Capstone_Notebook.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Capstone Project\n",
    "# Relocation Support\n",
    "### Sidclay da Silva\n",
    "### July 2020\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Introduction\n",
    "\n",
    "This notebook contains the Capstone Project as the Week 5 peer-graded assignment for the Course IBM Applied Data Science Capstone on Coursera, which requires to develop a solution for the problem proposed by the learner in his Project Proposal Week 4, the stablished condition requires to make use of __API Foursquare__ to solve the proposed problem.\n",
    "\n",
    "The proposed problem for this project is to support people to relocate giving them directions by creating a districts, or neighborhoods, rank for the target location using the relocator profile.\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 1 - Define the _Relocator Profile_ and _Target Location_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For this project the __Relocator Profile__ will be arbitrary defined to simulate a family relocating to also arbitrary defined __Target Location São Paulo, Brazil__.\n",
    "\n",
    "__Family components__;\n",
    "* 2 adults\n",
    "* 2 kids in primary school age\n",
    "* 1 dog as pet\n",
    "\n",
    "__Family priorities__;\n",
    "* __Primary school__ for the kids\n",
    "* __Outdoor park__ to go with the kids, walk with the dog and jogging\n",
    "* __Supermarket__ for the daily life\n",
    "* __Pharmacy__ in case of emergencies, specially having kids\n",
    "* __Subway or metro station__ to avoid traffic\n",
    "\n",
    "__Housing wishes__;\n",
    "* Apartment\n",
    "* 2 or 3 bedrooms\n",
    "* 80 m<sup>2</sup> approximately\n",
    "* 1 garage spot\n",
    "\n",
    "__Rental budget__;\n",
    "* BRL 2,000.00 / month\n",
    "\n",
    "Based on the Relocator Profile, define objects to be used to rank the districts of the target location.\n",
    "\n",
    "Family priorities will be translated into a _dictionary_ containing venues categories according to the __API Foursquare__, the _dictionary keys_ will be __CategoryId__ from __API Foursquare__ and _dictionary values_ the categories names. Housing wishes and Rental budget will be converted in a _variable_ containg the rental budget per square meter - BRL/m<sup>2</sup>. Enabling some flexibility and opening more options to the Relocator, a tolerance for rental prices will be set to +-5%."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Family priorities are: ['Elementary School', 'Park', 'Supermarket', 'Pharmacy', 'Metro Station'] \n",
      "\n",
      "Monthly rental budget is BRL 2,000.00 for a 80 sqm apartment = BRL 25.00/sqm/month\n",
      "\n",
      "Rental prices will be considered ranging from 23.75/sqm/month up to 26.25/sqm/month\n"
     ]
    }
   ],
   "source": [
    "# create priorities list according to API Foursquare categories {'CategoryId': 'Category Name'}\n",
    "prio_list = {'4f4533804b9074f6e4fb0105': 'Elementary School',\n",
    "             '4bf58dd8d48988d163941735': 'Park',\n",
    "             '52f2ab2ebcbc57f1066b8b46': 'Supermarket',\n",
    "             '4bf58dd8d48988d10f951735': 'Pharmacy',\n",
    "             '4bf58dd8d48988d1fd931735': 'Metro Station'}\n",
    "\n",
    "# calculate rental budget per square meter based in target area and budget\n",
    "target_area = 80\n",
    "target_budget = 2000\n",
    "tolera_budget = 0.05\n",
    "\n",
    "budget_sqm = target_budget / target_area\n",
    "budget_sqm_min = budget_sqm * (1 - tolera_budget)\n",
    "budget_sqm_max = budget_sqm * (1 + tolera_budget)\n",
    "\n",
    "# print objects\n",
    "print('Family priorities are:', list(prio_list.values()), '\\n')\n",
    "print('Monthly rental budget is BRL {:,.2f} for a {} sqm apartment = BRL {:,.2f}/sqm/month\\n'.\\\n",
    "      format(target_budget, target_area, budget_sqm))\n",
    "print('Rental prices will be considered ranging from {:,.2f}/sqm/month up to {:,.2f}/sqm/month'.\\\n",
    "      format(budget_sqm_min, budget_sqm_max))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 2 - Acquire Districts Data of _Target Location_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The city of São Paulo is divided in 5 geographical regions, 32 boroughs, 96 districts and hundreds of neighborhoods. The city division up to the district level can be found in the official city web site, its is stored in a XLSX table format into a HTML page. The project will be based on district level to create the rank.\n",
    "The current table is from 2017 and can be viewed at the following link;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Prefeitura de São Paulo (SP) Regiões, Prefeituras Regionas e Distritos](http://www.prefeitura.sp.gov.br/cidade/secretarias/upload/urbanismo/infocidade/htmls/3_regioes_prefeituras_regionais_e_distrito_2017_10895.html)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To create a dataframe for the Districts of São Paulo, a request to the above URL will be done, its content parsed, cleaned and organized, to be stored in so called __Districts Dataframe__.\n",
    "\n",
    "First thing, import required libraries.\n",
    "\n",
    "1. __Pandas__: manipulate dataframe objects\n",
    "1. __Numpy__: manipulate arrays and matrices calculations\n",
    "1. __Requests__: send and receive url requests\n",
    "1. __BeautifulSoup__: parse url content"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import requests\n",
    "from bs4 import BeautifulSoup as bs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Send request to the above URL using _requests.get_ method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data loaded, status 200\n"
     ]
    }
   ],
   "source": [
    "# define a variable for the url\n",
    "url = 'http://www.prefeitura.sp.gov.br/cidade/secretarias/upload/urbanismo/infocidade/htmls/3_regioes_prefeituras_regionais_e_distrito_2017_10895.html'\n",
    "\n",
    "# send a request to the URL and store the response\n",
    "raw = requests.get(url)\n",
    "\n",
    "# check if data was loaded [status 200 means success]\n",
    "if raw:\n",
    "    print('Data loaded, status', raw.status_code)\n",
    "else:\n",
    "    print('Error loading data', raw.status_code)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Parse the URL content using _BeautifulSoup_ with _HTML_ parser."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "URL content parsed.\n"
     ]
    }
   ],
   "source": [
    "# parse the raw data\n",
    "par = bs(raw.text, 'html.parser')\n",
    "print('URL content parsed.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check the returned content. Initially, how many tables it contains, the HTML tag _table_ will be the reference for counting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 table(s) found in the parsed URL.\n"
     ]
    }
   ],
   "source": [
    "# print number of tables\n",
    "print('{} table(s) found in the parsed URL.'.format(len(par.find_all('table'))))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As it is a table in _XLSX_ format, all its content is stored in one unique table, including table description, headers, data, summary and footnotes. The data should be extract from this unique table, but, first get the table out of the parsed content, HTML tag _table_ will be the reference as well."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get the table out of the parsed content\n",
    "par_table = par.find_all('table')[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check if there is any table header and how many rows the table contains. HTML tags _th_ and _tr_ will be respectively used for counting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 header(s) and 110 row(s) found in the table.\n"
     ]
    }
   ],
   "source": [
    "# print the number of headers and rows\n",
    "print('{} header(s) and {} row(s) found in the table.'.format(len(par_table.find_all('th')),\n",
    "                                                              len(par_table.find_all('tr'))))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the 110 rows, 96 of them should be the required districts data, the remaining rows should be titles, summaries and footnotes rows.\n",
    "\n",
    "Extract the data from the table to a list and check where the required data is located. This will be done running a nested loop through the rows, on the first level, and columns, on the second level. HTML tag _tr_ will be used to extract rows and _td_ to extract the columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "110 rows loaded.\n"
     ]
    }
   ],
   "source": [
    "# create an empty list for the entire table\n",
    "tabletmp = []\n",
    "\n",
    "# run a loop by row [tag 'tr']\n",
    "for i, row in enumerate(par_table.find_all('tr')):\n",
    "    # create an empty list for the current row\n",
    "    celltmp = []\n",
    "\n",
    "    # run a loop by column for the current row [tag 'td']\n",
    "    for j, column in enumerate(row.find_all('td')):\n",
    "        # append the text of current cell to the list\n",
    "        celltmp.append(column.get_text())\n",
    "\n",
    "    # append current line to the list\n",
    "    tabletmp.append(celltmp)\n",
    "        \n",
    "# inform the number of rows loaded\n",
    "print('{} rows loaded.'.format(len(tabletmp)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All the 110 rows have been loaded. Check the head and tail of the list to define the range of required data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Head 10 rows\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[['Regiões, Prefeituras Regionais e Distritos Municipais',\n",
       "  '\\xa0',\n",
       "  '',\n",
       "  '',\n",
       "  '',\n",
       "  '',\n",
       "  ''],\n",
       " ['Município de São Paulo', '\\xa0', '', '', '', ''],\n",
       " ['2017', '\\xa0', '\\xa0', '', '', '', ''],\n",
       " ['', '', '', '', ''],\n",
       " ['Regiões',\n",
       "  'Prefeituras\\r\\n  Regionais',\n",
       "  'Distritos',\n",
       "  'Área (ha)',\n",
       "  'Área (km²)',\n",
       "  '',\n",
       "  '',\n",
       "  ''],\n",
       " ['Centro', 'Sé', 'Bela Vista', '271,77', '2,72', '', '', ''],\n",
       " ['Bom Retiro', '420,54', '4,21', '', '', ''],\n",
       " ['Cambuci', '392,42', '3,92', '', '', ''],\n",
       " ['Consolação', '381,51', '3,82', '', '', ''],\n",
       " ['Liberdade', '365,07', '3,65', '', '', '']]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# print the first 10 rows\n",
    "print('Head 10 rows')\n",
    "tabletmp[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The first 4 rows are the table titles and can be ignored, row 5 contains the columns headers.\n",
    "\n",
    "Check the tail of the table."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tail 10 rows\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[['Vila Mariana', '859,56', '8,60', '', '', '', ''],\n",
       " ['Município de São\\r\\n  Paulo', '', '152.753,58', '1.527,54', '', '', '', ''],\n",
       " ['', '', '', '', '', '', '', ''],\n",
       " ['Fonte:\\r\\n  Prefeitura do Município de São Paulo. /\\xa0\\r\\n  Instituto\\xa0 Geográfico e\\r\\n  Cartográfico\\xa0 do Estado de São Paulo.',\n",
       "  '\\xa0',\n",
       "  '',\n",
       "  '',\n",
       "  '\\xa0'],\n",
       " ['Elaboração:\\r\\n  SMUL/Deinfo', '\\xa0', '\\xa0', '', '', '\\xa0'],\n",
       " ['Nota: Distritos Lei\\r\\n  nº 11.220/1992', '', '', ''],\n",
       " ['Subprefeituras\\r\\n  Lei nº 13.399/2002, alterada pelas Leis nº 13.682/2003 e nº 15.764/2013',\n",
       "  '',\n",
       "  '',\n",
       "  ''],\n",
       " ['Base\\r\\n  de cálculo das áreas: Mapa Digital da Cidade (MDC) - UTM/SAD69-96.',\n",
       "  '',\n",
       "  '',\n",
       "  ''],\n",
       " ['', '', '', '', '', '', '', ''],\n",
       " ['', '', '', '', '', '', '', '', '']]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#print the last 10 rows\n",
    "print('Tail 10 rows')\n",
    "tabletmp[-10:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The last 8 rows are the columns summary and footnotes, will also be ignored.\n",
    "\n",
    "Check the columns headers, row 5 (index 4)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Regiões',\n",
       " 'Prefeituras\\r\\n  Regionais',\n",
       " 'Distritos',\n",
       " 'Área (ha)',\n",
       " 'Área (km²)',\n",
       " '',\n",
       " '',\n",
       " '']"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check the columns headers\n",
    "tabletmp[4]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are 8 columns, but the last 3 are empty, the first 5 are the following:\n",
    "\n",
    "* __Region__ (Regiões), the first column is the city Region to which the District belongs\n",
    "* __Borough__ (Prefeituras Regionais), the second column is the Borough location\n",
    "* __District__ (Distritos), the third column is the District name\n",
    "* Area ha (Área (ha)), the fourth column is the neighborhood land area in hectares, it is not relevant for this project and it will be ignored\n",
    "* __Area km<sup>2</sup>__ (Área (km<sup>2</sup>)), the fifth column is the neighborhood land area in square kilometers\n",
    "\n",
    "Define the columns names for the __Districts Dataframe__."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# define the columns names\n",
    "column_names = ['region', 'borough', 'district', 'area_sqkm']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "At the top are 4 title rows and 1 header row, the first 5 rows at the top will be ignored. At the bottom 8 rows will be ignored, 1 summary and 7 footnotes rows. District data ranges from row 6 (index 5) to row 101 (index 100), counting 96 districts in São Paulo.\n",
    "\n",
    "Extract only the data rows from the table and count the number of rows left."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "96 rows left.\n"
     ]
    }
   ],
   "source": [
    "# extract only required rows\n",
    "tabletmp = tabletmp[5:101]\n",
    "\n",
    "# print number rows left\n",
    "print('{} rows left.'.format(len(tabletmp)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before extracting the data, check the head and tail to see how the data is stored."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Head 10 rows\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[['Centro', 'Sé', 'Bela Vista', '271,77', '2,72', '', '', ''],\n",
       " ['Bom Retiro', '420,54', '4,21', '', '', ''],\n",
       " ['Cambuci', '392,42', '3,92', '', '', ''],\n",
       " ['Consolação', '381,51', '3,82', '', '', ''],\n",
       " ['Liberdade', '365,07', '3,65', '', '', ''],\n",
       " ['República', '239,67', '2,40', '', '', ''],\n",
       " ['Santa Cecília', '375,92', '3,76', '', '', ''],\n",
       " ['Sé', '219,36', '2,19', '', '', ''],\n",
       " ['Leste',\n",
       "  'Aricanduva/Formosa/Carrão',\n",
       "  'Aricanduva',\n",
       "  '695,83',\n",
       "  '6,96',\n",
       "  '',\n",
       "  '',\n",
       "  ''],\n",
       " ['Carrão', '790,12', '7,90', '', '', '']]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# print the first 10 rows\n",
    "print('Head 10 rows')\n",
    "tabletmp[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check the tail."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tail 10 rows\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[[\"M'Boi Mirim\", 'Jardim Ângela', '3.741,13', '37,41', '', '', ''],\n",
       " ['Jardim São Luís', '2.604,72', '26,05', '', '', ''],\n",
       " ['Parelheiros', 'Marsilac', '20.818,52', '208,19', '', '', ''],\n",
       " ['Parelheiros', '15.260,75', '152,61', '', '', ''],\n",
       " ['Santo Amaro', 'Campo Belo', '876,98', '8,77', '', '', ''],\n",
       " ['Campo Grande', '1.295,08', '12,95', '', '', ''],\n",
       " ['Santo Amaro', '1.603,53', '16,04', '', '', '', ''],\n",
       " ['Vila Mariana', 'Moema', '907,87', '9,08', '', '', '', ''],\n",
       " ['Saúde', '931,12', '9,31', '', '', '', ''],\n",
       " ['Vila Mariana', '859,56', '8,60', '', '', '', '']]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#print the last 10 rows\n",
    "print('Tail 10 rows')\n",
    "tabletmp[-10:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The table seems to be structured in a group format, which is normally done for good visualization in _XLSX_ format tables. The region name, e.g. __Centro__, appears only once at its first borough __Sé__, and so the borough __Sé__ for its first district __Bela Vista__. There are rows with 5 columns, rows with 4 columns and rows with 3 columns, it makes things interesting.\n",
    "\n",
    "Something else to notice is that the columns aligned on the left side, in some of the rows the first column contains the region name, in some it contains the borough, and in many of them it contains the district, meaning that the columns are not at the same positions for all rows. To extract the data the reading should consider the number of features each row contains, and for rows missing region and/or borough information, they should be added, even more interesting.\n",
    "\n",
    "The last notice here is that the last columns are empty, for some rows 3 and for some 4 empty columns, they will be ignored.\n",
    "\n",
    "Extract the data to a temporary list, taking all the remarks above into consideration. This will be achieved running a loop through the rows. Actually it is one list object, which represents the source table, its elements are lists as well, each of them representing the rows of the table, and each element of the inner lists represents the cells of the table."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create an empty list to store data temporary\n",
    "listtmp = []\n",
    "\n",
    "# run a loop through the rows [list represents the table]\n",
    "for i, row in enumerate(tabletmp):\n",
    "    # check quantity of features in the current row [list represents a row]\n",
    "    nfeatures = len(row)-row.count('')\n",
    "    \n",
    "    # check the number of features\n",
    "    if nfeatures == 5:\n",
    "        # five features means complete row with new region and new borough, store them in variables\n",
    "        vregion = row[0]\n",
    "        vborough = row[1]\n",
    "    elif nfeatures == 4:\n",
    "        # four features means same region and new borough, store it in a variable\n",
    "        vborough = row[0]        \n",
    "\n",
    "    # 3 features means same region and borough, the variable above will be used to store the row data\n",
    "\n",
    "    # append the current row to the temporary list\n",
    "    listtmp.append([vregion, vborough, row[nfeatures-3], row[nfeatures-1]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check the resulting list, head and tail."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Head 10 rows\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[['Centro', 'Sé', 'Bela Vista', '2,72'],\n",
       " ['Centro', 'Sé', 'Bom Retiro', '4,21'],\n",
       " ['Centro', 'Sé', 'Cambuci', '3,92'],\n",
       " ['Centro', 'Sé', 'Consolação', '3,82'],\n",
       " ['Centro', 'Sé', 'Liberdade', '3,65'],\n",
       " ['Centro', 'Sé', 'República', '2,40'],\n",
       " ['Centro', 'Sé', 'Santa Cecília', '3,76'],\n",
       " ['Centro', 'Sé', 'Sé', '2,19'],\n",
       " ['Leste', 'Aricanduva/Formosa/Carrão', 'Aricanduva', '6,96'],\n",
       " ['Leste', 'Aricanduva/Formosa/Carrão', 'Carrão', '7,90']]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check results\n",
    "print('Head 10 rows')\n",
    "listtmp[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tail 10 rows\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[['Sul', \"M'Boi Mirim\", 'Jardim Ângela', '37,41'],\n",
       " ['Sul', \"M'Boi Mirim\", 'Jardim São Luís', '26,05'],\n",
       " ['Sul', 'Parelheiros', 'Marsilac', '208,19'],\n",
       " ['Sul', 'Parelheiros', 'Parelheiros', '152,61'],\n",
       " ['Sul', 'Santo Amaro', 'Campo Belo', '8,77'],\n",
       " ['Sul', 'Santo Amaro', 'Campo Grande', '12,95'],\n",
       " ['Sul', 'Santo Amaro', 'Santo Amaro', '16,04'],\n",
       " ['Sul', 'Vila Mariana', 'Moema', '9,08'],\n",
       " ['Sul', 'Vila Mariana', 'Saúde', '9,31'],\n",
       " ['Sul', 'Vila Mariana', 'Vila Mariana', '8,60']]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#print results\n",
    "print('Tail 10 rows')\n",
    "listtmp[-10:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The list is ready to be stored in a dataframe. Create the __Districts Dataframe__, using _pandas.DataFrame_ method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>region</th>\n",
       "      <th>borough</th>\n",
       "      <th>district</th>\n",
       "      <th>area_sqkm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Centro</td>\n",
       "      <td>Sé</td>\n",
       "      <td>Bela Vista</td>\n",
       "      <td>2,72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Centro</td>\n",
       "      <td>Sé</td>\n",
       "      <td>Bom Retiro</td>\n",
       "      <td>4,21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Centro</td>\n",
       "      <td>Sé</td>\n",
       "      <td>Cambuci</td>\n",
       "      <td>3,92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Centro</td>\n",
       "      <td>Sé</td>\n",
       "      <td>Consolação</td>\n",
       "      <td>3,82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Centro</td>\n",
       "      <td>Sé</td>\n",
       "      <td>Liberdade</td>\n",
       "      <td>3,65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>Sul</td>\n",
       "      <td>Santo Amaro</td>\n",
       "      <td>Campo Grande</td>\n",
       "      <td>12,95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>Sul</td>\n",
       "      <td>Santo Amaro</td>\n",
       "      <td>Santo Amaro</td>\n",
       "      <td>16,04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>Sul</td>\n",
       "      <td>Vila Mariana</td>\n",
       "      <td>Moema</td>\n",
       "      <td>9,08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>Sul</td>\n",
       "      <td>Vila Mariana</td>\n",
       "      <td>Saúde</td>\n",
       "      <td>9,31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>Sul</td>\n",
       "      <td>Vila Mariana</td>\n",
       "      <td>Vila Mariana</td>\n",
       "      <td>8,60</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>96 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    region       borough      district area_sqkm\n",
       "0   Centro            Sé    Bela Vista      2,72\n",
       "1   Centro            Sé    Bom Retiro      4,21\n",
       "2   Centro            Sé       Cambuci      3,92\n",
       "3   Centro            Sé    Consolação      3,82\n",
       "4   Centro            Sé     Liberdade      3,65\n",
       "..     ...           ...           ...       ...\n",
       "91     Sul   Santo Amaro  Campo Grande     12,95\n",
       "92     Sul   Santo Amaro   Santo Amaro     16,04\n",
       "93     Sul  Vila Mariana         Moema      9,08\n",
       "94     Sul  Vila Mariana         Saúde      9,31\n",
       "95     Sul  Vila Mariana  Vila Mariana      8,60\n",
       "\n",
       "[96 rows x 4 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create the dataframe\n",
    "df_districts = pd.DataFrame(data=listtmp, columns=column_names)\n",
    "\n",
    "# print results\n",
    "df_districts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data in __area_sqkm__ column doesn't have a good fit, as the decimal separator is comma, the Brazilian standard. For this reason it contains strings when it should contain numbers, _float_ in this case.\n",
    "\n",
    "Convert the data type of __area_sqkm__ to _float_. This will be done replacing the decimal separator, using _pandas.DataFrame.apply_ metohd, and then converting the column data type to _float_ in the dataframe using _pandas.dataframe.astype_ method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>region</th>\n",
       "      <th>borough</th>\n",
       "      <th>district</th>\n",
       "      <th>area_sqkm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Centro</td>\n",
       "      <td>Sé</td>\n",
       "      <td>Bela Vista</td>\n",
       "      <td>2.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Centro</td>\n",
       "      <td>Sé</td>\n",
       "      <td>Bom Retiro</td>\n",
       "      <td>4.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Centro</td>\n",
       "      <td>Sé</td>\n",
       "      <td>Cambuci</td>\n",
       "      <td>3.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Centro</td>\n",
       "      <td>Sé</td>\n",
       "      <td>Consolação</td>\n",
       "      <td>3.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Centro</td>\n",
       "      <td>Sé</td>\n",
       "      <td>Liberdade</td>\n",
       "      <td>3.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>Sul</td>\n",
       "      <td>Santo Amaro</td>\n",
       "      <td>Campo Grande</td>\n",
       "      <td>12.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>Sul</td>\n",
       "      <td>Santo Amaro</td>\n",
       "      <td>Santo Amaro</td>\n",
       "      <td>16.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>Sul</td>\n",
       "      <td>Vila Mariana</td>\n",
       "      <td>Moema</td>\n",
       "      <td>9.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>Sul</td>\n",
       "      <td>Vila Mariana</td>\n",
       "      <td>Saúde</td>\n",
       "      <td>9.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>Sul</td>\n",
       "      <td>Vila Mariana</td>\n",
       "      <td>Vila Mariana</td>\n",
       "      <td>8.60</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>96 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    region       borough      district area_sqkm\n",
       "0   Centro            Sé    Bela Vista      2.72\n",
       "1   Centro            Sé    Bom Retiro      4.21\n",
       "2   Centro            Sé       Cambuci      3.92\n",
       "3   Centro            Sé    Consolação      3.82\n",
       "4   Centro            Sé     Liberdade      3.65\n",
       "..     ...           ...           ...       ...\n",
       "91     Sul   Santo Amaro  Campo Grande     12.95\n",
       "92     Sul   Santo Amaro   Santo Amaro     16.04\n",
       "93     Sul  Vila Mariana         Moema      9.08\n",
       "94     Sul  Vila Mariana         Saúde      9.31\n",
       "95     Sul  Vila Mariana  Vila Mariana      8.60\n",
       "\n",
       "[96 rows x 4 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# change decimal separator\n",
    "df_districts['area_sqkm'] = df_districts['area_sqkm'].apply(lambda x : x.replace(',', '.'))\n",
    "\n",
    "# check the head\n",
    "df_districts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check the dataframe columns data types before converting, change the data type of __area_sqkm__ from _object_ to _float_, and check the dataframe columns data types after converting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "df_districts data types before converting:\n",
      " region       object\n",
      "borough      object\n",
      "district     object\n",
      "area_sqkm    object\n",
      "dtype: object \n",
      "\n",
      "df_districts data types after converting:\n",
      " region        object\n",
      "borough       object\n",
      "district      object\n",
      "area_sqkm    float64\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "# print the columns data types\n",
    "print('df_districts data types before converting:\\n', df_districts.dtypes, '\\n')\n",
    "\n",
    "# convert the area column to float\n",
    "df_districts = df_districts.astype({'area_sqkm': 'float64'})\n",
    "\n",
    "# print the columns data types\n",
    "print('df_districts data types after converting:\\n', df_districts.dtypes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>region</th>\n",
       "      <th>borough</th>\n",
       "      <th>district</th>\n",
       "      <th>area_sqkm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Centro</td>\n",
       "      <td>Sé</td>\n",
       "      <td>Bela Vista</td>\n",
       "      <td>2.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Centro</td>\n",
       "      <td>Sé</td>\n",
       "      <td>Bom Retiro</td>\n",
       "      <td>4.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Centro</td>\n",
       "      <td>Sé</td>\n",
       "      <td>Cambuci</td>\n",
       "      <td>3.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Centro</td>\n",
       "      <td>Sé</td>\n",
       "      <td>Consolação</td>\n",
       "      <td>3.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Centro</td>\n",
       "      <td>Sé</td>\n",
       "      <td>Liberdade</td>\n",
       "      <td>3.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>Sul</td>\n",
       "      <td>Santo Amaro</td>\n",
       "      <td>Campo Grande</td>\n",
       "      <td>12.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>Sul</td>\n",
       "      <td>Santo Amaro</td>\n",
       "      <td>Santo Amaro</td>\n",
       "      <td>16.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>Sul</td>\n",
       "      <td>Vila Mariana</td>\n",
       "      <td>Moema</td>\n",
       "      <td>9.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>Sul</td>\n",
       "      <td>Vila Mariana</td>\n",
       "      <td>Saúde</td>\n",
       "      <td>9.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>Sul</td>\n",
       "      <td>Vila Mariana</td>\n",
       "      <td>Vila Mariana</td>\n",
       "      <td>8.60</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>96 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    region       borough      district  area_sqkm\n",
       "0   Centro            Sé    Bela Vista       2.72\n",
       "1   Centro            Sé    Bom Retiro       4.21\n",
       "2   Centro            Sé       Cambuci       3.92\n",
       "3   Centro            Sé    Consolação       3.82\n",
       "4   Centro            Sé     Liberdade       3.65\n",
       "..     ...           ...           ...        ...\n",
       "91     Sul   Santo Amaro  Campo Grande      12.95\n",
       "92     Sul   Santo Amaro   Santo Amaro      16.04\n",
       "93     Sul  Vila Mariana         Moema       9.08\n",
       "94     Sul  Vila Mariana         Saúde       9.31\n",
       "95     Sul  Vila Mariana  Vila Mariana       8.60\n",
       "\n",
       "[96 rows x 4 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# print results\n",
    "df_districts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 3 - Acquire Rental Prices Data of _Target Location_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Searching on the internet, there are several real estate agencies websites in São Paulo, but to find a rental prices list per district or neighborhood is a hard task. A mixed rental prices list per district and neighborhood could be found in a real estate agency website called __Blog SP Imóvel__ (www.spimovel.com.br), which provides its services all around the city. It hosts additional four websites covering the regions of the city, as follwing;\n",
    "\n",
    "* __Blog ZN Imóvel__ (www.znimovel.com.br) for region _Norte_\n",
    "* __Blog ZS Imóvel__ (www.zsimovel.com.br) for region _Sul_\n",
    "* __Blog ZL Imóvel__ (www.zlimovel.com.br) for region _Leste_\n",
    "* __Blog ZO Imóvel__ (www.zoimovel.com.br) for region _Oeste_\n",
    "\n",
    "The websites contain each of them a list mixed with districts and neighborhoods names, it is not the complete city neighborhoods, but all the districts are represented. Unfortunately there is a missing relation between districts and neighborhoods, what could not be found, and for this reason missing data is expected to occour, and they will be treated accordingly."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To create the __Rental Prices Dataframe__ a request will be sent to each of the mentioned websites, their content parsed, cleaned and organized, to be combined and stored into a unique data frame.\n",
    "\n",
    "The lists can be viewed at the following links;\n",
    "\n",
    "[Blog ZN Imóvel list from 2020.02.26](https://www.znimovel.com.br/blog/qual-o-valor-do-metro-quadrado-do-aluguel-dos-apartamentos-na-zona-norte-de-sao-paulo/2834/)\n",
    "\n",
    "[Blog ZS Imóvel list from 2020.03.06](https://www.zsimovel.com.br/blog/qual-o-valor-do-metro-quadrado-do-aluguel-dos-apartamentos-na-zona-sul-de-sao-paulo/2874/)\n",
    "\n",
    "[Blog ZL Imóvel list from 2020.02.28](https://www.zlimovel.com.br/blog/qual-o-valor-do-metro-quadrado-do-aluguel-dos-apartamentos-na-zona-leste-de-sao-paulo/2824/)\n",
    "\n",
    "[Blog ZO Imóvel list from 2020.02.28](https://www.zoimovel.com.br/blog/qual-o-valor-do-metro-quadrado-do-aluguel-dos-apartamentos-na-zona-oeste-de-sao-paulo/2851/)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Region__ ___Norte___ - Send request to the URL using _requests.get_ method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data loaded, status 200\n"
     ]
    }
   ],
   "source": [
    "# define a variable for the url\n",
    "url = 'https://www.znimovel.com.br/blog/qual-o-valor-do-metro-quadrado-do-aluguel-dos-apartamentos-na-zona-norte-de-sao-paulo/2834/'\n",
    "\n",
    "# send a request to the URL and store the response\n",
    "raw = requests.get(url)\n",
    "\n",
    "# check if data was loaded [status 200 means success]\n",
    "if raw:\n",
    "    print('Data loaded, status', raw.status_code)\n",
    "else:\n",
    "    print('Error loading data', raw.status_code)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Parse the URL content using _BeautifulSoup_ with _HTML_ parser."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "URL content parsed.\n"
     ]
    }
   ],
   "source": [
    "# parse the raw data\n",
    "par = bs(raw.text, 'html.parser')\n",
    "print('URL content parsed.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check the returned content, initially, how many tables it contains, the HTML tag _table_ will be the reference for counting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3 table(s) found in the parsed content.\n"
     ]
    }
   ],
   "source": [
    "# print number of tables\n",
    "print('{} table(s) found in the parsed content.'.format(len(par.find_all('table'))))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check the titles of each table to know which of them is the relevant one to be used. The HTML tag _table_ will be used to select the tables, and the tag _tr_ to select the first row form the table."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Title of Table 0\n",
      "\n",
      "Valor médio do metro quadrado do Aluguel\n",
      "\t\t\tApartamentos 1, 2 e 3 dormitórios \n",
      "1 Vaga de Garagem\n",
      "\t\t\tZona Norte - São Paulo\n",
      "\n",
      "Title of Table 1\n",
      "\n",
      "Valor médio do metro quadrado do Aluguel\n",
      "\t\t\tApartamentos 2 e 3 dormitórios \n",
      "2 Vagas de Garagem\n",
      "\t\t\tZona Norte - São Paulo\n",
      "\n",
      "Title of Table 2\n",
      "\n",
      "Valor médio do metro quadrado do Aluguel\n",
      "\t\t\tApartamentos Alto Padrão com 3 SUÌTES ou 4 dormitórios\n",
      "3 ou mais  Vagas de Garagem\n",
      "\t\t\tZona Norte - São Paulo\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# run a loop through the tables [tag table]\n",
    "for i, title in enumerate(par.find_all('table')):\n",
    "    # print the first row [tag tr]\n",
    "    print('Title of Table', i)\n",
    "    print(title.find_all('tr')[0].get_text())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Translating results.\n",
    "\n",
    "__Table 0__ contains prices for apartments with 1, 2 or 3 bedrooms and 1 garage spot\n",
    "\n",
    "__Table 1__ contains prices for apartments with 2 or 3 bedrooms and 2 garage spots\n",
    "\n",
    "__Table 2__ contains prices for high standard apartments with 3 bedrooms with private suites or 4 bedrooms and 3 or more garage spots\n",
    "\n",
    "For this project the __Table 0__ will be used as it is the best fit for __Relocator Profile__."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Extract the data out of the table to a list and check where the required data is located. This will be done running a nested loop through the rows, on the first level, and columns, on the second level. HTML tag _tr_ will be used to extract rows and _td_ to extract the columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "28 rows loaded.\n"
     ]
    }
   ],
   "source": [
    "# get the table from the URL content [tag 'table']\n",
    "par_table = par.find_all('table')[0]\n",
    "\n",
    "# create an empty list for the entire table\n",
    "tabletmp = []\n",
    "\n",
    "# run a loop by row [tag 'tr']\n",
    "for i, row in enumerate(par_table.find_all('tr')):\n",
    "    # create an empty list for the current row\n",
    "    celltmp = []\n",
    "\n",
    "    # run a loop by column for the current row [tag 'td']\n",
    "    for j, column in enumerate(row.find_all('td')):\n",
    "        # append the text of current cell to the list\n",
    "        celltmp.append(column.get_text())\n",
    "\n",
    "    # append current line to the list\n",
    "    tabletmp.append(celltmp)\n",
    "        \n",
    "# print results\n",
    "print('{} rows loaded.'.format(len(tabletmp)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check the head and tail of the list to define the range of required data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Head 10 rows\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[['Valor médio do metro quadrado do Aluguel\\r\\n\\t\\t\\tApartamentos 1, 2 e 3 dormitórios\\xa0\\n1 Vaga de Garagem\\r\\n\\t\\t\\tZona Norte - São Paulo'],\n",
       " ['Bairros', 'Valor médio do m² Aluguel'],\n",
       " ['Santana', 'R$ 23,80'],\n",
       " ['Lauzane Paulista', 'R$ 22,10'],\n",
       " ['Mandaqui', 'R$ 21,70'],\n",
       " ['Tucuruvi', 'R$ 23,00'],\n",
       " ['Parada Inglesa', 'R$ 27,00'],\n",
       " ['Vila Guilherme', 'R$ 25,80'],\n",
       " ['Jardim São Paulo', 'R$ 22,40'],\n",
       " ['Vila Mazzei', 'R$ 23,60']]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# print the first 10 rows\n",
    "print('Head 10 rows')\n",
    "tabletmp[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The first row contains the table title, the second row the columns headers.\n",
    "\n",
    "Check the tail of the table."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tail 10 rows\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[['Cachoeirinha', 'R$ 21,00'],\n",
       " ['Vila Amália', 'R$ 22,30'],\n",
       " ['Vila Gustavo', 'R$ 23,70'],\n",
       " ['Limão', 'R$ 23,30'],\n",
       " ['Vila Medeiros', 'R$ 22,20'],\n",
       " ['Vila Nova Cachoeirinha', 'R$ 21,80'],\n",
       " ['Tremembé', 'R$ 20,10'],\n",
       " ['Horto Florestal', 'R$ 20,40'],\n",
       " ['Alto de Santana', 'R$ 23,80'],\n",
       " ['Dados Fevereiro 2020\\r\\n\\t\\t\\tPortal ZN Imóvel']]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#print the last 10 rows\n",
    "print('Tail 10 rows')\n",
    "tabletmp[-10:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The last row contais the footnote.\n",
    "\n",
    "There are 1 title and 1 header row, the first 2 rows at the top will be ignored. At the bottom 1 footnote row  will be ignored. The data ranges from row 3 (index 2) to row 27 (index 26), counting 25 neighborhoods.\n",
    "\n",
    "Extract only the data rows out of the table, store them into a temporary prices list and count the number of rows left."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "25 rows in total.\n"
     ]
    }
   ],
   "source": [
    "# extract only data rows and store in temporary prices list\n",
    "listtmp = tabletmp[2:-1]\n",
    "\n",
    "# print results\n",
    "print('{} rows in total.'.format(len(listtmp)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check the columns headers in row 2."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Bairros', 'Valor médio do m² Aluguel']"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check columns headers\n",
    "tabletmp[1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are 2 columns, an english header will be defined for the __Rental Prices Dataframe__ as following:\n",
    "\n",
    "* __neighborhood__ (Bairros), the first column is the Neighborhood name\n",
    "* __mean_price_sqm__ (Valor médio do m<sup>2</sup> Aluguel), the second column is the mean rental price per m<sup>2</sup> in BRL (BRL/m<sup>2</sup>)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# define the columns names\n",
    "column_names = ['neighborhood','mean_price_sqm']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For regions _Sul_, _Leste_ and _Oeste_ the task should be the same, but they will be performed less didatically as it is only repetition."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Region__ ___Sul___ - Send request to the URL and check its return."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data loaded, status 200\n"
     ]
    }
   ],
   "source": [
    "# define a variable for the url\n",
    "url = 'https://www.zsimovel.com.br/blog/qual-o-valor-do-metro-quadrado-do-aluguel-dos-apartamentos-na-zona-sul-de-sao-paulo/2874/'\n",
    "\n",
    "# send a request to the URL and store the response\n",
    "raw = requests.get(url)\n",
    "\n",
    "# check if data was loaded [status 200 means success]\n",
    "if raw:\n",
    "    print('Data loaded, status', raw.status_code)\n",
    "else:\n",
    "    print('Error loading data', raw.status_code)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Parse the URL content, check how many tables it contains and check the titles of each table to know which of them is the relevant one to be used."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4 table(s) found in the parsed URL.\n",
      "\n",
      "Title of Table 0\n",
      "\n",
      "Valor médio do metro quadrado do Aluguel\n",
      "\t\t\tApartamentos 1 ou 2 dormitórios\n",
      "1 Vaga de Garagem\n",
      "\t\t\tZona Sul - São Paulo\n",
      "\n",
      "Title of Table 1\n",
      "\n",
      "Valor médio do metro quadrado do Aluguel\n",
      "\t\t\tApartamentos 2 ou 3 dormitórios\n",
      "2 Vagas de Garagem\n",
      "\t\t\tZona Sul - São Paulo\n",
      "\n",
      "Title of Table 2\n",
      "\n",
      "Valor médio do metro quadrado do Aluguel\n",
      "\t\t\tApartamentos 3 Suítes ou 4 dormitórios\n",
      "3 Vagas de Garagem\n",
      "\t\t\tZona Sul - São Paulo\n",
      "\n",
      "Title of Table 3\n",
      "\n",
      "Valor médio do metro quadrado do Aluguel\n",
      "\t\t\tApartamentos 1 dormitório\n",
      "SEM VAGA de Garagem\n",
      "\t\t\tZona Sul - São Paulo\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# parse the raw data\n",
    "par = bs(raw.text, 'html.parser')\n",
    "\n",
    "# print number of tables\n",
    "print('{} table(s) found in the parsed URL.\\n'.format(len(par.find_all('table'))))\n",
    "\n",
    "# run a loop through the tables [tag table]\n",
    "for i, title in enumerate(par.find_all('table')):\n",
    "    # print the first row [tag tr]\n",
    "    print('Title of Table', i)\n",
    "    print(title.find_all('tr')[0].get_text())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Translating results.\n",
    "\n",
    "__Table 0__ contains prices for apartments with 1 or 2 bedrooms and 1 garage spot\n",
    "\n",
    "__Table 1__ contains prices for apartments with 2 or 3 bedrooms and 2 garage spots\n",
    "\n",
    "__Table 2__ contains prices for apartments with 3 bedrooms with private suites or 4 bedrooms and 3 garage spots\n",
    "\n",
    "__Table 3__ contains prices for apartments with 1 bedroom and no garage spot\n",
    "\n",
    "For this project the __Table 0__ will be used as it is the best fit for __Relocator Profile__."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Extract the data out of the table to a list and confirm where the required data is located."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20 rows loaded.\n",
      "Content of three first rows:\n",
      " [['Valor médio do metro quadrado do Aluguel\\r\\n\\t\\t\\tApartamentos 1 ou 2 dormitórios\\n1 Vaga de Garagem\\r\\n\\t\\t\\tZona Sul - São Paulo'], ['Bairros', 'Valor médio m² Aluguel'], ['Vila Mariana', 'R$ 29,90']] \n",
      "\n",
      "Content of 2 last rows:\n",
      " [['Campo Limpo', 'R$ 24,50'], ['Dados Março 2020\\r\\n\\t\\t\\tPortal ZS Imóvel']]\n"
     ]
    }
   ],
   "source": [
    "# get the table out of the parsed content [tag 'table']\n",
    "par_table = par.find_all('table')[0]\n",
    "\n",
    "# create an empty list for the entire table\n",
    "tabletmp = []\n",
    "\n",
    "# run a loop by row [tag 'tr']\n",
    "for i, row in enumerate(par_table.find_all('tr')):\n",
    "    # create an empty list for the current row\n",
    "    celltmp = []\n",
    "\n",
    "    # run a loop by column for the current row [tag 'td']\n",
    "    for j, column in enumerate(row.find_all('td')):\n",
    "        # append the text of current cell to the list\n",
    "        celltmp.append(column.get_text())\n",
    "\n",
    "    # append current line to the list\n",
    "    tabletmp.append(celltmp)\n",
    "        \n",
    "# inform the number of rows loaded\n",
    "print('{} rows loaded.'.format(len(tabletmp)))\n",
    "\n",
    "# print content of three first row\n",
    "print('Content of three first rows:\\n', tabletmp[:3], '\\n')\n",
    "\n",
    "# print content of last two row\n",
    "print('Content of 2 last rows:\\n', tabletmp[-2:])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The table structure is the same, 1 title, 1 header and 1 footnote row, the first 2 and the last 1 rows will be ignored. Add the data to the temporary prices list."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "17 rows added / 42 rows in total.\n"
     ]
    }
   ],
   "source": [
    "# store quantity of rows before adding new rows\n",
    "vlen = len(listtmp)\n",
    "\n",
    "# extend the temporary prices list with new data\n",
    "listtmp.extend(tabletmp[2:-1])\n",
    "\n",
    "# print results\n",
    "print('{} rows added / {} rows in total.'.format(len(listtmp)-vlen, len(listtmp)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Region__ ___Leste___ - Send request to the URL and check its return."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data loaded, status 200\n"
     ]
    }
   ],
   "source": [
    "# define a variable for the url\n",
    "url = 'https://www.zlimovel.com.br/blog/qual-o-valor-do-metro-quadrado-do-aluguel-dos-apartamentos-na-zona-leste-de-sao-paulo/2824/'\n",
    "\n",
    "# send a request to the URL and store the response\n",
    "raw = requests.get(url)\n",
    "\n",
    "# check if data was loaded [status 200 means success]\n",
    "if raw:\n",
    "    print('Data loaded, status', raw.status_code)\n",
    "else:\n",
    "    print('Error loading data', raw.status_code)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Parse the URL content, check how many tables it contains and check the titles of each table to know which of them is the relevant one to be used."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4 table(s) found in the parsed URL.\n",
      "\n",
      "Title of Table 0\n",
      "\n",
      "Valor médio do metro quadrado do Aluguel\n",
      "\t\t\tApartamentos 1, 2 e 3 dormitórios\n",
      "1 Vaga de Garagem\n",
      "\t\t\tZona Leste - São Paulo\n",
      "\n",
      "Title of Table 1\n",
      "\n",
      "Valor médio do metro quadrado do Aluguel\n",
      "Apartamentos 2 e 3 dormitórios\n",
      "2 Vagas de Garagem\n",
      "\t\t\tZona Leste - São Paulo\n",
      "\n",
      "Title of Table 2\n",
      "\n",
      "Valor médio do metro quadrado do Aluguel\n",
      "Apartamentos 3 Suítes ou 4 Dormitórios\n",
      "3 ou mais Vagas de Garagem\n",
      "\t\t\tZona Leste - São Paulo\n",
      "\n",
      "Title of Table 3\n",
      "\n",
      "Valor médio do metro quadrado do Aluguel\n",
      "Apartamentos 1, 2 e 3 dormitórios\n",
      "1 Vaga de Garagem\n",
      "\t\t\tCohab, Zona Leste - São Paulo\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# parse the raw data\n",
    "par = bs(raw.text, 'html.parser')\n",
    "\n",
    "# print number of tables\n",
    "print('{} table(s) found in the parsed URL.\\n'.format(len(par.find_all('table'))))\n",
    "\n",
    "# run a loop through the tables [tag table]\n",
    "for i, title in enumerate(par.find_all('table')):\n",
    "    # print the first row [tag tr]\n",
    "    print('Title of Table', i)\n",
    "    print(title.find_all('tr')[0].get_text())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Translating results.\n",
    "\n",
    "__Table 0__ contains prices for apartments with 1, 2 or 3 bedrooms and 1 garage spot\n",
    "\n",
    "__Table 1__ contains prices for apartments with 2 or 3 bedrooms and 2 garage spots\n",
    "\n",
    "__Table 2__ contains prices for apartments with 3 bedrooms with private suites or 4 bedrooms and 3 or more garage spots\n",
    "\n",
    "__Table 3__ contains prices for apartments with 1, 2 or 3 bedrooms and 1 garage spot from _Cohab_, which is government habitational support program\n",
    "\n",
    "For this project the __Table 0__ will be used as it is the best fit for __Relocator Profile__."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Extract the data out of the table to a list and confirm where the required data is located."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "28 rows loaded.\n",
      "Content of three first rows:\n",
      " [['Valor médio do metro quadrado do Aluguel\\r\\n\\t\\t\\tApartamentos 1, 2 e 3 dormitórios\\n1 Vaga de Garagem\\r\\n\\t\\t\\tZona Leste - São Paulo'], ['Bairros', '\\xa0Valor médio m² Aluguel'], ['Tatuapé', 'R$ 25,70']] \n",
      "\n",
      "Content of 2 last rows:\n",
      " [['Jd. Nove de Julho', 'R$ 21,90'], ['Dados Fevereiro 2020\\r\\n\\t\\t\\tPortal ZL Imóvel']]\n"
     ]
    }
   ],
   "source": [
    "# get the table out of the parsed content [tag 'table']\n",
    "par_table = par.find_all('table')[0]\n",
    "\n",
    "# create an empty list for the entire table\n",
    "tabletmp = []\n",
    "\n",
    "# run a loop by row [tag 'tr']\n",
    "for i, row in enumerate(par_table.find_all('tr')):\n",
    "    # create an empty list for the current row\n",
    "    celltmp = []\n",
    "\n",
    "    # run a loop by column for the current row [tag 'td']\n",
    "    for j, column in enumerate(row.find_all('td')):\n",
    "        # append the text of current cell to the list\n",
    "        celltmp.append(column.get_text())\n",
    "\n",
    "    # append current line to the list\n",
    "    tabletmp.append(celltmp)\n",
    "        \n",
    "# inform the number of rows loaded\n",
    "print('{} rows loaded.'.format(len(tabletmp)))\n",
    "\n",
    "# print content of three first row\n",
    "print('Content of three first rows:\\n', tabletmp[:3], '\\n')\n",
    "\n",
    "# print content of last two row\n",
    "print('Content of 2 last rows:\\n', tabletmp[-2:])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The table structure is the same, 1 title, 1 header and 1 footnote row, the first 2 and the last 1 rows will be ignored. Add the data to the temporary prices list."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "25 rows added / 67 rows in total rows.\n"
     ]
    }
   ],
   "source": [
    "# store quantity rows before adding new rows\n",
    "vlen = len(listtmp)\n",
    "\n",
    "# extend the temporary prices list with new data\n",
    "listtmp.extend(tabletmp[2:-1])\n",
    "\n",
    "# print results\n",
    "print('{} rows added / {} rows in total rows.'.format(len(listtmp)-vlen, len(listtmp)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Region__ ___Oeste___ - Send request to the URL and check its return."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data loaded, status 200\n"
     ]
    }
   ],
   "source": [
    "# define a variable for the url\n",
    "url = 'https://www.zoimovel.com.br/blog/qual-o-valor-do-metro-quadrado-do-aluguel-dos-apartamentos-na-zona-oeste-de-sao-paulo/2851/'\n",
    "\n",
    "# send a request to the URL and store the response\n",
    "raw = requests.get(url)\n",
    "\n",
    "# check if data was loaded [status 200 means success]\n",
    "if raw:\n",
    "    print('Data loaded, status', raw.status_code)\n",
    "else:\n",
    "    print('Error loading data', raw.status_code)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Parse the URL content, check how many tables it contains and check the titles of each table to know which of them is the relevant one to be used."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4 table(s) found in the parsed URL.\n",
      "\n",
      "Title of Table 0\n",
      "\n",
      "Valor médio do metro quadrado do Aluguel\n",
      "\t\t\tApartamentos 1 e 2 dormitórios\n",
      "1 Vaga de Garagem\n",
      "\t\t\tZona Oeste - São Paulo\n",
      "\n",
      "Title of Table 1\n",
      "\n",
      "Valor médio do metro quadrado do Aluguel\n",
      "Apartamentos 2 e 3 dormitórios\n",
      "2 Vagas de Garagem\n",
      "\t\t\tZona Oeste - São Paulo\n",
      "\n",
      "Title of Table 2\n",
      "\n",
      "Valor médio do metro quadrado do Aluguel\n",
      "Apartamentos 3 e 4 dormitórios\n",
      "3 Vagas de Garagem\n",
      "\t\t\tZona Oeste - São Paulo\n",
      "\n",
      "Title of Table 3\n",
      "\n",
      "Valor médio do metro quadrado do Aluguel\n",
      "Apartamentos ou Kitnets 1 dormitório\n",
      "SEM VAGA de Garagem\n",
      "\t\t\tZona Oeste - São Paulo\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# parse the raw data\n",
    "par = bs(raw.text, 'html.parser')\n",
    "\n",
    "# print number of tables\n",
    "print('{} table(s) found in the parsed URL.\\n'.format(len(par.find_all('table'))))\n",
    "\n",
    "# run a loop through the tables [tag table]\n",
    "for i, title in enumerate(par.find_all('table')):\n",
    "    # print the first row [tag tr]\n",
    "    print('Title of Table', i)\n",
    "    print(title.find_all('tr')[0].get_text())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Translating results.\n",
    "\n",
    "__Table 0__ contains prices for apartments with 1 or 2 bedrooms and 1 garage spot\n",
    "\n",
    "__Table 1__ contains prices for apartments with 2 or 3 bedrooms and 2 garage spots\n",
    "\n",
    "__Table 2__ contains prices for apartments with 3 or 4 bedrooms and 3 garage spots\n",
    "\n",
    "__Table 3__ contains prices for apartments with 1 bedroom and no garage spot\n",
    "\n",
    "For this project the __Table 0__ will be used as it is the best fit for __Relocator Profile__."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Extract the data out of the table to a list and confirm where the required data is located."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "24 rows loaded.\n",
      "Content of three first rows:\n",
      " [['Valor médio do metro quadrado do Aluguel\\r\\n\\t\\t\\tApartamentos 1 e 2 dormitórios\\n1 Vaga de Garagem\\r\\n\\t\\t\\tZona Oeste - São Paulo'], ['Bairros', 'Valor médio de m²'], ['Perdizes', 'R$ 27,80']] \n",
      "\n",
      "Content of 2 last rows:\n",
      " [['Vila Leopoldina', 'R$ 33,50'], ['Dados Fevereiro 2020\\r\\n\\t\\t\\tPortal ZO Imóvel']]\n"
     ]
    }
   ],
   "source": [
    "# get the table out of the parsed content [tag 'table']\n",
    "par_table = par.find_all('table')[0]\n",
    "\n",
    "# create an empty list for the entire table\n",
    "tabletmp = []\n",
    "\n",
    "# run a loop by row [tag 'tr']\n",
    "for i, row in enumerate(par_table.find_all('tr')):\n",
    "    # create an empty list for the current row\n",
    "    celltmp = []\n",
    "\n",
    "    # run a loop by column for the current row [tag 'td']\n",
    "    for j, column in enumerate(row.find_all('td')):\n",
    "        # append the text of current cell to the list\n",
    "        celltmp.append(column.get_text())\n",
    "\n",
    "    # append current line to the list\n",
    "    tabletmp.append(celltmp)\n",
    "        \n",
    "# inform the number of rows loaded\n",
    "print('{} rows loaded.'.format(len(tabletmp)))\n",
    "\n",
    "# print content of three first row\n",
    "print('Content of three first rows:\\n', tabletmp[:3], '\\n')\n",
    "\n",
    "# print content of last two row\n",
    "print('Content of 2 last rows:\\n', tabletmp[-2:])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The table structure is the same, 1 title, 1 header and 1 footnote row, the first 2 and the last 1 rows will be ignored. Add the data to the temporary prices list."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "21 rows added / 88 rows in total.\n"
     ]
    }
   ],
   "source": [
    "# store quantity of rows before adding new rows\n",
    "vlen = len(listtmp)\n",
    "\n",
    "# extend the temporary prices list with new data\n",
    "listtmp.extend(tabletmp[2:-1])\n",
    "\n",
    "# print results\n",
    "print('{} rows added / {} rows in total.'.format(len(listtmp)-vlen, len(listtmp)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The list is ready to be stored in a dataframe. Create the __Rental Prices Dataframe__, using _pandas.DataFrame_ method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>neighborhood</th>\n",
       "      <th>mean_price_sqm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Santana</td>\n",
       "      <td>R$ 23,80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Lauzane Paulista</td>\n",
       "      <td>R$ 22,10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Mandaqui</td>\n",
       "      <td>R$ 21,70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Tucuruvi</td>\n",
       "      <td>R$ 23,00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Parada Inglesa</td>\n",
       "      <td>R$ 27,00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>Pirituba</td>\n",
       "      <td>R$ 23,70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>Vila Sônia</td>\n",
       "      <td>R$ 25,90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>Vila São Francisco (ZO)</td>\n",
       "      <td>*Prejudicado</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>Jardins</td>\n",
       "      <td>R$ 32,20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>Vila Leopoldina</td>\n",
       "      <td>R$ 33,50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>88 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               neighborhood mean_price_sqm\n",
       "0                   Santana       R$ 23,80\n",
       "1          Lauzane Paulista       R$ 22,10\n",
       "2                  Mandaqui       R$ 21,70\n",
       "3                  Tucuruvi       R$ 23,00\n",
       "4            Parada Inglesa       R$ 27,00\n",
       "..                      ...            ...\n",
       "83                 Pirituba       R$ 23,70\n",
       "84               Vila Sônia       R$ 25,90\n",
       "85  Vila São Francisco (ZO)   *Prejudicado\n",
       "86                  Jardins       R$ 32,20\n",
       "87          Vila Leopoldina       R$ 33,50\n",
       "\n",
       "[88 rows x 2 columns]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create the rental prices dataframe\n",
    "df_rentalprices = pd.DataFrame(data=listtmp, columns=column_names)\n",
    "\n",
    "# print results\n",
    "df_rentalprices"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For neighborhoods which did not have enough samples to measure the mean rental price, the text __\"*Prejudicado\"__ has been added instead of the  mean price. Check the observations whitout mean rental price."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>neighborhood</th>\n",
       "      <th>mean_price_sqm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>República</td>\n",
       "      <td>*Prejudicado</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>Vila São Francisco (ZO)</td>\n",
       "      <td>*Prejudicado</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               neighborhood mean_price_sqm\n",
       "80                República   *Prejudicado\n",
       "85  Vila São Francisco (ZO)   *Prejudicado"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check entries with text in mean price column\n",
    "df_rentalprices[df_rentalprices['mean_price_sqm']=='*Prejudicado']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Drop the observations withou mean rental price, as they cannot be used. This will be done using _pandas.DataFrame.drop_ method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>neighborhood</th>\n",
       "      <th>mean_price_sqm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Santana</td>\n",
       "      <td>R$ 23,80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Lauzane Paulista</td>\n",
       "      <td>R$ 22,10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Mandaqui</td>\n",
       "      <td>R$ 21,70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Tucuruvi</td>\n",
       "      <td>R$ 23,00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Parada Inglesa</td>\n",
       "      <td>R$ 27,00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>Liberdade</td>\n",
       "      <td>R$ 30,70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>Pirituba</td>\n",
       "      <td>R$ 23,70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>Vila Sônia</td>\n",
       "      <td>R$ 25,90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>Jardins</td>\n",
       "      <td>R$ 32,20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>Vila Leopoldina</td>\n",
       "      <td>R$ 33,50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>86 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        neighborhood mean_price_sqm\n",
       "0            Santana       R$ 23,80\n",
       "1   Lauzane Paulista       R$ 22,10\n",
       "2           Mandaqui       R$ 21,70\n",
       "3           Tucuruvi       R$ 23,00\n",
       "4     Parada Inglesa       R$ 27,00\n",
       "..               ...            ...\n",
       "82         Liberdade       R$ 30,70\n",
       "83          Pirituba       R$ 23,70\n",
       "84        Vila Sônia       R$ 25,90\n",
       "86           Jardins       R$ 32,20\n",
       "87   Vila Leopoldina       R$ 33,50\n",
       "\n",
       "[86 rows x 2 columns]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# select the rows index to drop\n",
    "vidx = list(df_rentalprices[df_rentalprices['mean_price_sqm']=='*Prejudicado'].index)\n",
    "\n",
    "# drop entries with text in mean price column by index\n",
    "df_rentalprices.drop(vidx, axis=0, inplace=True)\n",
    "\n",
    "# print results\n",
    "df_rentalprices"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data in __mean_price_sqm__ column doesn't have a good fit, as it contains the currency symbol and the decimal separator is comma, the Brazilian standard.\n",
    "\n",
    "Convert the price _string_ to _float_ format. This will be done firstly removing the currency symbol, then replacing the decimal separator, both using _pandas.Dataframe.apply_ method, and finally converting _string_ to _float_ in the dataframe using _pandas.DataFrame.astype_ method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataframedata types:\n",
      " neighborhood       object\n",
      "mean_price_sqm    float64\n",
      "dtype: object \n",
      "\n"
     ]
    }
   ],
   "source": [
    "# remove currency symbol and change decimal separator\n",
    "df_rentalprices['mean_price_sqm'] = df_rentalprices['mean_price_sqm'].apply(lambda x : x.replace('R$ ', ''))\n",
    "df_rentalprices['mean_price_sqm'] = df_rentalprices['mean_price_sqm'].apply(lambda x : x.replace(',', '.'))\n",
    "\n",
    "# convert the price column to float\n",
    "df_rentalprices = df_rentalprices.astype({'mean_price_sqm': 'float64'})\n",
    "\n",
    "# print the columns data types\n",
    "print('Dataframedata types:\\n', df_rentalprices.dtypes, '\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>neighborhood</th>\n",
       "      <th>mean_price_sqm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Santana</td>\n",
       "      <td>23.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Lauzane Paulista</td>\n",
       "      <td>22.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Mandaqui</td>\n",
       "      <td>21.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Tucuruvi</td>\n",
       "      <td>23.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Parada Inglesa</td>\n",
       "      <td>27.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>Liberdade</td>\n",
       "      <td>30.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>Pirituba</td>\n",
       "      <td>23.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>Vila Sônia</td>\n",
       "      <td>25.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>Jardins</td>\n",
       "      <td>32.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>Vila Leopoldina</td>\n",
       "      <td>33.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>86 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        neighborhood  mean_price_sqm\n",
       "0            Santana            23.8\n",
       "1   Lauzane Paulista            22.1\n",
       "2           Mandaqui            21.7\n",
       "3           Tucuruvi            23.0\n",
       "4     Parada Inglesa            27.0\n",
       "..               ...             ...\n",
       "82         Liberdade            30.7\n",
       "83          Pirituba            23.7\n",
       "84        Vila Sônia            25.9\n",
       "86           Jardins            32.2\n",
       "87   Vila Leopoldina            33.5\n",
       "\n",
       "[86 rows x 2 columns]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# print results\n",
    "df_rentalprices"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check if there is any duplicated neighborhood in __Rental Prices Dataframe__. It will be done with _pandas.DataFrame.groupby_ method, grouping the dataframe by neighborhood and counting the mean prices."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>neighborhood</th>\n",
       "      <th>mean_price_sqm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [neighborhood, mean_price_sqm]\n",
       "Index: []"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# group the rental prices by neighborhoods counting the mean price\n",
    "df_group = df_rentalprices.groupby('neighborhood', sort=True).count().reset_index()\n",
    "\n",
    "# check for counting greater then 1\n",
    "df_group[df_group['mean_price_sqm'] > 1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All the observations in __Rental Prices Dataframe__ are unique.\n",
    "\n",
    "Merge __Districts Dataframe__ (__district__ as key) with __Rental Prices Dataframe__ (__neighborhood__ as key) using _pandas.merge_ method. The resulting dataframe will be __Districts Dataframe__ with mean rental prices. As stated before, missing data is expected to occour."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "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",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>region</th>\n",
       "      <th>borough</th>\n",
       "      <th>district</th>\n",
       "      <th>area_sqkm</th>\n",
       "      <th>neighborhood</th>\n",
       "      <th>mean_price_sqm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Centro</td>\n",
       "      <td>Sé</td>\n",
       "      <td>Bela Vista</td>\n",
       "      <td>2.72</td>\n",
       "      <td>Bela Vista</td>\n",
       "      <td>30.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Centro</td>\n",
       "      <td>Sé</td>\n",
       "      <td>Bom Retiro</td>\n",
       "      <td>4.21</td>\n",
       "      <td>Bom Retiro</td>\n",
       "      <td>29.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Centro</td>\n",
       "      <td>Sé</td>\n",
       "      <td>Cambuci</td>\n",
       "      <td>3.92</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Centro</td>\n",
       "      <td>Sé</td>\n",
       "      <td>Consolação</td>\n",
       "      <td>3.82</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Centro</td>\n",
       "      <td>Sé</td>\n",
       "      <td>Liberdade</td>\n",
       "      <td>3.65</td>\n",
       "      <td>Liberdade</td>\n",
       "      <td>30.7</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>Sul</td>\n",
       "      <td>Santo Amaro</td>\n",
       "      <td>Campo Grande</td>\n",
       "      <td>12.95</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>Sul</td>\n",
       "      <td>Santo Amaro</td>\n",
       "      <td>Santo Amaro</td>\n",
       "      <td>16.04</td>\n",
       "      <td>Santo Amaro</td>\n",
       "      <td>26.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>Sul</td>\n",
       "      <td>Vila Mariana</td>\n",
       "      <td>Moema</td>\n",
       "      <td>9.08</td>\n",
       "      <td>Moema</td>\n",
       "      <td>32.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>Sul</td>\n",
       "      <td>Vila Mariana</td>\n",
       "      <td>Saúde</td>\n",
       "      <td>9.31</td>\n",
       "      <td>Saúde</td>\n",
       "      <td>28.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>Sul</td>\n",
       "      <td>Vila Mariana</td>\n",
       "      <td>Vila Mariana</td>\n",
       "      <td>8.60</td>\n",
       "      <td>Vila Mariana</td>\n",
       "      <td>29.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>96 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    region       borough      district  area_sqkm  neighborhood  \\\n",
       "0   Centro            Sé    Bela Vista       2.72    Bela Vista   \n",
       "1   Centro            Sé    Bom Retiro       4.21    Bom Retiro   \n",
       "2   Centro            Sé       Cambuci       3.92           NaN   \n",
       "3   Centro            Sé    Consolação       3.82           NaN   \n",
       "4   Centro            Sé     Liberdade       3.65     Liberdade   \n",
       "..     ...           ...           ...        ...           ...   \n",
       "91     Sul   Santo Amaro  Campo Grande      12.95           NaN   \n",
       "92     Sul   Santo Amaro   Santo Amaro      16.04   Santo Amaro   \n",
       "93     Sul  Vila Mariana         Moema       9.08         Moema   \n",
       "94     Sul  Vila Mariana         Saúde       9.31         Saúde   \n",
       "95     Sul  Vila Mariana  Vila Mariana       8.60  Vila Mariana   \n",
       "\n",
       "    mean_price_sqm  \n",
       "0             30.6  \n",
       "1             29.1  \n",
       "2              NaN  \n",
       "3              NaN  \n",
       "4             30.7  \n",
       "..             ...  \n",
       "91             NaN  \n",
       "92            26.8  \n",
       "93            32.6  \n",
       "94            28.2  \n",
       "95            29.9  \n",
       "\n",
       "[96 rows x 6 columns]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# merge districts and rental prices dataframes\n",
    "df_districts = pd.merge(df_districts, df_rentalprices, how='left', left_on='district', right_on='neighborhood')\n",
    "\n",
    "# print results\n",
    "df_districts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The column __neighborhood__ contains the same information as __district__, it will be dropped from the data dataframe using _pandas.DataFrame.drop_ method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>region</th>\n",
       "      <th>borough</th>\n",
       "      <th>district</th>\n",
       "      <th>area_sqkm</th>\n",
       "      <th>mean_price_sqm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Centro</td>\n",
       "      <td>Sé</td>\n",
       "      <td>Bela Vista</td>\n",
       "      <td>2.72</td>\n",
       "      <td>30.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Centro</td>\n",
       "      <td>Sé</td>\n",
       "      <td>Bom Retiro</td>\n",
       "      <td>4.21</td>\n",
       "      <td>29.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Centro</td>\n",
       "      <td>Sé</td>\n",
       "      <td>Cambuci</td>\n",
       "      <td>3.92</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Centro</td>\n",
       "      <td>Sé</td>\n",
       "      <td>Consolação</td>\n",
       "      <td>3.82</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Centro</td>\n",
       "      <td>Sé</td>\n",
       "      <td>Liberdade</td>\n",
       "      <td>3.65</td>\n",
       "      <td>30.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>Sul</td>\n",
       "      <td>Santo Amaro</td>\n",
       "      <td>Campo Grande</td>\n",
       "      <td>12.95</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>Sul</td>\n",
       "      <td>Santo Amaro</td>\n",
       "      <td>Santo Amaro</td>\n",
       "      <td>16.04</td>\n",
       "      <td>26.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>Sul</td>\n",
       "      <td>Vila Mariana</td>\n",
       "      <td>Moema</td>\n",
       "      <td>9.08</td>\n",
       "      <td>32.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>Sul</td>\n",
       "      <td>Vila Mariana</td>\n",
       "      <td>Saúde</td>\n",
       "      <td>9.31</td>\n",
       "      <td>28.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>Sul</td>\n",
       "      <td>Vila Mariana</td>\n",
       "      <td>Vila Mariana</td>\n",
       "      <td>8.60</td>\n",
       "      <td>29.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>96 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    region       borough      district  area_sqkm  mean_price_sqm\n",
       "0   Centro            Sé    Bela Vista       2.72            30.6\n",
       "1   Centro            Sé    Bom Retiro       4.21            29.1\n",
       "2   Centro            Sé       Cambuci       3.92             NaN\n",
       "3   Centro            Sé    Consolação       3.82             NaN\n",
       "4   Centro            Sé     Liberdade       3.65            30.7\n",
       "..     ...           ...           ...        ...             ...\n",
       "91     Sul   Santo Amaro  Campo Grande      12.95             NaN\n",
       "92     Sul   Santo Amaro   Santo Amaro      16.04            26.8\n",
       "93     Sul  Vila Mariana         Moema       9.08            32.6\n",
       "94     Sul  Vila Mariana         Saúde       9.31            28.2\n",
       "95     Sul  Vila Mariana  Vila Mariana       8.60            29.9\n",
       "\n",
       "[96 rows x 5 columns]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# drop neighborhood column\n",
    "df_districts.drop('neighborhood', axis=1, inplace=True)\n",
    "\n",
    "# print results\n",
    "df_districts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Last step to have dataframe ready is to treat the __missing values__ in column __mean_price_sqm__. The missing values will be replaced by the mean price of respective region, but first for reference, calculate the mean price for each region, using _pandas.DataFrame.grouby_ method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
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       "        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>region</th>\n",
       "      <th>area_sqkm</th>\n",
       "      <th>mean_price_sqm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Centro</td>\n",
       "      <td>3.333750</td>\n",
       "      <td>29.925000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Leste</td>\n",
       "      <td>10.045758</td>\n",
       "      <td>23.053846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Norte</td>\n",
       "      <td>16.658333</td>\n",
       "      <td>22.725000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Oeste</td>\n",
       "      <td>8.598667</td>\n",
       "      <td>29.444444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Sul</td>\n",
       "      <td>33.662727</td>\n",
       "      <td>28.388889</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   region  area_sqkm  mean_price_sqm\n",
       "0  Centro   3.333750       29.925000\n",
       "1   Leste  10.045758       23.053846\n",
       "2   Norte  16.658333       22.725000\n",
       "3   Oeste   8.598667       29.444444\n",
       "4     Sul  33.662727       28.388889"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# group dataframe by region calculating the mean price\n",
    "df_districts.groupby('region', sort=True).mean().reset_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Replace the missing __mean_price_sqm__ by its respective region mean price. It will be done firstly defining a function to return the region mean price taking the region name as parameter, _pandas.DataFrame.group_ method will be used for this. Having the function defined, _pandas.DataFrame.apply_ method will be used to call the function for the observations where mean price is _NaN_, _numpy.isnan_ method will be used to chech for _NaN_."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Replace finished.\n"
     ]
    },
    {
     "data": {
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>region</th>\n",
       "      <th>borough</th>\n",
       "      <th>district</th>\n",
       "      <th>area_sqkm</th>\n",
       "      <th>mean_price_sqm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Centro</td>\n",
       "      <td>Sé</td>\n",
       "      <td>Bela Vista</td>\n",
       "      <td>2.72</td>\n",
       "      <td>30.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Centro</td>\n",
       "      <td>Sé</td>\n",
       "      <td>Bom Retiro</td>\n",
       "      <td>4.21</td>\n",
       "      <td>29.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Centro</td>\n",
       "      <td>Sé</td>\n",
       "      <td>Cambuci</td>\n",
       "      <td>3.92</td>\n",
       "      <td>29.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Centro</td>\n",
       "      <td>Sé</td>\n",
       "      <td>Consolação</td>\n",
       "      <td>3.82</td>\n",
       "      <td>29.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Centro</td>\n",
       "      <td>Sé</td>\n",
       "      <td>Liberdade</td>\n",
       "      <td>3.65</td>\n",
       "      <td>30.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>Sul</td>\n",
       "      <td>Santo Amaro</td>\n",
       "      <td>Campo Grande</td>\n",
       "      <td>12.95</td>\n",
       "      <td>28.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>Sul</td>\n",
       "      <td>Santo Amaro</td>\n",
       "      <td>Santo Amaro</td>\n",
       "      <td>16.04</td>\n",
       "      <td>26.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>Sul</td>\n",
       "      <td>Vila Mariana</td>\n",
       "      <td>Moema</td>\n",
       "      <td>9.08</td>\n",
       "      <td>32.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>Sul</td>\n",
       "      <td>Vila Mariana</td>\n",
       "      <td>Saúde</td>\n",
       "      <td>9.31</td>\n",
       "      <td>28.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>Sul</td>\n",
       "      <td>Vila Mariana</td>\n",
       "      <td>Vila Mariana</td>\n",
       "      <td>8.60</td>\n",
       "      <td>29.90</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>96 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    region       borough      district  area_sqkm  mean_price_sqm\n",
       "0   Centro            Sé    Bela Vista       2.72           30.60\n",
       "1   Centro            Sé    Bom Retiro       4.21           29.10\n",
       "2   Centro            Sé       Cambuci       3.92           29.93\n",
       "3   Centro            Sé    Consolação       3.82           29.93\n",
       "4   Centro            Sé     Liberdade       3.65           30.70\n",
       "..     ...           ...           ...        ...             ...\n",
       "91     Sul   Santo Amaro  Campo Grande      12.95           28.39\n",
       "92     Sul   Santo Amaro   Santo Amaro      16.04           26.80\n",
       "93     Sul  Vila Mariana         Moema       9.08           32.60\n",
       "94     Sul  Vila Mariana         Saúde       9.31           28.20\n",
       "95     Sul  Vila Mariana  Vila Mariana       8.60           29.90\n",
       "\n",
       "[96 rows x 5 columns]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# define a funciton to return the region mean price\n",
    "def get_region_mean(r):\n",
    "    m = df_districts[df_districts['region']==r].groupby('region', sort=True).mean().reset_index()['mean_price_sqm']\n",
    "    return round(float(m),2)\n",
    "\n",
    "# use apply to call the defined function when mean price is missing\n",
    "df_districts['mean_price_sqm'] = df_districts.apply(lambda x : get_region_mean(x.region) if np.isnan(x.mean_price_sqm) else x.mean_price_sqm, axis=1)\n",
    "\n",
    "# inform when it is finished\n",
    "print('Replace finished.')\n",
    "\n",
    "# print results\n",
    "df_districts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 4 - Exploratory Data Analysis on _Districts Dataframe_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import addional libraries.\n",
    "1. __Matplotlib__: create plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.colors as mcolors\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check how many districts each region contains. First the __Districts Dataframe__ will be grouped by __region__ with _pandas.DataFrame.groupby_ method, then a __bar plot__ will be constructed using _Matplotlib_."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# group dataframe by region counting the group observations\n",
    "vdf = df_districts.groupby('region').count().reset_index()\n",
    "\n",
    "# define plot style\n",
    "plt.style.use('seaborn')\n",
    "\n",
    "# set font size for axis ticks\n",
    "plt.rc('xtick', labelsize=13)\n",
    "plt.rc('ytick', labelsize=12)\n",
    "\n",
    "# create figure object\n",
    "fig, ax = plt.subplots(figsize=(10,6))\n",
    "\n",
    "# add bar plot to the figure\n",
    "ax.bar(vdf['region'], vdf['district'])\n",
    "\n",
    "# add plot title and ylable\n",
    "ax.set_title('São Paulo - Districts per Region', {'fontsize':14})\n",
    "ax.set_ylabel('units', {'fontsize':13})\n",
    "\n",
    "# show the figure\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check __Districts Dataframe__ statistics using _pandas.DataFrame.describe_ method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>region</th>\n",
       "      <th>borough</th>\n",
       "      <th>district</th>\n",
       "      <th>area_sqkm</th>\n",
       "      <th>mean_price_sqm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>96</td>\n",
       "      <td>96</td>\n",
       "      <td>96</td>\n",
       "      <td>96.000000</td>\n",
       "      <td>96.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>5</td>\n",
       "      <td>32</td>\n",
       "      <td>96</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>Leste</td>\n",
       "      <td>Sé</td>\n",
       "      <td>Ponte Rasa</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>33</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15.912396</td>\n",
       "      <td>25.785521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>26.968078</td>\n",
       "      <td>3.597908</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.190000</td>\n",
       "      <td>17.700000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.395000</td>\n",
       "      <td>23.050000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.780000</td>\n",
       "      <td>25.150000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13.560000</td>\n",
       "      <td>28.467500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>208.190000</td>\n",
       "      <td>33.500000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       region borough    district   area_sqkm  mean_price_sqm\n",
       "count      96      96          96   96.000000       96.000000\n",
       "unique      5      32          96         NaN             NaN\n",
       "top     Leste      Sé  Ponte Rasa         NaN             NaN\n",
       "freq       33       8           1         NaN             NaN\n",
       "mean      NaN     NaN         NaN   15.912396       25.785521\n",
       "std       NaN     NaN         NaN   26.968078        3.597908\n",
       "min       NaN     NaN         NaN    2.190000       17.700000\n",
       "25%       NaN     NaN         NaN    7.395000       23.050000\n",
       "50%       NaN     NaN         NaN    9.780000       25.150000\n",
       "75%       NaN     NaN         NaN   13.560000       28.467500\n",
       "max       NaN     NaN         NaN  208.190000       33.500000"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_districts.describe(include='all')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Interesting to notice the variation of rental prices across the city, what is expected for metropolis like São Paulo. The minimum mean rental price is __BRL 17.70/m<sup>2</sup>__ and the maximum __BRL 33.50/m<sup>2</sup>__, almost double. Mean at __25.78__ is balanced with the median at __25.15__. The range between __Q1 = 23.05__ and __Q3 = 28.47__ is not wide, but the relocator budget at __BRL 25.00/m<sup>2</sup>__ should not be an issue.\n",
    "\n",
    "There is also huge variation in districts land area, ranging from __2.19 km<sup>2</sup>__ to __208.19 km<sup>2</sup>__, what should be considered when looking for venues with __API Fousquare__, as the radius cannot be the same for all districts, to minimize overlaping and skiping areas.\n",
    "\n",
    "Check the mean prices frequencies with a histograms to have an idea on how is the Relocator Rental Budget positioned, _Matplotlib_ will be used."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# caculate bins edges\n",
    "cnt, bins = np.histogram(df_districts['mean_price_sqm'])\n",
    "\n",
    "# create figure object\n",
    "fig, ax = plt.subplots(figsize=(10,6))\n",
    "\n",
    "# add histogram to the figure\n",
    "ax.hist(df_districts['mean_price_sqm'], bins=bins)\n",
    "\n",
    "# define xticks values to match bins edges\n",
    "ax.set_xticks(bins)\n",
    "\n",
    "# get yticks (values on y-axis)\n",
    "ylocs, ylabels = plt.yticks()\n",
    "\n",
    "# add a vertical line for rental budget and annotate a text for it\n",
    "plt.axvline(x=budget_sqm, ymin=min(ylocs), ymax=max(ylocs), color='r')\n",
    "ax.annotate('Rental budget', xy=(budget_sqm*1.005, max(ylocs)*0.95), xycoords='data', color='r')\n",
    "\n",
    "# add a rectangle for the tolerance range and annotate a text for it\n",
    "rect = plt.Rectangle((budget_sqm_min, min(ylocs)), (budget_sqm_max-budget_sqm_min), (max(ylocs)-min(ylocs)), facecolor='r', alpha=0.1)\n",
    "ax.add_patch(rect)\n",
    "ax.annotate('Tolerance range', xy=(budget_sqm_max*1.005, max(ylocs)*0.85), xycoords='data', color='r')\n",
    "\n",
    "# add plot title and ylable\n",
    "ax.set_title('São Paulo - Mean Rental Prices (BRL/sqm)', {'fontsize':14})\n",
    "ax.set_xlabel('BRL/sqm', {'fontsize':13})\n",
    "ax.set_ylabel('frequencies', {'fontsize':13})\n",
    "\n",
    "# show the figure\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check the mean prices frequencies, but split between district, with the same bin setup for better comparison."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x864 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 576x396 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create a list of unique regions\n",
    "reglist = list(df_districts['region'].unique())\n",
    "\n",
    "# set the font size for axis ticks (values)\n",
    "plt.rc('xtick', labelsize=9)\n",
    "plt.rc('ytick', labelsize=9)\n",
    "\n",
    "# create figure object\n",
    "fig = plt.figure(figsize=(15,12))\n",
    "\n",
    "# run a loop through the regions list\n",
    "for i, r in enumerate(reglist):\n",
    "    # add plot for current region\n",
    "    ax = fig.add_subplot(3, 2, i+1)\n",
    "    ax.hist(df_districts[df_districts['region']==r]['mean_price_sqm'], bins=bins)\n",
    "\n",
    "    # define the xticks to match the bins edges of current region\n",
    "    ax.set_xticks(bins)\n",
    "    \n",
    "    # get yticks (values on y-axis)\n",
    "    ylocs, ylabels = plt.yticks()\n",
    "    \n",
    "    # add a vertical line for rental budget\n",
    "    plt.axvline(x=budget_sqm, ymin=min(ylocs), ymax=max(ylocs), color='r')\n",
    "    \n",
    "    # add a rectangle for the tolerance range\n",
    "    rect = plt.Rectangle((budget_sqm_min, min(ylocs)), (budget_sqm_max-budget_sqm_min), (max(ylocs)-min(ylocs)), facecolor='r', alpha=0.1)\n",
    "    ax.add_patch(rect)\n",
    "\n",
    "    # add plot title for the current region\n",
    "    ax.set_title(r, {'fontsize':12})\n",
    "\n",
    "# show the figure\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the regions histograms, it is visible that each region has a different rental prices range, with some overlaping in middle area, exception is _Centro_  region (downtown), which has higher rental prices starting from __BRL28.76/m<sup>2</sup>__. The high frequency between __22.44 and 24.02__, found in previous histogram, is concentrated in regions _Leste_ and _Norte_. Looking only at the rental prices, except from _Centro_, all the other regions are candidates for relocation."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 5 - Create a map to show the Districts of _Target Location_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import addional libraries.\n",
    "1. __Folium__: create maps\n",
    "1. __Geopy__: get geographical data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "import folium\n",
    "from geopy.geocoders import Nominatim"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To point the districts on the map, their coordinates are required. Get districts coodinates will be done with _geopy.geocoders.Nominatim_, for better accuracy, the search will be done with __district__ and __borough__ followed by __\"São Paulo, BR__\", if it is not found, __borough__ will be removed for a second search."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "96 coordinates acquired.\n"
     ]
    }
   ],
   "source": [
    "# define geolocator user agent\n",
    "geolc = Nominatim(user_agent=\"sp_search\")\n",
    "\n",
    "# create a temporary list\n",
    "coord = []\n",
    "\n",
    "# run a loop through the dataframe\n",
    "for b, d in zip(df_districts['borough'], df_districts['district']):\n",
    "    # set a variable to search location\n",
    "    vloc = d + ', ' + b + ', São Paulo, BR'\n",
    "\n",
    "    # send the geocode search\n",
    "    loct = geolc.geocode(vloc)\n",
    "    \n",
    "    # if it fails, try without borough\n",
    "    if loct == None:\n",
    "        vloc = d + ', São Paulo, BR'\n",
    "        loct = geolc.geocode(vloc)\n",
    "\n",
    "    # append results to the temporary list\n",
    "    if loct != None:\n",
    "        # append results into the list\n",
    "        coord.append([d, round(loct.latitude,4), round(loct.longitude,4)])\n",
    "\n",
    "# check results and print status\n",
    "if len(coord) > 0:\n",
    "    print('{} coordinates acquired.'.format(len(coord)))\n",
    "else:\n",
    "    print('Fail to acquire coordinates.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Store coordinates into __Coordinates Dataframe__ using _pandas.DataFrame_ method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>district</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bela Vista</td>\n",
       "      <td>-23.2970</td>\n",
       "      <td>-46.0267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Bom Retiro</td>\n",
       "      <td>-23.5271</td>\n",
       "      <td>-46.6368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Cambuci</td>\n",
       "      <td>-23.5661</td>\n",
       "      <td>-46.6137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Consolação</td>\n",
       "      <td>-23.5578</td>\n",
       "      <td>-46.6605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Liberdade</td>\n",
       "      <td>-23.5552</td>\n",
       "      <td>-46.6356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>Campo Grande</td>\n",
       "      <td>-23.6578</td>\n",
       "      <td>-46.6986</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>Santo Amaro</td>\n",
       "      <td>-23.6562</td>\n",
       "      <td>-46.7191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>Moema</td>\n",
       "      <td>-23.5827</td>\n",
       "      <td>-46.6490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>Saúde</td>\n",
       "      <td>-23.5973</td>\n",
       "      <td>-46.6359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>Vila Mariana</td>\n",
       "      <td>-23.5892</td>\n",
       "      <td>-46.6347</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>96 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        district  latitude  longitude\n",
       "0     Bela Vista  -23.2970   -46.0267\n",
       "1     Bom Retiro  -23.5271   -46.6368\n",
       "2        Cambuci  -23.5661   -46.6137\n",
       "3     Consolação  -23.5578   -46.6605\n",
       "4      Liberdade  -23.5552   -46.6356\n",
       "..           ...       ...        ...\n",
       "91  Campo Grande  -23.6578   -46.6986\n",
       "92   Santo Amaro  -23.6562   -46.7191\n",
       "93         Moema  -23.5827   -46.6490\n",
       "94         Saúde  -23.5973   -46.6359\n",
       "95  Vila Mariana  -23.5892   -46.6347\n",
       "\n",
       "[96 rows x 3 columns]"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# store coordinates into dataframe\n",
    "df_coord = pd.DataFrame(coord, columns=['district', 'latitude', 'longitude'])\n",
    "\n",
    "# print results\n",
    "df_coord"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Merge __Districts Dataframe__ with __Coordinates Dataframe__ using _pandas.merge_ method. The resulting dataframe will be __Districts Dataframe__ with coordinates."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>region</th>\n",
       "      <th>borough</th>\n",
       "      <th>district</th>\n",
       "      <th>area_sqkm</th>\n",
       "      <th>mean_price_sqm</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Centro</td>\n",
       "      <td>Sé</td>\n",
       "      <td>Bela Vista</td>\n",
       "      <td>2.72</td>\n",
       "      <td>30.60</td>\n",
       "      <td>-23.2970</td>\n",
       "      <td>-46.0267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Centro</td>\n",
       "      <td>Sé</td>\n",
       "      <td>Bom Retiro</td>\n",
       "      <td>4.21</td>\n",
       "      <td>29.10</td>\n",
       "      <td>-23.5271</td>\n",
       "      <td>-46.6368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Centro</td>\n",
       "      <td>Sé</td>\n",
       "      <td>Cambuci</td>\n",
       "      <td>3.92</td>\n",
       "      <td>29.93</td>\n",
       "      <td>-23.5661</td>\n",
       "      <td>-46.6137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Centro</td>\n",
       "      <td>Sé</td>\n",
       "      <td>Consolação</td>\n",
       "      <td>3.82</td>\n",
       "      <td>29.93</td>\n",
       "      <td>-23.5578</td>\n",
       "      <td>-46.6605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Centro</td>\n",
       "      <td>Sé</td>\n",
       "      <td>Liberdade</td>\n",
       "      <td>3.65</td>\n",
       "      <td>30.70</td>\n",
       "      <td>-23.5552</td>\n",
       "      <td>-46.6356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>Sul</td>\n",
       "      <td>Santo Amaro</td>\n",
       "      <td>Campo Grande</td>\n",
       "      <td>12.95</td>\n",
       "      <td>28.39</td>\n",
       "      <td>-23.6578</td>\n",
       "      <td>-46.6986</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>Sul</td>\n",
       "      <td>Santo Amaro</td>\n",
       "      <td>Santo Amaro</td>\n",
       "      <td>16.04</td>\n",
       "      <td>26.80</td>\n",
       "      <td>-23.6562</td>\n",
       "      <td>-46.7191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>Sul</td>\n",
       "      <td>Vila Mariana</td>\n",
       "      <td>Moema</td>\n",
       "      <td>9.08</td>\n",
       "      <td>32.60</td>\n",
       "      <td>-23.5827</td>\n",
       "      <td>-46.6490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>Sul</td>\n",
       "      <td>Vila Mariana</td>\n",
       "      <td>Saúde</td>\n",
       "      <td>9.31</td>\n",
       "      <td>28.20</td>\n",
       "      <td>-23.5973</td>\n",
       "      <td>-46.6359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>Sul</td>\n",
       "      <td>Vila Mariana</td>\n",
       "      <td>Vila Mariana</td>\n",
       "      <td>8.60</td>\n",
       "      <td>29.90</td>\n",
       "      <td>-23.5892</td>\n",
       "      <td>-46.6347</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>96 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    region       borough      district  area_sqkm  mean_price_sqm  latitude  \\\n",
       "0   Centro            Sé    Bela Vista       2.72           30.60  -23.2970   \n",
       "1   Centro            Sé    Bom Retiro       4.21           29.10  -23.5271   \n",
       "2   Centro            Sé       Cambuci       3.92           29.93  -23.5661   \n",
       "3   Centro            Sé    Consolação       3.82           29.93  -23.5578   \n",
       "4   Centro            Sé     Liberdade       3.65           30.70  -23.5552   \n",
       "..     ...           ...           ...        ...             ...       ...   \n",
       "91     Sul   Santo Amaro  Campo Grande      12.95           28.39  -23.6578   \n",
       "92     Sul   Santo Amaro   Santo Amaro      16.04           26.80  -23.6562   \n",
       "93     Sul  Vila Mariana         Moema       9.08           32.60  -23.5827   \n",
       "94     Sul  Vila Mariana         Saúde       9.31           28.20  -23.5973   \n",
       "95     Sul  Vila Mariana  Vila Mariana       8.60           29.90  -23.5892   \n",
       "\n",
       "    longitude  \n",
       "0    -46.0267  \n",
       "1    -46.6368  \n",
       "2    -46.6137  \n",
       "3    -46.6605  \n",
       "4    -46.6356  \n",
       "..        ...  \n",
       "91   -46.6986  \n",
       "92   -46.7191  \n",
       "93   -46.6490  \n",
       "94   -46.6359  \n",
       "95   -46.6347  \n",
       "\n",
       "[96 rows x 7 columns]"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# merge Districts and Coordinates dataframes\n",
    "df_districts = pd.merge(df_districts, df_coord, how='inner', on='district')\n",
    "\n",
    "# print results\n",
    "df_districts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define a __regions colors__ for better visualization on the map. Color definition will be done with _Matplotlib_ and then they will be attached to __Districts Dataframe__ using _pandas.merge_ method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>region</th>\n",
       "      <th>borough</th>\n",
       "      <th>district</th>\n",
       "      <th>area_sqkm</th>\n",
       "      <th>mean_price_sqm</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>region_color</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Centro</td>\n",
       "      <td>Sé</td>\n",
       "      <td>Bela Vista</td>\n",
       "      <td>2.72</td>\n",
       "      <td>30.60</td>\n",
       "      <td>-23.2970</td>\n",
       "      <td>-46.0267</td>\n",
       "      <td>#1f77b4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Centro</td>\n",
       "      <td>Sé</td>\n",
       "      <td>Bom Retiro</td>\n",
       "      <td>4.21</td>\n",
       "      <td>29.10</td>\n",
       "      <td>-23.5271</td>\n",
       "      <td>-46.6368</td>\n",
       "      <td>#1f77b4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Centro</td>\n",
       "      <td>Sé</td>\n",
       "      <td>Cambuci</td>\n",
       "      <td>3.92</td>\n",
       "      <td>29.93</td>\n",
       "      <td>-23.5661</td>\n",
       "      <td>-46.6137</td>\n",
       "      <td>#1f77b4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Centro</td>\n",
       "      <td>Sé</td>\n",
       "      <td>Consolação</td>\n",
       "      <td>3.82</td>\n",
       "      <td>29.93</td>\n",
       "      <td>-23.5578</td>\n",
       "      <td>-46.6605</td>\n",
       "      <td>#1f77b4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Centro</td>\n",
       "      <td>Sé</td>\n",
       "      <td>Liberdade</td>\n",
       "      <td>3.65</td>\n",
       "      <td>30.70</td>\n",
       "      <td>-23.5552</td>\n",
       "      <td>-46.6356</td>\n",
       "      <td>#1f77b4</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>Sul</td>\n",
       "      <td>Santo Amaro</td>\n",
       "      <td>Campo Grande</td>\n",
       "      <td>12.95</td>\n",
       "      <td>28.39</td>\n",
       "      <td>-23.6578</td>\n",
       "      <td>-46.6986</td>\n",
       "      <td>#17becf</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>Sul</td>\n",
       "      <td>Santo Amaro</td>\n",
       "      <td>Santo Amaro</td>\n",
       "      <td>16.04</td>\n",
       "      <td>26.80</td>\n",
       "      <td>-23.6562</td>\n",
       "      <td>-46.7191</td>\n",
       "      <td>#17becf</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>Sul</td>\n",
       "      <td>Vila Mariana</td>\n",
       "      <td>Moema</td>\n",
       "      <td>9.08</td>\n",
       "      <td>32.60</td>\n",
       "      <td>-23.5827</td>\n",
       "      <td>-46.6490</td>\n",
       "      <td>#17becf</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>Sul</td>\n",
       "      <td>Vila Mariana</td>\n",
       "      <td>Saúde</td>\n",
       "      <td>9.31</td>\n",
       "      <td>28.20</td>\n",
       "      <td>-23.5973</td>\n",
       "      <td>-46.6359</td>\n",
       "      <td>#17becf</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>Sul</td>\n",
       "      <td>Vila Mariana</td>\n",
       "      <td>Vila Mariana</td>\n",
       "      <td>8.60</td>\n",
       "      <td>29.90</td>\n",
       "      <td>-23.5892</td>\n",
       "      <td>-46.6347</td>\n",
       "      <td>#17becf</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>96 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    region       borough      district  area_sqkm  mean_price_sqm  latitude  \\\n",
       "0   Centro            Sé    Bela Vista       2.72           30.60  -23.2970   \n",
       "1   Centro            Sé    Bom Retiro       4.21           29.10  -23.5271   \n",
       "2   Centro            Sé       Cambuci       3.92           29.93  -23.5661   \n",
       "3   Centro            Sé    Consolação       3.82           29.93  -23.5578   \n",
       "4   Centro            Sé     Liberdade       3.65           30.70  -23.5552   \n",
       "..     ...           ...           ...        ...             ...       ...   \n",
       "91     Sul   Santo Amaro  Campo Grande      12.95           28.39  -23.6578   \n",
       "92     Sul   Santo Amaro   Santo Amaro      16.04           26.80  -23.6562   \n",
       "93     Sul  Vila Mariana         Moema       9.08           32.60  -23.5827   \n",
       "94     Sul  Vila Mariana         Saúde       9.31           28.20  -23.5973   \n",
       "95     Sul  Vila Mariana  Vila Mariana       8.60           29.90  -23.5892   \n",
       "\n",
       "    longitude region_color  \n",
       "0    -46.0267      #1f77b4  \n",
       "1    -46.6368      #1f77b4  \n",
       "2    -46.6137      #1f77b4  \n",
       "3    -46.6605      #1f77b4  \n",
       "4    -46.6356      #1f77b4  \n",
       "..        ...          ...  \n",
       "91   -46.6986      #17becf  \n",
       "92   -46.7191      #17becf  \n",
       "93   -46.6490      #17becf  \n",
       "94   -46.6359      #17becf  \n",
       "95   -46.6347      #17becf  \n",
       "\n",
       "[96 rows x 8 columns]"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create a list of unique boroughs\n",
    "reglist = df_districts['region'].unique()\n",
    "\n",
    "# create a color list by borough [RGBA format]\n",
    "trgb = plt.cm.tab10(np.linspace(0, 1, len(reglist)))\n",
    "\n",
    "# convert the color from RGBA to HEX\n",
    "thex = []\n",
    "for i in range(len(trgb)):\n",
    "    thex.append(mcolors.rgb2hex(trgb[i][:3]))\n",
    "\n",
    "# create a temporary list to assign colors to regions\n",
    "tmplist = list()\n",
    "for b, c in zip(reglist, thex):\n",
    "    tmplist.append([b,c])\n",
    "\n",
    "# create a dataframe from the temporary list\n",
    "colortable = pd.DataFrame(columns=['region','region_color'], data=tmplist)\n",
    "\n",
    "# merge Districts and color table data frames\n",
    "df_districts = pd.merge(df_districts, colortable, how='inner', on='region')\n",
    "\n",
    "# print results\n",
    "df_districts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a map of São Paulo pointing its Districts, it will be done _Folium_."
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "### Important notice:\n",
    "Unfortunately the Folium maps are not displayed in GitHub, they are interactive objects not supported, as an option to see the maps the [Jupyter nbviewer](https://nbviewer.org/) can be used. Once it is opened just need to paste the URL of the GitHub document.\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
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            popup_c0b68499597b43ffbcd7f9226935d7de.setContent(html_fefa42bf252e4123973451d4b28f3a61);
        

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            popup_5e08a881efa14d1480f4451b68b736bf.setContent(html_8519ecb0404a4595a393bd49a1cfec7e);
        

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            popup_a639aef66e8541179ef5cd81644e71dd.setContent(html_06e13816ddd44cf0814d652cb23c23b5);
        

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            popup_d3ee454c74db47e8a43f01415e1596fd.setContent(html_e0dc6aa369dd4152aaeee859a4a108ea);
        

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            ).addTo(map_78e6151711054753a538294762e4072d);
        
    
        var popup_6da3480ed3db4a239ceee1de19d6095d = L.popup({"maxWidth": "100%"});

        
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            popup_6da3480ed3db4a239ceee1de19d6095d.setContent(html_a266b1f7141845e9b80218313158a0db);
        

        circle_marker_caeafac6bc204673aa400069664a173d.bindPopup(popup_6da3480ed3db4a239ceee1de19d6095d)
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            var circle_marker_1e165a8a074342d39cdda22e4c326d90 = L.circleMarker(
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            var html_5f8b26d551534215a79a541da86d1068 = $(`<div id="html_5f8b26d551534215a79a541da86d1068" style="width: 100.0%; height: 100.0%;">Tatuapé (Leste)</div>`)[0];
            popup_49ec3e5a023a4e5e884e3a2810aca47b.setContent(html_5f8b26d551534215a79a541da86d1068);
        

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            var circle_marker_3c439543771540958d341d36fe9b5300 = L.circleMarker(
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            var html_df847e172d8649efb9b03fb59fc7d0d9 = $(`<div id="html_df847e172d8649efb9b03fb59fc7d0d9" style="width: 100.0%; height: 100.0%;">Artur Alvim (Leste)</div>`)[0];
            popup_76a99c72f3654d8dafaa89b91deb3f79.setContent(html_df847e172d8649efb9b03fb59fc7d0d9);
        

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            ).addTo(map_78e6151711054753a538294762e4072d);
        
    
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            var html_caadaa5cf86748cab72050338164ae47 = $(`<div id="html_caadaa5cf86748cab72050338164ae47" style="width: 100.0%; height: 100.0%;">Cangaíba (Leste)</div>`)[0];
            popup_19e9fbb1c1424163b9d7f6f2894eec25.setContent(html_caadaa5cf86748cab72050338164ae47);
        

        circle_marker_6d9f3fc2846b498091261c7d7d2acae5.bindPopup(popup_19e9fbb1c1424163b9d7f6f2894eec25)
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            var circle_marker_4da08a9f51cd4d3daaf5e48dc6c5db88 = L.circleMarker(
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            var html_da391d5cc07d4961a3441fe765380208 = $(`<div id="html_da391d5cc07d4961a3441fe765380208" style="width: 100.0%; height: 100.0%;">Penha (Leste)</div>`)[0];
            popup_4d7d74ea23214d9a8a12b01a8f597f50.setContent(html_da391d5cc07d4961a3441fe765380208);
        

        circle_marker_4da08a9f51cd4d3daaf5e48dc6c5db88.bindPopup(popup_4d7d74ea23214d9a8a12b01a8f597f50)
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            ).addTo(map_78e6151711054753a538294762e4072d);
        
    
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            var html_f8b8894c0413439a8643b8ffedbff2fc = $(`<div id="html_f8b8894c0413439a8643b8ffedbff2fc" style="width: 100.0%; height: 100.0%;">Vila Matilde (Leste)</div>`)[0];
            popup_ffc608069d8c48f8a5592e8aab2761f6.setContent(html_f8b8894c0413439a8643b8ffedbff2fc);
        

        circle_marker_206f68c7ceed44429953aaf739a381f5.bindPopup(popup_ffc608069d8c48f8a5592e8aab2761f6)
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            var circle_marker_522a4925792b4e7d9cc2d95735a8b44b = L.circleMarker(
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            ).addTo(map_78e6151711054753a538294762e4072d);
        
    
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            popup_6452e2e3169b42618a72e47aa73b5bed.setContent(html_0847ade0df3e475a8087a1728f200e89);
        

        circle_marker_522a4925792b4e7d9cc2d95735a8b44b.bindPopup(popup_6452e2e3169b42618a72e47aa73b5bed)
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            ).addTo(map_78e6151711054753a538294762e4072d);
        
    
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            popup_85c654fd4df74cce8203d79d567a8d7b.setContent(html_02c06451742643fb9c9f679e46bf0844);
        

        circle_marker_84aadfb87efd4f16b4d1db0e975e7f1f.bindPopup(popup_85c654fd4df74cce8203d79d567a8d7b)
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            var circle_marker_c8b45637c75b4690ae0e97b8287fc0a4 = L.circleMarker(
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            ).addTo(map_78e6151711054753a538294762e4072d);
        
    
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            var html_5d7323d2d5cc40438c915b9570a1a405 = $(`<div id="html_5d7323d2d5cc40438c915b9570a1a405" style="width: 100.0%; height: 100.0%;">São Rafael (Leste)</div>`)[0];
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        circle_marker_c8b45637c75b4690ae0e97b8287fc0a4.bindPopup(popup_020f55521b1f4716be502f0cb305a7fd)
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        circle_marker_2a63270eca084e1197dd278c189f65e4.bindPopup(popup_2c1483e1c95e4a4b8d7db82ec91e460d)
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            popup_a7d9981e0aaa40ab90bd39d8f756f7ca.setContent(html_f297521e2c744b65a2151462879cb8c9);
        

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            popup_f780fa3f17d941968a59813960cdc4ef.setContent(html_f72e0c4859ee4aac9297a94c14fd8dcd);
        

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            popup_2d2505cb75bd429f834ea8fa44d03b2a.setContent(html_ce1832b07b9b4bf0b0420be7476a45b8);
        

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        circle_marker_57c69f89532141e4a5d117e79454ab08.bindPopup(popup_cb395b416a844109883d733cb81f4236)
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            popup_63fea5be846340be96f2ad774bb2eb64.setContent(html_e588d5e9c0bb47b2a3ffcb6d13566a24);
        

        circle_marker_93342568c55c408c80c1ace75c3b6dbb.bindPopup(popup_63fea5be846340be96f2ad774bb2eb64)
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            var html_7ad55e55b81342aaa7e01928af03fe6a = $(`<div id="html_7ad55e55b81342aaa7e01928af03fe6a" style="width: 100.0%; height: 100.0%;">Jaraguá (Norte)</div>`)[0];
            popup_057f99af00034cfc92017aa860e50ad8.setContent(html_7ad55e55b81342aaa7e01928af03fe6a);
        

        circle_marker_a4b7a4888f2843d5b31bf0a5bce6d4f4.bindPopup(popup_057f99af00034cfc92017aa860e50ad8)
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            ).addTo(map_78e6151711054753a538294762e4072d);
        
    
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            var html_ce40b1d66f8549e58c9ea00227bda5d6 = $(`<div id="html_ce40b1d66f8549e58c9ea00227bda5d6" style="width: 100.0%; height: 100.0%;">Pirituba (Norte)</div>`)[0];
            popup_5cd97bad13fe4fbca232513bb240be94.setContent(html_ce40b1d66f8549e58c9ea00227bda5d6);
        

        circle_marker_45954763947e4ce38dcd31e71f2f7d6c.bindPopup(popup_5cd97bad13fe4fbca232513bb240be94)
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            var circle_marker_e3e5eb6eb91548598ff76af9585554aa = L.circleMarker(
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            ).addTo(map_78e6151711054753a538294762e4072d);
        
    
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            var html_bc191300c20c4a3abcc877f01f81e22c = $(`<div id="html_bc191300c20c4a3abcc877f01f81e22c" style="width: 100.0%; height: 100.0%;">São Domingos (Norte)</div>`)[0];
            popup_807287221d734449938d67c0bacb7482.setContent(html_bc191300c20c4a3abcc877f01f81e22c);
        

        circle_marker_e3e5eb6eb91548598ff76af9585554aa.bindPopup(popup_807287221d734449938d67c0bacb7482)
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            var circle_marker_5fc232d1be224503a6644c677b848ecc = L.circleMarker(
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            ).addTo(map_78e6151711054753a538294762e4072d);
        
    
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            var html_4f9e9eb6f5a346ec9f557e9e82054c26 = $(`<div id="html_4f9e9eb6f5a346ec9f557e9e82054c26" style="width: 100.0%; height: 100.0%;">Mandaqui (Norte)</div>`)[0];
            popup_a0e6be797874473e9bad96adab9c416b.setContent(html_4f9e9eb6f5a346ec9f557e9e82054c26);
        

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            var circle_marker_563b86f7362447e9931848095b82ae70 = L.circleMarker(
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            var html_4a8f38ce6c7d4583be9ce3d2c9f3b4d2 = $(`<div id="html_4a8f38ce6c7d4583be9ce3d2c9f3b4d2" style="width: 100.0%; height: 100.0%;">Santana (Norte)</div>`)[0];
            popup_4f76070704764bc3858b6613b4a232c6.setContent(html_4a8f38ce6c7d4583be9ce3d2c9f3b4d2);
        

        circle_marker_563b86f7362447e9931848095b82ae70.bindPopup(popup_4f76070704764bc3858b6613b4a232c6)
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            var circle_marker_7dc8ef801b5a43c9b5765195c73ad230 = L.circleMarker(
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            popup_5c24a5fe64ba4bf0b3f4eaaa716e9574.setContent(html_95b0871a77ed467db5984b27f91364ad);
        

        circle_marker_7dc8ef801b5a43c9b5765195c73ad230.bindPopup(popup_5c24a5fe64ba4bf0b3f4eaaa716e9574)
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            popup_faa655225bb741f999c9a85cc2c473da.setContent(html_488b59da87254911a838cc98b07decd3);
        

        circle_marker_be9d5bc5aba7451a917db408b8e9a08a.bindPopup(popup_faa655225bb741f999c9a85cc2c473da)
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        circle_marker_1e830aa5fdfc449ebd3a3ee476c7be03.bindPopup(popup_f6b5aaea13b348188fa5e3901b1fedba)
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            popup_ce16fabe482248f9b7c2dc967e2d940e.setContent(html_e5bc17fc33a349a79ca4c873a8aab0a9);
        

        circle_marker_d5d274346c8b4d6f9682c811e01ded1e.bindPopup(popup_ce16fabe482248f9b7c2dc967e2d940e)
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            popup_9fb3f11bb6374a64b24a43e4cb0367ba.setContent(html_f42399fad3284069bf19d4ea6d65958e);
        

        circle_marker_16edb0b855a74813b08d38adfd650678.bindPopup(popup_9fb3f11bb6374a64b24a43e4cb0367ba)
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            var circle_marker_312a11357b4c44b88b0b1fa6359053f9 = L.circleMarker(
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            popup_98646c746b4e4034af08970186427cf9.setContent(html_f271a8f6cdaf43aca916f38b0699cb21);
        

        circle_marker_312a11357b4c44b88b0b1fa6359053f9.bindPopup(popup_98646c746b4e4034af08970186427cf9)
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            var circle_marker_8f504e85adbd414e9829b8a9c90e2493 = L.circleMarker(
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            popup_26e7f29dd05e41dc9f9b1a8274b8cc66.setContent(html_bd2becaf0b7146d3a3180d861aface24);
        

        circle_marker_8f504e85adbd414e9829b8a9c90e2493.bindPopup(popup_26e7f29dd05e41dc9f9b1a8274b8cc66)
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                {"bubblingMouseEvents": true, "color": "#7f7f7f", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#7f7f7f", "fillOpacity": 0.3, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
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            popup_beffb56e587d4059b788df90d13b6d92.setContent(html_f365db495e31478f88fcc62b455e1aec);
        

        circle_marker_64e5c0b1d1b44ae7a1cf1ef1a92c7a96.bindPopup(popup_beffb56e587d4059b788df90d13b6d92)
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            var circle_marker_70eece6458d447128aafe40bcdfdf610 = L.circleMarker(
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                {"bubblingMouseEvents": true, "color": "#7f7f7f", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#7f7f7f", "fillOpacity": 0.3, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_78e6151711054753a538294762e4072d);
        
    
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            var html_78d146e66f1849d8883a019114747332 = $(`<div id="html_78d146e66f1849d8883a019114747332" style="width: 100.0%; height: 100.0%;">Vila Sônia (Oeste)</div>`)[0];
            popup_2ca2413dadb54008abd220c5dbe0a9f1.setContent(html_78d146e66f1849d8883a019114747332);
        

        circle_marker_70eece6458d447128aafe40bcdfdf610.bindPopup(popup_2ca2413dadb54008abd220c5dbe0a9f1)
        ;

        
    
    
            var circle_marker_d2997f3c91b0454b8f6d53640a13f559 = L.circleMarker(
                [-23.5255, -46.6675],
                {"bubblingMouseEvents": true, "color": "#7f7f7f", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#7f7f7f", "fillOpacity": 0.3, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_78e6151711054753a538294762e4072d);
        
    
        var popup_bb2fd52aab9e4f02bef95daa55b7aece = L.popup({"maxWidth": "100%"});

        
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            popup_bb2fd52aab9e4f02bef95daa55b7aece.setContent(html_0bbd1532bca247dc9eec812e2a28d9cc);
        

        circle_marker_d2997f3c91b0454b8f6d53640a13f559.bindPopup(popup_bb2fd52aab9e4f02bef95daa55b7aece)
        ;

        
    
    
            var circle_marker_bb5a6891de3642b7baa3a165fae6dbfb = L.circleMarker(
                [-23.5074, -46.7553],
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            ).addTo(map_78e6151711054753a538294762e4072d);
        
    
        var popup_a0b1294a165943dba1c2bbd600bbf7df = L.popup({"maxWidth": "100%"});

        
            var html_74600f3702124457916146bdf5fe2dce = $(`<div id="html_74600f3702124457916146bdf5fe2dce" style="width: 100.0%; height: 100.0%;">Jaguara (Oeste)</div>`)[0];
            popup_a0b1294a165943dba1c2bbd600bbf7df.setContent(html_74600f3702124457916146bdf5fe2dce);
        

        circle_marker_bb5a6891de3642b7baa3a165fae6dbfb.bindPopup(popup_a0b1294a165943dba1c2bbd600bbf7df)
        ;

        
    
    
            var circle_marker_196d3588bb43426d82bc8942864d68d2 = L.circleMarker(
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                {"bubblingMouseEvents": true, "color": "#7f7f7f", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#7f7f7f", "fillOpacity": 0.3, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_78e6151711054753a538294762e4072d);
        
    
        var popup_450b7f5531f54a91b42fde36f9a62a06 = L.popup({"maxWidth": "100%"});

        
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            popup_450b7f5531f54a91b42fde36f9a62a06.setContent(html_5c17e3d77f0a42cf844bb6e1e6cd255f);
        

        circle_marker_196d3588bb43426d82bc8942864d68d2.bindPopup(popup_450b7f5531f54a91b42fde36f9a62a06)
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            var circle_marker_77ddffff66744289943b51e6c82229ba = L.circleMarker(
                [-23.5146, -46.7243],
                {"bubblingMouseEvents": true, "color": "#7f7f7f", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#7f7f7f", "fillOpacity": 0.3, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_78e6151711054753a538294762e4072d);
        
    
        var popup_019175d78535435581a47a565df7175b = L.popup({"maxWidth": "100%"});

        
            var html_9d10ec0778014108bddcba87f8f76cf1 = $(`<div id="html_9d10ec0778014108bddcba87f8f76cf1" style="width: 100.0%; height: 100.0%;">Lapa (Oeste)</div>`)[0];
            popup_019175d78535435581a47a565df7175b.setContent(html_9d10ec0778014108bddcba87f8f76cf1);
        

        circle_marker_77ddffff66744289943b51e6c82229ba.bindPopup(popup_019175d78535435581a47a565df7175b)
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            var circle_marker_7882c7fef21242adbea8fc7bd637bd17 = L.circleMarker(
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            ).addTo(map_78e6151711054753a538294762e4072d);
        
    
        var popup_1d9af12528c54f578e958d1eb5f93afa = L.popup({"maxWidth": "100%"});

        
            var html_3fb462c0f5504e4cbe5b778147d9e422 = $(`<div id="html_3fb462c0f5504e4cbe5b778147d9e422" style="width: 100.0%; height: 100.0%;">Perdizes (Oeste)</div>`)[0];
            popup_1d9af12528c54f578e958d1eb5f93afa.setContent(html_3fb462c0f5504e4cbe5b778147d9e422);
        

        circle_marker_7882c7fef21242adbea8fc7bd637bd17.bindPopup(popup_1d9af12528c54f578e958d1eb5f93afa)
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            var circle_marker_243e9535cc974678a84157148923741c = L.circleMarker(
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                {"bubblingMouseEvents": true, "color": "#7f7f7f", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#7f7f7f", "fillOpacity": 0.3, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_78e6151711054753a538294762e4072d);
        
    
        var popup_616088456fec425588b17f89ef72d963 = L.popup({"maxWidth": "100%"});

        
            var html_f805cf7029cc41d89babb00133403417 = $(`<div id="html_f805cf7029cc41d89babb00133403417" style="width: 100.0%; height: 100.0%;">Vila Leopoldina (Oeste)</div>`)[0];
            popup_616088456fec425588b17f89ef72d963.setContent(html_f805cf7029cc41d89babb00133403417);
        

        circle_marker_243e9535cc974678a84157148923741c.bindPopup(popup_616088456fec425588b17f89ef72d963)
        ;

        
    
    
            var circle_marker_d8657a53232d4751bcb765c2ff9b1f1b = L.circleMarker(
                [-23.5499, -46.7076],
                {"bubblingMouseEvents": true, "color": "#7f7f7f", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#7f7f7f", "fillOpacity": 0.3, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_78e6151711054753a538294762e4072d);
        
    
        var popup_bbc285643d214acda63d49db8926f94f = L.popup({"maxWidth": "100%"});

        
            var html_d441c7b3a57b4a359c037cc5d1dbe591 = $(`<div id="html_d441c7b3a57b4a359c037cc5d1dbe591" style="width: 100.0%; height: 100.0%;">Alto de Pinheiros (Oeste)</div>`)[0];
            popup_bbc285643d214acda63d49db8926f94f.setContent(html_d441c7b3a57b4a359c037cc5d1dbe591);
        

        circle_marker_d8657a53232d4751bcb765c2ff9b1f1b.bindPopup(popup_bbc285643d214acda63d49db8926f94f)
        ;

        
    
    
            var circle_marker_0b4f49c1fd5b432291f4c790726a87a8 = L.circleMarker(
                [-23.5844, -46.6784],
                {"bubblingMouseEvents": true, "color": "#7f7f7f", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#7f7f7f", "fillOpacity": 0.3, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_78e6151711054753a538294762e4072d);
        
    
        var popup_c94668f167204555bfd5a83f06b50ccd = L.popup({"maxWidth": "100%"});

        
            var html_140e509a718b47acaebd76ada58d2e1f = $(`<div id="html_140e509a718b47acaebd76ada58d2e1f" style="width: 100.0%; height: 100.0%;">Itaim Bibi (Oeste)</div>`)[0];
            popup_c94668f167204555bfd5a83f06b50ccd.setContent(html_140e509a718b47acaebd76ada58d2e1f);
        

        circle_marker_0b4f49c1fd5b432291f4c790726a87a8.bindPopup(popup_c94668f167204555bfd5a83f06b50ccd)
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            var circle_marker_8f90ad62da1643ff9574066b90710ac2 = L.circleMarker(
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                {"bubblingMouseEvents": true, "color": "#7f7f7f", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#7f7f7f", "fillOpacity": 0.3, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_78e6151711054753a538294762e4072d);
        
    
        var popup_8a1c91d4c28542228148e9a61fe009a4 = L.popup({"maxWidth": "100%"});

        
            var html_39708638702744759f007c1be1b6aeb9 = $(`<div id="html_39708638702744759f007c1be1b6aeb9" style="width: 100.0%; height: 100.0%;">Jardim Paulista (Oeste)</div>`)[0];
            popup_8a1c91d4c28542228148e9a61fe009a4.setContent(html_39708638702744759f007c1be1b6aeb9);
        

        circle_marker_8f90ad62da1643ff9574066b90710ac2.bindPopup(popup_8a1c91d4c28542228148e9a61fe009a4)
        ;

        
    
    
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            popup_3d40854907fe44f9863e614bf8f775d5.setContent(html_d2da32a962ec4e5e93490871e4075681);
        

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            var html_6cdada6d075040ac819e995175babe91 = $(`<div id="html_6cdada6d075040ac819e995175babe91" style="width: 100.0%; height: 100.0%;">Sacomã (Sul)</div>`)[0];
            popup_7927e86026894ac382e05e7e24c648eb.setContent(html_6cdada6d075040ac819e995175babe91);
        

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            popup_32fe65470cae4c7f85c8b0e83608d1e1.setContent(html_223527afbea141c69fa9e826af89df6f);
        

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            var html_68be8cb5714243f88f900160d5dbb23c = $(`<div id="html_68be8cb5714243f88f900160d5dbb23c" style="width: 100.0%; height: 100.0%;">Jardim Ângela (Sul)</div>`)[0];
            popup_855d76f5fe1846e7af405b8cdc062ae2.setContent(html_68be8cb5714243f88f900160d5dbb23c);
        

        circle_marker_205d4423c10f493fbb8d2c7e3e7d15c9.bindPopup(popup_855d76f5fe1846e7af405b8cdc062ae2)
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            var circle_marker_9cd9450f8d2c4413be1a45daccd4af8c = L.circleMarker(
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            var html_e53c2bac0e2844c4b437a75b5c0a98c9 = $(`<div id="html_e53c2bac0e2844c4b437a75b5c0a98c9" style="width: 100.0%; height: 100.0%;">Jardim São Luís (Sul)</div>`)[0];
            popup_ac02d7d319a7466a8b3669d91802276d.setContent(html_e53c2bac0e2844c4b437a75b5c0a98c9);
        

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            var circle_marker_85c329b0fee04dba987ee2f36085c5a2 = L.circleMarker(
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            ).addTo(map_78e6151711054753a538294762e4072d);
        
    
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            popup_e11bdff7275d49f7b764bf199fadfe81.setContent(html_d75dbfc90ea34ac898d17cf3248b5da1);
        

        circle_marker_85c329b0fee04dba987ee2f36085c5a2.bindPopup(popup_e11bdff7275d49f7b764bf199fadfe81)
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            var circle_marker_46ab47b73cad431297efb5a1aeafa4ef = L.circleMarker(
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            ).addTo(map_78e6151711054753a538294762e4072d);
        
    
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            popup_92a40ff8dbc54f5fb93a0129bb642e68.setContent(html_472dbbdebc9640b68f14e085901d760e);
        

        circle_marker_46ab47b73cad431297efb5a1aeafa4ef.bindPopup(popup_92a40ff8dbc54f5fb93a0129bb642e68)
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            var circle_marker_6656f2883c87407880397e7b0a71f6f3 = L.circleMarker(
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            ).addTo(map_78e6151711054753a538294762e4072d);
        
    
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            var html_851d052572c04b06ba5e8a3339e5efbb = $(`<div id="html_851d052572c04b06ba5e8a3339e5efbb" style="width: 100.0%; height: 100.0%;">Campo Belo (Sul)</div>`)[0];
            popup_5a52e66e40d24727ace4e113246c6de0.setContent(html_851d052572c04b06ba5e8a3339e5efbb);
        

        circle_marker_6656f2883c87407880397e7b0a71f6f3.bindPopup(popup_5a52e66e40d24727ace4e113246c6de0)
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            ).addTo(map_78e6151711054753a538294762e4072d);
        
    
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            var html_bba4fdee9c2449b995f01625e4524c60 = $(`<div id="html_bba4fdee9c2449b995f01625e4524c60" style="width: 100.0%; height: 100.0%;">Campo Grande (Sul)</div>`)[0];
            popup_56815e4770854bfb8d3fe45f576a5953.setContent(html_bba4fdee9c2449b995f01625e4524c60);
        

        circle_marker_8ade66a53bae4f6f8a2bbe18acfb6408.bindPopup(popup_56815e4770854bfb8d3fe45f576a5953)
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</script> onload=\"this.contentDocument.open();this.contentDocument.write(atob(this.getAttribute('data-html')));this.contentDocument.close();\" allowfullscreen webkitallowfullscreen mozallowfullscreen></iframe></div></div>"
      ],
      "text/plain": [
       "<folium.folium.Map at 0x11c7e3080>"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get São Paulo coordenates to start the map\n",
    "loct = geolc.geocode('São Paulo, BR')\n",
    "latsp = round(loct.latitude, 4)\n",
    "lngsp = round(loct.longitude, 4)\n",
    "\n",
    "# create the map\n",
    "sp_map = folium.Map(location=[latsp, lngsp], width='70%', height='70%',\n",
    "                    min_zoom=9, max_zoom=12, zoom_start=10, control_scale=True)\n",
    "\n",
    "# run a loop through the dataframe creating the points (circle marks)\n",
    "for lat, lng, reg, dst, clr in zip(df_districts['latitude'], df_districts['longitude'], df_districts['region'],\n",
    "                                   df_districts['district'], df_districts['region_color']):\n",
    "    # create marker label\n",
    "    label = '{} ({})'.format(dst, reg)\n",
    "    label = folium.Popup(label, parse_html=True)\n",
    "    \n",
    "    # create the circle mark\n",
    "    folium.CircleMarker([lat, lng],\n",
    "                        popup = label, \n",
    "                        radius = 5,\n",
    "                        color = clr,\n",
    "                        fill = True,\n",
    "                        fill_color = clr,\n",
    "                        fill_opacity = 0.3,\n",
    "                        parse_html = False,\n",
    "                        no_touch=True).add_to(sp_map)\n",
    "\n",
    "# show the map\n",
    "sp_map"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 6 - Get Districts Venues Information"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import addional libraries.\n",
    "1. __Credentials__: API credentials, local user defined package\n",
    "1. __Json__: manipulate files in _json_ format\n",
    "1. __Math__: mathematical formulas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "import credentials\n",
    "import json\n",
    "import math"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Search for districts venues. The __API Foursquare__ will be used to search venues based on the districts coordinates, for each district a request will be sent to the API using _requests.get_ method and responses will be interpreted with _json_ method.\n",
    "\n",
    "Get __API Foursquare credentials__. The connection to __API Foursquare__ requires private user credentials, they have previouly been requested and stored into the local user defined package _Credentials_."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get the credentials, token will not be used\n",
    "client_id, client_secret, access_token = credentials.get_foursquare()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Will also be defined the __API version__ (required), __limit of venues__ to be returned and the __search radius__ around the location.\n",
    "\n",
    "API version and limit of venues will be fixed, radius will be set according to each district area (km<sup>2</sup>), limited to 2000 meters, as following: $$r=\\frac{\\sqrt{area}}{2}\\times 1000 = \\sqrt{area}\\times 500$$\n",
    "\n",
    "The search will be limited to the Relocator __Priority List__, the _dictionary indexes_ will be converted into a single string, having each of the _indexes_ separated by comma and without blank spaces to be used as input for the API request."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3650 venues returned.\n"
     ]
    }
   ],
   "source": [
    "# define API version and limit\n",
    "api_version = '20180605'\n",
    "limit = 50\n",
    "\n",
    "# convert priority list to a single string\n",
    "priocateg = str(list(prio_list.keys())).strip('[]').replace('\\'','').replace(' ','')\n",
    "\n",
    "# create an empty list to store venues information\n",
    "venueslist = []\n",
    "\n",
    "# run a loop through the dataframe\n",
    "for d, lat, lng, a in zip(df_districts['district'], df_districts['latitude'], df_districts['longitude'], df_districts['area_sqkm']):\n",
    "    # define radius for current district\n",
    "    radius = int(math.sqrt(a)*500) if round(math.sqrt(a)*500,0)<=2000 else 2000\n",
    "    \n",
    "    # create the API Foursquare request URL\n",
    "    url='https://api.foursquare.com/v2/venues/search?&client_id={}&client_secret={}&v={}&ll={},{}&radius={}&limit={}&categoryId={}'.\\\n",
    "    format(client_id, client_secret, api_version, lat, lng, radius, limit, priocateg)\n",
    "    \n",
    "    # make the GET request and store in JSON format\n",
    "    vresponse = requests.get(url).json()['response']['venues']\n",
    "\n",
    "    # add to temporary list only relevant information for each venue\n",
    "    for v in vresponse:\n",
    "        venueslist.append([d, v['name'], v['categories'][0]['name']])\n",
    "\n",
    "# print the quantity of venues returned\n",
    "print('{} venues returned.'.format(len(venueslist)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create __Venues Dataframe__ to store the response from __API Foursquare__, using _pandas.DataFrame_."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>district</th>\n",
       "      <th>venue_name</th>\n",
       "      <th>venue_category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bom Retiro</td>\n",
       "      <td>Extra Supermercado</td>\n",
       "      <td>Supermarket</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Bom Retiro</td>\n",
       "      <td>Estação Tiradentes (Metrô)</td>\n",
       "      <td>Metro Station</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Bom Retiro</td>\n",
       "      <td>Estação Luz (Metrô)</td>\n",
       "      <td>Metro Station</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Bom Retiro</td>\n",
       "      <td>Estação Santa Cecília (Metrô)</td>\n",
       "      <td>Metro Station</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Bom Retiro</td>\n",
       "      <td>Estação Armênia (Metrô)</td>\n",
       "      <td>Metro Station</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3645</th>\n",
       "      <td>Vila Mariana</td>\n",
       "      <td>Droga Raia</td>\n",
       "      <td>Pharmacy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3646</th>\n",
       "      <td>Vila Mariana</td>\n",
       "      <td>Praça Kant</td>\n",
       "      <td>Park</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3647</th>\n",
       "      <td>Vila Mariana</td>\n",
       "      <td>Master Supermercados</td>\n",
       "      <td>Supermarket</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3648</th>\n",
       "      <td>Vila Mariana</td>\n",
       "      <td>Colégio Cristo Rei</td>\n",
       "      <td>School</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3649</th>\n",
       "      <td>Vila Mariana</td>\n",
       "      <td>Med Tec Cirúrgica</td>\n",
       "      <td>Miscellaneous Shop</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3650 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          district                     venue_name      venue_category\n",
       "0       Bom Retiro             Extra Supermercado         Supermarket\n",
       "1       Bom Retiro     Estação Tiradentes (Metrô)       Metro Station\n",
       "2       Bom Retiro            Estação Luz (Metrô)       Metro Station\n",
       "3       Bom Retiro  Estação Santa Cecília (Metrô)       Metro Station\n",
       "4       Bom Retiro        Estação Armênia (Metrô)       Metro Station\n",
       "...            ...                            ...                 ...\n",
       "3645  Vila Mariana                     Droga Raia            Pharmacy\n",
       "3646  Vila Mariana                     Praça Kant                Park\n",
       "3647  Vila Mariana           Master Supermercados         Supermarket\n",
       "3648  Vila Mariana             Colégio Cristo Rei              School\n",
       "3649  Vila Mariana              Med Tec Cirúrgica  Miscellaneous Shop\n",
       "\n",
       "[3650 rows x 3 columns]"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create Venues Dataframe\n",
    "df_venues = pd.DataFrame(data=venueslist, columns=['district',\n",
    "                                                   'venue_name',\n",
    "                                                   'venue_category'])\n",
    "\n",
    "# print results\n",
    "df_venues"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check how many unique __categories__ have been returned, using _pandas.DataFrame.unique_ method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "43 unique categories returned.\n"
     ]
    }
   ],
   "source": [
    "# print quantity of unique categories\n",
    "print('{} unique categories returned.'.format(len(df_venues['venue_category'].unique())))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When specifying categories to __API Foursquare__, all the sub-categories are returned in response. There are more categories than the five in __Priority List__. Remove venues of not required categories, using _pandas.DataFrame.isin_ method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "175 venues removed.\n"
     ]
    }
   ],
   "source": [
    "# count observations before removing\n",
    "vlen = df_venues.shape[0]\n",
    "\n",
    "# remove venues of not required categories\n",
    "df_venues = df_venues[df_venues['venue_category'].isin(list(prio_list.values()))]\n",
    "\n",
    "# print quantity of removed observations\n",
    "print('{} venues removed.'.format(vlen-df_venues.shape[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create __Venues Count Dataframe__ with the venues quantity for each district, using _pandas.pivot_table_ method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>venue_category</th>\n",
       "      <th>Elementary School</th>\n",
       "      <th>Metro Station</th>\n",
       "      <th>Park</th>\n",
       "      <th>Pharmacy</th>\n",
       "      <th>Supermarket</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>district</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Alto de Pinheiros</th>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Anhanguera</th>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Aricanduva</th>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Artur Alvim</th>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Barra Funda</th>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vila Matilde</th>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vila Medeiros</th>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vila Prudente</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vila Sônia</th>\n",
       "      <td>7.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Água Rasa</th>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>94 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "venue_category     Elementary School  Metro Station  Park  Pharmacy  \\\n",
       "district                                                              \n",
       "Alto de Pinheiros                2.0            1.0  13.0      24.0   \n",
       "Anhanguera                       3.0            NaN   3.0       NaN   \n",
       "Aricanduva                       6.0            NaN   NaN      12.0   \n",
       "Artur Alvim                      4.0            4.0   NaN      25.0   \n",
       "Barra Funda                      4.0            3.0   2.0      22.0   \n",
       "...                              ...            ...   ...       ...   \n",
       "Vila Matilde                     6.0            2.0   3.0      25.0   \n",
       "Vila Medeiros                    6.0            NaN   1.0      29.0   \n",
       "Vila Prudente                    5.0            3.0   7.0      23.0   \n",
       "Vila Sônia                       7.0            NaN   4.0      29.0   \n",
       "Água Rasa                        4.0            1.0   3.0      30.0   \n",
       "\n",
       "venue_category     Supermarket  \n",
       "district                        \n",
       "Alto de Pinheiros          4.0  \n",
       "Anhanguera                 1.0  \n",
       "Aricanduva                 3.0  \n",
       "Artur Alvim                3.0  \n",
       "Barra Funda               12.0  \n",
       "...                        ...  \n",
       "Vila Matilde              12.0  \n",
       "Vila Medeiros              2.0  \n",
       "Vila Prudente              8.0  \n",
       "Vila Sônia                 9.0  \n",
       "Água Rasa                  9.0  \n",
       "\n",
       "[94 rows x 5 columns]"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create Venues Count dataframe\n",
    "df_venues_count = pd.pivot_table(df_venues, values='venue_name', index='district', columns='venue_category', aggfunc='count')\n",
    "\n",
    "# print results\n",
    "df_venues_count"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Change the columns names removing capitals and blank spaces, using _pandas.DataFrame,rename_ method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>venue_category</th>\n",
       "      <th>elementary_school</th>\n",
       "      <th>metro_station</th>\n",
       "      <th>park</th>\n",
       "      <th>pharmacy</th>\n",
       "      <th>supermarket</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>district</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Alto de Pinheiros</th>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Anhanguera</th>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Aricanduva</th>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Artur Alvim</th>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Barra Funda</th>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vila Matilde</th>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vila Medeiros</th>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vila Prudente</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vila Sônia</th>\n",
       "      <td>7.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Água Rasa</th>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>94 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "venue_category     elementary_school  metro_station  park  pharmacy  \\\n",
       "district                                                              \n",
       "Alto de Pinheiros                2.0            1.0  13.0      24.0   \n",
       "Anhanguera                       3.0            NaN   3.0       NaN   \n",
       "Aricanduva                       6.0            NaN   NaN      12.0   \n",
       "Artur Alvim                      4.0            4.0   NaN      25.0   \n",
       "Barra Funda                      4.0            3.0   2.0      22.0   \n",
       "...                              ...            ...   ...       ...   \n",
       "Vila Matilde                     6.0            2.0   3.0      25.0   \n",
       "Vila Medeiros                    6.0            NaN   1.0      29.0   \n",
       "Vila Prudente                    5.0            3.0   7.0      23.0   \n",
       "Vila Sônia                       7.0            NaN   4.0      29.0   \n",
       "Água Rasa                        4.0            1.0   3.0      30.0   \n",
       "\n",
       "venue_category     supermarket  \n",
       "district                        \n",
       "Alto de Pinheiros          4.0  \n",
       "Anhanguera                 1.0  \n",
       "Aricanduva                 3.0  \n",
       "Artur Alvim                3.0  \n",
       "Barra Funda               12.0  \n",
       "...                        ...  \n",
       "Vila Matilde              12.0  \n",
       "Vila Medeiros              2.0  \n",
       "Vila Prudente              8.0  \n",
       "Vila Sônia                 9.0  \n",
       "Água Rasa                  9.0  \n",
       "\n",
       "[94 rows x 5 columns]"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# replace upper case by lower case and remove blank spaces\n",
    "df_venues_count.rename(columns={'Elementary School': 'elementary_school',\n",
    "                                'Metro Station': 'metro_station',\n",
    "                                'Park': 'park',\n",
    "                                'Pharmacy': 'pharmacy',\n",
    "                                'Supermarket': 'supermarket'}, inplace=True)\n",
    "\n",
    "# print results\n",
    "df_venues_count"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Not all of the 96 districts have venues categories in __Priority List__. Merge the __Districts Dataframe__ with __Venues Count Dataframe__ to have all the 96 districts available for ranking, _pandas.merge_ will be used."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>district</th>\n",
       "      <th>elementary_school</th>\n",
       "      <th>metro_station</th>\n",
       "      <th>park</th>\n",
       "      <th>pharmacy</th>\n",
       "      <th>supermarket</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bela Vista</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Bom Retiro</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Cambuci</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Consolação</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Liberdade</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>Campo Grande</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>Santo Amaro</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>Moema</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>Saúde</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>Vila Mariana</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>96 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        district  elementary_school  metro_station  park  pharmacy  \\\n",
       "0     Bela Vista                NaN            NaN   NaN       NaN   \n",
       "1     Bom Retiro                2.0            5.0   4.0      19.0   \n",
       "2        Cambuci                3.0            NaN   1.0      21.0   \n",
       "3     Consolação                NaN            4.0   1.0      28.0   \n",
       "4      Liberdade                NaN            5.0   1.0      29.0   \n",
       "..           ...                ...            ...   ...       ...   \n",
       "91  Campo Grande                3.0            4.0   5.0      25.0   \n",
       "92   Santo Amaro                2.0            4.0   5.0      28.0   \n",
       "93         Moema                NaN            4.0   3.0      28.0   \n",
       "94         Saúde                NaN            7.0   2.0      31.0   \n",
       "95  Vila Mariana                1.0            5.0   2.0      26.0   \n",
       "\n",
       "    supermarket  \n",
       "0           NaN  \n",
       "1           5.0  \n",
       "2           3.0  \n",
       "3          12.0  \n",
       "4          10.0  \n",
       "..          ...  \n",
       "91          8.0  \n",
       "92          8.0  \n",
       "93         11.0  \n",
       "94          8.0  \n",
       "95         12.0  \n",
       "\n",
       "[96 rows x 6 columns]"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# merge Districts and Venues Count dataframes\n",
    "df_venues_count = pd.merge(left=df_districts['district'], right=df_venues_count, how='left', on='district')\n",
    "\n",
    "# print results\n",
    "df_venues_count"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Categories which have not been returned from __API Foursquare__ have been set as _NaN_ when creating the _Pivot Table_ dataframe. Replace _NaN_ entries by _0_ (zero) using _pandas.DataFrame.fillna_ method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>district</th>\n",
       "      <th>elementary_school</th>\n",
       "      <th>metro_station</th>\n",
       "      <th>park</th>\n",
       "      <th>pharmacy</th>\n",
       "      <th>supermarket</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bela Vista</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Bom Retiro</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Cambuci</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Consolação</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Liberdade</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>Campo Grande</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>Santo Amaro</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>Moema</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>Saúde</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>Vila Mariana</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>96 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        district  elementary_school  metro_station  park  pharmacy  \\\n",
       "0     Bela Vista                0.0            0.0   0.0       0.0   \n",
       "1     Bom Retiro                2.0            5.0   4.0      19.0   \n",
       "2        Cambuci                3.0            0.0   1.0      21.0   \n",
       "3     Consolação                0.0            4.0   1.0      28.0   \n",
       "4      Liberdade                0.0            5.0   1.0      29.0   \n",
       "..           ...                ...            ...   ...       ...   \n",
       "91  Campo Grande                3.0            4.0   5.0      25.0   \n",
       "92   Santo Amaro                2.0            4.0   5.0      28.0   \n",
       "93         Moema                0.0            4.0   3.0      28.0   \n",
       "94         Saúde                0.0            7.0   2.0      31.0   \n",
       "95  Vila Mariana                1.0            5.0   2.0      26.0   \n",
       "\n",
       "    supermarket  \n",
       "0           0.0  \n",
       "1           5.0  \n",
       "2           3.0  \n",
       "3          12.0  \n",
       "4          10.0  \n",
       "..          ...  \n",
       "91          8.0  \n",
       "92          8.0  \n",
       "93         11.0  \n",
       "94          8.0  \n",
       "95         12.0  \n",
       "\n",
       "[96 rows x 6 columns]"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# replace NaN entries by 0\n",
    "df_venues_count.fillna(value=0, inplace=True)\n",
    "\n",
    "# print results\n",
    "df_venues_count"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 7 - Create the _Districts Rank_ according to the _Relocator Profile_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import additional libraries.\n",
    "* __Scikit-Learn__: machine learning package"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cluster import DBSCAN"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The rank will be based on groups of districts, created on their similarities. To group them __Machine Learning Techniques__ will be used, for this project __Clustering Technique__ with __DBSCAN algorithm__, which stands for _Density-Based Spatial Clustering of Applications with Noise_. The choice for DBSCAN was done because it is capable to detect clusters with different densities, sizes and shapes, it does not require to define a number of clusters to start and it can also identify noise and outliers among the data.\n",
    "\n",
    "Create a __Clusters Dataframe__ to run the clustering process. The clustering will run based on categories from __Priority List__ each district contains, what is ready into __Venues Count Dataframe__ and also based on __mean rental prices__ per district, what is ready in __Districts Dataframe__. To create the __Clusters Dataframe__, __Venues Count__ and __Districts__ dataframes will be mergeded using _pandas.merge_ method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "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",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>district</th>\n",
       "      <th>elementary_school</th>\n",
       "      <th>metro_station</th>\n",
       "      <th>park</th>\n",
       "      <th>pharmacy</th>\n",
       "      <th>supermarket</th>\n",
       "      <th>mean_price_sqm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bela Vista</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Bom Retiro</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>29.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Cambuci</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>29.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Consolação</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>29.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Liberdade</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>30.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>Campo Grande</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>28.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>Santo Amaro</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>26.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>Moema</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>32.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>Saúde</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>28.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>Vila Mariana</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>29.90</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>96 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        district  elementary_school  metro_station  park  pharmacy  \\\n",
       "0     Bela Vista                0.0            0.0   0.0       0.0   \n",
       "1     Bom Retiro                2.0            5.0   4.0      19.0   \n",
       "2        Cambuci                3.0            0.0   1.0      21.0   \n",
       "3     Consolação                0.0            4.0   1.0      28.0   \n",
       "4      Liberdade                0.0            5.0   1.0      29.0   \n",
       "..           ...                ...            ...   ...       ...   \n",
       "91  Campo Grande                3.0            4.0   5.0      25.0   \n",
       "92   Santo Amaro                2.0            4.0   5.0      28.0   \n",
       "93         Moema                0.0            4.0   3.0      28.0   \n",
       "94         Saúde                0.0            7.0   2.0      31.0   \n",
       "95  Vila Mariana                1.0            5.0   2.0      26.0   \n",
       "\n",
       "    supermarket  mean_price_sqm  \n",
       "0           0.0           30.60  \n",
       "1           5.0           29.10  \n",
       "2           3.0           29.93  \n",
       "3          12.0           29.93  \n",
       "4          10.0           30.70  \n",
       "..          ...             ...  \n",
       "91          8.0           28.39  \n",
       "92          8.0           26.80  \n",
       "93         11.0           32.60  \n",
       "94          8.0           28.20  \n",
       "95         12.0           29.90  \n",
       "\n",
       "[96 rows x 7 columns]"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# merge Venues Count and Districts dataframes\n",
    "df_clusters = pd.merge(left=df_venues_count, right=df_districts[['district','mean_price_sqm']], how='inner', on='district')\n",
    "\n",
    "# print results\n",
    "df_clusters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Prepare data for clustering process. First, drop __district__ column, as it is not part of the features to be used for clustering, _pandas.DataFrame.drop_ will be used."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>elementary_school</th>\n",
       "      <th>metro_station</th>\n",
       "      <th>park</th>\n",
       "      <th>pharmacy</th>\n",
       "      <th>supermarket</th>\n",
       "      <th>mean_price_sqm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>29.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>29.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>29.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>30.70</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>28.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>26.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>32.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>28.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>29.90</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>96 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    elementary_school  metro_station  park  pharmacy  supermarket  \\\n",
       "0                 0.0            0.0   0.0       0.0          0.0   \n",
       "1                 2.0            5.0   4.0      19.0          5.0   \n",
       "2                 3.0            0.0   1.0      21.0          3.0   \n",
       "3                 0.0            4.0   1.0      28.0         12.0   \n",
       "4                 0.0            5.0   1.0      29.0         10.0   \n",
       "..                ...            ...   ...       ...          ...   \n",
       "91                3.0            4.0   5.0      25.0          8.0   \n",
       "92                2.0            4.0   5.0      28.0          8.0   \n",
       "93                0.0            4.0   3.0      28.0         11.0   \n",
       "94                0.0            7.0   2.0      31.0          8.0   \n",
       "95                1.0            5.0   2.0      26.0         12.0   \n",
       "\n",
       "    mean_price_sqm  \n",
       "0            30.60  \n",
       "1            29.10  \n",
       "2            29.93  \n",
       "3            29.93  \n",
       "4            30.70  \n",
       "..             ...  \n",
       "91           28.39  \n",
       "92           26.80  \n",
       "93           32.60  \n",
       "94           28.20  \n",
       "95           29.90  \n",
       "\n",
       "[96 rows x 6 columns]"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# drop district column\n",
    "df_clusters.drop('district', axis=1, inplace=True)\n",
    "\n",
    "# print results\n",
    "df_clusters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Second, convert features values of venues categories count (the first five columns) as follwing:\n",
    "\n",
    "* __0__ when __value = 0__\n",
    "* __1__ when __value > 0__\n",
    "\n",
    "Method _pandas.DataFrame.apply_ will be used to convert features values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>elementary_school</th>\n",
       "      <th>metro_station</th>\n",
       "      <th>park</th>\n",
       "      <th>pharmacy</th>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
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       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <th>91</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>1.0</td>\n",
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       "      <th>94</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>28.20</td>\n",
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       "      <th>95</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>96 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    elementary_school  metro_station  park  pharmacy  supermarket  \\\n",
       "0                 0.0            0.0   0.0       0.0          0.0   \n",
       "1                 1.0            1.0   1.0       1.0          1.0   \n",
       "2                 1.0            0.0   1.0       1.0          1.0   \n",
       "3                 0.0            1.0   1.0       1.0          1.0   \n",
       "4                 0.0            1.0   1.0       1.0          1.0   \n",
       "..                ...            ...   ...       ...          ...   \n",
       "91                1.0            1.0   1.0       1.0          1.0   \n",
       "92                1.0            1.0   1.0       1.0          1.0   \n",
       "93                0.0            1.0   1.0       1.0          1.0   \n",
       "94                0.0            1.0   1.0       1.0          1.0   \n",
       "95                1.0            1.0   1.0       1.0          1.0   \n",
       "\n",
       "    mean_price_sqm  \n",
       "0            30.60  \n",
       "1            29.10  \n",
       "2            29.93  \n",
       "3            29.93  \n",
       "4            30.70  \n",
       "..             ...  \n",
       "91           28.39  \n",
       "92           26.80  \n",
       "93           32.60  \n",
       "94           28.20  \n",
       "95           29.90  \n",
       "\n",
       "[96 rows x 6 columns]"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# run a loop through datafame for first five columns\n",
    "for i, c in enumerate(list(df_clusters.columns)):\n",
    "    # convert features values\n",
    "    if i < 5: df_clusters[c] = df_clusters[c].apply(lambda x : x if x == 0 else 1)\n",
    "\n",
    "# print results\n",
    "df_clusters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Third, convert __mean_price_sqm__ to three groups as following:\n",
    "\n",
    "* 0 when below rental prices tolerance range __(mean_price_sqm < 23.75)__\n",
    "* 1 when inside rental prices tolerance range __(23.75 <= mean_price_sqm <= 26.25)__\n",
    "* 2 when above rental prices tolerance range __(mean_price_sqm > 26.25)__\n",
    "\n",
    "It will be done defining a function to assign a group taking __mean_price_sqm__ and __tolerance range__ as parameters. Having the function defined, _pandas.DataFrame.apply_ method will be used to call the function and replace __mean_price_sqm__ by its assigned group. Finally the column name will be changed to __rental_group__."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>1.0</td>\n",
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       "      <th>95</th>\n",
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       "      <td>1.0</td>\n",
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      "text/plain": [
       "    elementary_school  metro_station  park  pharmacy  supermarket  \\\n",
       "0                 0.0            0.0   0.0       0.0          0.0   \n",
       "1                 1.0            1.0   1.0       1.0          1.0   \n",
       "2                 1.0            0.0   1.0       1.0          1.0   \n",
       "3                 0.0            1.0   1.0       1.0          1.0   \n",
       "4                 0.0            1.0   1.0       1.0          1.0   \n",
       "..                ...            ...   ...       ...          ...   \n",
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       "93                0.0            1.0   1.0       1.0          1.0   \n",
       "94                0.0            1.0   1.0       1.0          1.0   \n",
       "95                1.0            1.0   1.0       1.0          1.0   \n",
       "\n",
       "    rental_group  \n",
       "0              2  \n",
       "1              2  \n",
       "2              2  \n",
       "3              2  \n",
       "4              2  \n",
       "..           ...  \n",
       "91             2  \n",
       "92             2  \n",
       "93             2  \n",
       "94             2  \n",
       "95             2  \n",
       "\n",
       "[96 rows x 6 columns]"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# define a functon to assign price group\n",
    "def convert_price(p, tmin, tmax):\n",
    "    if p < tmin:\n",
    "        r = 0\n",
    "    elif p > tmax:\n",
    "        r = 2\n",
    "    else:\n",
    "        r = 1\n",
    "    \n",
    "    return r\n",
    "\n",
    "# convert the values to rental groups\n",
    "df_clusters['mean_price_sqm'] = df_clusters['mean_price_sqm'].apply(lambda x : convert_price(x, budget_sqm_min, budget_sqm_max))\n",
    "\n",
    "# change column name\n",
    "df_clusters.rename(columns={'mean_price_sqm': 'rental_group'}, inplace=True)\n",
    "\n",
    "# print results\n",
    "df_clusters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fourth, use __DBSCAN algorithm__ to cluster districts according the their similarities."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-1,  0,  1,  2,  2,  0, -1,  2,  3,  4,  5,  6,  4,  6,  4,  4,  4,\n",
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      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# fit DBSCAN model with Clusters dataframe\n",
    "vmodel = DBSCAN(eps=0.5, min_samples=3).fit(df_clusters)\n",
    "\n",
    "# run the model\n",
    "labels = vmodel.labels_\n",
    "\n",
    "# (cluster lables\n",
    "labels"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 8 - Cluster Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check clusters size, it wiill be done attaching _cluster labels_ to the __Clusters Dataframe__ and then group data by __cluster_label__ and counting the districts, _pandas.DataFrame.groupby_ will be used."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "               elementary_school  metro_station  park  pharmacy  supermarket  \\\n",
       "cluster_label                                                                  \n",
       "-1                             9              9     9         9            9   \n",
       " 0                            19             19    19        19           19   \n",
       " 1                            13             13    13        13           13   \n",
       " 2                             7              7     7         7            7   \n",
       " 3                             4              4     4         4            4   \n",
       " 4                            19             19    19        19           19   \n",
       " 5                             3              3     3         3            3   \n",
       " 6                            11             11    11        11           11   \n",
       " 7                             4              4     4         4            4   \n",
       " 8                             7              7     7         7            7   \n",
       "\n",
       "               rental_group  \n",
       "cluster_label                \n",
       "-1                        9  \n",
       " 0                       19  \n",
       " 1                       13  \n",
       " 2                        7  \n",
       " 3                        4  \n",
       " 4                       19  \n",
       " 5                        3  \n",
       " 6                       11  \n",
       " 7                        4  \n",
       " 8                        7  "
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# attached cluster lable to Clusters dataframe\n",
    "df_clusters['cluster_label'] = labels\n",
    "\n",
    "# group data by cluster lable\n",
    "df_clusters.groupby('cluster_label').count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check results in __Clusters Dataframe__, as the features are _0 / 1_ for venues categories and _0 / 1 / 2_ for rental group, the mean calculated per cluster labels will provide a good results overview, it will be done grouping __Clusters Dataframe__ by __cluster_label__ and calculating _mean_."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
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       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               elementary_school  metro_station      park  pharmacy  \\\n",
       "cluster_label                                                         \n",
       "-1                      0.333333       0.222222  0.333333  0.555556   \n",
       " 0                      1.000000       1.000000  1.000000  1.000000   \n",
       " 1                      1.000000       0.000000  1.000000  1.000000   \n",
       " 2                      0.000000       1.000000  1.000000  1.000000   \n",
       " 3                      1.000000       0.000000  0.000000  1.000000   \n",
       " 4                      1.000000       0.000000  1.000000  1.000000   \n",
       " 5                      1.000000       0.000000  1.000000  1.000000   \n",
       " 6                      1.000000       1.000000  1.000000  1.000000   \n",
       " 7                      1.000000       1.000000  0.000000  1.000000   \n",
       " 8                      1.000000       1.000000  1.000000  1.000000   \n",
       "\n",
       "               supermarket  rental_group  \n",
       "cluster_label                             \n",
       "-1                0.444444      0.888889  \n",
       " 0                1.000000      2.000000  \n",
       " 1                1.000000      2.000000  \n",
       " 2                1.000000      2.000000  \n",
       " 3                1.000000      0.000000  \n",
       " 4                1.000000      0.000000  \n",
       " 5                1.000000      1.000000  \n",
       " 6                1.000000      0.000000  \n",
       " 7                1.000000      0.000000  \n",
       " 8                1.000000      1.000000  "
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# group data by cluster lable\n",
    "df_clusters.groupby('cluster_label').mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Analysis of mean results:\n",
    "* Venues Categories (Elementary School / Metro Station / Park / Pharmacy / Supermarket)\n",
    "\n",
    "    mean = 0: cluster does not contain any districts with such category, see elementary_school in cluster 2\n",
    "    \n",
    "    mean = 1: cluster contains only districts with such category, see elementary_school in cluster 0\n",
    "    \n",
    "* Rental Group\n",
    "\n",
    "    mean = 0: cluster contains only districts with with rental group 0, see cluster 3\n",
    "    \n",
    "    mean = 1: cluster contains only districts with with rental group 1, see cluster 5\n",
    "    \n",
    "    mean = 2: cluster contains only districts with with rental group 2, see cluster 1\n",
    "    \n",
    "* Outliers\n",
    "\n",
    "    mean < 0: , districts which does not fit in any other cluster\n",
    "\n",
    "The goal is to find districts containing the five categories in __Priority List__ and rental prices in tolerance range, __rental group 1__. From the summary above our goal is achieved by districts in ___cluster 8___, which contains 7 districts with all the required characteristics.\n",
    "\n",
    "Check districts in selected cluster. The __cluster_label__ will be attached to __Venues Count DataFrame__ allowing to check the districts characteristics."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>district</th>\n",
       "      <th>elementary_school</th>\n",
       "      <th>metro_station</th>\n",
       "      <th>park</th>\n",
       "      <th>pharmacy</th>\n",
       "      <th>supermarket</th>\n",
       "      <th>cluster_label</th>\n",
       "      <th>mean_price_sqm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Tatuapé</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>8</td>\n",
       "      <td>25.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Vila Prudente</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8</td>\n",
       "      <td>25.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Santana</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8</td>\n",
       "      <td>23.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Vila Maria</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>8</td>\n",
       "      <td>24.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Butantã</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Campo Limpo</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>8</td>\n",
       "      <td>24.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Jabaquara</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>8</td>\n",
       "      <td>25.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        district  elementary_school  metro_station  park  pharmacy  \\\n",
       "0        Tatuapé                1.0            6.0   5.0      26.0   \n",
       "1  Vila Prudente                5.0            3.0   7.0      23.0   \n",
       "2        Santana                2.0            5.0   2.0      31.0   \n",
       "3     Vila Maria                8.0            1.0   4.0      26.0   \n",
       "4        Butantã                1.0            4.0   7.0      28.0   \n",
       "5    Campo Limpo                7.0            5.0   3.0      24.0   \n",
       "6      Jabaquara                5.0            3.0   1.0      24.0   \n",
       "\n",
       "   supermarket  cluster_label  mean_price_sqm  \n",
       "0          9.0              8            25.7  \n",
       "1          8.0              8            25.9  \n",
       "2          8.0              8            23.8  \n",
       "3          5.0              8            24.8  \n",
       "4          7.0              8            25.0  \n",
       "5         10.0              8            24.5  \n",
       "6         12.0              8            25.3  "
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# attach Cluster Label to Venues Count dataframe\n",
    "df_venues_count['cluster_label'] = labels\n",
    "\n",
    "# check data for selected cluster\n",
    "pd.merge(left=df_venues_count[df_venues_count['cluster_label']==8], right=df_districts[['district','mean_price_sqm']], how='inner', on='district')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 9 - Create a map to show the Reults"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a map of São Paulo pointing Districts which presents all required characteristics. Initially create a dataframe with the required information from __Districts__ and __Venues Count__ dataframes. It will be done with _pandas.DataFrame.merge_ method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>region</th>\n",
       "      <th>district</th>\n",
       "      <th>mean_price_sqm</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>elementary_school</th>\n",
       "      <th>metro_station</th>\n",
       "      <th>park</th>\n",
       "      <th>pharmacy</th>\n",
       "      <th>supermarket</th>\n",
       "      <th>cluster_label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Centro</td>\n",
       "      <td>Bela Vista</td>\n",
       "      <td>30.60</td>\n",
       "      <td>-23.2970</td>\n",
       "      <td>-46.0267</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Centro</td>\n",
       "      <td>Bom Retiro</td>\n",
       "      <td>29.10</td>\n",
       "      <td>-23.5271</td>\n",
       "      <td>-46.6368</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Centro</td>\n",
       "      <td>Cambuci</td>\n",
       "      <td>29.93</td>\n",
       "      <td>-23.5661</td>\n",
       "      <td>-46.6137</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Centro</td>\n",
       "      <td>Consolação</td>\n",
       "      <td>29.93</td>\n",
       "      <td>-23.5578</td>\n",
       "      <td>-46.6605</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Centro</td>\n",
       "      <td>Liberdade</td>\n",
       "      <td>30.70</td>\n",
       "      <td>-23.5552</td>\n",
       "      <td>-46.6356</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>Sul</td>\n",
       "      <td>Campo Grande</td>\n",
       "      <td>28.39</td>\n",
       "      <td>-23.6578</td>\n",
       "      <td>-46.6986</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>Sul</td>\n",
       "      <td>Santo Amaro</td>\n",
       "      <td>26.80</td>\n",
       "      <td>-23.6562</td>\n",
       "      <td>-46.7191</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>Sul</td>\n",
       "      <td>Moema</td>\n",
       "      <td>32.60</td>\n",
       "      <td>-23.5827</td>\n",
       "      <td>-46.6490</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>Sul</td>\n",
       "      <td>Saúde</td>\n",
       "      <td>28.20</td>\n",
       "      <td>-23.5973</td>\n",
       "      <td>-46.6359</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>Sul</td>\n",
       "      <td>Vila Mariana</td>\n",
       "      <td>29.90</td>\n",
       "      <td>-23.5892</td>\n",
       "      <td>-46.6347</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>96 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    region      district  mean_price_sqm  latitude  longitude  \\\n",
       "0   Centro    Bela Vista           30.60  -23.2970   -46.0267   \n",
       "1   Centro    Bom Retiro           29.10  -23.5271   -46.6368   \n",
       "2   Centro       Cambuci           29.93  -23.5661   -46.6137   \n",
       "3   Centro    Consolação           29.93  -23.5578   -46.6605   \n",
       "4   Centro     Liberdade           30.70  -23.5552   -46.6356   \n",
       "..     ...           ...             ...       ...        ...   \n",
       "91     Sul  Campo Grande           28.39  -23.6578   -46.6986   \n",
       "92     Sul   Santo Amaro           26.80  -23.6562   -46.7191   \n",
       "93     Sul         Moema           32.60  -23.5827   -46.6490   \n",
       "94     Sul         Saúde           28.20  -23.5973   -46.6359   \n",
       "95     Sul  Vila Mariana           29.90  -23.5892   -46.6347   \n",
       "\n",
       "    elementary_school  metro_station  park  pharmacy  supermarket  \\\n",
       "0                 0.0            0.0   0.0       0.0          0.0   \n",
       "1                 2.0            5.0   4.0      19.0          5.0   \n",
       "2                 3.0            0.0   1.0      21.0          3.0   \n",
       "3                 0.0            4.0   1.0      28.0         12.0   \n",
       "4                 0.0            5.0   1.0      29.0         10.0   \n",
       "..                ...            ...   ...       ...          ...   \n",
       "91                3.0            4.0   5.0      25.0          8.0   \n",
       "92                2.0            4.0   5.0      28.0          8.0   \n",
       "93                0.0            4.0   3.0      28.0         11.0   \n",
       "94                0.0            7.0   2.0      31.0          8.0   \n",
       "95                1.0            5.0   2.0      26.0         12.0   \n",
       "\n",
       "    cluster_label  \n",
       "0              -1  \n",
       "1               0  \n",
       "2               1  \n",
       "3               2  \n",
       "4               2  \n",
       "..            ...  \n",
       "91              0  \n",
       "92              0  \n",
       "93              2  \n",
       "94              2  \n",
       "95              0  \n",
       "\n",
       "[96 rows x 11 columns]"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create Cluster Map dataframe\n",
    "df_cluster_map = pd.merge(left=df_districts[['region','district','mean_price_sqm','latitude','longitude']],\n",
    "                          right=df_venues_count, how='inner', on='district')\n",
    "\n",
    "# print results\n",
    "df_cluster_map"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "### Important notice:\n",
    "Unfortunately the Folium maps are not displayed in GitHub, they are interactive objects not supported, as an option to see the maps the [Jupyter nbviewer](https://nbviewer.org/) can be used. Once it is opened just need to paste the URL of the GitHub document.\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create ___Cluster Map___ to show the results. _Folium_ will be used."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"width:100%;\"><div style=\"position:relative;width:100%;height:0;padding-bottom:60%;\"><span style=\"color:#565656\">Make this Notebook Trusted to load map: File -> Trust Notebook</span><iframe src=\"about:blank\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" data-html=<!DOCTYPE html>
<head>    
    <meta http-equiv="content-type" content="text/html; charset=UTF-8" />
    
        <script>
            L_NO_TOUCH = false;
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    <link rel="stylesheet" href="https://maxcdn.bootstrapcdn.com/font-awesome/4.6.3/css/font-awesome.min.css"/>
    <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/Leaflet.awesome-markers/2.0.2/leaflet.awesome-markers.css"/>
    <link rel="stylesheet" href="https://rawcdn.githack.com/python-visualization/folium/master/folium/templates/leaflet.awesome.rotate.css"/>
    <style>html, body {width: 100%;height: 100%;margin: 0;padding: 0;}</style>
    <style>#map {position:absolute;top:0;bottom:0;right:0;left:0;}</style>
    
            <meta name="viewport" content="width=device-width,
                initial-scale=1.0, maximum-scale=1.0, user-scalable=no" />
            <style>
                #map_ef0e65d7309a4fef8ed50563de7a6e98 {
                    position: relative;
                    width: 70.0%;
                    height: 70.0%;
                    left: 0.0%;
                    top: 0.0%;
                }
            </style>
        
</head>
<body>    
    
            <div class="folium-map" id="map_ef0e65d7309a4fef8ed50563de7a6e98" ></div>
        
</body>
<script>    
    
            var map_ef0e65d7309a4fef8ed50563de7a6e98 = L.map(
                "map_ef0e65d7309a4fef8ed50563de7a6e98",
                {
                    center: [-23.5507, -46.6334],
                    crs: L.CRS.EPSG3857,
                    zoom: 11,
                    zoomControl: true,
                    preferCanvas: false,
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            );
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            var tile_layer_419bb3f9c8a1488684dc4fcaaba602a6 = L.tileLayer(
                "https://{s}.tile.openstreetmap.org/{z}/{x}/{y}.png",
                {"attribution": "Data by \u0026copy; \u003ca href=\"http://openstreetmap.org\"\u003eOpenStreetMap\u003c/a\u003e, under \u003ca href=\"http://www.openstreetmap.org/copyright\"\u003eODbL\u003c/a\u003e.", "detectRetina": false, "maxNativeZoom": 12, "maxZoom": 12, "minZoom": 9, "noWrap": false, "opacity": 1, "subdomains": "abc", "tms": false}
            ).addTo(map_ef0e65d7309a4fef8ed50563de7a6e98);
        
    
            var circle_marker_5bf605ca06f44c8fa5a91bffe055622e = L.circleMarker(
                [-23.297, -46.0267],
                {"bubblingMouseEvents": true, "color": "gray", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "gray", "fillOpacity": 0.3, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_ef0e65d7309a4fef8ed50563de7a6e98);
        
    
        var popup_34dff6d9e14941e0af9c1eb55a2570dc = L.popup({"maxWidth": "100%"});

        
            var html_a7255f8808c24112afebb633abca642a = $(`<div id="html_a7255f8808c24112afebb633abca642a" style="width: 100.0%; height: 100.0%;">Bela Vista (Centro)  Mean Rental Price=30.60  El.shool=0.0  M.station=0.0  Park=0.0  Pharmacy=0.0  Supermkt=0.0</div>`)[0];
            popup_34dff6d9e14941e0af9c1eb55a2570dc.setContent(html_a7255f8808c24112afebb633abca642a);
        

        circle_marker_5bf605ca06f44c8fa5a91bffe055622e.bindPopup(popup_34dff6d9e14941e0af9c1eb55a2570dc)
        ;

        
    
    
            var circle_marker_b2760050bbb14803aad0afce43a4a137 = L.circleMarker(
                [-23.5271, -46.6368],
                {"bubblingMouseEvents": true, "color": "gray", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "gray", "fillOpacity": 0.3, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
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        var popup_2bfa4bdc04e94385b16e6b0c082cf58d = L.popup({"maxWidth": "100%"});

        
            var html_fd98610733504653a920ec120dbd5832 = $(`<div id="html_fd98610733504653a920ec120dbd5832" style="width: 100.0%; height: 100.0%;">Bom Retiro (Centro)  Mean Rental Price=29.10  El.shool=2.0  M.station=5.0  Park=4.0  Pharmacy=19.0  Supermkt=5.0</div>`)[0];
            popup_2bfa4bdc04e94385b16e6b0c082cf58d.setContent(html_fd98610733504653a920ec120dbd5832);
        

        circle_marker_b2760050bbb14803aad0afce43a4a137.bindPopup(popup_2bfa4bdc04e94385b16e6b0c082cf58d)
        ;

        
    
    
            var circle_marker_54a61b3e1bae49c4920968b5e28026ab = L.circleMarker(
                [-23.5661, -46.6137],
                {"bubblingMouseEvents": true, "color": "gray", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "gray", "fillOpacity": 0.3, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_ef0e65d7309a4fef8ed50563de7a6e98);
        
    
        var popup_53321dce926f4ded934aabd1c9675712 = L.popup({"maxWidth": "100%"});

        
            var html_fc80744300ff417b88cf1e6a03f127ab = $(`<div id="html_fc80744300ff417b88cf1e6a03f127ab" style="width: 100.0%; height: 100.0%;">Cambuci (Centro)  Mean Rental Price=29.93  El.shool=3.0  M.station=0.0  Park=1.0  Pharmacy=21.0  Supermkt=3.0</div>`)[0];
            popup_53321dce926f4ded934aabd1c9675712.setContent(html_fc80744300ff417b88cf1e6a03f127ab);
        

        circle_marker_54a61b3e1bae49c4920968b5e28026ab.bindPopup(popup_53321dce926f4ded934aabd1c9675712)
        ;

        
    
    
            var circle_marker_fce2f254ce3f48969fc87bda44839fc7 = L.circleMarker(
                [-23.5578, -46.6605],
                {"bubblingMouseEvents": true, "color": "gray", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "gray", "fillOpacity": 0.3, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_ef0e65d7309a4fef8ed50563de7a6e98);
        
    
        var popup_47c62f4a6f614e97b49914146af7d1dc = L.popup({"maxWidth": "100%"});

        
            var html_165b107c29ff418c818abeb4521a6308 = $(`<div id="html_165b107c29ff418c818abeb4521a6308" style="width: 100.0%; height: 100.0%;">Consolação (Centro)  Mean Rental Price=29.93  El.shool=0.0  M.station=4.0  Park=1.0  Pharmacy=28.0  Supermkt=12.0</div>`)[0];
            popup_47c62f4a6f614e97b49914146af7d1dc.setContent(html_165b107c29ff418c818abeb4521a6308);
        

        circle_marker_fce2f254ce3f48969fc87bda44839fc7.bindPopup(popup_47c62f4a6f614e97b49914146af7d1dc)
        ;

        
    
    
            var circle_marker_8634a717bb3a4e85b792fe45f367037a = L.circleMarker(
                [-23.5552, -46.6356],
                {"bubblingMouseEvents": true, "color": "gray", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "gray", "fillOpacity": 0.3, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_ef0e65d7309a4fef8ed50563de7a6e98);
        
    
        var popup_49869fa4ec4f4707b503f196089b5293 = L.popup({"maxWidth": "100%"});

        
            var html_ab826623e30e429d807fa1dd65b446c4 = $(`<div id="html_ab826623e30e429d807fa1dd65b446c4" style="width: 100.0%; height: 100.0%;">Liberdade (Centro)  Mean Rental Price=30.70  El.shool=0.0  M.station=5.0  Park=1.0  Pharmacy=29.0  Supermkt=10.0</div>`)[0];
            popup_49869fa4ec4f4707b503f196089b5293.setContent(html_ab826623e30e429d807fa1dd65b446c4);
        

        circle_marker_8634a717bb3a4e85b792fe45f367037a.bindPopup(popup_49869fa4ec4f4707b503f196089b5293)
        ;

        
    
    
            var circle_marker_10d36688a55447bb8a309f6549e0c4f3 = L.circleMarker(
                [-23.5453, -46.6423],
                {"bubblingMouseEvents": true, "color": "gray", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "gray", "fillOpacity": 0.3, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_ef0e65d7309a4fef8ed50563de7a6e98);
        
    
        var popup_84fa948dae9e43f387e7de396fd23e3b = L.popup({"maxWidth": "100%"});

        
            var html_44ebc70b584645d18c2d18af24f3e910 = $(`<div id="html_44ebc70b584645d18c2d18af24f3e910" style="width: 100.0%; height: 100.0%;">República (Centro)  Mean Rental Price=29.93  El.shool=1.0  M.station=3.0  Park=3.0  Pharmacy=33.0  Supermkt=8.0</div>`)[0];
            popup_84fa948dae9e43f387e7de396fd23e3b.setContent(html_44ebc70b584645d18c2d18af24f3e910);
        

        circle_marker_10d36688a55447bb8a309f6549e0c4f3.bindPopup(popup_84fa948dae9e43f387e7de396fd23e3b)
        ;

        
    
    
            var circle_marker_61fd1dec7b19436ca29688bad63b8af9 = L.circleMarker(
                [-23.5297, -46.6519],
                {"bubblingMouseEvents": true, "color": "gray", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "gray", "fillOpacity": 0.3, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_ef0e65d7309a4fef8ed50563de7a6e98);
        
    
        var popup_fab9e0b32cb9496d983c3e98b623ffa1 = L.popup({"maxWidth": "100%"});

        
            var html_8be65e77bd054f038505e0ef90170548 = $(`<div id="html_8be65e77bd054f038505e0ef90170548" style="width: 100.0%; height: 100.0%;">Santa Cecília (Centro)  Mean Rental Price=29.30  El.shool=2.0  M.station=2.0  Park=0.0  Pharmacy=29.0  Supermkt=10.0</div>`)[0];
            popup_fab9e0b32cb9496d983c3e98b623ffa1.setContent(html_8be65e77bd054f038505e0ef90170548);
        

        circle_marker_61fd1dec7b19436ca29688bad63b8af9.bindPopup(popup_fab9e0b32cb9496d983c3e98b623ffa1)
        ;

        
    
    
            var circle_marker_d1f97ac60b50460193a1dfde33a05bdb = L.circleMarker(
                [-23.5504, -46.6334],
                {"bubblingMouseEvents": true, "color": "gray", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "gray", "fillOpacity": 0.3, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_ef0e65d7309a4fef8ed50563de7a6e98);
        
    
        var popup_9253362b0c524a26acf9811dd40f726f = L.popup({"maxWidth": "100%"});

        
            var html_ee0320c64b334fde89dc40b2dfc6928e = $(`<div id="html_ee0320c64b334fde89dc40b2dfc6928e" style="width: 100.0%; height: 100.0%;">Sé (Centro)  Mean Rental Price=29.93  El.shool=0.0  M.station=10.0  Park=2.0  Pharmacy=28.0  Supermkt=5.0</div>`)[0];
            popup_9253362b0c524a26acf9811dd40f726f.setContent(html_ee0320c64b334fde89dc40b2dfc6928e);
        

        circle_marker_d1f97ac60b50460193a1dfde33a05bdb.bindPopup(popup_9253362b0c524a26acf9811dd40f726f)
        ;

        
    
    
            var circle_marker_f6c7a31e91424761b4007b26444bed6b = L.circleMarker(
                [-23.578, -46.5115],
                {"bubblingMouseEvents": true, "color": "gray", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "gray", "fillOpacity": 0.3, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_ef0e65d7309a4fef8ed50563de7a6e98);
        
    
        var popup_b1373dd73ea2473796d413c80aeb2e07 = L.popup({"maxWidth": "100%"});

        
            var html_8092b0e348314150930a0da5772a7201 = $(`<div id="html_8092b0e348314150930a0da5772a7201" style="width: 100.0%; height: 100.0%;">Aricanduva (Leste)  Mean Rental Price=22.40  El.shool=6.0  M.station=0.0  Park=0.0  Pharmacy=12.0  Supermkt=3.0</div>`)[0];
            popup_b1373dd73ea2473796d413c80aeb2e07.setContent(html_8092b0e348314150930a0da5772a7201);
        

        circle_marker_f6c7a31e91424761b4007b26444bed6b.bindPopup(popup_b1373dd73ea2473796d413c80aeb2e07)
        ;

        
    
    
            var circle_marker_65c6fc809caf4ecb8a0ed32ee9c08c11 = L.circleMarker(
                [-23.5515, -46.5378],
                {"bubblingMouseEvents": true, "color": "gray", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "gray", "fillOpacity": 0.3, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_ef0e65d7309a4fef8ed50563de7a6e98);
        
    
        var popup_381820fbe85246d5976d2d0ea6404893 = L.popup({"maxWidth": "100%"});

        
            var html_21324788d8764f2ab093481fe9afd5c1 = $(`<div id="html_21324788d8764f2ab093481fe9afd5c1" style="width: 100.0%; height: 100.0%;">Carrão (Leste)  Mean Rental Price=23.05  El.shool=8.0  M.station=0.0  Park=7.0  Pharmacy=28.0  Supermkt=4.0</div>`)[0];
            popup_381820fbe85246d5976d2d0ea6404893.setContent(html_21324788d8764f2ab093481fe9afd5c1);
        

        circle_marker_65c6fc809caf4ecb8a0ed32ee9c08c11.bindPopup(popup_381820fbe85246d5976d2d0ea6404893)
        ;

        
    
    
            var circle_marker_81665e5074a54179a94a75ac662f6e0e = L.circleMarker(
                [-23.5669, -46.5463],
                {"bubblingMouseEvents": true, "color": "gray", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "gray", "fillOpacity": 0.3, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_ef0e65d7309a4fef8ed50563de7a6e98);
        
    
        var popup_39c8d5be16614de98d8a9e2d808c3e78 = L.popup({"maxWidth": "100%"});

        
            var html_7ff8996dde83446d840477d3027040a2 = $(`<div id="html_7ff8996dde83446d840477d3027040a2" style="width: 100.0%; height: 100.0%;">Vila Formosa (Leste)  Mean Rental Price=24.00  El.shool=7.0  M.station=0.0  Park=3.0  Pharmacy=32.0  Supermkt=5.0</div>`)[0];
            popup_39c8d5be16614de98d8a9e2d808c3e78.setContent(html_7ff8996dde83446d840477d3027040a2);
        

        circle_marker_81665e5074a54179a94a75ac662f6e0e.bindPopup(popup_39c8d5be16614de98d8a9e2d808c3e78)
        ;

        
    
    
            var circle_marker_409fca3182a742f498bcf1815a48e088 = L.circleMarker(
                [-23.5825, -46.4092],
                {"bubblingMouseEvents": true, "color": "gray", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "gray", "fillOpacity": 0.3, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_ef0e65d7309a4fef8ed50563de7a6e98);
        
    
        var popup_7eb5fa71eb9447319d03a9da74174d65 = L.popup({"maxWidth": "100%"});

        
            var html_02aad98bcef94e81887df92e0b73a98e = $(`<div id="html_02aad98bcef94e81887df92e0b73a98e" style="width: 100.0%; height: 100.0%;">Cidade Tiradentes (Leste)  Mean Rental Price=23.05  El.shool=5.0  M.station=1.0  Park=3.0  Pharmacy=7.0  Supermkt=4.0</div>`)[0];
            popup_7eb5fa71eb9447319d03a9da74174d65.setContent(html_02aad98bcef94e81887df92e0b73a98e);
        

        circle_marker_409fca3182a742f498bcf1815a48e088.bindPopup(popup_7eb5fa71eb9447319d03a9da74174d65)
        ;

        
    
    
            var circle_marker_dccaf73e6ad14775a7c2a604616b9b05 = L.circleMarker(
                [-23.4903, -46.4824],
                {"bubblingMouseEvents": true, "color": "gray", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "gray", "fillOpacity": 0.3, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_ef0e65d7309a4fef8ed50563de7a6e98);
        
    
        var popup_0435be840bf745a8934ca80b88ee406e = L.popup({"maxWidth": "100%"});

        
            var html_fac8ae6b54ed44acaa79ca2ef9fd73fc = $(`<div id="html_fac8ae6b54ed44acaa79ca2ef9fd73fc" style="width: 100.0%; height: 100.0%;">Ermelino Matarazzo (Leste)  Mean Rental Price=23.05  El.shool=2.0  M.station=0.0  Park=4.0  Pharmacy=19.0  Supermkt=2.0</div>`)[0];
            popup_0435be840bf745a8934ca80b88ee406e.setContent(html_fac8ae6b54ed44acaa79ca2ef9fd73fc);
        

        circle_marker_dccaf73e6ad14775a7c2a604616b9b05.bindPopup(popup_0435be840bf745a8934ca80b88ee406e)
        ;

        
    
    
            var circle_marker_baee2b972cba4490af9db7052882a2ea = L.circleMarker(
                [-23.511, -46.4871],
                {"bubblingMouseEvents": true, "color": "gray", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "gray", "fillOpacity": 0.3, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_ef0e65d7309a4fef8ed50563de7a6e98);
        
    
        var popup_bccfca34635544caaff79444479eb9a1 = L.popup({"maxWidth": "100%"});

        
            var html_ff746035dc4441f4b206bc4f71713fb9 = $(`<div id="html_ff746035dc4441f4b206bc4f71713fb9" style="width: 100.0%; height: 100.0%;">Ponte Rasa (Leste)  Mean Rental Price=23.05  El.shool=5.0  M.station=1.0  Park=1.0  Pharmacy=24.0  Supermkt=2.0</div>`)[0];
            popup_bccfca34635544caaff79444479eb9a1.setContent(html_ff746035dc4441f4b206bc4f71713fb9);
        

        circle_marker_baee2b972cba4490af9db7052882a2ea.bindPopup(popup_bccfca34635544caaff79444479eb9a1)
        ;

        
    
    
            var circle_marker_562a3fd2a5364576b8d0cb0c5a0123d7 = L.circleMarker(
                [-23.5542, -46.4041],
                {"bubblingMouseEvents": true, "color": "gray", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "gray", "fillOpacity": 0.3, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_ef0e65d7309a4fef8ed50563de7a6e98);
        
    
        var popup_8baa246ca0654f1bbe210f356cbe3fa2 = L.popup({"maxWidth": "100%"});

        
            var html_1f804be0bd23422f917a8c5a5a07baa8 = $(`<div id="html_1f804be0bd23422f917a8c5a5a07baa8" style="width: 100.0%; height: 100.0%;">Guaianases (Leste)  Mean Rental Price=17.70  El.shool=4.0  M.station=0.0  Park=2.0  Pharmacy=8.0  Supermkt=1.0</div>`)[0];
            popup_8baa246ca0654f1bbe210f356cbe3fa2.setContent(html_1f804be0bd23422f917a8c5a5a07baa8);
        

        circle_marker_562a3fd2a5364576b8d0cb0c5a0123d7.bindPopup(popup_8baa246ca0654f1bbe210f356cbe3fa2)
        ;

        
    
    
            var circle_marker_b632b57fa3e946b5a5eebb265e694544 = L.circleMarker(
                [-23.5362, -46.41],
                {"bubblingMouseEvents": true, "color": "gray", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "gray", "fillOpacity": 0.3, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_ef0e65d7309a4fef8ed50563de7a6e98);
        
    
        var popup_cc1671e14b014bdea3d44f596910180d = L.popup({"maxWidth": "100%"});

        
            var html_55fa9377c2d14308a02694a4fc7e2985 = $(`<div id="html_55fa9377c2d14308a02694a4fc7e2985" style="width: 100.0%; height: 100.0%;">Lajeado (Leste)  Mean Rental Price=23.05  El.shool=7.0  M.station=0.0  Park=3.0  Pharmacy=10.0  Supermkt=1.0</div>`)[0];
            popup_cc1671e14b014bdea3d44f596910180d.setContent(html_55fa9377c2d14308a02694a4fc7e2985);
        

        circle_marker_b632b57fa3e946b5a5eebb265e694544.bindPopup(popup_cc1671e14b014bdea3d44f596910180d)
        ;

        
    
    
            var circle_marker_8e170c7e74ea4dfa892ad4d039d3341f = L.circleMarker(
                [-23.494, -46.4022],
                {"bubblingMouseEvents": true, "color": "gray", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "gray", "fillOpacity": 0.3, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_ef0e65d7309a4fef8ed50563de7a6e98);
        
    
        var popup_9c475310c6f6467c8108d07a9603e499 = L.popup({"maxWidth": "100%"});

        
            var html_97459e545357487f8e589f5d73cedead = $(`<div id="html_97459e545357487f8e589f5d73cedead" style="width: 100.0%; height: 100.0%;">Itaim Paulista (Leste)  Mean Rental Price=23.05  El.shool=8.0  M.station=0.0  Park=3.0  Pharmacy=15.0  Supermkt=4.0</div>`)[0];
            popup_9c475310c6f6467c8108d07a9603e499.setContent(html_97459e545357487f8e589f5d73cedead);
        

        circle_marker_8e170c7e74ea4dfa892ad4d039d3341f.bindPopup(popup_9c475310c6f6467c8108d07a9603e499)
        ;

        
    
    
            var circle_marker_9bbb8fe7351e4c9e8dde62c4c33ce54d = L.circleMarker(
                [-23.5102, -46.4179],
                {"bubblingMouseEvents": true, "color": "gray", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "gray", "fillOpacity": 0.3, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_ef0e65d7309a4fef8ed50563de7a6e98);
        
    
        var popup_88874fd5186445cfbfeebf32da911edf = L.popup({"maxWidth": "100%"});

        
            var html_0895103f72034274a926738a8e2d3782 = $(`<div id="html_0895103f72034274a926738a8e2d3782" style="width: 100.0%; height: 100.0%;">Vila Curuçá (Leste)  Mean Rental Price=23.05  El.shool=3.0  M.station=0.0  Park=5.0  Pharmacy=9.0  Supermkt=3.0</div>`)[0];
            popup_88874fd5186445cfbfeebf32da911edf.setContent(html_0895103f72034274a926738a8e2d3782);
        

        circle_marker_9bbb8fe7351e4c9e8dde62c4c33ce54d.bindPopup(popup_88874fd5186445cfbfeebf32da911edf)
        ;

        
    
    
            var circle_marker_e74ce4fc9bf7415d9d9d28072d6a1bb7 = L.circleMarker(
                [-23.5628, -46.4943],
                {"bubblingMouseEvents": true, "color": "gray", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "gray", "fillOpacity": 0.3, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_ef0e65d7309a4fef8ed50563de7a6e98);
        
    
        var popup_88596ff22fc8499880c66ff53cb6f2cd = L.popup({"maxWidth": "100%"});

        
            var html_e83801e1be2f4786a43533d3c82ed3ff = $(`<div id="html_e83801e1be2f4786a43533d3c82ed3ff" style="width: 100.0%; height: 100.0%;">Cidade Líder (Leste)  Mean Rental Price=20.70  El.shool=9.0  M.station=1.0  Park=0.0  Pharmacy=11.0  Supermkt=8.0</div>`)[0];
            popup_88596ff22fc8499880c66ff53cb6f2cd.setContent(html_e83801e1be2f4786a43533d3c82ed3ff);
        

        circle_marker_e74ce4fc9bf7415d9d9d28072d6a1bb7.bindPopup(popup_88596ff22fc8499880c66ff53cb6f2cd)
        ;

        
    
    
            var circle_marker_87d132b018494fc78f5ef9019233fa14 = L.circleMarker(
                [-23.5314, -46.4533],
                {"bubblingMouseEvents": true, "color": "gray", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "gray", "fillOpacity": 0.3, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_ef0e65d7309a4fef8ed50563de7a6e98);
        
    
        var popup_3e98167da0b0439aa4f5753d78ee6cce = L.popup({"maxWidth": "100%"});

        
            var html_357ade5b19614780b56a6806eacfb13b = $(`<div id="html_357ade5b19614780b56a6806eacfb13b" style="width: 100.0%; height: 100.0%;">Itaquera (Leste)  Mean Rental Price=21.20  El.shool=5.0  M.station=1.0  Park=3.0  Pharmacy=32.0  Supermkt=6.0</div>`)[0];
            popup_3e98167da0b0439aa4f5753d78ee6cce.setContent(html_357ade5b19614780b56a6806eacfb13b);
        

        circle_marker_87d132b018494fc78f5ef9019233fa14.bindPopup(popup_3e98167da0b0439aa4f5753d78ee6cce)
        ;

        
    
    
            var circle_marker_668cb00b431c4c828428b55940de5dfb = L.circleMarker(
                [-23.5641, -46.4348],
                {"bubblingMouseEvents": true, "color": "gray", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "gray", "fillOpacity": 0.3, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_ef0e65d7309a4fef8ed50563de7a6e98);
        
    
        var popup_72a8fcbad1434427912f3f3664bad2c9 = L.popup({"maxWidth": "100%"});

        
            var html_c1d1bf33d0934a1fb50f058f822d8cfa = $(`<div id="html_c1d1bf33d0934a1fb50f058f822d8cfa" style="width: 100.0%; height: 100.0%;">José Bonifácio (Leste)  Mean Rental Price=23.05  El.shool=6.0  M.station=0.0  Park=3.0  Pharmacy=8.0  Supermkt=5.0</div>`)[0];
            popup_72a8fcbad1434427912f3f3664bad2c9.setContent(html_c1d1bf33d0934a1fb50f058f822d8cfa);
        

        circle_marker_668cb00b431c4c828428b55940de5dfb.bindPopup(popup_72a8fcbad1434427912f3f3664bad2c9)
        ;

        
    
    
            var circle_marker_a30fadd3a6d84f578c630f054b5155da = L.circleMarker(
                [-23.5568, -46.4463],
                {"bubblingMouseEvents": true, "color": "gray", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "gray", "fillOpacity": 0.3, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_ef0e65d7309a4fef8ed50563de7a6e98);
        
    
        var popup_5f2c16b307c540199541a5f76f1c88a7 = L.popup({"maxWidth": "100%"});

        
            var html_bce46670166b47fbac15d9f42f4f4be6 = $(`<div id="html_bce46670166b47fbac15d9f42f4f4be6" style="width: 100.0%; height: 100.0%;">Parque do Carmo (Leste)  Mean Rental Price=23.05  El.shool=8.0  M.station=0.0  Park=4.0  Pharmacy=17.0  Supermkt=4.0</div>`)[0];
            popup_5f2c16b307c540199541a5f76f1c88a7.setContent(html_bce46670166b47fbac15d9f42f4f4be6);
        

        circle_marker_a30fadd3a6d84f578c630f054b5155da.bindPopup(popup_5f2c16b307c540199541a5f76f1c88a7)
        ;

        
    
    
            var circle_marker_be5d25b538544cd28222a20d03bdae00 = L.circleMarker(
                [-23.5745, -46.5793],
                {"bubblingMouseEvents": true, "color": "gray", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "gray", "fillOpacity": 0.3, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_ef0e65d7309a4fef8ed50563de7a6e98);
        
    
        var popup_480583fcbe7842c8815df0c46b23fef3 = L.popup({"maxWidth": "100%"});

        
            var html_e7179fad2ff445ddb06018f4de041654 = $(`<div id="html_e7179fad2ff445ddb06018f4de041654" style="width: 100.0%; height: 100.0%;">Água Rasa (Leste)  Mean Rental Price=23.05  El.shool=4.0  M.station=1.0  Park=3.0  Pharmacy=30.0  Supermkt=9.0</div>`)[0];
            popup_480583fcbe7842c8815df0c46b23fef3.setContent(html_e7179fad2ff445ddb06018f4de041654);
        

        circle_marker_be5d25b538544cd28222a20d03bdae00.bindPopup(popup_480583fcbe7842c8815df0c46b23fef3)
        ;

        
    
    
            var circle_marker_e00fbce38edb47d5b9ac1e60554ca619 = L.circleMarker(
                [-23.5444, -46.5949],
                {"bubblingMouseEvents": true, "color": "gray", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "gray", "fillOpacity": 0.3, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_ef0e65d7309a4fef8ed50563de7a6e98);
        
    
        var popup_6f3efe2e28da43e7a26fc25eea754352 = L.popup({"maxWidth": "100%"});

        
            var html_96afe8a8adb841c396607272d2b4bd58 = $(`<div id="html_96afe8a8adb841c396607272d2b4bd58" style="width: 100.0%; height: 100.0%;">Belém (Leste)  Mean Rental Price=27.50  El.shool=4.0  M.station=8.0  Park=4.0  Pharmacy=26.0  Supermkt=3.0</div>`)[0];
            popup_6f3efe2e28da43e7a26fc25eea754352.setContent(html_96afe8a8adb841c396607272d2b4bd58);
        

        circle_marker_e00fbce38edb47d5b9ac1e60554ca619.bindPopup(popup_6f3efe2e28da43e7a26fc25eea754352)
        ;

        
    
    
            var circle_marker_d20816adfb944834997defced76de508 = L.circleMarker(
                [-23.5545, -46.6108],
                {"bubblingMouseEvents": true, "color": "gray", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "gray", "fillOpacity": 0.3, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_ef0e65d7309a4fef8ed50563de7a6e98);
        
    
        var popup_f979b2924bc1438dbe468d84a6d6a618 = L.popup({"maxWidth": "100%"});

        
            var html_ab4c97aa08ba4557a7588e5558af79be = $(`<div id="html_ab4c97aa08ba4557a7588e5558af79be" style="width: 100.0%; height: 100.0%;">Brás (Leste)  Mean Rental Price=29.40  El.shool=1.0  M.station=8.0  Park=2.0  Pharmacy=13.0  Supermkt=5.0</div>`)[0];
            popup_f979b2924bc1438dbe468d84a6d6a618.setContent(html_ab4c97aa08ba4557a7588e5558af79be);
        

        circle_marker_d20816adfb944834997defced76de508.bindPopup(popup_f979b2924bc1438dbe468d84a6d6a618)
        ;

        
    
    
            var circle_marker_8c0aefc32a2c4845b287a819bdf99096 = L.circleMarker(
                [-23.558, -46.6083],
                {"bubblingMouseEvents": true, "color": "gray", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "gray", "fillOpacity": 0.3, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_ef0e65d7309a4fef8ed50563de7a6e98);
        
    
        var popup_f372284893a943d7b1c68a9892c11c39 = L.popup({"maxWidth": "100%"});

        
            var html_fb6d01f0377841ce9907aaca1499751c = $(`<div id="html_fb6d01f0377841ce9907aaca1499751c" style="width: 100.0%; height: 100.0%;">Moóca (Leste)  Mean Rental Price=23.05  El.shool=1.0  M.station=8.0  Park=4.0  Pharmacy=26.0  Supermkt=6.0</div>`)[0];
            popup_f372284893a943d7b1c68a9892c11c39.setContent(html_fb6d01f0377841ce9907aaca1499751c);
        

        circle_marker_8c0aefc32a2c4845b287a819bdf99096.bindPopup(popup_f372284893a943d7b1c68a9892c11c39)
        ;

        
    
    
            var circle_marker_254b9b4f48e44aa5bd568bb0e68f655b = L.circleMarker(
                [-23.533, -46.6158],
                {"bubblingMouseEvents": true, "color": "gray", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "gray", "fillOpacity": 0.3, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_ef0e65d7309a4fef8ed50563de7a6e98);
        
    
        var popup_7f349018194245c9a3e765f0a14cedd2 = L.popup({"maxWidth": "100%"});

        
            var html_59cf6e57917843d296b29d11ca730602 = $(`<div id="html_59cf6e57917843d296b29d11ca730602" style="width: 100.0%; height: 100.0%;">Pari (Leste)  Mean Rental Price=23.05  El.shool=0.0  M.station=2.0  Park=0.0  Pharmacy=7.0  Supermkt=1.0</div>`)[0];
            popup_7f349018194245c9a3e765f0a14cedd2.setContent(html_59cf6e57917843d296b29d11ca730602);
        

        circle_marker_254b9b4f48e44aa5bd568bb0e68f655b.bindPopup(popup_7f349018194245c9a3e765f0a14cedd2)
        ;

        
    
    
            var marker_02a585b4254f47139b9102431ba2fb50 = L.marker(
                [-23.5403, -46.5766],
                {}
            ).addTo(map_ef0e65d7309a4fef8ed50563de7a6e98);
        
    
            var icon_ddbcfbaf3cc440389fa1d0193be2acd5 = L.AwesomeMarkers.icon(
                {"extraClasses": "fa-rotate-0", "icon": "info-sign", "iconColor": "white", "markerColor": "blue", "prefix": "glyphicon"}
            );
            marker_02a585b4254f47139b9102431ba2fb50.setIcon(icon_ddbcfbaf3cc440389fa1d0193be2acd5);
        
    
        var popup_b959c4e015594dbd9aba51302f071c1b = L.popup({"maxWidth": "100%"});

        
            var html_d79a72a721db4f30b0edabacf49a91de = $(`<div id="html_d79a72a721db4f30b0edabacf49a91de" style="width: 100.0%; height: 100.0%;">Tatuapé (Leste)  Mean Rental Price=25.70  El.shool=1.0  M.station=6.0  Park=5.0  Pharmacy=26.0  Supermkt=9.0</div>`)[0];
            popup_b959c4e015594dbd9aba51302f071c1b.setContent(html_d79a72a721db4f30b0edabacf49a91de);
        

        marker_02a585b4254f47139b9102431ba2fb50.bindPopup(popup_b959c4e015594dbd9aba51302f071c1b)
        ;

        
    
    
            var circle_marker_38041d73b2104004b82d99478e08a3b5 = L.circleMarker(
                [-23.5275, -46.5006],
                {"bubblingMouseEvents": true, "color": "gray", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "gray", "fillOpacity": 0.3, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_ef0e65d7309a4fef8ed50563de7a6e98);
        
    
        var popup_1d862866339f4e0e980aa465b38299e2 = L.popup({"maxWidth": "100%"});

        
            var html_f0cba6d6d1de4a718809f43d835e37e0 = $(`<div id="html_f0cba6d6d1de4a718809f43d835e37e0" style="width: 100.0%; height: 100.0%;">Artur Alvim (Leste)  Mean Rental Price=18.70  El.shool=4.0  M.station=4.0  Park=0.0  Pharmacy=25.0  Supermkt=3.0</div>`)[0];
            popup_1d862866339f4e0e980aa465b38299e2.setContent(html_f0cba6d6d1de4a718809f43d835e37e0);
        

        circle_marker_38041d73b2104004b82d99478e08a3b5.bindPopup(popup_1d862866339f4e0e980aa465b38299e2)
        ;

        
    
    
            var circle_marker_f9eca6af0ee34076acced04aed839a18 = L.circleMarker(
                [-23.5126, -46.546],
                {"bubblingMouseEvents": true, "color": "gray", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "gray", "fillOpacity": 0.3, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_ef0e65d7309a4fef8ed50563de7a6e98);
        
    
        var popup_1bb268f864ed4c54a6c1c29cc46c1728 = L.popup({"maxWidth": "100%"});

        
            var html_e1bb82f8a6f5458f930a9fc8c4a78d14 = $(`<div id="html_e1bb82f8a6f5458f930a9fc8c4a78d14" style="width: 100.0%; height: 100.0%;">Cangaíba (Leste)  Mean Rental Price=21.20  El.shool=2.0  M.station=1.0  Park=3.0  Pharmacy=33.0  Supermkt=11.0</div>`)[0];
            popup_1bb268f864ed4c54a6c1c29cc46c1728.setContent(html_e1bb82f8a6f5458f930a9fc8c4a78d14);
        

        circle_marker_f9eca6af0ee34076acced04aed839a18.bindPopup(popup_1bb268f864ed4c54a6c1c29cc46c1728)
        ;

        
    
    
            var circle_marker_0f2e01d45d0b47d296bb16f69c7623dd = L.circleMarker(
                [-23.5342, -46.5429],
                {"bubblingMouseEvents": true, "color": "gray", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "gray", "fillOpacity": 0.3, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_ef0e65d7309a4fef8ed50563de7a6e98);
        
    
        var popup_c3c383b638be4c388907ae901edc8659 = L.popup({"maxWidth": "100%"});

        
            var html_51153692ca28442ea6d38ccd082745fb = $(`<div id="html_51153692ca28442ea6d38ccd082745fb" style="width: 100.0%; height: 100.0%;">Penha (Leste)  Mean Rental Price=23.10  El.shool=1.0  M.station=2.0  Park=1.0  Pharmacy=34.0  Supermkt=10.0</div>`)[0];
            popup_c3c383b638be4c388907ae901edc8659.setContent(html_51153692ca28442ea6d38ccd082745fb);
        

        circle_marker_0f2e01d45d0b47d296bb16f69c7623dd.bindPopup(popup_c3c383b638be4c388907ae901edc8659)
        ;

        
    
    
            var circle_marker_dc9de9a638d0442d915c4aacb61a5e9e = L.circleMarker(
                [-23.5362, -46.5246],
                {"bubblingMouseEvents": true, "color": "gray", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "gray", "fillOpacity": 0.3, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_ef0e65d7309a4fef8ed50563de7a6e98);
        
    
        var popup_d4499c5e9d0e4ae9b320665566b09748 = L.popup({"maxWidth": "100%"});

        
            var html_cfb605eb3b71416284f41d350ae5ceb9 = $(`<div id="html_cfb605eb3b71416284f41d350ae5ceb9" style="width: 100.0%; height: 100.0%;">Vila Matilde (Leste)  Mean Rental Price=22.20  El.shool=6.0  M.station=2.0  Park=3.0  Pharmacy=25.0  Supermkt=12.0</div>`)[0];
            popup_d4499c5e9d0e4ae9b320665566b09748.setContent(html_cfb605eb3b71416284f41d350ae5ceb9);
        

        circle_marker_dc9de9a638d0442d915c4aacb61a5e9e.bindPopup(popup_d4499c5e9d0e4ae9b320665566b09748)
        ;

        
    
    
            var circle_marker_0dc76cc19e4542568d7de9463dffd1a2 = L.circleMarker(
                [-23.6182, -46.419],
                {"bubblingMouseEvents": true, "color": "gray", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "gray", "fillOpacity": 0.3, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_ef0e65d7309a4fef8ed50563de7a6e98);
        
    
        var popup_30d59092e09046e3b04cf0d4b72d4df1 = L.popup({"maxWidth": "100%"});

        
            var html_1706412a90fa4c8eb2d6b785a3bc98ad = $(`<div id="html_1706412a90fa4c8eb2d6b785a3bc98ad" style="width: 100.0%; height: 100.0%;">Iguatemi (Leste)  Mean Rental Price=23.05  El.shool=0.0  M.station=0.0  Park=0.0  Pharmacy=1.0  Supermkt=0.0</div>`)[0];
            popup_30d59092e09046e3b04cf0d4b72d4df1.setContent(html_1706412a90fa4c8eb2d6b785a3bc98ad);
        

        circle_marker_0dc76cc19e4542568d7de9463dffd1a2.bindPopup(popup_30d59092e09046e3b04cf0d4b72d4df1)
        ;

        
    
    
            var circle_marker_e2fb6d493ad3434f8369b2cbde352271 = L.circleMarker(
                [-23.6123, -46.4773],
                {"bubblingMouseEvents": true, "color": "gray", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "gray", "fillOpacity": 0.3, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_ef0e65d7309a4fef8ed50563de7a6e98);
        
    
        var popup_490ed0ca88d1473fb1a78162eaf6ffd8 = L.popup({"maxWidth": "100%"});

        
            var html_46e1c8bc853b494db3a1214431f5d99b = $(`<div id="html_46e1c8bc853b494db3a1214431f5d99b" style="width: 100.0%; height: 100.0%;">São Mateus (Leste)  Mean Rental Price=23.05  El.shool=7.0  M.station=1.0  Park=4.0  Pharmacy=30.0  Supermkt=4.0</div>`)[0];
            popup_490ed0ca88d1473fb1a78162eaf6ffd8.setContent(html_46e1c8bc853b494db3a1214431f5d99b);
        

        circle_marker_e2fb6d493ad3434f8369b2cbde352271.bindPopup(popup_490ed0ca88d1473fb1a78162eaf6ffd8)
        ;

        
    
    
            var circle_marker_36d2bf39de39443e8b57a4db71e11cbd = L.circleMarker(
                [-23.6272, -46.4532],
                {"bubblingMouseEvents": true, "color": "gray", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "gray", "fillOpacity": 0.3, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_ef0e65d7309a4fef8ed50563de7a6e98);
        
    
        var popup_5f380ac193f44b9d85cecd6466b1f342 = L.popup({"maxWidth": "100%"});

        
            var html_ff408eb27d3a4e519ecb7f2fba9be905 = $(`<div id="html_ff408eb27d3a4e519ecb7f2fba9be905" style="width: 100.0%; height: 100.0%;">São Rafael (Leste)  Mean Rental Price=23.05  El.shool=1.0  M.station=0.0  Park=2.0  Pharmacy=10.0  Supermkt=2.0</div>`)[0];
            popup_5f380ac193f44b9d85cecd6466b1f342.setContent(html_ff408eb27d3a4e519ecb7f2fba9be905);
        

        circle_marker_36d2bf39de39443e8b57a4db71e11cbd.bindPopup(popup_5f380ac193f44b9d85cecd6466b1f342)
        ;

        
    
    
            var circle_marker_3cdc2d57169643dfa10705c1905a0933 = L.circleMarker(
                [-23.5027, -46.4383],
                {"bubblingMouseEvents": true, "color": "gray", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "gray", "fillOpacity": 0.3, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_ef0e65d7309a4fef8ed50563de7a6e98);
        
    
        var popup_ddb1b8adc0e04b939b160992cf689a48 = L.popup({"maxWidth": "100%"});

        
            var html_d361119b618f4628a4c3f2e18181da78 = $(`<div id="html_d361119b618f4628a4c3f2e18181da78" style="width: 100.0%; height: 100.0%;">Jardim Helena (Leste)  Mean Rental Price=23.05  El.shool=8.0  M.station=0.0  Park=3.0  Pharmacy=26.0  Supermkt=3.0</div>`)[0];
            popup_ddb1b8adc0e04b939b160992cf689a48.setContent(html_d361119b618f4628a4c3f2e18181da78);
        

        circle_marker_3cdc2d57169643dfa10705c1905a0933.bindPopup(popup_ddb1b8adc0e04b939b160992cf689a48)
        ;

        
    
    
            var circle_marker_47da938b4ac0486a93cd87bc5d620e4e = L.circleMarker(
                [-23.4949, -46.4391],
                {"bubblingMouseEvents": true, "color": "gray", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "gray", "fillOpacity": 0.3, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_ef0e65d7309a4fef8ed50563de7a6e98);
        
    
        var popup_bef6669382f342dfbcf026077f21ae47 = L.popup({"maxWidth": "100%"});

        
            var html_448ff4f14c034c2aadd87304e3729776 = $(`<div id="html_448ff4f14c034c2aadd87304e3729776" style="width: 100.0%; height: 100.0%;">São
   Miguel  (Leste)  Mean Rental Price=23.05  El.shool=8.0  M.station=0.0  Park=1.0  Pharmacy=22.0  Supermkt=2.0</div>`)[0];
            popup_bef6669382f342dfbcf026077f21ae47.setContent(html_448ff4f14c034c2aadd87304e3729776);
        

        circle_marker_47da938b4ac0486a93cd87bc5d620e4e.bindPopup(popup_bef6669382f342dfbcf026077f21ae47)
        ;

        
    
    
            var circle_marker_96c2e82db3c94d48a8aba70df3d34245 = L.circleMarker(
                [-23.5003, -46.4587],
                {"bubblingMouseEvents": true, "color": "gray", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "gray", "fillOpacity": 0.3, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_ef0e65d7309a4fef8ed50563de7a6e98);
        
    
        var popup_91f512214c6d4dfd997265366a3146bd = L.popup({"maxWidth": "100%"});

        
            var html_a492b623776649a196a10f74cc00e36f = $(`<div id="html_a492b623776649a196a10f74cc00e36f" style="width: 100.0%; height: 100.0%;">Vila Jacuí (Leste)  Mean Rental Price=23.05  El.shool=6.0  M.station=0.0  Park=4.0  Pharmacy=15.0  Supermkt=7.0</div>`)[0];
            popup_91f512214c6d4dfd997265366a3146bd.setContent(html_a492b623776649a196a10f74cc00e36f);
        

        circle_marker_96c2e82db3c94d48a8aba70df3d34245.bindPopup(popup_91f512214c6d4dfd997265366a3146bd)
        ;

        
    
    
            var circle_marker_caaeee95e93a49a5b0f81a0bddca9622 = L.circleMarker(
                [-23.6147, -46.5008],
                {"bubblingMouseEvents": true, "color": "gray", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "gray", "fillOpacity": 0.3, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_ef0e65d7309a4fef8ed50563de7a6e98);
        
    
        var popup_397c6551cc8144c5b69d147523d577d8 = L.popup({"maxWidth": "100%"});

        
            var html_98d11c048f254f7fbf7374f1f83f74d8 = $(`<div id="html_98d11c048f254f7fbf7374f1f83f74d8" style="width: 100.0%; height: 100.0%;">Sapopemba (Leste)  Mean Rental Price=23.05  El.shool=13.0  M.station=4.0  Park=3.0  Pharmacy=23.0  Supermkt=5.0</div>`)[0];
            popup_397c6551cc8144c5b69d147523d577d8.setContent(html_98d11c048f254f7fbf7374f1f83f74d8);
        

        circle_marker_caaeee95e93a49a5b0f81a0bddca9622.bindPopup(popup_397c6551cc8144c5b69d147523d577d8)
        ;

        
    
    
            var circle_marker_007283ccfc20485f944558d4601b6441 = L.circleMarker(
                [-23.5889, -46.5446],
                {"bubblingMouseEvents": true, "color": "gray", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "gray", "fillOpacity": 0.3, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_ef0e65d7309a4fef8ed50563de7a6e98);
        
    
        var popup_870f6f22a72941ef8fa77294260f338c = L.popup({"maxWidth": "100%"});

        
            var html_6ccaa1998919478a8aa9493f1ef24cdc = $(`<div id="html_6ccaa1998919478a8aa9493f1ef24cdc" style="width: 100.0%; height: 100.0%;">São Lucas (Leste)  Mean Rental Price=23.05  El.shool=10.0  M.station=1.0  Park=2.0  Pharmacy=28.0  Supermkt=8.0</div>`)[0];
            popup_870f6f22a72941ef8fa77294260f338c.setContent(html_6ccaa1998919478a8aa9493f1ef24cdc);
        

        circle_marker_007283ccfc20485f944558d4601b6441.bindPopup(popup_870f6f22a72941ef8fa77294260f338c)
        ;

        
    
    
            var marker_489aa0a09fa24b01a00dc181ae97ffaf = L.marker(
                [-23.5846, -46.5834],
                {}
            ).addTo(map_ef0e65d7309a4fef8ed50563de7a6e98);
        
    
            var icon_8378b5e48c24487d87a6c4ade0aadaa6 = L.AwesomeMarkers.icon(
                {"extraClasses": "fa-rotate-0", "icon": "info-sign", "iconColor": "white", "markerColor": "blue", "prefix": "glyphicon"}
            );
            marker_489aa0a09fa24b01a00dc181ae97ffaf.setIcon(icon_8378b5e48c24487d87a6c4ade0aadaa6);
        
    
        var popup_cf72e1c8d7154df7bf5b4632213d2297 = L.popup({"maxWidth": "100%"});

        
            var html_28d92aaaf25b4834a7f2d006dfb1c736 = $(`<div id="html_28d92aaaf25b4834a7f2d006dfb1c736" style="width: 100.0%; height: 100.0%;">Vila Prudente (Leste)  Mean Rental Price=25.90  El.shool=5.0  M.station=3.0  Park=7.0  Pharmacy=23.0  Supermkt=8.0</div>`)[0];
            popup_cf72e1c8d7154df7bf5b4632213d2297.setContent(html_28d92aaaf25b4834a7f2d006dfb1c736);
        

        marker_489aa0a09fa24b01a00dc181ae97ffaf.bindPopup(popup_cf72e1c8d7154df7bf5b4632213d2297)
        ;

        
    
    
            var circle_marker_a2d1ce99631e41839d7ab87ba6c4c189 = L.circleMarker(
                [-23.4495, -46.6637],
                {"bubblingMouseEvents": true, "color": "gray", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "gray", "fillOpacity": 0.3, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_ef0e65d7309a4fef8ed50563de7a6e98);
        
    
        var popup_be243049220b4a5ab9c4d7faae21b9b3 = L.popup({"maxWidth": "100%"});

        
            var html_70c068fb9e104382b84860e6cd2a8d05 = $(`<div id="html_70c068fb9e104382b84860e6cd2a8d05" style="width: 100.0%; height: 100.0%;">Cachoeirinha (Norte)  Mean Rental Price=21.00  El.shool=3.0  M.station=0.0  Park=1.0  Pharmacy=13.0  Supermkt=3.0</div>`)[0];
            popup_be243049220b4a5ab9c4d7faae21b9b3.setContent(html_70c068fb9e104382b84860e6cd2a8d05);
        

        circle_marker_a2d1ce99631e41839d7ab87ba6c4c189.bindPopup(popup_be243049220b4a5ab9c4d7faae21b9b3)
        ;

        
    
    
            var circle_marker_bc4044f230644f86b6fdf303eb8b9d69 = L.circleMarker(
                [-23.5059, -46.6561],
                {"bubblingMouseEvents": true, "color": "gray", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "gray", "fillOpacity": 0.3, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_ef0e65d7309a4fef8ed50563de7a6e98);
        
    
        var popup_2dcc35dd81cd42baa696c7022315af27 = L.popup({"maxWidth": "100%"});

        
            var html_b1d7ee1ac78749fe8fc8c9d050883067 = $(`<div id="html_b1d7ee1ac78749fe8fc8c9d050883067" style="width: 100.0%; height: 100.0%;">Casa Verde (Norte)  Mean Rental Price=23.10  El.shool=1.0  M.station=0.0  Park=0.0  Pharmacy=19.0  Supermkt=9.0</div>`)[0];
            popup_2dcc35dd81cd42baa696c7022315af27.setContent(html_b1d7ee1ac78749fe8fc8c9d050883067);
        

        circle_marker_bc4044f230644f86b6fdf303eb8b9d69.bindPopup(popup_2dcc35dd81cd42baa696c7022315af27)
        ;

        
    
    
            var circle_marker_d1194f5c80ec4798994887a8d27c743e = L.circleMarker(
                [-23.5033, -46.6753],
                {"bubblingMouseEvents": true, "color": "gray", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "gray", "fillOpacity": 0.3, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_ef0e65d7309a4fef8ed50563de7a6e98);
        
    
        var popup_58c3383b285a4336a857a9b419684ac6 = L.popup({"maxWidth": "100%"});

        
            var html_87d2b38b779841939292947ae6482d03 = $(`<div id="html_87d2b38b779841939292947ae6482d03" style="width: 100.0%; height: 100.0%;">Limão (Norte)  Mean Rental Price=23.30  El.shool=3.0  M.station=0.0  Park=0.0  Pharmacy=23.0  Supermkt=7.0</div>`)[0];
            popup_58c3383b285a4336a857a9b419684ac6.setContent(html_87d2b38b779841939292947ae6482d03);
        

        circle_marker_d1194f5c80ec4798994887a8d27c743e.bindPopup(popup_58c3383b285a4336a857a9b419684ac6)
        ;

        
    
    
            var circle_marker_44310117c11b49a7b12becfbcca581a8 = L.circleMarker(
                [-23.4717, -46.7059],
                {"bubblingMouseEvents": true, "color": "gray", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "gray", "fillOpacity": 0.3, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_ef0e65d7309a4fef8ed50563de7a6e98);
        
    
        var popup_88043ef3e6504173bfbe21d63ed44041 = L.popup({"maxWidth": "100%"});

        
            var html_022c531f5d7f486a95468b039cfe0930 = $(`<div id="html_022c531f5d7f486a95468b039cfe0930" style="width: 100.0%; height: 100.0%;">Brasilândia (Norte)  Mean Rental Price=22.73  El.shool=10.0  M.station=0.0  Park=4.0  Pharmacy=28.0  Supermkt=7.0</div>`)[0];
            popup_88043ef3e6504173bfbe21d63ed44041.setContent(html_022c531f5d7f486a95468b039cfe0930);
        

        circle_marker_44310117c11b49a7b12becfbcca581a8.bindPopup(popup_88043ef3e6504173bfbe21d63ed44041)
        ;

        
    
    
            var circle_marker_bb3555aa61ff454db4c852c164f6bf26 = L.circleMarker(
                [-23.4717, -46.7059],
                {"bubblingMouseEvents": true, "color": "gray", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "gray", "fillOpacity": 0.3, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_ef0e65d7309a4fef8ed50563de7a6e98);
        
    
        var popup_9ea8a43d508c4d998da99918b131b418 = L.popup({"maxWidth": "100%"});

        
            var html_048a7150214a4baa8010d513de13ff64 = $(`<div id="html_048a7150214a4baa8010d513de13ff64" style="width: 100.0%; height: 100.0%;">Freguesia do Ó (Norte)  Mean Rental Price=22.73  El.shool=11.0  M.station=0.0  Park=3.0  Pharmacy=19.0  Supermkt=6.0</div>`)[0];
            popup_9ea8a43d508c4d998da99918b131b418.setContent(html_048a7150214a4baa8010d513de13ff64);
        

        circle_marker_bb3555aa61ff454db4c852c164f6bf26.bindPopup(popup_9ea8a43d508c4d998da99918b131b418)
        ;

        
    
    
            var circle_marker_6826bd998dfe4647bd2ab3fb6b59ab83 = L.circleMarker(
                [-23.4083, -46.5804],
                {"bubblingMouseEvents": true, "color": "gray", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "gray", "fillOpacity": 0.3, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_ef0e65d7309a4fef8ed50563de7a6e98);
        
    
        var popup_f761c7302f2f4d30bbd8ada032d40828 = L.popup({"maxWidth": "100%"});

        
            var html_b9448d6279d04606a9c4c3f81c5e6e11 = $(`<div id="html_b9448d6279d04606a9c4c3f81c5e6e11" style="width: 100.0%; height: 100.0%;">Jaçanã (Norte)  Mean Rental Price=20.20  El.shool=0.0  M.station=0.0  Park=1.0  Pharmacy=0.0  Supermkt=0.0</div>`)[0];
            popup_f761c7302f2f4d30bbd8ada032d40828.setContent(html_b9448d6279d04606a9c4c3f81c5e6e11);
        

        circle_marker_6826bd998dfe4647bd2ab3fb6b59ab83.bindPopup(popup_f761c7302f2f4d30bbd8ada032d40828)
        ;

        
    
    
            var circle_marker_0df70b61624e4237a7502eac46889742 = L.circleMarker(
                [-23.4083, -46.5804],
                {"bubblingMouseEvents": true, "color": "gray", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "gray", "fillOpacity": 0.3, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_ef0e65d7309a4fef8ed50563de7a6e98);
        
    
        var popup_347d484b1d9d4e6c954510a6bbbe95ed = L.popup({"maxWidth": "100%"});

        
            var html_27522e0e9d2b4871b8b8a6391342df4f = $(`<div id="html_27522e0e9d2b4871b8b8a6391342df4f" style="width: 100.0%; height: 100.0%;">Tremembé (Norte)  Mean Rental Price=20.10  El.shool=1.0  M.station=0.0  Park=1.0  Pharmacy=1.0  Supermkt=0.0</div>`)[0];
            popup_347d484b1d9d4e6c954510a6bbbe95ed.setContent(html_27522e0e9d2b4871b8b8a6391342df4f);
        

        circle_marker_0df70b61624e4237a7502eac46889742.bindPopup(popup_347d484b1d9d4e6c954510a6bbbe95ed)
        ;

        
    
    
            var circle_marker_e44dfb53e1a14c949355e7ff7b7be04a = L.circleMarker(
                [-23.4252, -46.7771],
                {"bubblingMouseEvents": true, "color": "gray", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "gray", "fillOpacity": 0.3, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_ef0e65d7309a4fef8ed50563de7a6e98);
        
    
        var popup_e8f63b4e1b1f4a918066887e2c1a40dd = L.popup({"maxWidth": "100%"});

        
            var html_bdb801b14f0945758b0c28d0eaecdeb8 = $(`<div id="html_bdb801b14f0945758b0c28d0eaecdeb8" style="width: 100.0%; height: 100.0%;">Anhanguera (Norte)  Mean Rental Price=22.73  El.shool=3.0  M.station=0.0  Park=3.0  Pharmacy=0.0  Supermkt=1.0</div>`)[0];
            popup_e8f63b4e1b1f4a918066887e2c1a40dd.setContent(html_bdb801b14f0945758b0c28d0eaecdeb8);
        

        circle_marker_e44dfb53e1a14c949355e7ff7b7be04a.bindPopup(popup_e8f63b4e1b1f4a918066887e2c1a40dd)
        ;

        
    
    
            var circle_marker_e4ed81878874457fa63b970f85e31db8 = L.circleMarker(
                [-23.4074, -46.7522],
                {"bubblingMouseEvents": true, "color": "gray", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "gray", "fillOpacity": 0.3, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_ef0e65d7309a4fef8ed50563de7a6e98);
        
    
        var popup_6602d879858f4a18b46c3f1da4239388 = L.popup({"maxWidth": "100%"});

        
            var html_aeb42849b95a404683c6225d49574e75 = $(`<div id="html_aeb42849b95a404683c6225d49574e75" style="width: 100.0%; height: 100.0%;">Perus (Norte)  Mean Rental Price=22.73  El.shool=3.0  M.station=0.0  Park=0.0  Pharmacy=3.0  Supermkt=5.0</div>`)[0];
            popup_6602d879858f4a18b46c3f1da4239388.setContent(html_aeb42849b95a404683c6225d49574e75);
        

        circle_marker_e4ed81878874457fa63b970f85e31db8.bindPopup(popup_6602d879858f4a18b46c3f1da4239388)
        ;

        
    
    
            var circle_marker_6962e4e757a34ccbbadf7c02e7e59ad2 = L.circleMarker(
                [-23.4472, -46.7219],
                {"bubblingMouseEvents": true, "color": "gray", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "gray", "fillOpacity": 0.3, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_ef0e65d7309a4fef8ed50563de7a6e98);
        
    
        var popup_cce75287a48f4c4ab3363caf3e057835 = L.popup({"maxWidth": "100%"});

        
            var html_7ee48606c13b4ccb87a6e06de20054ba = $(`<div id="html_7ee48606c13b4ccb87a6e06de20054ba" style="width: 100.0%; height: 100.0%;">Jaraguá (Norte)  Mean Rental Price=22.73  El.shool=7.0  M.station=0.0  Park=4.0  Pharmacy=14.0  Supermkt=1.0</div>`)[0];
            popup_cce75287a48f4c4ab3363caf3e057835.setContent(html_7ee48606c13b4ccb87a6e06de20054ba);
        

        circle_marker_6962e4e757a34ccbbadf7c02e7e59ad2.bindPopup(popup_cce75287a48f4c4ab3363caf3e057835)
        ;

        
    
    
            var circle_marker_fa13d594806148e2bba316dae7505ce0 = L.circleMarker(
                [-23.4885, -46.726],
                {"bubblingMouseEvents": true, "color": "gray", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "gray", "fillOpacity": 0.3, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_ef0e65d7309a4fef8ed50563de7a6e98);
        
    
        var popup_842a505c01e3448ba80fc39bd77d8f0f = L.popup({"maxWidth": "100%"});

        
            var html_efa5122f3306404ca53189848b26c642 = $(`<div id="html_efa5122f3306404ca53189848b26c642" style="width: 100.0%; height: 100.0%;">Pirituba (Norte)  Mean Rental Price=23.70  El.shool=5.0  M.station=0.0  Park=7.0  Pharmacy=33.0  Supermkt=5.0</div>`)[0];
            popup_842a505c01e3448ba80fc39bd77d8f0f.setContent(html_efa5122f3306404ca53189848b26c642);
        

        circle_marker_fa13d594806148e2bba316dae7505ce0.bindPopup(popup_842a505c01e3448ba80fc39bd77d8f0f)
        ;

        
    
    
            var circle_marker_7dd1012725114c72815304009fbdf3b7 = L.circleMarker(
                [-23.4938, -46.7456],
                {"bubblingMouseEvents": true, "color": "gray", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "gray", "fillOpacity": 0.3, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_ef0e65d7309a4fef8ed50563de7a6e98);
        
    
        var popup_8f9358ee91284938b8bda2676097bfca = L.popup({"maxWidth": "100%"});

        
            var html_c7cdea65f8574e548bf58616ed7f2400 = $(`<div id="html_c7cdea65f8574e548bf58616ed7f2400" style="width: 100.0%; height: 100.0%;">São Domingos (Norte)  Mean Rental Price=22.73  El.shool=6.0  M.station=0.0  Park=5.0  Pharmacy=33.0  Supermkt=6.0</div>`)[0];
            popup_8f9358ee91284938b8bda2676097bfca.setContent(html_c7cdea65f8574e548bf58616ed7f2400);
        

        circle_marker_7dd1012725114c72815304009fbdf3b7.bindPopup(popup_8f9358ee91284938b8bda2676097bfca)
        ;

        
    
    
            var circle_marker_c2ae8e20d99741a38a0251b81a5c63da = L.circleMarker(
                [-23.4778, -46.6208],
                {"bubblingMouseEvents": true, "color": "gray", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "gray", "fillOpacity": 0.3, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_ef0e65d7309a4fef8ed50563de7a6e98);
        
    
        var popup_733d17b675d54b27ae5c517e835bbc17 = L.popup({"maxWidth": "100%"});

        
            var html_e1f84adc09054ddeb396b01aae671db8 = $(`<div id="html_e1f84adc09054ddeb396b01aae671db8" style="width: 100.0%; height: 100.0%;">Mandaqui (Norte)  Mean Rental Price=21.70  El.shool=4.0  M.station=3.0  Park=0.0  Pharmacy=29.0  Supermkt=9.0</div>`)[0];
            popup_733d17b675d54b27ae5c517e835bbc17.setContent(html_e1f84adc09054ddeb396b01aae671db8);
        

        circle_marker_c2ae8e20d99741a38a0251b81a5c63da.bindPopup(popup_733d17b675d54b27ae5c517e835bbc17)
        ;

        
    
    
            var marker_47bd67b1032a4f43b9b29458befcf1a6 = L.marker(
                [-23.5026, -46.6252],
                {}
            ).addTo(map_ef0e65d7309a4fef8ed50563de7a6e98);
        
    
            var icon_33a8b9cb403c41e29c64f620551661a8 = L.AwesomeMarkers.icon(
                {"extraClasses": "fa-rotate-0", "icon": "info-sign", "iconColor": "white", "markerColor": "blue", "prefix": "glyphicon"}
            );
            marker_47bd67b1032a4f43b9b29458befcf1a6.setIcon(icon_33a8b9cb403c41e29c64f620551661a8);
        
    
        var popup_455e5cb2ad59481d9006500976e40a2d = L.popup({"maxWidth": "100%"});

        
            var html_f16be45b77c54d398df0777efff4e101 = $(`<div id="html_f16be45b77c54d398df0777efff4e101" style="width: 100.0%; height: 100.0%;">Santana (Norte)  Mean Rental Price=23.80  El.shool=2.0  M.station=5.0  Park=2.0  Pharmacy=31.0  Supermkt=8.0</div>`)[0];
            popup_455e5cb2ad59481d9006500976e40a2d.setContent(html_f16be45b77c54d398df0777efff4e101);
        

        marker_47bd67b1032a4f43b9b29458befcf1a6.bindPopup(popup_455e5cb2ad59481d9006500976e40a2d)
        ;

        
    
    
            var circle_marker_0a9c2070400e4990b44cf967415596eb = L.circleMarker(
                [-23.4778, -46.6208],
                {"bubblingMouseEvents": true, "color": "gray", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "gray", "fillOpacity": 0.3, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_ef0e65d7309a4fef8ed50563de7a6e98);
        
    
        var popup_2dc207291a634b878e81d91a896698b8 = L.popup({"maxWidth": "100%"});

        
            var html_9d564c235fa342099c9733d4222c65f5 = $(`<div id="html_9d564c235fa342099c9733d4222c65f5" style="width: 100.0%; height: 100.0%;">Tucuruvi (Norte)  Mean Rental Price=23.00  El.shool=6.0  M.station=2.0  Park=0.0  Pharmacy=35.0  Supermkt=2.0</div>`)[0];
            popup_2dc207291a634b878e81d91a896698b8.setContent(html_9d564c235fa342099c9733d4222c65f5);
        

        circle_marker_0a9c2070400e4990b44cf967415596eb.bindPopup(popup_2dc207291a634b878e81d91a896698b8)
        ;

        
    
    
            var circle_marker_3fc1b58c5b7243158f38fc127a337ee6 = L.circleMarker(
                [-23.5171, -46.608],
                {"bubblingMouseEvents": true, "color": "gray", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "gray", "fillOpacity": 0.3, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_ef0e65d7309a4fef8ed50563de7a6e98);
        
    
        var popup_37a6c852f01b4acea63a302913ecb14e = L.popup({"maxWidth": "100%"});

        
            var html_1f51f604cd2340d0b0c11a17c65ad79f = $(`<div id="html_1f51f604cd2340d0b0c11a17c65ad79f" style="width: 100.0%; height: 100.0%;">Vila Guilherme (Norte)  Mean Rental Price=25.80  El.shool=2.0  M.station=0.0  Park=3.0  Pharmacy=18.0  Supermkt=2.0</div>`)[0];
            popup_37a6c852f01b4acea63a302913ecb14e.setContent(html_1f51f604cd2340d0b0c11a17c65ad79f);
        

        circle_marker_3fc1b58c5b7243158f38fc127a337ee6.bindPopup(popup_37a6c852f01b4acea63a302913ecb14e)
        ;

        
    
    
            var marker_75e63c73fa7b4153a75e9e476f1bb1be = L.marker(
                [-23.5132, -46.5892],
                {}
            ).addTo(map_ef0e65d7309a4fef8ed50563de7a6e98);
        
    
            var icon_e526e63827404ea4ab42c592a26339ef = L.AwesomeMarkers.icon(
                {"extraClasses": "fa-rotate-0", "icon": "info-sign", "iconColor": "white", "markerColor": "blue", "prefix": "glyphicon"}
            );
            marker_75e63c73fa7b4153a75e9e476f1bb1be.setIcon(icon_e526e63827404ea4ab42c592a26339ef);
        
    
        var popup_35f8f11ec88e495a9a5a996141bd30bc = L.popup({"maxWidth": "100%"});

        
            var html_b49fbf1ca5a947068019bd2738e03584 = $(`<div id="html_b49fbf1ca5a947068019bd2738e03584" style="width: 100.0%; height: 100.0%;">Vila Maria (Norte)  Mean Rental Price=24.80  El.shool=8.0  M.station=1.0  Park=4.0  Pharmacy=26.0  Supermkt=5.0</div>`)[0];
            popup_35f8f11ec88e495a9a5a996141bd30bc.setContent(html_b49fbf1ca5a947068019bd2738e03584);
        

        marker_75e63c73fa7b4153a75e9e476f1bb1be.bindPopup(popup_35f8f11ec88e495a9a5a996141bd30bc)
        ;

        
    
    
            var circle_marker_cf70d046f4bf4fd3a1fbcffacb5b74c4 = L.circleMarker(
                [-23.4877, -46.5845],
                {"bubblingMouseEvents": true, "color": "gray", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "gray", "fillOpacity": 0.3, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_ef0e65d7309a4fef8ed50563de7a6e98);
        
    
        var popup_91397cf02b4d4d6498d6a68e31dfc0d3 = L.popup({"maxWidth": "100%"});

        
            var html_3dafc8398d244311a6bab08277e97637 = $(`<div id="html_3dafc8398d244311a6bab08277e97637" style="width: 100.0%; height: 100.0%;">Vila Medeiros (Norte)  Mean Rental Price=22.20  El.shool=6.0  M.station=0.0  Park=1.0  Pharmacy=29.0  Supermkt=2.0</div>`)[0];
            popup_91397cf02b4d4d6498d6a68e31dfc0d3.setContent(html_3dafc8398d244311a6bab08277e97637);
        

        circle_marker_cf70d046f4bf4fd3a1fbcffacb5b74c4.bindPopup(popup_91397cf02b4d4d6498d6a68e31dfc0d3)
        ;

        
    
    
            var marker_bebdccd43d08454c926e568449c4eeba = L.marker(
                [-23.5719, -46.7081],
                {}
            ).addTo(map_ef0e65d7309a4fef8ed50563de7a6e98);
        
    
            var icon_4ee3a523b43642ec8040dcbe8570fef0 = L.AwesomeMarkers.icon(
                {"extraClasses": "fa-rotate-0", "icon": "info-sign", "iconColor": "white", "markerColor": "blue", "prefix": "glyphicon"}
            );
            marker_bebdccd43d08454c926e568449c4eeba.setIcon(icon_4ee3a523b43642ec8040dcbe8570fef0);
        
    
        var popup_7d55802bd88b4b66bd4cd7d25c367877 = L.popup({"maxWidth": "100%"});

        
            var html_315e7c8db08343f6bc38e0677335aee6 = $(`<div id="html_315e7c8db08343f6bc38e0677335aee6" style="width: 100.0%; height: 100.0%;">Butantã (Oeste)  Mean Rental Price=25.00  El.shool=1.0  M.station=4.0  Park=7.0  Pharmacy=28.0  Supermkt=7.0</div>`)[0];
            popup_7d55802bd88b4b66bd4cd7d25c367877.setContent(html_315e7c8db08343f6bc38e0677335aee6);
        

        marker_bebdccd43d08454c926e568449c4eeba.bindPopup(popup_7d55802bd88b4b66bd4cd7d25c367877)
        ;

        
    
    
            var circle_marker_cc3c8b5bef5d4ac186fa6a03b6953dbf = L.circleMarker(
                [-23.582, -46.7122],
                {"bubblingMouseEvents": true, "color": "gray", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "gray", "fillOpacity": 0.3, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_ef0e65d7309a4fef8ed50563de7a6e98);
        
    
        var popup_0fa96c4bd7c14fe7b414de74879bca53 = L.popup({"maxWidth": "100%"});

        
            var html_41f1ea8af90c4332a238daa405ec969c = $(`<div id="html_41f1ea8af90c4332a238daa405ec969c" style="width: 100.0%; height: 100.0%;">Morumbi (Oeste)  Mean Rental Price=27.40  El.shool=3.0  M.station=6.0  Park=10.0  Pharmacy=22.0  Supermkt=6.0</div>`)[0];
            popup_0fa96c4bd7c14fe7b414de74879bca53.setContent(html_41f1ea8af90c4332a238daa405ec969c);
        

        circle_marker_cc3c8b5bef5d4ac186fa6a03b6953dbf.bindPopup(popup_0fa96c4bd7c14fe7b414de74879bca53)
        ;

        
    
    
            var circle_marker_5188a9528aa2433587950dafd84e593b = L.circleMarker(
                [-23.5918, -46.7806],
                {"bubblingMouseEvents": true, "color": "gray", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "gray", "fillOpacity": 0.3, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_ef0e65d7309a4fef8ed50563de7a6e98);
        
    
        var popup_17637d16a2b14c2cbfabe5c329395b9f = L.popup({"maxWidth": "100%"});

        
            var html_5b8d120343404f5f941e61a6946f6dd5 = $(`<div id="html_5b8d120343404f5f941e61a6946f6dd5" style="width: 100.0%; height: 100.0%;">Raposo Tavares (Oeste)  Mean Rental Price=29.44  El.shool=9.0  M.station=0.0  Park=6.0  Pharmacy=16.0  Supermkt=5.0</div>`)[0];
            popup_17637d16a2b14c2cbfabe5c329395b9f.setContent(html_5b8d120343404f5f941e61a6946f6dd5);
        

        circle_marker_5188a9528aa2433587950dafd84e593b.bindPopup(popup_17637d16a2b14c2cbfabe5c329395b9f)
        ;

        
    
    
            var circle_marker_af4a826a83e8423da46d32720b635a2b = L.circleMarker(
                [-23.5685, -46.7569],
                {"bubblingMouseEvents": true, "color": "gray", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "gray", "fillOpacity": 0.3, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_ef0e65d7309a4fef8ed50563de7a6e98);
        
    
        var popup_dc58c839c9da47c4aa2f8b7a661c4bf3 = L.popup({"maxWidth": "100%"});

        
            var html_dc6823cceb6b4fffaf836f010d3b1a39 = $(`<div id="html_dc6823cceb6b4fffaf836f010d3b1a39" style="width: 100.0%; height: 100.0%;">Rio Pequeno (Oeste)  Mean Rental Price=29.44  El.shool=10.0  M.station=0.0  Park=8.0  Pharmacy=23.0  Supermkt=6.0</div>`)[0];
            popup_dc58c839c9da47c4aa2f8b7a661c4bf3.setContent(html_dc6823cceb6b4fffaf836f010d3b1a39);
        

        circle_marker_af4a826a83e8423da46d32720b635a2b.bindPopup(popup_dc58c839c9da47c4aa2f8b7a661c4bf3)
        ;

        
    
    
            var circle_marker_d7e99008ffab41b3b7a27581bc7d038c = L.circleMarker(
                [-23.5999, -46.7392],
                {"bubblingMouseEvents": true, "color": "gray", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "gray", "fillOpacity": 0.3, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_ef0e65d7309a4fef8ed50563de7a6e98);
        
    
        var popup_4004ddfaaff64f73a511e4ae742bb02b = L.popup({"maxWidth": "100%"});

        
            var html_8b87f0d62a654035aa5cb508274f2b1e = $(`<div id="html_8b87f0d62a654035aa5cb508274f2b1e" style="width: 100.0%; height: 100.0%;">Vila Sônia (Oeste)  Mean Rental Price=25.90  El.shool=7.0  M.station=0.0  Park=4.0  Pharmacy=29.0  Supermkt=9.0</div>`)[0];
            popup_4004ddfaaff64f73a511e4ae742bb02b.setContent(html_8b87f0d62a654035aa5cb508274f2b1e);
        

        circle_marker_d7e99008ffab41b3b7a27581bc7d038c.bindPopup(popup_4004ddfaaff64f73a511e4ae742bb02b)
        ;

        
    
    
            var circle_marker_2aecf608193d427fa83f27c777fa6184 = L.circleMarker(
                [-23.5255, -46.6675],
                {"bubblingMouseEvents": true, "color": "gray", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "gray", "fillOpacity": 0.3, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_ef0e65d7309a4fef8ed50563de7a6e98);
        
    
        var popup_ca7179d3b96e44ccb8186e5db20fe2e5 = L.popup({"maxWidth": "100%"});

        
            var html_67de3c0d41e84d979110e3571d0d445c = $(`<div id="html_67de3c0d41e84d979110e3571d0d445c" style="width: 100.0%; height: 100.0%;">Barra Funda (Oeste)  Mean Rental Price=33.50  El.shool=4.0  M.station=3.0  Park=2.0  Pharmacy=22.0  Supermkt=12.0</div>`)[0];
            popup_ca7179d3b96e44ccb8186e5db20fe2e5.setContent(html_67de3c0d41e84d979110e3571d0d445c);
        

        circle_marker_2aecf608193d427fa83f27c777fa6184.bindPopup(popup_ca7179d3b96e44ccb8186e5db20fe2e5)
        ;

        
    
    
            var circle_marker_4c6b759bc90245f19b64424435538de8 = L.circleMarker(
                [-23.5074, -46.7553],
                {"bubblingMouseEvents": true, "color": "gray", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "gray", "fillOpacity": 0.3, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_ef0e65d7309a4fef8ed50563de7a6e98);
        
    
        var popup_2f7a4e6f62c24769a6fb1242dc6ec73d = L.popup({"maxWidth": "100%"});

        
            var html_98889a34ba7940e4a4da82550822928c = $(`<div id="html_98889a34ba7940e4a4da82550822928c" style="width: 100.0%; height: 100.0%;">Jaguara (Oeste)  Mean Rental Price=29.44  El.shool=2.0  M.station=0.0  Park=3.0  Pharmacy=5.0  Supermkt=3.0</div>`)[0];
            popup_2f7a4e6f62c24769a6fb1242dc6ec73d.setContent(html_98889a34ba7940e4a4da82550822928c);
        

        circle_marker_4c6b759bc90245f19b64424435538de8.bindPopup(popup_2f7a4e6f62c24769a6fb1242dc6ec73d)
        ;

        
    
    
            var circle_marker_fc7a0d1d05c74399a35c8b44260affc6 = L.circleMarker(
                [-23.542, -46.7495],
                {"bubblingMouseEvents": true, "color": "gray", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "gray", "fillOpacity": 0.3, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_ef0e65d7309a4fef8ed50563de7a6e98);
        
    
        var popup_2e27bc2e05c0472a80720107b21521e4 = L.popup({"maxWidth": "100%"});

        
            var html_e5860a37b5dd40fa81acb24a86ab5f2a = $(`<div id="html_e5860a37b5dd40fa81acb24a86ab5f2a" style="width: 100.0%; height: 100.0%;">Jaguaré (Oeste)  Mean Rental Price=26.50  El.shool=1.0  M.station=0.0  Park=5.0  Pharmacy=13.0  Supermkt=7.0</div>`)[0];
            popup_2e27bc2e05c0472a80720107b21521e4.setContent(html_e5860a37b5dd40fa81acb24a86ab5f2a);
        

        circle_marker_fc7a0d1d05c74399a35c8b44260affc6.bindPopup(popup_2e27bc2e05c0472a80720107b21521e4)
        ;

        
    
    
            var circle_marker_1610d1aea3834952a1305a47fa14a18b = L.circleMarker(
                [-23.5146, -46.7243],
                {"bubblingMouseEvents": true, "color": "gray", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "gray", "fillOpacity": 0.3, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_ef0e65d7309a4fef8ed50563de7a6e98);
        
    
        var popup_33b975ee71284b67b305721ffefbcc80 = L.popup({"maxWidth": "100%"});

        
            var html_cbabaeea71cb407c97e934a13c22d97b = $(`<div id="html_cbabaeea71cb407c97e934a13c22d97b" style="width: 100.0%; height: 100.0%;">Lapa (Oeste)  Mean Rental Price=29.44  El.shool=4.0  M.station=1.0  Park=7.0  Pharmacy=28.0  Supermkt=5.0</div>`)[0];
            popup_33b975ee71284b67b305721ffefbcc80.setContent(html_cbabaeea71cb407c97e934a13c22d97b);
        

        circle_marker_1610d1aea3834952a1305a47fa14a18b.bindPopup(popup_33b975ee71284b67b305721ffefbcc80)
        ;

        
    
    
            var circle_marker_6b17b3022de844c5961f425c842d8f09 = L.circleMarker(
                [-23.5379, -46.6807],
                {"bubblingMouseEvents": true, "color": "gray", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "gray", "fillOpacity": 0.3, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_ef0e65d7309a4fef8ed50563de7a6e98);
        
    
        var popup_f994fc938e9444608e4a0a2eeb96fb4c = L.popup({"maxWidth": "100%"});

        
            var html_a874422e398942c5954514b0afd51f7f = $(`<div id="html_a874422e398942c5954514b0afd51f7f" style="width: 100.0%; height: 100.0%;">Perdizes (Oeste)  Mean Rental Price=27.80  El.shool=1.0  M.station=0.0  Park=3.0  Pharmacy=28.0  Supermkt=16.0</div>`)[0];
            popup_f994fc938e9444608e4a0a2eeb96fb4c.setContent(html_a874422e398942c5954514b0afd51f7f);
        

        circle_marker_6b17b3022de844c5961f425c842d8f09.bindPopup(popup_f994fc938e9444608e4a0a2eeb96fb4c)
        ;

        
    
    
            var circle_marker_9e70b80897d54f629bb9031a0ce19ab4 = L.circleMarker(
                [-23.5301, -46.7343],
                {"bubblingMouseEvents": true, "color": "gray", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "gray", "fillOpacity": 0.3, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_ef0e65d7309a4fef8ed50563de7a6e98);
        
    
        var popup_6509502048af498aa1886840cf1bc77d = L.popup({"maxWidth": "100%"});

        
            var html_265338fdf1c4414d8d629156f18b2e51 = $(`<div id="html_265338fdf1c4414d8d629156f18b2e51" style="width: 100.0%; height: 100.0%;">Vila Leopoldina (Oeste)  Mean Rental Price=33.50  El.shool=4.0  M.station=1.0  Park=6.0  Pharmacy=21.0  Supermkt=7.0</div>`)[0];
            popup_6509502048af498aa1886840cf1bc77d.setContent(html_265338fdf1c4414d8d629156f18b2e51);
        

        circle_marker_9e70b80897d54f629bb9031a0ce19ab4.bindPopup(popup_6509502048af498aa1886840cf1bc77d)
        ;

        
    
    
            var circle_marker_ba0633cd097d4fd2bdcc16b0b77fccc2 = L.circleMarker(
                [-23.5499, -46.7076],
                {"bubblingMouseEvents": true, "color": "gray", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "gray", "fillOpacity": 0.3, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_ef0e65d7309a4fef8ed50563de7a6e98);
        
    
        var popup_53b019e030cd47de9708598ab44e8271 = L.popup({"maxWidth": "100%"});

        
            var html_862f8ed91c154ae79e0d275ca943f1f8 = $(`<div id="html_862f8ed91c154ae79e0d275ca943f1f8" style="width: 100.0%; height: 100.0%;">Alto de Pinheiros (Oeste)  Mean Rental Price=29.44  El.shool=2.0  M.station=1.0  Park=13.0  Pharmacy=24.0  Supermkt=4.0</div>`)[0];
            popup_53b019e030cd47de9708598ab44e8271.setContent(html_862f8ed91c154ae79e0d275ca943f1f8);
        

        circle_marker_ba0633cd097d4fd2bdcc16b0b77fccc2.bindPopup(popup_53b019e030cd47de9708598ab44e8271)
        ;

        
    
    
            var circle_marker_42b668f69ba4486a96543d4f76fb657e = L.circleMarker(
                [-23.5844, -46.6784],
                {"bubblingMouseEvents": true, "color": "gray", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "gray", "fillOpacity": 0.3, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_ef0e65d7309a4fef8ed50563de7a6e98);
        
    
        var popup_93d57b6c0ac24d99b03fcc9715eed531 = L.popup({"maxWidth": "100%"});

        
            var html_2dbc2f987d6c442fa147ef8bb32b99bb = $(`<div id="html_2dbc2f987d6c442fa147ef8bb32b99bb" style="width: 100.0%; height: 100.0%;">Itaim Bibi (Oeste)  Mean Rental Price=33.50  El.shool=1.0  M.station=0.0  Park=5.0  Pharmacy=29.0  Supermkt=10.0</div>`)[0];
            popup_93d57b6c0ac24d99b03fcc9715eed531.setContent(html_2dbc2f987d6c442fa147ef8bb32b99bb);
        

        circle_marker_42b668f69ba4486a96543d4f76fb657e.bindPopup(popup_93d57b6c0ac24d99b03fcc9715eed531)
        ;

        
    
    
            var circle_marker_8eaf79476079405487e2ec9b399c9f4b = L.circleMarker(
                [-23.5674, -46.6637],
                {"bubblingMouseEvents": true, "color": "gray", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "gray", "fillOpacity": 0.3, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_ef0e65d7309a4fef8ed50563de7a6e98);
        
    
        var popup_a5977574dd304139ad791f4b1e0bacd3 = L.popup({"maxWidth": "100%"});

        
            var html_2001befee68d4a4684c0ba51eb61867f = $(`<div id="html_2001befee68d4a4684c0ba51eb61867f" style="width: 100.0%; height: 100.0%;">Jardim Paulista (Oeste)  Mean Rental Price=29.44  El.shool=0.0  M.station=6.0  Park=4.0  Pharmacy=29.0  Supermkt=8.0</div>`)[0];
            popup_a5977574dd304139ad791f4b1e0bacd3.setContent(html_2001befee68d4a4684c0ba51eb61867f);
        

        circle_marker_8eaf79476079405487e2ec9b399c9f4b.bindPopup(popup_a5977574dd304139ad791f4b1e0bacd3)
        ;

        
    
    
            var circle_marker_ff0ef3d3b9154266aef87d0451ee7a69 = L.circleMarker(
                [-23.5672, -46.702],
                {"bubblingMouseEvents": true, "color": "gray", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "gray", "fillOpacity": 0.3, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_ef0e65d7309a4fef8ed50563de7a6e98);
        
    
        var popup_54e4563941a7494eb7f45ef513af7546 = L.popup({"maxWidth": "100%"});

        
            var html_84865c33ff594d7487baa0b5702c929e = $(`<div id="html_84865c33ff594d7487baa0b5702c929e" style="width: 100.0%; height: 100.0%;">Pinheiros (Oeste)  Mean Rental Price=31.90  El.shool=0.0  M.station=6.0  Park=5.0  Pharmacy=29.0  Supermkt=8.0</div>`)[0];
            popup_54e4563941a7494eb7f45ef513af7546.setContent(html_84865c33ff594d7487baa0b5702c929e);
        

        circle_marker_ff0ef3d3b9154266aef87d0451ee7a69.bindPopup(popup_54e4563941a7494eb7f45ef513af7546)
        ;

        
    
    
            var marker_1bf0b536495d4ca0a17ed19c848279ac = L.marker(
                [-23.6492, -46.7589],
                {}
            ).addTo(map_ef0e65d7309a4fef8ed50563de7a6e98);
        
    
            var icon_1a5c8da2ade642fd94957f95e5c922f6 = L.AwesomeMarkers.icon(
                {"extraClasses": "fa-rotate-0", "icon": "info-sign", "iconColor": "white", "markerColor": "blue", "prefix": "glyphicon"}
            );
            marker_1bf0b536495d4ca0a17ed19c848279ac.setIcon(icon_1a5c8da2ade642fd94957f95e5c922f6);
        
    
        var popup_3ff1f6b085684b7ab89ecb38b4725743 = L.popup({"maxWidth": "100%"});

        
            var html_5dc0f5b791704762a3dd4fa699bf50e5 = $(`<div id="html_5dc0f5b791704762a3dd4fa699bf50e5" style="width: 100.0%; height: 100.0%;">Campo Limpo (Sul)  Mean Rental Price=24.50  El.shool=7.0  M.station=5.0  Park=3.0  Pharmacy=24.0  Supermkt=10.0</div>`)[0];
            popup_3ff1f6b085684b7ab89ecb38b4725743.setContent(html_5dc0f5b791704762a3dd4fa699bf50e5);
        

        marker_1bf0b536495d4ca0a17ed19c848279ac.bindPopup(popup_3ff1f6b085684b7ab89ecb38b4725743)
        ;

        
    
    
            var circle_marker_5122cc882c884225af999e0bbc7668e9 = L.circleMarker(
                [-23.6592, -46.768],
                {"bubblingMouseEvents": true, "color": "gray", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "gray", "fillOpacity": 0.3, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_ef0e65d7309a4fef8ed50563de7a6e98);
        
    
        var popup_86dd7a6ba22b47d5b03b501d004b33ed = L.popup({"maxWidth": "100%"});

        
            var html_b6e6bdd36ea14e618fe3d758c7bd49f5 = $(`<div id="html_b6e6bdd36ea14e618fe3d758c7bd49f5" style="width: 100.0%; height: 100.0%;">Capão Redondo (Sul)  Mean Rental Price=28.39  El.shool=7.0  M.station=4.0  Park=2.0  Pharmacy=27.0  Supermkt=7.0</div>`)[0];
            popup_86dd7a6ba22b47d5b03b501d004b33ed.setContent(html_b6e6bdd36ea14e618fe3d758c7bd49f5);
        

        circle_marker_5122cc882c884225af999e0bbc7668e9.bindPopup(popup_86dd7a6ba22b47d5b03b501d004b33ed)
        ;

        
    
    
            var circle_marker_1a35d79303a34061845072201d91c088 = L.circleMarker(
                [-23.6257, -46.727],
                {"bubblingMouseEvents": true, "color": "gray", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "gray", "fillOpacity": 0.3, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_ef0e65d7309a4fef8ed50563de7a6e98);
        
    
        var popup_b68930b89ae74bcaaf393d30537d0553 = L.popup({"maxWidth": "100%"});

        
            var html_7e983c35009d44d79bc3a7ab075b595f = $(`<div id="html_7e983c35009d44d79bc3a7ab075b595f" style="width: 100.0%; height: 100.0%;">Vila Andrade (Sul)  Mean Rental Price=29.70  El.shool=1.0  M.station=0.0  Park=7.0  Pharmacy=30.0  Supermkt=11.0</div>`)[0];
            popup_b68930b89ae74bcaaf393d30537d0553.setContent(html_7e983c35009d44d79bc3a7ab075b595f);
        

        circle_marker_1a35d79303a34061845072201d91c088.bindPopup(popup_b68930b89ae74bcaaf393d30537d0553)
        ;

        
    
    
            var circle_marker_99ed1b1bbc7a4efeb29e1b060c13fa49 = L.circleMarker(
                [-23.714, -46.6991],
                {"bubblingMouseEvents": true, "color": "gray", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "gray", "fillOpacity": 0.3, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_ef0e65d7309a4fef8ed50563de7a6e98);
        
    
        var popup_a9ad4d6867854fb5b18c8b047204a336 = L.popup({"maxWidth": "100%"});

        
            var html_e97b74ec94d74d8b93e7ee40abd71964 = $(`<div id="html_e97b74ec94d74d8b93e7ee40abd71964" style="width: 100.0%; height: 100.0%;">Cidade Dutra (Sul)  Mean Rental Price=28.39  El.shool=8.0  M.station=0.0  Park=7.0  Pharmacy=28.0  Supermkt=4.0</div>`)[0];
            popup_a9ad4d6867854fb5b18c8b047204a336.setContent(html_e97b74ec94d74d8b93e7ee40abd71964);
        

        circle_marker_99ed1b1bbc7a4efeb29e1b060c13fa49.bindPopup(popup_a9ad4d6867854fb5b18c8b047204a336)
        ;

        
    
    
            var circle_marker_5140e80562a94b0fb41c4426e81ec54b = L.circleMarker(
                [-23.7862, -46.665],
                {"bubblingMouseEvents": true, "color": "gray", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "gray", "fillOpacity": 0.3, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_ef0e65d7309a4fef8ed50563de7a6e98);
        
    
        var popup_66dbfe194f8e4d2685024f8953877294 = L.popup({"maxWidth": "100%"});

        
            var html_47b7dd941eb248dfbb2475eb88a7bc82 = $(`<div id="html_47b7dd941eb248dfbb2475eb88a7bc82" style="width: 100.0%; height: 100.0%;">Grajaú (Sul)  Mean Rental Price=28.39  El.shool=0.0  M.station=0.0  Park=0.0  Pharmacy=1.0  Supermkt=1.0</div>`)[0];
            popup_66dbfe194f8e4d2685024f8953877294.setContent(html_47b7dd941eb248dfbb2475eb88a7bc82);
        

        circle_marker_5140e80562a94b0fb41c4426e81ec54b.bindPopup(popup_66dbfe194f8e4d2685024f8953877294)
        ;

        
    
    
            var circle_marker_ff362b596d384ffba18bd61ec5660af1 = L.circleMarker(
                [-22.5903, -46.5249],
                {"bubblingMouseEvents": true, "color": "gray", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "gray", "fillOpacity": 0.3, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_ef0e65d7309a4fef8ed50563de7a6e98);
        
    
        var popup_a422aa80c8144efa8f642774552e6c64 = L.popup({"maxWidth": "100%"});

        
            var html_25ef8b77959248d194e8bc296bd4e156 = $(`<div id="html_25ef8b77959248d194e8bc296bd4e156" style="width: 100.0%; height: 100.0%;">Socorro (Sul)  Mean Rental Price=28.39  El.shool=2.0  M.station=0.0  Park=5.0  Pharmacy=8.0  Supermkt=1.0</div>`)[0];
            popup_a422aa80c8144efa8f642774552e6c64.setContent(html_25ef8b77959248d194e8bc296bd4e156);
        

        circle_marker_ff362b596d384ffba18bd61ec5660af1.bindPopup(popup_a422aa80c8144efa8f642774552e6c64)
        ;

        
    
    
            var circle_marker_c52e6aace7104901996907d453da2227 = L.circleMarker(
                [-23.6652, -46.6566],
                {"bubblingMouseEvents": true, "color": "gray", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "gray", "fillOpacity": 0.3, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_ef0e65d7309a4fef8ed50563de7a6e98);
        
    
        var popup_aaa7fc7228d44204bbf730114b553c75 = L.popup({"maxWidth": "100%"});

        
            var html_0fca7c938aac4c6490c4730b557742a1 = $(`<div id="html_0fca7c938aac4c6490c4730b557742a1" style="width: 100.0%; height: 100.0%;">Cidade Ademar (Sul)  Mean Rental Price=28.39  El.shool=7.0  M.station=1.0  Park=1.0  Pharmacy=30.0  Supermkt=8.0</div>`)[0];
            popup_aaa7fc7228d44204bbf730114b553c75.setContent(html_0fca7c938aac4c6490c4730b557742a1);
        

        circle_marker_c52e6aace7104901996907d453da2227.bindPopup(popup_aaa7fc7228d44204bbf730114b553c75)
        ;

        
    
    
            var circle_marker_4261d97406044b5d85a090c353874107 = L.circleMarker(
                [-23.6512, -46.6697],
                {"bubblingMouseEvents": true, "color": "gray", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "gray", "fillOpacity": 0.3, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_ef0e65d7309a4fef8ed50563de7a6e98);
        
    
        var popup_4f965d563cdb4d05af98a8e9155482cc = L.popup({"maxWidth": "100%"});

        
            var html_af089619a5414f90b3425a2fc4980403 = $(`<div id="html_af089619a5414f90b3425a2fc4980403" style="width: 100.0%; height: 100.0%;">Pedreira (Sul)  Mean Rental Price=28.39  El.shool=6.0  M.station=0.0  Park=5.0  Pharmacy=32.0  Supermkt=6.0</div>`)[0];
            popup_4f965d563cdb4d05af98a8e9155482cc.setContent(html_af089619a5414f90b3425a2fc4980403);
        

        circle_marker_4261d97406044b5d85a090c353874107.bindPopup(popup_4f965d563cdb4d05af98a8e9155482cc)
        ;

        
    
    
            var circle_marker_44eb89a301dd47608ba797252fb3547e = L.circleMarker(
                [-23.6323, -46.6196],
                {"bubblingMouseEvents": true, "color": "gray", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "gray", "fillOpacity": 0.3, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_ef0e65d7309a4fef8ed50563de7a6e98);
        
    
        var popup_464e884a0cc346059ef8dfd1334cc5c5 = L.popup({"maxWidth": "100%"});

        
            var html_03009659d569480da1af402ae2e402b8 = $(`<div id="html_03009659d569480da1af402ae2e402b8" style="width: 100.0%; height: 100.0%;">Cursino (Sul)  Mean Rental Price=28.39  El.shool=7.0  M.station=0.0  Park=2.0  Pharmacy=35.0  Supermkt=5.0</div>`)[0];
            popup_464e884a0cc346059ef8dfd1334cc5c5.setContent(html_03009659d569480da1af402ae2e402b8);
        

        circle_marker_44eb89a301dd47608ba797252fb3547e.bindPopup(popup_464e884a0cc346059ef8dfd1334cc5c5)
        ;

        
    
    
            var circle_marker_6e4296b694d0460b8182370c20d752c4 = L.circleMarker(
                [-23.5945, -46.6027],
                {"bubblingMouseEvents": true, "color": "gray", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "gray", "fillOpacity": 0.3, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_ef0e65d7309a4fef8ed50563de7a6e98);
        
    
        var popup_0fbfaebd4f7c4dc69ef4ea3b30170c55 = L.popup({"maxWidth": "100%"});

        
            var html_d8ec12a0c51943abb4d3d12fc0570a79 = $(`<div id="html_d8ec12a0c51943abb4d3d12fc0570a79" style="width: 100.0%; height: 100.0%;">Ipiranga (Sul)  Mean Rental Price=28.70  El.shool=2.0  M.station=5.0  Park=3.0  Pharmacy=25.0  Supermkt=11.0</div>`)[0];
            popup_0fbfaebd4f7c4dc69ef4ea3b30170c55.setContent(html_d8ec12a0c51943abb4d3d12fc0570a79);
        

        circle_marker_6e4296b694d0460b8182370c20d752c4.bindPopup(popup_0fbfaebd4f7c4dc69ef4ea3b30170c55)
        ;

        
    
    
            var circle_marker_0bf5526975a34d8e87e7f59295e6b3cd = L.circleMarker(
                [-23.6013, -46.6026],
                {"bubblingMouseEvents": true, "color": "gray", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "gray", "fillOpacity": 0.3, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_ef0e65d7309a4fef8ed50563de7a6e98);
        
    
        var popup_7d383fd05f7d47e8b9a8a5551800fcf4 = L.popup({"maxWidth": "100%"});

        
            var html_cbf158851222444fbfeea79f651cc1b6 = $(`<div id="html_cbf158851222444fbfeea79f651cc1b6" style="width: 100.0%; height: 100.0%;">Sacomã (Sul)  Mean Rental Price=28.39  El.shool=2.0  M.station=5.0  Park=1.0  Pharmacy=25.0  Supermkt=12.0</div>`)[0];
            popup_7d383fd05f7d47e8b9a8a5551800fcf4.setContent(html_cbf158851222444fbfeea79f651cc1b6);
        

        circle_marker_0bf5526975a34d8e87e7f59295e6b3cd.bindPopup(popup_7d383fd05f7d47e8b9a8a5551800fcf4)
        ;

        
    
    
            var marker_eba3baba6bb14113b75f24912eb73be8 = L.marker(
                [-23.6463, -46.641],
                {}
            ).addTo(map_ef0e65d7309a4fef8ed50563de7a6e98);
        
    
            var icon_68856dcc19934644b91dc5ca56dfae74 = L.AwesomeMarkers.icon(
                {"extraClasses": "fa-rotate-0", "icon": "info-sign", "iconColor": "white", "markerColor": "blue", "prefix": "glyphicon"}
            );
            marker_eba3baba6bb14113b75f24912eb73be8.setIcon(icon_68856dcc19934644b91dc5ca56dfae74);
        
    
        var popup_75bb67169e7e42658508b5af4fe78a58 = L.popup({"maxWidth": "100%"});

        
            var html_061e5ba6fbb9416087ebf7d6f6b052b6 = $(`<div id="html_061e5ba6fbb9416087ebf7d6f6b052b6" style="width: 100.0%; height: 100.0%;">Jabaquara (Sul)  Mean Rental Price=25.30  El.shool=5.0  M.station=3.0  Park=1.0  Pharmacy=24.0  Supermkt=12.0</div>`)[0];
            popup_75bb67169e7e42658508b5af4fe78a58.setContent(html_061e5ba6fbb9416087ebf7d6f6b052b6);
        

        marker_eba3baba6bb14113b75f24912eb73be8.bindPopup(popup_75bb67169e7e42658508b5af4fe78a58)
        ;

        
    
    
            var circle_marker_71f305c38888499f91a45e3bdb72d6ee = L.circleMarker(
                [-23.6763, -46.7578],
                {"bubblingMouseEvents": true, "color": "gray", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "gray", "fillOpacity": 0.3, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_ef0e65d7309a4fef8ed50563de7a6e98);
        
    
        var popup_5d13ba46d47e4d57a4bc1065306d2784 = L.popup({"maxWidth": "100%"});

        
            var html_48bbcf524c654e928a07e36547a50197 = $(`<div id="html_48bbcf524c654e928a07e36547a50197" style="width: 100.0%; height: 100.0%;">Jardim Ângela (Sul)  Mean Rental Price=28.39  El.shool=8.0  M.station=3.0  Park=2.0  Pharmacy=28.0  Supermkt=6.0</div>`)[0];
            popup_5d13ba46d47e4d57a4bc1065306d2784.setContent(html_48bbcf524c654e928a07e36547a50197);
        

        circle_marker_71f305c38888499f91a45e3bdb72d6ee.bindPopup(popup_5d13ba46d47e4d57a4bc1065306d2784)
        ;

        
    
    
            var circle_marker_5d0c0aedd10742ddb94343a663b756f9 = L.circleMarker(
                [-23.6763, -46.7578],
                {"bubblingMouseEvents": true, "color": "gray", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "gray", "fillOpacity": 0.3, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_ef0e65d7309a4fef8ed50563de7a6e98);
        
    
        var popup_3a9a75d2e6b9417bb4cc5e9fd47a75a6 = L.popup({"maxWidth": "100%"});

        
            var html_d981d67c311b4628a24675f36a9bd4c7 = $(`<div id="html_d981d67c311b4628a24675f36a9bd4c7" style="width: 100.0%; height: 100.0%;">Jardim São Luís (Sul)  Mean Rental Price=28.39  El.shool=8.0  M.station=3.0  Park=2.0  Pharmacy=28.0  Supermkt=6.0</div>`)[0];
            popup_3a9a75d2e6b9417bb4cc5e9fd47a75a6.setContent(html_d981d67c311b4628a24675f36a9bd4c7);
        

        circle_marker_5d0c0aedd10742ddb94343a663b756f9.bindPopup(popup_3a9a75d2e6b9417bb4cc5e9fd47a75a6)
        ;

        
    
    
            var circle_marker_6ba61cf7d656416b861487cccd24ef89 = L.circleMarker(
                [-23.854, -46.7557],
                {"bubblingMouseEvents": true, "color": "gray", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "gray", "fillOpacity": 0.3, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_ef0e65d7309a4fef8ed50563de7a6e98);
        
    
        var popup_7bcbb4710be04d8689591bb9f4357d5e = L.popup({"maxWidth": "100%"});

        
            var html_41467a7130404d47b3bfd4ffb1946802 = $(`<div id="html_41467a7130404d47b3bfd4ffb1946802" style="width: 100.0%; height: 100.0%;">Marsilac (Sul)  Mean Rental Price=28.39  El.shool=0.0  M.station=0.0  Park=0.0  Pharmacy=0.0  Supermkt=0.0</div>`)[0];
            popup_7bcbb4710be04d8689591bb9f4357d5e.setContent(html_41467a7130404d47b3bfd4ffb1946802);
        

        circle_marker_6ba61cf7d656416b861487cccd24ef89.bindPopup(popup_7bcbb4710be04d8689591bb9f4357d5e)
        ;

        
    
    
            var circle_marker_44d7ff29b9094422a348be26bc261590 = L.circleMarker(
                [-23.8273, -46.7278],
                {"bubblingMouseEvents": true, "color": "gray", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "gray", "fillOpacity": 0.3, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_ef0e65d7309a4fef8ed50563de7a6e98);
        
    
        var popup_434207a86224456d8e765f8ff6ad61b0 = L.popup({"maxWidth": "100%"});

        
            var html_67e216296ea24caa8afcb231c25cc720 = $(`<div id="html_67e216296ea24caa8afcb231c25cc720" style="width: 100.0%; height: 100.0%;">Parelheiros (Sul)  Mean Rental Price=28.39  El.shool=1.0  M.station=0.0  Park=2.0  Pharmacy=2.0  Supermkt=2.0</div>`)[0];
            popup_434207a86224456d8e765f8ff6ad61b0.setContent(html_67e216296ea24caa8afcb231c25cc720);
        

        circle_marker_44d7ff29b9094422a348be26bc261590.bindPopup(popup_434207a86224456d8e765f8ff6ad61b0)
        ;

        
    
    
            var circle_marker_66864f6bc9b44610b9dc92abb4859062 = L.circleMarker(
                [-23.6267, -46.6694],
                {"bubblingMouseEvents": true, "color": "gray", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "gray", "fillOpacity": 0.3, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_ef0e65d7309a4fef8ed50563de7a6e98);
        
    
        var popup_4d01f2085a484892be3a903027ef39c9 = L.popup({"maxWidth": "100%"});

        
            var html_b79493572ff142b088ab4491536ee3aa = $(`<div id="html_b79493572ff142b088ab4491536ee3aa" style="width: 100.0%; height: 100.0%;">Campo Belo (Sul)  Mean Rental Price=29.80  El.shool=5.0  M.station=2.0  Park=2.0  Pharmacy=29.0  Supermkt=10.0</div>`)[0];
            popup_4d01f2085a484892be3a903027ef39c9.setContent(html_b79493572ff142b088ab4491536ee3aa);
        

        circle_marker_66864f6bc9b44610b9dc92abb4859062.bindPopup(popup_4d01f2085a484892be3a903027ef39c9)
        ;

        
    
    
            var circle_marker_42f3dc5bcb6549bdbf89c50f6599c769 = L.circleMarker(
                [-23.6578, -46.6986],
                {"bubblingMouseEvents": true, "color": "gray", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "gray", "fillOpacity": 0.3, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_ef0e65d7309a4fef8ed50563de7a6e98);
        
    
        var popup_08cabcbc728e460a9a4a6db69c26651b = L.popup({"maxWidth": "100%"});

        
            var html_1e26f58911d84190b00cedfd894b1535 = $(`<div id="html_1e26f58911d84190b00cedfd894b1535" style="width: 100.0%; height: 100.0%;">Campo Grande (Sul)  Mean Rental Price=28.39  El.shool=3.0  M.station=4.0  Park=5.0  Pharmacy=25.0  Supermkt=8.0</div>`)[0];
            popup_08cabcbc728e460a9a4a6db69c26651b.setContent(html_1e26f58911d84190b00cedfd894b1535);
        

        circle_marker_42f3dc5bcb6549bdbf89c50f6599c769.bindPopup(popup_08cabcbc728e460a9a4a6db69c26651b)
        ;

        
    
    
            var circle_marker_f9e0cf0ee95d4dbea1700331aa9ee05e = L.circleMarker(
                [-23.6562, -46.7191],
                {"bubblingMouseEvents": true, "color": "gray", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "gray", "fillOpacity": 0.3, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_ef0e65d7309a4fef8ed50563de7a6e98);
        
    
        var popup_213a4325af9d46aa96bc56a272fe3fe4 = L.popup({"maxWidth": "100%"});

        
            var html_d592d6055b3e4faeaaf13c041f3072d2 = $(`<div id="html_d592d6055b3e4faeaaf13c041f3072d2" style="width: 100.0%; height: 100.0%;">Santo Amaro (Sul)  Mean Rental Price=26.80  El.shool=2.0  M.station=4.0  Park=5.0  Pharmacy=28.0  Supermkt=8.0</div>`)[0];
            popup_213a4325af9d46aa96bc56a272fe3fe4.setContent(html_d592d6055b3e4faeaaf13c041f3072d2);
        

        circle_marker_f9e0cf0ee95d4dbea1700331aa9ee05e.bindPopup(popup_213a4325af9d46aa96bc56a272fe3fe4)
        ;

        
    
    
            var circle_marker_2e4adb296ef5462aa058439818ef1395 = L.circleMarker(
                [-23.5827, -46.649],
                {"bubblingMouseEvents": true, "color": "gray", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "gray", "fillOpacity": 0.3, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_ef0e65d7309a4fef8ed50563de7a6e98);
        
    
        var popup_e2dd3458bcfe420996f0a7ea1645d2e2 = L.popup({"maxWidth": "100%"});

        
            var html_24d018ef28b7422b8c93e73debe897b9 = $(`<div id="html_24d018ef28b7422b8c93e73debe897b9" style="width: 100.0%; height: 100.0%;">Moema (Sul)  Mean Rental Price=32.60  El.shool=0.0  M.station=4.0  Park=3.0  Pharmacy=28.0  Supermkt=11.0</div>`)[0];
            popup_e2dd3458bcfe420996f0a7ea1645d2e2.setContent(html_24d018ef28b7422b8c93e73debe897b9);
        

        circle_marker_2e4adb296ef5462aa058439818ef1395.bindPopup(popup_e2dd3458bcfe420996f0a7ea1645d2e2)
        ;

        
    
    
            var circle_marker_a31938af384142e2ba4c76f9949344eb = L.circleMarker(
                [-23.5973, -46.6359],
                {"bubblingMouseEvents": true, "color": "gray", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "gray", "fillOpacity": 0.3, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_ef0e65d7309a4fef8ed50563de7a6e98);
        
    
        var popup_0e02d716e6e5489ebe675e17c16eea78 = L.popup({"maxWidth": "100%"});

        
            var html_13f98cd2f1d24d26b761345b283b9156 = $(`<div id="html_13f98cd2f1d24d26b761345b283b9156" style="width: 100.0%; height: 100.0%;">Saúde (Sul)  Mean Rental Price=28.20  El.shool=0.0  M.station=7.0  Park=2.0  Pharmacy=31.0  Supermkt=8.0</div>`)[0];
            popup_0e02d716e6e5489ebe675e17c16eea78.setContent(html_13f98cd2f1d24d26b761345b283b9156);
        

        circle_marker_a31938af384142e2ba4c76f9949344eb.bindPopup(popup_0e02d716e6e5489ebe675e17c16eea78)
        ;

        
    
    
            var circle_marker_4fe44b595fff4be4b553a6f43ac2140e = L.circleMarker(
                [-23.5892, -46.6347],
                {"bubblingMouseEvents": true, "color": "gray", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "gray", "fillOpacity": 0.3, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_ef0e65d7309a4fef8ed50563de7a6e98);
        
    
        var popup_cc5e179ca0664722be624be8fd74e06f = L.popup({"maxWidth": "100%"});

        
            var html_209291bd7e95452e98176a04e6a29537 = $(`<div id="html_209291bd7e95452e98176a04e6a29537" style="width: 100.0%; height: 100.0%;">Vila Mariana (Sul)  Mean Rental Price=29.90  El.shool=1.0  M.station=5.0  Park=2.0  Pharmacy=26.0  Supermkt=12.0</div>`)[0];
            popup_cc5e179ca0664722be624be8fd74e06f.setContent(html_209291bd7e95452e98176a04e6a29537);
        

        circle_marker_4fe44b595fff4be4b553a6f43ac2140e.bindPopup(popup_cc5e179ca0664722be624be8fd74e06f)
        ;

        
    
</script> onload=\"this.contentDocument.open();this.contentDocument.write(atob(this.getAttribute('data-html')));this.contentDocument.close();\" allowfullscreen webkitallowfullscreen mozallowfullscreen></iframe></div></div>"
      ],
      "text/plain": [
       "<folium.folium.Map at 0x126c76a20>"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create the map\n",
    "sp_map = folium.Map(location=[latsp, lngsp], width='70%', height='70%',\n",
    "                    min_zoom=9, max_zoom=12, zoom_start=11, control_scale=True)\n",
    "\n",
    "# run a loop through the dataframe creating the points (circle marks)\n",
    "for d,r,mp,es,ms,pk,ph,sm,lt,ln,cl in zip(df_cluster_map['district'],df_cluster_map['region'],df_cluster_map['mean_price_sqm'],\n",
    "                                          df_cluster_map['elementary_school'],df_cluster_map['metro_station'],df_cluster_map['park'],\n",
    "                                          df_cluster_map['pharmacy'],df_cluster_map['supermarket'],df_cluster_map['latitude'],\n",
    "                                          df_cluster_map['longitude'],df_cluster_map['cluster_label']):\n",
    "    # create marker label\n",
    "    label = '{} ({})\\n Mean Rental Price={:.2f}\\n El.shool={}\\n M.station={}\\n Park={}\\n Pharmacy={}\\n Supermkt={}'.\\\n",
    "    format(d,r,mp,es,ms,pk,ph,sm)\n",
    "\n",
    "    if cl == 8:\n",
    "        # create the infopoint mark\n",
    "        folium.Marker(location=[lt,ln], popup=label,\n",
    "                      icon=folium.Icon(color='blue',icon='info-sign')).add_to(sp_map)\n",
    "    else:\n",
    "        # create circle mark\n",
    "        folium.CircleMarker(location=[lt,ln], popup=label, radius=5, color='gray',\n",
    "                      fill=True, fill_color='gray', fill_opacity=0.3).add_to(sp_map)\n",
    "\n",
    "# show the map\n",
    "sp_map"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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