Draw_BoxPlot.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd  \n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy import stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv (\"/Users/mohadeseh/Documents/me/session8/data_mining/FDM_Project_dataset-99-2/divar_dataset/divar_posts_dataset.csv\")  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>id</th>\n",
       "      <th>image_count</th>\n",
       "      <th>mileage</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>947635.000000</td>\n",
       "      <td>9.476350e+05</td>\n",
       "      <td>947635.000000</td>\n",
       "      <td>120451.000000</td>\n",
       "      <td>9.476350e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>473817.000000</td>\n",
       "      <td>3.363293e+13</td>\n",
       "      <td>1.785852</td>\n",
       "      <td>100862.291944</td>\n",
       "      <td>3.398462e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
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       "      <th>min</th>\n",
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       "      <td>5.950553e+07</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>236908.500000</td>\n",
       "      <td>1.677974e+13</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>19000.000000</td>\n",
       "      <td>3.000000e+04</td>\n",
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       "      <td>5.049560e+13</td>\n",
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       "      <td>160000.000000</td>\n",
       "      <td>6.000000e+05</td>\n",
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       "      <td>16.000000</td>\n",
       "      <td>500000.000000</td>\n",
       "      <td>4.000000e+08</td>\n",
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       "          Unnamed: 0            id    image_count        mileage         price\n",
       "count  947635.000000  9.476350e+05  947635.000000  120451.000000  9.476350e+05\n",
       "mean   473817.000000  3.363293e+13       1.785852  100862.291944  3.398462e+06\n",
       "std    273558.805509  1.944109e+13       1.127712   98368.470916  1.371741e+07\n",
       "min         0.000000  5.950553e+07       0.000000       0.000000 -1.000000e+00\n",
       "25%    236908.500000  1.677974e+13       1.000000   19000.000000  3.000000e+04\n",
       "50%    473817.000000  3.362242e+13       2.000000   75000.000000  1.500000e+05\n",
       "75%    710725.500000  5.049560e+13       2.000000  160000.000000  6.000000e+05\n",
       "max    947634.000000  6.728036e+13      16.000000  500000.000000  4.000000e+08"
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     "execution_count": 34,
     "metadata": {},
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   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Unnamed: 0         4.738170e+05\n",
       "archive_by_user    1.000000e+00\n",
       "id                 3.362242e+13\n",
       "image_count        2.000000e+00\n",
       "mileage            7.500000e+04\n",
       "price              1.500000e+05\n",
       "dtype: float64"
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     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
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       "<p>947635 rows × 17 columns</p>\n",
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      "text/plain": [
       "        Unnamed: 0 archive_by_user                   brand          cat1  \\\n",
       "0                0            True  پراید صندوق‌دار::Pride  for-the-home   \n",
       "1                1             NaN                     NaN           NaN   \n",
       "2                2             NaN                     NaN           NaN   \n",
       "3                3             NaN                     NaN           NaN   \n",
       "4                4             NaN                     NaN           NaN   \n",
       "...            ...             ...                     ...           ...   \n",
       "947630      947630             NaN                     NaN           NaN   \n",
       "947631      947631             NaN                     NaN           NaN   \n",
       "947632      947632             NaN                     NaN           NaN   \n",
       "947633      947633             NaN                     NaN           NaN   \n",
       "947634      947634             NaN                     NaN           NaN   \n",
       "\n",
       "                             cat2   cat3    city     created_at        desc  \\\n",
       "0       furniture-and-home-decore  light  Tehran  Saturday 12PM  کاملا سالم   \n",
       "1                             NaN    NaN     NaN            NaN         NaN   \n",
       "2                             NaN    NaN     NaN            NaN         NaN   \n",
       "3                             NaN    NaN     NaN            NaN         NaN   \n",
       "4                             NaN    NaN     NaN            NaN         NaN   \n",
       "...                           ...    ...     ...            ...         ...   \n",
       "947630                        NaN    NaN     NaN            NaN         NaN   \n",
       "947631                        NaN    NaN     NaN            NaN         NaN   \n",
       "947632                        NaN    NaN     NaN            NaN         NaN   \n",
       "947633                        NaN    NaN     NaN            NaN         NaN   \n",
       "947634                        NaN    NaN     NaN            NaN         NaN   \n",
       "\n",
       "                    id  image_count   mileage platform  price title   type  \\\n",
       "0             59505527          1.0  200000.0   mobile   -1.0  بوفه  women   \n",
       "1            156800403          NaN       NaN      NaN    NaN   NaN    NaN   \n",
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       "...                ...          ...       ...      ...    ...   ...    ...   \n",
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       "947634  67280355378903          NaN       NaN      NaN    NaN   NaN    NaN   \n",
       "\n",
       "        year  \n",
       "0       1393  \n",
       "1        NaN  \n",
       "2        NaN  \n",
       "3        NaN  \n",
       "4        NaN  \n",
       "...      ...  \n",
       "947630   NaN  \n",
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       "\n",
       "[947635 rows x 17 columns]"
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     },
     "execution_count": 36,
     "metadata": {},
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   ],
   "source": [
    "df.mode()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ModeResult(mode=array([[59505527]]), count=array([[1]]))"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.mode(df.loc[:,['id']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Unnamed: 0           int64\n",
       "archive_by_user       bool\n",
       "brand               object\n",
       "cat1                object\n",
       "cat2                object\n",
       "cat3                object\n",
       "city                object\n",
       "created_at          object\n",
       "desc                object\n",
       "id                   int64\n",
       "image_count          int64\n",
       "mileage            float64\n",
       "platform            object\n",
       "price                int64\n",
       "title               object\n",
       "type                object\n",
       "year                object\n",
       "dtype: object"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-33794059581979.0"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Q1 = df.loc[:,['id']].quantile(0.25)\n",
    "Q3 = df.loc[:,['id']].quantile(0.75)\n",
    "IQR = Q3 - Q1\n",
    "outlier_above= (1.5*IQR)+Q3\n",
    "outlier_bottom= Q1-(1.5*IQR)\n",
    "outlier_bottom[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>brand</th>\n",
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       "      <th>city</th>\n",
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       "      <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",
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       "  </thead>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Unnamed: 0, archive_by_user, brand, cat1, cat2, cat3, city, created_at, desc, id, image_count, mileage, platform, price, title, type, year]\n",
       "Index: []"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "select = df['id'] < outlier_bottom[0]\n",
    "df.loc[select,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "Empty DataFrame\n",
       "Columns: [Unnamed: 0, archive_by_user, brand, cat1, cat2, cat3, city, created_at, desc, id, image_count, mileage, platform, price, title, type, year]\n",
       "Index: []"
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     "execution_count": 41,
     "metadata": {},
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   "source": [
    "select = df['id'] > outlier_above[0]\n",
    "df.loc[select,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-825000.0"
      ]
     },
     "execution_count": 42,
     "metadata": {},
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   "source": [
    "Q1_price = df.loc[:,['price']].quantile(0.25)\n",
    "Q3_price = df.loc[:,['price']].quantile(0.75)\n",
    "IQR_price = Q3_price - Q1_price\n",
    "outlier_price_above= (1.5 * IQR_price) + Q3_price\n",
    "outlier_price_bottom= Q1_price - (1.5 * IQR_price)\n",
    "outlier_price_bottom[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "Empty DataFrame\n",
       "Columns: [Unnamed: 0, archive_by_user, brand, cat1, cat2, cat3, city, created_at, desc, id, image_count, mileage, platform, price, title, type, year]\n",
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     "execution_count": 43,
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   "source": [
    "select = df['price'] < outlier_price_bottom[0]\n",
    "df.loc[select,:]"
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  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
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       "      <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",
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       "    <tr>\n",
       "      <th>18</th>\n",
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       "      <td>cars</td>\n",
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       "      <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",
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       "      <td>8500000</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>1381</td>\n",
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       "    <tr>\n",
       "      <th>22</th>\n",
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       "      <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",
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       "      <td>19500000</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>1372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>36</td>\n",
       "      <td>False</td>\n",
       "      <td>سمند::Samand</td>\n",
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       "      <td>cars</td>\n",
       "      <td>light</td>\n",
       "      <td>Tehran</td>\n",
       "      <td>Thursday 07AM</td>\n",
       "      <td>سمند ال ایکس بدون رنگ تمامی سرویس ها انجام شده...</td>\n",
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       "      <td>4</td>\n",
       "      <td>80000.0</td>\n",
       "      <td>web</td>\n",
       "      <td>23900000</td>\n",
       "      <td>سمند دوگانه کارخانه</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
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       "      <td>cars</td>\n",
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       "      <td>Thursday 08AM</td>\n",
       "      <td>بیمه یکسال ده سال تخفیف دور رنگ شاسی پلمپ سالم</td>\n",
       "      <td>20806323919288</td>\n",
       "      <td>3</td>\n",
       "      <td>125000.0</td>\n",
       "      <td>mobile</td>\n",
       "      <td>8500000</td>\n",
       "      <td>پژو405 مدل84</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1384</td>\n",
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       "      <th>947602</th>\n",
       "      <td>947602</td>\n",
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       "      <td>vehicles</td>\n",
       "      <td>cars</td>\n",
       "      <td>light</td>\n",
       "      <td>Isfahan</td>\n",
       "      <td>Tuesday 05PM</td>\n",
       "      <td>مدل 84 تمیز. بدون رنگ. سیستم صوتی. رینگ و لاست...</td>\n",
       "      <td>36679901713761</td>\n",
       "      <td>3</td>\n",
       "      <td>140000.0</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>947612</th>\n",
       "      <td>947612</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>vehicles</td>\n",
       "      <td>cars</td>\n",
       "      <td>heavy</td>\n",
       "      <td>Tehran</td>\n",
       "      <td>Wednesday 03PM</td>\n",
       "      <td>بادسان فوق بیرنگ سفید رنگ بیمه تابرج 3.96 تخفی...</td>\n",
       "      <td>21866530196097</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>mobile</td>\n",
       "      <td>48000000</td>\n",
       "      <td>بادسان 8b14 مدل83</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>947628</th>\n",
       "      <td>947628</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>for-the-home</td>\n",
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       "      <td>Tehran</td>\n",
       "      <td>Thursday 05PM</td>\n",
       "      <td>با دوسال ضمانت تشکر از دیوار</td>\n",
       "      <td>38646519610598</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>mobile</td>\n",
       "      <td>2200000</td>\n",
       "      <td>تولید کننده</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",
       "  </tbody>\n",
       "</table>\n",
       "<p>157133 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Unnamed: 0  archive_by_user                   brand          cat1  \\\n",
       "0                0            False                     NaN  for-the-home   \n",
       "18              18            False    پژو ۴۰۵::Peugeot 405      vehicles   \n",
       "22              22            False           نیسان::Nissan      vehicles   \n",
       "36              36            False            سمند::Samand      vehicles   \n",
       "38              38            False    پژو ۴۰۵::Peugeot 405      vehicles   \n",
       "...            ...              ...                     ...           ...   \n",
       "947602      947602             True  پراید صندوق‌دار::Pride      vehicles   \n",
       "947612      947612             True                     NaN      vehicles   \n",
       "947625      947625             True  پراید صندوق‌دار::Pride      vehicles   \n",
       "947628      947628             True                     NaN  for-the-home   \n",
       "947632      947632             True  پراید صندوق‌دار::Pride      vehicles   \n",
       "\n",
       "                             cat2           cat3     city      created_at  \\\n",
       "0       furniture-and-home-decore  sofa-armchair   Tehran    Tuesday 07PM   \n",
       "18                           cars          light    Karaj  Wednesday 11AM   \n",
       "22                           cars          light   Tehran  Wednesday 01PM   \n",
       "36                           cars          light   Tehran   Thursday 07AM   \n",
       "38                           cars          light   Tehran   Thursday 08AM   \n",
       "...                           ...            ...      ...             ...   \n",
       "947602                       cars          light  Isfahan    Tuesday 05PM   \n",
       "947612                       cars          heavy   Tehran  Wednesday 03PM   \n",
       "947625                       cars          light    Karaj   Thursday 03PM   \n",
       "947628  furniture-and-home-decore  sofa-armchair   Tehran   Thursday 05PM   \n",
       "947632                       cars          light   Shiraz     Friday 04PM   \n",
       "\n",
       "                                                     desc              id  \\\n",
       "0       کلاسیک و شیک و استثنایی\\nچرم مالزی\\nچوب راش\\nف...  54761638662241   \n",
       "18      پژو 2000فنی تازه تعمیر بدنه بدون خطو خش تخیف ب...  51728781836074   \n",
       "22      سلام.\\nماشین کاملا سالم همه چی فابریک متور زد ...  26591305369165   \n",
       "36      سمند ال ایکس بدون رنگ تمامی سرویس ها انجام شده...  35066474998268   \n",
       "38         بیمه یکسال ده سال تخفیف دور رنگ شاسی پلمپ سالم  20806323919288   \n",
       "...                                                   ...             ...   \n",
       "947602  مدل 84 تمیز. بدون رنگ. سیستم صوتی. رینگ و لاست...  36679901713761   \n",
       "947612  بادسان فوق بیرنگ سفید رنگ بیمه تابرج 3.96 تخفی...  21866530196097   \n",
       "947625  سند۲ برگ،بیمه تا برج$NUMتخفیفات بیمه 15 سال،مع...  30197850752025   \n",
       "947628                       با دوسال ضمانت تشکر از دیوار  38646519610598   \n",
       "947632  پراید دوگانه کارخانه میباشد و کم کارکرد.روکش ....  55514317985619   \n",
       "\n",
       "        image_count   mileage platform     price                        title  \\\n",
       "0                 5       NaN   mobile   3850000  ست مبلمان و نهارخوری ٩ نفره   \n",
       "18                0  290000.0      web   8500000              پژو مدل 81 مشکی   \n",
       "22                3  175000.0   mobile  19500000                   پاترول 2در   \n",
       "36                4   80000.0      web  23900000          سمند دوگانه کارخانه   \n",
       "38                3  125000.0   mobile   8500000                 پژو405 مدل84   \n",
       "...             ...       ...      ...       ...                          ...   \n",
       "947602            3  140000.0   mobile  11000000        پراید مدل 84 بدون رنگ   \n",
       "947612            2       NaN   mobile  48000000            بادسان 8b14 مدل83   \n",
       "947625            4  123000.0   mobile   6900000          پراید 79 بسیار سالم   \n",
       "947628            5       NaN   mobile   2200000                  تولید کننده   \n",
       "947632            3   43000.0   mobile  17400000   پراید دوگانه کارخانه مدل93   \n",
       "\n",
       "       type  year  \n",
       "0       NaN   NaN  \n",
       "18      NaN  1381  \n",
       "22      NaN  1372  \n",
       "36      NaN  1391  \n",
       "38      NaN  1384  \n",
       "...     ...   ...  \n",
       "947602  NaN  1384  \n",
       "947612  NaN   NaN  \n",
       "947625  NaN  1379  \n",
       "947628  NaN   NaN  \n",
       "947632  NaN  1393  \n",
       "\n",
       "[157133 rows x 17 columns]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "select = df['price'] > outlier_price_above[0]\n",
    "# select\n",
    "df.loc[select,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.5"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Q1_imgCount = df.loc[:,['image_count']].quantile(0.25)\n",
    "Q3_imgCount = df.loc[:,['image_count']].quantile(0.75)\n",
    "IQR_imgCount = Q3_imgCount - Q1_imgCount\n",
    "outlier_imgCount_above= (1.5 * IQR_imgCount) + Q3_imgCount\n",
    "outlier_imgCount_bottom= Q1_imgCount - (1.5 * IQR_imgCount)\n",
    "outlier_imgCount_bottom[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Unnamed: 0, archive_by_user, brand, cat1, cat2, cat3, city, created_at, desc, id, image_count, mileage, platform, price, title, type, year]\n",
       "Index: []"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "select3 = df['image_count'] < outlier_imgCount_bottom[0]\n",
    "df.loc[select3,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</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>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>5</th>\n",
       "      <td>5</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 09PM</td>\n",
       "      <td>بسیارتمیزوسالم وچوب راش فقط به علت جابه جایی و...</td>\n",
       "      <td>48029048440635</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>mobile</td>\n",
       "      <td>1000000</td>\n",
       "      <td>مبل استیل</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>12</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>personal</td>\n",
       "      <td>childrens-clothing-and-shoe</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Karaj</td>\n",
       "      <td>Wednesday 09AM</td>\n",
       "      <td>لباس های دخترانه از3تا9سال.فقط پیراهن قرمز به ...</td>\n",
       "      <td>21912367534021</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>mobile</td>\n",
       "      <td>30000</td>\n",
       "      <td>لباس های دخترانه 2تا9ساله</td>\n",
       "      <td>girls</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>947546</th>\n",
       "      <td>947546</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>for-the-home</td>\n",
       "      <td>furniture-and-home-decore</td>\n",
       "      <td>beds-bedroom</td>\n",
       "      <td>Karaj</td>\n",
       "      <td>Saturday 01PM</td>\n",
       "      <td>روتختی جنس دوخت ترک نونونو اصلا استفاده نشده</td>\n",
       "      <td>4714564630668</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>mobile</td>\n",
       "      <td>200000</td>\n",
       "      <td>روتختی ترک نونو</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>947578</th>\n",
       "      <td>947578</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>for-the-home</td>\n",
       "      <td>furniture-and-home-decore</td>\n",
       "      <td>tables-and-chairs</td>\n",
       "      <td>Tehran</td>\n",
       "      <td>Monday 11AM</td>\n",
       "      <td>میز غدا خوری 8 نفره تمیز و سالم \\nرویه  پارچه ...</td>\n",
       "      <td>43005258242240</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>mobile</td>\n",
       "      <td>1200000</td>\n",
       "      <td>میز نهار خوری 8 نفره</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>947614</th>\n",
       "      <td>947614</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>vehicles</td>\n",
       "      <td>cars</td>\n",
       "      <td>heavy</td>\n",
       "      <td>Isfahan</td>\n",
       "      <td>Wednesday 06PM</td>\n",
       "      <td>باسلام .تراکتورازصفری دست خودم بوده.سالم.ودارا...</td>\n",
       "      <td>44067752978166</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>mobile</td>\n",
       "      <td>-1</td>\n",
       "      <td>تراکتورگلدونی ۲۳۸ایتالیا</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>947628</th>\n",
       "      <td>947628</td>\n",
       "      <td>True</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>Thursday 05PM</td>\n",
       "      <td>با دوسال ضمانت تشکر از دیوار</td>\n",
       "      <td>38646519610598</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>mobile</td>\n",
       "      <td>2200000</td>\n",
       "      <td>تولید کننده</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>72245 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Unnamed: 0  archive_by_user                   brand          cat1  \\\n",
       "0                0            False                     NaN  for-the-home   \n",
       "2                2            False                     NaN      vehicles   \n",
       "3                3            False                     NaN  for-the-home   \n",
       "5                5            False                     NaN  for-the-home   \n",
       "12              12            False                     NaN      personal   \n",
       "...            ...              ...                     ...           ...   \n",
       "947546      947546             True                     NaN  for-the-home   \n",
       "947578      947578             True                     NaN  for-the-home   \n",
       "947614      947614             True                     NaN      vehicles   \n",
       "947625      947625             True  پراید صندوق‌دار::Pride      vehicles   \n",
       "947628      947628             True                     NaN  for-the-home   \n",
       "\n",
       "                               cat2               cat3     city  \\\n",
       "0         furniture-and-home-decore      sofa-armchair   Tehran   \n",
       "2                              cars              heavy  Mashhad   \n",
       "3         furniture-and-home-decore      sofa-armchair   Tehran   \n",
       "5         furniture-and-home-decore      sofa-armchair   Tehran   \n",
       "12      childrens-clothing-and-shoe                NaN    Karaj   \n",
       "...                             ...                ...      ...   \n",
       "947546    furniture-and-home-decore       beds-bedroom    Karaj   \n",
       "947578    furniture-and-home-decore  tables-and-chairs   Tehran   \n",
       "947614                         cars              heavy  Isfahan   \n",
       "947625                         cars              light    Karaj   \n",
       "947628    furniture-and-home-decore      sofa-armchair   Tehran   \n",
       "\n",
       "            created_at                                               desc  \\\n",
       "0         Tuesday 07PM  کلاسیک و شیک و استثنایی\\nچرم مالزی\\nچوب راش\\nف...   \n",
       "2         Tuesday 07PM  سریال 43j$NUMبدون شکستگی سه حلقه لاستیک نو یک ...   \n",
       "3         Tuesday 07PM  مبل راحتی هفت نفره شامل سه نفره یک عدد دونفره ...   \n",
       "5         Tuesday 09PM  بسیارتمیزوسالم وچوب راش فقط به علت جابه جایی و...   \n",
       "12      Wednesday 09AM  لباس های دخترانه از3تا9سال.فقط پیراهن قرمز به ...   \n",
       "...                ...                                                ...   \n",
       "947546   Saturday 01PM       روتختی جنس دوخت ترک نونونو اصلا استفاده نشده   \n",
       "947578     Monday 11AM  میز غدا خوری 8 نفره تمیز و سالم \\nرویه  پارچه ...   \n",
       "947614  Wednesday 06PM  باسلام .تراکتورازصفری دست خودم بوده.سالم.ودارا...   \n",
       "947625   Thursday 03PM  سند۲ برگ،بیمه تا برج$NUMتخفیفات بیمه 15 سال،مع...   \n",
       "947628   Thursday 05PM                       با دوسال ضمانت تشکر از دیوار   \n",
       "\n",
       "                    id  image_count   mileage platform    price  \\\n",
       "0       54761638662241            5       NaN   mobile  3850000   \n",
       "2       63194439667302            4       NaN   mobile       -1   \n",
       "3       19133025491169            4       NaN   mobile   600000   \n",
       "5       48029048440635            4       NaN   mobile  1000000   \n",
       "12      21912367534021            4       NaN   mobile    30000   \n",
       "...                ...          ...       ...      ...      ...   \n",
       "947546   4714564630668            4       NaN   mobile   200000   \n",
       "947578  43005258242240            4       NaN   mobile  1200000   \n",
       "947614  44067752978166            4       NaN   mobile       -1   \n",
       "947625  30197850752025            4  123000.0   mobile  6900000   \n",
       "947628  38646519610598            5       NaN   mobile  2200000   \n",
       "\n",
       "                                    title   type  year  \n",
       "0             ست مبلمان و نهارخوری ٩ نفره    NaN   NaN  \n",
       "2                     لودر کاتر پیلار 950    NaN   NaN  \n",
       "3       مبل راحتی هفت نفره بامیز جلو مبلی    NaN   NaN  \n",
       "5                               مبل استیل    NaN   NaN  \n",
       "12              لباس های دخترانه 2تا9ساله  girls   NaN  \n",
       "...                                   ...    ...   ...  \n",
       "947546                    روتختی ترک نونو    NaN   NaN  \n",
       "947578               میز نهار خوری 8 نفره    NaN   NaN  \n",
       "947614           تراکتورگلدونی ۲۳۸ایتالیا    NaN   NaN  \n",
       "947625                پراید 79 بسیار سالم    NaN  1379  \n",
       "947628                        تولید کننده    NaN   NaN  \n",
       "\n",
       "[72245 rows x 17 columns]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "select3 = df['image_count'] > outlier_imgCount_above[0]\n",
    "df.loc[select3,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-192500.0"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Q1_mileage = df.loc[:,['mileage']].quantile(0.25)\n",
    "Q3_mileage = df.loc[:,['mileage']].quantile(0.75)\n",
    "IQR_mileage = Q3_mileage - Q1_mileage\n",
    "outlier_mileage_above = (1.5 * IQR_mileage) + Q3_mileage\n",
    "outlier_mileage_bottom = Q1_mileage - (1.5 * IQR_mileage)\n",
    "outlier_mileage_bottom[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Unnamed: 0, archive_by_user, brand, cat1, cat2, cat3, city, created_at, desc, id, image_count, mileage, platform, price, title, type, year]\n",
       "Index: []"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "select4 = df['mileage'] < outlier_mileage_bottom[0]\n",
    "df.loc[select4,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>270</th>\n",
       "      <td>270</td>\n",
       "      <td>False</td>\n",
       "      <td>وانت</td>\n",
       "      <td>vehicles</td>\n",
       "      <td>cars</td>\n",
       "      <td>light</td>\n",
       "      <td>Tehran</td>\n",
       "      <td>Sunday 03PM</td>\n",
       "      <td>گاز سوز دارای سه سال تخفیف  بیمه لاستیک 75درصد...</td>\n",
       "      <td>7711513580902</td>\n",
       "      <td>0</td>\n",
       "      <td>450000.0</td>\n",
       "      <td>mobile</td>\n",
       "      <td>13100000</td>\n",
       "      <td>وانت نیسان گازسوز</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1383</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>352</th>\n",
       "      <td>352</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>Thursday 05AM</td>\n",
       "      <td>فنی سالم . مدل 86</td>\n",
       "      <td>16547724198274</td>\n",
       "      <td>0</td>\n",
       "      <td>440000.0</td>\n",
       "      <td>web</td>\n",
       "      <td>22000000</td>\n",
       "      <td>ون دلیکا مدا 86</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>441</th>\n",
       "      <td>441</td>\n",
       "      <td>False</td>\n",
       "      <td>وانت</td>\n",
       "      <td>vehicles</td>\n",
       "      <td>cars</td>\n",
       "      <td>light</td>\n",
       "      <td>Karaj</td>\n",
       "      <td>Saturday 06PM</td>\n",
       "      <td>موتوروگیربکس ودف سالم کابین عقب ازسه طرف باز م...</td>\n",
       "      <td>5045335444898</td>\n",
       "      <td>4</td>\n",
       "      <td>380000.0</td>\n",
       "      <td>mobile</td>\n",
       "      <td>18000000</td>\n",
       "      <td>میتسوبیشی دوکابین</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1371</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>744</th>\n",
       "      <td>744</td>\n",
       "      <td>False</td>\n",
       "      <td>پژو ۲۰۶‍::Peugeot 206</td>\n",
       "      <td>vehicles</td>\n",
       "      <td>cars</td>\n",
       "      <td>light</td>\n",
       "      <td>Karaj</td>\n",
       "      <td>Monday 11AM</td>\n",
       "      <td>پژو ۲۰۶ مدل ۸۲  تیپ ۲\\nرنگ یشمی متالیک\\nجلو و ...</td>\n",
       "      <td>26675716365250</td>\n",
       "      <td>4</td>\n",
       "      <td>400000.0</td>\n",
       "      <td>mobile</td>\n",
       "      <td>-1</td>\n",
       "      <td>پژو ۲۰۶ تیپ ۲</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>772</th>\n",
       "      <td>772</td>\n",
       "      <td>False</td>\n",
       "      <td>نیسان::Nissan</td>\n",
       "      <td>vehicles</td>\n",
       "      <td>cars</td>\n",
       "      <td>light</td>\n",
       "      <td>Mashhad</td>\n",
       "      <td>Monday 08PM</td>\n",
       "      <td>دوگانه لاستیک نوکولر وهیدرلیک سالم تودوزی فابر...</td>\n",
       "      <td>56938769532290</td>\n",
       "      <td>1</td>\n",
       "      <td>380000.0</td>\n",
       "      <td>mobile</td>\n",
       "      <td>17500000</td>\n",
       "      <td>پاترول6سیلند4درب</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1371</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>943614</th>\n",
       "      <td>943614</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>Tehran</td>\n",
       "      <td>Sunday 07AM</td>\n",
       "      <td>3 تیکه رنگ موتور به شرط جلو و عقب پلمپ تازه مو...</td>\n",
       "      <td>14974280737779</td>\n",
       "      <td>0</td>\n",
       "      <td>480000.0</td>\n",
       "      <td>mobile</td>\n",
       "      <td>9800000</td>\n",
       "      <td>مدل 85  فنی سالم</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>943739</th>\n",
       "      <td>943739</td>\n",
       "      <td>False</td>\n",
       "      <td>سمند::Samand</td>\n",
       "      <td>vehicles</td>\n",
       "      <td>cars</td>\n",
       "      <td>light</td>\n",
       "      <td>Ahvaz</td>\n",
       "      <td>Tuesday 08PM</td>\n",
       "      <td>این سمند تاکسی ابادانه مدل 84 اسقاطی میباشد وا...</td>\n",
       "      <td>11892343125434</td>\n",
       "      <td>1</td>\n",
       "      <td>400000.0</td>\n",
       "      <td>mobile</td>\n",
       "      <td>-1</td>\n",
       "      <td>سمند تاکسی اسقاطی</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>945078</th>\n",
       "      <td>945078</td>\n",
       "      <td>False</td>\n",
       "      <td>پژو روآ / آر‌دی::RD/ROA</td>\n",
       "      <td>vehicles</td>\n",
       "      <td>cars</td>\n",
       "      <td>light</td>\n",
       "      <td>Shiraz</td>\n",
       "      <td>Thursday 09AM</td>\n",
       "      <td>سلام مدل ۸۸ تمام فابریک یک درشو خودم پالیش کرد...</td>\n",
       "      <td>14324195865949</td>\n",
       "      <td>1</td>\n",
       "      <td>400000.0</td>\n",
       "      <td>mobile</td>\n",
       "      <td>12000000</td>\n",
       "      <td>پژو روآ اسپورت تمام فابریک</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>945349</th>\n",
       "      <td>945349</td>\n",
       "      <td>False</td>\n",
       "      <td>پژو روآ / آر‌دی::RD/ROA</td>\n",
       "      <td>vehicles</td>\n",
       "      <td>cars</td>\n",
       "      <td>light</td>\n",
       "      <td>Karaj</td>\n",
       "      <td>Tuesday 12PM</td>\n",
       "      <td>فوری فوری لطفأ خریدار واقعی تماس بگیرد تشکر وی...</td>\n",
       "      <td>58176222307961</td>\n",
       "      <td>0</td>\n",
       "      <td>420000.0</td>\n",
       "      <td>mobile</td>\n",
       "      <td>-1</td>\n",
       "      <td>آردی سفید مدل80</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>946550</th>\n",
       "      <td>946550</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>Tehran</td>\n",
       "      <td>Wednesday 01PM</td>\n",
       "      <td>پژو 405 مدل 83\\nفنی سالم\\nتمیز\\nقیمت توافقی</td>\n",
       "      <td>3042429111201</td>\n",
       "      <td>1</td>\n",
       "      <td>499999.0</td>\n",
       "      <td>mobile</td>\n",
       "      <td>-1</td>\n",
       "      <td>پژو 405 مدل 83</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1383</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1898 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Unnamed: 0  archive_by_user                    brand      cat1  cat2  \\\n",
       "270            270            False                     وانت  vehicles  cars   \n",
       "352            352            False                     سایر  vehicles  cars   \n",
       "441            441            False                     وانت  vehicles  cars   \n",
       "744            744            False    پژو ۲۰۶‍::Peugeot 206  vehicles  cars   \n",
       "772            772            False            نیسان::Nissan  vehicles  cars   \n",
       "...            ...              ...                      ...       ...   ...   \n",
       "943614      943614             True   پراید صندوق‌دار::Pride  vehicles  cars   \n",
       "943739      943739            False             سمند::Samand  vehicles  cars   \n",
       "945078      945078            False  پژو روآ / آر‌دی::RD/ROA  vehicles  cars   \n",
       "945349      945349            False  پژو روآ / آر‌دی::RD/ROA  vehicles  cars   \n",
       "946550      946550            False     پژو ۴۰۵::Peugeot 405  vehicles  cars   \n",
       "\n",
       "         cat3     city      created_at  \\\n",
       "270     light   Tehran     Sunday 03PM   \n",
       "352     light   Tehran   Thursday 05AM   \n",
       "441     light    Karaj   Saturday 06PM   \n",
       "744     light    Karaj     Monday 11AM   \n",
       "772     light  Mashhad     Monday 08PM   \n",
       "...       ...      ...             ...   \n",
       "943614  light   Tehran     Sunday 07AM   \n",
       "943739  light    Ahvaz    Tuesday 08PM   \n",
       "945078  light   Shiraz   Thursday 09AM   \n",
       "945349  light    Karaj    Tuesday 12PM   \n",
       "946550  light   Tehran  Wednesday 01PM   \n",
       "\n",
       "                                                     desc              id  \\\n",
       "270     گاز سوز دارای سه سال تخفیف  بیمه لاستیک 75درصد...   7711513580902   \n",
       "352                                     فنی سالم . مدل 86  16547724198274   \n",
       "441     موتوروگیربکس ودف سالم کابین عقب ازسه طرف باز م...   5045335444898   \n",
       "744     پژو ۲۰۶ مدل ۸۲  تیپ ۲\\nرنگ یشمی متالیک\\nجلو و ...  26675716365250   \n",
       "772     دوگانه لاستیک نوکولر وهیدرلیک سالم تودوزی فابر...  56938769532290   \n",
       "...                                                   ...             ...   \n",
       "943614  3 تیکه رنگ موتور به شرط جلو و عقب پلمپ تازه مو...  14974280737779   \n",
       "943739  این سمند تاکسی ابادانه مدل 84 اسقاطی میباشد وا...  11892343125434   \n",
       "945078  سلام مدل ۸۸ تمام فابریک یک درشو خودم پالیش کرد...  14324195865949   \n",
       "945349  فوری فوری لطفأ خریدار واقعی تماس بگیرد تشکر وی...  58176222307961   \n",
       "946550        پژو 405 مدل 83\\nفنی سالم\\nتمیز\\nقیمت توافقی   3042429111201   \n",
       "\n",
       "        image_count   mileage platform     price                       title  \\\n",
       "270               0  450000.0   mobile  13100000           وانت نیسان گازسوز   \n",
       "352               0  440000.0      web  22000000             ون دلیکا مدا 86   \n",
       "441               4  380000.0   mobile  18000000           میتسوبیشی دوکابین   \n",
       "744               4  400000.0   mobile        -1               پژو ۲۰۶ تیپ ۲   \n",
       "772               1  380000.0   mobile  17500000            پاترول6سیلند4درب   \n",
       "...             ...       ...      ...       ...                         ...   \n",
       "943614            0  480000.0   mobile   9800000            مدل 85  فنی سالم   \n",
       "943739            1  400000.0   mobile        -1           سمند تاکسی اسقاطی   \n",
       "945078            1  400000.0   mobile  12000000  پژو روآ اسپورت تمام فابریک   \n",
       "945349            0  420000.0   mobile        -1             آردی سفید مدل80   \n",
       "946550            1  499999.0   mobile        -1              پژو 405 مدل 83   \n",
       "\n",
       "       type  year  \n",
       "270     NaN  1383  \n",
       "352     NaN  1386  \n",
       "441     NaN  1371  \n",
       "744     NaN  1382  \n",
       "772     NaN  1371  \n",
       "...     ...   ...  \n",
       "943614  NaN  1385  \n",
       "943739  NaN  1384  \n",
       "945078  NaN  1388  \n",
       "945349  NaN  1380  \n",
       "946550  NaN  1383  \n",
       "\n",
       "[1898 rows x 17 columns]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "select4 = df['mileage'] > outlier_mileage_above[0]\n",
    "df.loc[select4,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "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": [
    "ax = df.loc[:,['id']].boxplot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "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": [
    "ax = df.loc[:,['price']].boxplot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "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": [
    "ax = df.loc[:,['image_count']].boxplot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "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": [
    "ax = df.loc[:,['mileage']].boxplot()"
   ]
  }
 ],
 "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": 4
}