project.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "innocent-university",
   "metadata": {},
   "source": [
    "# Real Estate Clean up\n",
    "\n",
    "This is a real dataset, and it was downloaded using web scraping techniques. The data contains registers from **Fotocasa** which is one of the most popular real estate websites in Spain. Please, do not do this (web scraping) unless it is for academic purposes.\n",
    "\n",
    "The dataset was downloaded a few years ago by Henry Navarro, and in no case were economic returns obtained from it.\n",
    "\n",
    "It contains thousands of data from real houses published on the web www.fotocasa.com. Your goal is to extract as much information as possible with the knowledge you have so far about data science, for example what is the most expensive house in the entire dataset?\n",
    "\n",
    "Let's start with precisely that question... Good luck!"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "multiple-glass",
   "metadata": {},
   "source": [
    "#### Exercise 00. Read the dataset assets/real_estate.csv and try to visualize the table (★☆☆)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 301,
   "id": "frank-heath",
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>id_realEstates</th>\n",
       "      <th>isNew</th>\n",
       "      <th>realEstate_name</th>\n",
       "      <th>phone_realEstate</th>\n",
       "      <th>url_inmueble</th>\n",
       "      <th>rooms</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>surface</th>\n",
       "      <th>price</th>\n",
       "      <th>...</th>\n",
       "      <th>level4Id</th>\n",
       "      <th>level5Id</th>\n",
       "      <th>level6Id</th>\n",
       "      <th>level7Id</th>\n",
       "      <th>level8Id</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>zipCode</th>\n",
       "      <th>customZone</th>\n",
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       "      <td>916350408.0</td>\n",
       "      <td>https://www.fotocasa.es/es/comprar/vivienda/ma...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>99.0</td>\n",
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       "      <td>40,4115646786438</td>\n",
       "      <td>-3,90662252135624</td>\n",
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       "      <td>4</td>\n",
       "      <td>152776331</td>\n",
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       "      <td>916358736.0</td>\n",
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       "      <td>3.0</td>\n",
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       "      <td>86.0</td>\n",
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       "      <td>153180188</td>\n",
       "      <td>False</td>\n",
       "      <td>ferrari 57 inmobiliaria</td>\n",
       "      <td>912177526.0</td>\n",
       "      <td>https://www.fotocasa.es/es/comprar/vivienda/ma...</td>\n",
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       "      <td>NaN</td>\n",
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       "      <th>15331</th>\n",
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       "      <td>150394373</td>\n",
       "      <td>False</td>\n",
       "      <td>inmobiliaria pulpon</td>\n",
       "      <td>912788039.0</td>\n",
       "      <td>https://www.fotocasa.es/es/comprar/vivienda/ma...</td>\n",
       "      <td>3.0</td>\n",
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       "      <td>False</td>\n",
       "      <td>tecnocasa torrelodones</td>\n",
       "      <td>912780348.0</td>\n",
       "      <td>https://www.fotocasa.es/es/comprar/vivienda/ma...</td>\n",
       "      <td>4.0</td>\n",
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       "    <tr>\n",
       "      <th>15333</th>\n",
       "      <td>15334</td>\n",
       "      <td>152607440</td>\n",
       "      <td>False</td>\n",
       "      <td>inmobiliaria pulpon</td>\n",
       "      <td>912788039.0</td>\n",
       "      <td>https://www.fotocasa.es/es/comprar/vivienda/ma...</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>15334</th>\n",
       "      <td>15335</td>\n",
       "      <td>153901356</td>\n",
       "      <td>False</td>\n",
       "      <td>infocasa consulting</td>\n",
       "      <td>911360461.0</td>\n",
       "      <td>https://www.fotocasa.es/es/comprar/vivienda/ma...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>152.0</td>\n",
       "      <td>765000</td>\n",
       "      <td>...</td>\n",
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       "      <td>40,45773</td>\n",
       "      <td>-3,69068</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>15335 rows × 37 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Unnamed: 0  id_realEstates  isNew                    realEstate_name  \\\n",
       "0               1       153771986  False            ferrari 57 inmobiliaria   \n",
       "1               2       153867863  False  tecnocasa fuenlabrada ferrocarril   \n",
       "2               3       153430440  False                 look find boadilla   \n",
       "3               4       152776331  False  tecnocasa fuenlabrada ferrocarril   \n",
       "4               5       153180188  False            ferrari 57 inmobiliaria   \n",
       "...           ...             ...    ...                                ...   \n",
       "15330       15331       153901377  False                infocasa consulting   \n",
       "15331       15332       150394373  False                inmobiliaria pulpon   \n",
       "15332       15333       153901397  False             tecnocasa torrelodones   \n",
       "15333       15334       152607440  False                inmobiliaria pulpon   \n",
       "15334       15335       153901356  False                infocasa consulting   \n",
       "\n",
       "       phone_realEstate                                       url_inmueble  \\\n",
       "0           912177526.0  https://www.fotocasa.es/es/comprar/vivienda/ma...   \n",
       "1           916358736.0  https://www.fotocasa.es/es/comprar/vivienda/ma...   \n",
       "2           916350408.0  https://www.fotocasa.es/es/comprar/vivienda/ma...   \n",
       "3           916358736.0  https://www.fotocasa.es/es/comprar/vivienda/ma...   \n",
       "4           912177526.0  https://www.fotocasa.es/es/comprar/vivienda/ma...   \n",
       "...                 ...                                                ...   \n",
       "15330       911360461.0  https://www.fotocasa.es/es/comprar/vivienda/ma...   \n",
       "15331       912788039.0  https://www.fotocasa.es/es/comprar/vivienda/ma...   \n",
       "15332       912780348.0  https://www.fotocasa.es/es/comprar/vivienda/ma...   \n",
       "15333       912788039.0  https://www.fotocasa.es/es/comprar/vivienda/ma...   \n",
       "15334       911360461.0  https://www.fotocasa.es/es/comprar/vivienda/ma...   \n",
       "\n",
       "       rooms  bathrooms  surface   price  ... level4Id level5Id level6Id  \\\n",
       "0        3.0        2.0    103.0  195000  ...        0        0        0   \n",
       "1        3.0        1.0      NaN   89000  ...        0        0        0   \n",
       "2        2.0        2.0     99.0  390000  ...        0        0        0   \n",
       "3        3.0        1.0     86.0   89000  ...        0        0        0   \n",
       "4        2.0        2.0    106.0  172000  ...        0        0        0   \n",
       "...      ...        ...      ...     ...  ...      ...      ...      ...   \n",
       "15330    2.0        1.0     96.0  259470  ...        0        0        0   \n",
       "15331    3.0        1.0    150.0  165000  ...        0        0        0   \n",
       "15332    4.0        2.0    175.0  495000  ...        0        0        0   \n",
       "15333    3.0        2.0    101.0  195000  ...        0        0        0   \n",
       "15334    3.0        2.0    152.0  765000  ...        0        0        0   \n",
       "\n",
       "      level7Id level8Id accuracy          latitude          longitude zipCode  \\\n",
       "0            0        0        0  40,2948276786438  -3,44402412135624     NaN   \n",
       "1            0        0        1          40,28674           -3,79351     NaN   \n",
       "2            0        0        0  40,4115646786438  -3,90662252135624     NaN   \n",
       "3            0        0        0  40,2853785786438  -3,79508142135624     NaN   \n",
       "4            0        0        0  40,2998774864376  -3,45226301356237     NaN   \n",
       "...        ...      ...      ...               ...                ...     ...   \n",
       "15330        0        0        0          40,45416           -3,70286     NaN   \n",
       "15331        0        0        0          40,36652           -3,48951     NaN   \n",
       "15332        0        0        0          40,57444           -3,92124     NaN   \n",
       "15333        0        0        0          40,36967           -3,48105     NaN   \n",
       "15334        0        0        0          40,45773           -3,69068     NaN   \n",
       "\n",
       "      customZone  \n",
       "0            NaN  \n",
       "1            NaN  \n",
       "2            NaN  \n",
       "3            NaN  \n",
       "4            NaN  \n",
       "...          ...  \n",
       "15330        NaN  \n",
       "15331        NaN  \n",
       "15332        NaN  \n",
       "15333        NaN  \n",
       "15334        NaN  \n",
       "\n",
       "[15335 rows x 37 columns]"
      ]
     },
     "execution_count": 301,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "# This CSV file contains semicolons instead of comas as separator\n",
    "ds = pd.read_csv('assets/real_estate.csv', sep=';')\n",
    "ds"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "latin-guest",
   "metadata": {},
   "source": [
    "#### Exercise 01. Which is the most expensive house in the dataset? (★☆☆)\n",
    "\n",
    "Print the address and the price of the selected house. For example:\n",
    "\n",
    "`The house with address General Street Nº5 is the most expensive and its price is 5000000 USD`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 302,
   "id": "developing-optimum",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The house with address El Escorial is the most expensive and its price is 8500000 USD\n"
     ]
    }
   ],
   "source": [
    "max_price = ds['price'].max()\n",
    "max_price_entry = ds[ds['price'] == max_price]\n",
    "max_price_address = max_price_entry[\"address\"].iloc[0]\n",
    "\n",
    "print(f\"The house with address {max_price_address} is the most expensive and its price is {max_price} USD\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "lesser-cosmetic",
   "metadata": {},
   "source": [
    "#### Exercise 02. Which is the cheapest house in the dataset? (★☆☆)\n",
    "\n",
    "Print the address and the price of the selected house. For example:\n",
    "\n",
    "`The house with address Concrete Street Nº1 is the cheapest and its price is 12000 USD`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 303,
   "id": "lovely-oasis",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The house with address Berlin, Coslada is the cheapest and its price is 600 USD\n"
     ]
    }
   ],
   "source": [
    "ds_filtered = ds[ds['price']>0]\n",
    "\n",
    "min_price = ds_filtered['price'].min()\n",
    "min_price_entry = ds_filtered[ds_filtered['price'] == min_price]\n",
    "min_price_address = min_price_entry[\"address\"].iloc[0]\n",
    "\n",
    "print(f\"The house with address {min_price_address} is the cheapest and its price is {min_price} USD\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "compliant-fellowship",
   "metadata": {},
   "source": [
    "#### Exercise 03. Which is the biggest and the smallest house in the dataset? (★☆☆)\n",
    "\n",
    "Print both the address and the surface of the selected houses. For example:\n",
    "\n",
    "`The biggest house is located on Yukka Street Nº10 and its surface is 5000 meters`\n",
    "\n",
    "`The smallest house is located on County Road 1 N and its surface is 200 meters`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 304,
   "id": "every-tiffany",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The biggest house is located on Sevilla la Nueva and its surface is 249000 meters\n",
      "The smallest house is located on Calle Amparo,  Madrid Capital and its surface is 15 meters\n"
     ]
    }
   ],
   "source": [
    "max_surface = round(ds['surface'].max())\n",
    "max_surface_entry = ds[ds['surface'] == max_surface]\n",
    "max_surface_address = max_surface_entry[\"address\"].iloc[0]\n",
    "\n",
    "min_surface = round(ds['surface'].min())\n",
    "min_surface_entry = ds[ds['surface'] == min_surface]\n",
    "min_surface_address = min_surface_entry[\"address\"].iloc[0]\n",
    "\n",
    "print(f\"The biggest house is located on {max_surface_address} and its surface is {max_surface} meters\")\n",
    "print(f\"The smallest house is located on {min_surface_address} and its surface is {min_surface} meters\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "danish-spirit",
   "metadata": {},
   "source": [
    "#### Exercise 04. How many populations (level5 column) the dataset contains? (★☆☆)\n",
    "\n",
    "Print the names of the populations with a comma as a separator. For example:\n",
    "\n",
    "`> print(populations)`\n",
    "\n",
    "`population1, population2, population3, ...`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 305,
   "id": "exciting-accreditation",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index([' Madrid Capital', 'Alcalá de Henares', 'Las Rozas de Madrid',\n",
      "       'Móstoles', 'Getafe', 'San Sebastián de los Reyes',\n",
      "       'Boadilla del Monte', 'Parla', 'Valdemoro', 'Torrejón de Ardoz',\n",
      "       ...\n",
      "       'Ambite', 'Navarredonda', 'Canencia', 'Lozoya', 'Navalafuente',\n",
      "       'Fresno de Torote', 'Pinilla del Valle', 'Rascafría', 'La Hiruela',\n",
      "       'Montejo de la Sierra'],\n",
      "      dtype='object', name='level5', length=168)\n"
     ]
    }
   ],
   "source": [
    "populations = ds['level5'].value_counts()\n",
    "\n",
    "print(populations.index)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "crazy-blame",
   "metadata": {},
   "source": [
    "#### Exercise 05. Does the dataset contain NAs? (★☆☆)\n",
    "\n",
    "Print a boolean value (`True` or `False`) followed by the rows/cols that contains NAs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 306,
   "id": "transparent-poetry",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Unnamed: 0              0\n",
       "id_realEstates          0\n",
       "isNew                   0\n",
       "realEstate_name        10\n",
       "phone_realEstate      794\n",
       "url_inmueble            0\n",
       "rooms                 353\n",
       "bathrooms             345\n",
       "surface              1250\n",
       "price                   0\n",
       "date                    0\n",
       "description           142\n",
       "address                 0\n",
       "country                 0\n",
       "level1                  0\n",
       "level2                  0\n",
       "level3                  0\n",
       "level4               6643\n",
       "level5                  0\n",
       "level6              14627\n",
       "level7               2277\n",
       "level8               8579\n",
       "upperLevel              0\n",
       "countryId               0\n",
       "level1Id                0\n",
       "level2Id                0\n",
       "level3Id                0\n",
       "level4Id                0\n",
       "level5Id                0\n",
       "level6Id                0\n",
       "level7Id                0\n",
       "level8Id                0\n",
       "accuracy                0\n",
       "latitude                0\n",
       "longitude               0\n",
       "zipCode             15335\n",
       "customZone          15335\n",
       "dtype: int64"
      ]
     },
     "execution_count": 306,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "na_sum = ds.isna().sum()\n",
    "na_sum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 307,
   "id": "451baa86",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{False: ['Unnamed: 0', 'id_realEstates', 'isNew', 'url_inmueble', 'price', 'date', 'address', 'country', 'level1', 'level2', 'level3', 'level5', 'upperLevel', 'countryId', 'level1Id', 'level2Id', 'level3Id', 'level4Id', 'level5Id', 'level6Id', 'level7Id', 'level8Id', 'accuracy', 'latitude', 'longitude'], True: ['realEstate_name', 'phone_realEstate', 'rooms', 'bathrooms', 'surface', 'description', 'level4', 'level6', 'level7', 'level8', 'zipCode', 'customZone']}\n"
     ]
    }
   ],
   "source": [
    "nan_cols_dict = {}\n",
    "\n",
    "for i, val in na_sum.items():\n",
    "    if val > 0:\n",
    "        nan_cols_dict.setdefault(True,[]).append(i)\n",
    "    else:\n",
    "        nan_cols_dict.setdefault(False,[]).append(i)\n",
    "\n",
    "print(nan_cols_dict)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "italic-hydrogen",
   "metadata": {},
   "source": [
    "#### Exercise 06. Delete the NAs of the dataset, if applicable (★★☆)\n",
    "\n",
    "Print a comparison between the dimensions of the original DataFrame versus the DataFrame after the deletions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 308,
   "id": "administrative-roads",
   "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>id_realEstates</th>\n",
       "      <th>isNew</th>\n",
       "      <th>realEstate_name</th>\n",
       "      <th>phone_realEstate</th>\n",
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       "      <th>bathrooms</th>\n",
       "      <th>surface</th>\n",
       "      <th>price</th>\n",
       "      <th>date</th>\n",
       "      <th>...</th>\n",
       "      <th>level2Id</th>\n",
       "      <th>level3Id</th>\n",
       "      <th>level4Id</th>\n",
       "      <th>level5Id</th>\n",
       "      <th>level6Id</th>\n",
       "      <th>level7Id</th>\n",
       "      <th>level8Id</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>153771986</td>\n",
       "      <td>False</td>\n",
       "      <td>ferrari 57 inmobiliaria</td>\n",
       "      <td>912177526.0</td>\n",
       "      <td>https://www.fotocasa.es/es/comprar/vivienda/ma...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>103.0</td>\n",
       "      <td>195000</td>\n",
       "      <td>2019-12-28T18:27:15.9975027Z</td>\n",
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       "      <td>0</td>\n",
       "      <td>40,2948276786438</td>\n",
       "      <td>-3,44402412135624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>153867863</td>\n",
       "      <td>False</td>\n",
       "      <td>tecnocasa fuenlabrada ferrocarril</td>\n",
       "      <td>916358736.0</td>\n",
       "      <td>https://www.fotocasa.es/es/comprar/vivienda/ma...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>89000</td>\n",
       "      <td>2019-12-28T18:27:15.9975027Z</td>\n",
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       "      <td>1</td>\n",
       "      <td>40,28674</td>\n",
       "      <td>-3,79351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>153430440</td>\n",
       "      <td>False</td>\n",
       "      <td>look find boadilla</td>\n",
       "      <td>916350408.0</td>\n",
       "      <td>https://www.fotocasa.es/es/comprar/vivienda/ma...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>390000</td>\n",
       "      <td>2019-12-28T18:27:15.9975027Z</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>40,4115646786438</td>\n",
       "      <td>-3,90662252135624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>152776331</td>\n",
       "      <td>False</td>\n",
       "      <td>tecnocasa fuenlabrada ferrocarril</td>\n",
       "      <td>916358736.0</td>\n",
       "      <td>https://www.fotocasa.es/es/comprar/vivienda/ma...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>89000</td>\n",
       "      <td>2019-12-28T18:27:15.9975027Z</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>40,2853785786438</td>\n",
       "      <td>-3,79508142135624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>153180188</td>\n",
       "      <td>False</td>\n",
       "      <td>ferrari 57 inmobiliaria</td>\n",
       "      <td>912177526.0</td>\n",
       "      <td>https://www.fotocasa.es/es/comprar/vivienda/ma...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>106.0</td>\n",
       "      <td>172000</td>\n",
       "      <td>2019-12-28T18:27:15.9975027Z</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>15330</th>\n",
       "      <td>153901377</td>\n",
       "      <td>False</td>\n",
       "      <td>infocasa consulting</td>\n",
       "      <td>911360461.0</td>\n",
       "      <td>https://www.fotocasa.es/es/comprar/vivienda/ma...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>259470</td>\n",
       "      <td>2019-12-17T18:19:00Z</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40,45416</td>\n",
       "      <td>-3,70286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15331</th>\n",
       "      <td>150394373</td>\n",
       "      <td>False</td>\n",
       "      <td>inmobiliaria pulpon</td>\n",
       "      <td>912788039.0</td>\n",
       "      <td>https://www.fotocasa.es/es/comprar/vivienda/ma...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>165000</td>\n",
       "      <td>2019-12-17T18:19:00Z</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>40,36652</td>\n",
       "      <td>-3,48951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15332</th>\n",
       "      <td>153901397</td>\n",
       "      <td>False</td>\n",
       "      <td>tecnocasa torrelodones</td>\n",
       "      <td>912780348.0</td>\n",
       "      <td>https://www.fotocasa.es/es/comprar/vivienda/ma...</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>175.0</td>\n",
       "      <td>495000</td>\n",
       "      <td>2019-12-17T18:19:00Z</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40,57444</td>\n",
       "      <td>-3,92124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15333</th>\n",
       "      <td>152607440</td>\n",
       "      <td>False</td>\n",
       "      <td>inmobiliaria pulpon</td>\n",
       "      <td>912788039.0</td>\n",
       "      <td>https://www.fotocasa.es/es/comprar/vivienda/ma...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>101.0</td>\n",
       "      <td>195000</td>\n",
       "      <td>2019-12-17T18:18:00Z</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40,36967</td>\n",
       "      <td>-3,48105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15334</th>\n",
       "      <td>153901356</td>\n",
       "      <td>False</td>\n",
       "      <td>infocasa consulting</td>\n",
       "      <td>911360461.0</td>\n",
       "      <td>https://www.fotocasa.es/es/comprar/vivienda/ma...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>152.0</td>\n",
       "      <td>765000</td>\n",
       "      <td>2019-12-17T18:17:00Z</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40,45773</td>\n",
       "      <td>-3,69068</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>15335 rows × 34 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       id_realEstates  isNew                    realEstate_name  \\\n",
       "0           153771986  False            ferrari 57 inmobiliaria   \n",
       "1           153867863  False  tecnocasa fuenlabrada ferrocarril   \n",
       "2           153430440  False                 look find boadilla   \n",
       "3           152776331  False  tecnocasa fuenlabrada ferrocarril   \n",
       "4           153180188  False            ferrari 57 inmobiliaria   \n",
       "...               ...    ...                                ...   \n",
       "15330       153901377  False                infocasa consulting   \n",
       "15331       150394373  False                inmobiliaria pulpon   \n",
       "15332       153901397  False             tecnocasa torrelodones   \n",
       "15333       152607440  False                inmobiliaria pulpon   \n",
       "15334       153901356  False                infocasa consulting   \n",
       "\n",
       "       phone_realEstate                                       url_inmueble  \\\n",
       "0           912177526.0  https://www.fotocasa.es/es/comprar/vivienda/ma...   \n",
       "1           916358736.0  https://www.fotocasa.es/es/comprar/vivienda/ma...   \n",
       "2           916350408.0  https://www.fotocasa.es/es/comprar/vivienda/ma...   \n",
       "3           916358736.0  https://www.fotocasa.es/es/comprar/vivienda/ma...   \n",
       "4           912177526.0  https://www.fotocasa.es/es/comprar/vivienda/ma...   \n",
       "...                 ...                                                ...   \n",
       "15330       911360461.0  https://www.fotocasa.es/es/comprar/vivienda/ma...   \n",
       "15331       912788039.0  https://www.fotocasa.es/es/comprar/vivienda/ma...   \n",
       "15332       912780348.0  https://www.fotocasa.es/es/comprar/vivienda/ma...   \n",
       "15333       912788039.0  https://www.fotocasa.es/es/comprar/vivienda/ma...   \n",
       "15334       911360461.0  https://www.fotocasa.es/es/comprar/vivienda/ma...   \n",
       "\n",
       "       rooms  bathrooms  surface   price                          date  ...  \\\n",
       "0        3.0        2.0    103.0  195000  2019-12-28T18:27:15.9975027Z  ...   \n",
       "1        3.0        1.0      NaN   89000  2019-12-28T18:27:15.9975027Z  ...   \n",
       "2        2.0        2.0     99.0  390000  2019-12-28T18:27:15.9975027Z  ...   \n",
       "3        3.0        1.0     86.0   89000  2019-12-28T18:27:15.9975027Z  ...   \n",
       "4        2.0        2.0    106.0  172000  2019-12-28T18:27:15.9975027Z  ...   \n",
       "...      ...        ...      ...     ...                           ...  ...   \n",
       "15330    2.0        1.0     96.0  259470          2019-12-17T18:19:00Z  ...   \n",
       "15331    3.0        1.0    150.0  165000          2019-12-17T18:19:00Z  ...   \n",
       "15332    4.0        2.0    175.0  495000          2019-12-17T18:19:00Z  ...   \n",
       "15333    3.0        2.0    101.0  195000          2019-12-17T18:18:00Z  ...   \n",
       "15334    3.0        2.0    152.0  765000          2019-12-17T18:17:00Z  ...   \n",
       "\n",
       "      level2Id level3Id level4Id level5Id level6Id level7Id level8Id accuracy  \\\n",
       "0            0        0        0        0        0        0        0        0   \n",
       "1            0        0        0        0        0        0        0        1   \n",
       "2            0        0        0        0        0        0        0        0   \n",
       "3            0        0        0        0        0        0        0        0   \n",
       "4            0        0        0        0        0        0        0        0   \n",
       "...        ...      ...      ...      ...      ...      ...      ...      ...   \n",
       "15330        0        0        0        0        0        0        0        0   \n",
       "15331        0        0        0        0        0        0        0        0   \n",
       "15332        0        0        0        0        0        0        0        0   \n",
       "15333        0        0        0        0        0        0        0        0   \n",
       "15334        0        0        0        0        0        0        0        0   \n",
       "\n",
       "               latitude          longitude  \n",
       "0      40,2948276786438  -3,44402412135624  \n",
       "1              40,28674           -3,79351  \n",
       "2      40,4115646786438  -3,90662252135624  \n",
       "3      40,2853785786438  -3,79508142135624  \n",
       "4      40,2998774864376  -3,45226301356237  \n",
       "...                 ...                ...  \n",
       "15330          40,45416           -3,70286  \n",
       "15331          40,36652           -3,48951  \n",
       "15332          40,57444           -3,92124  \n",
       "15333          40,36967           -3,48105  \n",
       "15334          40,45773           -3,69068  \n",
       "\n",
       "[15335 rows x 34 columns]"
      ]
     },
     "execution_count": 308,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds_clean = ds.drop([\"Unnamed: 0\",\"zipCode\",\"customZone\"], axis=1)\n",
    "ds_clean = ds_clean.dropna(axis=0,how='all')\n",
    "ds_clean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 309,
   "id": "678e900a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(15335, 37)\n",
      "(15335, 34)\n"
     ]
    }
   ],
   "source": [
    "print(ds.shape)\n",
    "print(ds_clean.shape)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "middle-china",
   "metadata": {},
   "source": [
    "#### Exercise 07. Which is the mean of prices in the population (level5 column) of \"Arroyomolinos (Madrid)\"? (★★☆)\n",
    "\n",
    "Print the obtained value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 310,
   "id": "nuclear-belief",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Arroyomolinos (Madrid) Mean Price: 298905\n"
     ]
    }
   ],
   "source": [
    "ds_clean['price'] = ds_clean['price'].replace(0,np.nan) # replace price values equal to zero by null so they don't affect the calculations\n",
    "\n",
    "arroyomolinos_subset = ds_clean[ds_clean[\"level5\"] == 'Arroyomolinos (Madrid)']\n",
    "arroyomolinos_mean = round(arroyomolinos_subset[\"price\"].mean())\n",
    "\n",
    "print(f\"Arroyomolinos (Madrid) Mean Price: {arroyomolinos_mean}\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "concerned-radical",
   "metadata": {},
   "source": [
    "#### Exercise 08. Plot the histogram of prices for the population (level5 column) of \"Arroyomolinos (Madrid)\" and explain what you observe (★★☆)\n",
    "\n",
    "Print the histogram of the prices and write in the Markdown cell a brief analysis about the plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 311,
   "id": "sudden-message",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (10, 5))\n",
    "plt.hist(arroyomolinos_subset[\"price\"], bins=30, alpha=0.7)\n",
    "plt.title(\"Arroyomolinos (Madrid): Price Distribution\")\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "impressed-combination",
   "metadata": {},
   "source": [
    "**Plot Analysis:**\n",
    "\n",
    "From the price distribution of the properties located in Arroyomolinos (Madrid), there are 2 entries missing price. The highest concentration of prices lies in the region between $200,000 and $400,000, with a few entries above and blelow this range.\n",
    "\n",
    "There are three different peaks in the distribution (bins with 12+ entries), suggesting that these may be frequent market prices for properties that share certain features. This could be further explored by looking at the features in the specific price brackets."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "actual-edinburgh",
   "metadata": {},
   "source": [
    "#### Exercise 09. Are the average prices of \"Valdemorillo\" and \"Galapagar\" the same? (★★☆)\n",
    "\n",
    "Print both average prices and then write a conclusion about them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 312,
   "id": "80c37dd1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Valdemorillo Mean Price: 363860\n",
      "Galapagar Mean Price: 360063\n"
     ]
    }
   ],
   "source": [
    "valdemorillo_subset = ds_clean[ds_clean[\"level5\"] == 'Valdemorillo']\n",
    "valdemorillo_mean = round(valdemorillo_subset[\"price\"].mean())\n",
    "\n",
    "galapagar_subset = ds_clean[ds_clean[\"level5\"] == 'Galapagar']\n",
    "galapagar_mean = round(galapagar_subset[\"price\"].mean())\n",
    "\n",
    "print(f\"Valdemorillo Mean Price: {valdemorillo_mean}\")\n",
    "print(f\"Galapagar Mean Price: {galapagar_mean}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 313,
   "id": "numeric-commerce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (10, 5))\n",
    "plt.hist(valdemorillo_subset[\"price\"], bins=30, alpha=0.5, label='Valdemorillo', histtype='step')\n",
    "plt.hist(galapagar_subset[\"price\"], bins=30, alpha=0.7, label='Galapagar', histtype='step')\n",
    "plt.title(\"Valdemorillo & Galapagar: Price Distribution\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c2b1b7e8",
   "metadata": {},
   "source": [
    "**Plot Analysis:**\n",
    "\n",
    "From the price distribution of the properties located in Valdemorillo and Galapagar, both populations show most of their properties priced between $100,000 and $500,000, while also having a few entries above $600,000.\n",
    "\n",
    "Valdemorillo's distribution shows one peak at around $250,000 and $300,000. Compared to Galapagar, Valdemorillo's distribution looks more compact around its most frequent values, while Galapagar's ditribution more spread to the sides, having two separate peaks. The first one being between $150,000 and $200,000, and the second one between $350,000 and $400,000.\n",
    "\n",
    " Having described the similarities and differences between both distributions, overall they still share a very similar average price per property."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "lonely-article",
   "metadata": {},
   "source": [
    "#### Exercise 10. Are the average prices per square meter (price/m2) of \"Valdemorillo\" and \"Galapagar\" the same? (★★☆)\n",
    "\n",
    "Print both average prices and then write a conclusion about it.\n",
    "\n",
    "Hint: Create a new column called `pps` (price per square meter) and then analyze the values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 314,
   "id": "hourly-globe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average PPS in Valdemorillo: 1039.54 ($/m2)\n",
      "Average PPS in Galapagar: 1178.53 ($/m2)\n"
     ]
    }
   ],
   "source": [
    "valdemorillo_avgpps = round(valdemorillo_mean/valdemorillo_subset[\"surface\"].mean(),2)\n",
    "galapagar_avgpps = round(galapagar_mean/galapagar_subset[\"surface\"].mean(),2)\n",
    "\n",
    "print(f\"Average PPS in Valdemorillo: {valdemorillo_avgpps} ($/m2)\")\n",
    "print(f\"Average PPS in Galapagar: {galapagar_avgpps} ($/m2)\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "pleasant-invite",
   "metadata": {},
   "source": [
    "#### Exercise 11. Analyze the relation between the surface and the price of the houses (★★☆)\n",
    "\n",
    "Hint: You can make a `scatter plot`, then write a conclusion about it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 315,
   "id": "common-drilling",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (10, 5))\n",
    "plt.scatter(x=ds_clean['surface'], y=ds_clean['price'])\n",
    "plt.xlabel('Surface (m2)')\n",
    "plt.ylabel('Price ($)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18045103",
   "metadata": {},
   "source": [
    "**Plot Analysis:** The data looks very clumped along the y-axis due to the influence of outliers in the surface field."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 316,
   "id": "9fb87b4c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (10, 5))\n",
    "plt.scatter(x=ds_clean[ds_clean['surface']<7500]['surface'], y=ds_clean[ds_clean['surface']<7500]['price']) # remove outliers\n",
    "plt.xlabel('Surface (m2)')\n",
    "plt.ylabel('Price ($)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9cd2f518",
   "metadata": {},
   "source": [
    "**Plot Analysis:** After removing the data points with surface values equal to or over 7500 m2, the scatterplot shows a moderate positive correlation between the surface and price variables. There is still however a lot of data clumped in the bottom left corner of the plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 317,
   "id": "ff453e69",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (10, 5))\n",
    "valdemorillo_scatter = plt.scatter(x=valdemorillo_subset['surface'], y=valdemorillo_subset['price'], alpha=0.5)\n",
    "galapagar_scatter = plt.scatter(x=galapagar_subset['surface'], y=galapagar_subset['price'], alpha=0.5)\n",
    "plt.xlabel('Surface (m2)')\n",
    "plt.ylabel(\"Price ($)\")\n",
    "plt.legend((valdemorillo_scatter, galapagar_scatter), ('Valdemorillo', 'Galapagar'))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "78b869a0",
   "metadata": {},
   "source": [
    "**Plot Analysis:** When isolating specifically the data of the populations of Valdemorillo and Galapagar, the positive correlation for both cases seem to be clearer and stronger. Most data points look clumped still due to surface outliers in the Galapagar population."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 318,
   "id": "bbd87743",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (10, 5))\n",
    "valdemorillo_scatter = plt.scatter(x=valdemorillo_subset['surface'], y=valdemorillo_subset['price'], alpha=0.5)\n",
    "galapagar_scatter = plt.scatter(x=galapagar_subset[galapagar_subset['surface']<5000]['surface'], y=galapagar_subset[galapagar_subset['surface']<5000]['price'], alpha=0.5) #remove outliers\n",
    "plt.xlabel('Surface (m2)')\n",
    "plt.ylabel(\"Price ($)\")\n",
    "plt.legend((valdemorillo_scatter, galapagar_scatter), ('Valdemorillo', 'Galapagar'))\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "ahead-liquid",
   "metadata": {},
   "source": [
    "**Plot Analysis:** With the surface outlier out of the way, the positive correlation between surface and price looks clearer for these two populations."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "coordinate-sunrise",
   "metadata": {},
   "source": [
    "#### Exercise 12. How many real estate agencies does the dataset contain? (★★☆)\n",
    "\n",
    "Print the obtained value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 319,
   "id": "valid-honolulu",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 1822 different real estate agencies in the dataset\n"
     ]
    }
   ],
   "source": [
    "n_agencies = len(ds_clean[\"realEstate_name\"].unique())\n",
    "\n",
    "print(f\"There are {n_agencies} different real estate agencies in the dataset\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "binding-ebony",
   "metadata": {},
   "source": [
    "#### Exercise 13. Which is the population (level5 column) that contains the most houses? (★★☆)\n",
    "\n",
    "Print both the population and the number of houses."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 320,
   "id": "static-perry",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Population with most houses:  Madrid Capital\n",
      "Number of houses: 6643\n"
     ]
    }
   ],
   "source": [
    "houses_per_pop = ds_clean['level5'].value_counts()\n",
    "top_pop_houses = houses_per_pop.iloc[0:1]\n",
    "\n",
    "print(f\"Population with most houses: {top_pop_houses.index[0]}\\nNumber of houses: {top_pop_houses.values[0]}\")\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "entire-classification",
   "metadata": {},
   "source": [
    "#### Exercise 14. Now let's work with the \"south belt\" of Madrid. Make a subset of the original DataFrame that contains the following populations (level5 column): \"Fuenlabrada\", \"Leganés\", \"Getafe\", \"Alcorcón\" (★★☆)\n",
    "\n",
    "Hint: Filter the original DataFrame using the column `level5` and the function `isin`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 321,
   "id": "binary-input",
   "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>id_realEstates</th>\n",
       "      <th>isNew</th>\n",
       "      <th>realEstate_name</th>\n",
       "      <th>phone_realEstate</th>\n",
       "      <th>url_inmueble</th>\n",
       "      <th>rooms</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>surface</th>\n",
       "      <th>price</th>\n",
       "      <th>date</th>\n",
       "      <th>...</th>\n",
       "      <th>level2Id</th>\n",
       "      <th>level3Id</th>\n",
       "      <th>level4Id</th>\n",
       "      <th>level5Id</th>\n",
       "      <th>level6Id</th>\n",
       "      <th>level7Id</th>\n",
       "      <th>level8Id</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>153867863</td>\n",
       "      <td>False</td>\n",
       "      <td>tecnocasa fuenlabrada ferrocarril</td>\n",
       "      <td>916358736.0</td>\n",
       "      <td>https://www.fotocasa.es/es/comprar/vivienda/ma...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>89000.0</td>\n",
       "      <td>2019-12-28T18:27:15.9975027Z</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>40,28674</td>\n",
       "      <td>-3,79351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>152776331</td>\n",
       "      <td>False</td>\n",
       "      <td>tecnocasa fuenlabrada ferrocarril</td>\n",
       "      <td>916358736.0</td>\n",
       "      <td>https://www.fotocasa.es/es/comprar/vivienda/ma...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>89000.0</td>\n",
       "      <td>2019-12-28T18:27:15.9975027Z</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40,2853785786438</td>\n",
       "      <td>-3,79508142135624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>153152077</td>\n",
       "      <td>False</td>\n",
       "      <td>sinergical inmobiliaria</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://www.fotocasa.es/es/comprar/vivienda/le...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>107000.0</td>\n",
       "      <td>2019-12-28T18:30:53.960102Z</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>40,35059</td>\n",
       "      <td>-3,82693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>153995577</td>\n",
       "      <td>False</td>\n",
       "      <td>viviendas365com</td>\n",
       "      <td>911226014.0</td>\n",
       "      <td>https://www.fotocasa.es/es/comprar/vivienda/le...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>320000.0</td>\n",
       "      <td>2019-12-28T18:30:53.960102Z</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40,31933</td>\n",
       "      <td>-3,77574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>153586414</td>\n",
       "      <td>False</td>\n",
       "      <td>area uno asesores inmobiliarios</td>\n",
       "      <td>912664081.0</td>\n",
       "      <td>https://www.fotocasa.es/es/comprar/vivienda/ma...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>142.0</td>\n",
       "      <td>425000.0</td>\n",
       "      <td>2019-12-28T18:32:32.5337942Z</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40,3313411</td>\n",
       "      <td>-3,8313868</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15275</th>\n",
       "      <td>153903887</td>\n",
       "      <td>False</td>\n",
       "      <td>aliseda servicios de gestion inmobiliaria</td>\n",
       "      <td>911368198.0</td>\n",
       "      <td>https://www.fotocasa.es/es/comprar/vivienda/al...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>138000.0</td>\n",
       "      <td>2019-12-17T19:02:00Z</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>40,31381</td>\n",
       "      <td>-3,83733</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15291</th>\n",
       "      <td>151697757</td>\n",
       "      <td>False</td>\n",
       "      <td>unipiso</td>\n",
       "      <td>912788631.0</td>\n",
       "      <td>https://www.fotocasa.es/es/comprar/vivienda/al...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>279000.0</td>\n",
       "      <td>2019-12-17T18:57:00Z</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40,3259051</td>\n",
       "      <td>-3,76318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15305</th>\n",
       "      <td>153902389</td>\n",
       "      <td>False</td>\n",
       "      <td>jadein ferrero</td>\n",
       "      <td>914871639.0</td>\n",
       "      <td>https://www.fotocasa.es/es/comprar/vivienda/ma...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>170000.0</td>\n",
       "      <td>2019-12-17T18:43:00Z</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40,2882193</td>\n",
       "      <td>-3,8098617</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15322</th>\n",
       "      <td>153871864</td>\n",
       "      <td>False</td>\n",
       "      <td>gestion comercial</td>\n",
       "      <td>911220662.0</td>\n",
       "      <td>https://www.fotocasa.es/es/comprar/vivienda/ma...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>91.0</td>\n",
       "      <td>112000.0</td>\n",
       "      <td>2019-12-17T18:25:00Z</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40,28282</td>\n",
       "      <td>-3,78892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15325</th>\n",
       "      <td>153901467</td>\n",
       "      <td>False</td>\n",
       "      <td>montehogar 68</td>\n",
       "      <td>911790675.0</td>\n",
       "      <td>https://www.fotocasa.es/es/comprar/vivienda/ma...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>215000.0</td>\n",
       "      <td>2019-12-17T18:24:00Z</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>40,28062</td>\n",
       "      <td>-3,79869</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>907 rows × 34 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       id_realEstates  isNew                            realEstate_name  \\\n",
       "1           153867863  False          tecnocasa fuenlabrada ferrocarril   \n",
       "3           152776331  False          tecnocasa fuenlabrada ferrocarril   \n",
       "85          153152077  False                    sinergical inmobiliaria   \n",
       "94          153995577  False                            viviendas365com   \n",
       "109         153586414  False            area uno asesores inmobiliarios   \n",
       "...               ...    ...                                        ...   \n",
       "15275       153903887  False  aliseda servicios de gestion inmobiliaria   \n",
       "15291       151697757  False                                    unipiso   \n",
       "15305       153902389  False                             jadein ferrero   \n",
       "15322       153871864  False                          gestion comercial   \n",
       "15325       153901467  False                              montehogar 68   \n",
       "\n",
       "       phone_realEstate                                       url_inmueble  \\\n",
       "1           916358736.0  https://www.fotocasa.es/es/comprar/vivienda/ma...   \n",
       "3           916358736.0  https://www.fotocasa.es/es/comprar/vivienda/ma...   \n",
       "85                  NaN  https://www.fotocasa.es/es/comprar/vivienda/le...   \n",
       "94          911226014.0  https://www.fotocasa.es/es/comprar/vivienda/le...   \n",
       "109         912664081.0  https://www.fotocasa.es/es/comprar/vivienda/ma...   \n",
       "...                 ...                                                ...   \n",
       "15275       911368198.0  https://www.fotocasa.es/es/comprar/vivienda/al...   \n",
       "15291       912788631.0  https://www.fotocasa.es/es/comprar/vivienda/al...   \n",
       "15305       914871639.0  https://www.fotocasa.es/es/comprar/vivienda/ma...   \n",
       "15322       911220662.0  https://www.fotocasa.es/es/comprar/vivienda/ma...   \n",
       "15325       911790675.0  https://www.fotocasa.es/es/comprar/vivienda/ma...   \n",
       "\n",
       "       rooms  bathrooms  surface     price                          date  ...  \\\n",
       "1        3.0        1.0      NaN   89000.0  2019-12-28T18:27:15.9975027Z  ...   \n",
       "3        3.0        1.0     86.0   89000.0  2019-12-28T18:27:15.9975027Z  ...   \n",
       "85       1.0        1.0     50.0  107000.0   2019-12-28T18:30:53.960102Z  ...   \n",
       "94       3.0        2.0    120.0  320000.0   2019-12-28T18:30:53.960102Z  ...   \n",
       "109      3.0        3.0    142.0  425000.0  2019-12-28T18:32:32.5337942Z  ...   \n",
       "...      ...        ...      ...       ...                           ...  ...   \n",
       "15275    3.0        1.0     78.0  138000.0          2019-12-17T19:02:00Z  ...   \n",
       "15291    3.0        2.0    110.0  279000.0          2019-12-17T18:57:00Z  ...   \n",
       "15305    3.0        2.0     85.0  170000.0          2019-12-17T18:43:00Z  ...   \n",
       "15322    3.0        1.0     91.0  112000.0          2019-12-17T18:25:00Z  ...   \n",
       "15325    2.0        2.0     99.0  215000.0          2019-12-17T18:24:00Z  ...   \n",
       "\n",
       "      level2Id level3Id level4Id level5Id level6Id level7Id level8Id accuracy  \\\n",
       "1            0        0        0        0        0        0        0        1   \n",
       "3            0        0        0        0        0        0        0        0   \n",
       "85           0        0        0        0        0        0        0        1   \n",
       "94           0        0        0        0        0        0        0        0   \n",
       "109          0        0        0        0        0        0        0        0   \n",
       "...        ...      ...      ...      ...      ...      ...      ...      ...   \n",
       "15275        0        0        0        0        0        0        0        1   \n",
       "15291        0        0        0        0        0        0        0        0   \n",
       "15305        0        0        0        0        0        0        0        0   \n",
       "15322        0        0        0        0        0        0        0        0   \n",
       "15325        0        0        0        0        0        0        0        1   \n",
       "\n",
       "               latitude          longitude  \n",
       "1              40,28674           -3,79351  \n",
       "3      40,2853785786438  -3,79508142135624  \n",
       "85             40,35059           -3,82693  \n",
       "94             40,31933           -3,77574  \n",
       "109          40,3313411         -3,8313868  \n",
       "...                 ...                ...  \n",
       "15275          40,31381           -3,83733  \n",
       "15291        40,3259051           -3,76318  \n",
       "15305        40,2882193         -3,8098617  \n",
       "15322          40,28282           -3,78892  \n",
       "15325          40,28062           -3,79869  \n",
       "\n",
       "[907 rows x 34 columns]"
      ]
     },
     "execution_count": 321,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "southbelt_pops_list = [\"Fuenlabrada\", \"Leganés\", \"Getafe\", \"Alcorcón\"]\n",
    "southbelt_subset = ds_clean[ds_clean[\"level5\"].isin(southbelt_pops_list)]\n",
    "southbelt_subset"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "severe-fisher",
   "metadata": {},
   "source": [
    "#### Exercise 15. Make a bar plot of the median of the prices and explain what you observe (you must use the subset obtained in Exercise 14) (★★★)\n",
    "\n",
    "Print the bar of the median of the prices and write in the Markdown cell a brief analysis about the plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 322,
   "id": "027636b2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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evXurVatWysnJ0cyZMzVlyhS9/fbbRs2mTZs0cOBAxcbG6quvvlJ0dLSio6O1c+dOoyY5OVlz587VggULlJ2dLW9vb9lsNp09e7aW7woAAAAA/MTidDqddd2EJFksFi1fvlzR0dGXrNmyZYvuuusuHTp0SDfffLOkn56wxcXFKS4ursp95s+fr5deekl2u13u7u6SpAkTJig1NVV79+6VJPXv318lJSVKS0sz9uvevbs6duyoBQsWyOl0ymq16rnnntPzzz8vSSouLpa/v79SUlI0YMCAKs9dWlqq0tJSY93hcCgoKEjFxcXy8fGp9r25WoInrKzrFgAAAIBr6uCMqLpuQdJP2cDX1/eK2eC6eoetuLhYFotFjRs3dtk+Y8YMNWvWTJ06ddLMmTNVVlZmjGVlZalHjx5GWJMkm82mvLw8nThxwqiJjIx0OabNZlNWVpYkKT8/X3a73aXG19dX4eHhRk1Vpk+fLl9fX2MJCgr6xdcOAAAA4MZz3QS2s2fPKiEhQQMHDnRJoM8++6yWLFmitWvX6qmnntKrr76q8ePHG+N2u13+/v4ux6pYt9vtl625ePzi/aqqqUpiYqKKi4uN5fDhwzW9bAAAAAA3sPp13UB1nD9/Xr///e/ldDo1f/58l7H4+Hjjz+3bt5e7u7ueeuopTZ8+XR4eHte6VRceHh513gMAAACA65fpn7BVhLVDhw4pIyPjiu9+hYeHq6ysTAcPHpQkBQQEqLCw0KWmYj0gIOCyNRePX7xfVTUAAAAAUNtMHdgqwtq+ffu0Zs0aNWvW7Ir75Obmys3NTX5+fpKkiIgIbdiwQefPnzdqMjIy1Lp1azVp0sSoyczMdDlORkaGIiIiJEkhISEKCAhwqXE4HMrOzjZqAAAAAKC21elPIk+dOqX9+/cb6/n5+crNzVXTpk0VGBioxx9/XNu2bVNaWpouXLhgvC/WtGlTubu7KysrS9nZ2erVq5caNWqkrKwsjRs3Tn/4wx+MMPbEE09o6tSpio2NVUJCgnbu3Kk5c+Zo1qxZxnnHjh2rnj176vXXX1dUVJSWLFmirVu3GlP/WywWxcXF6ZVXXtHtt9+ukJAQTZo0SVar9bKzWgIAAADAf6JOp/Vft26devXqVWl7TEyMpkyZopCQkCr3W7t2re6//35t27ZNf/zjH7V3716VlpYqJCREgwcPVnx8vMu7Yzt27NCoUaO0ZcsWNW/eXGPGjFFCQoLLMZctW6aJEyfq4MGDuv3225WcnKyHHnrIGHc6nZo8ebLefvttFRUV6d5779Wbb76pO+64o9rXW92pO68VpvUHAADAjeZ6m9bfNN9huxEQ2AAAAIC6db0FNlO/wwYAAAAANzICGwAAAACYFIENAAAAAEyKwAYAAAAAJkVgAwAAAACTIrABAAAAgEkR2AAAAADApAhsAAAAAGBSBDYAAAAAMCkCGwAAAACYFIENAAAAAEyKwAYAAAAAJkVgAwAAAACTIrABAAAAgEkR2AAAAADApAhsAAAAAGBSBDYAAAAAMCkCGwAAAACYFIENAAAAAEyKwAYAAAAAJkVgAwAAAACTIrABAAAAgEkR2AAAAADApAhsAAAAAGBSBDYAAAAAMCkCGwAAAACYFIENAAAAAEyKwAYAAAAAJkVgAwAAAACTIrABAAAAgEkR2AAAAADApAhsAAAAAGBSBDYAAAAAMCkCGwAAAACYFIENAAAAAEyKwAYAAAAAJlW/JsVFRUVavny5vvjiCx06dEinT59WixYt1KlTJ9lsNt19991Xq08AAAAAuOFU6wnbkSNH9OSTTyowMFCvvPKKzpw5o44dO+qBBx5Qy5YttXbtWv3ud79TaGioli5derV7BgAAAIAbQrWesHXq1EkxMTHKyclRaGholTVnzpxRamqqZs+ercOHD+v555+v1UYBAAAA4EZTrcC2e/duNWvW7LI1Xl5eGjhwoAYOHKgffvihVpoDAAAAgBtZtX4SeaWw9p/WAwAAAAAq+49nidyzZ48++OAD5ebm1njfDRs2qG/fvrJarbJYLEpNTXUZdzqdSkpKUmBgoLy8vBQZGal9+/a51Pz4448aNGiQfHx81LhxY8XGxurUqVMuNTt27NB9990nT09PBQUFKTk5uVIvy5YtU5s2beTp6amwsDB9+umnNe4FAAAAAGpTjQLbtGnTNHPmTGN97dq16tixo1544QV169ZNixcvrtHJS0pK1KFDB82bN6/K8eTkZM2dO1cLFixQdna2vL29ZbPZdPbsWaNm0KBB2rVrlzIyMpSWlqYNGzZo5MiRxrjD4VDv3r3VqlUr5eTkaObMmZoyZYrefvtto2bTpk0aOHCgYmNj9dVXXyk6OlrR0dHauXNnjXoBAAAAgNpkcTqdzuoWt2/fXtOnT1dUVJQkKTIyUp06ddLMmTP11ltvae7cudq1a9cva8Ri0fLlyxUdHS3ppydaVqtVzz33nDGBSXFxsfz9/ZWSkqIBAwZoz549Cg0N1ZYtW9S1a1dJUnp6uh566CF99913slqtmj9/vl566SXZ7Xa5u7tLkiZMmKDU1FTt3btXktS/f3+VlJQoLS3N6Kd79+7q2LGjFixYUK1eqsPhcMjX11fFxcXy8fH5RfepNgVPWFnXLQAAAADX1MEZUXXdgqTqZ4NqPWFbtGiRFi5cqIMHDyo3N9dY37hxoxo2bKhFixapvLxc3377rRYtWqRFixb9xxeQn58vu92uyMhIY5uvr6/Cw8OVlZUlScrKylLjxo2NsCb9FCLd3NyUnZ1t1PTo0cMIa5Jks9mUl5enEydOGDUXn6eipuI81emlKqWlpXI4HC4LAAAAAFRXtWaJbNWqlSTJ3d1d/v7+atWqlXJzc+Xj46NevXrJ6XSqtLRUFotFwcHBqsFDu0uy2+2SJH9/f5ft/v7+xpjdbpefn5/rBdWvr6ZNm7rUhISEVDpGxViTJk1kt9uveJ4r9VKV6dOna+rUqVe+WAAAAACoQrWesPXs2VM9e/ZU586dlZaWJnd3d+Onhz169FDPnj0VGBiooKAgYx1SYmKiiouLjeXw4cN13RIAAACA60iNJh2ZOXOmcnNzdc899+jQoUOaNm2aMZaSkqI+ffrUWmMBAQGSpMLCQpfthYWFxlhAQICOHTvmMl5WVqYff/zRpaaqY1x8jkvVXDx+pV6q4uHhIR8fH5cFAAAAAKqrRoGtQ4cOOnjwoI4fP67du3crKCjIGHv++ef16quv1lpjISEhCggIUGZmprHN4XAoOztbERERkqSIiAgVFRUpJyfHqPn8889VXl6u8PBwo2bDhg06f/68UZORkaHWrVurSZMmRs3F56moqThPdXoBAAAAgNr2i77DVtWHsQMDA+Xt7V2j45w6dUq5ubnGN9zy8/OVm5urgoICWSwWxcXF6ZVXXtGKFSv09ddfa8iQIbJarcZMkm3btlWfPn00YsQIbd68WRs3btTo0aM1YMAAWa1WSdITTzwhd3d3xcbGateuXVq6dKnmzJmj+Ph4o4+xY8cqPT1dr7/+uvbu3aspU6Zo69atGj16tCRVqxcAAAAAqG3VmnRkyZIl1Z66/vDhwyooKNA999xzxdqtW7eqV69exnpFiIqJiVFKSorGjx+vkpISjRw5UkVFRbr33nuVnp4uT09PY5/Fixdr9OjReuCBB+Tm5qZ+/fpp7ty5xrivr69Wr16tUaNGqUuXLmrevLmSkpJcvtV2991368MPP9TEiRP14osv6vbbb1dqaqratWtn1FSnFwAAAACoTdX6DlvPnj117NgxDRs2TH379lXbtm1dxouLi7Vx40b99a9/VUZGht577z098sgjV63p6xXfYQMAAADq1vX2HbZqPWFbv369VqxYof/93/9VYmKivL295e/vL09PT504cUJ2u13NmzfX0KFDtXPnzkrT3wMAAAAAaq5agU2SHnnkET3yyCP6/vvv9eWXX+rQoUM6c+aMmjdvrk6dOqlTp05yc/tFr8QBAAAAAKpQ7cBWoXnz5ky0AQAAAADXAI/EAAAAAMCkCGwAAAAAYFIENgAAAAAwKQIbAAAAAJjULw5s586dU15ensrKymqzHwAAAADA/6lxYDt9+rRiY2PVoEED3XnnnSooKJAkjRkzRjNmzKj1BgEAAADgRlXjwJaYmKjt27dr3bp18vT0NLZHRkZq6dKltdocAAAAANzIavwdttTUVC1dulTdu3eXxWIxtt955506cOBArTYHAAAAADeyGj9hO378uPz8/CptLykpcQlwAAAAAID/TI0DW9euXbVy5UpjvSKkvfvuu4qIiKi9zgAAAADgBlfjn0S++uqrevDBB7V7926VlZVpzpw52r17tzZt2qT169dfjR4BAAAA4IZU4yds9957r3Jzc1VWVqawsDCtXr1afn5+ysrKUpcuXa5GjwAAAABwQ6rxEzZJuvXWW/XOO+/Udi8AAAAAgIvU+Anbp59+qlWrVlXavmrVKn322We10hQAAAAA4BcEtgkTJujChQuVtjudTk2YMKFWmgIAAAAA/ILAtm/fPoWGhlba3qZNG+3fv79WmgIAAAAA/ILA5uvrq2+//bbS9v3798vb27tWmgIAAAAA/ILA9uijjyouLk4HDhwwtu3fv1/PPfecHnnkkVptDgAAAABuZDUObMnJyfL29labNm0UEhKikJAQtW3bVs2aNdP//M//XI0eAQAAAOCGVONp/X19fbVp0yZlZGRo+/bt8vLyUvv27dWjR4+r0R8AAAAA3LB+0XfYLBaLevfurd69e9d2PwAAAACA/1OtwDZ37lyNHDlSnp6emjt37mVrn3322VppDAAAAABudNUKbLNmzdKgQYPk6empWbNmXbLOYrEQ2AAAAACgllQrsOXn51f5ZwAAAADA1VOjWSLPnz+vW2+9VXv27Lla/QAAAAAA/k+NAttNN92ks2fPXq1eAAAAAAAXqfF32EaNGqXXXntNZWVlV6MfAAAAAMD/qfG0/lu2bFFmZqZWr16tsLAweXt7u4x//PHHtdYcAAAAANzIahzYGjdurH79+l2NXgAAAAAAF6lxYPvggw+uRh8AAAAAgJ+p9jts5eXleu2113TPPfeoW7dumjBhgs6cOXM1ewMAAACAG1q1A9uf/vQnvfjii2rYsKF+85vfaM6cORo1atTV7A0AAAAAbmjVDmyLFi3Sm2++qVWrVik1NVX//Oc/tXjxYpWXl1/N/gAAAADghlXtwFZQUKCHHnrIWI+MjJTFYtGRI0euSmMAAAAAcKOrdmArKyuTp6eny7abbrpJ58+fr/WmAAAAAAA1mCXS6XRq6NCh8vDwMLadPXtWTz/9tMu32PgOGwAAAADUjmoHtpiYmErb/vCHP9RqMwAAAACAf6t2YOP7awAAAABwbVX7Hba6EhwcLIvFUmmp+KTA/fffX2ns6aefdjlGQUGBoqKi1KBBA/n5+emFF15QWVmZS826devUuXNneXh46LbbblNKSkqlXubNm6fg4GB5enoqPDxcmzdvvmrXDQAAAACmD2xbtmzR0aNHjSUjI0OS9N///d9GzYgRI1xqkpOTjbELFy4oKipK586d06ZNm7Rw4UKlpKQoKSnJqMnPz1dUVJR69eql3NxcxcXF6cknn9SqVauMmqVLlyo+Pl6TJ0/Wtm3b1KFDB9lsNh07duwa3AUAAAAANyKL0+l01nUTNREXF6e0tDTt27dPFotF999/vzp27KjZs2dXWf/ZZ5/p4Ycf1pEjR+Tv7y9JWrBggRISEnT8+HG5u7srISFBK1eu1M6dO439BgwYoKKiIqWnp0uSwsPD1a1bN73xxhuSpPLycgUFBWnMmDGaMGFCtXp3OBzy9fVVcXGxfHx8/oO7UDuCJ6ys6xYAAACAa+rgjKi6bkFS9bOB6Z+wXezcuXP661//quHDh8tisRjbFy9erObNm6tdu3ZKTEzU6dOnjbGsrCyFhYUZYU2SbDabHA6Hdu3aZdRERka6nMtmsykrK8s4b05OjkuNm5ubIiMjjZqqlJaWyuFwuCwAAAAAUF3VnnTEDFJTU1VUVKShQ4ca25544gm1atVKVqtVO3bsUEJCgvLy8ozPC9jtdpewJslYt9vtl61xOBw6c+aMTpw4oQsXLlRZs3fv3kv2O336dE2dOvUXXy8AAACAG9t1Fdjee+89Pfjgg7Jarca2kSNHGn8OCwtTYGCgHnjgAR04cEC33nprXbRpSExMVHx8vLHucDgUFBRUhx0BAAAAuJ5cN4Ht0KFDWrNmzRU/zB0eHi5J2r9/v2699VYFBARUms2xsLBQkhQQEGD8s2LbxTU+Pj7y8vJSvXr1VK9evSprKo5RFQ8PD5cPjQMAAABATVw377B98MEH8vPzU1TU5V8SzM3NlSQFBgZKkiIiIvT111+7zOaYkZEhHx8fhYaGGjWZmZkux8nIyFBERIQkyd3dXV26dHGpKS8vV2ZmplEDAAAAALXtughs5eXl+uCDDxQTE6P69f/9UPDAgQN6+eWXlZOTo4MHD2rFihUaMmSIevToofbt20uSevfurdDQUA0ePFjbt2/XqlWrNHHiRI0aNcp4+vX000/r22+/1fjx47V37169+eab+uijjzRu3DjjXPHx8XrnnXe0cOFC7dmzR88884xKSko0bNiwa3szAAAAANwwroufRK5Zs0YFBQUaPny4y3Z3d3etWbNGs2fPVklJiYKCgtSvXz9NnDjRqKlXr57S0tL0zDPPKCIiQt7e3oqJidG0adOMmpCQEK1cuVLjxo3TnDlz1LJlS7377ruy2WxGTf/+/XX8+HElJSXJbrerY8eOSk9PrzQRCQAAAADUluvuO2zXM77DBgAAANQtvsMGAAAAAKgVBDYAAAAAMCkCGwAAAACYFIENAAAAAEyKwAYAAAAAJkVgAwAAAACTIrABAAAAgEkR2AAAAADApAhsAAAAAGBSBDYAAAAAMCkCGwAAAACYFIENAAAAAEyKwAYAAAAAJkVgAwAAAACTIrABAAAAgEkR2AAAAADApAhsAAAAAGBSBDYAAAAAMCkCGwAAAACYFIENAAAAAEyKwAYAAAAAJkVgAwAAAACTIrABAAAAgEkR2AAAAADApAhsAAAAAGBSBDYAAAAAMCkCGwAAAACYFIENAAAAAEyKwAYAAAAAJkVgAwAAAACTIrABAAAAgEkR2AAAAADApAhsAAAAAGBSBDYAAAAAMCkCGwAAAACYFIENAAAAAEyKwAYAAAAAJkVgAwAAAACTIrABAAAAgEkR2AAAAADApAhsAAAAAGBSBDYAAAAAMClTB7YpU6bIYrG4LG3atDHGz549q1GjRqlZs2Zq2LCh+vXrp8LCQpdjFBQUKCoqSg0aNJCfn59eeOEFlZWVudSsW7dOnTt3loeHh2677TalpKRU6mXevHkKDg6Wp6enwsPDtXnz5qtyzQAAAABQwdSBTZLuvPNOHT161Fi+/PJLY2zcuHH65z//qWXLlmn9+vU6cuSIHnvsMWP8woULioqK0rlz57Rp0yYtXLhQKSkpSkpKMmry8/MVFRWlXr16KTc3V3FxcXryySe1atUqo2bp0qWKj4/X5MmTtW3bNnXo0EE2m03Hjh27NjcBAAAAwA3J4nQ6nXXdxKVMmTJFqampys3NrTRWXFysFi1a6MMPP9Tjjz8uSdq7d6/atm2rrKwsde/eXZ999pkefvhhHTlyRP7+/pKkBQsWKCEhQcePH5e7u7sSEhK0cuVK7dy50zj2gAEDVFRUpPT0dElSeHi4unXrpjfeeEOSVF5erqCgII0ZM0YTJkyo9vU4HA75+vqquLhYPj4+v/S21JrgCSvrugUAAADgmjo4I6quW5BU/Wxg+ids+/btk9Vq1S233KJBgwapoKBAkpSTk6Pz588rMjLSqG3Tpo1uvvlmZWVlSZKysrIUFhZmhDVJstlscjgc2rVrl1Fz8TEqaiqOce7cOeXk5LjUuLm5KTIy0qi5lNLSUjkcDpcFAAAAAKrL1IEtPDxcKSkpSk9P1/z585Wfn6/77rtPJ0+elN1ul7u7uxo3buyyj7+/v+x2uyTJbre7hLWK8Yqxy9U4HA6dOXNG33//vS5cuFBlTcUxLmX69Ony9fU1lqCgoBrfAwAAAAA3rvp13cDlPPjgg8af27dvr/DwcLVq1UofffSRvLy86rCz6klMTFR8fLyx7nA4CG0AAAAAqs3UT9h+rnHjxrrjjju0f/9+BQQE6Ny5cyoqKnKpKSwsVEBAgCQpICCg0qyRFetXqvHx8ZGXl5eaN2+uevXqVVlTcYxL8fDwkI+Pj8sCAAAAANV1XQW2U6dO6cCBAwoMDFSXLl100003KTMz0xjPy8tTQUGBIiIiJEkRERH6+uuvXWZzzMjIkI+Pj0JDQ42ai49RUVNxDHd3d3Xp0sWlpry8XJmZmUYNAAAAAFwNpg5szz//vNavX6+DBw9q06ZN+q//+i/Vq1dPAwcOlK+vr2JjYxUfH6+1a9cqJydHw4YNU0REhLp37y5J6t27t0JDQzV48GBt375dq1at0sSJEzVq1Ch5eHhIkp5++ml9++23Gj9+vPbu3as333xTH330kcaNG2f0ER8fr3feeUcLFy7Unj179Mwzz6ikpETDhg2rk/sCAAAA4MZg6nfYvvvuOw0cOFA//PCDWrRooXvvvVf/+te/1KJFC0nSrFmz5Obmpn79+qm0tFQ2m01vvvmmsX+9evWUlpamZ555RhEREfL29lZMTIymTZtm1ISEhGjlypUaN26c5syZo5YtW+rdd9+VzWYzavr376/jx48rKSlJdrtdHTt2VHp6eqWJSAAAAACgNpn6O2y/NnyHDQAAAKhbfIcNAAAAAFArCGwAAAAAYFIENgAAAAAwKQIbAAAAAJgUgQ0AAAAATIrABgAAAAAmRWADAAAAAJMisAEAAACASRHYAAAAAMCkCGwAAAAAYFIENgAAAAAwKQIbAAAAAJgUgQ0AAAAATIrABgAAAAAmRWADAAAAAJMisAEAAACASRHYAAAAAMCkCGwAAAAAYFIENgAAAAAwKQIbAAAAAJgUgQ0AAAAATIrABgAAAAAmRWADAAAAAJMisAEAAACASRHYAAAAAMCkCGwAAAAAYFIENgAAAAAwKQIbAAAAAJgUgQ0AAAAATIrABgAAAAAmRWADAAAAAJMisAEAAACASRHYAAAAAMCkCGwAAAAAYFIENgAAAAAwKQIbAAAAAJgUgQ0AAAAATIrABgAAAAAmRWADAAAAAJMisAEAAACASRHYAAAAAMCkTB3Ypk+frm7duqlRo0by8/NTdHS08vLyXGruv/9+WSwWl+Xpp592qSkoKFBUVJQaNGggPz8/vfDCCyorK3OpWbdunTp37iwPDw/ddtttSklJqdTPvHnzFBwcLE9PT4WHh2vz5s21fs0AAAAAUMHUgW39+vUaNWqU/vWvfykjI0Pnz59X7969VVJS4lI3YsQIHT161FiSk5ONsQsXLigqKkrnzp3Tpk2btHDhQqWkpCgpKcmoyc/PV1RUlHr16qXc3FzFxcXpySef1KpVq4yapUuXKj4+XpMnT9a2bdvUoUMH2Ww2HTt27OrfCAAAAAA3JIvT6XTWdRPVdfz4cfn5+Wn9+vXq0aOHpJ+esHXs2FGzZ8+ucp/PPvtMDz/8sI4cOSJ/f39J0oIFC5SQkKDjx4/L3d1dCQkJWrlypXbu3GnsN2DAABUVFSk9PV2SFB4erm7duumNN96QJJWXlysoKEhjxozRhAkTqtW/w+GQr6+viouL5ePj80tvQ60JnrCyrlsAAAAArqmDM6LqugVJ1c8Gpn7C9nPFxcWSpKZNm7psX7x4sZo3b6527dopMTFRp0+fNsaysrIUFhZmhDVJstlscjgc2rVrl1ETGRnpckybzaasrCxJ0rlz55STk+NS4+bmpsjISKOmKqWlpXI4HC4LAAAAAFRX/bpuoLrKy8sVFxene+65R+3atTO2P/HEE2rVqpWsVqt27NihhIQE5eXl6eOPP5Yk2e12l7AmyVi32+2XrXE4HDpz5oxOnDihCxcuVFmzd+/eS/Y8ffp0TZ069ZdfNAAAAIAb2nUT2EaNGqWdO3fqyy+/dNk+cuRI489hYWEKDAzUAw88oAMHDujWW2+91m26SExMVHx8vLHucDgUFBRUhx0BAAAAuJ5cF4Ft9OjRSktL04YNG9SyZcvL1oaHh0uS9u/fr1tvvVUBAQGVZnMsLCyUJAUEBBj/rNh2cY2Pj4+8vLxUr1491atXr8qaimNUxcPDQx4eHtW7SAAAAAD4GVO/w+Z0OjV69GgtX75cn3/+uUJCQq64T25uriQpMDBQkhQREaGvv/7aZTbHjIwM+fj4KDQ01KjJzMx0OU5GRoYiIiIkSe7u7urSpYtLTXl5uTIzM40aAAAAAKhtpn7CNmrUKH344Yf65JNP1KhRI+OdM19fX3l5eenAgQP68MMP9dBDD6lZs2basWOHxo0bpx49eqh9+/aSpN69eys0NFSDBw9WcnKy7Ha7Jk6cqFGjRhlPv55++mm98cYbGj9+vIYPH67PP/9cH330kVau/PcsivHx8YqJiVHXrl111113afbs2SopKdGwYcOu/Y0BAAAAcEMwdWCbP3++pJ+m7r/YBx98oKFDh8rd3V1r1qwxwlNQUJD69euniRMnGrX16tVTWlqannnmGUVERMjb21sxMTGaNm2aURMSEqKVK1dq3LhxmjNnjlq2bKl3331XNpvNqOnfv7+OHz+upKQk2e12dezYUenp6ZUmIgEAAACA2nJdfYftesd32AAAAIC6xXfYAAAAAAC1gsAGAAAAACZFYAMAAAAAkyKwAQAAAIBJEdgAAAAAwKQIbAAAAABgUgQ2AAAAADApAhsAAAAAmBSBDQAAAABMisAGAAAAACZFYAMAAAAAkyKwAQAAAIBJEdgAAAAAwKQIbAAAAABgUgQ2AAAAADApAhsAAAAAmBSBDQAAAABMisAGAAAAACZFYAMAAAAAkyKwAQAAAIBJEdgAAAAAwKQIbAAAAABgUgQ2AAAAADApAhsAAAAAmBSBDQAAAABMisAGAAAAACZFYAMAAAAAkyKwAQAAAIBJEdgAAAAAwKQIbAAAAABgUgQ2AAAAADApAhsAAAAAmBSBDQAAAABMisAGAAAAACZFYAMAAAAAkyKwAQAAAIBJEdgAAAAAwKQIbAAAAABgUgQ2AAAAADApAhsAAAAAmBSBDQAAAABMisAGAAAAACZFYKuhefPmKTg4WJ6engoPD9fmzZvruiUAAAAAv1IEthpYunSp4uPjNXnyZG3btk0dOnSQzWbTsWPH6ro1AAAAAL9C9eu6gevJn//8Z40YMULDhg2TJC1YsEArV67U+++/rwkTJlSqLy0tVWlpqbFeXFwsSXI4HNem4SsoLz1d1y0AAAAA15RZ/l+8og+n03nZOgJbNZ07d045OTlKTEw0trm5uSkyMlJZWVlV7jN9+nRNnTq10vagoKCr1icAAACAS/OdXdcduDp58qR8fX0vOU5gq6bvv/9eFy5ckL+/v8t2f39/7d27t8p9EhMTFR8fb6yXl5frxx9/VLNmzWSxWK5qvwAAc3I4HAoKCtLhw4fl4+NT1+0AAOqI0+nUyZMnZbVaL1tHYLuKPDw85OHh4bKtcePGddMMAMBUfHx8CGwAcIO73JO1Ckw6Uk3NmzdXvXr1VFhY6LK9sLBQAQEBddQVAAAAgF8zAls1ubu7q0uXLsrMzDS2lZeXKzMzUxEREXXYGQAAAIBfK34SWQPx8fGKiYlR165dddddd2n27NkqKSkxZo0EAOBKPDw8NHny5Eo/mQcAoCoW55XmkYSLN954QzNnzpTdblfHjh01d+5chYeH13VbAAAAAH6FCGwAAAAAYFK8wwYAAAAAJkVgAwAAAACTIrABAAAAgEkR2AAAprZu3TpZLBYVFRXVyfktFotSU1Pr5NySdP/99ysuLq7Ozl9bgoODNXv2bGO9ru8rAFwvCGwAgCs6fvy4nnnmGd18883y8PBQQECAbDabNm7cWKvnqc1wYrFYjKV+/fq6+eabFR8fr9LS0l98zIMHD8pisSg3N7fG+06ZMsWln+DgYI0bN06nTp267H4ff/yxXn755V/Y8ZVVBOImTZro7NmzLmNbtmwxeq5tR48e1YMPPljrxwWAXxu+wwYAuKJ+/frp3LlzWrhwoW655RYVFhYqMzNTP/zwQ123dlkffPCB+vTpo/Pnz2v79u0aNmyYvL29r2oAupw777xTa9asUVlZmTZu3Kjhw4fr9OnTeuuttyrVnjt3Tu7u7mratOk16a1Ro0Zavny5Bg4caGx77733dPPNN6ugoKDWzxcQEFDrxwSAXyOesAEALquoqEhffPGFXnvtNfXq1UutWrXSXXfdpcTERD3yyCNGXUFBgR599FE1bNhQPj4++v3vf6/CwkJjfOjQoYqOjnY5dlxcnO6//35jfP369ZozZ47xVOfgwYNGbU5Ojrp27aoGDRro7rvvVl5e3hV7b9y4sQICAhQUFKSHH35Yjz76qLZt2+ZS88knn6hz587y9PTULbfcoqlTp6qsrKzK44WEhEiSOnXqJIvFYvReXfXr11dAQIBatmyp/v37a9CgQVqxYoWkn57AdezYUe+++65CQkLk6ekpqfJTx9LSUiUkJCgoKEgeHh667bbb9N577xnjO3fu1IMPPqiGDRvK399fgwcP1vfff3/F3mJiYvT+++8b62fOnNGSJUsUExNTqfbLL7/UfffdJy8vLwUFBenZZ59VSUmJMX7s2DH17dtXXl5eCgkJ0eLFiysd4+c/iUxISNAdd9yhBg0a6JZbbtGkSZN0/vx5Y7zi/vzlL39RcHCwfH19NWDAAJ08efKK1wYA1zMCGwDgsho2bKiGDRsqNTX1kj8nLC8v16OPPqoff/xR69evV0ZGhr799lv179+/2ueZM2eOIiIiNGLECB09elRHjx5VUFCQMf7SSy/p9ddf19atW1W/fn0NHz68RtfxzTff6PPPP1d4eLix7YsvvtCQIUM0duxY7d69W2+99ZZSUlL0pz/9qcpjbN68WZK0Zs0aHT16VB9//LGkf/+s8OKAWR1eXl46d+6csb5//3794x//0Mcff3zJn10OGTJEf/vb3zR37lzt2bNHb731lho2bCjpp3D929/+Vp06ddLWrVuVnp6uwsJC/f73v79iL4MHD9YXX3xhPE37xz/+oeDgYHXu3Nml7sCBA+rTp4/69eunHTt2aOnSpfryyy81evRoo2bo0KE6fPiw1q5dq7///e968803dezYscuev1GjRkpJSdHu3bs1Z84cvfPOO5o1a1alc6empiotLU1paWlav369ZsyYccVrA4DrmhMAgCv4+9//7mzSpInT09PTeffddzsTExOd27dvN8ZXr17trFevnrOgoMDYtmvXLqck5+bNm51Op9MZExPjfPTRR12OO3bsWGfPnj2N9Z49ezrHjh3rUrN27VqnJOeaNWuMbStXrnRKcp45c+aSPUtyenp6Or29vZ0eHh5OSc6HH37Yee7cOaPmgQcecL766qsu+/3lL39xBgYGuhxn+fLlTqfT6czPz3dKcn711Vcu+2RnZztbt27t/O677y7Zz+TJk50dOnQw1rdu3eps3ry58/HHHzfGb7rpJuexY8dc9rv4nuTl5TklOTMyMqo8x8svv+zs3bu3y7bDhw87JTnz8vKq3Kfi/p44ccIZHR3tnDp1qtPpdDp79erlnDNnjnP58uXOi/93ITY21jly5EiXY3zxxRdONzc355kzZ4weK/69O51O5549e5ySnLNmzTK2XXxfqzJz5kxnly5djPXJkyc7GzRo4HQ4HMa2F154wRkeHn7JYwDArwFP2AAAV9SvXz8dOXJEK1asUJ8+fbRu3Tp17txZKSkpkqQ9e/YoKCjI5YlYaGioGjdurD179tRKD+3btzf+HBgYKElXfGoza9Ys5ebmavv27UpLS9M333yjwYMHG+Pbt2/XtGnTjKeIDRs2NJ7wnT59utq93XXXXdq7d69+85vfXLbu66+/VsOGDeXl5aW77rpLEREReuONN4zxVq1aqUWLFpfcPzc3V/Xq1VPPnj2rHN++fbvWrl3rcj1t2rSR9NPTqSsZPny4UlJS9O233yorK0uDBg2q8hwpKSku57DZbCovL1d+fr727Nmj+vXrq0uXLsY+bdq0UePGjS977qVLl+qee+5RQECAGjZsqIkTJ1Z6dy44OFiNGjUy1gMDA6/4dwAArndMOgIAqBZPT0/97ne/0+9+9ztNmjRJTz75pCZPnqyhQ4dWa383Nzc5nU6XbRe/o3QlN910k/HnilkLy8vLL7tPQECAbrvtNklS69atdfLkSQ0cOFCvvPKKbrvtNp06dUpTp07VY489VmnfinfIalPr1q21YsUK1a9fX1arVe7u7i7j3t7el93fy8vrsuOnTp1S37599dprr1Uaqwi5l/Pggw9q5MiRio2NVd++fdWsWbMqz/HUU0/p2WefrTR2880365tvvrnieX6uIhxOnTpVNptNvr6+WrJkiV5//XWXuov/Dkg//T240t8BALjeEdgAAL9IaGioMWlE27ZtdfjwYR0+fNh4yrZ7924VFRUpNDRUktSiRQvt3LnT5Ri5ubku/xPu7u6uCxcuXLWe69WrJ+mnCTUkqXPnzsrLyzNC3ZVUBKxf2qO7u3u1z1WVsLAwlZeXa/369YqMjKw03rlzZ+Pds/r1a/6f+Pr162vIkCFKTk7WZ599VmVN586dtXv37kteR5s2bVRWVqacnBx169ZNkpSXl3fZ7+ht2rRJrVq10ksvvWRsO3ToUI37B4BfI34SCQC4rB9++EG//e1v9de//lU7duxQfn6+li1bpuTkZD366KOSpMjISIWFhWnQoEHatm2bNm/erCFDhqhnz57q2rWrJOm3v/2ttm7dqkWLFmnfvn2aPHlypQAXHBys7OxsHTx4UN9///1//PSkqKhIdrtdR44c0fr16zVt2jTdcccdatu2rSQpKSlJixYt0tSpU7Vr1y7t2bNHS5Ys0cSJE6s8np+fn7y8vIzJPIqLiyX9NBlJmzZt9P/+3//7j/q9kuDgYMXExGj48OFKTU1Vfn6+1q1bp48++kiSNGrUKP34448aOHCgtmzZogMHDmjVqlUaNmxYtUPmyy+/rOPHj8tms1U5npCQoE2bNmn06NHKzc3Vvn379MknnxiTjrRu3Vp9+vTRU089pezsbOXk5OjJJ5+87NPB22+/XQUFBVqyZIkOHDiguXPnavny5TW8OwDw60RgAwBcVsOGDRUeHq5Zs2apR48eateunSZNmqQRI0YY719ZLBZ98sknatKkiXr06KHIyEjdcsstWrp0qXEcm82mSZMmafz48erWrZtOnjypIUOGuJzr+eefV7169RQaGqoWLVr8x9//GjZsmAIDA9WyZUsNHDhQd955pz777DPj6ZPNZlNaWppWr16tbt26qXv37po1a5ZatWpV5fHq16+vuXPn6q233pLVajUC6+nTp5WXl1ejn3j+UvPnz9fjjz+uP/7xj2rTpo1GjBhhTKlvtVq1ceNGXbhwQb1791ZYWJji4uLUuHFjublV7z/57u7uat68+SU/lt2+fXutX79e33zzje677z516tRJSUlJslqtRs0HH3wgq9Wqnj176rHHHtPIkSPl5+d3yXM+8sgjGjdunEaPHq2OHTtq06ZNmjRpUg3uCgD8elmcP3+hAAAAAABgCjxhAwAAAACTIrABAAAAgEkR2AAAAADApAhsAAAAAGBSBDYAAAAAMCkCGwAAAACYFIENAAAAAEyKwAYAAAAAJkVgAwAAAACTIrABAAAAgEkR2AAAAADApP4/k0x2Of/1lEkAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb_price_median = southbelt_subset[\"price\"].median()\n",
    "\n",
    "plt.figure(figsize=(10,5))\n",
    "plt.bar(\"South Belt: Price Median\", sb_price_median, width=0.01)\n",
    "plt.ylabel(\"Price ($)\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c62cf2c",
   "metadata": {},
   "source": [
    "**Plot Analysis:** The price median of the populations included within Madrid's South Belt is $175,000. This means that this value divides the distribution of the prices in half, with one half being above this price and the other one below. A histogram could reveal further insights about the distribution of the prices under and above the median."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 323,
   "id": "lyric-bunch",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb_median_by_pop = southbelt_subset.groupby('level5')[\"price\"].median()\n",
    "\n",
    "plt.figure(figsize=(10,5))\n",
    "plt.bar(sb_median_by_pop.index, sb_median_by_pop.values)\n",
    "plt.xlabel(\"Population\")\n",
    "plt.ylabel(\"Price ($)\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "35c5b359",
   "metadata": {},
   "source": [
    "**Plot Analysis:** The price medians of Alcorcón, Fuenlabrada and Leganés are not too far from each other, with the three of them laying roughly between $150,000 and $180,000.  This suggests that there are several common price brackets for the properties in these three populations. Getafe on the other hand has a higher median of almost $300,000, meaning that overall this population has higher prices compared to the other three."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "speaking-diamond",
   "metadata": {},
   "source": [
    "#### Exercise 16. Calculate the sample mean and variance of the variables: price, rooms, surface area and bathrooms (you must use the subset obtained in Exercise 14) (★★★)\n",
    "\n",
    "Print both values for each variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 324,
   "id": "random-feeling",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Price Mean: $226085.69\n",
      "Price Variance: 226085.69\n",
      "Rooms Mean: 3.02\n",
      "Rooms Variance: 3.02\n",
      "Surface Mean: 111.75 m2\n",
      "Surface Variance: 111.75\n",
      "Bathrooms Mean: 1.63\n",
      "Bathrooms Variance: 1.63\n"
     ]
    }
   ],
   "source": [
    "vars = ['price', 'rooms', 'surface', 'bathrooms']\n",
    "vars_dict = {}\n",
    "\n",
    "for col in southbelt_subset.columns:\n",
    "    if col in vars:\n",
    "        for i in vars:\n",
    "            vars_dict[f\"{i.title()} Mean\"] = float(round(southbelt_subset[i].mean(),2))\n",
    "            vars_dict[f\"{i.title()} Variance\"] = float(round(southbelt_subset[i].mean(),2))\n",
    "    else:\n",
    "        pass\n",
    "\n",
    "print(f\"Price Mean: ${vars_dict['Price Mean']}\\nPrice Variance: {vars_dict['Price Variance']}\\nRooms Mean: {vars_dict['Rooms Mean']}\\nRooms Variance: {vars_dict['Rooms Variance']}\\nSurface Mean: {vars_dict['Surface Mean']} m2\\nSurface Variance: {vars_dict['Surface Variance']}\\nBathrooms Mean: {vars_dict['Bathrooms Mean']}\\nBathrooms Variance: {vars_dict['Bathrooms Variance']}\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "revolutionary-matrix",
   "metadata": {},
   "source": [
    "#### Exercise 17. What is the most expensive house in each population? You must use the subset obtained in Exercise 14 (★★☆)\n",
    "\n",
    "Print both the address and the price of the selected house of each population. You can print a DataFrame or a single line for each population."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 325,
   "id": "fifteen-browse",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fuenlabrada Most Expensive Property Value: $490000\n",
      "Leganés Most Expensive Property Value: $650000\n",
      "Getafe Most Expensive Property Value: $1050000\n",
      "Alcorcón Most Expensive Property Value: $950000\n"
     ]
    }
   ],
   "source": [
    "for pop in southbelt_pops_list:\n",
    "    max_price = southbelt_subset[southbelt_subset['level5']==pop]['price'].max()\n",
    "    print(pop + ' Most Expensive Property Value: $' + str(int(max_price)))\n",
    "\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "activated-knight",
   "metadata": {},
   "source": [
    "#### Exercise 18. Normalize the variable of prices for each population and plot the 4 histograms in the same plot (you must use the subset obtained in Exercise 14) (★★★)\n",
    "\n",
    "For the normalization method, you can use the one you consider; there is not a single correct answer to this question. Print the plot and write in the Markdown cell a brief analysis about the plot.\n",
    "\n",
    "Hint: You can help yourself by reviewing the *multihist* demo of Matplotlib."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 326,
   "id": "civic-meditation",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_729/1426427023.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  southbelt_subset.loc[:,'normalized price'] = southbelt_subset['price']\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "southbelt_subset.loc[:,'normalized price'] = southbelt_subset['price']\n",
    "\n",
    "plt.figure(figsize=(10,5))\n",
    "\n",
    "for pop in southbelt_pops_list:\n",
    "    mask = southbelt_subset['level5'] == pop\n",
    "    pop_data = southbelt_subset.loc[mask, 'normalized price']\n",
    "    if mask.any():\n",
    "       max_price = pop_data.max()\n",
    "       min_price = pop_data.min()\n",
    "       southbelt_subset.loc[mask,'normalized price'] = (pop_data - min_price)/(max_price - min_price)\n",
    "    plt.hist(southbelt_subset[southbelt_subset['level5']==pop]['normalized price'],bins=30, alpha=0.5, label=str(pop), histtype='step')\n",
    "    plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "precise-heavy",
   "metadata": {},
   "source": [
    "**Plot Analysis:** Comparing the normalized distributions of the different populations in the south belt, there is a clear concentration of lower price properties in Alcorcón and a big portion of the properties in Getafe (between 0.0 and 0.05).\n",
    "\n",
    "Fuenlabrada shows a pronounced peak not very far from Alcorcón's and Getafe's (between 0.1 and 0.15). It shares in this area several data points with the other three populations, especially with Leganés that has its peak in that range, but at a much lower frequency and a distribution skewed to the right, where the rest of its values lay, on a relatively wide tail compared to the other distributions.\n",
    "\n",
    "Getafe shows a second peak on its distribution, between 0.3 and 0.35 and standing out in the plot. This suggests that this population may have two different features (or groups of features) that determine on which of the regions around the peaks the price of the property belongs."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "patent-jonathan",
   "metadata": {},
   "source": [
    "#### Exercise 19. What can you say about the price per square meter (price/m2) between the towns of \"Getafe\" and \"Alcorcón\"? You must use the subset obtained in Exercise 14 (★★☆)\n",
    "\n",
    "Hint: Create a new column called `pps` (price per square meter) and then analyze the values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 327,
   "id": "initial-liverpool",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_729/945939426.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  southbelt_subset.loc[:,'pps'] = southbelt_subset['price']/southbelt_subset['surface']\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "southbelt_subset.loc[:,'pps'] = southbelt_subset['price']/southbelt_subset['surface']\n",
    "\n",
    "plt.figure(figsize=(10,5))\n",
    "plt.hist(southbelt_subset[southbelt_subset['level5']=='Getafe']['pps'], bins=30, alpha=0.5, label=\"Getafe\", histtype='step')\n",
    "plt.hist(southbelt_subset[southbelt_subset['level5']=='Alcorcón']['pps'], bins=30, alpha=0.5, label=\"Alcorcón\", histtype='step')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d54eafc6",
   "metadata": {},
   "source": [
    "**Plot Analysis:** Getafe shows a large concentration of properties around the 1,500 and 1,700 pps values on its peak. Alcorcón shows a more spread distribution, with relatively similar frequencies (13 to 17) between the pps bins ranging from 1,700 to 2,400. Some properties in Getafe stand out in the higher bins, where some bins between 2,500 and 3,000 show frequencies between 10 to 12."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "enhanced-moscow",
   "metadata": {},
   "source": [
    "#### Exercise 20. Make the same plot for 4 different populations (level5 column) and rearrange them on the same graph. You must use the subset obtained in Exercise 14 (★★☆)\n",
    " \n",
    "Hint: Make a scatter plot of each population using subplots."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 328,
   "id": "accepting-airfare",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,5))\n",
    "\n",
    "for pop in southbelt_pops_list:\n",
    "    plt.hist(southbelt_subset[southbelt_subset['level5']==pop]['pps'],bins=30, alpha=0.5, label=str(pop), histtype='step')\n",
    "    plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "blocked-effects",
   "metadata": {},
   "source": [
    "#### Exercise 21. Make a plot of the coordinates (latitude and longitude columns) of the south belt of Madrid by color of each population (you must use the subset obtained in Exercise 14) (★★★★)\n",
    "\n",
    "Execute the following cell, and then start coding in the next one. You must implement a simple code that transforms the coordinates columns in a Python dictionary (add more information if needed) and then add it to the map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 329,
   "id": "headed-privacy",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "445cb2c464b94c94be5762856ed7ab63",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Map(center=[40.4168, -3.7038], controls=(ZoomControl(options=['position', 'zoom_in_text', 'zoom_in_title', 'zo…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from ipyleaflet import Map, basemaps, Marker, AwesomeIcon\n",
    "from IPython.display import display\n",
    "\n",
    "# Map centered on (60 degrees latitude and -2.2 degrees longitude)\n",
    "# Latitude, longitude\n",
    "map = Map(center = (40.4168, -3.7038), zoom = 10, min_zoom = 1, max_zoom = 20, basemap=basemaps.OpenStreetMap.Mapnik)\n",
    "display(map)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 330,
   "id": "bb9b061e",
   "metadata": {},
   "outputs": [],
   "source": [
    "southbelt_subset.loc[:, 'latitude'] = southbelt_subset['latitude'].str.replace(',','.').astype('float')\n",
    "southbelt_subset.loc[:, 'longitude'] = southbelt_subset['longitude'].str.replace(',','.').astype('float')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 331,
   "id": "present-mistress",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "445cb2c464b94c94be5762856ed7ab63",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Map(center=[40.4168, -3.7038], controls=(ZoomControl(options=['position', 'zoom_in_text', 'zoom_in_title', 'zo…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "map.layers = [map.layers[0]]\n",
    "\n",
    "## HERE: plot the coordinates of the estates\n",
    "\n",
    "\n",
    "## PUT HERE YOUR CODE:\n",
    "coordinates_dict = {}\n",
    "\n",
    "def get_marker_icon(pop):\n",
    "    if pop == 'Fuenlabrada':\n",
    "        return AwesomeIcon(icon='info-sign', marker_color ='blue', icon_color='blue')\n",
    "    elif pop == 'Leganés':\n",
    "        return AwesomeIcon(icon='info-sign', marker_color ='orange', icon_color='orange')\n",
    "    elif pop == 'Getafe':\n",
    "        return AwesomeIcon(icon='info-sign', marker_color ='green', icon_color='green')\n",
    "    else:\n",
    "        return AwesomeIcon(icon='info-sign', marker_color ='red', icon_color='red')\n",
    "\n",
    "for idx, row in southbelt_subset.iterrows():\n",
    "    if row['latitude'] != 0:\n",
    "        coordinates_dict[int(row['id_realEstates'])] = (row['latitude'], row['longitude']), get_marker_icon(row['level5'])\n",
    "\n",
    "for id, ((lat, lon), icon) in coordinates_dict.items():\n",
    "    marker = Marker(location=(lat, lon), draggable=False, icon=icon)\n",
    "    map.add_layer(marker)\n",
    "\n",
    "display(map) "
   ]
  }
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