       "3                   sofa-armchair   Tehran  Tuesday 07PM   \n",
       "4                   personal-toys    Karaj  Tuesday 08PM   \n",
       "...                           ...      ...           ...   \n",
       "947630          stove-and-heating   Shiraz   Friday 07AM   \n",
       "947631           camera-camcoders   Tehran   Friday 02PM   \n",
       "947632                      light   Shiraz   Friday 04PM   \n",
       "947633  mobile-tablet-accessories  Mashhad   Friday 05PM   \n",
       "947634                    watches   Shiraz   Friday 05PM   \n",
       "\n",
       "                                                     desc              id  \\\n",
       "0       کلاسیک و شیک و استثنایی\\nچرم مالزی\\nچوب راش\\nف...  54761638662241   \n",
       "1       سلام,یک عدد گلدون نخل سه طبقه ی سالم دارم با پ...  42727631379133   \n",
       "2       سریال 43j$NUMبدون شکستگی سه حلقه لاستیک نو یک ...  63194439667302   \n",
       "3       مبل راحتی هفت نفره شامل سه نفره یک عدد دونفره ...  19133025491169   \n",
       "4       شارژی کنترلی سویچ حمل تا 35 کیلو صندلی برای دو...  58998998335444   \n",
       "...                                                   ...             ...   \n",
       "947630  دوعدد یکی یک متری و دیگری 1.4 متری موجود است\\n...  16127180351093   \n",
       "947631  دوربین هندی کم کنون مدل لگریا fs36\\n\\nعکاسی و ...  29805298491725   \n",
       "947632  پراید دوگانه کارخانه میباشد و کم کارکرد.روکش ....  55514317985619   \n",
       "947633  مونو پد یانگ تد همراه با پایه و اینه عقبش\\nواس...  65938921533442   \n",
       "947634                           جواب فقط تماس و. وات ساپ  18312774360687   \n",
       "\n",
       "        image_count  mileage platform     price  \\\n",
       "0                 5      NaN   mobile   3850000   \n",
       "1                 1      NaN   mobile     30000   \n",
       "2                 4      NaN   mobile        -1   \n",
       "3                 4      NaN   mobile    600000   \n",
       "4                 2      NaN   mobile    450000   \n",
       "...             ...      ...      ...       ...   \n",
       "947630            1      NaN      web    180000   \n",
       "947631            1      NaN   mobile    350000   \n",
       "947632            3  43000.0   mobile  17400000   \n",
       "947633            1      NaN   mobile     40000   \n",
       "947634            1      NaN   mobile     25000   \n",
       "\n",
       "                                    title type  year  \n",
       "0             ست مبلمان و نهارخوری ٩ نفره  NaN   NaN  \n",
       "1                        گلدون مصنوعی نخل  NaN   NaN  \n",
       "2                     لودر کاتر پیلار 950  NaN   NaN  \n",
       "3       مبل راحتی هفت نفره بامیز جلو مبلی  NaN   NaN  \n",
       "4                             ماشین شارژی  NaN   NaN  \n",
       "...                                   ...  ...   ...  \n",
       "947630               رادیاتور پنلی نو ترک  NaN   NaN  \n",
       "947631               دوربین هندی کم  کنون  NaN   NaN  \n",
       "947632         پراید دوگانه کارخانه مدل93  NaN  1393  \n",
       "947633      مونوپد یانگ تد خیلی تمیز و نو  NaN   NaN  \n",
       "947634                          ساعت مارک  NaN   NaN  \n",
       "\n",
       "[945877 rows x 17 columns]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df[(df['cat2'].notnull())]\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "a80a78db",
   "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>Unnamed: 0</th>\n",
       "      <th>archive_by_user</th>\n",
       "      <th>brand</th>\n",
       "      <th>cat1</th>\n",
       "      <th>cat2</th>\n",
       "      <th>cat3</th>\n",
       "      <th>city</th>\n",
       "      <th>created_at</th>\n",
       "      <th>desc</th>\n",
       "      <th>id</th>\n",
       "      <th>image_count</th>\n",
       "      <th>mileage</th>\n",
       "      <th>platform</th>\n",
       "      <th>price</th>\n",
       "      <th>title</th>\n",
       "      <th>type</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>for-the-home</td>\n",
       "      <td>furniture-and-home-decore</td>\n",
       "      <td>sofa-armchair</td>\n",
       "      <td>Tehran</td>\n",
       "      <td>Tuesday 07PM</td>\n",
       "      <td>کلاسیک و شیک و استثنایی\\nچرم مالزی\\nچوب راش\\nف...</td>\n",
       "      <td>54761638662241</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>mobile</td>\n",
       "      <td>3850000</td>\n",
       "      <td>ست مبلمان و نهارخوری ٩ نفره</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>for-the-home</td>\n",
       "      <td>furniture-and-home-decore</td>\n",
       "      <td>antiques-and-art</td>\n",
       "      <td>Mashhad</td>\n",
       "      <td>Tuesday 07PM</td>\n",
       "      <td>سلام,یک عدد گلدون نخل سه طبقه ی سالم دارم با پ...</td>\n",
       "      <td>42727631379133</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>mobile</td>\n",
       "      <td>30000</td>\n",
       "      <td>گلدون مصنوعی نخل</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>vehicles</td>\n",
       "      <td>cars</td>\n",
       "      <td>heavy</td>\n",
       "      <td>Mashhad</td>\n",
       "      <td>Tuesday 07PM</td>\n",
       "      <td>سریال 43j$NUMبدون شکستگی سه حلقه لاستیک نو یک ...</td>\n",
       "      <td>63194439667302</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>mobile</td>\n",
       "      <td>-1</td>\n",
       "      <td>لودر کاتر پیلار 950</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>for-the-home</td>\n",
       "      <td>furniture-and-home-decore</td>\n",
       "      <td>sofa-armchair</td>\n",
       "      <td>Tehran</td>\n",
       "      <td>Tuesday 07PM</td>\n",
       "      <td>مبل راحتی هفت نفره شامل سه نفره یک عدد دونفره ...</td>\n",
       "      <td>19133025491169</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>mobile</td>\n",
       "      <td>600000</td>\n",
       "      <td>مبل راحتی هفت نفره بامیز جلو مبلی</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>personal</td>\n",
       "      <td>baby-and-toys</td>\n",
       "      <td>personal-toys</td>\n",
       "      <td>Karaj</td>\n",
       "      <td>Tuesday 08PM</td>\n",
       "      <td>شارژی کنترلی سویچ حمل تا 35 کیلو صندلی برای دو...</td>\n",
       "      <td>58998998335444</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>mobile</td>\n",
       "      <td>450000</td>\n",
       "      <td>ماشین شارژی</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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",
       "      <td>...</td>\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>947630</th>\n",
       "      <td>947630</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>for-the-home</td>\n",
       "      <td>building-and-garden</td>\n",
       "      <td>stove-and-heating</td>\n",
       "      <td>Shiraz</td>\n",
       "      <td>Friday 07AM</td>\n",
       "      <td>دوعدد یکی یک متری و دیگری 1.4 متری موجود است\\n...</td>\n",
       "      <td>16127180351093</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>web</td>\n",
       "      <td>180000</td>\n",
       "      <td>رادیاتور پنلی نو ترک</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>947631</th>\n",
       "      <td>947631</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>electronic-devices</td>\n",
       "      <td>audio-video</td>\n",
       "      <td>camera-camcoders</td>\n",
       "      <td>Tehran</td>\n",
       "      <td>Friday 02PM</td>\n",
       "      <td>دوربین هندی کم کنون مدل لگریا fs36\\n\\nعکاسی و ...</td>\n",
       "      <td>29805298491725</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>mobile</td>\n",
       "      <td>350000</td>\n",
       "      <td>دوربین هندی کم  کنون</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>947632</th>\n",
       "      <td>947632</td>\n",
       "      <td>True</td>\n",
       "      <td>پراید صندوق‌دار::Pride</td>\n",
       "      <td>vehicles</td>\n",
       "      <td>cars</td>\n",
       "      <td>light</td>\n",
       "      <td>Shiraz</td>\n",
       "      <td>Friday 04PM</td>\n",
       "      <td>پراید دوگانه کارخانه میباشد و کم کارکرد.روکش ....</td>\n",
       "      <td>55514317985619</td>\n",
       "      <td>3</td>\n",
       "      <td>43000.0</td>\n",
       "      <td>mobile</td>\n",
       "      <td>17400000</td>\n",
       "      <td>پراید دوگانه کارخانه مدل93</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>947633</th>\n",
       "      <td>947633</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>electronic-devices</td>\n",
       "      <td>mobile-tablet</td>\n",
       "      <td>mobile-tablet-accessories</td>\n",
       "      <td>Mashhad</td>\n",
       "      <td>Friday 05PM</td>\n",
       "      <td>مونو پد یانگ تد همراه با پایه و اینه عقبش\\nواس...</td>\n",
       "      <td>65938921533442</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>mobile</td>\n",
       "      <td>40000</td>\n",
       "      <td>مونوپد یانگ تد خیلی تمیز و نو</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>947634</th>\n",
       "      <td>947634</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>personal</td>\n",
       "      <td>jewelry-and-watches</td>\n",
       "      <td>watches</td>\n",
       "      <td>Shiraz</td>\n",
       "      <td>Friday 05PM</td>\n",
       "      <td>جواب فقط تماس و. وات ساپ</td>\n",
       "      <td>18312774360687</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>mobile</td>\n",
       "      <td>25000</td>\n",
       "      <td>ساعت مارک</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>775301 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Unnamed: 0  archive_by_user                   brand  \\\n",
       "0                0            False                     NaN   \n",
       "1                1            False                     NaN   \n",
       "2                2            False                     NaN   \n",
       "3                3            False                     NaN   \n",
       "4                4            False                     NaN   \n",
       "...            ...              ...                     ...   \n",
       "947630      947630             True                     NaN   \n",
       "947631      947631             True                     NaN   \n",
       "947632      947632             True  پراید صندوق‌دار::Pride   \n",
       "947633      947633             True                     NaN   \n",
       "947634      947634             True                     NaN   \n",
       "\n",
       "                      cat1                       cat2  \\\n",
       "0             for-the-home  furniture-and-home-decore   \n",
       "1             for-the-home  furniture-and-home-decore   \n",
       "2                 vehicles                       cars   \n",
       "3             for-the-home  furniture-and-home-decore   \n",
       "4                 personal              baby-and-toys   \n",
       "...                    ...                        ...   \n",
       "947630        for-the-home        building-and-garden   \n",
       "947631  electronic-devices                audio-video   \n",
       "947632            vehicles                       cars   \n",
       "947633  electronic-devices              mobile-tablet   \n",
       "947634            personal        jewelry-and-watches   \n",
       "\n",
       "                             cat3     city    created_at  \\\n",
       "0                   sofa-armchair   Tehran  Tuesday 07PM   \n",
       "1                antiques-and-art  Mashhad  Tuesday 07PM   \n",
       "2                           heavy  Mashhad  Tuesday 07PM   \n",
       "3                   sofa-armchair   Tehran  Tuesday 07PM   \n",
       "4                   personal-toys    Karaj  Tuesday 08PM   \n",
       "...                           ...      ...           ...   \n",
       "947630          stove-and-heating   Shiraz   Friday 07AM   \n",
       "947631           camera-camcoders   Tehran   Friday 02PM   \n",
       "947632                      light   Shiraz   Friday 04PM   \n",
       "947633  mobile-tablet-accessories  Mashhad   Friday 05PM   \n",
       "947634                    watches   Shiraz   Friday 05PM   \n",
       "\n",
       "                                                     desc              id  \\\n",
       "0       کلاسیک و شیک و استثنایی\\nچرم مالزی\\nچوب راش\\nف...  54761638662241   \n",
       "1       سلام,یک عدد گلدون نخل سه طبقه ی سالم دارم با پ...  42727631379133   \n",
       "2       سریال 43j$NUMبدون شکستگی سه حلقه لاستیک نو یک ...  63194439667302   \n",
       "3       مبل راحتی هفت نفره شامل سه نفره یک عدد دونفره ...  19133025491169   \n",
       "4       شارژی کنترلی سویچ حمل تا 35 کیلو صندلی برای دو...  58998998335444   \n",
       "...                                                   ...             ...   \n",
       "947630  دوعدد یکی یک متری و دیگری 1.4 متری موجود است\\n...  16127180351093   \n",
       "947631  دوربین هندی کم کنون مدل لگریا fs36\\n\\nعکاسی و ...  29805298491725   \n",
       "947632  پراید دوگانه کارخانه میباشد و کم کارکرد.روکش ....  55514317985619   \n",
       "947633  مونو پد یانگ تد همراه با پایه و اینه عقبش\\nواس...  65938921533442   \n",
       "947634                           جواب فقط تماس و. وات ساپ  18312774360687   \n",
       "\n",
       "        image_count  mileage platform     price  \\\n",
       "0                 5      NaN   mobile   3850000   \n",
       "1                 1      NaN   mobile     30000   \n",
       "2                 4      NaN   mobile        -1   \n",
       "3                 4      NaN   mobile    600000   \n",
       "4                 2      NaN   mobile    450000   \n",
       "...             ...      ...      ...       ...   \n",
       "947630            1      NaN      web    180000   \n",
       "947631            1      NaN   mobile    350000   \n",
       "947632            3  43000.0   mobile  17400000   \n",
       "947633            1      NaN   mobile     40000   \n",
       "947634            1      NaN   mobile     25000   \n",
       "\n",
       "                                    title type  year  \n",
       "0             ست مبلمان و نهارخوری ٩ نفره  NaN   NaN  \n",
       "1                        گلدون مصنوعی نخل  NaN   NaN  \n",
       "2                     لودر کاتر پیلار 950  NaN   NaN  \n",
       "3       مبل راحتی هفت نفره بامیز جلو مبلی  NaN   NaN  \n",
       "4                             ماشین شارژی  NaN   NaN  \n",
       "...                                   ...  ...   ...  \n",
       "947630               رادیاتور پنلی نو ترک  NaN   NaN  \n",
       "947631               دوربین هندی کم  کنون  NaN   NaN  \n",
       "947632         پراید دوگانه کارخانه مدل93  NaN  1393  \n",
       "947633      مونوپد یانگ تد خیلی تمیز و نو  NaN   NaN  \n",
       "947634                          ساعت مارک  NaN   NaN  \n",
       "\n",
       "[775301 rows x 17 columns]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df[(df['cat3'].notnull())]\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "5b70f9cd",
   "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>Unnamed: 0</th>\n",
       "      <th>archive_by_user</th>\n",
       "      <th>brand</th>\n",
       "      <th>cat1</th>\n",
       "      <th>cat2</th>\n",
       "      <th>cat3</th>\n",
       "      <th>city</th>\n",
       "      <th>created_at</th>\n",
       "      <th>desc</th>\n",
       "      <th>id</th>\n",
       "      <th>image_count</th>\n",
       "      <th>mileage</th>\n",
       "      <th>platform</th>\n",
       "      <th>price</th>\n",
       "      <th>title</th>\n",
       "      <th>type</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>False</td>\n",
       "      <td>سایر</td>\n",
       "      <td>vehicles</td>\n",
       "      <td>cars</td>\n",
       "      <td>light</td>\n",
       "      <td>Tehran</td>\n",
       "      <td>Wednesday 04AM</td>\n",
       "      <td>دوگانه ال پی جی بیمه با تخفیف دارد \\nتودوزی نو...</td>\n",
       "      <td>59878876364899</td>\n",
       "      <td>3</td>\n",
       "      <td>180000.0</td>\n",
       "      <td>mobile</td>\n",
       "      <td>-1</td>\n",
       "      <td>پاترول شش سیلندر چهار درب</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>11</td>\n",
       "      <td>False</td>\n",
       "      <td>Nokia::نوکیا</td>\n",
       "      <td>electronic-devices</td>\n",
       "      <td>mobile-tablet</td>\n",
       "      <td>mobile-phones</td>\n",
       "      <td>Qom</td>\n",
       "      <td>Wednesday 07AM</td>\n",
       "      <td>سلام.یه گوشیه6303سالم که فقط دوتا خط کوچیک رو ...</td>\n",
       "      <td>36762902012926</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>mobile</td>\n",
       "      <td>60000</td>\n",
       "      <td>نوکیا6303</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>18</td>\n",
       "      <td>False</td>\n",
       "      <td>پژو ۴۰۵::Peugeot 405</td>\n",
       "      <td>vehicles</td>\n",
       "      <td>cars</td>\n",
       "      <td>light</td>\n",
       "      <td>Karaj</td>\n",
       "      <td>Wednesday 11AM</td>\n",
       "      <td>پژو 2000فنی تازه تعمیر بدنه بدون خطو خش تخیف ب...</td>\n",
       "      <td>51728781836074</td>\n",
       "      <td>0</td>\n",
       "      <td>290000.0</td>\n",
       "      <td>web</td>\n",
       "      <td>8500000</td>\n",
       "      <td>پژو مدل 81 مشکی</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>19</td>\n",
       "      <td>False</td>\n",
       "      <td>Apple::اپل</td>\n",
       "      <td>electronic-devices</td>\n",
       "      <td>mobile-tablet</td>\n",
       "      <td>mobile-phones</td>\n",
       "      <td>Tehran</td>\n",
       "      <td>Wednesday 11AM</td>\n",
       "      <td>درحد نو سالم اصلى بدون ضربه مهلت تست میدم</td>\n",
       "      <td>54942524705708</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>mobile</td>\n",
       "      <td>1150000</td>\n",
       "      <td>ایفون ٥اس٣٢گیگ</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>22</td>\n",
       "      <td>False</td>\n",
       "      <td>نیسان::Nissan</td>\n",
       "      <td>vehicles</td>\n",
       "      <td>cars</td>\n",
       "      <td>light</td>\n",
       "      <td>Tehran</td>\n",
       "      <td>Wednesday 01PM</td>\n",
       "      <td>سلام.\\nماشین کاملا سالم همه چی فابریک متور زد ...</td>\n",
       "      <td>26591305369165</td>\n",
       "      <td>3</td>\n",
       "      <td>175000.0</td>\n",
       "      <td>mobile</td>\n",
       "      <td>19500000</td>\n",
       "      <td>پاترول 2در</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1372</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",
       "      <td>...</td>\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>947615</th>\n",
       "      <td>947615</td>\n",
       "      <td>True</td>\n",
       "      <td>سمند::Samand</td>\n",
       "      <td>vehicles</td>\n",
       "      <td>cars</td>\n",
       "      <td>light</td>\n",
       "      <td>Shiraz</td>\n",
       "      <td>Wednesday 06PM</td>\n",
       "      <td>ماشینم تصادفیه با خریدار توافق میکنم سر قیمتش</td>\n",
       "      <td>32248935135739</td>\n",
       "      <td>3</td>\n",
       "      <td>20000.0</td>\n",
       "      <td>web</td>\n",
       "      <td>-1</td>\n",
       "      <td>خودرو سمند تصادفی مدل92</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>947621</th>\n",
       "      <td>947621</td>\n",
       "      <td>True</td>\n",
       "      <td>Samsung::سامسونگ</td>\n",
       "      <td>electronic-devices</td>\n",
       "      <td>mobile-tablet</td>\n",
       "      <td>mobile-phones</td>\n",
       "      <td>Tehran</td>\n",
       "      <td>Thursday 09AM</td>\n",
       "      <td>سالم و تمیز با لوازم\\nاندروید 5. باتری اورجینا...</td>\n",
       "      <td>20251684311504</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>mobile</td>\n",
       "      <td>475000</td>\n",
       "      <td>گلکسی galaxy s4 i9500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>947622</th>\n",
       "      <td>947622</td>\n",
       "      <td>True</td>\n",
       "      <td>Sony::سونی</td>\n",
       "      <td>electronic-devices</td>\n",
       "      <td>mobile-tablet</td>\n",
       "      <td>mobile-phones</td>\n",
       "      <td>Mashhad</td>\n",
       "      <td>Thursday 11AM</td>\n",
       "      <td>فروش یا معاوضه فوری فوری\\nسفید تمیز همراه با گ...</td>\n",
       "      <td>48122459746674</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>web</td>\n",
       "      <td>650000</td>\n",
       "      <td>سونی Z2فورجی سفیدD6503</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>947625</th>\n",
       "      <td>947625</td>\n",
       "      <td>True</td>\n",
       "      <td>پراید صندوق‌دار::Pride</td>\n",
       "      <td>vehicles</td>\n",
       "      <td>cars</td>\n",
       "      <td>light</td>\n",
       "      <td>Karaj</td>\n",
       "      <td>Thursday 03PM</td>\n",
       "      <td>سند۲ برگ،بیمه تا برج$NUMتخفیفات بیمه 15 سال،مع...</td>\n",
       "      <td>30197850752025</td>\n",
       "      <td>4</td>\n",
       "      <td>123000.0</td>\n",
       "      <td>mobile</td>\n",
       "      <td>6900000</td>\n",
       "      <td>پراید 79 بسیار سالم</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1379</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>947632</th>\n",
       "      <td>947632</td>\n",
       "      <td>True</td>\n",
       "      <td>پراید صندوق‌دار::Pride</td>\n",
       "      <td>vehicles</td>\n",
       "      <td>cars</td>\n",
       "      <td>light</td>\n",
       "      <td>Shiraz</td>\n",
       "      <td>Friday 04PM</td>\n",
       "      <td>پراید دوگانه کارخانه میباشد و کم کارکرد.روکش ....</td>\n",
       "      <td>55514317985619</td>\n",
       "      <td>3</td>\n",
       "      <td>43000.0</td>\n",
       "      <td>mobile</td>\n",
       "      <td>17400000</td>\n",
       "      <td>پراید دوگانه کارخانه مدل93</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1393</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>190892 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Unnamed: 0  archive_by_user                   brand  \\\n",
       "8                8            False                    سایر   \n",
       "11              11            False            Nokia::نوکیا   \n",
       "18              18            False    پژو ۴۰۵::Peugeot 405   \n",
       "19              19            False              Apple::اپل   \n",
       "22              22            False           نیسان::Nissan   \n",
       "...            ...              ...                     ...   \n",
       "947615      947615             True            سمند::Samand   \n",
       "947621      947621             True        Samsung::سامسونگ   \n",
       "947622      947622             True              Sony::سونی   \n",
       "947625      947625             True  پراید صندوق‌دار::Pride   \n",
       "947632      947632             True  پراید صندوق‌دار::Pride   \n",
       "\n",
       "                      cat1           cat2           cat3     city  \\\n",
       "8                 vehicles           cars          light   Tehran   \n",
       "11      electronic-devices  mobile-tablet  mobile-phones      Qom   \n",
       "18                vehicles           cars          light    Karaj   \n",
       "19      electronic-devices  mobile-tablet  mobile-phones   Tehran   \n",
       "22                vehicles           cars          light   Tehran   \n",
       "...                    ...            ...            ...      ...   \n",
       "947615            vehicles           cars          light   Shiraz   \n",
       "947621  electronic-devices  mobile-tablet  mobile-phones   Tehran   \n",
       "947622  electronic-devices  mobile-tablet  mobile-phones  Mashhad   \n",
       "947625            vehicles           cars          light    Karaj   \n",
       "947632            vehicles           cars          light   Shiraz   \n",
       "\n",
       "            created_at                                               desc  \\\n",
       "8       Wednesday 04AM  دوگانه ال پی جی بیمه با تخفیف دارد \\nتودوزی نو...   \n",
       "11      Wednesday 07AM  سلام.یه گوشیه6303سالم که فقط دوتا خط کوچیک رو ...   \n",
       "18      Wednesday 11AM  پژو 2000فنی تازه تعمیر بدنه بدون خطو خش تخیف ب...   \n",
       "19      Wednesday 11AM          درحد نو سالم اصلى بدون ضربه مهلت تست میدم   \n",
       "22      Wednesday 01PM  سلام.\\nماشین کاملا سالم همه چی فابریک متور زد ...   \n",
       "...                ...                                                ...   \n",
       "947615  Wednesday 06PM      ماشینم تصادفیه با خریدار توافق میکنم سر قیمتش   \n",
       "947621   Thursday 09AM  سالم و تمیز با لوازم\\nاندروید 5. باتری اورجینا...   \n",
       "947622   Thursday 11AM  فروش یا معاوضه فوری فوری\\nسفید تمیز همراه با گ...   \n",
       "947625   Thursday 03PM  سند۲ برگ،بیمه تا برج$NUMتخفیفات بیمه 15 سال،مع...   \n",
       "947632     Friday 04PM  پراید دوگانه کارخانه میباشد و کم کارکرد.روکش ....   \n",
       "\n",
       "                    id  image_count   mileage platform     price  \\\n",
       "8       59878876364899            3  180000.0   mobile        -1   \n",
       "11      36762902012926            2       NaN   mobile     60000   \n",
       "18      51728781836074            0  290000.0      web   8500000   \n",
       "19      54942524705708            0       NaN   mobile   1150000   \n",
       "22      26591305369165            3  175000.0   mobile  19500000   \n",
       "...                ...          ...       ...      ...       ...   \n",
       "947615  32248935135739            3   20000.0      web        -1   \n",
       "947621  20251684311504            2       NaN   mobile    475000   \n",
       "947622  48122459746674            0       NaN      web    650000   \n",
       "947625  30197850752025            4  123000.0   mobile   6900000   \n",
       "947632  55514317985619            3   43000.0   mobile  17400000   \n",
       "\n",
       "                             title type  year  \n",
       "8        پاترول شش سیلندر چهار درب  NaN  1366  \n",
       "11                       نوکیا6303  NaN   NaN  \n",
       "18                 پژو مدل 81 مشکی  NaN  1381  \n",
       "19                  ایفون ٥اس٣٢گیگ  NaN   NaN  \n",
       "22                      پاترول 2در  NaN  1372  \n",
       "...                            ...  ...   ...  \n",
       "947615     خودرو سمند تصادفی مدل92  NaN  1392  \n",
       "947621       گلکسی galaxy s4 i9500  NaN   NaN  \n",
       "947622      سونی Z2فورجی سفیدD6503  NaN   NaN  \n",
       "947625         پراید 79 بسیار سالم  NaN  1379  \n",
       "947632  پراید دوگانه کارخانه مدل93  NaN  1393  \n",
       "\n",
       "[190892 rows x 17 columns]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df[(df['brand'].notnull())]\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "0cf1de6e",
   "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>cat1_electronic-devices</th>\n",
       "      <th>cat1_vehicles</th>\n",
       "      <th>cat2_cars</th>\n",
       "      <th>cat2_computers</th>\n",
       "      <th>cat2_mobile-tablet</th>\n",
       "      <th>cat3_laptops</th>\n",
       "      <th>cat3_light</th>\n",
       "      <th>cat3_mobile-phones</th>\n",
       "      <th>cat3_tablet</th>\n",
       "      <th>brand_Acer::ایسر</th>\n",
       "      <th>...</th>\n",
       "      <th>brand_وانت</th>\n",
       "      <th>brand_پراید صندوق‌دار::Pride</th>\n",
       "      <th>brand_پراید هاچ‌بک::Pride</th>\n",
       "      <th>brand_پژو روآ / آر‌دی::RD/ROA</th>\n",
       "      <th>brand_پژو پارس::Peugeot Pars</th>\n",
       "      <th>brand_پژو ۲۰۶‍ صندوق‌دار::Peugeot 206</th>\n",
       "      <th>brand_پژو ۲۰۶‍::Peugeot 206</th>\n",
       "      <th>brand_پژو ۴۰۵::Peugeot 405</th>\n",
       "      <th>brand_پیکان::Peykan</th>\n",
       "      <th>brand_کیا::Kia</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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",
       "      <td>...</td>\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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>947615</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>947621</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>947622</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>947625</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>947632</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>190892 rows × 62 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        cat1_electronic-devices  cat1_vehicles  cat2_cars  cat2_computers  \\\n",
       "8                             0              1          1               0   \n",
       "11                            1              0          0               0   \n",
       "18                            0              1          1               0   \n",
       "19                            1              0          0               0   \n",
       "22                            0              1          1               0   \n",
       "...                         ...            ...        ...             ...   \n",
       "947615                        0              1          1               0   \n",
       "947621                        1              0          0               0   \n",
       "947622                        1              0          0               0   \n",
       "947625                        0              1          1               0   \n",
       "947632                        0              1          1               0   \n",
       "\n",
       "        cat2_mobile-tablet  cat3_laptops  cat3_light  cat3_mobile-phones  \\\n",
       "8                        0             0           1                   0   \n",
       "11                       1             0           0                   1   \n",
       "18                       0             0           1                   0   \n",
       "19                       1             0           0                   1   \n",
       "22                       0             0           1                   0   \n",
       "...                    ...           ...         ...                 ...   \n",
       "947615                   0             0           1                   0   \n",
       "947621                   1             0           0                   1   \n",
       "947622                   1             0           0                   1   \n",
       "947625                   0             0           1                   0   \n",
       "947632                   0             0           1                   0   \n",
       "\n",
       "        cat3_tablet  brand_Acer::ایسر  ...  brand_وانت  \\\n",
       "8                 0                 0  ...           0   \n",
       "11                0                 0  ...           0   \n",
       "18                0                 0  ...           0   \n",
       "19                0                 0  ...           0   \n",
       "22                0                 0  ...           0   \n",
       "...             ...               ...  ...         ...   \n",
       "947615            0                 0  ...           0   \n",
       "947621            0                 0  ...           0   \n",
       "947622            0                 0  ...           0   \n",
       "947625            0                 0  ...           0   \n",
       "947632            0                 0  ...           0   \n",
       "\n",
       "        brand_پراید صندوق‌دار::Pride  brand_پراید هاچ‌بک::Pride  \\\n",
       "8                                  0                          0   \n",
       "11                                 0                          0   \n",
       "18                                 0                          0   \n",
       "19                                 0                          0   \n",
       "22                                 0                          0   \n",
       "...                              ...                        ...   \n",
       "947615                             0                          0   \n",
       "947621                             0                          0   \n",
       "947622                             0                          0   \n",
       "947625                             1                          0   \n",
       "947632                             1                          0   \n",
       "\n",
       "        brand_پژو روآ / آر‌دی::RD/ROA  brand_پژو پارس::Peugeot Pars  \\\n",
       "8                                   0                             0   \n",
       "11                                  0                             0   \n",
       "18                                  0                             0   \n",
       "19                                  0                             0   \n",
       "22                                  0                             0   \n",
       "...                               ...                           ...   \n",
       "947615                              0                             0   \n",
       "947621                              0                             0   \n",
       "947622                              0                             0   \n",
       "947625                              0                             0   \n",
       "947632                              0                             0   \n",
       "\n",
       "        brand_پژو ۲۰۶‍ صندوق‌دار::Peugeot 206  brand_پژو ۲۰۶‍::Peugeot 206  \\\n",
       "8                                           0                            0   \n",
       "11                                          0                            0   \n",
       "18                                          0                            0   \n",
       "19                                          0                            0   \n",
       "22                                          0                            0   \n",
       "...                                       ...                          ...   \n",
       "947615                                      0                            0   \n",
       "947621                                      0                            0   \n",
       "947622                                      0                            0   \n",
       "947625                                      0                            0   \n",
       "947632                                      0                            0   \n",
       "\n",
       "        brand_پژو ۴۰۵::Peugeot 405  brand_پیکان::Peykan  brand_کیا::Kia  \n",
       "8                                0                    0               0  \n",
       "11                               0                    0               0  \n",
       "18                               1                    0               0  \n",
       "19                               0                    0               0  \n",
       "22                               0                    0               0  \n",
       "...                            ...                  ...             ...  \n",
       "947615                           0                    0               0  \n",
       "947621                           0                    0               0  \n",
       "947622                           0                    0               0  \n",
       "947625                           0                    0               0  \n",
       "947632                           0                    0               0  \n",
       "\n",
       "[190892 rows x 62 columns]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sub_df = df[['cat1','cat2','cat3','brand']]\n",
    "df_new = pd.get_dummies(sub_df)\n",
    "df_new"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "8e24f0a6",
   "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>city</th>\n",
       "      <th>cat1_electronic-devices</th>\n",
       "      <th>cat1_vehicles</th>\n",
       "      <th>cat2_cars</th>\n",
       "      <th>cat2_computers</th>\n",
       "      <th>cat2_mobile-tablet</th>\n",
       "      <th>cat3_laptops</th>\n",
       "      <th>cat3_light</th>\n",
       "      <th>cat3_mobile-phones</th>\n",
       "      <th>cat3_tablet</th>\n",
       "      <th>...</th>\n",
       "      <th>brand_وانت</th>\n",
       "      <th>brand_پراید صندوق‌دار::Pride</th>\n",
       "      <th>brand_پراید هاچ‌بک::Pride</th>\n",
       "      <th>brand_پژو روآ / آر‌دی::RD/ROA</th>\n",
       "      <th>brand_پژو پارس::Peugeot Pars</th>\n",
       "      <th>brand_پژو ۲۰۶‍ صندوق‌دار::Peugeot 206</th>\n",
       "      <th>brand_پژو ۲۰۶‍::Peugeot 206</th>\n",
       "      <th>brand_پژو ۴۰۵::Peugeot 405</th>\n",
       "      <th>brand_پیکان::Peykan</th>\n",
       "      <th>brand_کیا::Kia</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Tehran</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Qom</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Karaj</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Tehran</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Tehran</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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",
       "      <td>...</td>\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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>947615</th>\n",
       "      <td>Shiraz</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>947621</th>\n",
       "      <td>Tehran</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>947622</th>\n",
       "      <td>Mashhad</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>947625</th>\n",
       "      <td>Karaj</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>947632</th>\n",
       "      <td>Shiraz</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>190892 rows × 63 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           city  cat1_electronic-devices  cat1_vehicles  cat2_cars  \\\n",
       "8        Tehran                        0              1          1   \n",
       "11          Qom                        1              0          0   \n",
       "18        Karaj                        0              1          1   \n",
       "19       Tehran                        1              0          0   \n",
       "22       Tehran                        0              1          1   \n",
       "...         ...                      ...            ...        ...   \n",
       "947615   Shiraz                        0              1          1   \n",
       "947621   Tehran                        1              0          0   \n",
       "947622  Mashhad                        1              0          0   \n",
       "947625    Karaj                        0              1          1   \n",
       "947632   Shiraz                        0              1          1   \n",
       "\n",
       "        cat2_computers  cat2_mobile-tablet  cat3_laptops  cat3_light  \\\n",
       "8                    0                   0             0           1   \n",
       "11                   0                   1             0           0   \n",
       "18                   0                   0             0           1   \n",
       "19                   0                   1             0           0   \n",
       "22                   0                   0             0           1   \n",
       "...                ...                 ...           ...         ...   \n",
       "947615               0                   0             0           1   \n",
       "947621               0                   1             0           0   \n",
       "947622               0                   1             0           0   \n",
       "947625               0                   0             0           1   \n",
       "947632               0                   0             0           1   \n",
       "\n",
       "        cat3_mobile-phones  cat3_tablet  ...  brand_وانت  \\\n",
       "8                        0            0  ...           0   \n",
       "11                       1            0  ...           0   \n",
       "18                       0            0  ...           0   \n",
       "19                       1            0  ...           0   \n",
       "22                       0            0  ...           0   \n",
       "...                    ...          ...  ...         ...   \n",
       "947615                   0            0  ...           0   \n",
       "947621                   1            0  ...           0   \n",
       "947622                   1            0  ...           0   \n",
       "947625                   0            0  ...           0   \n",
       "947632                   0            0  ...           0   \n",
       "\n",
       "        brand_پراید صندوق‌دار::Pride  brand_پراید هاچ‌بک::Pride  \\\n",
       "8                                  0                          0   \n",
       "11                                 0                          0   \n",
       "18                                 0                          0   \n",
       "19                                 0                          0   \n",
       "22                                 0                          0   \n",
       "...                              ...                        ...   \n",
       "947615                             0                          0   \n",
       "947621                             0                          0   \n",
       "947622                             0                          0   \n",
       "947625                             1                          0   \n",
       "947632                             1                          0   \n",
       "\n",
       "        brand_پژو روآ / آر‌دی::RD/ROA  brand_پژو پارس::Peugeot Pars  \\\n",
       "8                                   0                             0   \n",
       "11                                  0                             0   \n",
       "18                                  0                             0   \n",
       "19                                  0                             0   \n",
       "22                                  0                             0   \n",
       "...                               ...                           ...   \n",
       "947615                              0                             0   \n",
       "947621                              0                             0   \n",
       "947622                              0                             0   \n",
       "947625                              0                             0   \n",
       "947632                              0                             0   \n",
       "\n",
       "        brand_پژو ۲۰۶‍ صندوق‌دار::Peugeot 206  brand_پژو ۲۰۶‍::Peugeot 206  \\\n",
       "8                                           0                            0   \n",
       "11                                          0                            0   \n",
       "18                                          0                            0   \n",
       "19                                          0                            0   \n",
       "22                                          0                            0   \n",
       "...                                       ...                          ...   \n",
       "947615                                      0                            0   \n",
       "947621                                      0                            0   \n",
       "947622                                      0                            0   \n",
       "947625                                      0                            0   \n",
       "947632                                      0                            0   \n",
       "\n",
       "        brand_پژو ۴۰۵::Peugeot 405  brand_پیکان::Peykan  brand_کیا::Kia  \n",
       "8                                0                    0               0  \n",
       "11                               0                    0               0  \n",
       "18                               1                    0               0  \n",
       "19                               0                    0               0  \n",
       "22                               0                    0               0  \n",
       "...                            ...                  ...             ...  \n",
       "947615                           0                    0               0  \n",
       "947621                           0                    0               0  \n",
       "947622                           0                    0               0  \n",
       "947625                           0                    0               0  \n",
       "947632                           0                    0               0  \n",
       "\n",
       "[190892 rows x 63 columns]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "city = df[['city']]\n",
    "df_new1 = pd.concat([city,df_new], axis=1)\n",
    "df_new1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "4aaa1fcd",
   "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>city</th>\n",
       "      <th>cat1_electronic-devices</th>\n",
       "      <th>cat1_vehicles</th>\n",
       "      <th>cat2_cars</th>\n",
       "      <th>cat2_computers</th>\n",
       "      <th>cat2_mobile-tablet</th>\n",
       "      <th>cat3_laptops</th>\n",
       "      <th>cat3_light</th>\n",
       "      <th>cat3_mobile-phones</th>\n",
       "      <th>cat3_tablet</th>\n",
       "      <th>...</th>\n",
       "      <th>brand_وانت</th>\n",
       "      <th>brand_پراید صندوق‌دار::Pride</th>\n",
       "      <th>brand_پراید هاچ‌بک::Pride</th>\n",
       "      <th>brand_پژو روآ / آر‌دی::RD/ROA</th>\n",
       "      <th>brand_پژو پارس::Peugeot Pars</th>\n",
       "      <th>brand_پژو ۲۰۶‍ صندوق‌دار::Peugeot 206</th>\n",
       "      <th>brand_پژو ۲۰۶‍::Peugeot 206</th>\n",
       "      <th>brand_پژو ۴۰۵::Peugeot 405</th>\n",
       "      <th>brand_پیکان::Peykan</th>\n",
       "      <th>brand_کیا::Kia</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Ahvaz</td>\n",
       "      <td>4296.0</td>\n",
       "      <td>4879.0</td>\n",
       "      <td>4879.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4296.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4879.0</td>\n",
       "      <td>4296.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1599.0</td>\n",
       "      <td>351.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>571.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>402.0</td>\n",
       "      <td>718.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Isfahan</td>\n",
       "      <td>5408.0</td>\n",
       "      <td>8773.0</td>\n",
       "      <td>8773.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5408.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8773.0</td>\n",
       "      <td>5021.0</td>\n",
       "      <td>387.0</td>\n",
       "      <td>...</td>\n",
       "      <td>344.0</td>\n",
       "      <td>2179.0</td>\n",
       "      <td>610.0</td>\n",
       "      <td>466.0</td>\n",
       "      <td>957.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>767.0</td>\n",
       "      <td>867.0</td>\n",
       "      <td>620.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Karaj</td>\n",
       "      <td>6269.0</td>\n",
       "      <td>11057.0</td>\n",
       "      <td>11057.0</td>\n",
       "      <td>405.0</td>\n",
       "      <td>5864.0</td>\n",
       "      <td>405.0</td>\n",
       "      <td>11057.0</td>\n",
       "      <td>5443.0</td>\n",
       "      <td>421.0</td>\n",
       "      <td>...</td>\n",
       "      <td>508.0</td>\n",
       "      <td>3241.0</td>\n",
       "      <td>641.0</td>\n",
       "      <td>516.0</td>\n",
       "      <td>604.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1028.0</td>\n",
       "      <td>1184.0</td>\n",
       "      <td>571.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Kermanshah</td>\n",
       "      <td>2582.0</td>\n",
       "      <td>2277.0</td>\n",
       "      <td>2277.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2582.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2277.0</td>\n",
       "      <td>2582.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>961.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>457.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>497.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Mashhad</td>\n",
       "      <td>10862.0</td>\n",
       "      <td>15088.0</td>\n",
       "      <td>15088.0</td>\n",
       "      <td>681.0</td>\n",
       "      <td>10181.0</td>\n",
       "      <td>681.0</td>\n",
       "      <td>15088.0</td>\n",
       "      <td>9560.0</td>\n",
       "      <td>621.0</td>\n",
       "      <td>...</td>\n",
       "      <td>586.0</td>\n",
       "      <td>3523.0</td>\n",
       "      <td>1004.0</td>\n",
       "      <td>596.0</td>\n",
       "      <td>1277.0</td>\n",
       "      <td>592.0</td>\n",
       "      <td>1064.0</td>\n",
       "      <td>1221.0</td>\n",
       "      <td>528.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Qom</td>\n",
       "      <td>2925.0</td>\n",
       "      <td>1683.0</td>\n",
       "      <td>1683.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2925.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1683.0</td>\n",
       "      <td>2925.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1133.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>550.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Shiraz</td>\n",
       "      <td>6600.0</td>\n",
       "      <td>18344.0</td>\n",
       "      <td>18344.0</td>\n",
       "      <td>370.0</td>\n",
       "      <td>6230.0</td>\n",
       "      <td>370.0</td>\n",
       "      <td>18344.0</td>\n",
       "      <td>5771.0</td>\n",
       "      <td>459.0</td>\n",
       "      <td>...</td>\n",
       "      <td>985.0</td>\n",
       "      <td>3874.0</td>\n",
       "      <td>1080.0</td>\n",
       "      <td>456.0</td>\n",
       "      <td>1550.0</td>\n",
       "      <td>518.0</td>\n",
       "      <td>1149.0</td>\n",
       "      <td>1802.0</td>\n",
       "      <td>1174.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Tabriz</td>\n",
       "      <td>3491.0</td>\n",
       "      <td>4421.0</td>\n",
       "      <td>4421.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3491.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4421.0</td>\n",
       "      <td>3491.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1289.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>687.0</td>\n",
       "      <td>332.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>613.0</td>\n",
       "      <td>572.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Tehran</td>\n",
       "      <td>28008.0</td>\n",
       "      <td>53929.0</td>\n",
       "      <td>53929.0</td>\n",
       "      <td>2468.0</td>\n",
       "      <td>25540.0</td>\n",
       "      <td>2468.0</td>\n",
       "      <td>53929.0</td>\n",
       "      <td>23087.0</td>\n",
       "      <td>2453.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1946.0</td>\n",
       "      <td>10500.0</td>\n",
       "      <td>2288.0</td>\n",
       "      <td>1159.0</td>\n",
       "      <td>2770.0</td>\n",
       "      <td>1622.0</td>\n",
       "      <td>5417.0</td>\n",
       "      <td>4466.0</td>\n",
       "      <td>1376.0</td>\n",
       "      <td>1237.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9 rows × 63 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         city  cat1_electronic-devices  cat1_vehicles  cat2_cars  \\\n",
       "0       Ahvaz                   4296.0         4879.0     4879.0   \n",
       "1     Isfahan                   5408.0         8773.0     8773.0   \n",
       "2       Karaj                   6269.0        11057.0    11057.0   \n",
       "3  Kermanshah                   2582.0         2277.0     2277.0   \n",
       "4     Mashhad                  10862.0        15088.0    15088.0   \n",
       "5         Qom                   2925.0         1683.0     1683.0   \n",
       "6      Shiraz                   6600.0        18344.0    18344.0   \n",
       "7      Tabriz                   3491.0         4421.0     4421.0   \n",
       "8      Tehran                  28008.0        53929.0    53929.0   \n",
       "\n",
       "   cat2_computers  cat2_mobile-tablet  cat3_laptops  cat3_light  \\\n",
       "0             0.0              4296.0           0.0      4879.0   \n",
       "1             0.0              5408.0           0.0      8773.0   \n",
       "2           405.0              5864.0         405.0     11057.0   \n",
       "3             0.0              2582.0           0.0      2277.0   \n",
       "4           681.0             10181.0         681.0     15088.0   \n",
       "5             0.0              2925.0           0.0      1683.0   \n",
       "6           370.0              6230.0         370.0     18344.0   \n",
       "7             0.0              3491.0           0.0      4421.0   \n",
       "8          2468.0             25540.0        2468.0     53929.0   \n",
       "\n",
       "   cat3_mobile-phones  cat3_tablet  ...  brand_وانت  \\\n",
       "0              4296.0          0.0  ...         0.0   \n",
       "1              5021.0        387.0  ...       344.0   \n",
       "2              5443.0        421.0  ...       508.0   \n",
       "3              2582.0          0.0  ...         0.0   \n",
       "4              9560.0        621.0  ...       586.0   \n",
       "5              2925.0          0.0  ...         0.0   \n",
       "6              5771.0        459.0  ...       985.0   \n",
       "7              3491.0          0.0  ...         0.0   \n",
       "8             23087.0       2453.0  ...      1946.0   \n",
       "\n",
       "   brand_پراید صندوق‌دار::Pride  brand_پراید هاچ‌بک::Pride  \\\n",
       "0                        1599.0                      351.0   \n",
       "1                        2179.0                      610.0   \n",
       "2                        3241.0                      641.0   \n",
       "3                         961.0                        0.0   \n",
       "4                        3523.0                     1004.0   \n",
       "5                        1133.0                        0.0   \n",
       "6                        3874.0                     1080.0   \n",
       "7                        1289.0                        0.0   \n",
       "8                       10500.0                     2288.0   \n",
       "\n",
       "   brand_پژو روآ / آر‌دی::RD/ROA  brand_پژو پارس::Peugeot Pars  \\\n",
       "0                            0.0                         571.0   \n",
       "1                          466.0                         957.0   \n",
       "2                          516.0                         604.0   \n",
       "3                            0.0                         457.0   \n",
       "4                          596.0                        1277.0   \n",
       "5                            0.0                           0.0   \n",
       "6                          456.0                        1550.0   \n",
       "7                          687.0                         332.0   \n",
       "8                         1159.0                        2770.0   \n",
       "\n",
       "   brand_پژو ۲۰۶‍ صندوق‌دار::Peugeot 206  brand_پژو ۲۰۶‍::Peugeot 206  \\\n",
       "0                                    0.0                        402.0   \n",
       "1                                    0.0                        767.0   \n",
       "2                                    0.0                       1028.0   \n",
       "3                                    0.0                          0.0   \n",
       "4                                  592.0                       1064.0   \n",
       "5                                    0.0                          0.0   \n",
       "6                                  518.0                       1149.0   \n",
       "7                                    0.0                          0.0   \n",
       "8                                 1622.0                       5417.0   \n",
       "\n",
       "   brand_پژو ۴۰۵::Peugeot 405  brand_پیکان::Peykan  brand_کیا::Kia  \n",
       "0                       718.0                  0.0             0.0  \n",
       "1                       867.0                620.0             0.0  \n",
       "2                      1184.0                571.0             0.0  \n",
       "3                       497.0                  0.0             0.0  \n",
       "4                      1221.0                528.0             0.0  \n",
       "5                       550.0                  0.0             0.0  \n",
       "6                      1802.0               1174.0             0.0  \n",
       "7                       613.0                572.0             0.0  \n",
       "8                      4466.0               1376.0          1237.0  \n",
       "\n",
       "[9 rows x 63 columns]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new1 = df_new1.groupby(by=['city'],as_index=False).sum()\n",
    "df_new1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "35ca65d6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         Ahvaz\n",
       "1       Isfahan\n",
       "2         Karaj\n",
       "3    Kermanshah\n",
       "4       Mashhad\n",
       "5           Qom\n",
       "6        Shiraz\n",
       "7        Tabriz\n",
       "8        Tehran\n",
       "Name: city, dtype: object"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "groupsOFCity = df_new1['city']\n",
    "groupsOFCity"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "93b6abdb",
   "metadata": {},
   "source": [
    "# agglomerative"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "4fd399c2",
   "metadata": {},
   "outputs": [],
   "source": [
    "AggloX = df_new1.iloc[:,1:]\n",
    "clustering_agglomerative = AgglomerativeClustering().fit(AggloX)\n",
    "AggloX['agglo'] = clustering_agglomerative.labels_\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "6ee82e07",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0\n",
       "1    0\n",
       "2    0\n",
       "3    0\n",
       "4    0\n",
       "5    0\n",
       "6    0\n",
       "7    0\n",
       "8    1\n",
       "Name: agglo, dtype: int64"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "AggloX.iloc[:, 62]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "6ed5d225",
   "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": [
    "AggloXX = np.array(AggloX)\n",
    "plt.scatter(AggloXX[:, 1], AggloXX[:, 2], c=AggloXX[:, 62], s=100, cmap='viridis')\n",
    "plt.rcParams[\"figure.figsize\"] = (20,3)\n",
    "# plt.scatter(centers[:, 0], centers[:, 1], c='black', s=50, alpha=0.5);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "b621fc1c",
   "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>city</th>\n",
       "      <th>cat1_electronic-devices</th>\n",
       "      <th>cat1_vehicles</th>\n",
       "      <th>cat2_cars</th>\n",
       "      <th>cat2_computers</th>\n",
       "      <th>cat2_mobile-tablet</th>\n",
       "      <th>cat3_laptops</th>\n",
       "      <th>cat3_light</th>\n",
       "      <th>cat3_mobile-phones</th>\n",
       "      <th>cat3_tablet</th>\n",
       "      <th>...</th>\n",
       "      <th>brand_پراید صندوق‌دار::Pride</th>\n",
       "      <th>brand_پراید هاچ‌بک::Pride</th>\n",
       "      <th>brand_پژو روآ / آر‌دی::RD/ROA</th>\n",
       "      <th>brand_پژو پارس::Peugeot Pars</th>\n",
       "      <th>brand_پژو ۲۰۶‍ صندوق‌دار::Peugeot 206</th>\n",
       "      <th>brand_پژو ۲۰۶‍::Peugeot 206</th>\n",
       "      <th>brand_پژو ۴۰۵::Peugeot 405</th>\n",
       "      <th>brand_پیکان::Peykan</th>\n",
       "      <th>brand_کیا::Kia</th>\n",
       "      <th>agglo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Ahvaz</td>\n",
       "      <td>4296.0</td>\n",
       "      <td>4879.0</td>\n",
       "      <td>4879.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4296.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4879.0</td>\n",
       "      <td>4296.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1599.0</td>\n",
       "      <td>351.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>571.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>402.0</td>\n",
       "      <td>718.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Isfahan</td>\n",
       "      <td>5408.0</td>\n",
       "      <td>8773.0</td>\n",
       "      <td>8773.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5408.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8773.0</td>\n",
       "      <td>5021.0</td>\n",
       "      <td>387.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2179.0</td>\n",
       "      <td>610.0</td>\n",
       "      <td>466.0</td>\n",
       "      <td>957.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>767.0</td>\n",
       "      <td>867.0</td>\n",
       "      <td>620.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Karaj</td>\n",
       "      <td>6269.0</td>\n",
       "      <td>11057.0</td>\n",
       "      <td>11057.0</td>\n",
       "      <td>405.0</td>\n",
       "      <td>5864.0</td>\n",
       "      <td>405.0</td>\n",
       "      <td>11057.0</td>\n",
       "      <td>5443.0</td>\n",
       "      <td>421.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3241.0</td>\n",
       "      <td>641.0</td>\n",
       "      <td>516.0</td>\n",
       "      <td>604.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1028.0</td>\n",
       "      <td>1184.0</td>\n",
       "      <td>571.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Kermanshah</td>\n",
       "      <td>2582.0</td>\n",
       "      <td>2277.0</td>\n",
       "      <td>2277.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2582.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2277.0</td>\n",
       "      <td>2582.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>961.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>457.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>497.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Mashhad</td>\n",
       "      <td>10862.0</td>\n",
       "      <td>15088.0</td>\n",
       "      <td>15088.0</td>\n",
       "      <td>681.0</td>\n",
       "      <td>10181.0</td>\n",
       "      <td>681.0</td>\n",
       "      <td>15088.0</td>\n",
       "      <td>9560.0</td>\n",
       "      <td>621.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3523.0</td>\n",
       "      <td>1004.0</td>\n",
       "      <td>596.0</td>\n",
       "      <td>1277.0</td>\n",
       "      <td>592.0</td>\n",
       "      <td>1064.0</td>\n",
       "      <td>1221.0</td>\n",
       "      <td>528.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Qom</td>\n",
       "      <td>2925.0</td>\n",
       "      <td>1683.0</td>\n",
       "      <td>1683.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2925.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1683.0</td>\n",
       "      <td>2925.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1133.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>550.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Shiraz</td>\n",
       "      <td>6600.0</td>\n",
       "      <td>18344.0</td>\n",
       "      <td>18344.0</td>\n",
       "      <td>370.0</td>\n",
       "      <td>6230.0</td>\n",
       "      <td>370.0</td>\n",
       "      <td>18344.0</td>\n",
       "      <td>5771.0</td>\n",
       "      <td>459.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3874.0</td>\n",
       "      <td>1080.0</td>\n",
       "      <td>456.0</td>\n",
       "      <td>1550.0</td>\n",
       "      <td>518.0</td>\n",
       "      <td>1149.0</td>\n",
       "      <td>1802.0</td>\n",
       "      <td>1174.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Tabriz</td>\n",
       "      <td>3491.0</td>\n",
       "      <td>4421.0</td>\n",
       "      <td>4421.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3491.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4421.0</td>\n",
       "      <td>3491.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1289.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>687.0</td>\n",
       "      <td>332.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>613.0</td>\n",
       "      <td>572.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Tehran</td>\n",
       "      <td>28008.0</td>\n",
       "      <td>53929.0</td>\n",
       "      <td>53929.0</td>\n",
       "      <td>2468.0</td>\n",
       "      <td>25540.0</td>\n",
       "      <td>2468.0</td>\n",
       "      <td>53929.0</td>\n",
       "      <td>23087.0</td>\n",
       "      <td>2453.0</td>\n",
       "      <td>...</td>\n",
       "      <td>10500.0</td>\n",
       "      <td>2288.0</td>\n",
       "      <td>1159.0</td>\n",
       "      <td>2770.0</td>\n",
       "      <td>1622.0</td>\n",
       "      <td>5417.0</td>\n",
       "      <td>4466.0</td>\n",
       "      <td>1376.0</td>\n",
       "      <td>1237.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9 rows × 64 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         city  cat1_electronic-devices  cat1_vehicles  cat2_cars  \\\n",
       "0       Ahvaz                   4296.0         4879.0     4879.0   \n",
       "1     Isfahan                   5408.0         8773.0     8773.0   \n",
       "2       Karaj                   6269.0        11057.0    11057.0   \n",
       "3  Kermanshah                   2582.0         2277.0     2277.0   \n",
       "4     Mashhad                  10862.0        15088.0    15088.0   \n",
       "5         Qom                   2925.0         1683.0     1683.0   \n",
       "6      Shiraz                   6600.0        18344.0    18344.0   \n",
       "7      Tabriz                   3491.0         4421.0     4421.0   \n",
       "8      Tehran                  28008.0        53929.0    53929.0   \n",
       "\n",
       "   cat2_computers  cat2_mobile-tablet  cat3_laptops  cat3_light  \\\n",
       "0             0.0              4296.0           0.0      4879.0   \n",
       "1             0.0              5408.0           0.0      8773.0   \n",
       "2           405.0              5864.0         405.0     11057.0   \n",
       "3             0.0              2582.0           0.0      2277.0   \n",
       "4           681.0             10181.0         681.0     15088.0   \n",
       "5             0.0              2925.0           0.0      1683.0   \n",
       "6           370.0              6230.0         370.0     18344.0   \n",
       "7             0.0              3491.0           0.0      4421.0   \n",
       "8          2468.0             25540.0        2468.0     53929.0   \n",
       "\n",
       "   cat3_mobile-phones  cat3_tablet  ...  brand_پراید صندوق‌دار::Pride  \\\n",
       "0              4296.0          0.0  ...                        1599.0   \n",
       "1              5021.0        387.0  ...                        2179.0   \n",
       "2              5443.0        421.0  ...                        3241.0   \n",
       "3              2582.0          0.0  ...                         961.0   \n",
       "4              9560.0        621.0  ...                        3523.0   \n",
       "5              2925.0          0.0  ...                        1133.0   \n",
       "6              5771.0        459.0  ...                        3874.0   \n",
       "7              3491.0          0.0  ...                        1289.0   \n",
       "8             23087.0       2453.0  ...                       10500.0   \n",
       "\n",
       "   brand_پراید هاچ‌بک::Pride  brand_پژو روآ / آر‌دی::RD/ROA  \\\n",
       "0                      351.0                            0.0   \n",
       "1                      610.0                          466.0   \n",
       "2                      641.0                          516.0   \n",
       "3                        0.0                            0.0   \n",
       "4                     1004.0                          596.0   \n",
       "5                        0.0                            0.0   \n",
       "6                     1080.0                          456.0   \n",
       "7                        0.0                          687.0   \n",
       "8                     2288.0                         1159.0   \n",
       "\n",
       "   brand_پژو پارس::Peugeot Pars  brand_پژو ۲۰۶‍ صندوق‌دار::Peugeot 206  \\\n",
       "0                         571.0                                    0.0   \n",
       "1                         957.0                                    0.0   \n",
       "2                         604.0                                    0.0   \n",
       "3                         457.0                                    0.0   \n",
       "4                        1277.0                                  592.0   \n",
       "5                           0.0                                    0.0   \n",
       "6                        1550.0                                  518.0   \n",
       "7                         332.0                                    0.0   \n",
       "8                        2770.0                                 1622.0   \n",
       "\n",
       "   brand_پژو ۲۰۶‍::Peugeot 206  brand_پژو ۴۰۵::Peugeot 405  \\\n",
       "0                        402.0                       718.0   \n",
       "1                        767.0                       867.0   \n",
       "2                       1028.0                      1184.0   \n",
       "3                          0.0                       497.0   \n",
       "4                       1064.0                      1221.0   \n",
       "5                          0.0                       550.0   \n",
       "6                       1149.0                      1802.0   \n",
       "7                          0.0                       613.0   \n",
       "8                       5417.0                      4466.0   \n",
       "\n",
       "   brand_پیکان::Peykan  brand_کیا::Kia  agglo  \n",
       "0                  0.0             0.0      0  \n",
       "1                620.0             0.0      0  \n",
       "2                571.0             0.0      0  \n",
       "3                  0.0             0.0      0  \n",
       "4                528.0             0.0      0  \n",
       "5                  0.0             0.0      0  \n",
       "6               1174.0             0.0      0  \n",
       "7                572.0             0.0      0  \n",
       "8               1376.0          1237.0      1  \n",
       "\n",
       "[9 rows x 64 columns]"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "AggloX\n",
    "AggloCityLabels = pd.concat([groupsOFCity,AggloX],axis=1)\n",
    "AggloCityLabels"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0e8f9644",
   "metadata": {},
   "source": [
    "# kmeans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "ccfe3a37",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_kmeans1 = df_new1.iloc[:,1:]\n",
    "\n",
    "distortions = []\n",
    "K = range(1,10)\n",
    "for k in K:\n",
    "    kmeanModel = KMeans(n_clusters=k)\n",
    "    kmeanModel.fit(X_kmeans1)\n",
    "    distortions.append(kmeanModel.inertia_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "7201bfb5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16,8))\n",
    "plt.plot(K, distortions, 'bx-')\n",
    "plt.xlabel('k')\n",
    "plt.ylabel('Distortion')\n",
    "plt.title('The Elbow Method showing the optimal k')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "5fd2eca9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KMeans(n_clusters=3)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeanModel = KMeans(n_clusters=3)\n",
    "kmeanModel.fit(X_kmeans1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "8fb810c2",
   "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>cat1_electronic-devices</th>\n",
       "      <th>cat1_vehicles</th>\n",
       "      <th>cat2_cars</th>\n",
       "      <th>cat2_computers</th>\n",
       "      <th>cat2_mobile-tablet</th>\n",
       "      <th>cat3_laptops</th>\n",
       "      <th>cat3_light</th>\n",
       "      <th>cat3_mobile-phones</th>\n",
       "      <th>cat3_tablet</th>\n",
       "      <th>brand_Acer::ایسر</th>\n",
       "      <th>...</th>\n",
       "      <th>brand_پراید صندوق‌دار::Pride</th>\n",
       "      <th>brand_پراید هاچ‌بک::Pride</th>\n",
       "      <th>brand_پژو روآ / آر‌دی::RD/ROA</th>\n",
       "      <th>brand_پژو پارس::Peugeot Pars</th>\n",
       "      <th>brand_پژو ۲۰۶‍ صندوق‌دار::Peugeot 206</th>\n",
       "      <th>brand_پژو ۲۰۶‍::Peugeot 206</th>\n",
       "      <th>brand_پژو ۴۰۵::Peugeot 405</th>\n",
       "      <th>brand_پیکان::Peykan</th>\n",
       "      <th>brand_کیا::Kia</th>\n",
       "      <th>k_means</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4296.0</td>\n",
       "      <td>4879.0</td>\n",
       "      <td>4879.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4296.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4879.0</td>\n",
       "      <td>4296.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1599.0</td>\n",
       "      <td>351.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>571.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>402.0</td>\n",
       "      <td>718.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5408.0</td>\n",
       "      <td>8773.0</td>\n",
       "      <td>8773.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5408.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8773.0</td>\n",
       "      <td>5021.0</td>\n",
       "      <td>387.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2179.0</td>\n",
       "      <td>610.0</td>\n",
       "      <td>466.0</td>\n",
       "      <td>957.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>767.0</td>\n",
       "      <td>867.0</td>\n",
       "      <td>620.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6269.0</td>\n",
       "      <td>11057.0</td>\n",
       "      <td>11057.0</td>\n",
       "      <td>405.0</td>\n",
       "      <td>5864.0</td>\n",
       "      <td>405.0</td>\n",
       "      <td>11057.0</td>\n",
       "      <td>5443.0</td>\n",
       "      <td>421.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3241.0</td>\n",
       "      <td>641.0</td>\n",
       "      <td>516.0</td>\n",
       "      <td>604.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1028.0</td>\n",
       "      <td>1184.0</td>\n",
       "      <td>571.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2582.0</td>\n",
       "      <td>2277.0</td>\n",
       "      <td>2277.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2582.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2277.0</td>\n",
       "      <td>2582.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>...</td>\n",
       "      <td>961.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>457.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>497.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10862.0</td>\n",
       "      <td>15088.0</td>\n",
       "      <td>15088.0</td>\n",
       "      <td>681.0</td>\n",
       "      <td>10181.0</td>\n",
       "      <td>681.0</td>\n",
       "      <td>15088.0</td>\n",
       "      <td>9560.0</td>\n",
       "      <td>621.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3523.0</td>\n",
       "      <td>1004.0</td>\n",
       "      <td>596.0</td>\n",
       "      <td>1277.0</td>\n",
       "      <td>592.0</td>\n",
       "      <td>1064.0</td>\n",
       "      <td>1221.0</td>\n",
       "      <td>528.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2925.0</td>\n",
       "      <td>1683.0</td>\n",
       "      <td>1683.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2925.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1683.0</td>\n",
       "      <td>2925.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1133.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>550.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6600.0</td>\n",
       "      <td>18344.0</td>\n",
       "      <td>18344.0</td>\n",
       "      <td>370.0</td>\n",
       "      <td>6230.0</td>\n",
       "      <td>370.0</td>\n",
       "      <td>18344.0</td>\n",
       "      <td>5771.0</td>\n",
       "      <td>459.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3874.0</td>\n",
       "      <td>1080.0</td>\n",
       "      <td>456.0</td>\n",
       "      <td>1550.0</td>\n",
       "      <td>518.0</td>\n",
       "      <td>1149.0</td>\n",
       "      <td>1802.0</td>\n",
       "      <td>1174.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>3491.0</td>\n",
       "      <td>4421.0</td>\n",
       "      <td>4421.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3491.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4421.0</td>\n",
       "      <td>3491.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1289.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>687.0</td>\n",
       "      <td>332.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>613.0</td>\n",
       "      <td>572.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>28008.0</td>\n",
       "      <td>53929.0</td>\n",
       "      <td>53929.0</td>\n",
       "      <td>2468.0</td>\n",
       "      <td>25540.0</td>\n",
       "      <td>2468.0</td>\n",
       "      <td>53929.0</td>\n",
       "      <td>23087.0</td>\n",
       "      <td>2453.0</td>\n",
       "      <td>224.0</td>\n",
       "      <td>...</td>\n",
       "      <td>10500.0</td>\n",
       "      <td>2288.0</td>\n",
       "      <td>1159.0</td>\n",
       "      <td>2770.0</td>\n",
       "      <td>1622.0</td>\n",
       "      <td>5417.0</td>\n",
       "      <td>4466.0</td>\n",
       "      <td>1376.0</td>\n",
       "      <td>1237.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9 rows × 63 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   cat1_electronic-devices  cat1_vehicles  cat2_cars  cat2_computers  \\\n",
       "0                   4296.0         4879.0     4879.0             0.0   \n",
       "1                   5408.0         8773.0     8773.0             0.0   \n",
       "2                   6269.0        11057.0    11057.0           405.0   \n",
       "3                   2582.0         2277.0     2277.0             0.0   \n",
       "4                  10862.0        15088.0    15088.0           681.0   \n",
       "5                   2925.0         1683.0     1683.0             0.0   \n",
       "6                   6600.0        18344.0    18344.0           370.0   \n",
       "7                   3491.0         4421.0     4421.0             0.0   \n",
       "8                  28008.0        53929.0    53929.0          2468.0   \n",
       "\n",
       "   cat2_mobile-tablet  cat3_laptops  cat3_light  cat3_mobile-phones  \\\n",
       "0              4296.0           0.0      4879.0              4296.0   \n",
       "1              5408.0           0.0      8773.0              5021.0   \n",
       "2              5864.0         405.0     11057.0              5443.0   \n",
       "3              2582.0           0.0      2277.0              2582.0   \n",
       "4             10181.0         681.0     15088.0              9560.0   \n",
       "5              2925.0           0.0      1683.0              2925.0   \n",
       "6              6230.0         370.0     18344.0              5771.0   \n",
       "7              3491.0           0.0      4421.0              3491.0   \n",
       "8             25540.0        2468.0     53929.0             23087.0   \n",
       "\n",
       "   cat3_tablet  brand_Acer::ایسر  ...  brand_پراید صندوق‌دار::Pride  \\\n",
       "0          0.0               4.0  ...                        1599.0   \n",
       "1        387.0               6.0  ...                        2179.0   \n",
       "2        421.0              39.0  ...                        3241.0   \n",
       "3          0.0               5.0  ...                         961.0   \n",
       "4        621.0              88.0  ...                        3523.0   \n",
       "5          0.0               6.0  ...                        1133.0   \n",
       "6        459.0              32.0  ...                        3874.0   \n",
       "7          0.0               0.0  ...                        1289.0   \n",
       "8       2453.0             224.0  ...                       10500.0   \n",
       "\n",
       "   brand_پراید هاچ‌بک::Pride  brand_پژو روآ / آر‌دی::RD/ROA  \\\n",
       "0                      351.0                            0.0   \n",
       "1                      610.0                          466.0   \n",
       "2                      641.0                          516.0   \n",
       "3                        0.0                            0.0   \n",
       "4                     1004.0                          596.0   \n",
       "5                        0.0                            0.0   \n",
       "6                     1080.0                          456.0   \n",
       "7                        0.0                          687.0   \n",
       "8                     2288.0                         1159.0   \n",
       "\n",
       "   brand_پژو پارس::Peugeot Pars  brand_پژو ۲۰۶‍ صندوق‌دار::Peugeot 206  \\\n",
       "0                         571.0                                    0.0   \n",
       "1                         957.0                                    0.0   \n",
       "2                         604.0                                    0.0   \n",
       "3                         457.0                                    0.0   \n",
       "4                        1277.0                                  592.0   \n",
       "5                           0.0                                    0.0   \n",
       "6                        1550.0                                  518.0   \n",
       "7                         332.0                                    0.0   \n",
       "8                        2770.0                                 1622.0   \n",
       "\n",
       "   brand_پژو ۲۰۶‍::Peugeot 206  brand_پژو ۴۰۵::Peugeot 405  \\\n",
       "0                        402.0                       718.0   \n",
       "1                        767.0                       867.0   \n",
       "2                       1028.0                      1184.0   \n",
       "3                          0.0                       497.0   \n",
       "4                       1064.0                      1221.0   \n",
       "5                          0.0                       550.0   \n",
       "6                       1149.0                      1802.0   \n",
       "7                          0.0                       613.0   \n",
       "8                       5417.0                      4466.0   \n",
       "\n",
       "   brand_پیکان::Peykan  brand_کیا::Kia  k_means  \n",
       "0                  0.0             0.0        0  \n",
       "1                620.0             0.0        0  \n",
       "2                571.0             0.0        2  \n",
       "3                  0.0             0.0        0  \n",
       "4                528.0             0.0        2  \n",
       "5                  0.0             0.0        0  \n",
       "6               1174.0             0.0        2  \n",
       "7                572.0             0.0        0  \n",
       "8               1376.0          1237.0        1  \n",
       "\n",
       "[9 rows x 63 columns]"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_kmeans1 = kmeanModel.predict(X_kmeans1)\n",
    "X_kmeans1['k_means'] = y_kmeans1\n",
    "centers = kmeanModel.cluster_centers_\n",
    "\n",
    "X_kmeans1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "56ad6920",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_kmeans11= np.array(X_kmeans1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "66031b98",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x16dffa100>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X_kmeans11[:, 1], X_kmeans11[:, 2], c=y_kmeans1, s=100, cmap='viridis')\n",
    "# plt.scatter(centers[:, 0], centers[:, 1], c='black', s=50, alpha=0.5)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad26a972",
   "metadata": {},
   "source": [
    "# DBSCAN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "3d9eb8ce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-1, -1, -1, -1, -1, -1, -1, -1, -1])"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.cluster import DBSCAN\n",
    "X = df_new1.iloc[:,1:]\n",
    "clustering = DBSCAN(eps=0.4, min_samples=2).fit(X)\n",
    "clustering.labels_"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18be15ee",
   "metadata": {},
   "source": [
    "# q2. Clustring the cities based on selling products\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "7ffe92d1",
   "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_Order</th>\n",
       "      <th>ID_Customer</th>\n",
       "      <th>ID_Item</th>\n",
       "      <th>DateTime_CartFinalize</th>\n",
       "      <th>Amount_Gross_Order</th>\n",
       "      <th>city_name_fa</th>\n",
       "      <th>Quantity_item</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2714054</td>\n",
       "      <td>469662</td>\n",
       "      <td>21386</td>\n",
       "      <td>2015-10-15 08:50:56.000</td>\n",
       "      <td>597982.0</td>\n",
       "      <td>محمود آباد</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>11104039</td>\n",
       "      <td>3063877</td>\n",
       "      <td>248497</td>\n",
       "      <td>2018-02-11 00:29:26.000</td>\n",
       "      <td>980000.0</td>\n",
       "      <td>خرمدره</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4228130</td>\n",
       "      <td>3184893</td>\n",
       "      <td>50144</td>\n",
       "      <td>2016-06-14 00:30:08.000</td>\n",
       "      <td>229358.0</td>\n",
       "      <td>قرچک</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>22225624</td>\n",
       "      <td>6888562</td>\n",
       "      <td>70208</td>\n",
       "      <td>2018-09-03 14:37:19.000</td>\n",
       "      <td>16514.0</td>\n",
       "      <td>قم</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4068771</td>\n",
       "      <td>2533490</td>\n",
       "      <td>67627</td>\n",
       "      <td>2016-05-21 11:51:02.000</td>\n",
       "      <td>133028.0</td>\n",
       "      <td>تهران</td>\n",
       "      <td>1.0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199995</th>\n",
       "      <td>12172771</td>\n",
       "      <td>4403268</td>\n",
       "      <td>445940</td>\n",
       "      <td>2018-04-03 12:07:01.000</td>\n",
       "      <td>344037.0</td>\n",
       "      <td>تهران</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199996</th>\n",
       "      <td>4425393</td>\n",
       "      <td>3745774</td>\n",
       "      <td>131645</td>\n",
       "      <td>2016-07-09 19:14:09.000</td>\n",
       "      <td>600000.0</td>\n",
       "      <td>محمدیه</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199997</th>\n",
       "      <td>6671889</td>\n",
       "      <td>4845514</td>\n",
       "      <td>215045</td>\n",
       "      <td>2017-03-08 23:47:06.000</td>\n",
       "      <td>450000.0</td>\n",
       "      <td>اصفهان</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199998</th>\n",
       "      <td>6315995</td>\n",
       "      <td>2541418</td>\n",
       "      <td>264021</td>\n",
       "      <td>2017-02-08 14:59:39.000</td>\n",
       "      <td>193486.0</td>\n",
       "      <td>همدان</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199999</th>\n",
       "      <td>7584127</td>\n",
       "      <td>1342585</td>\n",
       "      <td>89427</td>\n",
       "      <td>2017-06-17 10:08:06.000</td>\n",
       "      <td>55046.0</td>\n",
       "      <td>بندر انزلی</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>200000 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        ID_Order  ID_Customer  ID_Item    DateTime_CartFinalize  \\\n",
       "0        2714054       469662    21386  2015-10-15 08:50:56.000   \n",
       "1       11104039      3063877   248497  2018-02-11 00:29:26.000   \n",
       "2        4228130      3184893    50144  2016-06-14 00:30:08.000   \n",
       "3       22225624      6888562    70208  2018-09-03 14:37:19.000   \n",
       "4        4068771      2533490    67627  2016-05-21 11:51:02.000   \n",
       "...          ...          ...      ...                      ...   \n",
       "199995  12172771      4403268   445940  2018-04-03 12:07:01.000   \n",
       "199996   4425393      3745774   131645  2016-07-09 19:14:09.000   \n",
       "199997   6671889      4845514   215045  2017-03-08 23:47:06.000   \n",
       "199998   6315995      2541418   264021  2017-02-08 14:59:39.000   \n",
       "199999   7584127      1342585    89427  2017-06-17 10:08:06.000   \n",
       "\n",
       "        Amount_Gross_Order city_name_fa  Quantity_item  \n",
       "0                 597982.0   محمود آباد            1.0  \n",
       "1                 980000.0       خرمدره            1.0  \n",
       "2                 229358.0         قرچک            1.0  \n",
       "3                  16514.0           قم            1.0  \n",
       "4                 133028.0        تهران            1.0  \n",
       "...                    ...          ...            ...  \n",
       "199995            344037.0        تهران            2.0  \n",
       "199996            600000.0       محمدیه            1.0  \n",
       "199997            450000.0       اصفهان            1.0  \n",
       "199998            193486.0        همدان            1.0  \n",
       "199999             55046.0   بندر انزلی            1.0  \n",
       "\n",
       "[200000 rows x 7 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "orders"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d448bdac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "orders['ID_Item'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d9f080be",
   "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_Order</th>\n",
       "      <th>ID_Customer</th>\n",
       "      <th>ID_Item</th>\n",
       "      <th>DateTime_CartFinalize</th>\n",
       "      <th>Amount_Gross_Order</th>\n",
       "      <th>city_name_fa</th>\n",
       "      <th>Quantity_item</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4068771</td>\n",
       "      <td>2533490</td>\n",
       "      <td>67627</td>\n",
       "      <td>2016-05-21 11:51:02.000</td>\n",
       "      <td>133028.0</td>\n",
       "      <td>تهران</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9916301</td>\n",
       "      <td>1145709</td>\n",
       "      <td>102588</td>\n",
       "      <td>2017-12-11 19:33:59.000</td>\n",
       "      <td>34862.0</td>\n",
       "      <td>تهران</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>4605740</td>\n",
       "      <td>637880</td>\n",
       "      <td>138296</td>\n",
       "      <td>2016-08-02 10:48:18.000</td>\n",
       "      <td>469358.0</td>\n",
       "      <td>تهران</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13469523</td>\n",
       "      <td>3819595</td>\n",
       "      <td>564993</td>\n",
       "      <td>2018-06-13 12:28:55.000</td>\n",
       "      <td>525000.0</td>\n",
       "      <td>تهران</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>24233238</td>\n",
       "      <td>500391</td>\n",
       "      <td>1707686</td>\n",
       "      <td>2018-11-21 07:44:36.000</td>\n",
       "      <td>49000.0</td>\n",
       "      <td>تهران</td>\n",
       "      <td>1.0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199986</th>\n",
       "      <td>22105540</td>\n",
       "      <td>4343929</td>\n",
       "      <td>738895</td>\n",
       "      <td>2018-08-30 09:42:40.000</td>\n",
       "      <td>192156.0</td>\n",
       "      <td>تهران</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199989</th>\n",
       "      <td>8320537</td>\n",
       "      <td>659553</td>\n",
       "      <td>192749</td>\n",
       "      <td>2017-08-24 11:38:29.000</td>\n",
       "      <td>37798.0</td>\n",
       "      <td>تهران</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199991</th>\n",
       "      <td>23311624</td>\n",
       "      <td>4293370</td>\n",
       "      <td>1193629</td>\n",
       "      <td>2018-10-15 18:03:08.000</td>\n",
       "      <td>370000.0</td>\n",
       "      <td>تهران</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199994</th>\n",
       "      <td>4228418</td>\n",
       "      <td>3720408</td>\n",
       "      <td>123312</td>\n",
       "      <td>2016-06-14 05:05:39.000</td>\n",
       "      <td>120000.0</td>\n",
       "      <td>تهران</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199995</th>\n",
       "      <td>12172771</td>\n",
       "      <td>4403268</td>\n",
       "      <td>445940</td>\n",
       "      <td>2018-04-03 12:07:01.000</td>\n",
       "      <td>344037.0</td>\n",
       "      <td>تهران</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>108306 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        ID_Order  ID_Customer  ID_Item    DateTime_CartFinalize  \\\n",
       "4        4068771      2533490    67627  2016-05-21 11:51:02.000   \n",
       "8        9916301      1145709   102588  2017-12-11 19:33:59.000   \n",
       "10       4605740       637880   138296  2016-08-02 10:48:18.000   \n",
       "12      13469523      3819595   564993  2018-06-13 12:28:55.000   \n",
       "13      24233238       500391  1707686  2018-11-21 07:44:36.000   \n",
       "...          ...          ...      ...                      ...   \n",
       "199986  22105540      4343929   738895  2018-08-30 09:42:40.000   \n",
       "199989   8320537       659553   192749  2017-08-24 11:38:29.000   \n",
       "199991  23311624      4293370  1193629  2018-10-15 18:03:08.000   \n",
       "199994   4228418      3720408   123312  2016-06-14 05:05:39.000   \n",
       "199995  12172771      4403268   445940  2018-04-03 12:07:01.000   \n",
       "\n",
       "        Amount_Gross_Order city_name_fa  Quantity_item  \n",
       "4                 133028.0        تهران            1.0  \n",
       "8                  34862.0        تهران            1.0  \n",
       "10                469358.0        تهران            1.0  \n",
       "12                525000.0        تهران            1.0  \n",
       "13                 49000.0        تهران            1.0  \n",
       "...                    ...          ...            ...  \n",
       "199986            192156.0        تهران            2.0  \n",
       "199989             37798.0        تهران           24.0  \n",
       "199991            370000.0        تهران            1.0  \n",
       "199994            120000.0        تهران            1.0  \n",
       "199995            344037.0        تهران            2.0  \n",
       "\n",
       "[108306 rows x 7 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "otherCity = orders[ (orders['city_name_fa'] =='اهواز') | ( orders['city_name_fa'] == 'تبریز')|\n",
    "                         (orders['city_name_fa'] == 'اصفهان') | (orders['city_name_fa'] == 'کرج') |\n",
    "                         (orders['city_name_fa'] == 'کرمانشاه') | (orders['city_name_fa'] == 'مشهد') |\n",
    "                         (orders['city_name_fa'] == 'قم') | (orders['city_name_fa'] == 'شیراز') ]\n",
    "                         \n",
    "tehranCity = orders[(orders['city_name_fa'] == 'تهران' )]\n",
    "tehranCity\n",
    "# otherCity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "38ed9099",
   "metadata": {},
   "outputs": [],
   "source": [
    "orders_sameCity = pd.concat([tehranCity.iloc[:60000,:],otherCity])\n",
    "# orders_sameCity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "066fac03",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "orders_sameCity['ID_Item'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "bc08c794",
   "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>86</th>\n",
       "      <th>91</th>\n",
       "      <th>93</th>\n",
       "      <th>99</th>\n",
       "      <th>113</th>\n",
       "      <th>136</th>\n",
       "      <th>147</th>\n",
       "      <th>152</th>\n",
       "      <th>216</th>\n",
       "      <th>226</th>\n",
       "      <th>...</th>\n",
       "      <th>2050131</th>\n",
       "      <th>2052576</th>\n",
       "      <th>2056354</th>\n",
       "      <th>2059919</th>\n",
       "      <th>2063420</th>\n",
       "      <th>2071547</th>\n",
       "      <th>2082626</th>\n",
       "      <th>2082782</th>\n",
       "      <th>2084160</th>\n",
       "      <th>2093722</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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",
       "      <td>...</td>\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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199960</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199969</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199975</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199978</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199997</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>113594 rows × 63114 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        86       91       93       99       113      136      147      \\\n",
       "4             0        0        0        0        0        0        0   \n",
       "8             0        0        0        0        0        0        0   \n",
       "10            0        0        0        0        0        0        0   \n",
       "12            0        0        0        0        0        0        0   \n",
       "13            0        0        0        0        0        0        0   \n",
       "...         ...      ...      ...      ...      ...      ...      ...   \n",
       "199960        0        0        0        0        0        0        0   \n",
       "199969        0        0        0        0        0        0        0   \n",
       "199975        0        0        0        0        0        0        0   \n",
       "199978        0        0        0        0        0        0        0   \n",
       "199997        0        0        0        0        0        0        0   \n",
       "\n",
       "        152      216      226      ...  2050131  2052576  2056354  2059919  \\\n",
       "4             0        0        0  ...        0        0        0        0   \n",
       "8             0        0        0  ...        0        0        0        0   \n",
       "10            0        0        0  ...        0        0        0        0   \n",
       "12            0        0        0  ...        0        0        0        0   \n",
       "13            0        0        0  ...        0        0        0        0   \n",
       "...         ...      ...      ...  ...      ...      ...      ...      ...   \n",
       "199960        0        0        0  ...        0        0        0        0   \n",
       "199969        0        0        0  ...        0        0        0        0   \n",
       "199975        0        0        0  ...        0        0        0        0   \n",
       "199978        0        0        0  ...        0        0        0        0   \n",
       "199997        0        0        0  ...        0        0        0        0   \n",
       "\n",
       "        2063420  2071547  2082626  2082782  2084160  2093722  \n",
       "4             0        0        0        0        0        0  \n",
       "8             0        0        0        0        0        0  \n",
       "10            0        0        0        0        0        0  \n",
       "12            0        0        0        0        0        0  \n",
       "13            0        0        0        0        0        0  \n",
       "...         ...      ...      ...      ...      ...      ...  \n",
       "199960        0        0        0        0        0        0  \n",
       "199969        0        0        0        0        0        0  \n",
       "199975        0        0        0        0        0        0  \n",
       "199978        0        0        0        0        0        0  \n",
       "199997        0        0        0        0        0        0  \n",
       "\n",
       "[113594 rows x 63114 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sub_orders = orders_sameCity['ID_Item']\n",
    "sub_orders = pd.get_dummies(sub_orders)\n",
    "sub_orders"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "7763e37c",
   "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>city_name_fa</th>\n",
       "      <th>86</th>\n",
       "      <th>91</th>\n",
       "      <th>93</th>\n",
       "      <th>99</th>\n",
       "      <th>113</th>\n",
       "      <th>136</th>\n",
       "      <th>147</th>\n",
       "      <th>152</th>\n",
       "      <th>216</th>\n",
       "      <th>...</th>\n",
       "      <th>2050131</th>\n",
       "      <th>2052576</th>\n",
       "      <th>2056354</th>\n",
       "      <th>2059919</th>\n",
       "      <th>2063420</th>\n",
       "      <th>2071547</th>\n",
       "      <th>2082626</th>\n",
       "      <th>2082782</th>\n",
       "      <th>2084160</th>\n",
       "      <th>2093722</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>تهران</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>تهران</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>تهران</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>تهران</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>تهران</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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",
       "      <td>...</td>\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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199960</th>\n",
       "      <td>مشهد</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199969</th>\n",
       "      <td>مشهد</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199975</th>\n",
       "      <td>کرج</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199978</th>\n",
       "      <td>اصفهان</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199997</th>\n",
       "      <td>اصفهان</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>113594 rows × 63115 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       city_name_fa  86  91  93  99  113  136  147  152  216  ...  2050131  \\\n",
       "4             تهران   0   0   0   0    0    0    0    0    0  ...        0   \n",
       "8             تهران   0   0   0   0    0    0    0    0    0  ...        0   \n",
       "10            تهران   0   0   0   0    0    0    0    0    0  ...        0   \n",
       "12            تهران   0   0   0   0    0    0    0    0    0  ...        0   \n",
       "13            تهران   0   0   0   0    0    0    0    0    0  ...        0   \n",
       "...             ...  ..  ..  ..  ..  ...  ...  ...  ...  ...  ...      ...   \n",
       "199960         مشهد   0   0   0   0    0    0    0    0    0  ...        0   \n",
       "199969         مشهد   0   0   0   0    0    0    0    0    0  ...        0   \n",
       "199975          کرج   0   0   0   0    0    0    0    0    0  ...        0   \n",
       "199978       اصفهان   0   0   0   0    0    0    0    0    0  ...        0   \n",
       "199997       اصفهان   0   0   0   0    0    0    0    0    0  ...        0   \n",
       "\n",
       "        2052576  2056354  2059919  2063420  2071547  2082626  2082782  \\\n",
       "4             0        0        0        0        0        0        0   \n",
       "8             0        0        0        0        0        0        0   \n",
       "10            0        0        0        0        0        0        0   \n",
       "12            0        0        0        0        0        0        0   \n",
       "13            0        0        0        0        0        0        0   \n",
       "...         ...      ...      ...      ...      ...      ...      ...   \n",
       "199960        0        0        0        0        0        0        0   \n",
       "199969        0        0        0        0        0        0        0   \n",
       "199975        0        0        0        0        0        0        0   \n",
       "199978        0        0        0        0        0        0        0   \n",
       "199997        0        0        0        0        0        0        0   \n",
       "\n",
       "        2084160  2093722  \n",
       "4             0        0  \n",
       "8             0        0  \n",
       "10            0        0  \n",
       "12            0        0  \n",
       "13            0        0  \n",
       "...         ...      ...  \n",
       "199960        0        0  \n",
       "199969        0        0  \n",
       "199975        0        0  \n",
       "199978        0        0  \n",
       "199997        0        0  \n",
       "\n",
       "[113594 rows x 63115 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "city = orders_sameCity[['city_name_fa']]\n",
    "df2 = pd.concat([city,sub_orders], axis=1)\n",
    "df2\n",
    "\n",
    "# sub_orders = orders[['city_name_fa','ID_Item']]\n",
    "# sub_orders"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "f3d957aa",
   "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>city_name_fa</th>\n",
       "      <th>86</th>\n",
       "      <th>91</th>\n",
       "      <th>93</th>\n",
       "      <th>99</th>\n",
       "      <th>113</th>\n",
       "      <th>136</th>\n",
       "      <th>147</th>\n",
       "      <th>152</th>\n",
       "      <th>216</th>\n",
       "      <th>...</th>\n",
       "      <th>2050131</th>\n",
       "      <th>2052576</th>\n",
       "      <th>2056354</th>\n",
       "      <th>2059919</th>\n",
       "      <th>2063420</th>\n",
       "      <th>2071547</th>\n",
       "      <th>2082626</th>\n",
       "      <th>2082782</th>\n",
       "      <th>2084160</th>\n",
       "      <th>2093722</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>اصفهان</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>اهواز</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>تبریز</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>تهران</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>شیراز</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>قم</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>مشهد</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>کرج</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>کرمانشاه</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9 rows × 63115 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  city_name_fa  86  91  93  99  113  136  147  152  216  ...  2050131  \\\n",
       "0       اصفهان   0   0   1   0    0    1    1    0    0  ...        0   \n",
       "1        اهواز   0   0   0   0    0    0    0    0    1  ...        0   \n",
       "2        تبریز   0   0   0   0    0    0    0    0    0  ...        0   \n",
       "3        تهران   1   1   1   0    1    3    0    2    2  ...        1   \n",
       "4        شیراز   0   0   0   0    1    0    0    0    0  ...        0   \n",
       "5           قم   0   1   0   0    0    0    0    0    0  ...        0   \n",
       "6         مشهد   0   1   0   0    0    0    0    0    0  ...        0   \n",
       "7          کرج   2   2   0   1    0    1    0    0    0  ...        0   \n",
       "8     کرمانشاه   0   0   0   0    0    0    0    0    0  ...        0   \n",
       "\n",
       "   2052576  2056354  2059919  2063420  2071547  2082626  2082782  2084160  \\\n",
       "0        0        0        0        0        0        0        0        0   \n",
       "1        0        0        1        0        0        0        0        0   \n",
       "2        0        0        0        0        0        0        0        0   \n",
       "3        1        1        0        1        1        1        1        1   \n",
       "4        0        0        0        0        0        0        0        0   \n",
       "5        0        0        0        0        0        0        0        0   \n",
       "6        0        0        0        0        0        0        0        0   \n",
       "7        0        0        0        0        0        0        0        0   \n",
       "8        0        0        0        0        0        0        0        0   \n",
       "\n",
       "   2093722  \n",
       "0        0  \n",
       "1        0  \n",
       "2        0  \n",
       "3        1  \n",
       "4        0  \n",
       "5        0  \n",
       "6        0  \n",
       "7        0  \n",
       "8        0  \n",
       "\n",
       "[9 rows x 63115 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = df2.groupby(by=['city_name_fa'],as_index=False).sum()\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "1001d733",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      اصفهان\n",
       "1       اهواز\n",
       "2       تبریز\n",
       "3       تهران\n",
       "4       شیراز\n",
       "5          قم\n",
       "6        مشهد\n",
       "7         کرج\n",
       "8    کرمانشاه\n",
       "Name: city_name_fa, dtype: object"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "groupsOfcity2 = df2['city_name_fa']\n",
    "groupsOfcity2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "938a34f4",
   "metadata": {},
   "source": [
    "# agglomerative"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "9ad4070b",
   "metadata": {},
   "outputs": [],
   "source": [
    "AggloX2 = df2.iloc[:,1:]\n",
    "AggloX2\n",
    "clustering_agglomerative = AgglomerativeClustering(n_clusters=3).fit(AggloX2)\n",
    "AggloX2['agglo'] = clustering_agglomerative.labels_\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "b9161894",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x173dedfa0>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "AggloXX2 = np.array(AggloX2)\n",
    "plt.scatter(AggloXX2[:, 1], AggloXX2[:, 2], c=AggloXX2[:, 152], s=100, cmap='viridis')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c5064b38",
   "metadata": {},
   "source": [
    "# k_means"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "155ca34e",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_kmeans2 = df2.iloc[:,1:]\n",
    "distortions2 = []\n",
    "K = range(1,10)\n",
    "for k in K:\n",
    "    kmeanModel2 = KMeans(n_clusters=k)\n",
    "    kmeanModel2.fit(X_kmeans2)\n",
    "    distortions2.append(kmeanModel2.inertia_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "2ea4810f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16,8))\n",
    "plt.plot(K, distortions2, 'bx-')\n",
    "plt.xlabel('k')\n",
    "plt.ylabel('Distortion')\n",
    "plt.title('The Elbow Method showing the optimal k')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "847ddd73",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KMeans(n_clusters=3)"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeanModel2 = KMeans(n_clusters=3)\n",
    "kmeanModel2.fit(X_kmeans2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "30de6571",
   "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>86</th>\n",
       "      <th>91</th>\n",
       "      <th>93</th>\n",
       "      <th>99</th>\n",
       "      <th>113</th>\n",
       "      <th>136</th>\n",
       "      <th>147</th>\n",
       "      <th>152</th>\n",
       "      <th>216</th>\n",
       "      <th>226</th>\n",
       "      <th>...</th>\n",
       "      <th>2052576</th>\n",
       "      <th>2056354</th>\n",
       "      <th>2059919</th>\n",
       "      <th>2063420</th>\n",
       "      <th>2071547</th>\n",
       "      <th>2082626</th>\n",
       "      <th>2082782</th>\n",
       "      <th>2084160</th>\n",
       "      <th>2093722</th>\n",
       "      <th>k_means</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9 rows × 63115 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   86  91  93  99  113  136  147  152  216  226  ...  2052576  2056354  \\\n",
       "0   0   0   1   0    0    1    1    0    0    0  ...        0        0   \n",
       "1   0   0   0   0    0    0    0    0    1    0  ...        0        0   \n",
       "2   0   0   0   0    0    0    0    0    0    0  ...        0        0   \n",
       "3   1   1   1   0    1    3    0    2    2    2  ...        1        1   \n",
       "4   0   0   0   0    1    0    0    0    0    0  ...        0        0   \n",
       "5   0   1   0   0    0    0    0    0    0    0  ...        0        0   \n",
       "6   0   1   0   0    0    0    0    0    0    0  ...        0        0   \n",
       "7   2   2   0   1    0    1    0    0    0    0  ...        0        0   \n",
       "8   0   0   0   0    0    0    0    0    0    0  ...        0        0   \n",
       "\n",
       "   2059919  2063420  2071547  2082626  2082782  2084160  2093722  k_means  \n",
       "0        0        0        0        0        0        0        0        0  \n",
       "1        1        0        0        0        0        0        0        0  \n",
       "2        0        0        0        0        0        0        0        0  \n",
       "3        0        1        1        1        1        1        1        1  \n",
       "4        0        0        0        0        0        0        0        0  \n",
       "5        0        0        0        0        0        0        0        0  \n",
       "6        0        0        0        0        0        0        0        2  \n",
       "7        0        0        0        0        0        0        0        0  \n",
       "8        0        0        0        0        0        0        0        0  \n",
       "\n",
       "[9 rows x 63115 columns]"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_kmeans2 = kmeanModel2.predict(X_kmeans2)\n",
    "X_kmeans2['k_means'] = y_kmeans2\n",
    "centers2 = kmeanModel2.cluster_centers_\n",
    "X_kmeans2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "3bca3651",
   "metadata": {},
   "outputs": [],
   "source": [
    "# X_kmeans22 = np.array(X_kmeans2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "464cf2c3",
   "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": [
    "plt.scatter(X_kmeans22[:, 1], X_kmeans22[:, 2], c=y_kmeans2, s=100, cmap='viridis')\n",
    "plt.scatter(centers2[:, 0], centers2[:, 1], c='black', s=50, alpha=0.5);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1994e771",
   "metadata": {},
   "source": [
    "# DBSCAN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "f1a0329c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DBSCAN(eps=3, min_samples=4)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X2 = df2.iloc[:,1:]\n",
    "clustering = DBSCAN(eps=3, min_samples=4).fit(X2)\n",
    "clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "db5d47aa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-1, -1, -1, -1, -1, -1, -1, -1, -1])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clustering.labels_"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "57370efa",
   "metadata": {},
   "source": [
    "# q3."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "e517e04b",
   "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>cat1_electronic-devices</th>\n",
       "      <th>cat1_vehicles</th>\n",
       "      <th>cat2_cars</th>\n",
       "      <th>cat2_computers</th>\n",
       "      <th>cat2_mobile-tablet</th>\n",
       "      <th>cat3_laptops</th>\n",
       "      <th>cat3_light</th>\n",
       "      <th>cat3_mobile-phones</th>\n",
       "      <th>cat3_tablet</th>\n",
       "      <th>brand_Acer::ایسر</th>\n",
       "      <th>...</th>\n",
       "      <th>brand_پراید صندوق‌دار::Pride</th>\n",
       "      <th>brand_پراید هاچ‌بک::Pride</th>\n",
       "      <th>brand_پژو روآ / آر‌دی::RD/ROA</th>\n",
       "      <th>brand_پژو پارس::Peugeot Pars</th>\n",
       "      <th>brand_پژو ۲۰۶‍ صندوق‌دار::Peugeot 206</th>\n",
       "      <th>brand_پژو ۲۰۶‍::Peugeot 206</th>\n",
       "      <th>brand_پژو ۴۰۵::Peugeot 405</th>\n",
       "      <th>brand_پیکان::Peykan</th>\n",
       "      <th>brand_کیا::Kia</th>\n",
       "      <th>k_means</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4296.0</td>\n",
       "      <td>4879.0</td>\n",
       "      <td>4879.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4296.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4879.0</td>\n",
       "      <td>4296.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1599.0</td>\n",
       "      <td>351.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>571.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>402.0</td>\n",
       "      <td>718.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5408.0</td>\n",
       "      <td>8773.0</td>\n",
       "      <td>8773.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5408.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8773.0</td>\n",
       "      <td>5021.0</td>\n",
       "      <td>387.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2179.0</td>\n",
       "      <td>610.0</td>\n",
       "      <td>466.0</td>\n",
       "      <td>957.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>767.0</td>\n",
       "      <td>867.0</td>\n",
       "      <td>620.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6269.0</td>\n",
       "      <td>11057.0</td>\n",
       "      <td>11057.0</td>\n",
       "      <td>405.0</td>\n",
       "      <td>5864.0</td>\n",
       "      <td>405.0</td>\n",
       "      <td>11057.0</td>\n",
       "      <td>5443.0</td>\n",
       "      <td>421.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3241.0</td>\n",
       "      <td>641.0</td>\n",
       "      <td>516.0</td>\n",
       "      <td>604.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1028.0</td>\n",
       "      <td>1184.0</td>\n",
       "      <td>571.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2582.0</td>\n",
       "      <td>2277.0</td>\n",
       "      <td>2277.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2582.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2277.0</td>\n",
       "      <td>2582.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>...</td>\n",
       "      <td>961.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>457.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>497.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10862.0</td>\n",
       "      <td>15088.0</td>\n",
       "      <td>15088.0</td>\n",
       "      <td>681.0</td>\n",
       "      <td>10181.0</td>\n",
       "      <td>681.0</td>\n",
       "      <td>15088.0</td>\n",
       "      <td>9560.0</td>\n",
       "      <td>621.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3523.0</td>\n",
       "      <td>1004.0</td>\n",
       "      <td>596.0</td>\n",
       "      <td>1277.0</td>\n",
       "      <td>592.0</td>\n",
       "      <td>1064.0</td>\n",
       "      <td>1221.0</td>\n",
       "      <td>528.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2925.0</td>\n",
       "      <td>1683.0</td>\n",
       "      <td>1683.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2925.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1683.0</td>\n",
       "      <td>2925.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1133.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>550.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6600.0</td>\n",
       "      <td>18344.0</td>\n",
       "      <td>18344.0</td>\n",
       "      <td>370.0</td>\n",
       "      <td>6230.0</td>\n",
       "      <td>370.0</td>\n",
       "      <td>18344.0</td>\n",
       "      <td>5771.0</td>\n",
       "      <td>459.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3874.0</td>\n",
       "      <td>1080.0</td>\n",
       "      <td>456.0</td>\n",
       "      <td>1550.0</td>\n",
       "      <td>518.0</td>\n",
       "      <td>1149.0</td>\n",
       "      <td>1802.0</td>\n",
       "      <td>1174.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>3491.0</td>\n",
       "      <td>4421.0</td>\n",
       "      <td>4421.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3491.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4421.0</td>\n",
       "      <td>3491.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1289.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>687.0</td>\n",
       "      <td>332.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>613.0</td>\n",
       "      <td>572.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>28008.0</td>\n",
       "      <td>53929.0</td>\n",
       "      <td>53929.0</td>\n",
       "      <td>2468.0</td>\n",
       "      <td>25540.0</td>\n",
       "      <td>2468.0</td>\n",
       "      <td>53929.0</td>\n",
       "      <td>23087.0</td>\n",
       "      <td>2453.0</td>\n",
       "      <td>224.0</td>\n",
       "      <td>...</td>\n",
       "      <td>10500.0</td>\n",
       "      <td>2288.0</td>\n",
       "      <td>1159.0</td>\n",
       "      <td>2770.0</td>\n",
       "      <td>1622.0</td>\n",
       "      <td>5417.0</td>\n",
       "      <td>4466.0</td>\n",
       "      <td>1376.0</td>\n",
       "      <td>1237.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9 rows × 63 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   cat1_electronic-devices  cat1_vehicles  cat2_cars  cat2_computers  \\\n",
       "0                   4296.0         4879.0     4879.0             0.0   \n",
       "1                   5408.0         8773.0     8773.0             0.0   \n",
       "2                   6269.0        11057.0    11057.0           405.0   \n",
       "3                   2582.0         2277.0     2277.0             0.0   \n",
       "4                  10862.0        15088.0    15088.0           681.0   \n",
       "5                   2925.0         1683.0     1683.0             0.0   \n",
       "6                   6600.0        18344.0    18344.0           370.0   \n",
       "7                   3491.0         4421.0     4421.0             0.0   \n",
       "8                  28008.0        53929.0    53929.0          2468.0   \n",
       "\n",
       "   cat2_mobile-tablet  cat3_laptops  cat3_light  cat3_mobile-phones  \\\n",
       "0              4296.0           0.0      4879.0              4296.0   \n",
       "1              5408.0           0.0      8773.0              5021.0   \n",
       "2              5864.0         405.0     11057.0              5443.0   \n",
       "3              2582.0           0.0      2277.0              2582.0   \n",
       "4             10181.0         681.0     15088.0              9560.0   \n",
       "5              2925.0           0.0      1683.0              2925.0   \n",
       "6              6230.0         370.0     18344.0              5771.0   \n",
       "7              3491.0           0.0      4421.0              3491.0   \n",
       "8             25540.0        2468.0     53929.0             23087.0   \n",
       "\n",
       "   cat3_tablet  brand_Acer::ایسر  ...  brand_پراید صندوق‌دار::Pride  \\\n",
       "0          0.0               4.0  ...                        1599.0   \n",
       "1        387.0               6.0  ...                        2179.0   \n",
       "2        421.0              39.0  ...                        3241.0   \n",
       "3          0.0               5.0  ...                         961.0   \n",
       "4        621.0              88.0  ...                        3523.0   \n",
       "5          0.0               6.0  ...                        1133.0   \n",
       "6        459.0              32.0  ...                        3874.0   \n",
       "7          0.0               0.0  ...                        1289.0   \n",
       "8       2453.0             224.0  ...                       10500.0   \n",
       "\n",
       "   brand_پراید هاچ‌بک::Pride  brand_پژو روآ / آر‌دی::RD/ROA  \\\n",
       "0                      351.0                            0.0   \n",
       "1                      610.0                          466.0   \n",
       "2                      641.0                          516.0   \n",
       "3                        0.0                            0.0   \n",
       "4                     1004.0                          596.0   \n",
       "5                        0.0                            0.0   \n",
       "6                     1080.0                          456.0   \n",
       "7                        0.0                          687.0   \n",
       "8                     2288.0                         1159.0   \n",
       "\n",
       "   brand_پژو پارس::Peugeot Pars  brand_پژو ۲۰۶‍ صندوق‌دار::Peugeot 206  \\\n",
       "0                         571.0                                    0.0   \n",
       "1                         957.0                                    0.0   \n",
       "2                         604.0                                    0.0   \n",
       "3                         457.0                                    0.0   \n",
       "4                        1277.0                                  592.0   \n",
       "5                           0.0                                    0.0   \n",
       "6                        1550.0                                  518.0   \n",
       "7                         332.0                                    0.0   \n",
       "8                        2770.0                                 1622.0   \n",
       "\n",
       "   brand_پژو ۲۰۶‍::Peugeot 206  brand_پژو ۴۰۵::Peugeot 405  \\\n",
       "0                        402.0                       718.0   \n",
       "1                        767.0                       867.0   \n",
       "2                       1028.0                      1184.0   \n",
       "3                          0.0                       497.0   \n",
       "4                       1064.0                      1221.0   \n",
       "5                          0.0                       550.0   \n",
       "6                       1149.0                      1802.0   \n",
       "7                          0.0                       613.0   \n",
       "8                       5417.0                      4466.0   \n",
       "\n",
       "   brand_پیکان::Peykan  brand_کیا::Kia  k_means  \n",
       "0                  0.0             0.0        0  \n",
       "1                620.0             0.0        0  \n",
       "2                571.0             0.0        2  \n",
       "3                  0.0             0.0        0  \n",
       "4                528.0             0.0        2  \n",
       "5                  0.0             0.0        0  \n",
       "6               1174.0             0.0        2  \n",
       "7                572.0             0.0        0  \n",
       "8               1376.0          1237.0        1  \n",
       "\n",
       "[9 rows x 63 columns]"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_kmeans1\n",
    "# groupsOFCity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "aa53b4e0",
   "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>city</th>\n",
       "      <th>cat1_electronic-devices</th>\n",
       "      <th>cat1_vehicles</th>\n",
       "      <th>cat2_cars</th>\n",
       "      <th>cat2_computers</th>\n",
       "      <th>cat2_mobile-tablet</th>\n",
       "      <th>cat3_laptops</th>\n",
       "      <th>cat3_light</th>\n",
       "      <th>cat3_mobile-phones</th>\n",
       "      <th>cat3_tablet</th>\n",
       "      <th>...</th>\n",
       "      <th>brand_پراید صندوق‌دار::Pride</th>\n",
       "      <th>brand_پراید هاچ‌بک::Pride</th>\n",
       "      <th>brand_پژو روآ / آر‌دی::RD/ROA</th>\n",
       "      <th>brand_پژو پارس::Peugeot Pars</th>\n",
       "      <th>brand_پژو ۲۰۶‍ صندوق‌دار::Peugeot 206</th>\n",
       "      <th>brand_پژو ۲۰۶‍::Peugeot 206</th>\n",
       "      <th>brand_پژو ۴۰۵::Peugeot 405</th>\n",
       "      <th>brand_پیکان::Peykan</th>\n",
       "      <th>brand_کیا::Kia</th>\n",
       "      <th>k_means</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Ahvaz</td>\n",
       "      <td>4296.0</td>\n",
       "      <td>4879.0</td>\n",
       "      <td>4879.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4296.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4879.0</td>\n",
       "      <td>4296.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1599.0</td>\n",
       "      <td>351.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>571.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>402.0</td>\n",
       "      <td>718.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Isfahan</td>\n",
       "      <td>5408.0</td>\n",
       "      <td>8773.0</td>\n",
       "      <td>8773.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5408.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8773.0</td>\n",
       "      <td>5021.0</td>\n",
       "      <td>387.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2179.0</td>\n",
       "      <td>610.0</td>\n",
       "      <td>466.0</td>\n",
       "      <td>957.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>767.0</td>\n",
       "      <td>867.0</td>\n",
       "      <td>620.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Karaj</td>\n",
       "      <td>6269.0</td>\n",
       "      <td>11057.0</td>\n",
       "      <td>11057.0</td>\n",
       "      <td>405.0</td>\n",
       "      <td>5864.0</td>\n",
       "      <td>405.0</td>\n",
       "      <td>11057.0</td>\n",
       "      <td>5443.0</td>\n",
       "      <td>421.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3241.0</td>\n",
       "      <td>641.0</td>\n",
       "      <td>516.0</td>\n",
       "      <td>604.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1028.0</td>\n",
       "      <td>1184.0</td>\n",
       "      <td>571.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Kermanshah</td>\n",
       "      <td>2582.0</td>\n",
       "      <td>2277.0</td>\n",
       "      <td>2277.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2582.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2277.0</td>\n",
       "      <td>2582.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>961.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>457.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>497.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Mashhad</td>\n",
       "      <td>10862.0</td>\n",
       "      <td>15088.0</td>\n",
       "      <td>15088.0</td>\n",
       "      <td>681.0</td>\n",
       "      <td>10181.0</td>\n",
       "      <td>681.0</td>\n",
       "      <td>15088.0</td>\n",
       "      <td>9560.0</td>\n",
       "      <td>621.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3523.0</td>\n",
       "      <td>1004.0</td>\n",
       "      <td>596.0</td>\n",
       "      <td>1277.0</td>\n",
       "      <td>592.0</td>\n",
       "      <td>1064.0</td>\n",
       "      <td>1221.0</td>\n",
       "      <td>528.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Qom</td>\n",
       "      <td>2925.0</td>\n",
       "      <td>1683.0</td>\n",
       "      <td>1683.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2925.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1683.0</td>\n",
       "      <td>2925.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1133.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>550.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Shiraz</td>\n",
       "      <td>6600.0</td>\n",
       "      <td>18344.0</td>\n",
       "      <td>18344.0</td>\n",
       "      <td>370.0</td>\n",
       "      <td>6230.0</td>\n",
       "      <td>370.0</td>\n",
       "      <td>18344.0</td>\n",
       "      <td>5771.0</td>\n",
       "      <td>459.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3874.0</td>\n",
       "      <td>1080.0</td>\n",
       "      <td>456.0</td>\n",
       "      <td>1550.0</td>\n",
       "      <td>518.0</td>\n",
       "      <td>1149.0</td>\n",
       "      <td>1802.0</td>\n",
       "      <td>1174.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Tabriz</td>\n",
       "      <td>3491.0</td>\n",
       "      <td>4421.0</td>\n",
       "      <td>4421.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3491.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4421.0</td>\n",
       "      <td>3491.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1289.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>687.0</td>\n",
       "      <td>332.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>613.0</td>\n",
       "      <td>572.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Tehran</td>\n",
       "      <td>28008.0</td>\n",
       "      <td>53929.0</td>\n",
       "      <td>53929.0</td>\n",
       "      <td>2468.0</td>\n",
       "      <td>25540.0</td>\n",
       "      <td>2468.0</td>\n",
       "      <td>53929.0</td>\n",
       "      <td>23087.0</td>\n",
       "      <td>2453.0</td>\n",
       "      <td>...</td>\n",
       "      <td>10500.0</td>\n",
       "      <td>2288.0</td>\n",
       "      <td>1159.0</td>\n",
       "      <td>2770.0</td>\n",
       "      <td>1622.0</td>\n",
       "      <td>5417.0</td>\n",
       "      <td>4466.0</td>\n",
       "      <td>1376.0</td>\n",
       "      <td>1237.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9 rows × 64 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         city  cat1_electronic-devices  cat1_vehicles  cat2_cars  \\\n",
       "0       Ahvaz                   4296.0         4879.0     4879.0   \n",
       "1     Isfahan                   5408.0         8773.0     8773.0   \n",
       "2       Karaj                   6269.0        11057.0    11057.0   \n",
       "3  Kermanshah                   2582.0         2277.0     2277.0   \n",
       "4     Mashhad                  10862.0        15088.0    15088.0   \n",
       "5         Qom                   2925.0         1683.0     1683.0   \n",
       "6      Shiraz                   6600.0        18344.0    18344.0   \n",
       "7      Tabriz                   3491.0         4421.0     4421.0   \n",
       "8      Tehran                  28008.0        53929.0    53929.0   \n",
       "\n",
       "   cat2_computers  cat2_mobile-tablet  cat3_laptops  cat3_light  \\\n",
       "0             0.0              4296.0           0.0      4879.0   \n",
       "1             0.0              5408.0           0.0      8773.0   \n",
       "2           405.0              5864.0         405.0     11057.0   \n",
       "3             0.0              2582.0           0.0      2277.0   \n",
       "4           681.0             10181.0         681.0     15088.0   \n",
       "5             0.0              2925.0           0.0      1683.0   \n",
       "6           370.0              6230.0         370.0     18344.0   \n",
       "7             0.0              3491.0           0.0      4421.0   \n",
       "8          2468.0             25540.0        2468.0     53929.0   \n",
       "\n",
       "   cat3_mobile-phones  cat3_tablet  ...  brand_پراید صندوق‌دار::Pride  \\\n",
       "0              4296.0          0.0  ...                        1599.0   \n",
       "1              5021.0        387.0  ...                        2179.0   \n",
       "2              5443.0        421.0  ...                        3241.0   \n",
       "3              2582.0          0.0  ...                         961.0   \n",
       "4              9560.0        621.0  ...                        3523.0   \n",
       "5              2925.0          0.0  ...                        1133.0   \n",
       "6              5771.0        459.0  ...                        3874.0   \n",
       "7              3491.0          0.0  ...                        1289.0   \n",
       "8             23087.0       2453.0  ...                       10500.0   \n",
       "\n",
       "   brand_پراید هاچ‌بک::Pride  brand_پژو روآ / آر‌دی::RD/ROA  \\\n",
       "0                      351.0                            0.0   \n",
       "1                      610.0                          466.0   \n",
       "2                      641.0                          516.0   \n",
       "3                        0.0                            0.0   \n",
       "4                     1004.0                          596.0   \n",
       "5                        0.0                            0.0   \n",
       "6                     1080.0                          456.0   \n",
       "7                        0.0                          687.0   \n",
       "8                     2288.0                         1159.0   \n",
       "\n",
       "   brand_پژو پارس::Peugeot Pars  brand_پژو ۲۰۶‍ صندوق‌دار::Peugeot 206  \\\n",
       "0                         571.0                                    0.0   \n",
       "1                         957.0                                    0.0   \n",
       "2                         604.0                                    0.0   \n",
       "3                         457.0                                    0.0   \n",
       "4                        1277.0                                  592.0   \n",
       "5                           0.0                                    0.0   \n",
       "6                        1550.0                                  518.0   \n",
       "7                         332.0                                    0.0   \n",
       "8                        2770.0                                 1622.0   \n",
       "\n",
       "   brand_پژو ۲۰۶‍::Peugeot 206  brand_پژو ۴۰۵::Peugeot 405  \\\n",
       "0                        402.0                       718.0   \n",
       "1                        767.0                       867.0   \n",
       "2                       1028.0                      1184.0   \n",
       "3                          0.0                       497.0   \n",
       "4                       1064.0                      1221.0   \n",
       "5                          0.0                       550.0   \n",
       "6                       1149.0                      1802.0   \n",
       "7                          0.0                       613.0   \n",
       "8                       5417.0                      4466.0   \n",
       "\n",
       "   brand_پیکان::Peykan  brand_کیا::Kia  k_means  \n",
       "0                  0.0             0.0        0  \n",
       "1                620.0             0.0        0  \n",
       "2                571.0             0.0        2  \n",
       "3                  0.0             0.0        0  \n",
       "4                528.0             0.0        2  \n",
       "5                  0.0             0.0        0  \n",
       "6               1174.0             0.0        2  \n",
       "7                572.0             0.0        0  \n",
       "8               1376.0          1237.0        1  \n",
       "\n",
       "[9 rows x 64 columns]"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmean_prediction1 = pd.concat([groupsOFCity,X_kmeans1],axis=1)\n",
    "kmean_prediction1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "45b40eb9",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_kmeans1= np.array(X_kmeans1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "62f90c36",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X_kmeans1[:, 1], X_kmeans1[:, 2], c=y_kmeans1, s=100, cmap='viridis')\n",
    "plt.scatter(centers[:, 0], centers[:, 1], c='black', s=50, alpha=0.5);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "fa9e2b70",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x176fabeb0>"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(kmean_prediction1['city'], X_kmeans1[:, 4], c=y_kmeans1, s=100, cmap='viridis')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "60ccff6e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x177131850>"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(kmean_prediction1['city'],y_kmeans1, s=100, cmap='viridis')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "7f9202e6",
   "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>city</th>\n",
       "      <th>cat1_electronic-devices</th>\n",
       "      <th>cat1_vehicles</th>\n",
       "      <th>cat2_cars</th>\n",
       "      <th>cat2_computers</th>\n",
       "      <th>cat2_mobile-tablet</th>\n",
       "      <th>cat3_laptops</th>\n",
       "      <th>cat3_light</th>\n",
       "      <th>cat3_mobile-phones</th>\n",
       "      <th>cat3_tablet</th>\n",
       "      <th>...</th>\n",
       "      <th>brand_پراید صندوق‌دار::Pride</th>\n",
       "      <th>brand_پراید هاچ‌بک::Pride</th>\n",
       "      <th>brand_پژو روآ / آر‌دی::RD/ROA</th>\n",
       "      <th>brand_پژو پارس::Peugeot Pars</th>\n",
       "      <th>brand_پژو ۲۰۶‍ صندوق‌دار::Peugeot 206</th>\n",
       "      <th>brand_پژو ۲۰۶‍::Peugeot 206</th>\n",
       "      <th>brand_پژو ۴۰۵::Peugeot 405</th>\n",
       "      <th>brand_پیکان::Peykan</th>\n",
       "      <th>brand_کیا::Kia</th>\n",
       "      <th>k_means</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Ahvaz</td>\n",
       "      <td>4296.0</td>\n",
       "      <td>4879.0</td>\n",
       "      <td>4879.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4296.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4879.0</td>\n",
       "      <td>4296.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1599.0</td>\n",
       "      <td>351.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>571.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>402.0</td>\n",
       "      <td>718.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Isfahan</td>\n",
       "      <td>5408.0</td>\n",
       "      <td>8773.0</td>\n",
       "      <td>8773.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5408.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8773.0</td>\n",
       "      <td>5021.0</td>\n",
       "      <td>387.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2179.0</td>\n",
       "      <td>610.0</td>\n",
       "      <td>466.0</td>\n",
       "      <td>957.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>767.0</td>\n",
       "      <td>867.0</td>\n",
       "      <td>620.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Kermanshah</td>\n",
       "      <td>2582.0</td>\n",
       "      <td>2277.0</td>\n",
       "      <td>2277.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2582.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2277.0</td>\n",
       "      <td>2582.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>961.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>457.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>497.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Qom</td>\n",
       "      <td>2925.0</td>\n",
       "      <td>1683.0</td>\n",
       "      <td>1683.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2925.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1683.0</td>\n",
       "      <td>2925.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1133.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>550.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Tabriz</td>\n",
       "      <td>3491.0</td>\n",
       "      <td>4421.0</td>\n",
       "      <td>4421.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3491.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4421.0</td>\n",
       "      <td>3491.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1289.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>687.0</td>\n",
       "      <td>332.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>613.0</td>\n",
       "      <td>572.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 64 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         city  cat1_electronic-devices  cat1_vehicles  cat2_cars  \\\n",
       "0       Ahvaz                   4296.0         4879.0     4879.0   \n",
       "1     Isfahan                   5408.0         8773.0     8773.0   \n",
       "3  Kermanshah                   2582.0         2277.0     2277.0   \n",
       "5         Qom                   2925.0         1683.0     1683.0   \n",
       "7      Tabriz                   3491.0         4421.0     4421.0   \n",
       "\n",
       "   cat2_computers  cat2_mobile-tablet  cat3_laptops  cat3_light  \\\n",
       "0             0.0              4296.0           0.0      4879.0   \n",
       "1             0.0              5408.0           0.0      8773.0   \n",
       "3             0.0              2582.0           0.0      2277.0   \n",
       "5             0.0              2925.0           0.0      1683.0   \n",
       "7             0.0              3491.0           0.0      4421.0   \n",
       "\n",
       "   cat3_mobile-phones  cat3_tablet  ...  brand_پراید صندوق‌دار::Pride  \\\n",
       "0              4296.0          0.0  ...                        1599.0   \n",
       "1              5021.0        387.0  ...                        2179.0   \n",
       "3              2582.0          0.0  ...                         961.0   \n",
       "5              2925.0          0.0  ...                        1133.0   \n",
       "7              3491.0          0.0  ...                        1289.0   \n",
       "\n",
       "   brand_پراید هاچ‌بک::Pride  brand_پژو روآ / آر‌دی::RD/ROA  \\\n",
       "0                      351.0                            0.0   \n",
       "1                      610.0                          466.0   \n",
       "3                        0.0                            0.0   \n",
       "5                        0.0                            0.0   \n",
       "7                        0.0                          687.0   \n",
       "\n",
       "   brand_پژو پارس::Peugeot Pars  brand_پژو ۲۰۶‍ صندوق‌دار::Peugeot 206  \\\n",
       "0                         571.0                                    0.0   \n",
       "1                         957.0                                    0.0   \n",
       "3                         457.0                                    0.0   \n",
       "5                           0.0                                    0.0   \n",
       "7                         332.0                                    0.0   \n",
       "\n",
       "   brand_پژو ۲۰۶‍::Peugeot 206  brand_پژو ۴۰۵::Peugeot 405  \\\n",
       "0                        402.0                       718.0   \n",
       "1                        767.0                       867.0   \n",
       "3                          0.0                       497.0   \n",
       "5                          0.0                       550.0   \n",
       "7                          0.0                       613.0   \n",
       "\n",
       "   brand_پیکان::Peykan  brand_کیا::Kia  k_means  \n",
       "0                  0.0             0.0        0  \n",
       "1                620.0             0.0        0  \n",
       "3                  0.0             0.0        0  \n",
       "5                  0.0             0.0        0  \n",
       "7                572.0             0.0        0  \n",
       "\n",
       "[5 rows x 64 columns]"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CitieWithinACluster0 = kmean_prediction1[kmean_prediction1['k_means'] == 0 ]\n",
    "CitieWithinACluster0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "060be4ae",
   "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>city</th>\n",
       "      <th>cat1_electronic-devices</th>\n",
       "      <th>cat1_vehicles</th>\n",
       "      <th>cat2_cars</th>\n",
       "      <th>cat2_computers</th>\n",
       "      <th>cat2_mobile-tablet</th>\n",
       "      <th>cat3_laptops</th>\n",
       "      <th>cat3_light</th>\n",
       "      <th>cat3_mobile-phones</th>\n",
       "      <th>cat3_tablet</th>\n",
       "      <th>...</th>\n",
       "      <th>brand_پراید صندوق‌دار::Pride</th>\n",
       "      <th>brand_پراید هاچ‌بک::Pride</th>\n",
       "      <th>brand_پژو روآ / آر‌دی::RD/ROA</th>\n",
       "      <th>brand_پژو پارس::Peugeot Pars</th>\n",
       "      <th>brand_پژو ۲۰۶‍ صندوق‌دار::Peugeot 206</th>\n",
       "      <th>brand_پژو ۲۰۶‍::Peugeot 206</th>\n",
       "      <th>brand_پژو ۴۰۵::Peugeot 405</th>\n",
       "      <th>brand_پیکان::Peykan</th>\n",
       "      <th>brand_کیا::Kia</th>\n",
       "      <th>k_means</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Tehran</td>\n",
       "      <td>28008.0</td>\n",
       "      <td>53929.0</td>\n",
       "      <td>53929.0</td>\n",
       "      <td>2468.0</td>\n",
       "      <td>25540.0</td>\n",
       "      <td>2468.0</td>\n",
       "      <td>53929.0</td>\n",
       "      <td>23087.0</td>\n",
       "      <td>2453.0</td>\n",
       "      <td>...</td>\n",
       "      <td>10500.0</td>\n",
       "      <td>2288.0</td>\n",
       "      <td>1159.0</td>\n",
       "      <td>2770.0</td>\n",
       "      <td>1622.0</td>\n",
       "      <td>5417.0</td>\n",
       "      <td>4466.0</td>\n",
       "      <td>1376.0</td>\n",
       "      <td>1237.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 64 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     city  cat1_electronic-devices  cat1_vehicles  cat2_cars  cat2_computers  \\\n",
       "8  Tehran                  28008.0        53929.0    53929.0          2468.0   \n",
       "\n",
       "   cat2_mobile-tablet  cat3_laptops  cat3_light  cat3_mobile-phones  \\\n",
       "8             25540.0        2468.0     53929.0             23087.0   \n",
       "\n",
       "   cat3_tablet  ...  brand_پراید صندوق‌دار::Pride  brand_پراید هاچ‌بک::Pride  \\\n",
       "8       2453.0  ...                       10500.0                     2288.0   \n",
       "\n",
       "   brand_پژو روآ / آر‌دی::RD/ROA  brand_پژو پارس::Peugeot Pars  \\\n",
       "8                         1159.0                        2770.0   \n",
       "\n",
       "   brand_پژو ۲۰۶‍ صندوق‌دار::Peugeot 206  brand_پژو ۲۰۶‍::Peugeot 206  \\\n",
       "8                                 1622.0                       5417.0   \n",
       "\n",
       "   brand_پژو ۴۰۵::Peugeot 405  brand_پیکان::Peykan  brand_کیا::Kia  k_means  \n",
       "8                      4466.0               1376.0          1237.0        1  \n",
       "\n",
       "[1 rows x 64 columns]"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CitieWithinACluster1 = kmean_prediction1[kmean_prediction1['k_means'] == 1 ]\n",
    "CitieWithinACluster1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "b053c74d",
   "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>city</th>\n",
       "      <th>cat1_electronic-devices</th>\n",
       "      <th>cat1_vehicles</th>\n",
       "      <th>cat2_cars</th>\n",
       "      <th>cat2_computers</th>\n",
       "      <th>cat2_mobile-tablet</th>\n",
       "      <th>cat3_laptops</th>\n",
       "      <th>cat3_light</th>\n",
       "      <th>cat3_mobile-phones</th>\n",
       "      <th>cat3_tablet</th>\n",
       "      <th>...</th>\n",
       "      <th>brand_پراید صندوق‌دار::Pride</th>\n",
       "      <th>brand_پراید هاچ‌بک::Pride</th>\n",
       "      <th>brand_پژو روآ / آر‌دی::RD/ROA</th>\n",
       "      <th>brand_پژو پارس::Peugeot Pars</th>\n",
       "      <th>brand_پژو ۲۰۶‍ صندوق‌دار::Peugeot 206</th>\n",
       "      <th>brand_پژو ۲۰۶‍::Peugeot 206</th>\n",
       "      <th>brand_پژو ۴۰۵::Peugeot 405</th>\n",
       "      <th>brand_پیکان::Peykan</th>\n",
       "      <th>brand_کیا::Kia</th>\n",
       "      <th>k_means</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Karaj</td>\n",
       "      <td>6269.0</td>\n",
       "      <td>11057.0</td>\n",
       "      <td>11057.0</td>\n",
       "      <td>405.0</td>\n",
       "      <td>5864.0</td>\n",
       "      <td>405.0</td>\n",
       "      <td>11057.0</td>\n",
       "      <td>5443.0</td>\n",
       "      <td>421.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3241.0</td>\n",
       "      <td>641.0</td>\n",
       "      <td>516.0</td>\n",
       "      <td>604.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1028.0</td>\n",
       "      <td>1184.0</td>\n",
       "      <td>571.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Mashhad</td>\n",
       "      <td>10862.0</td>\n",
       "      <td>15088.0</td>\n",
       "      <td>15088.0</td>\n",
       "      <td>681.0</td>\n",
       "      <td>10181.0</td>\n",
       "      <td>681.0</td>\n",
       "      <td>15088.0</td>\n",
       "      <td>9560.0</td>\n",
       "      <td>621.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3523.0</td>\n",
       "      <td>1004.0</td>\n",
       "      <td>596.0</td>\n",
       "      <td>1277.0</td>\n",
       "      <td>592.0</td>\n",
       "      <td>1064.0</td>\n",
       "      <td>1221.0</td>\n",
       "      <td>528.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Shiraz</td>\n",
       "      <td>6600.0</td>\n",
       "      <td>18344.0</td>\n",
       "      <td>18344.0</td>\n",
       "      <td>370.0</td>\n",
       "      <td>6230.0</td>\n",
       "      <td>370.0</td>\n",
       "      <td>18344.0</td>\n",
       "      <td>5771.0</td>\n",
       "      <td>459.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3874.0</td>\n",
       "      <td>1080.0</td>\n",
       "      <td>456.0</td>\n",
       "      <td>1550.0</td>\n",
       "      <td>518.0</td>\n",
       "      <td>1149.0</td>\n",
       "      <td>1802.0</td>\n",
       "      <td>1174.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 64 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      city  cat1_electronic-devices  cat1_vehicles  cat2_cars  cat2_computers  \\\n",
       "2    Karaj                   6269.0        11057.0    11057.0           405.0   \n",
       "4  Mashhad                  10862.0        15088.0    15088.0           681.0   \n",
       "6   Shiraz                   6600.0        18344.0    18344.0           370.0   \n",
       "\n",
       "   cat2_mobile-tablet  cat3_laptops  cat3_light  cat3_mobile-phones  \\\n",
       "2              5864.0         405.0     11057.0              5443.0   \n",
       "4             10181.0         681.0     15088.0              9560.0   \n",
       "6              6230.0         370.0     18344.0              5771.0   \n",
       "\n",
       "   cat3_tablet  ...  brand_پراید صندوق‌دار::Pride  brand_پراید هاچ‌بک::Pride  \\\n",
       "2        421.0  ...                        3241.0                      641.0   \n",
       "4        621.0  ...                        3523.0                     1004.0   \n",
       "6        459.0  ...                        3874.0                     1080.0   \n",
       "\n",
       "   brand_پژو روآ / آر‌دی::RD/ROA  brand_پژو پارس::Peugeot Pars  \\\n",
       "2                          516.0                         604.0   \n",
       "4                          596.0                        1277.0   \n",
       "6                          456.0                        1550.0   \n",
       "\n",
       "   brand_پژو ۲۰۶‍ صندوق‌دار::Peugeot 206  brand_پژو ۲۰۶‍::Peugeot 206  \\\n",
       "2                                    0.0                       1028.0   \n",
       "4                                  592.0                       1064.0   \n",
       "6                                  518.0                       1149.0   \n",
       "\n",
       "   brand_پژو ۴۰۵::Peugeot 405  brand_پیکان::Peykan  brand_کیا::Kia  k_means  \n",
       "2                      1184.0                571.0             0.0        2  \n",
       "4                      1221.0                528.0             0.0        2  \n",
       "6                      1802.0               1174.0             0.0        2  \n",
       "\n",
       "[3 rows x 64 columns]"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CitieWithinACluster2 = kmean_prediction1[kmean_prediction1['k_means'] == 2 ]\n",
    "CitieWithinACluster2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "a637f2f6",
   "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>city_name_fa</th>\n",
       "      <th>86</th>\n",
       "      <th>91</th>\n",
       "      <th>93</th>\n",
       "      <th>99</th>\n",
       "      <th>113</th>\n",
       "      <th>136</th>\n",
       "      <th>147</th>\n",
       "      <th>152</th>\n",
       "      <th>216</th>\n",
       "      <th>...</th>\n",
       "      <th>2052576</th>\n",
       "      <th>2056354</th>\n",
       "      <th>2059919</th>\n",
       "      <th>2063420</th>\n",
       "      <th>2071547</th>\n",
       "      <th>2082626</th>\n",
       "      <th>2082782</th>\n",
       "      <th>2084160</th>\n",
       "      <th>2093722</th>\n",
       "      <th>k_means</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>اصفهان</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>اهواز</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>تبریز</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>تهران</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>شیراز</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>قم</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>مشهد</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>کرج</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>کرمانشاه</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9 rows × 63116 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  city_name_fa  86  91  93  99  113  136  147  152  216  ...  2052576  \\\n",
       "0       اصفهان   0   0   1   0    0    1    1    0    0  ...        0   \n",
       "1        اهواز   0   0   0   0    0    0    0    0    1  ...        0   \n",
       "2        تبریز   0   0   0   0    0    0    0    0    0  ...        0   \n",
       "3        تهران   1   1   1   0    1    3    0    2    2  ...        1   \n",
       "4        شیراز   0   0   0   0    1    0    0    0    0  ...        0   \n",
       "5           قم   0   1   0   0    0    0    0    0    0  ...        0   \n",
       "6         مشهد   0   1   0   0    0    0    0    0    0  ...        0   \n",
       "7          کرج   2   2   0   1    0    1    0    0    0  ...        0   \n",
       "8     کرمانشاه   0   0   0   0    0    0    0    0    0  ...        0   \n",
       "\n",
       "   2056354  2059919  2063420  2071547  2082626  2082782  2084160  2093722  \\\n",
       "0        0        0        0        0        0        0        0        0   \n",
       "1        0        1        0        0        0        0        0        0   \n",
       "2        0        0        0        0        0        0        0        0   \n",
       "3        1        0        1        1        1        1        1        1   \n",
       "4        0        0        0        0        0        0        0        0   \n",
       "5        0        0        0        0        0        0        0        0   \n",
       "6        0        0        0        0        0        0        0        0   \n",
       "7        0        0        0        0        0        0        0        0   \n",
       "8        0        0        0        0        0        0        0        0   \n",
       "\n",
       "   k_means  \n",
       "0        0  \n",
       "1        0  \n",
       "2        0  \n",
       "3        1  \n",
       "4        0  \n",
       "5        0  \n",
       "6        2  \n",
       "7        0  \n",
       "8        0  \n",
       "\n",
       "[9 rows x 63116 columns]"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_kmeans2\n",
    "groupsOfcity2\n",
    "kmean_prediction2 = pd.concat([groupsOfcity2,X_kmeans2],axis=1)\n",
    "kmean_prediction2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "d210bc28",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 0, 1, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       ...,\n",
       "       [0, 1, 0, ..., 0, 0, 2],\n",
       "       [2, 2, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0]], dtype=int32)"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_kmeans2 = np.array(X_kmeans2)\n",
    "X_kmeans2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "c53d7c4d",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x173e5e220>"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABIEAAADDCAYAAAAV6QG1AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+j8jraAAAgAElEQVR4nO3deZQcdbnG8eeZPTshGbzcJJAoMRIWlzuCBhFkMwgmiIqJG17RXFRcUUFE5EaUXUAFMV42RQkgIhEDBAUXUDQTwSggEIJAEEgIkIVk9vf+UT3QTGbpZLq7Zqa+n3Ny0l3166p3TnXX8tSvqhwRAgAAAAAAwNBWkXYBAAAAAAAAKD1CIAAAAAAAgAwgBAIAAAAAAMgAQiAAAAAAAIAMIAQCAAAAAADIAEIgAAAAAACADKhKa8bjx4+PyZMnpzV7AAAAAACAIWfZsmXPRER9d+NSC4EmT56sxsbGtGYPAEDBmtpadfeTT6qpvU07j9lOrxy7fdoloUzWr92gh/66Uh3tHZq8+06qnzgu7ZIAAAB6ZfvRnsalFgIBADDQbW5t1dl//IOuvvcfqqywLKm1o0NTthurk/fdX2+etFPaJaJE1j75nC763GW665eNqq6tliS1Nrdqt31eo09d8N/aefqklCsEAADYeo6IVGbc0NAQ9AQCAAxUTW2teu+1C7Xi2bVqbm/fYnxdVZXOPXimDp06LYXqUErPPLFWn2w4QevXblB7W8fLxtlS3cg6ffu387XL66ekVCEAAEDPbC+LiIbuxvV5Y2jbl9pebfsfPYy37e/YXmF7ue039LdgAADS9t2/3NVjACRJTW1tOv7Wm7WuqanMlaHUzjz6e90GQJIUIW3e0KSvv+sspXUiDQAAYFsV8nSwyyXN7GX8oZKm5v7Nk/T9/pcFAEB6WtrbdeXye3oMgPJde1+350gwSD31r9W6948PdBsA5dvw7EbdczvLHgAADC59hkAR8XtJz/bSZLakH0XiLknb2d6xWAUCAFBuD6x9RoV08mhqa9MtDz9U+oJQNvfc9g9VVvZ9jmzzxiYtvfmeMlQEAABQPIX0BOrLBEmP571flRu2BdvzbDfablyzZk0RZg0AQPG1trfLLqxtSwG9hTB4tLa0KToKu8yrZXNziasBAAAormKEQAWLiAUR0RARDfX13T6yHgCA1E0aM6agS8EqbU0bN74MFaFcJk37T1VU9b17VDeiVjvvxtPhAADA4FKMEOgJSfnPSZ2YGwYAwKBUP3yE9p7Q9yPAayor9ZHX8TyEoWTP/aZr2MhhfbaLjtAB739LGSoCAAAonmKEQIskfTj3lLA3SVoXEU8WYboAAKTmxLe8VcOqqnocX1dVpX13mqzp9TuUsSqUWkVFhY77zkdVO7ymxzZ1w2s196QjNWL08DJWBgAA0H+FPCL+Kkl/kjTN9irbx9g+1vaxuSaLJa2UtELSDyV9smTVAgBQJruOr9flR7xbo2trNaK6+sXhVbbqqqp0wORX6oKZh6VYIUpl33e/SZ+56OOqGVajuuG1Lw6vqatWdW213v2Fw/X+k45MsUIAAIBt4yjk8Scl0NDQEI2NjanMGwCAQjW3tenmhx/Srx58QJvbWrXL9uP0wT1eq1dtPy7t0lBiL6x7QUuu+K2W3nyPOjpC02dM02EfP0jjdhybdmkAAAA9sr0sIhq6HUcIBAAAAAAAMDT0FgKV9elgAAAAAAAASAchEAAAAAAAQAYQAgEAAAAAAGQAIRAAAAAAAEAGEAIBAAAAAABkACEQAAAAAABABhACAQAAAAAAZAAhEAAAAAAAQAYQAgEAAAAAAGQAIRAAAAAAAEAGEAIBAAAAAABkACEQAAAAAABABhACAQAAAAAAZAAhEAAAAAAAQAYQAgEAAAAAAGQAIRAAAAAAAEAGEAIBAAAAAABkACEQAAAAAABABhACAQAAAAAAZEBBIZDtmbYfsL3C9ondjN/J9u2277a93PY7il8qAAAAAAAAtlWfIZDtSkkXSjpU0nRJc21P79LsZEnXRMTrJc2RdFGxCwUAAAAAAMC2K6Qn0F6SVkTEyohokbRQ0uwubULS6NzrMZL+XbwSAQAAAAAA0F9VBbSZIOnxvPerJO3dpc2pkpbY/rSkEZIOKkp1AAAAAAAAKIpi3Rh6rqTLI2KipHdI+rHtLaZte57tRtuNa9asKdKsAQAAAAAA0JdCQqAnJE3Kez8xNyzfMZKukaSI+JOkOknju04oIhZERENENNTX129bxQAAAAAAANhqhYRASyVNtT3Fdo2SGz8v6tLmMUkHSpLtXZWEQHT1AQAAAAAAGCD6DIEiok3ScZJukXS/kqeA3Wt7vu1ZuWbHS/q47b9JukrSRyIiSlU0AAAAAAAAtk4hN4ZWRCyWtLjLsFPyXt8naZ/ilgYAAAAAAIBiKdaNoQEAAAAAADCAEQIBAAAAAABkACEQAAAAAABABhACAQAAAAAAZAAhEAAAAAAAQAYQAgEAAAAAAGQAIRAAAAAAAEAGEAIBAAAAAABkACEQAAAAAABABhACAQAAAAAAZAAhEAAAAAAAQAYQAgEAAAAAAGQAIRAAAAAAAEAGEAIBAAAAAABkACEQAAAAAABABhACAQAAAAAAZAAhEAAAAAAAQAYQAgEAAAAAAGQAIRAAAAAAAEAGEAIBAAAAAABkACEQAAAAAABABhQUAtmeafsB2ytsn9hDm6Ns32f7Xts/LW6ZAAAAAAAA6I+qvhrYrpR0oaSDJa2StNT2ooi4L6/NVElfkbRPRDxne4dSFQwAAAAAAICtV0hPoL0krYiIlRHRImmhpNld2nxc0oUR8ZwkRcTq4pYJAAAAAACA/igkBJog6fG896tyw/K9WtKrbd9p+y7bM7ubkO15thttN65Zs2bbKgYAAAAAAMBWK9aNoaskTZW0v6S5kn5oe7uujSJiQUQ0RERDfX19kWYNAAAAAACAvhQSAj0haVLe+4m5YflWSVoUEa0R8YikB5WEQgAAAAAAABgACgmBlkqaanuK7RpJcyQt6tLmF0p6Acn2eCWXh60sYp0AAAAAAADohz5DoIhok3ScpFsk3S/pmoi41/Z827NyzW6RtNb2fZJul/SliFhbqqIBAAAAAACwdRwRqcy4oaEhGhsbU5k3AAAAAADAUGR7WUQ0dDeuWDeGBgAAAAAAwABGCAQAAAAAAJABhEAAAAAAAAAZQAgEAAAAAACQAYRAAAAAAAAAGUAIBAAAAAAAkAGEQAAAAAAAABlACAQAAAAAAJABhEAAAAAAAAAZQAgEAAAAAACQAYRAAAAAAAAAGUAIBAAAAAAAkAGEQAAAAAAAABlACAQAAAAAAJABhEAAAAAAAAAZQAgEAAAAAACQAYRAAAAAAAAAGUAIBAAAAAAAkAGEQAAAAAAAABlACAQAAAAAAJABhEAAAAAAAAAZUFAIZHum7Qdsr7B9Yi/t3m07bDcUr0QAAAAAAAD0V58hkO1KSRdKOlTSdElzbU/vpt0oSZ+V9OdiFwkAAAAAAID+KaQn0F6SVkTEyohokbRQ0uxu2n1D0pmSmopYHwAAAAAAAIqgkBBogqTH896vyg17ke03SJoUEb/qbUK259lutN24Zs2arS4WAAAAAAAA26bfN4a2XSHp25KO76ttRCyIiIaIaKivr+/vrAEAAAAAAFCgQkKgJyRNyns/MTes0yhJu0v6re1/SXqTpEXcHBoAAAAAAGDgKCQEWippqu0ptmskzZG0qHNkRKyLiPERMTkiJku6S9KsiGgsScUAAAAAAADYan2GQBHRJuk4SbdIul/SNRFxr+35tmeVukAAAAAAAAD0X1UhjSJisaTFXYad0kPb/ftfFgAAAAAAAIqp3zeGBgAAAAAAwMBHCAQAAAAAAJABhEAAAAAAAAAZQAgEAAAAAACQAYRAAAAAAAAAGUAIBAAAAAAAkAGEQAAAAAAAABlACAQAAAAAAJABhEAAAAAAAAAZQAgEAAAAAACQAYRAAAAAAAAAGUAIBAAAAAAAkAGEQAAAAAAAABlACAQAAAAAAJABhEAAAAAAAAAZQAgEAAAAAACQAYRAAAAAAAAAGUAIBAAAAAAAkAGEQAAAAAAAABlACAQAAAAAAJABBYVAtmfafsD2CtsndjP+C7bvs73c9m9s71z8UgEAAAAAALCt+gyBbFdKulDSoZKmS5pre3qXZndLaoiIPSX9TNJZxS4UAAAAAAAA266QnkB7SVoRESsjokXSQkmz8xtExO0RsSn39i5JE4tbJgAAAAAAAPqjkBBogqTH896vyg3ryTGSbupPUQAAAAAAACiuqmJOzPYHJTVI2q+H8fMkzZOknXbaqZizBgAAAAAAQC8K6Qn0hKRJee8n5oa9jO2DJH1V0qyIaO5uQhGxICIaIqKhvr5+W+oFAAAAAADANigkBFoqaartKbZrJM2RtCi/ge3XS/qBkgBodfHLBAAAAAAAQH/0GQJFRJuk4yTdIul+SddExL2259uelWt2tqSRkq61fY/tRT1MDgAAAAAAACko6J5AEbFY0uIuw07Je31QkesCAAAAAABAERVyORgAAAAAAAAGOUIgAAAAAACADCAEAgAAAAAAyABCIAAAAAAAgAwgBAIAAAAAAMgAQiAAAAAAAIAMIAQCAAAAAADIAEIgAAAAAACADCAEAgAAAAAAyABCIAAAAAAAgAwgBAIAAAAAAMgAQiAAAAAAAIAMIAQCAAAAAADIAEIgAAAAAACADCAEAgAAAAAAyABCIAAAAAAAgAwgBAIAAAAAAMgAQiAAAAAAAIAMIATqp5amFrW3taddBsosItS0qVkdHR1pl4Iy6+joUNOmZkVE2qUAAEqspblVba1taZeBMosIRTQpgn38rOncx2c/L3uytI9flXYBg9H6tRv0i+/dpBu+d7M2Pv+CoiP06je+SnNOOEL7HLGXbKddIkrkyZVP69pzF2nJFb9Ta3OrbKlh5us198R3abcZ09IuDyX04LKHtfCM6/XHGxoVEaqqrtQBc9+io748W5OmTUi7PABAkWzasFk3/uBWXXfejXr+6ecVIe282yTNOeEI7T9nhiorK9MuESUS7U8rNl0hbVooxWZJoah5ozziWLl2n7TLQwk9et/juvqsG/Tbq+9Ue1uHKiortM8Re2nOiUdol9dNSbs8lNDy39+nhadfr2W/Xi5Jqqmr0cyPvk3vPf6d2mGn+pSrKw2nlXQ1NDREY2NjKvPujycfeVqfnfFVvbBuk1qaWl82rm5ErfY98k360uWfIggagv7+h/t10mHfUmtzq9pbXzozZEs1w2r036fN1bs/d3iKFaJUbrr0N7rwM5eqpalV0fHSOrOiskLVtdX6+nVf1Bvf/roUKwQAFMNzq9fpszO+qrVPPqeWzS0vG1c3olZ77Dtd82/4sqqqOY861ETrPxXPfkCKJkmtXcYOk4Z/UBWjv5RGaSixP96wVN/6wAVqbW5VR/tLvfwrKqzqump9fsGxOvD9+6ZYIUrlqjOu109Ou07Nm5pfNryqulLVddU6c8kp2nXvqSlV1z+2l0VEQ3fjCroczPZM2w/YXmH7xG7G19q+Ojf+z7Yn96/kgamjo0MnHPwNrVuzfosASJKaXmjWH667Sz8//1cpVIdSWvfMep18+Olq2tj0sgBIkiKk5k0tuuzkhbrn9n+kVCFK5YGlK3Thpy9V86aWlwVAktTR3qHmTc2a/55ztPrxZ1KqEABQLKfMOkOrH39miwBISvbzlv/uXl1y0k9TqAylFNGsePZoKTZoywBIkjZLm65UbGYff6j598NP6VsfuEDNm5pfFgBJUkdHqHlTi86bd7FWLn80pQpRKn+56e5uAyBJamtt1+YNTfrKzNP0wroXUqiutPoMgWxXSrpQ0qGSpkuaa3t6l2bHSHouInaRdJ6kM4td6ECw7Nblen71OnV09Nx7qmlTs64643q1t3MN8VBy0//9Rm193PupeVOzrjztZ2WqCOVy1enXdxv65mtvbdcN37u5TBUBAEphxd2P6JF/PLbFyZ58zZtbdOPFS9TUzUEDBrGmxZL6WqabFRu/W45qUEbXnf8rtfdx36/W5jZdfdYNZaoI5fLj+dd2GwDla29t15If/a5MFZVPIT2B9pK0IiJWRkSLpIWSZndpM1vSFbnXP5N0oIfg9VBLLv+tNm9s6rNdS1OLHlj6cBkqQrncfNlt3Z4V7OreO/6pTRs2l6EilEN7e7vuunFZnzeIa21p060/HnobCADIktuvukOtfYT+klRRUaFlS/5WhopQLrHpGik29d2w/d+KtsdLXxDK5vaf3qG2XoJfKen5/Yfr7ipTRSiH9Ws3aMXdj/TZrmlTsxb/8NdlqKi8CgmBJkjKX9utyg3rtk1EtElaJ2lc1wnZnme70XbjmjVrtq3iFD2/el1B7SoqKrTh2Y0lrgbltPH5AnYMJFVWV+qFdYW1xcBXSPDXadN6wj8AGMye7aO3d6eOjg5teG7oXR6QaR3PF9bOVVIUdjyAwWHzxsL231qbW3ki9BCy4bmNqq4p7N5uG4fg+r6sj4iPiAUR0RARDfX1g+9O2+MnjSvohs/t7R0a+4oxZagI5TJm/OiC2rW3dWjU9iNLXA3KpXZ4rSoqC+vUOJrlDgCD2g4Tx6myqu8nf1VUVrCfN9RUFnhcEq1SxRbnuTGIjdxuREHt6kbUFrR+wOAwZvxotbb0fhlgp7Gv2K7E1ZRfISHQE5Im5b2fmBvWbRvbVZLGSFpbjAIHksM+dqBqh9f02W7U2BGa+oZXlqEilMs7P3GI6kbU9trGlhre/jrVDe+9HQaPiooK7XfUDFVU9r6qrKmr1mH/c1CZqgIAlMJBH3qrKqv63jW2rTcctEcZKkK5ePgHJBcQBlRNlSt3LH1BKJuZxxyg6trqXttUVlXqoA++tUwVoRxGbjdCe+y7a5/t6kbWatanZpahovIqJARaKmmq7Sm2ayTNkbSoS5tFko7OvX6PpNsirWfPl9Bu+7xGE6buqMrqnlPg2uG1Onr++3hE/BBz8If3U01d7xuImroaffDkd5epIpTLnBOOUHVt791Fq2qq9I6PH1ymigAApTBp2gTtud/0Xg8Ia4fX6qgvzVJ1Te/7BBhkag+QPFq9HxrVyaM+V66KUCazPzVTVb0c20nJft57jn9nmSpCuXz46+9V7bCeO3jYUt3wOu3/vhllrKo8+gyBcvf4OU7SLZLul3RNRNxre77tWblml0gaZ3uFpC9I2uIx8kOBbZ1+88naccoOW/QKcYVVO7xGR37uHXr70W9LqUKUyojRw3XObadq1PYjVdNlZVFZXanaYTX6wiWf0LQ37pJShSiVnadP0skLv6Da4bWq6nLtcE1dtYaPHqYzl3xNY3fg0gAAGOy+ds3xmrLHTlvu5zm5HORtc2Zo7leOTKk6lIpdLW9/pVQxXtKwLmMrJdVJo74o19IbZKgZP2GcvnXTVzVsVN0WJ3yra6pUN6JWp/78S5qwCz3Ahprd37KrPn3Rx1Q7rGaLS/1qh9do9LhROve3/zskr/JwWh12GhoaorGxMZV591fz5mbdvvCPuvacRXr60dWqrKrSfx2yp957/CztuvfUtMtDCa1/doMW//A3WnTRzVr/zAbVDq/V/u+boXd99jBNnMrGYSh7+tE1uv47i/XrK3+vpo1NGjl2pA6bd5AOP/YQAiAAGELaWtt0x8//rKvPukGPP/BvVVRWaPcZ03TUl2frtfvvRm/vISw6Nio2Xy9tukJqXy25Rqo9UB7xUbl6WtrloYTWPvmcfnnxEt30f7/Rxude0PDRw3TI0ftr9nEztcOk8WmXhxJ67J9P6Ofn36jf/+wutWxu0XY7jNHs42bq0GMOLPieUQOR7WUR0dDtOEIgAAAAAACAoaG3EKisTwcDAAAAAABAOlLrCWR7jaRHU5l58Y2X9EzaRSAVLPtsYrlnF8s+u1j22cWyzy6WfXax7LNpKC33nSOivrsRqYVAQ4ntxp66WmFoY9lnE8s9u1j22cWyzy6WfXax7LOLZZ9NWVnuXA4GAAAAAACQAYRAAAAAAAAAGUAIVBwL0i4AqWHZZxPLPbtY9tnFss8uln12seyzi2WfTZlY7twTCAAAAAAAIAPoCQQAAAAAAJABhEAAAAAAAGBQsj3S9rG2b9vKz60qUT2n2v5YKaZdDJkMgUq1sAuY70dsn5bGvLF1tuU7ktb3CkC6BvqGHuXDdmDosH2F7Ytsj067FgClt60hAtJle5jtCyT9VdIESR9KuaRBIZMhEAAAANCTiDg6Ij4ZEevTrgXlZXui7TPSrgPlQYgw6L1L0m6Sdo+Ir0XEE+Wcue0ryzm/YiEEKhLbp9h+ddp1ABj4bH/Y9qFp1wFAsv0m28elXQcGDttVtq+y/S/bs9KuB2V3hqQ1tl9f6AdsH2F7nxLWhNJJNURAvy1REt7tXu4Z2z5HUqPtd5Z73v1FCFQkETE/Ih5Muw4MHLaH274k7TrQM9vftH1858rb9hjbO/bxmS92eV9he2Q37ebYvs/2cttvzx8XET+KiJuK8TcA6LfjJL3Kdn2xJ8x2YNB6o6RdJb1V0nnSi+v679t+0PZJqVaHbWb7GNsP277S9rBuxtdExAcj4lxJ9+WGzbY9L/f6M7Yfsv0d2yPyPnqnpEttjy/LH4JiSi1EwNZzYlTn+4h4RtJcSQttv6JL29Fd3p9ie0Xu9+sepj/e9uTc6/faruuhXbWkL0XE+ZJuydW1l+2vd9O2p/VGagiBemD7lbZvtP1P29/u6YuSa/tm23+3vaTzy1bowrb9WtvVuS8cPQMGmNxyWWr7j7YP2JrPRsSmiDimVLVh2+R+r9+VpIj4akScGxG/zI1+m6T5ud/9Fr9b2zWSTugyuErSvd3M6kRJn5c0S9L2Xaazj+0F/fxTUCQ9hYG9rcdzPQWG9zLNhtz/U2y/pbR/AbaV7drcAd/nJa3PDfuE7cPz2lTbvtT2Stu/tD22yzRG2u52f4rtwOBhu95252UgyyU9K+kmSTvkhu0n6b+UhEPvL0VoiNKyXSvpAiUh33pJh3XT7Brbe+Rer7JdJaleUucJohMkNUh6RNJFnR+KiDWSrpX0vtJUj2LZlhDB9t6547zbbD9t+3627ak5WMnv+EURcY+kyyX9T5e2P7e9m5Tsj+XG7y5pFyXr8xfZrrQ9JiKeiYh/5QbvL+lwde/svHF3SnqVpNGSJnfTttv1RpoIgfLkFv6RtndSsuH/npLugXtLmtbLR49TcqZouV768vW6sHNnB2sj4m8R0SppnaQf9LQjidTMlXSLpDmSzrE9TZJsf972I7bP7u3Dtv8yUBJfvGh7SXvmn62zvb3tIyR9U9LNkkYob+Pg5Hrx/STdIOnq/IlFRIuSIEi2D7G9Z27UpyWdLGmBpN93qWGNpKnF/KOwdQoJA9X7evw/JP2um+mOsl0dEY25QS9IurgUfwOK4k+50G+EpIdzwyZIGpfXZjdJb1KyTvihpP/sMo3zJfV4EoftwKAxU7n1ckS8EBEHKOkJ9LztGUrW56slrVRy75C1aRWKrWd7e0m3SrpO0jIlB4J3OrnsL/9M/18kzbG9s6TxkmZI2knSU7nx7ZLaJN2Wm0bnSYEaSa+RtLEMfw76Z1tChHty48YpuVxwv4i4o/SlohttkvawPTUX6A2zPVvJsdrDXdo+I2mc7VdJukJJWPNPSR2S7u/S9hBJ3+ky7J9Kwh3Zvtgvv1qgc10xUklO0HVdkW+L9UbaMh045L48l3W+j4h2JTvriyUdq+Tg/+1KEr21tve1/YNuJvUTJTsH75PUkhvW18K+TMmXpXPerUoODDmzNLBUSWqStErJjl/nfZ9OlfRmSfva7u1gvk3JTgQGjsWSbpf0Z9urbD+q5GD+MEkfi4jrlIR/37D9lO1/Sfq7klDn0oh42b1DbO8vqfP68c8pFwhFxB8iYl9JKyQd2aWGI5WExkhPIWFgb+vxZ/TyoKDTryVN7HwTEaslje2mHcrM9gjbP+kyuFHSUUrCvv+w/UrlduJyYX+FpL8p2c7fKemNEdG1519P34VObAcGh4lKQp58JytZ9h+SdGVEHB4RO0fEhyOio+wVoj8OlvRI7obfUyLirUq2A2Mjoimv3URJR0v6s5J9vR/nPtt5AmiBpAeVHFB+OTfsJ0pOFqzLvcbAttUhQkQ0S/qBpM9FxHm5bTtSEBG3SbpE0lWSHlOyjT5S0vER8eLvLxf8vlXJPvx7JN0eEUdFxOTcuvyFLpMeqy3D/WmSns4FPftFxJN54yYqOWn4iJKOIycruQKgu6ygu/VGqqrSLiBlhyovBbRtScMljVES0lQqSQDfExGdN4h7re3Jnd3Ecp+5V9JZkr4oaVFucp0Le42k47uZ98u+aLkzCDtKer6Ifx/67zpJN0qapyQUXJIbfr6Sg4exSrqKP9T1g7keZVOUBEgYICIilOzYndpLmzuUdP3vke1xSg4eT9FL3b8XSzrd9kIl3419lOxknpJbV+ys5EzSHEl0I07XYiWXBPw5d4lAu5LLA+5SEgb+yfau6nk9PkfJOqCrruv2er10cgDp2l/Shi7DJirpAWIlvb/uUHLG925JZ0fEebl237R9rpLLQ87ufGJU7rszW8mB4hbYDgwqD0g6xvZFSvYDvybptUpOEHxfUqRYG/pvmLZchpWSXmF7jJJem51BwJ4R0fmb/d/8D0TEaZJO6zKMS8AGkYi4zcm92q6S9ApJmyX9SUmIcGtnu7wQ4RO2t5M0ISJud3L7jtERcXU3k0cZRMTF6qGXtZN7fR0k6UxJ346I53LDul2HR0Tnibt7JH3N9nkR8bjtA5VkBV+VVC1pVK4n0FNKvhdfljQzr+f3SV2me2re6y3WG2nLZAiUt7BD0mty1/vWKFmYj0XE9B4+tyTX6+Oa3NljKzlweExJl7BDIuKRXNvuNhKX5739q5KdjS8q6ZF1tqTrc0kzUtb5HYmIxyTt2c34r9s+S8kZg4fzP5O7zvgwSadL+kquhxkGKSePiT0/Ip7KG3aekg3ArZLe3BkKR8T3bP9FSbjwlKQTI+Kh3GfuVNL99BeSXh8RBL4pKjAM3GI9ntsB+IiSy4APzNa97TkAAAHRSURBVLXLn8ZfJc2z/W1JI5VcRjYgrv+GKiXtlLv0o0PSx5Qc5E+NiE25NvOl5CbAkjbY/qaS3nxTJL1D0gURsT4X/sxQ0mvs1529g9gODGrXKwnnH1LSA/inkt4REU3u+baQGMQiYrntXynZhx+m5KTuQXkBEIaobQgRRuulnt482GOAsv0+JSdn/yLpmIj4U6GfjYj7bM+XdH0uAPy7pHdGxLrctM9Q0tt7lJLeP3PyAqBBx8l+cDblftALlBywVSs5A3xSRKwow7yHSzpH0gFKwqTrJJ2au78IBjjbr1ZyYLcqIj6SN/x4Jd2I/yBpQUT8LZ0KARSb7WuUXCp0o5Lf9xbdwXM9f86XtJeSHkCXSTo3sryxHSCcPMnju0p27qslLZX05YhY2UP7zl4+4yX9S9KdEbHOyWOgL1HSBf2yiLi5y+fYDgDAINQlRFiQHyLYvlXJduMcSc8p2c5viIj70qgV6I9Mh0DA1rK9t5LrvZ9WEtxdwBleAAAAYOjKneQ5XckJ/GolVwLMi4gHUy0M2AaEQAAAAAAAABmQ6aeDAQAAAAAAZAUhEAAAAAAAQAYQAgEAAAAAAGQAIRAAAAAAAEAGEAIBAAAAAABkwP8D6WiCUvvOxPgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1440x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(kmean_prediction2['city_name_fa'], X_kmeans2[:, 4], c=y_kmeans2, s=100, cmap='viridis')\n",
    "# plt.scatter(centers2[:, 0], centers2[:, 1], c='black', s=50, alpha=0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "ad9d85ea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x174a13130>"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(kmean_prediction2['city_name_fa'],y_kmeans2, s=100, cmap='viridis')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "f803a994",
   "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>city_name_fa</th>\n",
       "      <th>86</th>\n",
       "      <th>91</th>\n",
       "      <th>93</th>\n",
       "      <th>99</th>\n",
       "      <th>113</th>\n",
       "      <th>136</th>\n",
       "      <th>147</th>\n",
       "      <th>152</th>\n",
       "      <th>216</th>\n",
       "      <th>...</th>\n",
       "      <th>2052576</th>\n",
       "      <th>2056354</th>\n",
       "      <th>2059919</th>\n",
       "      <th>2063420</th>\n",
       "      <th>2071547</th>\n",
       "      <th>2082626</th>\n",
       "      <th>2082782</th>\n",
       "      <th>2084160</th>\n",
       "      <th>2093722</th>\n",
       "      <th>k_means</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>اصفهان</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>اهواز</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>تبریز</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>شیراز</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>قم</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>کرج</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>کرمانشاه</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7 rows × 63116 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  city_name_fa  86  91  93  99  113  136  147  152  216  ...  2052576  \\\n",
       "0       اصفهان   0   0   1   0    0    1    1    0    0  ...        0   \n",
       "1        اهواز   0   0   0   0    0    0    0    0    1  ...        0   \n",
       "2        تبریز   0   0   0   0    0    0    0    0    0  ...        0   \n",
       "4        شیراز   0   0   0   0    1    0    0    0    0  ...        0   \n",
       "5           قم   0   1   0   0    0    0    0    0    0  ...        0   \n",
       "7          کرج   2   2   0   1    0    1    0    0    0  ...        0   \n",
       "8     کرمانشاه   0   0   0   0    0    0    0    0    0  ...        0   \n",
       "\n",
       "   2056354  2059919  2063420  2071547  2082626  2082782  2084160  2093722  \\\n",
       "0        0        0        0        0        0        0        0        0   \n",
       "1        0        1        0        0        0        0        0        0   \n",
       "2        0        0        0        0        0        0        0        0   \n",
       "4        0        0        0        0        0        0        0        0   \n",
       "5        0        0        0        0        0        0        0        0   \n",
       "7        0        0        0        0        0        0        0        0   \n",
       "8        0        0        0        0        0        0        0        0   \n",
       "\n",
       "   k_means  \n",
       "0        0  \n",
       "1        0  \n",
       "2        0  \n",
       "4        0  \n",
       "5        0  \n",
       "7        0  \n",
       "8        0  \n",
       "\n",
       "[7 rows x 63116 columns]"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CitiesDigikalaWithinACluster0 = kmean_prediction2[kmean_prediction2['k_means'] == 0 ]\n",
    "CitiesDigikalaWithinACluster0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "c29f0c2c",
   "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>city_name_fa</th>\n",
       "      <th>86</th>\n",
       "      <th>91</th>\n",
       "      <th>93</th>\n",
       "      <th>99</th>\n",
       "      <th>113</th>\n",
       "      <th>136</th>\n",
       "      <th>147</th>\n",
       "      <th>152</th>\n",
       "      <th>216</th>\n",
       "      <th>...</th>\n",
       "      <th>2052576</th>\n",
       "      <th>2056354</th>\n",
       "      <th>2059919</th>\n",
       "      <th>2063420</th>\n",
       "      <th>2071547</th>\n",
       "      <th>2082626</th>\n",
       "      <th>2082782</th>\n",
       "      <th>2084160</th>\n",
       "      <th>2093722</th>\n",
       "      <th>k_means</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>تهران</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 63116 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  city_name_fa  86  91  93  99  113  136  147  152  216  ...  2052576  \\\n",
       "3        تهران   1   1   1   0    1    3    0    2    2  ...        1   \n",
       "\n",
       "   2056354  2059919  2063420  2071547  2082626  2082782  2084160  2093722  \\\n",
       "3        1        0        1        1        1        1        1        1   \n",
       "\n",
       "   k_means  \n",
       "3        1  \n",
       "\n",
       "[1 rows x 63116 columns]"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CitiesDigikalaWithinACluster1 = kmean_prediction2[kmean_prediction2['k_means'] == 1 ]\n",
    "CitiesDigikalaWithinACluster1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "3ea3ff30",
   "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>city_name_fa</th>\n",
       "      <th>86</th>\n",
       "      <th>91</th>\n",
       "      <th>93</th>\n",
       "      <th>99</th>\n",
       "      <th>113</th>\n",
       "      <th>136</th>\n",
       "      <th>147</th>\n",
       "      <th>152</th>\n",
       "      <th>216</th>\n",
       "      <th>...</th>\n",
       "      <th>2052576</th>\n",
       "      <th>2056354</th>\n",
       "      <th>2059919</th>\n",
       "      <th>2063420</th>\n",
       "      <th>2071547</th>\n",
       "      <th>2082626</th>\n",
       "      <th>2082782</th>\n",
       "      <th>2084160</th>\n",
       "      <th>2093722</th>\n",
       "      <th>k_means</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>مشهد</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 63116 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  city_name_fa  86  91  93  99  113  136  147  152  216  ...  2052576  \\\n",
       "6         مشهد   0   1   0   0    0    0    0    0    0  ...        0   \n",
       "\n",
       "   2056354  2059919  2063420  2071547  2082626  2082782  2084160  2093722  \\\n",
       "6        0        0        0        0        0        0        0        0   \n",
       "\n",
       "   k_means  \n",
       "6        2  \n",
       "\n",
       "[1 rows x 63116 columns]"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CitiesDigikalaWithinACluster2 = kmean_prediction2[kmean_prediction2['k_means'] == 2 ]\n",
    "CitiesDigikalaWithinACluster2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "f2455e28",
   "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>city_name_fa</th>\n",
       "      <th>86</th>\n",
       "      <th>91</th>\n",
       "      <th>93</th>\n",
       "      <th>99</th>\n",
       "      <th>113</th>\n",
       "      <th>136</th>\n",
       "      <th>147</th>\n",
       "      <th>152</th>\n",
       "      <th>216</th>\n",
       "      <th>...</th>\n",
       "      <th>2052576</th>\n",
       "      <th>2056354</th>\n",
       "      <th>2059919</th>\n",
       "      <th>2063420</th>\n",
       "      <th>2071547</th>\n",
       "      <th>2082626</th>\n",
       "      <th>2082782</th>\n",
       "      <th>2084160</th>\n",
       "      <th>2093722</th>\n",
       "      <th>k_means</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>کرج</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 63116 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  city_name_fa  86  91  93  99  113  136  147  152  216  ...  2052576  \\\n",
       "7          کرج   2   2   0   1    0    1    0    0    0  ...        0   \n",
       "\n",
       "   2056354  2059919  2063420  2071547  2082626  2082782  2084160  2093722  \\\n",
       "7        0        0        0        0        0        0        0        0   \n",
       "\n",
       "   k_means  \n",
       "7        3  \n",
       "\n",
       "[1 rows x 63116 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# CitiesDigikalaWithinACluster2 = kmean_prediction2[kmean_prediction2['k_means'] == 3 ]\n",
    "# CitiesDigikalaWithinACluster2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "5045da93",
   "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>city_name_fa</th>\n",
       "      <th>86</th>\n",
       "      <th>91</th>\n",
       "      <th>93</th>\n",
       "      <th>99</th>\n",
       "      <th>113</th>\n",
       "      <th>136</th>\n",
       "      <th>147</th>\n",
       "      <th>152</th>\n",
       "      <th>216</th>\n",
       "      <th>...</th>\n",
       "      <th>2052576</th>\n",
       "      <th>2056354</th>\n",
       "      <th>2059919</th>\n",
       "      <th>2063420</th>\n",
       "      <th>2071547</th>\n",
       "      <th>2082626</th>\n",
       "      <th>2082782</th>\n",
       "      <th>2084160</th>\n",
       "      <th>2093722</th>\n",
       "      <th>k_means</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>اصفهان</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 63116 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  city_name_fa  86  91  93  99  113  136  147  152  216  ...  2052576  \\\n",
       "0       اصفهان   0   0   1   0    0    1    1    0    0  ...        0   \n",
       "\n",
       "   2056354  2059919  2063420  2071547  2082626  2082782  2084160  2093722  \\\n",
       "0        0        0        0        0        0        0        0        0   \n",
       "\n",
       "   k_means  \n",
       "0        4  \n",
       "\n",
       "[1 rows x 63116 columns]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# CitiesDigikalaWithinACluster3 = kmean_prediction2[kmean_prediction2['k_means'] == 4 ]\n",
    "# CitiesDigikalaWithinACluster3"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a24c4c14",
   "metadata": {},
   "source": [
    "# q4 . clustering items based on price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "id": "fdf70cb3",
   "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>cat2</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>furniture-and-home-decore</td>\n",
       "      <td>3850000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>furniture-and-home-decore</td>\n",
       "      <td>30000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>cars</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>furniture-and-home-decore</td>\n",
       "      <td>600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>baby-and-toys</td>\n",
       "      <td>450000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>947630</th>\n",
       "      <td>building-and-garden</td>\n",
       "      <td>180000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>947631</th>\n",
       "      <td>audio-video</td>\n",
       "      <td>350000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>947632</th>\n",
       "      <td>cars</td>\n",
       "      <td>17400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>947633</th>\n",
       "      <td>mobile-tablet</td>\n",
       "      <td>40000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>947634</th>\n",
       "      <td>jewelry-and-watches</td>\n",
       "      <td>25000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>947635 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                             cat2     price\n",
       "0       furniture-and-home-decore   3850000\n",
       "1       furniture-and-home-decore     30000\n",
       "2                            cars        -1\n",
       "3       furniture-and-home-decore    600000\n",
       "4                   baby-and-toys    450000\n",
       "...                           ...       ...\n",
       "947630        building-and-garden    180000\n",
       "947631                audio-video    350000\n",
       "947632                       cars  17400000\n",
       "947633              mobile-tablet     40000\n",
       "947634        jewelry-and-watches     25000\n",
       "\n",
       "[947635 rows x 2 columns]"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_itemPrice = pd.read_csv (\"/Users/mohadeseh/Documents/me/session8/data_mining/FDM_Project_dataset-99-2/divar_dataset/divar_posts_dataset.csv\") \n",
    "df_itemPrice = df_itemPrice[['cat2','price']]\n",
    "df_itemPrice"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "id": "bfbfe7cf",
   "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>cat2</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>furniture-and-home-decore</td>\n",
       "      <td>3850000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>furniture-and-home-decore</td>\n",
       "      <td>30000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>cars</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>furniture-and-home-decore</td>\n",
       "      <td>600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>baby-and-toys</td>\n",
       "      <td>450000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>947630</th>\n",
       "      <td>building-and-garden</td>\n",
       "      <td>180000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>947631</th>\n",
       "      <td>audio-video</td>\n",
       "      <td>350000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>947632</th>\n",
       "      <td>cars</td>\n",
       "      <td>17400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>947633</th>\n",
       "      <td>mobile-tablet</td>\n",
       "      <td>40000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>947634</th>\n",
       "      <td>jewelry-and-watches</td>\n",
       "      <td>25000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>945877 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                             cat2     price\n",
       "0       furniture-and-home-decore   3850000\n",
       "1       furniture-and-home-decore     30000\n",
       "2                            cars        -1\n",
       "3       furniture-and-home-decore    600000\n",
       "4                   baby-and-toys    450000\n",
       "...                           ...       ...\n",
       "947630        building-and-garden    180000\n",
       "947631                audio-video    350000\n",
       "947632                       cars  17400000\n",
       "947633              mobile-tablet     40000\n",
       "947634        jewelry-and-watches     25000\n",
       "\n",
       "[945877 rows x 2 columns]"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_itemPrice = df_itemPrice[(df_itemPrice['cat2'].notnull())]\n",
    "df_itemPrice"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "id": "f4e49c6c",
   "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>cat2</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>furniture-and-home-decore</td>\n",
       "      <td>3850000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>furniture-and-home-decore</td>\n",
       "      <td>30000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>furniture-and-home-decore</td>\n",
       "      <td>600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>baby-and-toys</td>\n",
       "      <td>450000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>furniture-and-home-decore</td>\n",
       "      <td>1000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>947630</th>\n",
       "      <td>building-and-garden</td>\n",
       "      <td>180000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>947631</th>\n",
       "      <td>audio-video</td>\n",
       "      <td>350000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>947632</th>\n",
       "      <td>cars</td>\n",
       "      <td>17400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>947633</th>\n",
       "      <td>mobile-tablet</td>\n",
       "      <td>40000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>947634</th>\n",
       "      <td>jewelry-and-watches</td>\n",
       "      <td>25000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>784931 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                             cat2     price\n",
       "0       furniture-and-home-decore   3850000\n",
       "1       furniture-and-home-decore     30000\n",
       "3       furniture-and-home-decore    600000\n",
       "4                   baby-and-toys    450000\n",
       "5       furniture-and-home-decore   1000000\n",
       "...                           ...       ...\n",
       "947630        building-and-garden    180000\n",
       "947631                audio-video    350000\n",
       "947632                       cars  17400000\n",
       "947633              mobile-tablet     40000\n",
       "947634        jewelry-and-watches     25000\n",
       "\n",
       "[784931 rows x 2 columns]"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_itemPrice = df_itemPrice[(df_itemPrice['price'] != -1)]\n",
    "df_itemPrice"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "id": "4a7a8374",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 400000000)"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_itemPrice['price'].min(),df_itemPrice['price'].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "id": "fafc016a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/pandas/core/indexing.py:966: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  self.obj[item] = s\n"
     ]
    }
   ],
   "source": [
    "# df_itemPrice.loc[df_itemPrice[(df_itemPrice['price']>50)]['price'].index,'price']=0\n",
    "\n",
    "df_itemPrice.loc[df_itemPrice[(df_itemPrice['price']<=50)]['price'].index,'price']=0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "id": "dcd4690b",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_itemPrice.loc[df_itemPrice[((50 < df_itemPrice['price']) & (df_itemPrice['price'] <= 100))]['price'].index,'price']=1\n",
    "\n",
    "df_itemPrice.loc[df_itemPrice[((100 < df_itemPrice['price']) & (df_itemPrice['price'] <= 200))]['price'].index,'price']=2\n",
    "\n",
    "df_itemPrice.loc[df_itemPrice[((200 < df_itemPrice['price']) & (df_itemPrice['price'] <= 300))]['price'].index,'price']=3\n",
    "\n",
    "df_itemPrice.loc[df_itemPrice[((300 < df_itemPrice['price']) & (df_itemPrice['price'] <= 400))]['price'].index,'price']=4\n",
    "\n",
    "df_itemPrice.loc[df_itemPrice[((400 < df_itemPrice['price']) & (df_itemPrice['price'] <= 500))]['price'].index,'price']=5\n",
    "\n",
    "df_itemPrice.loc[df_itemPrice[((500 < df_itemPrice['price']) & (df_itemPrice['price'] <= 600))]['price'].index,'price']=6\n",
    "\n",
    "\n",
    "df_itemPrice.loc[df_itemPrice[((600 < df_itemPrice['price']) & (df_itemPrice['price'] <= 700))]['price'].index,'price']=7\n",
    "\n",
    "df_itemPrice.loc[df_itemPrice[((700 < df_itemPrice['price']) & (df_itemPrice['price'] <= 800))]['price'].index,'price']=8\n",
    "\n",
    "df_itemPrice.loc[df_itemPrice[((800 < df_itemPrice['price']) & (df_itemPrice['price'] <= 900))]['price'].index,'price']=9\n",
    "\n",
    "df_itemPrice.loc[df_itemPrice[((900 < df_itemPrice['price']) & (df_itemPrice['price'] <= 1000))]['price'].index,'price']=10\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "id": "665bfc36",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_itemPrice.loc[df_itemPrice[((1000 < df_itemPrice['price']) & (df_itemPrice['price'] <= 3000))]['price'].index,'price']=11\n",
    "\n",
    "df_itemPrice.loc[df_itemPrice[((3000 < df_itemPrice['price']) & (df_itemPrice['price'] <= 5000))]['price'].index,'price']=12\n",
    "\n",
    "df_itemPrice.loc[df_itemPrice[((5000 < df_itemPrice['price']) & (df_itemPrice['price'] <= 7000))]['price'].index,'price']=13\n",
    "\n",
    "df_itemPrice.loc[df_itemPrice[((7000 < df_itemPrice['price']) & (df_itemPrice['price'] <= 10000))]['price'].index,'price']=14\n",
    "\n",
    "\n",
    "df_itemPrice.loc[df_itemPrice[((10000 < df_itemPrice['price']) & (df_itemPrice['price'] <= 50000))]['price'].index,'price']=15\n",
    "\n",
    "df_itemPrice.loc[df_itemPrice[((50000 < df_itemPrice['price']) & (df_itemPrice['price'] <= 100000))]['price'].index,'price']=16\n",
    "\n",
    "df_itemPrice.loc[df_itemPrice[((100000 < df_itemPrice['price']) & (df_itemPrice['price'] <= 500000))]['price'].index,'price']=17\n",
    "\n",
    "df_itemPrice.loc[df_itemPrice[((500000 < df_itemPrice['price']) & (df_itemPrice['price'] <= 1000000))]['price'].index,'price']=18\n",
    "\n",
    "df_itemPrice.loc[df_itemPrice[((1000000 < df_itemPrice['price']) & (df_itemPrice['price'] <= 10000000))]['price'].index,'price']=19\n",
    "\n",
    "df_itemPrice.loc[df_itemPrice[((10000000 < df_itemPrice['price']) & (df_itemPrice['price'] <= 20000000))]['price'].index,'price']=20\n",
    "\n",
    "df_itemPrice.loc[df_itemPrice[((20000000 < df_itemPrice['price']) & (df_itemPrice['price'] <= 30000000))]['price'].index,'price']=21\n",
    "\n",
    "df_itemPrice.loc[df_itemPrice[((30000000 < df_itemPrice['price']) & (df_itemPrice['price'] <= 40000000))]['price'].index,'price']=22\n",
    "\n",
    "df_itemPrice.loc[df_itemPrice[((40000000 < df_itemPrice['price']) & (df_itemPrice['price'] <= 50000000))]['price'].index,'price']=23\n",
    "\n",
    "df_itemPrice.loc[df_itemPrice[((50000000 < df_itemPrice['price']) & (df_itemPrice['price'] <= 60000000))]['price'].index,'price']=24\n",
    "\n",
    "df_itemPrice.loc[df_itemPrice[((60000000 < df_itemPrice['price']) & (df_itemPrice['price'] <= 70000000))]['price'].index,'price']=25\n",
    "\n",
    "df_itemPrice.loc[df_itemPrice[((70000000 < df_itemPrice['price']) & (df_itemPrice['price'] <= 80000000))]['price'].index,'price']=26\n",
    "\n",
    "df_itemPrice.loc[df_itemPrice[((80000000 < df_itemPrice['price']) & (df_itemPrice['price'] <= 90000000))]['price'].index,'price']=27\n",
    "\n",
    "df_itemPrice.loc[df_itemPrice[((90000000 < df_itemPrice['price']) & (df_itemPrice['price'] <= 100000000))]['price'].index,'price']=28\n",
    "\n",
    "df_itemPrice.loc[df_itemPrice[((100000000 < df_itemPrice['price']) & (df_itemPrice['price'] <= 120000000))]['price'].index,'price']=29\n",
    "\n",
    "df_itemPrice.loc[df_itemPrice[((120000000 < df_itemPrice['price']) & (df_itemPrice['price'] <= 140000000))]['price'].index,'price']=30\n",
    "\n",
    "df_itemPrice.loc[df_itemPrice[((140000000 < df_itemPrice['price']) & (df_itemPrice['price'] <= 160000000))]['price'].index,'price']=31\n",
    "\n",
    "df_itemPrice.loc[df_itemPrice[((160000000 < df_itemPrice['price']) & (df_itemPrice['price'] <= 180000000))]['price'].index,'price']=32\n",
    "\n",
    "df_itemPrice.loc[df_itemPrice[((180000000 < df_itemPrice['price']) & (df_itemPrice['price'] <= 200000000))]['price'].index,'price']=33\n",
    "\n",
    "df_itemPrice.loc[df_itemPrice[((200000000 < df_itemPrice['price']) & (df_itemPrice['price'] <= 220000000))]['price'].index,'price']=34\n",
    "\n",
    "df_itemPrice.loc[df_itemPrice[((220000000 < df_itemPrice['price']) & (df_itemPrice['price'] <= 240000000))]['price'].index,'price']=35\n",
    "\n",
    "df_itemPrice.loc[df_itemPrice[((240000000 < df_itemPrice['price']) & (df_itemPrice['price'] <= 260000000))]['price'].index,'price']=36\n",
    "\n",
    "df_itemPrice.loc[df_itemPrice[((260000000 < df_itemPrice['price']) & (df_itemPrice['price'] <= 280000000))]['price'].index,'price']=37\n",
    "\n",
    "df_itemPrice.loc[df_itemPrice[((280000000 < df_itemPrice['price']) & (df_itemPrice['price'] <= 300000000))]['price'].index,'price']=38\n",
    "\n",
    "df_itemPrice.loc[df_itemPrice[((300000000 < df_itemPrice['price']) & (df_itemPrice['price'] <= 320000000))]['price'].index,'price']=39\n",
    "\n",
    "df_itemPrice.loc[df_itemPrice[((320000000 < df_itemPrice['price']) & (df_itemPrice['price'] <= 340000000))]['price'].index,'price']=40\n",
    "\n",
    "df_itemPrice.loc[df_itemPrice[((340000000 < df_itemPrice['price']) & (df_itemPrice['price'] <= 360000000))]['price'].index,'price']=41\n",
    "\n",
    "df_itemPrice.loc[df_itemPrice[((360000000 < df_itemPrice['price']) & (df_itemPrice['price'] <= 380000000))]['price'].index,'price']=42\n",
    "\n",
    "df_itemPrice.loc[df_itemPrice[((380000000 < df_itemPrice['price']) & (df_itemPrice['price'] <= 400000000))]['price'].index,'price']=43\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "id": "4ed21b04",
   "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>cat2</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>furniture-and-home-decore</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>furniture-and-home-decore</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>furniture-and-home-decore</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>baby-and-toys</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>furniture-and-home-decore</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>947630</th>\n",
       "      <td>building-and-garden</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>947631</th>\n",
       "      <td>audio-video</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>947632</th>\n",
       "      <td>cars</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>947633</th>\n",
       "      <td>mobile-tablet</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>947634</th>\n",
       "      <td>jewelry-and-watches</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>784931 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                             cat2  price\n",
       "0       furniture-and-home-decore     19\n",
       "1       furniture-and-home-decore     15\n",
       "3       furniture-and-home-decore     18\n",
       "4                   baby-and-toys     17\n",
       "5       furniture-and-home-decore     18\n",
       "...                           ...    ...\n",
       "947630        building-and-garden     17\n",
       "947631                audio-video     17\n",
       "947632                       cars     20\n",
       "947633              mobile-tablet     15\n",
       "947634        jewelry-and-watches     15\n",
       "\n",
       "[784931 rows x 2 columns]"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_itemPrice"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "id": "f685850e",
   "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>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>947630</th>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>947631</th>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>947632</th>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>947633</th>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>947634</th>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>784931 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        price\n",
       "0          19\n",
       "1          15\n",
       "3          18\n",
       "4          17\n",
       "5          18\n",
       "...       ...\n",
       "947630     17\n",
       "947631     17\n",
       "947632     20\n",
       "947633     15\n",
       "947634     15\n",
       "\n",
       "[784931 rows x 1 columns]"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d=df_itemPrice.iloc[:,1:]\n",
    "d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "id": "fc7aa986",
   "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>price_0</th>\n",
       "      <th>price_1</th>\n",
       "      <th>price_10</th>\n",
       "      <th>price_11</th>\n",
       "      <th>price_12</th>\n",
       "      <th>price_13</th>\n",
       "      <th>price_14</th>\n",
       "      <th>price_15</th>\n",
       "      <th>price_16</th>\n",
       "      <th>price_17</th>\n",
       "      <th>...</th>\n",
       "      <th>price_4</th>\n",
       "      <th>price_40</th>\n",
       "      <th>price_41</th>\n",
       "      <th>price_42</th>\n",
       "      <th>price_43</th>\n",
       "      <th>price_5</th>\n",
       "      <th>price_6</th>\n",
       "      <th>price_7</th>\n",
       "      <th>price_8</th>\n",
       "      <th>price_9</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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",
       "      <td>...</td>\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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>947630</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>947631</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>947632</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>947633</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>947634</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>784931 rows × 44 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        price_0  price_1  price_10  price_11  price_12  price_13  price_14  \\\n",
       "0             0        0         0         0         0         0         0   \n",
       "1             0        0         0         0         0         0         0   \n",
       "3             0        0         0         0         0         0         0   \n",
       "4             0        0         0         0         0         0         0   \n",
       "5             0        0         0         0         0         0         0   \n",
       "...         ...      ...       ...       ...       ...       ...       ...   \n",
       "947630        0        0         0         0         0         0         0   \n",
       "947631        0        0         0         0         0         0         0   \n",
       "947632        0        0         0         0         0         0         0   \n",
       "947633        0        0         0         0         0         0         0   \n",
       "947634        0        0         0         0         0         0         0   \n",
       "\n",
       "        price_15  price_16  price_17  ...  price_4  price_40  price_41  \\\n",
       "0              0         0         0  ...        0         0         0   \n",
       "1              1         0         0  ...        0         0         0   \n",
       "3              0         0         0  ...        0         0         0   \n",
       "4              0         0         1  ...        0         0         0   \n",
       "5              0         0         0  ...        0         0         0   \n",
       "...          ...       ...       ...  ...      ...       ...       ...   \n",
       "947630         0         0         1  ...        0         0         0   \n",
       "947631         0         0         1  ...        0         0         0   \n",
       "947632         0         0         0  ...        0         0         0   \n",
       "947633         1         0         0  ...        0         0         0   \n",
       "947634         1         0         0  ...        0         0         0   \n",
       "\n",
       "        price_42  price_43  price_5  price_6  price_7  price_8  price_9  \n",
       "0              0         0        0        0        0        0        0  \n",
       "1              0         0        0        0        0        0        0  \n",
       "3              0         0        0        0        0        0        0  \n",
       "4              0         0        0        0        0        0        0  \n",
       "5              0         0        0        0        0        0        0  \n",
       "...          ...       ...      ...      ...      ...      ...      ...  \n",
       "947630         0         0        0        0        0        0        0  \n",
       "947631         0         0        0        0        0        0        0  \n",
       "947632         0         0        0        0        0        0        0  \n",
       "947633         0         0        0        0        0        0        0  \n",
       "947634         0         0        0        0        0        0        0  \n",
       "\n",
       "[784931 rows x 44 columns]"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new = pd.get_dummies(d.astype(str))\n",
    "df_new"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "id": "cef835d9",
   "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>cat2</th>\n",
       "      <th>price_0</th>\n",
       "      <th>price_1</th>\n",
       "      <th>price_10</th>\n",
       "      <th>price_11</th>\n",
       "      <th>price_12</th>\n",
       "      <th>price_13</th>\n",
       "      <th>price_14</th>\n",
       "      <th>price_15</th>\n",
       "      <th>price_16</th>\n",
       "      <th>...</th>\n",
       "      <th>price_4</th>\n",
       "      <th>price_40</th>\n",
       "      <th>price_41</th>\n",
       "      <th>price_42</th>\n",
       "      <th>price_43</th>\n",
       "      <th>price_5</th>\n",
       "      <th>price_6</th>\n",
       "      <th>price_7</th>\n",
       "      <th>price_8</th>\n",
       "      <th>price_9</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>furniture-and-home-decore</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>furniture-and-home-decore</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>furniture-and-home-decore</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>baby-and-toys</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>furniture-and-home-decore</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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",
       "      <td>...</td>\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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>947630</th>\n",
       "      <td>building-and-garden</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>947631</th>\n",
       "      <td>audio-video</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>947632</th>\n",
       "      <td>cars</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>947633</th>\n",
       "      <td>mobile-tablet</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>947634</th>\n",
       "      <td>jewelry-and-watches</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>784931 rows × 45 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                             cat2  price_0  price_1  price_10  price_11  \\\n",
       "0       furniture-and-home-decore        0        0         0         0   \n",
       "1       furniture-and-home-decore        0        0         0         0   \n",
       "3       furniture-and-home-decore        0        0         0         0   \n",
       "4                   baby-and-toys        0        0         0         0   \n",
       "5       furniture-and-home-decore        0        0         0         0   \n",
       "...                           ...      ...      ...       ...       ...   \n",
       "947630        building-and-garden        0        0         0         0   \n",
       "947631                audio-video        0        0         0         0   \n",
       "947632                       cars        0        0         0         0   \n",
       "947633              mobile-tablet        0        0         0         0   \n",
       "947634        jewelry-and-watches        0        0         0         0   \n",
       "\n",
       "        price_12  price_13  price_14  price_15  price_16  ...  price_4  \\\n",
       "0              0         0         0         0         0  ...        0   \n",
       "1              0         0         0         1         0  ...        0   \n",
       "3              0         0         0         0         0  ...        0   \n",
       "4              0         0         0         0         0  ...        0   \n",
       "5              0         0         0         0         0  ...        0   \n",
       "...          ...       ...       ...       ...       ...  ...      ...   \n",
       "947630         0         0         0         0         0  ...        0   \n",
       "947631         0         0         0         0         0  ...        0   \n",
       "947632         0         0         0         0         0  ...        0   \n",
       "947633         0         0         0         1         0  ...        0   \n",
       "947634         0         0         0         1         0  ...        0   \n",
       "\n",
       "        price_40  price_41  price_42  price_43  price_5  price_6  price_7  \\\n",
       "0              0         0         0         0        0        0        0   \n",
       "1              0         0         0         0        0        0        0   \n",
       "3              0         0         0         0        0        0        0   \n",
       "4              0         0         0         0        0        0        0   \n",
       "5              0         0         0         0        0        0        0   \n",
       "...          ...       ...       ...       ...      ...      ...      ...   \n",
       "947630         0         0         0         0        0        0        0   \n",
       "947631         0         0         0         0        0        0        0   \n",
       "947632         0         0         0         0        0        0        0   \n",
       "947633         0         0         0         0        0        0        0   \n",
       "947634         0         0         0         0        0        0        0   \n",
       "\n",
       "        price_8  price_9  \n",
       "0             0        0  \n",
       "1             0        0  \n",
       "3             0        0  \n",
       "4             0        0  \n",
       "5             0        0  \n",
       "...         ...      ...  \n",
       "947630        0        0  \n",
       "947631        0        0  \n",
       "947632        0        0  \n",
       "947633        0        0  \n",
       "947634        0        0  \n",
       "\n",
       "[784931 rows x 45 columns]"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new = pd.concat([df_itemPrice.iloc[:,:-1],df_new],axis=1)\n",
    "df_new"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "id": "9667f473",
   "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>cat2</th>\n",
       "      <th>price_0</th>\n",
       "      <th>price_1</th>\n",
       "      <th>price_10</th>\n",
       "      <th>price_11</th>\n",
       "      <th>price_12</th>\n",
       "      <th>price_13</th>\n",
       "      <th>price_14</th>\n",
       "      <th>price_15</th>\n",
       "      <th>price_16</th>\n",
       "      <th>...</th>\n",
       "      <th>price_4</th>\n",
       "      <th>price_40</th>\n",
       "      <th>price_41</th>\n",
       "      <th>price_42</th>\n",
       "      <th>price_43</th>\n",
       "      <th>price_5</th>\n",
       "      <th>price_6</th>\n",
       "      <th>price_7</th>\n",
       "      <th>price_8</th>\n",
       "      <th>price_9</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>animals</td>\n",
       "      <td>38.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>195.0</td>\n",
       "      <td>187.0</td>\n",
       "      <td>141.0</td>\n",
       "      <td>836.0</td>\n",
       "      <td>11998.0</td>\n",
       "      <td>10067.0</td>\n",
       "      <td>...</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>audio-video</td>\n",
       "      <td>3.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>2075.0</td>\n",
       "      <td>4058.0</td>\n",
       "      <td>...</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>baby-and-toys</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>155.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>342.0</td>\n",
       "      <td>4653.0</td>\n",
       "      <td>3735.0</td>\n",
       "      <td>...</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>batch</td>\n",
       "      <td>33.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>473.0</td>\n",
       "      <td>656.0</td>\n",
       "      <td>479.0</td>\n",
       "      <td>740.0</td>\n",
       "      <td>2454.0</td>\n",
       "      <td>406.0</td>\n",
       "      <td>...</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>bicycle</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>665.0</td>\n",
       "      <td>1295.0</td>\n",
       "      <td>...</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>book-student-literature</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>141.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>283.0</td>\n",
       "      <td>992.0</td>\n",
       "      <td>234.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>building-and-garden</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>1453.0</td>\n",
       "      <td>2049.0</td>\n",
       "      <td>...</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>cars</td>\n",
       "      <td>16.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>482.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>childrens-clothing-and-shoe</td>\n",
       "      <td>21.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>354.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>1038.0</td>\n",
       "      <td>7292.0</td>\n",
       "      <td>1314.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>clothing-and-shoes</td>\n",
       "      <td>179.0</td>\n",
       "      <td>133.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>146.0</td>\n",
       "      <td>662.0</td>\n",
       "      <td>266.0</td>\n",
       "      <td>2531.0</td>\n",
       "      <td>34417.0</td>\n",
       "      <td>18934.0</td>\n",
       "      <td>...</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>computers</td>\n",
       "      <td>5.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>339.0</td>\n",
       "      <td>5324.0</td>\n",
       "      <td>3081.0</td>\n",
       "      <td>...</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>equipments-and-machinery</td>\n",
       "      <td>18.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>201.0</td>\n",
       "      <td>146.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>201.0</td>\n",
       "      <td>1991.0</td>\n",
       "      <td>2610.0</td>\n",
       "      <td>...</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>furniture-and-home-decore</td>\n",
       "      <td>87.0</td>\n",
       "      <td>124.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>261.0</td>\n",
       "      <td>477.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>1185.0</td>\n",
       "      <td>22249.0</td>\n",
       "      <td>26033.0</td>\n",
       "      <td>...</td>\n",
       "      <td>90.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>game-consoles-and-video-games</td>\n",
       "      <td>14.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>119.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>240.0</td>\n",
       "      <td>3506.0</td>\n",
       "      <td>7519.0</td>\n",
       "      <td>...</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>health-beauty</td>\n",
       "      <td>9.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>104.0</td>\n",
       "      <td>151.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>306.0</td>\n",
       "      <td>2643.0</td>\n",
       "      <td>2065.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>hobby-collectibles</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>263.0</td>\n",
       "      <td>321.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>jewelry-and-watches</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>138.0</td>\n",
       "      <td>1284.0</td>\n",
       "      <td>1245.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>leisure-hobbies-toys</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>122.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>mobile-tablet</td>\n",
       "      <td>23.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>276.0</td>\n",
       "      <td>303.0</td>\n",
       "      <td>127.0</td>\n",
       "      <td>654.0</td>\n",
       "      <td>5058.0</td>\n",
       "      <td>5009.0</td>\n",
       "      <td>...</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>motorcycles</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>837.0</td>\n",
       "      <td>382.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>musical-instruments</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>155.0</td>\n",
       "      <td>169.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>parts-accessories</td>\n",
       "      <td>22.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>170.0</td>\n",
       "      <td>112.0</td>\n",
       "      <td>630.0</td>\n",
       "      <td>8620.0</td>\n",
       "      <td>6729.0</td>\n",
       "      <td>...</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>phone</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>209.0</td>\n",
       "      <td>230.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>sport-leisure</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>1427.0</td>\n",
       "      <td>1275.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>travel-packages</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>utensils-and-appliances</td>\n",
       "      <td>29.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>127.0</td>\n",
       "      <td>240.0</td>\n",
       "      <td>127.0</td>\n",
       "      <td>790.0</td>\n",
       "      <td>11040.0</td>\n",
       "      <td>8622.0</td>\n",
       "      <td>...</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>utility</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>915.0</td>\n",
       "      <td>1240.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>27 rows × 45 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                             cat2  price_0  price_1  price_10  price_11  \\\n",
       "0                         animals     38.0     35.0       7.0     195.0   \n",
       "1                     audio-video      3.0     11.0       1.0      18.0   \n",
       "2                   baby-and-toys     10.0     10.0       2.0      48.0   \n",
       "3                           batch     33.0      6.0      11.0     473.0   \n",
       "4                         bicycle      1.0      5.0       1.0       1.0   \n",
       "5         book-student-literature      3.0      4.0       1.0      63.0   \n",
       "6             building-and-garden      2.0      6.0       8.0      41.0   \n",
       "7                            cars     16.0     13.0       0.0       3.0   \n",
       "8     childrens-clothing-and-shoe     21.0      9.0       2.0      47.0   \n",
       "9              clothing-and-shoes    179.0    133.0       2.0     146.0   \n",
       "10                      computers      5.0     11.0       3.0      32.0   \n",
       "11       equipments-and-machinery     18.0     22.0      17.0     201.0   \n",
       "12      furniture-and-home-decore     87.0    124.0      17.0     261.0   \n",
       "13  game-consoles-and-video-games     14.0     49.0       8.0     102.0   \n",
       "14                  health-beauty      9.0      7.0       4.0     104.0   \n",
       "15             hobby-collectibles      0.0      0.0       3.0      14.0   \n",
       "16            jewelry-and-watches      4.0      1.0       2.0      40.0   \n",
       "17           leisure-hobbies-toys      0.0      0.0       1.0       1.0   \n",
       "18                  mobile-tablet     23.0     10.0      14.0     276.0   \n",
       "19                    motorcycles      5.0      2.0       0.0      63.0   \n",
       "20            musical-instruments      1.0      0.0       0.0       3.0   \n",
       "21              parts-accessories     22.0     21.0       4.0      63.0   \n",
       "22                          phone      0.0      0.0       0.0       3.0   \n",
       "23                  sport-leisure      4.0      6.0       0.0      27.0   \n",
       "24                travel-packages      1.0      0.0       3.0       2.0   \n",
       "25        utensils-and-appliances     29.0     43.0       7.0     127.0   \n",
       "26                        utility      2.0      1.0       5.0       9.0   \n",
       "\n",
       "    price_12  price_13  price_14  price_15  price_16  ...  price_4  price_40  \\\n",
       "0      187.0     141.0     836.0   11998.0   10067.0  ...      8.0       0.0   \n",
       "1       21.0       6.0      53.0    2075.0    4058.0  ...      8.0       0.0   \n",
       "2      155.0      59.0     342.0    4653.0    3735.0  ...      9.0       0.0   \n",
       "3      656.0     479.0     740.0    2454.0     406.0  ...     12.0       0.0   \n",
       "4       11.0       3.0      22.0     665.0    1295.0  ...     11.0       0.0   \n",
       "5      141.0      60.0     283.0     992.0     234.0  ...      0.0       0.0   \n",
       "6       35.0      45.0      94.0    1453.0    2049.0  ...      6.0       0.0   \n",
       "7       12.0      33.0      63.0     482.0      26.0  ...      3.0      20.0   \n",
       "8      354.0     102.0    1038.0    7292.0    1314.0  ...      0.0       0.0   \n",
       "9      662.0     266.0    2531.0   34417.0   18934.0  ...     22.0       0.0   \n",
       "10      81.0      35.0     339.0    5324.0    3081.0  ...      8.0       0.0   \n",
       "11     146.0      75.0     201.0    1991.0    2610.0  ...     15.0       0.0   \n",
       "12     477.0     190.0    1185.0   22249.0   26033.0  ...     90.0       0.0   \n",
       "13     119.0      54.0     240.0    3506.0    7519.0  ...     13.0       0.0   \n",
       "14     151.0      86.0     306.0    2643.0    2065.0  ...      0.0       0.0   \n",
       "15      11.0       2.0      37.0     263.0     321.0  ...      1.0       0.0   \n",
       "16      62.0      32.0     138.0    1284.0    1245.0  ...      1.0       0.0   \n",
       "17       3.0       0.0      11.0     122.0      77.0  ...      1.0       0.0   \n",
       "18     303.0     127.0     654.0    5058.0    5009.0  ...     19.0       0.0   \n",
       "19     120.0      51.0      94.0     837.0     382.0  ...      1.0       0.0   \n",
       "20       3.0       0.0       5.0     155.0     169.0  ...      2.0       0.0   \n",
       "21     170.0     112.0     630.0    8620.0    6729.0  ...     14.0       0.0   \n",
       "22       1.0       1.0       5.0     209.0     230.0  ...      1.0       0.0   \n",
       "23      14.0       5.0      70.0    1427.0    1275.0  ...      1.0       0.0   \n",
       "24       0.0       0.0       0.0      11.0      18.0  ...      2.0       0.0   \n",
       "25     240.0     127.0     790.0   11040.0    8622.0  ...     21.0       0.0   \n",
       "26      12.0       4.0      71.0     915.0    1240.0  ...      5.0       0.0   \n",
       "\n",
       "    price_41  price_42  price_43  price_5  price_6  price_7  price_8  price_9  \n",
       "0        0.0       0.0       0.0      7.0      7.0      2.0     12.0      3.0  \n",
       "1        0.0       0.0       0.0      8.0      3.0      0.0      6.0      2.0  \n",
       "2        0.0       0.0       0.0      4.0      3.0      1.0      0.0      1.0  \n",
       "3        0.0       0.0       0.0      3.0      5.0      3.0      1.0      4.0  \n",
       "4        0.0       0.0       0.0      5.0      8.0      3.0      4.0      1.0  \n",
       "5        0.0       0.0       0.0      1.0      0.0      0.0      0.0      0.0  \n",
       "6        0.0       0.0       0.0      2.0      4.0      1.0      1.0      5.0  \n",
       "7        6.0       6.0       3.0      0.0      0.0      0.0      0.0      0.0  \n",
       "8        0.0       0.0       0.0      0.0      0.0      0.0      0.0      0.0  \n",
       "9        0.0       0.0       0.0      8.0      3.0      3.0      3.0      2.0  \n",
       "10       0.0       0.0       0.0      6.0      5.0      3.0      6.0      0.0  \n",
       "11       0.0       0.0       0.0     27.0     11.0     11.0      8.0      6.0  \n",
       "12       0.0       0.0       0.0     70.0     40.0     28.0     25.0     11.0  \n",
       "13       0.0       0.0       0.0      9.0      6.0      9.0     10.0      6.0  \n",
       "14       0.0       0.0       0.0      2.0      1.0      1.0      0.0      0.0  \n",
       "15       0.0       0.0       0.0      0.0      0.0      0.0      0.0      0.0  \n",
       "16       0.0       0.0       0.0      0.0      2.0      1.0      0.0      3.0  \n",
       "17       0.0       0.0       0.0      0.0      0.0      0.0      0.0      0.0  \n",
       "18       0.0       0.0       0.0     30.0     20.0     21.0     21.0     22.0  \n",
       "19       0.0       0.0       0.0      1.0      0.0      1.0      2.0      1.0  \n",
       "20       0.0       0.0       0.0      1.0      2.0      0.0      1.0      0.0  \n",
       "21       0.0       0.0       0.0     14.0      9.0      4.0      4.0      0.0  \n",
       "22       0.0       0.0       0.0      0.0      0.0      0.0      0.0      0.0  \n",
       "23       0.0       0.0       0.0      0.0      6.0      3.0      7.0      6.0  \n",
       "24       0.0       0.0       0.0      0.0      0.0      1.0      0.0      1.0  \n",
       "25       0.0       0.0       0.0     21.0      8.0      4.0      7.0      4.0  \n",
       "26       0.0       0.0       0.0      1.0      3.0      1.0      1.0      1.0  \n",
       "\n",
       "[27 rows x 45 columns]"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "group_item = df_new.groupby(by=['cat2'],as_index=False).sum()\n",
    "group_item"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cd79a338",
   "metadata": {},
   "source": [
    "# agglomerative"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "id": "281aafdf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0])"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X4 = group_item.iloc[:,1:]\n",
    "X4 \n",
    "clustering_agglomerative = AgglomerativeClustering(n_clusters=3).fit(X4)\n",
    "clustering_agglomerative.labels_"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a81fb9c",
   "metadata": {},
   "source": [
    "# kmeans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "id": "fdb2d4ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "X4 = group_item.iloc[:,1:]\n",
    "distortions = []\n",
    "K = range(1,10)\n",
    "for k in K:\n",
    "    kmeanModel4 = KMeans(n_clusters=k)\n",
    "    kmeanModel4.fit(X4)\n",
    "    distortions.append(kmeanModel4.inertia_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "id": "fe107953",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16,8))\n",
    "plt.plot(K, distortions, 'bx-')\n",
    "plt.xlabel('k')\n",
    "plt.ylabel('Distortion')\n",
    "plt.title('The Elbow Method showing the optimal k')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "id": "a8c3e8ab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KMeans(n_clusters=5)"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeanModel4 = KMeans(n_clusters=5)\n",
    "kmeanModel4.fit(X4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "id": "d432c79a",
   "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>price_0</th>\n",
       "      <th>price_1</th>\n",
       "      <th>price_10</th>\n",
       "      <th>price_11</th>\n",
       "      <th>price_12</th>\n",
       "      <th>price_13</th>\n",
       "      <th>price_14</th>\n",
       "      <th>price_15</th>\n",
       "      <th>price_16</th>\n",
       "      <th>price_17</th>\n",
       "      <th>...</th>\n",
       "      <th>price_40</th>\n",
       "      <th>price_41</th>\n",
       "      <th>price_42</th>\n",
       "      <th>price_43</th>\n",
       "      <th>price_5</th>\n",
       "      <th>price_6</th>\n",
       "      <th>price_7</th>\n",
       "      <th>price_8</th>\n",
       "      <th>price_9</th>\n",
       "      <th>k_means</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>38.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>195.0</td>\n",
       "      <td>187.0</td>\n",
       "      <td>141.0</td>\n",
       "      <td>836.0</td>\n",
       "      <td>11998.0</td>\n",
       "      <td>10067.0</td>\n",
       "      <td>14895.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>2075.0</td>\n",
       "      <td>4058.0</td>\n",
       "      <td>12361.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>155.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>342.0</td>\n",
       "      <td>4653.0</td>\n",
       "      <td>3735.0</td>\n",
       "      <td>8282.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>33.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>473.0</td>\n",
       "      <td>656.0</td>\n",
       "      <td>479.0</td>\n",
       "      <td>740.0</td>\n",
       "      <td>2454.0</td>\n",
       "      <td>406.0</td>\n",
       "      <td>260.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>665.0</td>\n",
       "      <td>1295.0</td>\n",
       "      <td>5666.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>141.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>283.0</td>\n",
       "      <td>992.0</td>\n",
       "      <td>234.0</td>\n",
       "      <td>127.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>1453.0</td>\n",
       "      <td>2049.0</td>\n",
       "      <td>9159.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>16.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>482.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>122.0</td>\n",
       "      <td>...</td>\n",
       "      <td>20.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>21.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>354.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>1038.0</td>\n",
       "      <td>7292.0</td>\n",
       "      <td>1314.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>179.0</td>\n",
       "      <td>133.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>146.0</td>\n",
       "      <td>662.0</td>\n",
       "      <td>266.0</td>\n",
       "      <td>2531.0</td>\n",
       "      <td>34417.0</td>\n",
       "      <td>18934.0</td>\n",
       "      <td>19303.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>5.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>339.0</td>\n",
       "      <td>5324.0</td>\n",
       "      <td>3081.0</td>\n",
       "      <td>8285.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>18.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>201.0</td>\n",
       "      <td>146.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>201.0</td>\n",
       "      <td>1991.0</td>\n",
       "      <td>2610.0</td>\n",
       "      <td>11408.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>87.0</td>\n",
       "      <td>124.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>261.0</td>\n",
       "      <td>477.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>1185.0</td>\n",
       "      <td>22249.0</td>\n",
       "      <td>26033.0</td>\n",
       "      <td>80604.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>119.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>240.0</td>\n",
       "      <td>3506.0</td>\n",
       "      <td>7519.0</td>\n",
       "      <td>10356.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>9.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>104.0</td>\n",
       "      <td>151.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>306.0</td>\n",
       "      <td>2643.0</td>\n",
       "      <td>2065.0</td>\n",
       "      <td>2319.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>263.0</td>\n",
       "      <td>321.0</td>\n",
       "      <td>826.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>138.0</td>\n",
       "      <td>1284.0</td>\n",
       "      <td>1245.0</td>\n",
       "      <td>4442.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>122.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>198.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>23.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>276.0</td>\n",
       "      <td>303.0</td>\n",
       "      <td>127.0</td>\n",
       "      <td>654.0</td>\n",
       "      <td>5058.0</td>\n",
       "      <td>5009.0</td>\n",
       "      <td>26632.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>837.0</td>\n",
       "      <td>382.0</td>\n",
       "      <td>905.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>155.0</td>\n",
       "      <td>169.0</td>\n",
       "      <td>1351.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>22.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>170.0</td>\n",
       "      <td>112.0</td>\n",
       "      <td>630.0</td>\n",
       "      <td>8620.0</td>\n",
       "      <td>6729.0</td>\n",
       "      <td>20321.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>209.0</td>\n",
       "      <td>230.0</td>\n",
       "      <td>384.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>1427.0</td>\n",
       "      <td>1275.0</td>\n",
       "      <td>2533.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>287.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>29.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>127.0</td>\n",
       "      <td>240.0</td>\n",
       "      <td>127.0</td>\n",
       "      <td>790.0</td>\n",
       "      <td>11040.0</td>\n",
       "      <td>8622.0</td>\n",
       "      <td>22235.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>915.0</td>\n",
       "      <td>1240.0</td>\n",
       "      <td>4030.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>27 rows × 45 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    price_0  price_1  price_10  price_11  price_12  price_13  price_14  \\\n",
       "0      38.0     35.0       7.0     195.0     187.0     141.0     836.0   \n",
       "1       3.0     11.0       1.0      18.0      21.0       6.0      53.0   \n",
       "2      10.0     10.0       2.0      48.0     155.0      59.0     342.0   \n",
       "3      33.0      6.0      11.0     473.0     656.0     479.0     740.0   \n",
       "4       1.0      5.0       1.0       1.0      11.0       3.0      22.0   \n",
       "5       3.0      4.0       1.0      63.0     141.0      60.0     283.0   \n",
       "6       2.0      6.0       8.0      41.0      35.0      45.0      94.0   \n",
       "7      16.0     13.0       0.0       3.0      12.0      33.0      63.0   \n",
       "8      21.0      9.0       2.0      47.0     354.0     102.0    1038.0   \n",
       "9     179.0    133.0       2.0     146.0     662.0     266.0    2531.0   \n",
       "10      5.0     11.0       3.0      32.0      81.0      35.0     339.0   \n",
       "11     18.0     22.0      17.0     201.0     146.0      75.0     201.0   \n",
       "12     87.0    124.0      17.0     261.0     477.0     190.0    1185.0   \n",
       "13     14.0     49.0       8.0     102.0     119.0      54.0     240.0   \n",
       "14      9.0      7.0       4.0     104.0     151.0      86.0     306.0   \n",
       "15      0.0      0.0       3.0      14.0      11.0       2.0      37.0   \n",
       "16      4.0      1.0       2.0      40.0      62.0      32.0     138.0   \n",
       "17      0.0      0.0       1.0       1.0       3.0       0.0      11.0   \n",
       "18     23.0     10.0      14.0     276.0     303.0     127.0     654.0   \n",
       "19      5.0      2.0       0.0      63.0     120.0      51.0      94.0   \n",
       "20      1.0      0.0       0.0       3.0       3.0       0.0       5.0   \n",
       "21     22.0     21.0       4.0      63.0     170.0     112.0     630.0   \n",
       "22      0.0      0.0       0.0       3.0       1.0       1.0       5.0   \n",
       "23      4.0      6.0       0.0      27.0      14.0       5.0      70.0   \n",
       "24      1.0      0.0       3.0       2.0       0.0       0.0       0.0   \n",
       "25     29.0     43.0       7.0     127.0     240.0     127.0     790.0   \n",
       "26      2.0      1.0       5.0       9.0      12.0       4.0      71.0   \n",
       "\n",
       "    price_15  price_16  price_17  ...  price_40  price_41  price_42  price_43  \\\n",
       "0    11998.0   10067.0   14895.0  ...       0.0       0.0       0.0       0.0   \n",
       "1     2075.0    4058.0   12361.0  ...       0.0       0.0       0.0       0.0   \n",
       "2     4653.0    3735.0    8282.0  ...       0.0       0.0       0.0       0.0   \n",
       "3     2454.0     406.0     260.0  ...       0.0       0.0       0.0       0.0   \n",
       "4      665.0    1295.0    5666.0  ...       0.0       0.0       0.0       0.0   \n",
       "5      992.0     234.0     127.0  ...       0.0       0.0       0.0       0.0   \n",
       "6     1453.0    2049.0    9159.0  ...       0.0       0.0       0.0       0.0   \n",
       "7      482.0      26.0     122.0  ...      20.0       6.0       6.0       3.0   \n",
       "8     7292.0    1314.0      82.0  ...       0.0       0.0       0.0       0.0   \n",
       "9    34417.0   18934.0   19303.0  ...       0.0       0.0       0.0       0.0   \n",
       "10    5324.0    3081.0    8285.0  ...       0.0       0.0       0.0       0.0   \n",
       "11    1991.0    2610.0   11408.0  ...       0.0       0.0       0.0       0.0   \n",
       "12   22249.0   26033.0   80604.0  ...       0.0       0.0       0.0       0.0   \n",
       "13    3506.0    7519.0   10356.0  ...       0.0       0.0       0.0       0.0   \n",
       "14    2643.0    2065.0    2319.0  ...       0.0       0.0       0.0       0.0   \n",
       "15     263.0     321.0     826.0  ...       0.0       0.0       0.0       0.0   \n",
       "16    1284.0    1245.0    4442.0  ...       0.0       0.0       0.0       0.0   \n",
       "17     122.0      77.0     198.0  ...       0.0       0.0       0.0       0.0   \n",
       "18    5058.0    5009.0   26632.0  ...       0.0       0.0       0.0       0.0   \n",
       "19     837.0     382.0     905.0  ...       0.0       0.0       0.0       0.0   \n",
       "20     155.0     169.0    1351.0  ...       0.0       0.0       0.0       0.0   \n",
       "21    8620.0    6729.0   20321.0  ...       0.0       0.0       0.0       0.0   \n",
       "22     209.0     230.0     384.0  ...       0.0       0.0       0.0       0.0   \n",
       "23    1427.0    1275.0    2533.0  ...       0.0       0.0       0.0       0.0   \n",
       "24      11.0      18.0     287.0  ...       0.0       0.0       0.0       0.0   \n",
       "25   11040.0    8622.0   22235.0  ...       0.0       0.0       0.0       0.0   \n",
       "26     915.0    1240.0    4030.0  ...       0.0       0.0       0.0       0.0   \n",
       "\n",
       "    price_5  price_6  price_7  price_8  price_9  k_means  \n",
       "0       7.0      7.0      2.0     12.0      3.0        1  \n",
       "1       8.0      3.0      0.0      6.0      2.0        0  \n",
       "2       4.0      3.0      1.0      0.0      1.0        0  \n",
       "3       3.0      5.0      3.0      1.0      4.0        0  \n",
       "4       5.0      8.0      3.0      4.0      1.0        0  \n",
       "5       1.0      0.0      0.0      0.0      0.0        0  \n",
       "6       2.0      4.0      1.0      1.0      5.0        0  \n",
       "7       0.0      0.0      0.0      0.0      0.0        2  \n",
       "8       0.0      0.0      0.0      0.0      0.0        0  \n",
       "9       8.0      3.0      3.0      3.0      2.0        4  \n",
       "10      6.0      5.0      3.0      6.0      0.0        0  \n",
       "11     27.0     11.0     11.0      8.0      6.0        0  \n",
       "12     70.0     40.0     28.0     25.0     11.0        3  \n",
       "13      9.0      6.0      9.0     10.0      6.0        0  \n",
       "14      2.0      1.0      1.0      0.0      0.0        0  \n",
       "15      0.0      0.0      0.0      0.0      0.0        0  \n",
       "16      0.0      2.0      1.0      0.0      3.0        0  \n",
       "17      0.0      0.0      0.0      0.0      0.0        0  \n",
       "18     30.0     20.0     21.0     21.0     22.0        1  \n",
       "19      1.0      0.0      1.0      2.0      1.0        0  \n",
       "20      1.0      2.0      0.0      1.0      0.0        0  \n",
       "21     14.0      9.0      4.0      4.0      0.0        1  \n",
       "22      0.0      0.0      0.0      0.0      0.0        0  \n",
       "23      0.0      6.0      3.0      7.0      6.0        0  \n",
       "24      0.0      0.0      1.0      0.0      1.0        0  \n",
       "25     21.0      8.0      4.0      7.0      4.0        1  \n",
       "26      1.0      3.0      1.0      1.0      1.0        0  \n",
       "\n",
       "[27 rows x 45 columns]"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X4['k_means']= kmeanModel4.predict(X4)\n",
    "X4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "754a1688",
   "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>cat2</th>\n",
       "      <th>price_0</th>\n",
       "      <th>price_1</th>\n",
       "      <th>price_10</th>\n",
       "      <th>price_11</th>\n",
       "      <th>price_12</th>\n",
       "      <th>price_13</th>\n",
       "      <th>price_14</th>\n",
       "      <th>price_15</th>\n",
       "      <th>price_16</th>\n",
       "      <th>...</th>\n",
       "      <th>price_40</th>\n",
       "      <th>price_41</th>\n",
       "      <th>price_42</th>\n",
       "      <th>price_43</th>\n",
       "      <th>price_5</th>\n",
       "      <th>price_6</th>\n",
       "      <th>price_7</th>\n",
       "      <th>price_8</th>\n",
       "      <th>price_9</th>\n",
       "      <th>k_means</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>animals</td>\n",
       "      <td>38.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>195.0</td>\n",
       "      <td>187.0</td>\n",
       "      <td>141.0</td>\n",
       "      <td>836.0</td>\n",
       "      <td>11998.0</td>\n",
       "      <td>10067.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>audio-video</td>\n",
       "      <td>3.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>2075.0</td>\n",
       "      <td>4058.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>baby-and-toys</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>155.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>342.0</td>\n",
       "      <td>4653.0</td>\n",
       "      <td>3735.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>batch</td>\n",
       "      <td>33.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>473.0</td>\n",
       "      <td>656.0</td>\n",
       "      <td>479.0</td>\n",
       "      <td>740.0</td>\n",
       "      <td>2454.0</td>\n",
       "      <td>406.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>bicycle</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>665.0</td>\n",
       "      <td>1295.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>book-student-literature</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>141.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>283.0</td>\n",
       "      <td>992.0</td>\n",
       "      <td>234.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>building-and-garden</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>1453.0</td>\n",
       "      <td>2049.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>cars</td>\n",
       "      <td>16.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>482.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>...</td>\n",
       "      <td>20.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>childrens-clothing-and-shoe</td>\n",
       "      <td>21.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>354.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>1038.0</td>\n",
       "      <td>7292.0</td>\n",
       "      <td>1314.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>clothing-and-shoes</td>\n",
       "      <td>179.0</td>\n",
       "      <td>133.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>146.0</td>\n",
       "      <td>662.0</td>\n",
       "      <td>266.0</td>\n",
       "      <td>2531.0</td>\n",
       "      <td>34417.0</td>\n",
       "      <td>18934.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>computers</td>\n",
       "      <td>5.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>339.0</td>\n",
       "      <td>5324.0</td>\n",
       "      <td>3081.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>equipments-and-machinery</td>\n",
       "      <td>18.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>201.0</td>\n",
       "      <td>146.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>201.0</td>\n",
       "      <td>1991.0</td>\n",
       "      <td>2610.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>furniture-and-home-decore</td>\n",
       "      <td>87.0</td>\n",
       "      <td>124.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>261.0</td>\n",
       "      <td>477.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>1185.0</td>\n",
       "      <td>22249.0</td>\n",
       "      <td>26033.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>game-consoles-and-video-games</td>\n",
       "      <td>14.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>119.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>240.0</td>\n",
       "      <td>3506.0</td>\n",
       "      <td>7519.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>health-beauty</td>\n",
       "      <td>9.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>104.0</td>\n",
       "      <td>151.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>306.0</td>\n",
       "      <td>2643.0</td>\n",
       "      <td>2065.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>hobby-collectibles</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>263.0</td>\n",
       "      <td>321.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>jewelry-and-watches</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>138.0</td>\n",
       "      <td>1284.0</td>\n",
       "      <td>1245.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>leisure-hobbies-toys</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>122.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>mobile-tablet</td>\n",
       "      <td>23.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>276.0</td>\n",
       "      <td>303.0</td>\n",
       "      <td>127.0</td>\n",
       "      <td>654.0</td>\n",
       "      <td>5058.0</td>\n",
       "      <td>5009.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>motorcycles</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>837.0</td>\n",
       "      <td>382.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>musical-instruments</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>155.0</td>\n",
       "      <td>169.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>parts-accessories</td>\n",
       "      <td>22.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>170.0</td>\n",
       "      <td>112.0</td>\n",
       "      <td>630.0</td>\n",
       "      <td>8620.0</td>\n",
       "      <td>6729.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>phone</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>209.0</td>\n",
       "      <td>230.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>sport-leisure</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>1427.0</td>\n",
       "      <td>1275.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>travel-packages</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>utensils-and-appliances</td>\n",
       "      <td>29.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>127.0</td>\n",
       "      <td>240.0</td>\n",
       "      <td>127.0</td>\n",
       "      <td>790.0</td>\n",
       "      <td>11040.0</td>\n",
       "      <td>8622.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>utility</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>915.0</td>\n",
       "      <td>1240.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>27 rows × 46 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                             cat2  price_0  price_1  price_10  price_11  \\\n",
       "0                         animals     38.0     35.0       7.0     195.0   \n",
       "1                     audio-video      3.0     11.0       1.0      18.0   \n",
       "2                   baby-and-toys     10.0     10.0       2.0      48.0   \n",
       "3                           batch     33.0      6.0      11.0     473.0   \n",
       "4                         bicycle      1.0      5.0       1.0       1.0   \n",
       "5         book-student-literature      3.0      4.0       1.0      63.0   \n",
       "6             building-and-garden      2.0      6.0       8.0      41.0   \n",
       "7                            cars     16.0     13.0       0.0       3.0   \n",
       "8     childrens-clothing-and-shoe     21.0      9.0       2.0      47.0   \n",
       "9              clothing-and-shoes    179.0    133.0       2.0     146.0   \n",
       "10                      computers      5.0     11.0       3.0      32.0   \n",
       "11       equipments-and-machinery     18.0     22.0      17.0     201.0   \n",
       "12      furniture-and-home-decore     87.0    124.0      17.0     261.0   \n",
       "13  game-consoles-and-video-games     14.0     49.0       8.0     102.0   \n",
       "14                  health-beauty      9.0      7.0       4.0     104.0   \n",
       "15             hobby-collectibles      0.0      0.0       3.0      14.0   \n",
       "16            jewelry-and-watches      4.0      1.0       2.0      40.0   \n",
       "17           leisure-hobbies-toys      0.0      0.0       1.0       1.0   \n",
       "18                  mobile-tablet     23.0     10.0      14.0     276.0   \n",
       "19                    motorcycles      5.0      2.0       0.0      63.0   \n",
       "20            musical-instruments      1.0      0.0       0.0       3.0   \n",
       "21              parts-accessories     22.0     21.0       4.0      63.0   \n",
       "22                          phone      0.0      0.0       0.0       3.0   \n",
       "23                  sport-leisure      4.0      6.0       0.0      27.0   \n",
       "24                travel-packages      1.0      0.0       3.0       2.0   \n",
       "25        utensils-and-appliances     29.0     43.0       7.0     127.0   \n",
       "26                        utility      2.0      1.0       5.0       9.0   \n",
       "\n",
       "    price_12  price_13  price_14  price_15  price_16  ...  price_40  price_41  \\\n",
       "0      187.0     141.0     836.0   11998.0   10067.0  ...       0.0       0.0   \n",
       "1       21.0       6.0      53.0    2075.0    4058.0  ...       0.0       0.0   \n",
       "2      155.0      59.0     342.0    4653.0    3735.0  ...       0.0       0.0   \n",
       "3      656.0     479.0     740.0    2454.0     406.0  ...       0.0       0.0   \n",
       "4       11.0       3.0      22.0     665.0    1295.0  ...       0.0       0.0   \n",
       "5      141.0      60.0     283.0     992.0     234.0  ...       0.0       0.0   \n",
       "6       35.0      45.0      94.0    1453.0    2049.0  ...       0.0       0.0   \n",
       "7       12.0      33.0      63.0     482.0      26.0  ...      20.0       6.0   \n",
       "8      354.0     102.0    1038.0    7292.0    1314.0  ...       0.0       0.0   \n",
       "9      662.0     266.0    2531.0   34417.0   18934.0  ...       0.0       0.0   \n",
       "10      81.0      35.0     339.0    5324.0    3081.0  ...       0.0       0.0   \n",
       "11     146.0      75.0     201.0    1991.0    2610.0  ...       0.0       0.0   \n",
       "12     477.0     190.0    1185.0   22249.0   26033.0  ...       0.0       0.0   \n",
       "13     119.0      54.0     240.0    3506.0    7519.0  ...       0.0       0.0   \n",
       "14     151.0      86.0     306.0    2643.0    2065.0  ...       0.0       0.0   \n",
       "15      11.0       2.0      37.0     263.0     321.0  ...       0.0       0.0   \n",
       "16      62.0      32.0     138.0    1284.0    1245.0  ...       0.0       0.0   \n",
       "17       3.0       0.0      11.0     122.0      77.0  ...       0.0       0.0   \n",
       "18     303.0     127.0     654.0    5058.0    5009.0  ...       0.0       0.0   \n",
       "19     120.0      51.0      94.0     837.0     382.0  ...       0.0       0.0   \n",
       "20       3.0       0.0       5.0     155.0     169.0  ...       0.0       0.0   \n",
       "21     170.0     112.0     630.0    8620.0    6729.0  ...       0.0       0.0   \n",
       "22       1.0       1.0       5.0     209.0     230.0  ...       0.0       0.0   \n",
       "23      14.0       5.0      70.0    1427.0    1275.0  ...       0.0       0.0   \n",
       "24       0.0       0.0       0.0      11.0      18.0  ...       0.0       0.0   \n",
       "25     240.0     127.0     790.0   11040.0    8622.0  ...       0.0       0.0   \n",
       "26      12.0       4.0      71.0     915.0    1240.0  ...       0.0       0.0   \n",
       "\n",
       "    price_42  price_43  price_5  price_6  price_7  price_8  price_9  k_means  \n",
       "0        0.0       0.0      7.0      7.0      2.0     12.0      3.0        3  \n",
       "1        0.0       0.0      8.0      3.0      0.0      6.0      2.0        0  \n",
       "2        0.0       0.0      4.0      3.0      1.0      0.0      1.0        0  \n",
       "3        0.0       0.0      3.0      5.0      3.0      1.0      4.0        0  \n",
       "4        0.0       0.0      5.0      8.0      3.0      4.0      1.0        0  \n",
       "5        0.0       0.0      1.0      0.0      0.0      0.0      0.0        0  \n",
       "6        0.0       0.0      2.0      4.0      1.0      1.0      5.0        0  \n",
       "7        6.0       3.0      0.0      0.0      0.0      0.0      0.0        2  \n",
       "8        0.0       0.0      0.0      0.0      0.0      0.0      0.0        0  \n",
       "9        0.0       0.0      8.0      3.0      3.0      3.0      2.0        4  \n",
       "10       0.0       0.0      6.0      5.0      3.0      6.0      0.0        0  \n",
       "11       0.0       0.0     27.0     11.0     11.0      8.0      6.0        0  \n",
       "12       0.0       0.0     70.0     40.0     28.0     25.0     11.0        1  \n",
       "13       0.0       0.0      9.0      6.0      9.0     10.0      6.0        0  \n",
       "14       0.0       0.0      2.0      1.0      1.0      0.0      0.0        0  \n",
       "15       0.0       0.0      0.0      0.0      0.0      0.0      0.0        0  \n",
       "16       0.0       0.0      0.0      2.0      1.0      0.0      3.0        0  \n",
       "17       0.0       0.0      0.0      0.0      0.0      0.0      0.0        0  \n",
       "18       0.0       0.0     30.0     20.0     21.0     21.0     22.0        3  \n",
       "19       0.0       0.0      1.0      0.0      1.0      2.0      1.0        0  \n",
       "20       0.0       0.0      1.0      2.0      0.0      1.0      0.0        0  \n",
       "21       0.0       0.0     14.0      9.0      4.0      4.0      0.0        3  \n",
       "22       0.0       0.0      0.0      0.0      0.0      0.0      0.0        0  \n",
       "23       0.0       0.0      0.0      6.0      3.0      7.0      6.0        0  \n",
       "24       0.0       0.0      0.0      0.0      1.0      0.0      1.0        0  \n",
       "25       0.0       0.0     21.0      8.0      4.0      7.0      4.0        3  \n",
       "26       0.0       0.0      1.0      3.0      1.0      1.0      1.0        0  \n",
       "\n",
       "[27 rows x 46 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmean_prediction4 = pd.concat([group_item.iloc[:,0],X4],axis=1)\n",
    "kmean_prediction4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "bce19208",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1872x936 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(kmean_prediction4['k_means'],kmean_prediction4['cat2'])\n",
    "plt.rcParams[\"figure.figsize\"] = (26,10)\n",
    "plt.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "id": "271d7a82",
   "metadata": {},
   "outputs": [],
   "source": [
    "X4 = np.array(X4)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "id": "606c7b3d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1872x2160 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X4[:,4],kmean_prediction4['cat2'] , c=kmean_prediction4['k_means'], s=100, cmap='viridis')\n",
    "plt.rcParams[\"figure.figsize\"] = (26,30)\n",
    "# plt.figure(figsize=(16,8))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1288a05d",
   "metadata": {},
   "source": [
    "# DBSCAN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "6ab00f33",
   "metadata": {},
   "outputs": [],
   "source": [
    "X4 = group_item.iloc[:,1:]\n",
    "clustering = DBSCAN(eps=20, min_samples=2).fit(X4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "id": "47dcfaf6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,\n",
       "       -1, -1, -1, -1, -1, -1, -1, -1, -1, -1])"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clustering.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4803f602",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.8.1 64-bit",
   "language": "python",
   "name": "python38164bit11fad06b1a964775a8334c24e6debefa"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}