03_HOUSE_RENTS.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Rents analysis \n",
    "In this Dataset, we have information on almost 4700+ Houses/Apartments/Flats Available for Rent with different parameters like BHK, Rent, Size, No. of Floors, Area Type, Area Locality, City, Furnishing Status, Type of Tenant Preferred, No. of Bathrooms, Point of Contact.\n",
    "____"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import matplotlib as mplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "rent = pd.read_csv(\"01_HOUSE_RENTS.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>Posted On</th>\n",
       "      <th>BHK</th>\n",
       "      <th>Rent</th>\n",
       "      <th>Size</th>\n",
       "      <th>Floor</th>\n",
       "      <th>Area Type</th>\n",
       "      <th>Area Locality</th>\n",
       "      <th>City</th>\n",
       "      <th>Furnishing Status</th>\n",
       "      <th>Tenant Preferred</th>\n",
       "      <th>Bathroom</th>\n",
       "      <th>Point of Contact</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2022-05-18</td>\n",
       "      <td>2</td>\n",
       "      <td>10000</td>\n",
       "      <td>1100</td>\n",
       "      <td>Ground out of 2</td>\n",
       "      <td>Super Area</td>\n",
       "      <td>Bandel</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Unfurnished</td>\n",
       "      <td>Bachelors/Family</td>\n",
       "      <td>2</td>\n",
       "      <td>Contact Owner</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2022-05-13</td>\n",
       "      <td>2</td>\n",
       "      <td>20000</td>\n",
       "      <td>800</td>\n",
       "      <td>1 out of 3</td>\n",
       "      <td>Super Area</td>\n",
       "      <td>Phool Bagan, Kankurgachi</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Semi-Furnished</td>\n",
       "      <td>Bachelors/Family</td>\n",
       "      <td>1</td>\n",
       "      <td>Contact Owner</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2022-05-16</td>\n",
       "      <td>2</td>\n",
       "      <td>17000</td>\n",
       "      <td>1000</td>\n",
       "      <td>1 out of 3</td>\n",
       "      <td>Super Area</td>\n",
       "      <td>Salt Lake City Sector 2</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Semi-Furnished</td>\n",
       "      <td>Bachelors/Family</td>\n",
       "      <td>1</td>\n",
       "      <td>Contact Owner</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2022-07-04</td>\n",
       "      <td>2</td>\n",
       "      <td>10000</td>\n",
       "      <td>800</td>\n",
       "      <td>1 out of 2</td>\n",
       "      <td>Super Area</td>\n",
       "      <td>Dumdum Park</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Unfurnished</td>\n",
       "      <td>Bachelors/Family</td>\n",
       "      <td>1</td>\n",
       "      <td>Contact Owner</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2022-05-09</td>\n",
       "      <td>2</td>\n",
       "      <td>7500</td>\n",
       "      <td>850</td>\n",
       "      <td>1 out of 2</td>\n",
       "      <td>Carpet Area</td>\n",
       "      <td>South Dum Dum</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Unfurnished</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>1</td>\n",
       "      <td>Contact Owner</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Posted On  BHK   Rent  Size            Floor    Area Type  \\\n",
       "0  2022-05-18    2  10000  1100  Ground out of 2   Super Area   \n",
       "1  2022-05-13    2  20000   800       1 out of 3   Super Area   \n",
       "2  2022-05-16    2  17000  1000       1 out of 3   Super Area   \n",
       "3  2022-07-04    2  10000   800       1 out of 2   Super Area   \n",
       "4  2022-05-09    2   7500   850       1 out of 2  Carpet Area   \n",
       "\n",
       "              Area Locality     City Furnishing Status  Tenant Preferred  \\\n",
       "0                    Bandel  Kolkata       Unfurnished  Bachelors/Family   \n",
       "1  Phool Bagan, Kankurgachi  Kolkata    Semi-Furnished  Bachelors/Family   \n",
       "2   Salt Lake City Sector 2  Kolkata    Semi-Furnished  Bachelors/Family   \n",
       "3               Dumdum Park  Kolkata       Unfurnished  Bachelors/Family   \n",
       "4             South Dum Dum  Kolkata       Unfurnished         Bachelors   \n",
       "\n",
       "   Bathroom Point of Contact  \n",
       "0         2    Contact Owner  \n",
       "1         1    Contact Owner  \n",
       "2         1    Contact Owner  \n",
       "3         1    Contact Owner  \n",
       "4         1    Contact Owner  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rent.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**How many homes does de dataset has?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Posted On            4746\n",
       "BHK                  4746\n",
       "Rent                 4746\n",
       "Size                 4746\n",
       "Floor                4746\n",
       "Area Type            4746\n",
       "Area Locality        4746\n",
       "City                 4746\n",
       "Furnishing Status    4746\n",
       "Tenant Preferred     4746\n",
       "Bathroom             4746\n",
       "Point of Contact     4746\n",
       "dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rent.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 4746 entries, 0 to 4745\n",
      "Data columns (total 12 columns):\n",
      " #   Column             Non-Null Count  Dtype \n",
      "---  ------             --------------  ----- \n",
      " 0   Posted On          4746 non-null   object\n",
      " 1   BHK                4746 non-null   int64 \n",
      " 2   Rent               4746 non-null   int64 \n",
      " 3   Size               4746 non-null   int64 \n",
      " 4   Floor              4746 non-null   object\n",
      " 5   Area Type          4746 non-null   object\n",
      " 6   Area Locality      4746 non-null   object\n",
      " 7   City               4746 non-null   object\n",
      " 8   Furnishing Status  4746 non-null   object\n",
      " 9   Tenant Preferred   4746 non-null   object\n",
      " 10  Bathroom           4746 non-null   int64 \n",
      " 11  Point of Contact   4746 non-null   object\n",
      "dtypes: int64(4), object(8)\n",
      "memory usage: 445.1+ KB\n"
     ]
    }
   ],
   "source": [
    "rent.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Wich is the oldest published home?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2022-04-13'"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rent[\"Posted On\"].min()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Wich is the latest published home?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2022-07-11'"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rent[\"Posted On\"].max()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Furnished, semi-furnished and unfurnished homes**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt = sns.countplot(x=\"Furnishing Status\", data=rent, palette=\"crest\").set_title(\"Furnishing Status\")\n",
    "sns.set_style(\"whitegrid\")\n",
    "sns.despine(left=True, bottom=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>Furnishing Status</th>\n",
       "      <th>BHK</th>\n",
       "      <th>Rent</th>\n",
       "      <th>Size</th>\n",
       "      <th>Bathroom</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Furnished</td>\n",
       "      <td>2.169118</td>\n",
       "      <td>56110.305882</td>\n",
       "      <td>1047.386765</td>\n",
       "      <td>2.127941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Semi-Furnished</td>\n",
       "      <td>2.200800</td>\n",
       "      <td>38718.810751</td>\n",
       "      <td>1077.342959</td>\n",
       "      <td>2.099511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Unfurnished</td>\n",
       "      <td>1.906887</td>\n",
       "      <td>22461.635813</td>\n",
       "      <td>801.316253</td>\n",
       "      <td>1.739394</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Furnishing Status       BHK          Rent         Size  Bathroom\n",
       "0         Furnished  2.169118  56110.305882  1047.386765  2.127941\n",
       "1    Semi-Furnished  2.200800  38718.810751  1077.342959  2.099511\n",
       "2       Unfurnished  1.906887  22461.635813   801.316253  1.739394"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "by_fur = rent.groupby (\"Furnishing Status\").mean()\n",
    "by_fur.reset_index(inplace=True)\n",
    "by_fur "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt2 = sns.barplot(x=\"Furnishing Status\", y=\"Rent\", data=rent, palette=\"crest\").set_title(\"Relation between furnishing status\")\n",
    "sns.set_style(\"whitegrid\")\n",
    "sns.despine(left=True, bottom=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Wich is the cheapest home in dataset?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1200"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rent[\"Rent\"].min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>Posted On</th>\n",
       "      <th>BHK</th>\n",
       "      <th>Rent</th>\n",
       "      <th>Size</th>\n",
       "      <th>Floor</th>\n",
       "      <th>Area Type</th>\n",
       "      <th>Area Locality</th>\n",
       "      <th>City</th>\n",
       "      <th>Furnishing Status</th>\n",
       "      <th>Tenant Preferred</th>\n",
       "      <th>Bathroom</th>\n",
       "      <th>Point of Contact</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4076</th>\n",
       "      <td>2022-05-31</td>\n",
       "      <td>3</td>\n",
       "      <td>1200</td>\n",
       "      <td>2100</td>\n",
       "      <td>1 out of 3</td>\n",
       "      <td>Carpet Area</td>\n",
       "      <td>Uppal, NH 2 2</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>Furnished</td>\n",
       "      <td>Bachelors/Family</td>\n",
       "      <td>3</td>\n",
       "      <td>Contact Owner</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Posted On  BHK  Rent  Size       Floor    Area Type  Area Locality  \\\n",
       "4076 2022-05-31    3  1200  2100  1 out of 3  Carpet Area  Uppal, NH 2 2   \n",
       "\n",
       "           City Furnishing Status  Tenant Preferred  Bathroom Point of Contact  \n",
       "4076  Hyderabad         Furnished  Bachelors/Family         3    Contact Owner  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rent[rent[\"Rent\"] == 1200]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Wich is the most expensive home in dataset?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3500000"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rent[\"Rent\"].max()"
   ]
  },
  {
   "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",
       "    }\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>Posted On</th>\n",
       "      <th>BHK</th>\n",
       "      <th>Rent</th>\n",
       "      <th>Size</th>\n",
       "      <th>Floor</th>\n",
       "      <th>Area Type</th>\n",
       "      <th>Area Locality</th>\n",
       "      <th>City</th>\n",
       "      <th>Furnishing Status</th>\n",
       "      <th>Tenant Preferred</th>\n",
       "      <th>Bathroom</th>\n",
       "      <th>Point of Contact</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1837</th>\n",
       "      <td>2022-06-08</td>\n",
       "      <td>3</td>\n",
       "      <td>3500000</td>\n",
       "      <td>2500</td>\n",
       "      <td>4 out of 4</td>\n",
       "      <td>Carpet Area</td>\n",
       "      <td>Marathahalli</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>Semi-Furnished</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>3</td>\n",
       "      <td>Contact Agent</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Posted On  BHK     Rent  Size       Floor    Area Type Area Locality  \\\n",
       "1837 2022-06-08    3  3500000  2500  4 out of 4  Carpet Area  Marathahalli   \n",
       "\n",
       "           City Furnishing Status Tenant Preferred  Bathroom Point of Contact  \n",
       "1837  Bangalore    Semi-Furnished        Bachelors         3    Contact Agent  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rent[rent[\"Rent\"] == 3500000]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Avergae rent cost:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "34993.45132743363"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rent[\"Rent\"].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Wich is the cheapest locality area?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2235"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rent[\"Area Locality\"].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "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>Area Locality</th>\n",
       "      <th>BHK</th>\n",
       "      <th>Rent</th>\n",
       "      <th>Size</th>\n",
       "      <th>Bathroom</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Beeramguda, Ramachandra Puram, NH 9</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>8000.0</td>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>in Boduppal, NH 2 2</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4700.000000</td>\n",
       "      <td>850.0</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>in Erragadda, NH 9</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>12000.000000</td>\n",
       "      <td>1150.0</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>in Miyapur, NH 9</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>15000.000000</td>\n",
       "      <td>1550.0</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>117 Residency, Chembur East</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>47000.000000</td>\n",
       "      <td>650.0</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2230</th>\n",
       "      <td>vanamali chs ghatla, Ghatla</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>52000.000000</td>\n",
       "      <td>750.0</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2231</th>\n",
       "      <td>venkatapuram</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>13500.000000</td>\n",
       "      <td>600.0</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2232</th>\n",
       "      <td>venkatesa perumal nagar</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>9000.000000</td>\n",
       "      <td>700.0</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2233</th>\n",
       "      <td>villvam towers tnhb colony</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>15000.000000</td>\n",
       "      <td>600.0</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2234</th>\n",
       "      <td>whitefield</td>\n",
       "      <td>2.583333</td>\n",
       "      <td>57458.333333</td>\n",
       "      <td>1960.0</td>\n",
       "      <td>2.416667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2235 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                             Area Locality       BHK           Rent    Size  \\\n",
       "0      Beeramguda, Ramachandra Puram, NH 9  1.000000  200000.000000  8000.0   \n",
       "1                      in Boduppal, NH 2 2  1.000000    4700.000000   850.0   \n",
       "2                       in Erragadda, NH 9  2.000000   12000.000000  1150.0   \n",
       "3                         in Miyapur, NH 9  3.000000   15000.000000  1550.0   \n",
       "4              117 Residency, Chembur East  2.000000   47000.000000   650.0   \n",
       "...                                    ...       ...            ...     ...   \n",
       "2230           vanamali chs ghatla, Ghatla  2.000000   52000.000000   750.0   \n",
       "2231                          venkatapuram  1.000000   13500.000000   600.0   \n",
       "2232               venkatesa perumal nagar  1.000000    9000.000000   700.0   \n",
       "2233            villvam towers tnhb colony  2.000000   15000.000000   600.0   \n",
       "2234                            whitefield  2.583333   57458.333333  1960.0   \n",
       "\n",
       "       Bathroom  \n",
       "0     10.000000  \n",
       "1      2.000000  \n",
       "2      2.000000  \n",
       "3      2.000000  \n",
       "4      2.000000  \n",
       "...         ...  \n",
       "2230   2.000000  \n",
       "2231   1.000000  \n",
       "2232   1.000000  \n",
       "2233   2.000000  \n",
       "2234   2.416667  \n",
       "\n",
       "[2235 rows x 5 columns]"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "by_loc = rent.groupby (\"Area Locality\").mean()\n",
    "by_loc.reset_index(inplace=True)\n",
    "by_loc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "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>Area Locality</th>\n",
       "      <th>BHK</th>\n",
       "      <th>Rent</th>\n",
       "      <th>Size</th>\n",
       "      <th>Bathroom</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>262</th>\n",
       "      <td>Behala silpara</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2200.0</td>\n",
       "      <td>550.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>563</th>\n",
       "      <td>Ennore</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>350.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>703</th>\n",
       "      <td>Hatiara</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>500.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1675</th>\n",
       "      <td>Rhoda Mistri Nagar</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>295</th>\n",
       "      <td>Bhawanipur</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3200.0</td>\n",
       "      <td>800.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Area Locality  BHK    Rent   Size  Bathroom\n",
       "262       Behala silpara  2.0  2200.0  550.0       1.0\n",
       "563               Ennore  1.0  3000.0  350.0       1.0\n",
       "703              Hatiara  1.0  3000.0  500.0       1.0\n",
       "1675  Rhoda Mistri Nagar  1.0  3000.0   80.0       1.0\n",
       "295           Bhawanipur  3.0  3200.0  800.0       2.0"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "by_loc_cheap = by_loc.sort_values(by = \"Rent\", ascending=True).head()\n",
    "by_loc_cheap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax1  = sns.barplot(x=\"Area Locality\", y=\"Rent\", data = by_loc_cheap, palette=\"crest\").set_title(\"Cheapest Area Locality\")\n",
    "sns.set_style(\"whitegrid\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Wich is the most expensive locality area?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "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>Area Locality</th>\n",
       "      <th>BHK</th>\n",
       "      <th>Rent</th>\n",
       "      <th>Size</th>\n",
       "      <th>Bathroom</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1611</th>\n",
       "      <td>Raheja Artesia, Worli</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1000000.0</td>\n",
       "      <td>3064.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1223</th>\n",
       "      <td>Marathahalli</td>\n",
       "      <td>2.0</td>\n",
       "      <td>715780.0</td>\n",
       "      <td>1070.0</td>\n",
       "      <td>1.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1073</th>\n",
       "      <td>Lady Ratan Tower, Worli</td>\n",
       "      <td>4.0</td>\n",
       "      <td>700000.0</td>\n",
       "      <td>3200.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2081</th>\n",
       "      <td>Vettuvankeni</td>\n",
       "      <td>2.0</td>\n",
       "      <td>600000.0</td>\n",
       "      <td>950.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>224</th>\n",
       "      <td>Bandra East</td>\n",
       "      <td>5.0</td>\n",
       "      <td>600000.0</td>\n",
       "      <td>3200.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Area Locality  BHK       Rent    Size  Bathroom\n",
       "1611    Raheja Artesia, Worli  4.0  1000000.0  3064.0       4.0\n",
       "1223             Marathahalli  2.0   715780.0  1070.0       1.6\n",
       "1073  Lady Ratan Tower, Worli  4.0   700000.0  3200.0       4.0\n",
       "2081             Vettuvankeni  2.0   600000.0   950.0       2.0\n",
       "224               Bandra East  5.0   600000.0  3200.0       5.0"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "by_loc_ex = by_loc.sort_values(by = \"Rent\", ascending=False).head()\n",
    "by_loc_ex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax2 = sns.barplot(x=\"Area Locality\", y=\"Rent\", data = by_loc_ex, palette=\"crest\").set_title(\"Most expensive Area Locality\")\n",
    "sns.set_style(\"whitegrid\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Wich is the cheapest and the most expensive city in the dataset?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                BHK          Rent         Size  Bathroom\n",
      "City                                                    \n",
      "Bangalore  1.930023  24966.365688   985.925508  1.793454\n",
      "Chennai    2.124579  21614.092031  1031.713805  1.998878\n",
      "Delhi      2.114050  29461.983471   786.406612  1.852893\n",
      "Hyderabad  2.206221  20555.048387  1186.669355  2.149770\n",
      "Kolkata    1.965649  11645.173664   787.366412  1.431298\n",
      "Mumbai     2.122428  85321.204733   905.904321  2.287037\n"
     ]
    }
   ],
   "source": [
    "by_city = rent.groupby(\"City\").mean()\n",
    "print (by_city)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "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>index</th>\n",
       "      <th>City</th>\n",
       "      <th>BHK</th>\n",
       "      <th>Rent</th>\n",
       "      <th>Size</th>\n",
       "      <th>Bathroom</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>1.930023</td>\n",
       "      <td>24966.365688</td>\n",
       "      <td>985.925508</td>\n",
       "      <td>1.793454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Chennai</td>\n",
       "      <td>2.124579</td>\n",
       "      <td>21614.092031</td>\n",
       "      <td>1031.713805</td>\n",
       "      <td>1.998878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>2.114050</td>\n",
       "      <td>29461.983471</td>\n",
       "      <td>786.406612</td>\n",
       "      <td>1.852893</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>2.206221</td>\n",
       "      <td>20555.048387</td>\n",
       "      <td>1186.669355</td>\n",
       "      <td>2.149770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>1.965649</td>\n",
       "      <td>11645.173664</td>\n",
       "      <td>787.366412</td>\n",
       "      <td>1.431298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>2.122428</td>\n",
       "      <td>85321.204733</td>\n",
       "      <td>905.904321</td>\n",
       "      <td>2.287037</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index       City       BHK          Rent         Size  Bathroom\n",
       "0      0  Bangalore  1.930023  24966.365688   985.925508  1.793454\n",
       "1      1    Chennai  2.124579  21614.092031  1031.713805  1.998878\n",
       "2      2      Delhi  2.114050  29461.983471   786.406612  1.852893\n",
       "3      3  Hyderabad  2.206221  20555.048387  1186.669355  2.149770\n",
       "4      4    Kolkata  1.965649  11645.173664   787.366412  1.431298\n",
       "5      5     Mumbai  2.122428  85321.204733   905.904321  2.287037"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "by_city.reset_index(inplace=True)\n",
    "by_city"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax3 = sns.barplot(x=\"City\", y=\"Rent\", data = by_city, palette=\"crest\").set_title(\"By city\")\n",
    "sns.set_style(\"whitegrid\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Una casa ofertada por el owner es más barata que una ofertada por una inmobiliaria?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                       BHK          Rent         Size  Bathroom\n",
      "Point of Contact                                               \n",
      "Contact Agent     2.362982  73481.158927  1164.812296  2.386527\n",
      "Contact Builder   1.000000   5500.000000   400.000000  1.000000\n",
      "Contact Owner     1.951493  16704.206468   873.853545  1.766169\n"
     ]
    }
   ],
   "source": [
    "by_pc = rent.groupby(\"Point of Contact\").mean()\n",
    "by_pc.reset_index()\n",
    "print (by_pc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.barplot(x=\"Point of Contact\", y=\"Rent\", data = by_pc, palette=\"crest\").set_title(\"Rent price by Contact\")\n",
    "sns.set_style(\"whitegrid\")\n",
    "sns.despine(left=True, bottom=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Wich is the best option?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.10.5 64-bit",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.5"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "aee8b7b246df8f9039afb4144a1f6fd8d2ca17a180786b69acc140d282b71a49"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}