VacationPy.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "1c244485",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import the dependencies.\n",
    "import pandas as pd\n",
    "import gmaps\n",
    "import requests\n",
    "# Import the API key.\n",
    "from config import g_key"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "622c7d3c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>City_ID</th>\n",
       "      <th>City</th>\n",
       "      <th>Country</th>\n",
       "      <th>Date</th>\n",
       "      <th>Lat</th>\n",
       "      <th>Lng</th>\n",
       "      <th>Max Temp</th>\n",
       "      <th>Humidity</th>\n",
       "      <th>Cloudiness</th>\n",
       "      <th>Wind Speed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Arraial Do Cabo</td>\n",
       "      <td>BR</td>\n",
       "      <td>2021-11-25 23:16:40</td>\n",
       "      <td>-22.9661</td>\n",
       "      <td>-42.0278</td>\n",
       "      <td>73.36</td>\n",
       "      <td>83</td>\n",
       "      <td>0</td>\n",
       "      <td>11.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Ushuaia</td>\n",
       "      <td>AR</td>\n",
       "      <td>2021-11-25 23:17:57</td>\n",
       "      <td>-54.8000</td>\n",
       "      <td>-68.3000</td>\n",
       "      <td>42.46</td>\n",
       "      <td>70</td>\n",
       "      <td>40</td>\n",
       "      <td>23.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>Sorland</td>\n",
       "      <td>NO</td>\n",
       "      <td>2021-11-25 23:17:57</td>\n",
       "      <td>67.6670</td>\n",
       "      <td>12.6934</td>\n",
       "      <td>34.00</td>\n",
       "      <td>42</td>\n",
       "      <td>100</td>\n",
       "      <td>10.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>Barrow</td>\n",
       "      <td>US</td>\n",
       "      <td>2021-11-25 23:16:54</td>\n",
       "      <td>71.2906</td>\n",
       "      <td>-156.7887</td>\n",
       "      <td>-9.38</td>\n",
       "      <td>77</td>\n",
       "      <td>20</td>\n",
       "      <td>16.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Kanye</td>\n",
       "      <td>BW</td>\n",
       "      <td>2021-11-25 23:17:58</td>\n",
       "      <td>-24.9667</td>\n",
       "      <td>25.3327</td>\n",
       "      <td>60.26</td>\n",
       "      <td>73</td>\n",
       "      <td>0</td>\n",
       "      <td>24.65</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   City_ID             City Country                 Date      Lat       Lng  \\\n",
       "0        0  Arraial Do Cabo      BR  2021-11-25 23:16:40 -22.9661  -42.0278   \n",
       "1        1          Ushuaia      AR  2021-11-25 23:17:57 -54.8000  -68.3000   \n",
       "2        2          Sorland      NO  2021-11-25 23:17:57  67.6670   12.6934   \n",
       "3        3           Barrow      US  2021-11-25 23:16:54  71.2906 -156.7887   \n",
       "4        4            Kanye      BW  2021-11-25 23:17:58 -24.9667   25.3327   \n",
       "\n",
       "   Max Temp  Humidity  Cloudiness  Wind Speed  \n",
       "0     73.36        83           0       11.50  \n",
       "1     42.46        70          40       23.02  \n",
       "2     34.00        42         100       10.18  \n",
       "3     -9.38        77          20       16.11  \n",
       "4     60.26        73           0       24.65  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Store the CSV you saved created in part one into a DataFrame.\n",
    "city_data_df = pd.read_csv(\"weather_data/cities.csv\")\n",
    "city_data_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "65e0c8cc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Configure gmaps to use your Google API key.\n",
    "gmaps.configure(api_key=g_key)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d21dd17a",
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "Figure(layout=FigureLayout(height='420px'))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Heatmap of temperature\n",
    "# Get the latitude and longitude.\n",
    "locations = city_data_df[[\"Lat\", \"Lng\"]]\n",
    "# Get the maximum temperature.\n",
    "max_temp = city_data_df[\"Max Temp\"]\n",
    "# Assign the figure variable.\n",
    "fig = gmaps.figure()\n",
    "fig = gmaps.figure(center=(30.0, 31.0), zoom_level=1.5)\n",
    "# Assign the heatmap variable.\n",
    "heat_layer = gmaps.heatmap_layer(locations, weights=[max(temp,0) for temp in max_temp], dissipating=False, max_intensity=300, point_radius=4)\n",
    "# Add the heatmap layer.\n",
    "fig.add_layer(heat_layer)\n",
    "# Call the figure to plot the data.\n",
    "fig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "cbf225da",
   "metadata": {},
   "outputs": [
    {
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       "Figure(layout=FigureLayout(height='420px'))"
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   ],
   "source": [
    "# Heatmap of percent humidity\n",
    "locations = city_data_df[[\"Lat\", \"Lng\"]]\n",
    "humidity = city_data_df[\"Humidity\"]\n",
    "fig = gmaps.figure(center=(30.0, 31.0), zoom_level=1.5)\n",
    "heat_layer = gmaps.heatmap_layer(locations, weights=humidity, dissipating=False, max_intensity=300, point_radius=4)\n",
    "\n",
    "fig.add_layer(heat_layer)\n",
    "# Call the figure to plot the data.\n",
    "fig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "615e160d",
   "metadata": {},
   "outputs": [
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   ],
   "source": [
    "# Heatmap of percent cloudiness\n",
    "locations = city_data_df[[\"Lat\", \"Lng\"]]\n",
    "clouds = city_data_df[\"Cloudiness\"]\n",
    "fig = gmaps.figure(center=(30.0, 31.0), zoom_level=1.5)\n",
    "heat_layer = gmaps.heatmap_layer(locations, weights=clouds, dissipating=False, max_intensity=300, point_radius=4)\n",
    "\n",
    "fig.add_layer(heat_layer)\n",
    "# Call the figure to plot the data.\n",
    "fig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "094b4668",
   "metadata": {},
   "outputs": [
    {
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       "Figure(layout=FigureLayout(height='420px'))"
      ]
     },
     "metadata": {},
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    }
   ],
   "source": [
    "# Heatmap of Wind Speed\n",
    "locations = city_data_df[[\"Lat\", \"Lng\"]]\n",
    "wind = city_data_df[\"Wind Speed\"]\n",
    "fig = gmaps.figure(center=(30.0, 31.0), zoom_level=1.5)\n",
    "heat_layer = gmaps.heatmap_layer(locations, weights=wind, dissipating=False, max_intensity=300, point_radius=4)\n",
    "\n",
    "fig.add_layer(heat_layer)\n",
    "# Call the figure to plot the data.\n",
    "fig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "325c9fde",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "What is the minimum temperature you would like for your trip? 75\n",
      "What is the maximum temperature you would like for your trip? 90\n"
     ]
    }
   ],
   "source": [
    "# Ask the customer to add a minimum and maximum temperature value.\n",
    "min_temp = float(input(\"What is the minimum temperature you would like for your trip? \"))\n",
    "max_temp = float(input(\"What is the maximum temperature you would like for your trip? \"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "fbdda7b5",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>City_ID</th>\n",
       "      <th>City</th>\n",
       "      <th>Country</th>\n",
       "      <th>Date</th>\n",
       "      <th>Lat</th>\n",
       "      <th>Lng</th>\n",
       "      <th>Max Temp</th>\n",
       "      <th>Humidity</th>\n",
       "      <th>Cloudiness</th>\n",
       "      <th>Wind Speed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>Rikitea</td>\n",
       "      <td>PF</td>\n",
       "      <td>2021-11-25 23:17:58</td>\n",
       "      <td>-23.1203</td>\n",
       "      <td>-134.9692</td>\n",
       "      <td>76.57</td>\n",
       "      <td>75</td>\n",
       "      <td>8</td>\n",
       "      <td>10.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>Vaini</td>\n",
       "      <td>TO</td>\n",
       "      <td>2021-11-25 23:18:00</td>\n",
       "      <td>-21.2000</td>\n",
       "      <td>-175.2000</td>\n",
       "      <td>87.96</td>\n",
       "      <td>79</td>\n",
       "      <td>75</td>\n",
       "      <td>6.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>15</td>\n",
       "      <td>Faanui</td>\n",
       "      <td>PF</td>\n",
       "      <td>2021-11-25 23:18:02</td>\n",
       "      <td>-16.4833</td>\n",
       "      <td>-151.7500</td>\n",
       "      <td>82.24</td>\n",
       "      <td>74</td>\n",
       "      <td>7</td>\n",
       "      <td>18.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>18</td>\n",
       "      <td>Butaritari</td>\n",
       "      <td>KI</td>\n",
       "      <td>2021-11-25 23:18:04</td>\n",
       "      <td>3.0707</td>\n",
       "      <td>172.7902</td>\n",
       "      <td>82.72</td>\n",
       "      <td>72</td>\n",
       "      <td>89</td>\n",
       "      <td>16.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>24</td>\n",
       "      <td>Alenquer</td>\n",
       "      <td>BR</td>\n",
       "      <td>2021-11-25 23:18:08</td>\n",
       "      <td>-1.9417</td>\n",
       "      <td>-54.7383</td>\n",
       "      <td>80.94</td>\n",
       "      <td>76</td>\n",
       "      <td>99</td>\n",
       "      <td>2.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>26</td>\n",
       "      <td>Mitsamiouli</td>\n",
       "      <td>KM</td>\n",
       "      <td>2021-11-25 23:18:08</td>\n",
       "      <td>-11.3847</td>\n",
       "      <td>43.2844</td>\n",
       "      <td>79.30</td>\n",
       "      <td>76</td>\n",
       "      <td>23</td>\n",
       "      <td>6.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>27</td>\n",
       "      <td>Saint-Philippe</td>\n",
       "      <td>RE</td>\n",
       "      <td>2021-11-25 23:18:09</td>\n",
       "      <td>-21.3585</td>\n",
       "      <td>55.7679</td>\n",
       "      <td>78.39</td>\n",
       "      <td>82</td>\n",
       "      <td>52</td>\n",
       "      <td>3.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>29</td>\n",
       "      <td>Songkhla</td>\n",
       "      <td>TH</td>\n",
       "      <td>2021-11-25 23:18:10</td>\n",
       "      <td>6.8333</td>\n",
       "      <td>100.6667</td>\n",
       "      <td>75.25</td>\n",
       "      <td>98</td>\n",
       "      <td>100</td>\n",
       "      <td>2.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>30</td>\n",
       "      <td>Benjamin Aceval</td>\n",
       "      <td>PY</td>\n",
       "      <td>2021-11-25 23:18:10</td>\n",
       "      <td>-24.9667</td>\n",
       "      <td>-57.5667</td>\n",
       "      <td>77.59</td>\n",
       "      <td>92</td>\n",
       "      <td>98</td>\n",
       "      <td>10.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>31</td>\n",
       "      <td>Avarua</td>\n",
       "      <td>CK</td>\n",
       "      <td>2021-11-25 23:18:11</td>\n",
       "      <td>-21.2078</td>\n",
       "      <td>-159.7750</td>\n",
       "      <td>82.45</td>\n",
       "      <td>78</td>\n",
       "      <td>4</td>\n",
       "      <td>9.22</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    City_ID             City Country                 Date      Lat       Lng  \\\n",
       "5         5          Rikitea      PF  2021-11-25 23:17:58 -23.1203 -134.9692   \n",
       "9         9            Vaini      TO  2021-11-25 23:18:00 -21.2000 -175.2000   \n",
       "15       15           Faanui      PF  2021-11-25 23:18:02 -16.4833 -151.7500   \n",
       "18       18       Butaritari      KI  2021-11-25 23:18:04   3.0707  172.7902   \n",
       "24       24         Alenquer      BR  2021-11-25 23:18:08  -1.9417  -54.7383   \n",
       "26       26      Mitsamiouli      KM  2021-11-25 23:18:08 -11.3847   43.2844   \n",
       "27       27   Saint-Philippe      RE  2021-11-25 23:18:09 -21.3585   55.7679   \n",
       "29       29         Songkhla      TH  2021-11-25 23:18:10   6.8333  100.6667   \n",
       "30       30  Benjamin Aceval      PY  2021-11-25 23:18:10 -24.9667  -57.5667   \n",
       "31       31           Avarua      CK  2021-11-25 23:18:11 -21.2078 -159.7750   \n",
       "\n",
       "    Max Temp  Humidity  Cloudiness  Wind Speed  \n",
       "5      76.57        75           8       10.76  \n",
       "9      87.96        79          75        6.91  \n",
       "15     82.24        74           7       18.12  \n",
       "18     82.72        72          89       16.46  \n",
       "24     80.94        76          99        2.91  \n",
       "26     79.30        76          23        6.55  \n",
       "27     78.39        82          52        3.36  \n",
       "29     75.25        98         100        2.80  \n",
       "30     77.59        92          98       10.47  \n",
       "31     82.45        78           4        9.22  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Filter the dataset to find the cities that fit the criteria.\n",
    "preferred_cities_df = city_data_df.loc[(city_data_df[\"Max Temp\"] <= max_temp) & \\\n",
    "                                       (city_data_df[\"Max Temp\"] >= min_temp)]\n",
    "preferred_cities_df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "2ab57569",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "City_ID       138\n",
       "City          138\n",
       "Country       138\n",
       "Date          138\n",
       "Lat           138\n",
       "Lng           138\n",
       "Max Temp      138\n",
       "Humidity      138\n",
       "Cloudiness    138\n",
       "Wind Speed    138\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preferred_cities_df.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "b7a1749a",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>City</th>\n",
       "      <th>Country</th>\n",
       "      <th>Max Temp</th>\n",
       "      <th>Lat</th>\n",
       "      <th>Lng</th>\n",
       "      <th>Hotel Name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Rikitea</td>\n",
       "      <td>PF</td>\n",
       "      <td>76.57</td>\n",
       "      <td>-23.1203</td>\n",
       "      <td>-134.9692</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Vaini</td>\n",
       "      <td>TO</td>\n",
       "      <td>87.96</td>\n",
       "      <td>-21.2000</td>\n",
       "      <td>-175.2000</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Faanui</td>\n",
       "      <td>PF</td>\n",
       "      <td>82.24</td>\n",
       "      <td>-16.4833</td>\n",
       "      <td>-151.7500</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Butaritari</td>\n",
       "      <td>KI</td>\n",
       "      <td>82.72</td>\n",
       "      <td>3.0707</td>\n",
       "      <td>172.7902</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Alenquer</td>\n",
       "      <td>BR</td>\n",
       "      <td>80.94</td>\n",
       "      <td>-1.9417</td>\n",
       "      <td>-54.7383</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Mitsamiouli</td>\n",
       "      <td>KM</td>\n",
       "      <td>79.30</td>\n",
       "      <td>-11.3847</td>\n",
       "      <td>43.2844</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Saint-Philippe</td>\n",
       "      <td>RE</td>\n",
       "      <td>78.39</td>\n",
       "      <td>-21.3585</td>\n",
       "      <td>55.7679</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Songkhla</td>\n",
       "      <td>TH</td>\n",
       "      <td>75.25</td>\n",
       "      <td>6.8333</td>\n",
       "      <td>100.6667</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Benjamin Aceval</td>\n",
       "      <td>PY</td>\n",
       "      <td>77.59</td>\n",
       "      <td>-24.9667</td>\n",
       "      <td>-57.5667</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Avarua</td>\n",
       "      <td>CK</td>\n",
       "      <td>82.45</td>\n",
       "      <td>-21.2078</td>\n",
       "      <td>-159.7750</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               City Country  Max Temp      Lat       Lng Hotel Name\n",
       "5           Rikitea      PF     76.57 -23.1203 -134.9692           \n",
       "9             Vaini      TO     87.96 -21.2000 -175.2000           \n",
       "15           Faanui      PF     82.24 -16.4833 -151.7500           \n",
       "18       Butaritari      KI     82.72   3.0707  172.7902           \n",
       "24         Alenquer      BR     80.94  -1.9417  -54.7383           \n",
       "26      Mitsamiouli      KM     79.30 -11.3847   43.2844           \n",
       "27   Saint-Philippe      RE     78.39 -21.3585   55.7679           \n",
       "29         Songkhla      TH     75.25   6.8333  100.6667           \n",
       "30  Benjamin Aceval      PY     77.59 -24.9667  -57.5667           \n",
       "31           Avarua      CK     82.45 -21.2078 -159.7750           "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create DataFrame called hotel_df to store hotel names along with city, country, max temp, and coordinates.\n",
    "hotel_df = preferred_cities_df[[\"City\", \"Country\", \"Max Temp\", \"Lat\", \"Lng\"]].copy()\n",
    "hotel_df[\"Hotel Name\"] = \"\"\n",
    "hotel_df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "472366f7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set parameters to search for a hotel.\n",
    "params = {\n",
    "    \"radius\": 5000,\n",
    "    \"type\": \"lodging\",\n",
    "    \"key\": g_key\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "24303dee",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hotel not found... skipping.\n",
      "Hotel not found... skipping.\n",
      "Hotel not found... skipping.\n",
      "Hotel not found... skipping.\n",
      "Hotel not found... skipping.\n",
      "Hotel not found... skipping.\n",
      "Hotel not found... skipping.\n",
      "Hotel not found... skipping.\n",
      "Hotel not found... skipping.\n",
      "Hotel not found... skipping.\n",
      "Hotel not found... skipping.\n",
      "Hotel not found... skipping.\n",
      "Hotel not found... skipping.\n",
      "Hotel not found... skipping.\n"
     ]
    }
   ],
   "source": [
    "# Iterate through the DataFrame.\n",
    "for index, row in hotel_df.iterrows():\n",
    "    # Get the latitude and longitude.\n",
    "    lat = row[\"Lat\"]\n",
    "    lng = row[\"Lng\"]\n",
    "\n",
    "    # Add the latitude and longitude to location key for the params dictionary.\n",
    "    params[\"location\"] = f\"{lat},{lng}\"\n",
    "\n",
    "    # Use the search term: \"lodging\" and our latitude and longitude.\n",
    "    base_url = \"https://maps.googleapis.com/maps/api/place/nearbysearch/json\"\n",
    "    # Make request and get the JSON data from the search.\n",
    "    hotels = requests.get(base_url, params=params).json()\n",
    "    # Grab the first hotel from the results and store the name.\n",
    "    try:\n",
    "        hotel_df.loc[index, \"Hotel Name\"] = hotels[\"results\"][0][\"name\"]\n",
    "    except (IndexError):\n",
    "        print(\"Hotel not found... skipping.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "81784feb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "27bb9333c365416fa9eb23d412fbe7af",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Figure(layout=FigureLayout(height='420px'))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Add a heatmap of temperature for the vacation spots.\n",
    "locations = hotel_df[[\"Lat\", \"Lng\"]]\n",
    "max_temp = hotel_df[\"Max Temp\"]\n",
    "fig = gmaps.figure(center=(30.0, 31.0), zoom_level=1.5)\n",
    "heat_layer = gmaps.heatmap_layer(locations, weights=max_temp, dissipating=False,\n",
    "             max_intensity=300, point_radius=4)\n",
    "\n",
    "marker_layer = gmaps.marker_layer(locations)\n",
    "fig.add_layer(heat_layer)\n",
    "fig.add_layer(marker_layer)\n",
    "# Call the figure to plot the data.\n",
    "fig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "89d6726b",
   "metadata": {},
   "outputs": [],
   "source": [
    "info_box_template = \"\"\"\n",
    "<dl>\n",
    "<dt>Hotel Name</dt><dd>{Hotel Name}</dd>\n",
    "<dt>City</dt><dd>{City}</dd>\n",
    "<dt>Country</dt><dd>{Country}</dd>\n",
    "<dt>Max Temp</dt><dd>{Max Temp} °F</dd>\n",
    "</dl>\n",
    "\"\"\"\n",
    "\n",
    "# Store the DataFrame Row.\n",
    "hotel_info = [info_box_template.format(**row) for index, row in hotel_df.iterrows()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "37f72e5a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f3d6fe5bba1a40b3a20227117a30b2d3",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Figure(layout=FigureLayout(height='420px'))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Add a heatmap of temperature for the vacation spots and a pop-up marker for each city.\n",
    "locations = hotel_df[[\"Lat\", \"Lng\"]]\n",
    "max_temp = hotel_df[\"Max Temp\"]\n",
    "fig = gmaps.figure(center=(30.0, 31.0), zoom_level=1.5)\n",
    "heat_layer = gmaps.heatmap_layer(locations, weights=max_temp,dissipating=False,\n",
    "             max_intensity=300, point_radius=4)\n",
    "marker_layer = gmaps.marker_layer(locations, info_box_content=hotel_info)\n",
    "fig.add_layer(heat_layer)\n",
    "fig.add_layer(marker_layer)\n",
    "\n",
    "# Call the figure to plot the data.\n",
    "fig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "96f597c0",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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