project3notebook.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a7e3a40f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Edu Project III"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 427,
   "id": "f2aa8acb",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pymongo import MongoClient\n",
    "client = MongoClient(\"mongodb://localhost:27017\")\n",
    "db = client[\"Ironhack\"]\n",
    "c = db.get_collection('I')\n",
    "from bson.decimal128 import Decimal128\n",
    "import re"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 428,
   "id": "ee3d3b32",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Developers like to be near successful tech startups that have raised at least 1 Million dollars.\n",
    "#Designers like to go to design talks and share knowledge. There must be some nearby companies that also do design.\n",
    "\n",
    "#^^ First 2 priorities to narrow down cities\n",
    "# I will look for video game companies so the designers have other video game designers close\n",
    "# Then I will look for companies below 200 employees which have raised at least 1Million (Startups)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 283,
   "id": "e1f9c942",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'_id': 'USA', 'count': 587},\n",
       " {'_id': 'ESP', 'count': 14},\n",
       " {'_id': 'ARM', 'count': 1},\n",
       " {'_id': 'QAT', 'count': 1},\n",
       " {'_id': 'ISR', 'count': 20},\n",
       " {'_id': 'MEX', 'count': 4},\n",
       " {'_id': 'CRI', 'count': 1},\n",
       " {'_id': 'LVA', 'count': 1},\n",
       " {'_id': 'FRA', 'count': 26},\n",
       " {'_id': 'BRB', 'count': 1},\n",
       " {'_id': 'RUS', 'count': 4},\n",
       " {'_id': 'NLD', 'count': 10},\n",
       " {'_id': 'NOR', 'count': 3},\n",
       " {'_id': 'FIN', 'count': 3},\n",
       " {'_id': 'ROM', 'count': 2},\n",
       " {'_id': 'AUT', 'count': 3},\n",
       " {'_id': 'ARG', 'count': 5},\n",
       " {'_id': 'BGD', 'count': 1},\n",
       " {'_id': 'TUR', 'count': 2},\n",
       " {'_id': 'TWN', 'count': 2},\n",
       " {'_id': 'MLT', 'count': 3},\n",
       " {'_id': 'ITA', 'count': 7},\n",
       " {'_id': 'BEL', 'count': 2},\n",
       " {'_id': 'ISL', 'count': 2},\n",
       " {'_id': 'CAN', 'count': 35},\n",
       " {'_id': 'BRA', 'count': 2},\n",
       " {'_id': 'DNK', 'count': 5},\n",
       " {'_id': 'HKG', 'count': 1},\n",
       " {'_id': 'SWE', 'count': 11},\n",
       " {'_id': 'MYS', 'count': 2},\n",
       " {'_id': 'GRC', 'count': 1},\n",
       " {'_id': 'SVN', 'count': 1},\n",
       " {'_id': 'CHN', 'count': 17},\n",
       " {'_id': 'IND', 'count': 15},\n",
       " {'_id': 'PRT', 'count': 1},\n",
       " {'_id': 'IRL', 'count': 9},\n",
       " {'_id': 'CZE', 'count': 1},\n",
       " {'_id': 'UKR', 'count': 3},\n",
       " {'_id': 'SGP', 'count': 2},\n",
       " {'_id': 'NZL', 'count': 1},\n",
       " {'_id': 'IOT', 'count': 1},\n",
       " {'_id': 'COL', 'count': 1},\n",
       " {'_id': 'KOR', 'count': 3},\n",
       " {'_id': 'DEU', 'count': 30},\n",
       " {'_id': 'JPN', 'count': 14},\n",
       " {'_id': 'LBN', 'count': 1},\n",
       " {'_id': 'GBR', 'count': 74},\n",
       " {'_id': 'AUS', 'count': 9},\n",
       " {'_id': 'EST', 'count': 1},\n",
       " {'_id': 'ARA', 'count': 1},\n",
       " {'_id': 'CYP', 'count': 2},\n",
       " {'_id': 'HUN', 'count': 1},\n",
       " {'_id': 'CHE', 'count': 6}]"
      ]
     },
     "execution_count": 283,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipeline = [\n",
    "    {\"$match\": {\"category_code\": \"games_video\"}},\n",
    "    {\"$unwind\": \"$offices\"},\n",
    "    {\"$group\": {\"_id\": \"$offices.country_code\", \"count\": {\"$sum\": 1}}}\n",
    "]\n",
    "\n",
    "result = list(c.aggregate(pipeline))\n",
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 285,
   "id": "eb4d40ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "condition1 = {\"category_code\": \"games_video\"} #vid game design\n",
    "condition2 = {f\"number_of_employees\": {\"$lt\": 200}} #start-up characteristic\n",
    "condition3 = {f\"founded_year\": {\"$gte\": 2010}} #start-up characteristic\n",
    "regex_pattern = re.compile(r'\\d+\\.\\d+M')\n",
    "condition4 = {\"total_money_raised\": {\"$regex\": regex_pattern}}# Query condition to match companies with at least 1 million raised\n",
    "condition5 = {f\"offices.country_code\": {\"$in\": [\"ESP\", \"GBR\", \"FRA\"]}}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 431,
   "id": "f3554d37",
   "metadata": {},
   "outputs": [],
   "source": [
    "pipeline = [\n",
    "    {\"$match\": {\"$and\": [condition2, condition4]}},\n",
    "    {\"$unwind\": \"$offices\"},\n",
    "    {\"$group\": {\"_id\": \"$offices.city\", \"count\": {\"$sum\": 1}}},\n",
    "    {\"$sort\": {\"count\": -1}}\n",
    "    ]\n",
    "\n",
    "result = list(c.aggregate(pipeline))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 432,
   "id": "2cc3fcbf",
   "metadata": {},
   "outputs": [],
   "source": [
    "pipeline = [\n",
    "    {\"$match\": {\"$and\": [condition1]}},\n",
    "    {\"$unwind\": \"$offices\"},\n",
    "    {\"$group\": {\"_id\": \"$offices.city\", \"count\": {\"$sum\": 1}}},\n",
    "    {\"$sort\": {\"count\": -1}}\n",
    "    ]\n",
    "\n",
    "result = list(c.aggregate(pipeline))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 293,
   "id": "1dbf1d5e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# This is going to be too much work unless I find a quick and efficient way of coding it all and applying it to 3 cities (I won't)\n",
    "# Using the info on video game companies and start ups I will look at the location of companies in one city and choose a neighbourhood in that city. No comparing between cities.\n",
    "# Still use similar filter criteria to above"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 294,
   "id": "e70fccab",
   "metadata": {},
   "outputs": [],
   "source": [
    "#1. What are the 6 start ups with over 1M raised in Madrid? Location (DF)\n",
    "#2. What are the 6 video game companies in Madrid? Location (DF)\n",
    "\n",
    "#For these locations, look for closest: Bars, nurseries, parks for children/parks, + distance from Barajas.\n",
    "#Check if they get any bonus points for proximity to starbucks, vegan places, basketball stadium, dog hairdresser."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 429,
   "id": "ace6084b",
   "metadata": {},
   "outputs": [],
   "source": [
    "condition1 = {\"category_code\": \"games_video\"} #vid game design\n",
    "condition2 = {f\"number_of_employees\": {\"$lt\": 200}} #start-up characteristic\n",
    "condition3 = {f\"founded_year\": {\"$gte\": 2010}} #start-up characteristic\n",
    "regex_pattern = re.compile(r'\\d+\\.\\d+M')\n",
    "condition4 = {\"total_money_raised\": {\"$regex\": regex_pattern}}# Query condition to match companies with at least 1 million raised\n",
    "condition5 = {f\"offices.country_code\": {\"$in\": [\"ESP\", \"GBR\", \"FRA\"]}}\n",
    "condition6 = {\"offices.city\": \"Madrid\"}\n",
    "\n",
    "madrid_startups = list(c.find({\"$and\": [condition6, condition2, condition4]}, projection = {\"_id\":0, \"name\":1, \"offices.city\":1, \"total_money_raised\":1, \"number_of_employees\":1, \"category_code\": 1, \"offices.latitude\":1, \"offices.longitude\":1}))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 430,
   "id": "5d9318de",
   "metadata": {},
   "outputs": [],
   "source": [
    "madrid_vidgames = list(c.find({\"$and\": [condition6, condition1]}, projection = {\"_id\":0, \"name\":1, \"offices.city\":1, \"total_money_raised\":1, \"number_of_employees\":1, \"category_code\": 1, \"offices.latitude\":1, \"offices.longitude\":1}))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 297,
   "id": "56bf6fd3",
   "metadata": {},
   "outputs": [],
   "source": [
    "#now - for each of these locations, I want \n",
    "        #the 10 closest bars + their distance from the location\n",
    "        #5 closest nurseries + distance from location\n",
    "        #5 closest parks + distance from location\n",
    "        #Distance from airport/metro lines to airport.\n",
    "        \n",
    "        #Proximity to a starbucks\n",
    "        \n",
    "        #Basketball stadium (Wizink)?, Vegan place + dog hairdresser"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 298,
   "id": "eecaac29",
   "metadata": {},
   "outputs": [],
   "source": [
    "def foursquare_bar (lat, lon, sort = \"DISTANCE\"):\n",
    "    url = f\"https://api.foursquare.com/v3/places/search?query=bar&ll={lat}%2C{lon}&radius=1000&sort=DISTANCE&limit=10\"\n",
    "    headers = {\n",
    "        \"accept\": \"application/json\",\n",
    "        \"Authorization\": \"fsq3zJlP+zylHII9gRWfq9KurwhB8+0LPJw/XnnFyamdXaE=\"}\n",
    "        \n",
    "    response = requests.get(url, headers=headers)\n",
    "    return response.json()\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 299,
   "id": "4aedab83",
   "metadata": {},
   "outputs": [],
   "source": [
    "#I'm now trying to apply this function to each office and concatenating\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 300,
   "id": "f842e194",
   "metadata": {},
   "outputs": [],
   "source": [
    "#attempt 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 301,
   "id": "78995773",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "def bars_dataframe():\n",
    "    vidgame_company_data = {}\n",
    "    for i in madrid_vidgames:\n",
    "        company_city = i['offices'][0]['city']\n",
    "        company_name = i['name']\n",
    "        company_type = 'Video Games'\n",
    "        company_latitude = i['offices'][0][\"latitude\"]\n",
    "        company_longitude = i['offices'][0][\"longitude\"]\n",
    "        if company_latitude and company_longitude:\n",
    "\n",
    "            if company_city == \"Madrid\":\n",
    "                office_w_bars= foursquare_bar(company_latitude, company_longitude)\n",
    "                bar_list= []\n",
    "                for bar in office_w_bars['results']:\n",
    "\n",
    "                    bar_info = {\n",
    "                        'name': bar['name'],\n",
    "                        'distance': bar['distance'],\n",
    "                        'latitude': bar['geocodes']['main']['latitude'],\n",
    "                        'longitude': bar['geocodes']['main']['longitude'],\n",
    "                        'category': 'Bar'\n",
    "                    }\n",
    "\n",
    "                    bar_list.append(bar_info)\n",
    "\n",
    "\n",
    "                company_info = {\n",
    "                    'company_name': company_name,\n",
    "                    'company_type': company_type,\n",
    "                    'company_city': company_city,\n",
    "                    'company_latitude': company_latitude,\n",
    "                    'company_longitude': company_longitude,\n",
    "                    'company_bars': bar_list            \n",
    "                }\n",
    "\n",
    "                vidgame_company_data[company_name] = company_info\n",
    "    df_vidgame = pd.DataFrame(vidgame_company_data).T\n",
    "    df_vidgame = df_vidgame.explode(\"company_bars\")\n",
    "    df_vidgame['venue_category'] = df_vidgame['company_bars'].apply(lambda x: x['category'] if x else None)\n",
    "    df_vidgame['venue_name'] = df_vidgame['company_bars'].apply(lambda x: x['name'] if x else None)\n",
    "    df_vidgame['venue_distance'] = df_vidgame['company_bars'].apply(lambda x: x['distance'] if x else None)\n",
    "    df_vidgame['venue_latitude'] = df_vidgame['company_bars'].apply(lambda x: x['latitude'] if x else None)\n",
    "    df_vidgame['venue_longitude'] = df_vidgame['company_bars'].apply(lambda x: x['longitude'] if x else None)\n",
    "    df_vidgame = df_vidgame.drop(columns='company_bars')\n",
    "\n",
    "    startup_company_data = {}\n",
    "    for y in madrid_startups:\n",
    "        company_city_startups = y['offices'][0]['city']\n",
    "        company_name_startups = y['name']\n",
    "        company_type_startups = 'Start Up (>$1M Raised)'\n",
    "        company_latitude_startups = y['offices'][0][\"latitude\"]\n",
    "        company_longitude_startups = y['offices'][0][\"longitude\"]\n",
    "        if company_latitude_startups and company_longitude_startups:\n",
    "\n",
    "            if company_city_startups == \"Madrid\":\n",
    "                office_w_bars_startups= foursquare_bar(company_latitude_startups, company_longitude_startups)\n",
    "                bar_list_startups= []\n",
    "                for bar in office_w_bars_startups['results']:\n",
    "\n",
    "                    bar_info_startups = {\n",
    "                        'name': bar['name'],\n",
    "                        'distance': bar['distance'],\n",
    "                        'latitude': bar['geocodes']['main']['latitude'],\n",
    "                        'longitude': bar['geocodes']['main']['longitude'],\n",
    "                        'category': 'Bar'\n",
    "                    }\n",
    "\n",
    "                    bar_list_startups.append(bar_info_startups)\n",
    "\n",
    "\n",
    "                startup_company_info = {\n",
    "                    'company_name': company_name_startups,\n",
    "                    'company_type': company_type_startups,\n",
    "                    'company_city': company_city_startups,\n",
    "                    'company_latitude': company_latitude_startups,\n",
    "                    'company_longitude': company_longitude_startups,\n",
    "                    'company_bars': bar_list_startups\n",
    "                }\n",
    "\n",
    "                startup_company_data[company_name_startups] = startup_company_info\n",
    "    df_startups = pd.DataFrame(startup_company_data).T\n",
    "    df_startups = df_startups.explode(\"company_bars\")\n",
    "    df_startups['venue_category'] = df_startups['company_bars'].apply(lambda x: x['category'] if x else None)\n",
    "    df_startups['venue_name'] = df_startups['company_bars'].apply(lambda x: x['name'] if x else None)\n",
    "    df_startups['venue_distance'] = df_startups['company_bars'].apply(lambda x: x['distance'] if x else None)\n",
    "    df_startups['venue_latitude'] = df_startups['company_bars'].apply(lambda x: x['latitude'] if x else None)\n",
    "    df_startups['venue_longitude'] = df_startups['company_bars'].apply(lambda x: x['longitude'] if x else None)\n",
    "    df_startups = df_startups.drop(columns='company_bars')\n",
    "\n",
    "    concatenated_df_companies_bars = pd.concat([df_vidgame, df_startups], ignore_index=False)\n",
    "    subset_columns = ['company_name', 'company_latitude', 'company_longitude', 'venue_category', 'venue_name', 'venue_distance']\n",
    "    concatenated_df_companies_bars = concatenated_df_companies_bars.drop_duplicates(subset=subset_columns, keep='first')\n",
    "    concatenated_df_companies_bars.to_csv('data/concatenated_df_companies_bars.csv', index=False)\n",
    "    \n",
    "    return concatenated_df_companies_bars"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 316,
   "id": "27ba12a8",
   "metadata": {},
   "outputs": [],
   "source": [
    "concatenated_df_companies_bars = bars_dataframe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 303,
   "id": "be4df624",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Now going to try a function which adds the same info bur for nurseries (3) airport (1), and starbucks (1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 304,
   "id": "342fdabc",
   "metadata": {},
   "outputs": [],
   "source": [
    "#first edit the foursquare function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 305,
   "id": "577aca13",
   "metadata": {},
   "outputs": [],
   "source": [
    "def foursquare_add_venues (venue, lat, lon, radius, limit, sort = \"DISTANCE\"):\n",
    "    \n",
    "    url = f\"https://api.foursquare.com/v3/places/search?query={venue}&ll={lat}%2C{lon}&radius={radius}&sort=DISTANCE&limit={limit}\"\n",
    "    headers = {\n",
    "        \"accept\": \"application/json\",\n",
    "        \"Authorization\": \"fsq3zJlP+zylHII9gRWfq9KurwhB8+0LPJw/XnnFyamdXaE=\"\n",
    "    }\n",
    "    \n",
    "    if venue == 'nursery, daycare, school':\n",
    "        url2 = f\"https://api.foursquare.com/v3/places/search?ll={lat}%2C{lon}&radius={radius}&categories=11026%2C12055%2C12057&sort=DISTANCE&limit={limit}\"\n",
    "        response = requests.get(url2, headers=headers)\n",
    "    \n",
    "    elif venue == 'Starbucks':\n",
    "        response = requests.get(url, headers=headers)\n",
    "    \n",
    "    elif venue == 'Airport':\n",
    "        url3 = f\"https://api.foursquare.com/v3/places/search?query=Madrid%20Airport%20Barajas&ll={lat}%2C{lon}&radius={radius}&categories=19031&limit={limit}\"\n",
    "\n",
    "        response = requests.get(url3, headers=headers)\n",
    "    \n",
    "    else:\n",
    "        return 'nursery, Starbucks or Airport'\n",
    "        \n",
    "    return response.json()\n",
    "    pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f44e568e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#and apply - one function for schools, one for starbucks, one for airport"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 308,
   "id": "54bc894e",
   "metadata": {},
   "outputs": [],
   "source": [
    "def schools_dataframe():\n",
    "    vidgame_company_data_schools = {}\n",
    "    for i in madrid_vidgames:\n",
    "        company_city = i['offices'][0]['city']\n",
    "        company_name = i['name']\n",
    "        company_type = 'Video Games'\n",
    "        company_latitude = i['offices'][0][\"latitude\"]\n",
    "        company_longitude = i['offices'][0][\"longitude\"]\n",
    "        if company_latitude and company_longitude:\n",
    "\n",
    "            if company_city == \"Madrid\":\n",
    "                office_w_school = foursquare_add_venues('nursery, daycare, school', company_latitude, company_longitude, 1000, 5)\n",
    "                school_list = []\n",
    "                for school in office_w_school['results']:\n",
    "                    \n",
    "                    school_info = {\n",
    "                        'name': school['name'],\n",
    "                        'distance': school['distance'],\n",
    "                        'latitude': school['geocodes']['main']['latitude'],\n",
    "                        'longitude': school['geocodes']['main']['longitude'],\n",
    "                        'category': 'School'\n",
    "                    }\n",
    "                    \n",
    "                    school_list.append(school_info)\n",
    "                    \n",
    "                company_info_schools = {\n",
    "                    'company_name': company_name,\n",
    "                    'company_type': company_type,\n",
    "                    'company_city': company_city,\n",
    "                    'company_latitude': company_latitude,\n",
    "                    'company_longitude': company_longitude,\n",
    "                    'company_schools': school_list\n",
    "                }\n",
    "\n",
    "                vidgame_company_data_schools[company_name] = company_info_schools\n",
    "    df_vidgame_schools = pd.DataFrame(vidgame_company_data_schools).T\n",
    "    df_vidgame_schools = df_vidgame_schools.explode(\"company_schools\")\n",
    "    \n",
    "    df_vidgame_schools['venue_category'] = df_vidgame_schools['company_schools'].apply(lambda x: x['category'] if x else None)\n",
    "    df_vidgame_schools['venue_name'] = df_vidgame_schools['company_schools'].apply(lambda x: x['name'] if x else None)\n",
    "    df_vidgame_schools['venue_distance'] = df_vidgame_schools['company_schools'].apply(lambda x: x['distance'] if x else None)\n",
    "    df_vidgame_schools['venue_latitude'] = df_vidgame_schools['company_schools'].apply(lambda x: x['latitude'] if x else None)\n",
    "    df_vidgame_schools['venue_longitude'] = df_vidgame_schools['company_schools'].apply(lambda x: x['longitude'] if x else None)\n",
    "    df_vidgame_schools = df_vidgame_schools.drop(columns='company_schools')\n",
    "\n",
    "    startup_company_data_schools = {}\n",
    "    for y in madrid_startups:\n",
    "        company_city_startups = y['offices'][0]['city']\n",
    "        company_name_startups = y['name']\n",
    "        company_type_startups = 'Start Up (>$1M Raised)'\n",
    "        company_latitude_startups = y['offices'][0][\"latitude\"]\n",
    "        company_longitude_startups = y['offices'][0][\"longitude\"]\n",
    "        if company_latitude_startups and company_longitude_startups:\n",
    "\n",
    "            if company_city_startups == \"Madrid\":\n",
    "                office_w_school_startups= foursquare_add_venues('nursery, daycare, school', company_latitude_startups, company_longitude_startups, 1000, 5)\n",
    "                school_list_startups= []\n",
    "                for school in office_w_school_startups['results']:\n",
    "\n",
    "                    school_info_startups = {\n",
    "                        'name': school['name'],\n",
    "                        'distance': school['distance'],\n",
    "                        'latitude': school['geocodes']['main']['latitude'],\n",
    "                        'longitude': school['geocodes']['main']['longitude'],\n",
    "                        'category': 'School'\n",
    "                    }\n",
    "\n",
    "                    school_list_startups.append(school_info_startups)\n",
    "\n",
    "                startup_company_info_schools = {\n",
    "                    'company_name': company_name_startups,\n",
    "                    'company_type': company_type_startups,\n",
    "                    'company_city': company_city_startups,\n",
    "                    'company_latitude': company_latitude_startups,\n",
    "                    'company_longitude': company_longitude_startups,\n",
    "                    'company_schools': school_list_startups\n",
    "                }\n",
    "\n",
    "                startup_company_data_schools[company_name_startups] = startup_company_info_schools\n",
    "    df_startups_schools = pd.DataFrame(startup_company_data_schools).T\n",
    "    df_startups_schools = df_startups_schools.explode(\"company_schools\")\n",
    "    df_startups_schools['venue_category'] = df_startups_schools['company_schools'].apply(lambda x: x['category'] if x else None)\n",
    "    df_startups_schools['venue_name'] = df_startups_schools['company_schools'].apply(lambda x: x['name'] if x else None)\n",
    "    df_startups_schools['venue_distance'] = df_startups_schools['company_schools'].apply(lambda x: x['distance'] if x else None)\n",
    "    df_startups_schools['venue_latitude'] = df_startups_schools['company_schools'].apply(lambda x: x['latitude'] if x else None)\n",
    "    df_startups_schools['venue_longitude'] = df_startups_schools['company_schools'].apply(lambda x: x['longitude'] if x else None)\n",
    "    df_startups_schools = df_startups_schools.drop(columns='company_schools')\n",
    "\n",
    "    concatenated_df_companies_schools = pd.concat([df_vidgame_schools, df_startups_schools], ignore_index=False)\n",
    "    subset_columns = ['company_name', 'company_latitude', 'company_longitude', 'venue_category', 'venue_name', 'venue_distance']\n",
    "    concatenated_df_companies_schools = concatenated_df_companies_schools.drop_duplicates(subset=subset_columns, keep='first')\n",
    "    concatenated_df_companies_schools.to_csv('data/concatenated_df_companies_schools.csv', index=False)\n",
    "    \n",
    "    return concatenated_df_companies_schools"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 318,
   "id": "62471ab3",
   "metadata": {},
   "outputs": [],
   "source": [
    "concatenated_df_companies_schools = schools_dataframe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 310,
   "id": "e4a504f2",
   "metadata": {},
   "outputs": [],
   "source": [
    "def starbucks_dataframe():\n",
    "    vidgame_company_data_starbucks = {}\n",
    "    for i in madrid_vidgames:\n",
    "        company_city = i['offices'][0]['city']\n",
    "        company_name = i['name']\n",
    "        company_type = 'Video Games'\n",
    "        company_latitude = i['offices'][0][\"latitude\"]\n",
    "        company_longitude = i['offices'][0][\"longitude\"]\n",
    "        if company_latitude and company_longitude:\n",
    "\n",
    "            if company_city == \"Madrid\":\n",
    "                office_w_starbucks = foursquare_add_venues('Starbucks', company_latitude, company_longitude, 10000, 1)\n",
    "                starbucks_list = []\n",
    "                for starbucks in office_w_starbucks['results']:\n",
    "                    \n",
    "                    starbucks_info = {\n",
    "                        'name': starbucks['name'],\n",
    "                        'distance': starbucks['distance'],\n",
    "                        'latitude': starbucks['geocodes']['main']['latitude'],\n",
    "                        'longitude': starbucks['geocodes']['main']['longitude'],\n",
    "                        'category': 'Starbucks'\n",
    "                    }\n",
    "                    \n",
    "                    starbucks_list.append(starbucks_info)\n",
    "                    \n",
    "                company_info_starbucks = {\n",
    "                    'company_name': company_name,\n",
    "                    'company_type': company_type,\n",
    "                    'company_city': company_city,\n",
    "                    'company_latitude': company_latitude,\n",
    "                    'company_longitude': company_longitude,\n",
    "                    'company_starbucks': starbucks_list\n",
    "                }\n",
    "\n",
    "                vidgame_company_data_starbucks[company_name] = company_info_starbucks\n",
    "    df_vidgame_starbucks = pd.DataFrame(vidgame_company_data_starbucks).T\n",
    "    df_vidgame_starbucks = df_vidgame_starbucks.explode(\"company_starbucks\")\n",
    "    df_vidgame_starbucks = df_vidgame_starbucks.dropna()\n",
    "\n",
    "    \n",
    "    df_vidgame_starbucks['venue_category'] = df_vidgame_starbucks['company_starbucks'].apply(lambda x: x['category'] if x else None)\n",
    "    df_vidgame_starbucks['venue_name'] = df_vidgame_starbucks['company_starbucks'].apply(lambda x: x['name'] if x else None)\n",
    "    df_vidgame_starbucks['venue_distance'] = df_vidgame_starbucks['company_starbucks'].apply(lambda x: x['distance'] if x else None)\n",
    "    df_vidgame_starbucks['venue_latitude'] = df_vidgame_starbucks['company_starbucks'].apply(lambda x: x['latitude'] if x else None)\n",
    "    df_vidgame_starbucks['venue_longitude'] = df_vidgame_starbucks['company_starbucks'].apply(lambda x: x['longitude'] if x else None)\n",
    "    df_vidgame_starbucks = df_vidgame_starbucks.drop(columns='company_starbucks')\n",
    "    \n",
    "\n",
    "    startup_company_data_starbucks = {}\n",
    "    for y in madrid_startups:\n",
    "        company_city_startups = y['offices'][0]['city']\n",
    "        company_name_startups = y['name']\n",
    "        company_type_startups = 'Start Up (>$1M Raised)'\n",
    "        company_latitude_startups = y['offices'][0][\"latitude\"]\n",
    "        company_longitude_startups = y['offices'][0][\"longitude\"]\n",
    "        if company_latitude_startups and company_longitude_startups:\n",
    "\n",
    "            if company_city_startups == \"Madrid\":\n",
    "                office_w_starbucks_startups= foursquare_add_venues('Starbucks', company_latitude_startups, company_longitude_startups, 10000, 1)\n",
    "                starbucks_list_startups= []\n",
    "                for starbucks in office_w_starbucks_startups['results']:\n",
    "\n",
    "                    starbucks_info_startups = {\n",
    "                        'name': starbucks['name'],\n",
    "                        'distance': starbucks['distance'],\n",
    "                        'latitude': starbucks['geocodes']['main']['latitude'],\n",
    "                        'longitude': starbucks['geocodes']['main']['longitude'],\n",
    "                        'category': 'Starbucks'\n",
    "                    }\n",
    "\n",
    "                    starbucks_list_startups.append(starbucks_info_startups)\n",
    "\n",
    "                startup_company_info_starbucks = {\n",
    "                    'company_name': company_name_startups,\n",
    "                    'company_type': company_type_startups,\n",
    "                    'company_city': company_city_startups,\n",
    "                    'company_latitude': company_latitude_startups,\n",
    "                    'company_longitude': company_longitude_startups,\n",
    "                    'company_starbucks': starbucks_list_startups\n",
    "                }\n",
    "\n",
    "                startup_company_data_starbucks[company_name_startups] = startup_company_info_starbucks\n",
    "    df_startups_starbucks = pd.DataFrame(startup_company_data_starbucks).T\n",
    "    df_startups_starbucks = df_startups_starbucks.explode(\"company_starbucks\")\n",
    "    \n",
    "    ##DROP NAN\n",
    "    \n",
    "    df_startups_starbucks = df_startups_starbucks.dropna()\n",
    "    \n",
    "    df_startups_starbucks['venue_category'] = df_startups_starbucks['company_starbucks'].apply(lambda x: x['category'] if x else None)\n",
    "    df_startups_starbucks['venue_name'] = df_startups_starbucks['company_starbucks'].apply(lambda x: x['name'] if x else None)\n",
    "    df_startups_starbucks['venue_distance'] = df_startups_starbucks['company_starbucks'].apply(lambda x: x['distance'] if x else None)\n",
    "    df_startups_starbucks['venue_latitude'] = df_startups_starbucks['company_starbucks'].apply(lambda x: x['latitude'] if x else None)\n",
    "    df_startups_starbucks['venue_longitude'] = df_startups_starbucks['company_starbucks'].apply(lambda x: x['longitude'] if x else None)\n",
    "    df_startups_starbucks = df_startups_starbucks.drop(columns='company_starbucks')\n",
    "    \n",
    "\n",
    "    concatenated_df_companies_starbucks = pd.concat([df_vidgame_starbucks, df_startups_starbucks], ignore_index=False)\n",
    "    subset_columns = ['company_name', 'company_latitude', 'company_longitude', 'venue_category', 'venue_name', 'venue_distance']\n",
    "    concatenated_df_companies_starbucks = concatenated_df_companies_starbucks.drop_duplicates(subset=subset_columns, keep='first')\n",
    "    concatenated_df_companies_starbucks.to_csv('data/concatenated_df_companies_starbucks.csv', index=False)\n",
    "    \n",
    "    return concatenated_df_companies_starbucks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 319,
   "id": "1c8c69e4",
   "metadata": {},
   "outputs": [],
   "source": [
    "concatenated_df_companies_starbucks = starbucks_dataframe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 312,
   "id": "b01ca118",
   "metadata": {},
   "outputs": [],
   "source": [
    "def airport_dataframe():\n",
    "    vidgame_company_data_airport = {}\n",
    "    for i in madrid_vidgames:\n",
    "        company_city = i['offices'][0]['city']\n",
    "        company_name = i['name']\n",
    "        company_type = 'Video Games'\n",
    "        company_latitude = i['offices'][0][\"latitude\"]\n",
    "        company_longitude = i['offices'][0][\"longitude\"]\n",
    "        if company_latitude and company_longitude:\n",
    "\n",
    "            if company_city == \"Madrid\":\n",
    "                office_w_airport = foursquare_add_venues('Airport', company_latitude, company_longitude, 50000, 1)\n",
    "                airport_list = []\n",
    "                for airport in office_w_airport['results']:\n",
    "                    \n",
    "                    airport_info = {\n",
    "                        'name': airport['name'],\n",
    "                        'distance': airport['distance'],\n",
    "                        'latitude': airport['geocodes']['main']['latitude'],\n",
    "                        'longitude': airport['geocodes']['main']['longitude'],\n",
    "                        'category': 'Airport'\n",
    "                    }\n",
    "                    \n",
    "                    airport_list.append(airport_info)\n",
    "                    \n",
    "                company_info_airport = {\n",
    "                    'company_name': company_name,\n",
    "                    'company_type': company_type,\n",
    "                    'company_city': company_city,\n",
    "                    'company_latitude': company_latitude,\n",
    "                    'company_longitude': company_longitude,\n",
    "                    'company_airport': airport_list\n",
    "                }\n",
    "\n",
    "                vidgame_company_data_airport[company_name] = company_info_airport\n",
    "    df_vidgame_airport = pd.DataFrame(vidgame_company_data_airport).T\n",
    "    df_vidgame_airport = df_vidgame_airport.explode(\"company_airport\")\n",
    "    df_vidgame_airport = df_vidgame_airport.dropna()\n",
    "\n",
    "    \n",
    "    df_vidgame_airport['venue_category'] = df_vidgame_airport['company_airport'].apply(lambda x: x['category'] if x else None)\n",
    "    df_vidgame_airport['venue_name'] = df_vidgame_airport['company_airport'].apply(lambda x: x['name'] if x else None)\n",
    "    df_vidgame_airport['venue_distance'] = df_vidgame_airport['company_airport'].apply(lambda x: x['distance'] if x else None)\n",
    "    df_vidgame_airport['venue_latitude'] = df_vidgame_airport['company_airport'].apply(lambda x: x['latitude'] if x else None)\n",
    "    df_vidgame_airport['venue_longitude'] = df_vidgame_airport['company_airport'].apply(lambda x: x['longitude'] if x else None)\n",
    "    df_vidgame_airport = df_vidgame_airport.drop(columns='company_airport')\n",
    "    \n",
    "\n",
    "    startup_company_data_airport = {}\n",
    "    for y in madrid_startups:\n",
    "        company_city_startups = y['offices'][0]['city']\n",
    "        company_name_startups = y['name']\n",
    "        company_type_startups = 'Start Up (>$1M Raised)'\n",
    "        company_latitude_startups = y['offices'][0][\"latitude\"]\n",
    "        company_longitude_startups = y['offices'][0][\"longitude\"]\n",
    "        if company_latitude_startups and company_longitude_startups:\n",
    "\n",
    "            if company_city_startups == \"Madrid\":\n",
    "                office_w_airport_startups= foursquare_add_venues('Airport', company_latitude_startups, company_longitude_startups, 50000, 1)\n",
    "                airport_list_startups= []\n",
    "                for airport in office_w_airport_startups['results']:\n",
    "\n",
    "                    airport_info_startups = {\n",
    "                        'name': airport['name'],\n",
    "                        'distance': airport['distance'],\n",
    "                        'latitude': airport['geocodes']['main']['latitude'],\n",
    "                        'longitude': airport['geocodes']['main']['longitude'],\n",
    "                        'category': 'Airport'\n",
    "                    }\n",
    "\n",
    "                    airport_list_startups.append(airport_info_startups)\n",
    "\n",
    "                startup_company_info_airport = {\n",
    "                    'company_name': company_name_startups,\n",
    "                    'company_type': company_type_startups,\n",
    "                    'company_city': company_city_startups,\n",
    "                    'company_latitude': company_latitude_startups,\n",
    "                    'company_longitude': company_longitude_startups,\n",
    "                    'company_airport': airport_list_startups\n",
    "                }\n",
    "\n",
    "                startup_company_data_airport[company_name_startups] = startup_company_info_airport\n",
    "    df_startups_airport = pd.DataFrame(startup_company_data_airport).T\n",
    "    df_startups_airport = df_startups_airport.explode(\"company_airport\")\n",
    "    \n",
    "    ##DROP NAN\n",
    "    \n",
    "    df_startups_airport = df_startups_airport.dropna()\n",
    "    \n",
    "    df_startups_airport['venue_category'] = df_startups_airport['company_airport'].apply(lambda x: x['category'] if x else None)\n",
    "    df_startups_airport['venue_name'] = df_startups_airport['company_airport'].apply(lambda x: x['name'] if x else None)\n",
    "    df_startups_airport['venue_distance'] = df_startups_airport['company_airport'].apply(lambda x: x['distance'] if x else None)\n",
    "    df_startups_airport['venue_latitude'] = df_startups_airport['company_airport'].apply(lambda x: x['latitude'] if x else None)\n",
    "    df_startups_airport['venue_longitude'] = df_startups_airport['company_airport'].apply(lambda x: x['longitude'] if x else None)\n",
    "    df_startups_airport = df_startups_airport.drop(columns='company_airport')\n",
    "    \n",
    "\n",
    "    concatenated_df_companies_airport = pd.concat([df_vidgame_airport, df_startups_airport], ignore_index=False)\n",
    "    subset_columns = ['company_name', 'company_latitude', 'company_longitude', 'venue_category', 'venue_name', 'venue_distance']\n",
    "    concatenated_df_companies_airport = concatenated_df_companies_airport.drop_duplicates(subset=subset_columns, keep='first')\n",
    "    concatenated_df_companies_airport.to_csv('data/concatenated_df_companies_airport.csv', index=False)\n",
    "    \n",
    "    return concatenated_df_companies_airport"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 320,
   "id": "02cb55db",
   "metadata": {},
   "outputs": [],
   "source": [
    "concatenated_df_companies_airport = airport_dataframe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 314,
   "id": "4c56b4b5",
   "metadata": {},
   "outputs": [],
   "source": [
    "#concatenated_df_companies_bars, concatenated_df_companies_schools, concatenated_df_companies_starbucks, concatenated_df_companies_airport --> concatenate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 433,
   "id": "32199e50",
   "metadata": {},
   "outputs": [],
   "source": [
    "final_df = pd.concat([concatenated_df_companies_bars, concatenated_df_companies_schools, concatenated_df_companies_starbucks, concatenated_df_companies_airport], ignore_index=False)\n",
    "final_df = final_df.sort_values(by=['company_name', 'venue_category', 'venue_distance'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8ef16085",
   "metadata": {},
   "outputs": [],
   "source": [
    "# simplifying"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 329,
   "id": "481819a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "final_df2 = final_df.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "id": "ff1fb514",
   "metadata": {},
   "outputs": [],
   "source": [
    "final_df2.reset_index(inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 358,
   "id": "c0247e52",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "final_df2 = final_df2.groupby([\"company_name\", \"venue_category\"]).agg(\n",
    "    mean_distance=(\"venue_distance\", \"mean\"),\n",
    "    min_distance=(\"venue_distance\", \"min\")\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 434,
   "id": "afaf2587",
   "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></th>\n",
       "      <th>mean_distance</th>\n",
       "      <th>min_distance</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>company_name</th>\n",
       "      <th>venue_category</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">Busuu</th>\n",
       "      <th>Airport</th>\n",
       "      <td>12689.0</td>\n",
       "      <td>12689</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bar</th>\n",
       "      <td>107.4</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>School</th>\n",
       "      <td>299.4</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Starbucks</th>\n",
       "      <td>284.0</td>\n",
       "      <td>284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">Comunitae</th>\n",
       "      <th>Airport</th>\n",
       "      <td>11542.0</td>\n",
       "      <td>11542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bar</th>\n",
       "      <td>364.5</td>\n",
       "      <td>178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>School</th>\n",
       "      <td>282.0</td>\n",
       "      <td>105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Starbucks</th>\n",
       "      <td>1255.0</td>\n",
       "      <td>1255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">Entropy Computational Services</th>\n",
       "      <th>Airport</th>\n",
       "      <td>11180.0</td>\n",
       "      <td>11180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bar</th>\n",
       "      <td>187.1</td>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>School</th>\n",
       "      <td>352.6</td>\n",
       "      <td>267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Starbucks</th>\n",
       "      <td>567.0</td>\n",
       "      <td>567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">Undead Code Studios</th>\n",
       "      <th>Airport</th>\n",
       "      <td>39676.0</td>\n",
       "      <td>39676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bar</th>\n",
       "      <td>622.8</td>\n",
       "      <td>250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>School</th>\n",
       "      <td>333.6</td>\n",
       "      <td>300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Starbucks</th>\n",
       "      <td>9987.0</td>\n",
       "      <td>9987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">Unkasoft Advergaming</th>\n",
       "      <th>Airport</th>\n",
       "      <td>10294.0</td>\n",
       "      <td>10294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bar</th>\n",
       "      <td>150.9</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>School</th>\n",
       "      <td>382.2</td>\n",
       "      <td>353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Starbucks</th>\n",
       "      <td>1000.0</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">Virgin Play</th>\n",
       "      <th>Airport</th>\n",
       "      <td>12800.0</td>\n",
       "      <td>12800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bar</th>\n",
       "      <td>137.1</td>\n",
       "      <td>84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>School</th>\n",
       "      <td>536.6</td>\n",
       "      <td>358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Starbucks</th>\n",
       "      <td>257.0</td>\n",
       "      <td>257</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">eRepublik</th>\n",
       "      <th>Airport</th>\n",
       "      <td>12974.0</td>\n",
       "      <td>12974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bar</th>\n",
       "      <td>204.7</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>School</th>\n",
       "      <td>343.4</td>\n",
       "      <td>208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Starbucks</th>\n",
       "      <td>385.0</td>\n",
       "      <td>385</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               mean_distance  min_distance\n",
       "company_name                   venue_category                             \n",
       "Busuu                          Airport               12689.0         12689\n",
       "                               Bar                     107.4            68\n",
       "                               School                  299.4            32\n",
       "                               Starbucks               284.0           284\n",
       "Comunitae                      Airport               11542.0         11542\n",
       "                               Bar                     364.5           178\n",
       "                               School                  282.0           105\n",
       "                               Starbucks              1255.0          1255\n",
       "Entropy Computational Services Airport               11180.0         11180\n",
       "                               Bar                     187.1            57\n",
       "                               School                  352.6           267\n",
       "                               Starbucks               567.0           567\n",
       "Undead Code Studios            Airport               39676.0         39676\n",
       "                               Bar                     622.8           250\n",
       "                               School                  333.6           300\n",
       "                               Starbucks              9987.0          9987\n",
       "Unkasoft Advergaming           Airport               10294.0         10294\n",
       "                               Bar                     150.9            85\n",
       "                               School                  382.2           353\n",
       "                               Starbucks              1000.0          1000\n",
       "Virgin Play                    Airport               12800.0         12800\n",
       "                               Bar                     137.1            84\n",
       "                               School                  536.6           358\n",
       "                               Starbucks               257.0           257\n",
       "eRepublik                      Airport               12974.0         12974\n",
       "                               Bar                     204.7            88\n",
       "                               School                  343.4           208\n",
       "                               Starbucks               385.0           385"
      ]
     },
     "execution_count": 434,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 334,
   "id": "e442aad8",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Busuu is the winner :) :) 2nd and 3rd eRepublik and Virgin Play - this for the plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 416,
   "id": "4bdc30c2",
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt \n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 417,
   "id": "8835fa1a",
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.set_context(\"poster\")\n",
    "sns.set(rc={\"figure.figsize\": (12.,6.)})\n",
    "sns.set_style(\"whitegrid\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 425,
   "id": "5a4835d6",
   "metadata": {},
   "outputs": [],
   "source": [
    "filtered_df = final_df2.query(\"venue_category != 'Airport' and company_name in ['Busuu', 'eRepublik', 'Virgin Play']\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 443,
   "id": "ce19d9f8",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x='company_name', hue = 'venue_category', y='mean_distance', data=filtered_df)\n",
    "\n",
    "# Customize the plot\n",
    "plt.title('Mean Distance to Amenities by Company')\n",
    "plt.xlabel('Amenity Category and Company')\n",
    "plt.ylabel('Mean Distance')\n",
    "plt.xticks(rotation=45)  # Rotate x-axis labels for better readability\n",
    "plt.savefig(\"images/figure1.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 336,
   "id": "17b13d17",
   "metadata": {
    "scrolled": false
   },
   "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>company_name</th>\n",
       "      <th>company_type</th>\n",
       "      <th>company_city</th>\n",
       "      <th>company_latitude</th>\n",
       "      <th>company_longitude</th>\n",
       "      <th>venue_category</th>\n",
       "      <th>venue_name</th>\n",
       "      <th>venue_distance</th>\n",
       "      <th>venue_latitude</th>\n",
       "      <th>venue_longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Busuu</th>\n",
       "      <td>Busuu</td>\n",
       "      <td>Start Up (&gt;$1M Raised)</td>\n",
       "      <td>Madrid</td>\n",
       "      <td>40.422549</td>\n",
       "      <td>-3.683032</td>\n",
       "      <td>Airport</td>\n",
       "      <td>Sala VIP Neptuno</td>\n",
       "      <td>12689</td>\n",
       "      <td>40.494892</td>\n",
       "      <td>-3.566847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Busuu</th>\n",
       "      <td>Busuu</td>\n",
       "      <td>Start Up (&gt;$1M Raised)</td>\n",
       "      <td>Madrid</td>\n",
       "      <td>40.422549</td>\n",
       "      <td>-3.683032</td>\n",
       "      <td>Bar</td>\n",
       "      <td>Goiko Grill</td>\n",
       "      <td>68</td>\n",
       "      <td>40.423127</td>\n",
       "      <td>-3.682795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Busuu</th>\n",
       "      <td>Busuu</td>\n",
       "      <td>Start Up (&gt;$1M Raised)</td>\n",
       "      <td>Madrid</td>\n",
       "      <td>40.422549</td>\n",
       "      <td>-3.683032</td>\n",
       "      <td>Bar</td>\n",
       "      <td>Goizeko Wellington</td>\n",
       "      <td>70</td>\n",
       "      <td>40.422183</td>\n",
       "      <td>-3.684035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Busuu</th>\n",
       "      <td>Busuu</td>\n",
       "      <td>Start Up (&gt;$1M Raised)</td>\n",
       "      <td>Madrid</td>\n",
       "      <td>40.422549</td>\n",
       "      <td>-3.683032</td>\n",
       "      <td>Bar</td>\n",
       "      <td>La Terraza del Hotel Wellington</td>\n",
       "      <td>91</td>\n",
       "      <td>40.421899</td>\n",
       "      <td>-3.683696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Busuu</th>\n",
       "      <td>Busuu</td>\n",
       "      <td>Start Up (&gt;$1M Raised)</td>\n",
       "      <td>Madrid</td>\n",
       "      <td>40.422549</td>\n",
       "      <td>-3.683032</td>\n",
       "      <td>Bar</td>\n",
       "      <td>Hotel Wellington</td>\n",
       "      <td>106</td>\n",
       "      <td>40.421776</td>\n",
       "      <td>-3.683752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Busuu</th>\n",
       "      <td>Busuu</td>\n",
       "      <td>Start Up (&gt;$1M Raised)</td>\n",
       "      <td>Madrid</td>\n",
       "      <td>40.422549</td>\n",
       "      <td>-3.683032</td>\n",
       "      <td>Bar</td>\n",
       "      <td>Garten True Food</td>\n",
       "      <td>111</td>\n",
       "      <td>40.422503</td>\n",
       "      <td>-3.681833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Busuu</th>\n",
       "      <td>Busuu</td>\n",
       "      <td>Start Up (&gt;$1M Raised)</td>\n",
       "      <td>Madrid</td>\n",
       "      <td>40.422549</td>\n",
       "      <td>-3.683032</td>\n",
       "      <td>Bar</td>\n",
       "      <td>La Llave de Oro</td>\n",
       "      <td>120</td>\n",
       "      <td>40.422221</td>\n",
       "      <td>-3.684172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Busuu</th>\n",
       "      <td>Busuu</td>\n",
       "      <td>Start Up (&gt;$1M Raised)</td>\n",
       "      <td>Madrid</td>\n",
       "      <td>40.422549</td>\n",
       "      <td>-3.683032</td>\n",
       "      <td>Bar</td>\n",
       "      <td>Numa Pompilio</td>\n",
       "      <td>123</td>\n",
       "      <td>40.423338</td>\n",
       "      <td>-3.684054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Busuu</th>\n",
       "      <td>Busuu</td>\n",
       "      <td>Start Up (&gt;$1M Raised)</td>\n",
       "      <td>Madrid</td>\n",
       "      <td>40.422549</td>\n",
       "      <td>-3.683032</td>\n",
       "      <td>Bar</td>\n",
       "      <td>Kabuki Wellington</td>\n",
       "      <td>124</td>\n",
       "      <td>40.421869</td>\n",
       "      <td>-3.684167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Busuu</th>\n",
       "      <td>Busuu</td>\n",
       "      <td>Start Up (&gt;$1M Raised)</td>\n",
       "      <td>Madrid</td>\n",
       "      <td>40.422549</td>\n",
       "      <td>-3.683032</td>\n",
       "      <td>Bar</td>\n",
       "      <td>Ornella</td>\n",
       "      <td>125</td>\n",
       "      <td>40.423414</td>\n",
       "      <td>-3.684052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Busuu</th>\n",
       "      <td>Busuu</td>\n",
       "      <td>Start Up (&gt;$1M Raised)</td>\n",
       "      <td>Madrid</td>\n",
       "      <td>40.422549</td>\n",
       "      <td>-3.683032</td>\n",
       "      <td>Bar</td>\n",
       "      <td>Salutteria</td>\n",
       "      <td>136</td>\n",
       "      <td>40.421924</td>\n",
       "      <td>-3.684604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Busuu</th>\n",
       "      <td>Busuu</td>\n",
       "      <td>Start Up (&gt;$1M Raised)</td>\n",
       "      <td>Madrid</td>\n",
       "      <td>40.422549</td>\n",
       "      <td>-3.683032</td>\n",
       "      <td>School</td>\n",
       "      <td>Academia Aries</td>\n",
       "      <td>32</td>\n",
       "      <td>40.422775</td>\n",
       "      <td>-3.682790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Busuu</th>\n",
       "      <td>Busuu</td>\n",
       "      <td>Start Up (&gt;$1M Raised)</td>\n",
       "      <td>Madrid</td>\n",
       "      <td>40.422549</td>\n",
       "      <td>-3.683032</td>\n",
       "      <td>School</td>\n",
       "      <td>Juan Evangelista Ruiz de Burgos</td>\n",
       "      <td>284</td>\n",
       "      <td>40.424175</td>\n",
       "      <td>-3.680439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Busuu</th>\n",
       "      <td>Busuu</td>\n",
       "      <td>Start Up (&gt;$1M Raised)</td>\n",
       "      <td>Madrid</td>\n",
       "      <td>40.422549</td>\n",
       "      <td>-3.683032</td>\n",
       "      <td>School</td>\n",
       "      <td>Institut d'estudis aplicats</td>\n",
       "      <td>312</td>\n",
       "      <td>40.425244</td>\n",
       "      <td>-3.681979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Busuu</th>\n",
       "      <td>Busuu</td>\n",
       "      <td>Start Up (&gt;$1M Raised)</td>\n",
       "      <td>Madrid</td>\n",
       "      <td>40.422549</td>\n",
       "      <td>-3.683032</td>\n",
       "      <td>School</td>\n",
       "      <td>I.E.S. Beatriz Galindo</td>\n",
       "      <td>406</td>\n",
       "      <td>40.425155</td>\n",
       "      <td>-3.686227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Busuu</th>\n",
       "      <td>Busuu</td>\n",
       "      <td>Start Up (&gt;$1M Raised)</td>\n",
       "      <td>Madrid</td>\n",
       "      <td>40.422549</td>\n",
       "      <td>-3.683032</td>\n",
       "      <td>School</td>\n",
       "      <td>Academia Qz Barrio Salamanca</td>\n",
       "      <td>463</td>\n",
       "      <td>40.426680</td>\n",
       "      <td>-3.683823</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Busuu</th>\n",
       "      <td>Busuu</td>\n",
       "      <td>Start Up (&gt;$1M Raised)</td>\n",
       "      <td>Madrid</td>\n",
       "      <td>40.422549</td>\n",
       "      <td>-3.683032</td>\n",
       "      <td>Starbucks</td>\n",
       "      <td>Starbucks</td>\n",
       "      <td>284</td>\n",
       "      <td>40.424959</td>\n",
       "      <td>-3.683936</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      company_name            company_type company_city company_latitude  \\\n",
       "Busuu        Busuu  Start Up (>$1M Raised)       Madrid        40.422549   \n",
       "Busuu        Busuu  Start Up (>$1M Raised)       Madrid        40.422549   \n",
       "Busuu        Busuu  Start Up (>$1M Raised)       Madrid        40.422549   \n",
       "Busuu        Busuu  Start Up (>$1M Raised)       Madrid        40.422549   \n",
       "Busuu        Busuu  Start Up (>$1M Raised)       Madrid        40.422549   \n",
       "Busuu        Busuu  Start Up (>$1M Raised)       Madrid        40.422549   \n",
       "Busuu        Busuu  Start Up (>$1M Raised)       Madrid        40.422549   \n",
       "Busuu        Busuu  Start Up (>$1M Raised)       Madrid        40.422549   \n",
       "Busuu        Busuu  Start Up (>$1M Raised)       Madrid        40.422549   \n",
       "Busuu        Busuu  Start Up (>$1M Raised)       Madrid        40.422549   \n",
       "Busuu        Busuu  Start Up (>$1M Raised)       Madrid        40.422549   \n",
       "Busuu        Busuu  Start Up (>$1M Raised)       Madrid        40.422549   \n",
       "Busuu        Busuu  Start Up (>$1M Raised)       Madrid        40.422549   \n",
       "Busuu        Busuu  Start Up (>$1M Raised)       Madrid        40.422549   \n",
       "Busuu        Busuu  Start Up (>$1M Raised)       Madrid        40.422549   \n",
       "Busuu        Busuu  Start Up (>$1M Raised)       Madrid        40.422549   \n",
       "Busuu        Busuu  Start Up (>$1M Raised)       Madrid        40.422549   \n",
       "\n",
       "      company_longitude venue_category                       venue_name  \\\n",
       "Busuu         -3.683032        Airport                 Sala VIP Neptuno   \n",
       "Busuu         -3.683032            Bar                      Goiko Grill   \n",
       "Busuu         -3.683032            Bar               Goizeko Wellington   \n",
       "Busuu         -3.683032            Bar  La Terraza del Hotel Wellington   \n",
       "Busuu         -3.683032            Bar                 Hotel Wellington   \n",
       "Busuu         -3.683032            Bar                 Garten True Food   \n",
       "Busuu         -3.683032            Bar                  La Llave de Oro   \n",
       "Busuu         -3.683032            Bar                    Numa Pompilio   \n",
       "Busuu         -3.683032            Bar                Kabuki Wellington   \n",
       "Busuu         -3.683032            Bar                          Ornella   \n",
       "Busuu         -3.683032            Bar                       Salutteria   \n",
       "Busuu         -3.683032         School                   Academia Aries   \n",
       "Busuu         -3.683032         School  Juan Evangelista Ruiz de Burgos   \n",
       "Busuu         -3.683032         School      Institut d'estudis aplicats   \n",
       "Busuu         -3.683032         School           I.E.S. Beatriz Galindo   \n",
       "Busuu         -3.683032         School     Academia Qz Barrio Salamanca   \n",
       "Busuu         -3.683032      Starbucks                        Starbucks   \n",
       "\n",
       "       venue_distance  venue_latitude  venue_longitude  \n",
       "Busuu           12689       40.494892        -3.566847  \n",
       "Busuu              68       40.423127        -3.682795  \n",
       "Busuu              70       40.422183        -3.684035  \n",
       "Busuu              91       40.421899        -3.683696  \n",
       "Busuu             106       40.421776        -3.683752  \n",
       "Busuu             111       40.422503        -3.681833  \n",
       "Busuu             120       40.422221        -3.684172  \n",
       "Busuu             123       40.423338        -3.684054  \n",
       "Busuu             124       40.421869        -3.684167  \n",
       "Busuu             125       40.423414        -3.684052  \n",
       "Busuu             136       40.421924        -3.684604  \n",
       "Busuu              32       40.422775        -3.682790  \n",
       "Busuu             284       40.424175        -3.680439  \n",
       "Busuu             312       40.425244        -3.681979  \n",
       "Busuu             406       40.425155        -3.686227  \n",
       "Busuu             463       40.426680        -3.683823  \n",
       "Busuu             284       40.424959        -3.683936  "
      ]
     },
     "execution_count": 336,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "condition_winner = final_df['company_name'] == 'Busuu'\n",
    "final_df_winner = final_df[condition_winner]\n",
    "final_df_winner"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 435,
   "id": "a5f6cb76",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Now checking for less important requirements which are only for one person in the copmany - vegan restaurant, basketball stadium, and dog hairdresser"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 337,
   "id": "94801cec",
   "metadata": {},
   "outputs": [],
   "source": [
    "def foursquare_add_caprichos (venue, radius, limit, sort = \"DISTANCE\"):\n",
    "    \n",
    "    url = f\"https://api.foursquare.com/v3/places/search?query={venue}&ll=40.422549%2C-3.683032&radius={radius}&sort=DISTANCE&limit={limit}\"\n",
    "    headers = {\n",
    "        \"accept\": \"application/json\",\n",
    "        \"Authorization\": \"fsq3zJlP+zylHII9gRWfq9KurwhB8+0LPJw/XnnFyamdXaE=\"\n",
    "    }\n",
    "    \n",
    "    if venue == 'vegan':\n",
    "        url_vegan = f\"https://api.foursquare.com/v3/places/search?ll=40.422549%2C-3.683032&radius={radius}&categories=13377&sort=DISTANCE&limit={limit}\"\n",
    "        response = requests.get(url_vegan, headers=headers)\n",
    "    \n",
    "    elif venue == 'basketball':\n",
    "        url_bball = f\"https://api.foursquare.com/v3/places/search?query=WiZink%20Center%20Madrid&ll=40.422549%2C-3.683032&radius=10000&limit={limit}\"\n",
    "        response = requests.get(url_bball, headers=headers)\n",
    "        \n",
    "    \n",
    "    elif venue == 'dog hairdresser':\n",
    "        url_dog = f\"https://api.foursquare.com/v3/places/search?query=Dog%20saloon&ll=40.422549%2C-3.683032&radius={radius}&sort=DISTANCE&limit={limit}\"\n",
    "        response = requests.get(url_dog, headers=headers)\n",
    "    \n",
    "    else:\n",
    "        return 'vegan, basketball or dog hairdresser'\n",
    "        \n",
    "    return response.json()\n",
    "    pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 339,
   "id": "1c538c22",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'results': [{'fsq_id': '57685c25498ee3f78cb522c5',\n",
       "   'categories': [{'id': 13377,\n",
       "     'name': 'Vegan and Vegetarian Restaurant',\n",
       "     'short_name': 'Vegan and Vegetarian Restaurant',\n",
       "     'plural_name': 'Vegan and Vegetarian Restaurants',\n",
       "     'icon': {'prefix': 'https://ss3.4sqi.net/img/categories_v2/food/vegetarian_',\n",
       "      'suffix': '.png'}}],\n",
       "   'chains': [],\n",
       "   'closed_bucket': 'LikelyOpen',\n",
       "   'distance': 398,\n",
       "   'geocodes': {'drop_off': {'latitude': 40.426014, 'longitude': -3.684052},\n",
       "    'main': {'latitude': 40.42603, 'longitude': -3.68425},\n",
       "    'roof': {'latitude': 40.42603, 'longitude': -3.68425}},\n",
       "   'link': '/v3/places/57685c25498ee3f78cb522c5',\n",
       "   'location': {'address': 'Velázquez, 39',\n",
       "    'admin_region': 'Comunidad de Madrid',\n",
       "    'country': 'ES',\n",
       "    'cross_street': '',\n",
       "    'formatted_address': 'Velázquez, 39, 28002 Madrid Madrid',\n",
       "    'locality': 'Madrid',\n",
       "    'postcode': '28002',\n",
       "    'region': 'Madrid'},\n",
       "   'name': 'Fit Food Velázquez 39',\n",
       "   'related_places': {},\n",
       "   'timezone': 'Europe/Madrid'}],\n",
       " 'context': {'geo_bounds': {'circle': {'center': {'latitude': 40.422549,\n",
       "     'longitude': -3.683032},\n",
       "    'radius': 10000}}}}"
      ]
     },
     "execution_count": 339,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "foursquare_add_caprichos ('vegan', 10000, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 341,
   "id": "a9b01a86",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'results': [{'fsq_id': '4e329dc6c65bfd5a4e27f5b3',\n",
       "   'categories': [{'id': 11128,\n",
       "     'name': 'Coworking Space',\n",
       "     'short_name': 'Coworking Space',\n",
       "     'plural_name': 'Coworking Spaces',\n",
       "     'icon': {'prefix': 'https://ss3.4sqi.net/img/categories_v2/building/office_coworkingspace_',\n",
       "      'suffix': '.png'}}],\n",
       "   'chains': [],\n",
       "   'closed_bucket': 'LikelyClosed',\n",
       "   'distance': 2034,\n",
       "   'geocodes': {'main': {'latitude': 40.430303, 'longitude': -3.661318},\n",
       "    'roof': {'latitude': 40.430303, 'longitude': -3.661318}},\n",
       "   'link': '/v3/places/4e329dc6c65bfd5a4e27f5b3',\n",
       "   'location': {'address': 'Calle Bocangel, 27',\n",
       "    'admin_region': 'Comunidad de Madrid',\n",
       "    'country': 'ES',\n",
       "    'cross_street': '',\n",
       "    'formatted_address': 'Calle Bocangel, 27, 28028 Madrid Madrid',\n",
       "    'locality': 'Madrid',\n",
       "    'postcode': '28028',\n",
       "    'region': 'Madrid'},\n",
       "   'name': 'Media Center Madrid',\n",
       "   'related_places': {},\n",
       "   'timezone': 'Europe/Madrid'}],\n",
       " 'context': {'geo_bounds': {'circle': {'center': {'latitude': 40.422549,\n",
       "     'longitude': -3.683032},\n",
       "    'radius': 10000}}}}"
      ]
     },
     "execution_count": 341,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "foursquare_add_caprichos ('basketball', 40000, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cb96a2ec",
   "metadata": {},
   "outputs": [],
   "source": [
    "#^ will do this manually for Wizink Center"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 342,
   "id": "4357c187",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'results': [{'fsq_id': '4ba4c276f964a52069b238e3',\n",
       "   'categories': [{'id': 17082,\n",
       "     'name': 'Furniture and Home Store',\n",
       "     'short_name': 'Furniture and Home Store',\n",
       "     'plural_name': 'Furniture and Home Stores',\n",
       "     'icon': {'prefix': 'https://ss3.4sqi.net/img/categories_v2/shops/furniture_',\n",
       "      'suffix': '.png'}}],\n",
       "   'chains': [],\n",
       "   'closed_bucket': 'LikelyOpen',\n",
       "   'distance': 213,\n",
       "   'geocodes': {'main': {'latitude': 40.424244, 'longitude': -3.684009},\n",
       "    'roof': {'latitude': 40.424244, 'longitude': -3.684009}},\n",
       "   'link': '/v3/places/4ba4c276f964a52069b238e3',\n",
       "   'location': {'address': 'Velázquez, 24',\n",
       "    'admin_region': 'Comunidad de Madrid',\n",
       "    'country': 'ES',\n",
       "    'cross_street': '',\n",
       "    'formatted_address': 'Velázquez, 24, 28001 Madrid Madrid',\n",
       "    'locality': 'Madrid',\n",
       "    'postcode': '28001',\n",
       "    'region': 'Madrid'},\n",
       "   'name': 'A loja do gato preto',\n",
       "   'related_places': {},\n",
       "   'timezone': 'Europe/Madrid'}],\n",
       " 'context': {'geo_bounds': {'circle': {'center': {'latitude': 40.422549,\n",
       "     'longitude': -3.683032},\n",
       "    'radius': 40000}}}}"
      ]
     },
     "execution_count": 342,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "foursquare_add_caprichos ('dog hairdresser', 40000, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 344,
   "id": "8bbbddee",
   "metadata": {},
   "outputs": [],
   "source": [
    "caprichos = {\n",
    "    'venue_category': ['Vegan Restaurant', 'Basketball Stadium', 'Dog Saloon'],\n",
    "    'venue_name': ['Fit Food Velázquez 39', 'Wizink Center', 'A loja do gato preto'],\n",
    "    'latitude': ['40.42603', '40.4239', '40.424244'],\n",
    "    'longitude': ['-3.68425', '-3.6718', '-3.684009'],\n",
    "    'distance_from_Busuu': ['398', '1000', '213']\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 346,
   "id": "050cb8cb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "\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>venue_category</th>\n",
       "      <th>venue_name</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>distance_from_Busuu</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Vegan Restaurant</td>\n",
       "      <td>Fit Food Velázquez 39</td>\n",
       "      <td>40.42603</td>\n",
       "      <td>-3.68425</td>\n",
       "      <td>398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Basketball Stadium</td>\n",
       "      <td>Wizink Center</td>\n",
       "      <td>40.4239</td>\n",
       "      <td>-3.6718</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Dog Saloon</td>\n",
       "      <td>A loja do gato preto</td>\n",
       "      <td>40.424244</td>\n",
       "      <td>-3.684009</td>\n",
       "      <td>213</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       venue_category             venue_name   latitude  longitude  \\\n",
       "0    Vegan Restaurant  Fit Food Velázquez 39   40.42603   -3.68425   \n",
       "1  Basketball Stadium          Wizink Center    40.4239    -3.6718   \n",
       "2          Dog Saloon   A loja do gato preto  40.424244  -3.684009   \n",
       "\n",
       "  distance_from_Busuu  \n",
       "0                 398  \n",
       "1                1000  \n",
       "2                 213  "
      ]
     },
     "execution_count": 346,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_caprichos = pd.DataFrame(caprichos)\n",
    "df_caprichos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 436,
   "id": "fa551ea0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# - for map: final_df_winner for bars, schools, airport and starbucks\n",
    "            #df_caprichos for bball, dog hairdresser, vegan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 437,
   "id": "c9bc95a0",
   "metadata": {},
   "outputs": [],
   "source": [
    " pd.set_option('display.max_rows', None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 438,
   "id": "d0778df4",
   "metadata": {},
   "outputs": [],
   "source": [
    "import folium\n",
    "from folium import Choropleth, Circle, Marker, Icon, Map\n",
    "from folium.plugins import HeatMap, MarkerCluster\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 439,
   "id": "e75e91ed",
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests\n",
    "import json\n",
    "import pandas as pd\n",
    "from getpass import getpass\n",
    "\n",
    "import os\n",
    "import requests\n",
    "import json\n",
    "from dotenv import load_dotenv\n",
    "import pandas as pd\n",
    "\n",
    "import geopandas as gpd\n",
    "\n",
    "token = os.getenv(\"token\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 458,
   "id": "0852dc6e",
   "metadata": {},
   "outputs": [],
   "source": [
    "mad_lat = 40.422549\n",
    "mad_lon = -3.683032\n",
    "mad_map = folium.Map(location = [mad_lat, mad_lon], zoom_start = 15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dfa7fcaa",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 459,
   "id": "0484fe69",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# How to loop a df?\n",
    "for index, row in final_df_winner.iterrows():\n",
    "    \n",
    "    # 1. location of the accident\n",
    "    lat_venue = row[\"venue_latitude\"]\n",
    "    lon_venue = row[\"venue_longitude\"]\n",
    "    \n",
    "    #dict_coordinates = {\"location\": [row[\"lat\"], row[\"lon\"]]}\n",
    "        # Marker(location = [])\n",
    "    \n",
    "    if row[\"venue_category\"] == \"Airport\":\n",
    "        icon = Icon (\n",
    "            icon = \"plane\",\n",
    "            prefix = \"fa\",\n",
    "            color = \"black\",\n",
    "            icon_color = \"white\"\n",
    "        )\n",
    "        \n",
    "    elif row[\"venue_category\"] == \"Bar\":\n",
    "        icon = Icon (\n",
    "            icon = \"beer\",\n",
    "            prefix = \"fa\",\n",
    "            color = \"red\",\n",
    "            icon_color = \"white\"\n",
    "        )\n",
    "            \n",
    "    elif row[\"venue_category\"] == \"School\":\n",
    "        icon = Icon (\n",
    "            icon = \"book\",\n",
    "            prefix = \"fa\",\n",
    "            color = \"green\",\n",
    "            icon_color = \"white\")\n",
    "            \n",
    "    elif row[\"venue_category\"] == \"Starbucks\":\n",
    "        icon = Icon (\n",
    "            icon = \"coffee\",\n",
    "            prefix = \"fa\",\n",
    "            color = \"orange\",\n",
    "            icon_color = \"white\"\n",
    "        )\n",
    "    \n",
    "    new_marker = Marker(location = [lat_venue, lon_venue], icon=icon)\n",
    "    \n",
    "    new_marker.add_to(mad_map)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 460,
   "id": "d61a54b2",
   "metadata": {},
   "outputs": [],
   "source": [
    "for index, row in df_caprichos.iterrows():\n",
    "    \n",
    "    # 1. location of the accident\n",
    "    lat_venue_caprichos = row[\"latitude\"]\n",
    "    lon_venue_caprichos = row[\"longitude\"]\n",
    "    \n",
    "    #dict_coordinates = {\"location\": [row[\"lat\"], row[\"lon\"]]}\n",
    "        # Marker(location = [])\n",
    "    \n",
    "    if row[\"venue_category\"] == \"Vegan Restaurant\":\n",
    "        icon = Icon (\n",
    "            icon = \"leaf\",\n",
    "            prefix = \"fa\",\n",
    "            color = \"darkgreen\",\n",
    "            icon_color = \"white\"\n",
    "        )\n",
    "        \n",
    "    elif row[\"venue_category\"] == \"Basketball Stadium\":\n",
    "        icon = Icon (\n",
    "            icon = \"circle\",\n",
    "            prefix = \"fa\",\n",
    "            color = \"darkblue\",\n",
    "            icon_color = \"white\"\n",
    "        )\n",
    "            \n",
    "    elif row[\"venue_category\"] == \"Dog Saloon\":\n",
    "        icon = Icon (\n",
    "            icon = \"paw\",\n",
    "            prefix = \"fa\",\n",
    "            color = \"pink\",\n",
    "            icon_color = \"white\")\n",
    "            \n",
    "        \n",
    "    \n",
    "    new_marker_caprichos = Marker(location = [lat_venue_caprichos, lon_venue_caprichos], icon=icon)\n",
    "    \n",
    "    new_marker_caprichos.add_to(mad_map)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 461,
   "id": "47d36a30",
   "metadata": {},
   "outputs": [],
   "source": [
    "Busuu_loc = {\n",
    "    'name': 'Busuu',\n",
    "    'latitude': '40.422549',\n",
    "    'longitude': '-3.683032'\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 462,
   "id": "430fcfdf",
   "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",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Busuu</td>\n",
       "      <td>40.422549</td>\n",
       "      <td>-3.683032</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "</div>"
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      "text/plain": [
       "    name   latitude  longitude\n",
       "0  Busuu  40.422549  -3.683032"
      ]
     },
     "execution_count": 462,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_busuu_loc = pd.DataFrame([Busuu_loc], index=[0])\n",
    "df_busuu_loc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 463,
   "id": "8788e75a",
   "metadata": {},
   "outputs": [
    {
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       "                    width: 100.0%;\n",
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       "        \n",
       "&lt;/body&gt;\n",
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       "    \n",
       "    \n",
       "            var map_e91deaa139f0437a7e90adf630f88959 = L.map(\n",
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       "            ).addTo(map_e91deaa139f0437a7e90adf630f88959);\n",
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       "    \n",
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       "                [40.494892, -3.566847],\n",
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       "        \n",
       "    \n",
       "            var icon_849193a8484e9e6ad9b34f7f69e2d9cc = L.AwesomeMarkers.icon(\n",
       "                {&quot;extraClasses&quot;: &quot;fa-rotate-0&quot;, &quot;icon&quot;: &quot;plane&quot;, &quot;iconColor&quot;: &quot;white&quot;, &quot;markerColor&quot;: &quot;black&quot;, &quot;prefix&quot;: &quot;fa&quot;}\n",
       "            );\n",
       "            marker_d3b00c9e3aa46cf021419df8159a5d4f.setIcon(icon_849193a8484e9e6ad9b34f7f69e2d9cc);\n",
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       "    \n",
       "            var marker_e9f229e6db544447ef82646a0b77fa2b = L.marker(\n",
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       "            );\n",
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       "            );\n",
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       "    \n",
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       "                [40.421899, -3.683696],\n",
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       "    \n",
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       "    \n",
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       "                [40.421776, -3.683752],\n",
       "                {}\n",
       "            ).addTo(map_e91deaa139f0437a7e90adf630f88959);\n",
       "        \n",
       "    \n",
       "            var icon_05918621378303f71b0c097a411b3e82 = L.AwesomeMarkers.icon(\n",
       "                {&quot;extraClasses&quot;: &quot;fa-rotate-0&quot;, &quot;icon&quot;: &quot;beer&quot;, &quot;iconColor&quot;: &quot;white&quot;, &quot;markerColor&quot;: &quot;red&quot;, &quot;prefix&quot;: &quot;fa&quot;}\n",
       "            );\n",
       "            marker_cff893774dab593036b1b6999f17d8bc.setIcon(icon_05918621378303f71b0c097a411b3e82);\n",
       "        \n",
       "    \n",
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       "        \n",
       "    \n",
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       "    \n",
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       "    \n",
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       "                {&quot;extraClasses&quot;: &quot;fa-rotate-0&quot;, &quot;icon&quot;: &quot;beer&quot;, &quot;iconColor&quot;: &quot;white&quot;, &quot;markerColor&quot;: &quot;red&quot;, &quot;prefix&quot;: &quot;fa&quot;}\n",
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       "    \n",
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       "                [40.423338, -3.684054],\n",
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       "        \n",
       "    \n",
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       "                {&quot;extraClasses&quot;: &quot;fa-rotate-0&quot;, &quot;icon&quot;: &quot;beer&quot;, &quot;iconColor&quot;: &quot;white&quot;, &quot;markerColor&quot;: &quot;red&quot;, &quot;prefix&quot;: &quot;fa&quot;}\n",
       "            );\n",
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       "        \n",
       "    \n",
       "            var marker_405a1b4b49251c12f8a303d126f4db36 = L.marker(\n",
       "                [40.421869, -3.684167],\n",
       "                {}\n",
       "            ).addTo(map_e91deaa139f0437a7e90adf630f88959);\n",
       "        \n",
       "    \n",
       "            var icon_27b4a707126fdfcba08b70caf5b52196 = L.AwesomeMarkers.icon(\n",
       "                {&quot;extraClasses&quot;: &quot;fa-rotate-0&quot;, &quot;icon&quot;: &quot;beer&quot;, &quot;iconColor&quot;: &quot;white&quot;, &quot;markerColor&quot;: &quot;red&quot;, &quot;prefix&quot;: &quot;fa&quot;}\n",
       "            );\n",
       "            marker_405a1b4b49251c12f8a303d126f4db36.setIcon(icon_27b4a707126fdfcba08b70caf5b52196);\n",
       "        \n",
       "    \n",
       "            var marker_47e9dfa1c508f097e5898a42adcf6607 = L.marker(\n",
       "                [40.423414, -3.684052],\n",
       "                {}\n",
       "            ).addTo(map_e91deaa139f0437a7e90adf630f88959);\n",
       "        \n",
       "    \n",
       "            var icon_e4f082585becf2b47dec341298c860b9 = L.AwesomeMarkers.icon(\n",
       "                {&quot;extraClasses&quot;: &quot;fa-rotate-0&quot;, &quot;icon&quot;: &quot;beer&quot;, &quot;iconColor&quot;: &quot;white&quot;, &quot;markerColor&quot;: &quot;red&quot;, &quot;prefix&quot;: &quot;fa&quot;}\n",
       "            );\n",
       "            marker_47e9dfa1c508f097e5898a42adcf6607.setIcon(icon_e4f082585becf2b47dec341298c860b9);\n",
       "        \n",
       "    \n",
       "            var marker_a1635532cc6404f0f6adc1a326583642 = L.marker(\n",
       "                [40.421924, -3.684604],\n",
       "                {}\n",
       "            ).addTo(map_e91deaa139f0437a7e90adf630f88959);\n",
       "        \n",
       "    \n",
       "            var icon_17c690dca7246740deb0a525479b895b = L.AwesomeMarkers.icon(\n",
       "                {&quot;extraClasses&quot;: &quot;fa-rotate-0&quot;, &quot;icon&quot;: &quot;beer&quot;, &quot;iconColor&quot;: &quot;white&quot;, &quot;markerColor&quot;: &quot;red&quot;, &quot;prefix&quot;: &quot;fa&quot;}\n",
       "            );\n",
       "            marker_a1635532cc6404f0f6adc1a326583642.setIcon(icon_17c690dca7246740deb0a525479b895b);\n",
       "        \n",
       "    \n",
       "            var marker_7885fd34b7283449ef5ab24c7814db51 = L.marker(\n",
       "                [40.422775, -3.68279],\n",
       "                {}\n",
       "            ).addTo(map_e91deaa139f0437a7e90adf630f88959);\n",
       "        \n",
       "    \n",
       "            var icon_dfe292bd56707ccd546b26d2068baff3 = L.AwesomeMarkers.icon(\n",
       "                {&quot;extraClasses&quot;: &quot;fa-rotate-0&quot;, &quot;icon&quot;: &quot;book&quot;, &quot;iconColor&quot;: &quot;white&quot;, &quot;markerColor&quot;: &quot;green&quot;, &quot;prefix&quot;: &quot;fa&quot;}\n",
       "            );\n",
       "            marker_7885fd34b7283449ef5ab24c7814db51.setIcon(icon_dfe292bd56707ccd546b26d2068baff3);\n",
       "        \n",
       "    \n",
       "            var marker_f745de4bd1d9bbb8b2b789ef100a0567 = L.marker(\n",
       "                [40.424175, -3.680439],\n",
       "                {}\n",
       "            ).addTo(map_e91deaa139f0437a7e90adf630f88959);\n",
       "        \n",
       "    \n",
       "            var icon_d8eb2a761b62fd3fbf94aaed74d7b4ab = L.AwesomeMarkers.icon(\n",
       "                {&quot;extraClasses&quot;: &quot;fa-rotate-0&quot;, &quot;icon&quot;: &quot;book&quot;, &quot;iconColor&quot;: &quot;white&quot;, &quot;markerColor&quot;: &quot;green&quot;, &quot;prefix&quot;: &quot;fa&quot;}\n",
       "            );\n",
       "            marker_f745de4bd1d9bbb8b2b789ef100a0567.setIcon(icon_d8eb2a761b62fd3fbf94aaed74d7b4ab);\n",
       "        \n",
       "    \n",
       "            var marker_24a406dc789561ad4271dcdff370dfc7 = L.marker(\n",
       "                [40.425244, -3.681979],\n",
       "                {}\n",
       "            ).addTo(map_e91deaa139f0437a7e90adf630f88959);\n",
       "        \n",
       "    \n",
       "            var icon_06652433f53bb5e701237ee2c484f6c7 = L.AwesomeMarkers.icon(\n",
       "                {&quot;extraClasses&quot;: &quot;fa-rotate-0&quot;, &quot;icon&quot;: &quot;book&quot;, &quot;iconColor&quot;: &quot;white&quot;, &quot;markerColor&quot;: &quot;green&quot;, &quot;prefix&quot;: &quot;fa&quot;}\n",
       "            );\n",
       "            marker_24a406dc789561ad4271dcdff370dfc7.setIcon(icon_06652433f53bb5e701237ee2c484f6c7);\n",
       "        \n",
       "    \n",
       "            var marker_4e51dbee03abf38dd176611b61d9849b = L.marker(\n",
       "                [40.425155, -3.686227],\n",
       "                {}\n",
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       "        \n",
       "    \n",
       "            var icon_3e796150c0b755574d52988580aa6d29 = L.AwesomeMarkers.icon(\n",
       "                {&quot;extraClasses&quot;: &quot;fa-rotate-0&quot;, &quot;icon&quot;: &quot;book&quot;, &quot;iconColor&quot;: &quot;white&quot;, &quot;markerColor&quot;: &quot;green&quot;, &quot;prefix&quot;: &quot;fa&quot;}\n",
       "            );\n",
       "            marker_4e51dbee03abf38dd176611b61d9849b.setIcon(icon_3e796150c0b755574d52988580aa6d29);\n",
       "        \n",
       "    \n",
       "            var marker_a4f18b6fd1a378bd99fb937ffd34ace9 = L.marker(\n",
       "                [40.42668, -3.683823],\n",
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       "        \n",
       "    \n",
       "            var icon_53e1914b23d6c3572fcf3565b0c8c534 = L.AwesomeMarkers.icon(\n",
       "                {&quot;extraClasses&quot;: &quot;fa-rotate-0&quot;, &quot;icon&quot;: &quot;book&quot;, &quot;iconColor&quot;: &quot;white&quot;, &quot;markerColor&quot;: &quot;green&quot;, &quot;prefix&quot;: &quot;fa&quot;}\n",
       "            );\n",
       "            marker_a4f18b6fd1a378bd99fb937ffd34ace9.setIcon(icon_53e1914b23d6c3572fcf3565b0c8c534);\n",
       "        \n",
       "    \n",
       "            var marker_f9474cf7daaf8588fe01c9aac2be23f9 = L.marker(\n",
       "                [40.424959, -3.683936],\n",
       "                {}\n",
       "            ).addTo(map_e91deaa139f0437a7e90adf630f88959);\n",
       "        \n",
       "    \n",
       "            var icon_ba9793d7811cbe9fadceb6601372bbc8 = L.AwesomeMarkers.icon(\n",
       "                {&quot;extraClasses&quot;: &quot;fa-rotate-0&quot;, &quot;icon&quot;: &quot;coffee&quot;, &quot;iconColor&quot;: &quot;white&quot;, &quot;markerColor&quot;: &quot;orange&quot;, &quot;prefix&quot;: &quot;fa&quot;}\n",
       "            );\n",
       "            marker_f9474cf7daaf8588fe01c9aac2be23f9.setIcon(icon_ba9793d7811cbe9fadceb6601372bbc8);\n",
       "        \n",
       "    \n",
       "            var marker_180020c071d5f3416941c994b0da1154 = L.marker(\n",
       "                [40.42603, -3.68425],\n",
       "                {}\n",
       "            ).addTo(map_e91deaa139f0437a7e90adf630f88959);\n",
       "        \n",
       "    \n",
       "            var icon_87226522b44e13fbecfad012bf6fa57d = L.AwesomeMarkers.icon(\n",
       "                {&quot;extraClasses&quot;: &quot;fa-rotate-0&quot;, &quot;icon&quot;: &quot;leaf&quot;, &quot;iconColor&quot;: &quot;white&quot;, &quot;markerColor&quot;: &quot;darkgreen&quot;, &quot;prefix&quot;: &quot;fa&quot;}\n",
       "            );\n",
       "            marker_180020c071d5f3416941c994b0da1154.setIcon(icon_87226522b44e13fbecfad012bf6fa57d);\n",
       "        \n",
       "    \n",
       "            var marker_084407e87e1922f83607f2ef9ca4f210 = L.marker(\n",
       "                [40.4239, -3.6718],\n",
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       "        \n",
       "    \n",
       "            var icon_dec9e3a09374fdd80174137eaf40d1ab = L.AwesomeMarkers.icon(\n",
       "                {&quot;extraClasses&quot;: &quot;fa-rotate-0&quot;, &quot;icon&quot;: &quot;circle&quot;, &quot;iconColor&quot;: &quot;white&quot;, &quot;markerColor&quot;: &quot;darkblue&quot;, &quot;prefix&quot;: &quot;fa&quot;}\n",
       "            );\n",
       "            marker_084407e87e1922f83607f2ef9ca4f210.setIcon(icon_dec9e3a09374fdd80174137eaf40d1ab);\n",
       "        \n",
       "    \n",
       "            var marker_b167e49d3785688b9f62ff26e162b1ef = L.marker(\n",
       "                [40.424244, -3.684009],\n",
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       "        \n",
       "    \n",
       "            var icon_baa9f2aad4b2889f3ecab360cd71a049 = L.AwesomeMarkers.icon(\n",
       "                {&quot;extraClasses&quot;: &quot;fa-rotate-0&quot;, &quot;icon&quot;: &quot;paw&quot;, &quot;iconColor&quot;: &quot;white&quot;, &quot;markerColor&quot;: &quot;pink&quot;, &quot;prefix&quot;: &quot;fa&quot;}\n",
       "            );\n",
       "            marker_b167e49d3785688b9f62ff26e162b1ef.setIcon(icon_baa9f2aad4b2889f3ecab360cd71a049);\n",
       "        \n",
       "    \n",
       "            var marker_840148acc31961c584fb2a546c14a2c3 = L.marker(\n",
       "                [40.422549, -3.683032],\n",
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       "        \n",
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       "            var icon_00eee3cda69706f8170eaa6588dc39a3 = L.AwesomeMarkers.icon(\n",
       "                {&quot;extraClasses&quot;: &quot;fa-rotate-0&quot;, &quot;icon&quot;: &quot;briefcase&quot;, &quot;iconColor&quot;: &quot;white&quot;, &quot;markerColor&quot;: &quot;purple&quot;, &quot;prefix&quot;: &quot;fa&quot;}\n",
       "            );\n",
       "            marker_840148acc31961c584fb2a546c14a2c3.setIcon(icon_00eee3cda69706f8170eaa6588dc39a3);\n",
       "        \n",
       "&lt;/script&gt;\n",
       "&lt;/html&gt;\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" allowfullscreen webkitallowfullscreen mozallowfullscreen></iframe></div></div>"
      ],
      "text/plain": [
       "<folium.folium.Map at 0x124814710>"
      ]
     },
     "execution_count": 463,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for index, row in df_busuu_loc.iterrows():\n",
    "    \n",
    "    # 1. location of the accident\n",
    "    lat_busuu = row[\"latitude\"]\n",
    "    lon_busuu = row[\"longitude\"]\n",
    "    \n",
    "    #dict_coordinates = {\"location\": [row[\"lat\"], row[\"lon\"]]}\n",
    "        # Marker(location = [])\n",
    "    \n",
    "    if row[\"name\"] == \"Busuu\":\n",
    "        icon = Icon (\n",
    "            icon = \"briefcase\",\n",
    "            prefix = \"fa\",\n",
    "            color = \"purple\",)\n",
    "    \n",
    "    new_marker_busuu = Marker(location = [lat_busuu, lon_busuu], icon=icon)\n",
    "    \n",
    "    new_marker_busuu.add_to(mad_map)\n",
    "mad_map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6a0a9c37",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1f753901",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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