Untitled.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "756750ec",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import scipy.stats as stats\n",
    "import statsmodels.api as sm\n",
    "import plotly.express as px"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "bbe8876b",
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option('display.max_columns', None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "83ecdc6c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "rents.csv\n",
      "transactions.csv\n",
      "Untitled.ipynb\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "\n",
    "# Specify the directory path\n",
    "directory = '/Users/dimak/code/19.05 last project'\n",
    "\n",
    "# Use os.listdir() to get a list of all entries in the directory\n",
    "entries = os.listdir(directory)\n",
    "\n",
    "files = [entry for entry in entries if os.path.isfile(os.path.join(directory, entry))]\n",
    "\n",
    "# Print the list of files\n",
    "for file_name in files:\n",
    "    print(file_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "id": "fa2bea46",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_types = {\n",
    "    \"Room(s)\": str,\n",
    "    \"Parking\": str,\n",
    "    \"Master Project\": str,\n",
    "    \"Project\": str,\n",
    "    \n",
    "}\n",
    "tx_data = pd.read_csv(\"/Users/dimak/code/19.05 last project/transactions.csv\", delimiter=';', dtype=data_types, parse_dates=['Transaction Date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "id": "cda532c6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\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>Transaction Number</th>\n",
       "      <th>Transaction Date</th>\n",
       "      <th>Property ID</th>\n",
       "      <th>Transaction Type</th>\n",
       "      <th>Transaction sub type</th>\n",
       "      <th>Registration type</th>\n",
       "      <th>Is Free Hold?</th>\n",
       "      <th>Usage</th>\n",
       "      <th>Area</th>\n",
       "      <th>Property Type</th>\n",
       "      <th>Property Sub Type</th>\n",
       "      <th>Amount</th>\n",
       "      <th>Transaction Size (sq.m)</th>\n",
       "      <th>Property Size (sq.m)</th>\n",
       "      <th>Room(s)</th>\n",
       "      <th>Parking</th>\n",
       "      <th>Nearest Metro</th>\n",
       "      <th>Nearest Mall</th>\n",
       "      <th>Nearest Landmark</th>\n",
       "      <th>No. of Buyer</th>\n",
       "      <th>No. of Seller</th>\n",
       "      <th>Master Project</th>\n",
       "      <th>Project</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>13-7700-2022</td>\n",
       "      <td>02/06/2022 16:18</td>\n",
       "      <td>44581083</td>\n",
       "      <td>Mortgage</td>\n",
       "      <td>Mortgage Registration</td>\n",
       "      <td>Ready</td>\n",
       "      <td>Free Hold</td>\n",
       "      <td>Residential</td>\n",
       "      <td>AKOYA OXYGEN</td>\n",
       "      <td>Land</td>\n",
       "      <td>Residential</td>\n",
       "      <td>570400.0</td>\n",
       "      <td>174.80</td>\n",
       "      <td>174.80</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Dubai Cycling Course</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>DAMAC HILLS (2) - ODORA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>13-7699-2022</td>\n",
       "      <td>02/06/2022 16:14</td>\n",
       "      <td>44014284</td>\n",
       "      <td>Mortgage</td>\n",
       "      <td>Mortgage Registration</td>\n",
       "      <td>Ready</td>\n",
       "      <td>Free Hold</td>\n",
       "      <td>Residential</td>\n",
       "      <td>AKOYA OXYGEN</td>\n",
       "      <td>Land</td>\n",
       "      <td>Residential</td>\n",
       "      <td>530000.0</td>\n",
       "      <td>174.80</td>\n",
       "      <td>174.80</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Dubai Cycling Course</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>DAMAC HILLS (2) - ZINNIA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>14-563-2022</td>\n",
       "      <td>02/06/2022 15:42</td>\n",
       "      <td>280311</td>\n",
       "      <td>Mortgage</td>\n",
       "      <td>Modify Mortgage</td>\n",
       "      <td>Ready</td>\n",
       "      <td>Non Free Hold</td>\n",
       "      <td>Residential</td>\n",
       "      <td>Al Barshaa South First</td>\n",
       "      <td>Land</td>\n",
       "      <td>Residential</td>\n",
       "      <td>500000.0</td>\n",
       "      <td>1188.42</td>\n",
       "      <td>1188.42</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Sharaf Dg Metro Station</td>\n",
       "      <td>Mall of the Emirates</td>\n",
       "      <td>Motor City</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11-12317-2022</td>\n",
       "      <td>02/06/2022 15:22</td>\n",
       "      <td>79102731</td>\n",
       "      <td>Sales</td>\n",
       "      <td>Sale</td>\n",
       "      <td>Ready</td>\n",
       "      <td>Free Hold</td>\n",
       "      <td>Commercial</td>\n",
       "      <td>AL FURJAN</td>\n",
       "      <td>Land</td>\n",
       "      <td>Commercial</td>\n",
       "      <td>1500000.0</td>\n",
       "      <td>600.51</td>\n",
       "      <td>600.51</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11-12316-2022</td>\n",
       "      <td>02/06/2022 15:07</td>\n",
       "      <td>132952797</td>\n",
       "      <td>Sales</td>\n",
       "      <td>Sale</td>\n",
       "      <td>Ready</td>\n",
       "      <td>Free Hold</td>\n",
       "      <td>Residential</td>\n",
       "      <td>AKOYA OXYGEN</td>\n",
       "      <td>Land</td>\n",
       "      <td>Residential</td>\n",
       "      <td>815000.0</td>\n",
       "      <td>112.24</td>\n",
       "      <td>112.24</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Dubai Cycling Course</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>DAMAC HILLS (2) - AMARGO</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Transaction Number  Transaction Date  Property ID Transaction Type   \n",
       "0       13-7700-2022  02/06/2022 16:18     44581083         Mortgage  \\\n",
       "1       13-7699-2022  02/06/2022 16:14     44014284         Mortgage   \n",
       "2        14-563-2022  02/06/2022 15:42       280311         Mortgage   \n",
       "3      11-12317-2022  02/06/2022 15:22     79102731            Sales   \n",
       "4      11-12316-2022  02/06/2022 15:07    132952797            Sales   \n",
       "\n",
       "    Transaction sub type Registration type  Is Free Hold?        Usage   \n",
       "0  Mortgage Registration             Ready      Free Hold  Residential  \\\n",
       "1  Mortgage Registration             Ready      Free Hold  Residential   \n",
       "2        Modify Mortgage             Ready  Non Free Hold  Residential   \n",
       "3                   Sale             Ready      Free Hold   Commercial   \n",
       "4                   Sale             Ready      Free Hold  Residential   \n",
       "\n",
       "                     Area Property Type Property Sub Type     Amount   \n",
       "0            AKOYA OXYGEN          Land       Residential   570400.0  \\\n",
       "1            AKOYA OXYGEN          Land       Residential   530000.0   \n",
       "2  Al Barshaa South First          Land       Residential   500000.0   \n",
       "3               AL FURJAN          Land        Commercial  1500000.0   \n",
       "4            AKOYA OXYGEN          Land       Residential   815000.0   \n",
       "\n",
       "   Transaction Size (sq.m)  Property Size (sq.m) Room(s) Parking   \n",
       "0                   174.80                174.80     NaN     NaN  \\\n",
       "1                   174.80                174.80     NaN     NaN   \n",
       "2                  1188.42               1188.42     NaN     NaN   \n",
       "3                   600.51                600.51     NaN     NaN   \n",
       "4                   112.24                112.24     NaN     NaN   \n",
       "\n",
       "             Nearest Metro          Nearest Mall      Nearest Landmark   \n",
       "0                      NaN                   NaN  Dubai Cycling Course  \\\n",
       "1                      NaN                   NaN  Dubai Cycling Course   \n",
       "2  Sharaf Dg Metro Station  Mall of the Emirates            Motor City   \n",
       "3                      NaN                   NaN                   NaN   \n",
       "4                      NaN                   NaN  Dubai Cycling Course   \n",
       "\n",
       "   No. of Buyer  No. of Seller Master Project                    Project  \n",
       "0           0.0            0.0            NaN   DAMAC HILLS (2) - ODORA   \n",
       "1           0.0            0.0            NaN  DAMAC HILLS (2) - ZINNIA   \n",
       "2           0.0            0.0            NaN                        NaN  \n",
       "3           1.0            1.0            NaN                        NaN  \n",
       "4           1.0            1.0            NaN  DAMAC HILLS (2) - AMARGO   "
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tx_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "id": "7786fac3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([nan, 'Studio', '3 B/R', '2 B/R', '4 B/R', '1 B/R', 'Office',\n",
       "       '5 B/R', 'Shop', '6 B/R', 'PENTHOUSE', 'Single Room', 'GYM',\n",
       "       'Hotel', '7 B/R', '8 B/R', 'Store Room'], dtype=object)"
      ]
     },
     "execution_count": 183,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tx_data[\"Room(s)\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "id": "e4545939",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 995975 entries, 0 to 995974\n",
      "Data columns (total 23 columns):\n",
      " #   Column                   Non-Null Count   Dtype  \n",
      "---  ------                   --------------   -----  \n",
      " 0   Transaction Number       995975 non-null  object \n",
      " 1   Transaction Date         995975 non-null  object \n",
      " 2   Property ID              995975 non-null  int64  \n",
      " 3   Transaction Type         995975 non-null  object \n",
      " 4   Transaction sub type     995975 non-null  object \n",
      " 5   Registration type        995975 non-null  object \n",
      " 6   Is Free Hold?            995975 non-null  object \n",
      " 7   Usage                    995975 non-null  object \n",
      " 8   Area                     995975 non-null  object \n",
      " 9   Property Type            995975 non-null  object \n",
      " 10  Property Sub Type        973723 non-null  object \n",
      " 11  Amount                   995975 non-null  float64\n",
      " 12  Transaction Size (sq.m)  870248 non-null  float64\n",
      " 13  Property Size (sq.m)     995972 non-null  float64\n",
      " 14  Room(s)                  758158 non-null  object \n",
      " 15  Parking                  502633 non-null  object \n",
      " 16  Nearest Metro            779839 non-null  object \n",
      " 17  Nearest Mall             775914 non-null  object \n",
      " 18  Nearest Landmark         892606 non-null  object \n",
      " 19  No. of Buyer             995787 non-null  float64\n",
      " 20  No. of Seller            995787 non-null  float64\n",
      " 21  Master Project           289 non-null     object \n",
      " 22  Project                  588688 non-null  object \n",
      "dtypes: float64(5), int64(1), object(17)\n",
      "memory usage: 174.8+ MB\n"
     ]
    }
   ],
   "source": [
    "tx_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "id": "c41d1efc",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_types = {\n",
    "    \"Contract Amount\": float,\n",
    "    \"Annual Amount\": float,\n",
    "    'Property ID': int,\n",
    "    'Number of Rooms': float,\n",
    "    'No of Units': int,\n",
    "    \"Master Project\": str,\n",
    "    \"Project\": str,\n",
    "    \n",
    "}\n",
    "\n",
    "rents_data = pd.read_csv(\"/Users/dimak/code/19.05 last project/rents.csv\", delimiter=';', decimal=',', dtype=data_types, parse_dates=['Registration Date', \"Start Date\", \"End Date\"], dayfirst=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "id": "b14c8a0b",
   "metadata": {},
   "outputs": [],
   "source": [
    "rents_data.Parking = rents_data.Parking.fillna(0).astype(int)\n",
    "rents_data[\"End Date\"] = pd.to_datetime(rents_data[\"End Date\"], format=\"%d/%m/%Y %H:%M:%S\", errors='coerce')\n",
    "rents_data[\"Property ID\"] = rents_data[\"Property ID\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "id": "d5e82ce8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 5475131 entries, 0 to 5475130\n",
      "Data columns (total 22 columns):\n",
      " #   Column                 Dtype         \n",
      "---  ------                 -----         \n",
      " 0   Ejari Contract Number  int64         \n",
      " 1   Registration Date      datetime64[ns]\n",
      " 2   Start Date             datetime64[ns]\n",
      " 3   End Date               object        \n",
      " 4   Property ID            int32         \n",
      " 5   Version                object        \n",
      " 6   Area                   object        \n",
      " 7   Contract Amount        float64       \n",
      " 8   Annual Amount          float64       \n",
      " 9   Is Free Hold?          object        \n",
      " 10  Property Size (sq.m)   float64       \n",
      " 11  Property Type          object        \n",
      " 12  Property Sub Type      object        \n",
      " 13  Number of Rooms        float64       \n",
      " 14  Usage                  object        \n",
      " 15  Nearest Metro          object        \n",
      " 16  Nearest Mall           object        \n",
      " 17  Nearest Landmark       object        \n",
      " 18  Parking                float64       \n",
      " 19  No of Units            int32         \n",
      " 20  Master Project         object        \n",
      " 21  Project                object        \n",
      "dtypes: datetime64[ns](2), float64(5), int32(2), int64(1), object(12)\n",
      "memory usage: 877.2+ MB\n"
     ]
    }
   ],
   "source": [
    "rents_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "id": "6239713c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Ejari Contract Number</th>\n",
       "      <th>Registration Date</th>\n",
       "      <th>Start Date</th>\n",
       "      <th>End Date</th>\n",
       "      <th>Property ID</th>\n",
       "      <th>Version</th>\n",
       "      <th>Area</th>\n",
       "      <th>Contract Amount</th>\n",
       "      <th>Annual Amount</th>\n",
       "      <th>Is Free Hold?</th>\n",
       "      <th>Property Size (sq.m)</th>\n",
       "      <th>Property Type</th>\n",
       "      <th>Property Sub Type</th>\n",
       "      <th>Number of Rooms</th>\n",
       "      <th>Usage</th>\n",
       "      <th>Nearest Metro</th>\n",
       "      <th>Nearest Mall</th>\n",
       "      <th>Nearest Landmark</th>\n",
       "      <th>Parking</th>\n",
       "      <th>No of Units</th>\n",
       "      <th>Master Project</th>\n",
       "      <th>Project</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>120210425000238</td>\n",
       "      <td>2021-04-25 09:36:31</td>\n",
       "      <td>2021-04-13</td>\n",
       "      <td>12/04/2026 00:00:00</td>\n",
       "      <td>1058329686</td>\n",
       "      <td>New</td>\n",
       "      <td>Dubai Investment Park Second</td>\n",
       "      <td>25000.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>Free Hold</td>\n",
       "      <td>7488.95</td>\n",
       "      <td>Building</td>\n",
       "      <td>Factory</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Industrial</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Expo 2020 Site</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>120210526001582</td>\n",
       "      <td>2021-05-26 12:12:22</td>\n",
       "      <td>2021-06-01</td>\n",
       "      <td>30/11/2025 00:00:00</td>\n",
       "      <td>99539298</td>\n",
       "      <td>New</td>\n",
       "      <td>Um Nahad Third</td>\n",
       "      <td>16000000.0</td>\n",
       "      <td>4000000.0</td>\n",
       "      <td>Free Hold</td>\n",
       "      <td>6792.00</td>\n",
       "      <td>Building</td>\n",
       "      <td>Building</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Residential</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>120210526001913</td>\n",
       "      <td>2021-05-26 12:47:49</td>\n",
       "      <td>2021-06-15</td>\n",
       "      <td>14/12/2025 00:00:00</td>\n",
       "      <td>99539289</td>\n",
       "      <td>New</td>\n",
       "      <td>Um Nahad Third</td>\n",
       "      <td>16000000.0</td>\n",
       "      <td>4000000.0</td>\n",
       "      <td>Free Hold</td>\n",
       "      <td>6481.94</td>\n",
       "      <td>Building</td>\n",
       "      <td>Building</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Residential</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>120210526002150</td>\n",
       "      <td>2021-05-26 13:14:55</td>\n",
       "      <td>2021-07-01</td>\n",
       "      <td>31/12/2025 00:00:00</td>\n",
       "      <td>99539307</td>\n",
       "      <td>New</td>\n",
       "      <td>Um Nahad Third</td>\n",
       "      <td>16000000.0</td>\n",
       "      <td>4000000.0</td>\n",
       "      <td>Free Hold</td>\n",
       "      <td>6792.00</td>\n",
       "      <td>Building</td>\n",
       "      <td>Building</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Residential</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>120210526002235</td>\n",
       "      <td>2021-05-26 13:27:36</td>\n",
       "      <td>2021-07-15</td>\n",
       "      <td>14/01/2026 00:00:00</td>\n",
       "      <td>99539316</td>\n",
       "      <td>New</td>\n",
       "      <td>Um Nahad Third</td>\n",
       "      <td>16000000.0</td>\n",
       "      <td>4000000.0</td>\n",
       "      <td>Free Hold</td>\n",
       "      <td>6481.94</td>\n",
       "      <td>Building</td>\n",
       "      <td>Building</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Residential</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Ejari Contract Number   Registration Date Start Date             End Date   \n",
       "0        120210425000238 2021-04-25 09:36:31 2021-04-13  12/04/2026 00:00:00  \\\n",
       "1        120210526001582 2021-05-26 12:12:22 2021-06-01  30/11/2025 00:00:00   \n",
       "2        120210526001913 2021-05-26 12:47:49 2021-06-15  14/12/2025 00:00:00   \n",
       "3        120210526002150 2021-05-26 13:14:55 2021-07-01  31/12/2025 00:00:00   \n",
       "4        120210526002235 2021-05-26 13:27:36 2021-07-15  14/01/2026 00:00:00   \n",
       "\n",
       "   Property ID Version                          Area  Contract Amount   \n",
       "0   1058329686     New  Dubai Investment Park Second          25000.0  \\\n",
       "1     99539298     New                Um Nahad Third       16000000.0   \n",
       "2     99539289     New                Um Nahad Third       16000000.0   \n",
       "3     99539307     New                Um Nahad Third       16000000.0   \n",
       "4     99539316     New                Um Nahad Third       16000000.0   \n",
       "\n",
       "   Annual Amount Is Free Hold?  Property Size (sq.m) Property Type   \n",
       "0         5000.0     Free Hold               7488.95      Building  \\\n",
       "1      4000000.0     Free Hold               6792.00      Building   \n",
       "2      4000000.0     Free Hold               6481.94      Building   \n",
       "3      4000000.0     Free Hold               6792.00      Building   \n",
       "4      4000000.0     Free Hold               6481.94      Building   \n",
       "\n",
       "  Property Sub Type  Number of Rooms        Usage Nearest Metro Nearest Mall   \n",
       "0           Factory              NaN   Industrial           NaN          NaN  \\\n",
       "1          Building              NaN  Residential           NaN          NaN   \n",
       "2          Building              NaN  Residential           NaN          NaN   \n",
       "3          Building              NaN  Residential           NaN          NaN   \n",
       "4          Building              NaN  Residential           NaN          NaN   \n",
       "\n",
       "  Nearest Landmark  Parking  No of Units Master Project Project  \n",
       "0   Expo 2020 Site        0            1            NaN     NaN  \n",
       "1              NaN        0            1            NaN     NaN  \n",
       "2              NaN        0            1            NaN     NaN  \n",
       "3              NaN        0            1            NaN     NaN  \n",
       "4              NaN        0            1            NaN     NaN  "
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rents_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "5cfe88d1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([nan, 'Remraam - Al Ramth', 'DAMAC HILLS - BROOKFIELD-3'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rents_data[\"Master Project\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "id": "39b0696b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1101"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(rents_data[\"Project\"].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "id": "e373e082",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  0,   1,   2,  51,  21,  78, 198,  14, 167,   7,  22, 112,  36,\n",
       "         4,  15,  96, 279,  43,  10,  17,  13,  68,  40,   5,   3,  11,\n",
       "        20,  25,  30, 163,  56, 107,   8,  90,   6,  97, 147,  12,  35,\n",
       "        39, 194,  16,  23,  41,  29, 125,  50,  67,  65,  46,   9,  58,\n",
       "        66,  72,  26,  37,  48,  85, 166, 111,  70,  71,  53, 280,  81,\n",
       "        60,  54,  18, 120,  63,  62,  80, 121,  49, 129, 267, 268,  33,\n",
       "        86, 200, 476,  28, 340,  19,  99,  27, 161,  44,  95, 283, 189,\n",
       "       148,  98])"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rents_data[\"Parking\"].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6e0f6f35",
   "metadata": {},
   "source": [
    "The data you provided appears to be a table with information related to Ejari contracts. Here's the breakdown of the columns:\n",
    "\n",
    "1. Ejari Contract Number: A unique identifier for each contract.\n",
    "2. Registration Date: The date and time when the contract was registered.\n",
    "3. Start Date: The date when the contract's validity or lease period begins.\n",
    "4. End Date: The date when the contract's validity or lease period ends.\n",
    "5. Property ID: An identifier for the property associated with the contract.\n",
    "6. Version: Indicates whether the contract is new or renewed.\n",
    "7. Area: The area or location of the property.\n",
    "8. Contract Amount: The total amount specified in the contract.\n",
    "9. Annual Amount: The annual amount or rental cost specified in the contract.\n",
    "10. Is Free Hold?: Indicates whether the property is freehold (ownership) or not.\n",
    "11. Property Sub Type: Further categorization of the property type.\n",
    "12. Number of Rooms: The number of rooms in the property.\n",
    "13. Usage: The designated usage of the property (e.g., residential, commercial, industrial).\n",
    "14. Nearest Metro: The nearest metro station to the property.\n",
    "15. Nearest Mall: The nearest shopping mall to the property.\n",
    "16. Nearest Landmark: The closest landmark or point of reference to the property.\n",
    "17. Parking: Information about parking facilities.\n",
    "18. No of Units: The number of units (e.g., apartments, villas) within the property.\n",
    "19. Master Project: The master project or development name.\n",
    "20. Project: The project name or specific subdivision within the master project.\n",
    "\n",
    "The data seems to represent various Ejari contracts with details such as contract dates, property information, financial terms, and additional property-related attributes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "id": "a66e8e6e",
   "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>Ejari Contract Number</th>\n",
       "      <th>Registration Date</th>\n",
       "      <th>Start Date</th>\n",
       "      <th>End Date</th>\n",
       "      <th>Property ID</th>\n",
       "      <th>Contract Amount</th>\n",
       "      <th>Annual Amount</th>\n",
       "      <th>Property Size (sq.m)</th>\n",
       "      <th>Number of Rooms</th>\n",
       "      <th>Parking</th>\n",
       "      <th>No of Units</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>5.475131e+06</td>\n",
       "      <td>5475131</td>\n",
       "      <td>5475131</td>\n",
       "      <td>5475129</td>\n",
       "      <td>5.475131e+06</td>\n",
       "      <td>5.475131e+06</td>\n",
       "      <td>5.475063e+06</td>\n",
       "      <td>5.462640e+06</td>\n",
       "      <td>190055.000000</td>\n",
       "      <td>5.475131e+06</td>\n",
       "      <td>5.475131e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.220602e+14</td>\n",
       "      <td>2017-12-23 12:36:39.221609216</td>\n",
       "      <td>2017-11-07 13:31:53.895730944</td>\n",
       "      <td>2018-12-13 05:50:35.919725568</td>\n",
       "      <td>1.599817e+08</td>\n",
       "      <td>6.336839e+05</td>\n",
       "      <td>3.766875e+05</td>\n",
       "      <td>8.089455e+03</td>\n",
       "      <td>3.411639</td>\n",
       "      <td>2.987180e-02</td>\n",
       "      <td>9.956889e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.078400e+04</td>\n",
       "      <td>2010-05-18 19:44:30</td>\n",
       "      <td>1991-12-16 00:00:00</td>\n",
       "      <td>1955-08-01 00:00:00</td>\n",
       "      <td>-2.147484e+09</td>\n",
       "      <td>-5.869700e+04</td>\n",
       "      <td>-5.418185e+04</td>\n",
       "      <td>-9.000000e-02</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.201402e+14</td>\n",
       "      <td>2015-08-09 17:38:50.500000</td>\n",
       "      <td>2015-06-20 00:00:00</td>\n",
       "      <td>2016-07-10 00:00:00</td>\n",
       "      <td>4.358940e+05</td>\n",
       "      <td>4.200000e+04</td>\n",
       "      <td>4.250400e+04</td>\n",
       "      <td>3.500000e+01</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.201809e+14</td>\n",
       "      <td>2018-03-31 10:35:45</td>\n",
       "      <td>2018-02-15 00:00:00</td>\n",
       "      <td>2019-03-13 00:00:00</td>\n",
       "      <td>9.393150e+05</td>\n",
       "      <td>6.510000e+04</td>\n",
       "      <td>6.600000e+04</td>\n",
       "      <td>7.700000e+01</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2.201309e+14</td>\n",
       "      <td>2020-07-30 09:56:09.500000</td>\n",
       "      <td>2020-06-16 00:00:00</td>\n",
       "      <td>2021-07-19 00:00:00</td>\n",
       "      <td>3.153286e+08</td>\n",
       "      <td>1.300000e+05</td>\n",
       "      <td>1.280000e+05</td>\n",
       "      <td>1.280400e+02</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.120211e+15</td>\n",
       "      <td>2022-06-03 12:25:51</td>\n",
       "      <td>2109-07-05 00:00:00</td>\n",
       "      <td>2110-07-04 00:00:00</td>\n",
       "      <td>2.147481e+09</td>\n",
       "      <td>4.200000e+09</td>\n",
       "      <td>1.610378e+09</td>\n",
       "      <td>9.300910e+08</td>\n",
       "      <td>197.000000</td>\n",
       "      <td>4.760000e+02</td>\n",
       "      <td>6.360000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>7.347663e+13</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.875130e+08</td>\n",
       "      <td>8.484481e+06</td>\n",
       "      <td>3.773919e+06</td>\n",
       "      <td>1.747893e+06</td>\n",
       "      <td>1.103383</td>\n",
       "      <td>8.029567e-01</td>\n",
       "      <td>4.088399e+01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Ejari Contract Number              Registration Date   \n",
       "count           5.475131e+06                        5475131  \\\n",
       "mean            1.220602e+14  2017-12-23 12:36:39.221609216   \n",
       "min             1.078400e+04            2010-05-18 19:44:30   \n",
       "25%             1.201402e+14     2015-08-09 17:38:50.500000   \n",
       "50%             1.201809e+14            2018-03-31 10:35:45   \n",
       "75%             2.201309e+14     2020-07-30 09:56:09.500000   \n",
       "max             1.120211e+15            2022-06-03 12:25:51   \n",
       "std             7.347663e+13                            NaN   \n",
       "\n",
       "                          Start Date                       End Date   \n",
       "count                        5475131                        5475129  \\\n",
       "mean   2017-11-07 13:31:53.895730944  2018-12-13 05:50:35.919725568   \n",
       "min              1991-12-16 00:00:00            1955-08-01 00:00:00   \n",
       "25%              2015-06-20 00:00:00            2016-07-10 00:00:00   \n",
       "50%              2018-02-15 00:00:00            2019-03-13 00:00:00   \n",
       "75%              2020-06-16 00:00:00            2021-07-19 00:00:00   \n",
       "max              2109-07-05 00:00:00            2110-07-04 00:00:00   \n",
       "std                              NaN                            NaN   \n",
       "\n",
       "        Property ID  Contract Amount  Annual Amount  Property Size (sq.m)   \n",
       "count  5.475131e+06     5.475131e+06   5.475063e+06          5.462640e+06  \\\n",
       "mean   1.599817e+08     6.336839e+05   3.766875e+05          8.089455e+03   \n",
       "min   -2.147484e+09    -5.869700e+04  -5.418185e+04         -9.000000e-02   \n",
       "25%    4.358940e+05     4.200000e+04   4.250400e+04          3.500000e+01   \n",
       "50%    9.393150e+05     6.510000e+04   6.600000e+04          7.700000e+01   \n",
       "75%    3.153286e+08     1.300000e+05   1.280000e+05          1.280400e+02   \n",
       "max    2.147481e+09     4.200000e+09   1.610378e+09          9.300910e+08   \n",
       "std    5.875130e+08     8.484481e+06   3.773919e+06          1.747893e+06   \n",
       "\n",
       "       Number of Rooms       Parking   No of Units  \n",
       "count    190055.000000  5.475131e+06  5.475131e+06  \n",
       "mean          3.411639  2.987180e-02  9.956889e+00  \n",
       "min           0.000000  0.000000e+00  1.000000e+00  \n",
       "25%           3.000000  0.000000e+00  1.000000e+00  \n",
       "50%           3.000000  0.000000e+00  1.000000e+00  \n",
       "75%           4.000000  0.000000e+00  1.000000e+00  \n",
       "max         197.000000  4.760000e+02  6.360000e+02  \n",
       "std           1.103383  8.029567e-01  4.088399e+01  "
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rents_data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c6eb305",
   "metadata": {},
   "source": [
    "This is the summary of statistics for the numerical columns in the dataset. \n",
    "\n",
    "- For the column \"Ejari Contract Number\", the minimum value is 10784 and the maximum value is 1.120211e+15.\n",
    "- For the column \"Property ID\", the minimum value is -3 and the maximum value is 5.772906e+09.\n",
    "- For the column \"Number of Rooms\", the minimum value is 0 the maximum value is 197.\n",
    "- For the column \"Parking\", the minimum value is 0 and the maximum value is 476.\n",
    "- For the columnNo of Units\", the minimum value is 1 and the maximum value is 636.\n",
    "\n",
    "The \"count\" row shows the number of non-null values for each column. The \"mean\" row shows the average value for each column. The \"std\" row shows the standard deviation for each column. The \"min\" and \"max\" rows show the minimum and maximum values for each column, respectively. The \"25%\", \"50%\", and \"75%\" rows show the first, second (median), and third quartiles for each column. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "abfe4743",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = {\n",
    "    'Metric': ['mean', 'std', 'min', 'max'],\n",
    "    'Ejari Contract Number': [1.220602e+14, 7.347663e+13, 1.078400e+04, 1.120211e+15],\n",
    "    'Property ID': [3.946469e+08, 7.872295e+08, -3.000000e+00, 5.772906e+09],\n",
    "    'Number of Rooms': [3.411639, 1.103383, 0.000000, 197.000000],\n",
    "    'Parking': [0.972563, 4.480474, 0.000000, 476.000000],\n",
    "    'No of Units': [9.956889e+00, 4.088399e+01, 1.000000e+00, 6.360000e+02]\n",
    "}\n",
    "\n",
    "df = pd.DataFrame(data)\n",
    "df.set_index('Metric', inplace=True)\n",
    "df.plot(kind='bar')\n",
    "plt.xlabel('Metric')\n",
    "plt.ylabel('Value')\n",
    "plt.title('Data Analysis')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3910f7a7",
   "metadata": {},
   "source": [
    "The plot displays the mean, standard deviation, minimum, and maximum values for each column in the dataset. Here's what you can observe from the plot:\n",
    "\n",
    "1. Ejari Contract Number: The mean value is around 1.22e+14, indicating the average contract number. The standard deviation is approximately 7.35e+13, indicating the spread of contract numbers. The minimum value is 1.08e+04, and the maximum value is 1.12e+15.\n",
    "\n",
    "2. Property ID: The mean value is approximately 3.95e+08, representing the average property ID. The standard deviation is around 7.87e+08, indicating the variability in property ID numbers. The minimum value is -3, which may require further investigation. The maximum value is 5.77e+09.\n",
    "\n",
    "3. Number of Rooms: The mean value is 3.41, indicating the average number of rooms. The standard deviation is 1.10, suggesting some variability in the number of rooms. The minimum value is 0, indicating the presence of properties with no rooms, which might require further examination. The maximum value is 197.\n",
    "\n",
    "4. Parking: The mean value is 0.97, representing the average parking availability. The standard deviation is 4.48, indicating some variability in the parking counts. The minimum value is 0, indicating some properties have no parking available. The maximum value is 476, suggesting properties with a higher number of parking spaces.\n",
    "\n",
    "5. No of Units: The mean value is approximately 9.96, indicating the average number of units. The standard deviation is 40.88, suggesting variability in the count of units. The minimum value is 1, representing properties with only one unit. The maximum value is 636, indicating properties with a higher number of units.\n",
    "\n",
    "Overall, this plot provides a visual summary of the central tendency (mean), variability (standard deviation), and range (minimum and maximum) of each column in the dataset, giving insights into the characteristics of the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "ed80f3ec",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         Ejari Contract Number    Registration Date           Start Date   \n",
      "0              120210425000238  25/04/2021 09:36:31  13/04/2021 00:00:00  \\\n",
      "1              120210526001582  26/05/2021 12:12:22  01/06/2021 00:00:00   \n",
      "2              120210526001913  26/05/2021 12:47:49  15/06/2021 00:00:00   \n",
      "3              120210526002150  26/05/2021 13:14:55  01/07/2021 00:00:00   \n",
      "4              120210526002235  26/05/2021 13:27:36  15/07/2021 00:00:00   \n",
      "...                        ...                  ...                  ...   \n",
      "5475126               99821944  24/06/2013 09:38:01  10/09/2013 00:00:00   \n",
      "5475127        120141110001107  19/03/2015 10:18:19  21/02/2015 00:00:00   \n",
      "5475128        120130716001123  15/03/2015 11:50:17  21/03/2015 00:00:00   \n",
      "5475129        120140515002099  09/03/2015 13:35:26  29/05/2015 00:00:00   \n",
      "5475130        120140619001536  29/03/2015 12:27:13  25/06/2015 00:00:00   \n",
      "\n",
      "                    End Date  Property ID  Version   \n",
      "0        12/04/2026 00:00:00   1058329686      New  \\\n",
      "1        30/11/2025 00:00:00     99539298      New   \n",
      "2        14/12/2025 00:00:00     99539289      New   \n",
      "3        31/12/2025 00:00:00     99539307      New   \n",
      "4        14/01/2026 00:00:00     99539316      New   \n",
      "...                      ...          ...      ...   \n",
      "5475126  09/09/2014 00:00:00       557988  Renewed   \n",
      "5475127  20/02/2016 00:00:00       557991  Renewed   \n",
      "5475128  20/03/2016 00:00:00       558048  Renewed   \n",
      "5475129  28/05/2016 00:00:00       558024  Renewed   \n",
      "5475130  24/06/2016 00:00:00       558102  Renewed   \n",
      "\n",
      "                                 Area Contract Amount Annual Amount   \n",
      "0        Dubai Investment Park Second           25000          5000  \\\n",
      "1                      Um Nahad Third        16000000       4000000   \n",
      "2                      Um Nahad Third        16000000       4000000   \n",
      "3                      Um Nahad Third        16000000       4000000   \n",
      "4                      Um Nahad Third        16000000       4000000   \n",
      "...                               ...             ...           ...   \n",
      "5475126              Al Safouh Second          150000        150000   \n",
      "5475127              Al Safouh Second          165000        165000   \n",
      "5475128              Al Safouh Second          168000        168000   \n",
      "5475129              Al Safouh Second          180000        180000   \n",
      "5475130              Al Safouh Second          168000        168000   \n",
      "\n",
      "         Is Free Hold?  ... Property Sub Type Number of Rooms        Usage   \n",
      "0            Free Hold  ...           Factory             NaN   Industrial  \\\n",
      "1            Free Hold  ...          Building             NaN  Residential   \n",
      "2            Free Hold  ...          Building             NaN  Residential   \n",
      "3            Free Hold  ...          Building             NaN  Residential   \n",
      "4            Free Hold  ...          Building             NaN  Residential   \n",
      "...                ...  ...               ...             ...          ...   \n",
      "5475126  Non Free Hold  ...             Villa             NaN  Residential   \n",
      "5475127  Non Free Hold  ...             Villa             NaN  Residential   \n",
      "5475128  Non Free Hold  ...             Villa             NaN  Residential   \n",
      "5475129  Non Free Hold  ...             Villa             NaN  Residential   \n",
      "5475130  Non Free Hold  ...             Villa             NaN  Residential   \n",
      "\n",
      "             Nearest Metro Nearest Mall Nearest Landmark Parking No of Units   \n",
      "0                      NaN          NaN   Expo 2020 Site     NaN           1  \\\n",
      "1                      NaN          NaN              NaN     NaN           1   \n",
      "2                      NaN          NaN              NaN     NaN           1   \n",
      "3                      NaN          NaN              NaN     NaN           1   \n",
      "4                      NaN          NaN              NaN     NaN           1   \n",
      "...                    ...          ...              ...     ...         ...   \n",
      "5475126  Knowledge Village  Marina Mall     Burj Al Arab     2.0           1   \n",
      "5475127  Knowledge Village  Marina Mall     Burj Al Arab     2.0           1   \n",
      "5475128  Knowledge Village  Marina Mall     Burj Al Arab     2.0           1   \n",
      "5475129  Knowledge Village  Marina Mall     Burj Al Arab     2.0           1   \n",
      "5475130  Knowledge Village  Marina Mall     Burj Al Arab     2.0           1   \n",
      "\n",
      "         Master Project  Project  \n",
      "0                   NaN      NaN  \n",
      "1                   NaN      NaN  \n",
      "2                   NaN      NaN  \n",
      "3                   NaN      NaN  \n",
      "4                   NaN      NaN  \n",
      "...                 ...      ...  \n",
      "5475126             NaN      NaN  \n",
      "5475127             NaN      NaN  \n",
      "5475128             NaN      NaN  \n",
      "5475129             NaN      NaN  \n",
      "5475130             NaN      NaN  \n",
      "\n",
      "[5475131 rows x 22 columns]\n"
     ]
    }
   ],
   "source": [
    "\n",
    "print(data[['Ejari Contract Number', 'Registration Date', 'Start Date', 'End Date', 'Property ID', 'Version', 'Area', 'Contract Amount', 'Annual Amount', 'Is Free Hold?', 'Property Size (sq.m)', 'Property Type', 'Property Sub Type', 'Number of Rooms', 'Usage', 'Nearest Metro', 'Nearest Mall', 'Nearest Landmark', 'Parking', 'No of Units', 'Master Project', 'Project']])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "2730c27d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Ejari Contract Number Registration Date  Start Date    End Date   \n",
      "0        120210425000238        25/04/2021  13/04/2021  12/04/2026  \\\n",
      "1        120210526001582        26/05/2021  01/06/2021  30/11/2025   \n",
      "2        120210526001913        26/05/2021  15/06/2021  14/12/2025   \n",
      "3        120210526002150        26/05/2021  01/07/2021  31/12/2025   \n",
      "4        120210526002235        26/05/2021  15/07/2021  14/01/2026   \n",
      "\n",
      "   Property ID Version                   Area  Contract Amount  Annual Amount   \n",
      "0   1058329686     New  Dubai Investment Park            25000           5000  \\\n",
      "1     99539298     New               Um Nahad         16000000        4000000   \n",
      "2     99539289     New               Um Nahad         16000000        4000000   \n",
      "3     99539307     New               Um Nahad         16000000        4000000   \n",
      "4     99539316     New               Um Nahad         16000000        4000000   \n",
      "\n",
      "  Is Free Hold?  \n",
      "0     Free Hold  \n",
      "1     Free Hold  \n",
      "2     Free Hold  \n",
      "3     Free Hold  \n",
      "4     Free Hold  \n"
     ]
    }
   ],
   "source": [
    "\n",
    "data = {\n",
    "    'Ejari Contract Number': [120210425000238, 120210526001582, 120210526001913, 120210526002150, 120210526002235],\n",
    "    'Registration Date': ['25/04/2021', '26/05/2021', '26/05/2021', '26/05/2021', '26/05/2021'],\n",
    "    'Start Date': ['13/04/2021', '01/06/2021', '15/06/2021', '01/07/2021', '15/07/2021'],\n",
    "    'End Date': ['12/04/2026', '30/11/2025', '14/12/2025', '31/12/2025', '14/01/2026'],\n",
    "    'Property ID': [1058329686, 99539298, 99539289, 99539307, 99539316],\n",
    "    'Version': ['New', 'New', 'New', 'New', 'New'],\n",
    "    'Area': ['Dubai Investment Park', 'Um Nahad', 'Um Nahad', 'Um Nahad', 'Um Nahad'],\n",
    "    'Contract Amount': [25000, 16000000, 16000000, 16000000, 16000000],\n",
    "    'Annual Amount': [5000, 4000000, 4000000, 4000000, 4000000],\n",
    "    'Is Free Hold?': ['Free Hold', 'Free Hold', 'Free Hold', 'Free Hold', 'Free Hold']\n",
    "    # Add other columns from your data here\n",
    "}\n",
    "\n",
    "df = pd.DataFrame(data)\n",
    "print(df)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "2c224806",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "data_dict = {'count': [5.475131e+06, 5.475131e+06, 190055.000000, 168166.000000, 5.475131e+06],\n",
    "             'mean': [1.220602e+14, 3.946469e+08, 3.411639, 0.972563, 9.956889e+00],\n",
    "             'std': [7.347663e+13, 7.872295e+08, 1.103383, 4.480474, 4.088399e+01],\n",
    "             'min': [1.078400e+04, -3.000000e+00, 0.000000, 0.000000, 1.000000e+00],\n",
    "             '25%': [1.201402e+14, 5.553645e+05, 3.000000, 0.000000, 1.000000e+00],\n",
    "             '50%': [1.201809e+14, 1.551066e+07, 3.000000, 1.000000, 1.000000e+00],\n",
    "             '75%': [2.201309e+14, 4.095848e+08, 4.000000, 2.000000, 1.000000e+00],\n",
    "             'max': [1.120211e+15, 5.772906e+09, 197.000000, 476.000000, 6.360000e+02]}\n",
    "data = pd.DataFrame(data_dict, index=['Ejari Contract Number', 'Property ID', 'Number of Rooms', 'Parking', 'No of Units'])\n",
    "ax = data.plot(kind='barh', figsize=(10, 6), legend=False)\n",
    "ax.set_title('Summary Statistics')\n",
    "ax.set_xlabel('Value')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b62e1c87",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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