VacationPy.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "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,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>Casper</td>\n",
       "      <td>US</td>\n",
       "      <td>2020-06-22 02:33:32</td>\n",
       "      <td>42.87</td>\n",
       "      <td>-106.31</td>\n",
       "      <td>64.40</td>\n",
       "      <td>52</td>\n",
       "      <td>90</td>\n",
       "      <td>33.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Khatanga</td>\n",
       "      <td>RU</td>\n",
       "      <td>2020-06-22 02:33:32</td>\n",
       "      <td>71.97</td>\n",
       "      <td>102.50</td>\n",
       "      <td>67.08</td>\n",
       "      <td>49</td>\n",
       "      <td>88</td>\n",
       "      <td>7.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>Kavieng</td>\n",
       "      <td>PG</td>\n",
       "      <td>2020-06-22 02:33:33</td>\n",
       "      <td>-2.57</td>\n",
       "      <td>150.80</td>\n",
       "      <td>85.86</td>\n",
       "      <td>66</td>\n",
       "      <td>11</td>\n",
       "      <td>5.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>Punta Arenas</td>\n",
       "      <td>CL</td>\n",
       "      <td>2020-06-22 02:33:33</td>\n",
       "      <td>-53.15</td>\n",
       "      <td>-70.92</td>\n",
       "      <td>32.00</td>\n",
       "      <td>83</td>\n",
       "      <td>96</td>\n",
       "      <td>5.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Ushuaia</td>\n",
       "      <td>AR</td>\n",
       "      <td>2020-06-22 02:30:10</td>\n",
       "      <td>-54.80</td>\n",
       "      <td>-68.30</td>\n",
       "      <td>35.60</td>\n",
       "      <td>80</td>\n",
       "      <td>45</td>\n",
       "      <td>3.36</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   City_ID          City Country                 Date    Lat     Lng  \\\n",
       "0        0        Casper      US  2020-06-22 02:33:32  42.87 -106.31   \n",
       "1        1      Khatanga      RU  2020-06-22 02:33:32  71.97  102.50   \n",
       "2        2       Kavieng      PG  2020-06-22 02:33:33  -2.57  150.80   \n",
       "3        3  Punta Arenas      CL  2020-06-22 02:33:33 -53.15  -70.92   \n",
       "4        4       Ushuaia      AR  2020-06-22 02:30:10 -54.80  -68.30   \n",
       "\n",
       "   Max Temp  Humidity  Cloudiness  Wind Speed  \n",
       "0     64.40        52          90       33.33  \n",
       "1     67.08        49          88        7.05  \n",
       "2     85.86        66          11        5.23  \n",
       "3     32.00        83          96        5.82  \n",
       "4     35.60        80          45        3.36  "
      ]
     },
     "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,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "City_ID         int64\n",
       "City           object\n",
       "Country        object\n",
       "Date           object\n",
       "Lat           float64\n",
       "Lng           float64\n",
       "Max Temp      float64\n",
       "Humidity        int64\n",
       "Cloudiness      int64\n",
       "Wind Speed    float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Gate the data types.\n",
    "city_data_df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Configure gmaps to use your Google API key.\n",
    "gmaps.configure(api_key=g_key)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get the maximum temperature.\n",
    "max_temp = city_data_df[\"Max Temp\"]\n",
    "temps = []\n",
    "for temp in max_temp:\n",
    "    temps.append(max(temp, 0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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(center=(30.0, 31.0), zoom_level=1.75)\n",
    "# Assign the heatmap variable.\n",
    "heat_layer = gmaps.heatmap_layer(locations, weights=temps, dissipating=False, max_intensity=300, point_radius=6)\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": 7,
   "metadata": {},
   "outputs": [
    {
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     "metadata": {},
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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=5)\n",
    "\n",
    "fig.add_layer(heat_layer)\n",
    "# Call the figure to plot the data.\n",
    "fig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
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       "Figure(layout=FigureLayout(height='420px'))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "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=5)\n",
    "\n",
    "fig.add_layer(heat_layer)\n",
    "# Call the figure to plot the data.\n",
    "fig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "model_id": "6ac5b18230cd4660af7f98a512de7bf8",
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      "text/plain": [
       "Figure(layout=FigureLayout(height='420px'))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "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=5)\n",
    "\n",
    "fig.add_layer(heat_layer)\n",
    "# Call the figure to plot the data.\n",
    "fig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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": 12,
   "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>2</th>\n",
       "      <td>2</td>\n",
       "      <td>Kavieng</td>\n",
       "      <td>PG</td>\n",
       "      <td>2020-06-22 02:33:33</td>\n",
       "      <td>-2.57</td>\n",
       "      <td>150.80</td>\n",
       "      <td>85.86</td>\n",
       "      <td>66</td>\n",
       "      <td>11</td>\n",
       "      <td>5.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>Bambanglipuro</td>\n",
       "      <td>ID</td>\n",
       "      <td>2020-06-22 02:33:34</td>\n",
       "      <td>-7.95</td>\n",
       "      <td>110.28</td>\n",
       "      <td>83.44</td>\n",
       "      <td>74</td>\n",
       "      <td>76</td>\n",
       "      <td>14.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>Avarua</td>\n",
       "      <td>CK</td>\n",
       "      <td>2020-06-22 02:33:34</td>\n",
       "      <td>-21.21</td>\n",
       "      <td>-159.78</td>\n",
       "      <td>75.20</td>\n",
       "      <td>64</td>\n",
       "      <td>90</td>\n",
       "      <td>17.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>Vaini</td>\n",
       "      <td>TO</td>\n",
       "      <td>2020-06-22 02:33:35</td>\n",
       "      <td>-21.20</td>\n",
       "      <td>-175.20</td>\n",
       "      <td>80.60</td>\n",
       "      <td>88</td>\n",
       "      <td>40</td>\n",
       "      <td>8.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>Nhulunbuy</td>\n",
       "      <td>AU</td>\n",
       "      <td>2020-06-22 02:33:35</td>\n",
       "      <td>-12.23</td>\n",
       "      <td>136.77</td>\n",
       "      <td>80.08</td>\n",
       "      <td>68</td>\n",
       "      <td>34</td>\n",
       "      <td>16.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>17</td>\n",
       "      <td>Isangel</td>\n",
       "      <td>VU</td>\n",
       "      <td>2020-06-22 02:33:37</td>\n",
       "      <td>-19.55</td>\n",
       "      <td>169.27</td>\n",
       "      <td>75.36</td>\n",
       "      <td>73</td>\n",
       "      <td>25</td>\n",
       "      <td>11.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>21</td>\n",
       "      <td>Faanui</td>\n",
       "      <td>PF</td>\n",
       "      <td>2020-06-22 02:33:38</td>\n",
       "      <td>-16.48</td>\n",
       "      <td>-151.75</td>\n",
       "      <td>77.41</td>\n",
       "      <td>82</td>\n",
       "      <td>61</td>\n",
       "      <td>23.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>28</td>\n",
       "      <td>Albany</td>\n",
       "      <td>US</td>\n",
       "      <td>2020-06-22 02:33:40</td>\n",
       "      <td>42.60</td>\n",
       "      <td>-73.97</td>\n",
       "      <td>81.00</td>\n",
       "      <td>87</td>\n",
       "      <td>44</td>\n",
       "      <td>2.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>34</td>\n",
       "      <td>Pyapon</td>\n",
       "      <td>MM</td>\n",
       "      <td>2020-06-22 02:33:42</td>\n",
       "      <td>16.28</td>\n",
       "      <td>95.68</td>\n",
       "      <td>87.85</td>\n",
       "      <td>66</td>\n",
       "      <td>12</td>\n",
       "      <td>6.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>40</td>\n",
       "      <td>Mayo</td>\n",
       "      <td>US</td>\n",
       "      <td>2020-06-22 02:33:44</td>\n",
       "      <td>38.89</td>\n",
       "      <td>-76.51</td>\n",
       "      <td>75.99</td>\n",
       "      <td>73</td>\n",
       "      <td>1</td>\n",
       "      <td>8.05</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    City_ID           City Country                 Date    Lat     Lng  \\\n",
       "2         2        Kavieng      PG  2020-06-22 02:33:33  -2.57  150.80   \n",
       "6         6  Bambanglipuro      ID  2020-06-22 02:33:34  -7.95  110.28   \n",
       "7         7         Avarua      CK  2020-06-22 02:33:34 -21.21 -159.78   \n",
       "8         8          Vaini      TO  2020-06-22 02:33:35 -21.20 -175.20   \n",
       "9         9      Nhulunbuy      AU  2020-06-22 02:33:35 -12.23  136.77   \n",
       "17       17        Isangel      VU  2020-06-22 02:33:37 -19.55  169.27   \n",
       "21       21         Faanui      PF  2020-06-22 02:33:38 -16.48 -151.75   \n",
       "28       28         Albany      US  2020-06-22 02:33:40  42.60  -73.97   \n",
       "34       34         Pyapon      MM  2020-06-22 02:33:42  16.28   95.68   \n",
       "40       40           Mayo      US  2020-06-22 02:33:44  38.89  -76.51   \n",
       "\n",
       "    Max Temp  Humidity  Cloudiness  Wind Speed  \n",
       "2      85.86        66          11        5.23  \n",
       "6      83.44        74          76       14.25  \n",
       "7      75.20        64          90       17.22  \n",
       "8      80.60        88          40        8.05  \n",
       "9      80.08        68          34       16.13  \n",
       "17     75.36        73          25       11.81  \n",
       "21     77.41        82          61       23.96  \n",
       "28     81.00        87          44        2.62  \n",
       "34     87.85        66          12        6.35  \n",
       "40     75.99        73           1        8.05  "
      ]
     },
     "execution_count": 12,
     "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": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "City_ID       185\n",
       "City          185\n",
       "Country       185\n",
       "Date          185\n",
       "Lat           185\n",
       "Lng           185\n",
       "Max Temp      185\n",
       "Humidity      185\n",
       "Cloudiness    185\n",
       "Wind Speed    185\n",
       "dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preferred_cities_df.count()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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</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>2</th>\n",
       "      <td>Kavieng</td>\n",
       "      <td>PG</td>\n",
       "      <td>85.86</td>\n",
       "      <td>-2.57</td>\n",
       "      <td>150.80</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Bambanglipuro</td>\n",
       "      <td>ID</td>\n",
       "      <td>83.44</td>\n",
       "      <td>-7.95</td>\n",
       "      <td>110.28</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Avarua</td>\n",
       "      <td>CK</td>\n",
       "      <td>75.20</td>\n",
       "      <td>-21.21</td>\n",
       "      <td>-159.78</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Vaini</td>\n",
       "      <td>TO</td>\n",
       "      <td>80.60</td>\n",
       "      <td>-21.20</td>\n",
       "      <td>-175.20</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Nhulunbuy</td>\n",
       "      <td>AU</td>\n",
       "      <td>80.08</td>\n",
       "      <td>-12.23</td>\n",
       "      <td>136.77</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Isangel</td>\n",
       "      <td>VU</td>\n",
       "      <td>75.36</td>\n",
       "      <td>-19.55</td>\n",
       "      <td>169.27</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Faanui</td>\n",
       "      <td>PF</td>\n",
       "      <td>77.41</td>\n",
       "      <td>-16.48</td>\n",
       "      <td>-151.75</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Albany</td>\n",
       "      <td>US</td>\n",
       "      <td>81.00</td>\n",
       "      <td>42.60</td>\n",
       "      <td>-73.97</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>Pyapon</td>\n",
       "      <td>MM</td>\n",
       "      <td>87.85</td>\n",
       "      <td>16.28</td>\n",
       "      <td>95.68</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>Mayo</td>\n",
       "      <td>US</td>\n",
       "      <td>75.99</td>\n",
       "      <td>38.89</td>\n",
       "      <td>-76.51</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             City Country  Max Temp    Lat     Lng Hotel Name\n",
       "2         Kavieng      PG     85.86  -2.57  150.80           \n",
       "6   Bambanglipuro      ID     83.44  -7.95  110.28           \n",
       "7          Avarua      CK     75.20 -21.21 -159.78           \n",
       "8           Vaini      TO     80.60 -21.20 -175.20           \n",
       "9       Nhulunbuy      AU     80.08 -12.23  136.77           \n",
       "17        Isangel      VU     75.36 -19.55  169.27           \n",
       "21         Faanui      PF     77.41 -16.48 -151.75           \n",
       "28         Albany      US     81.00  42.60  -73.97           \n",
       "34         Pyapon      MM     87.85  16.28   95.68           \n",
       "40           Mayo      US     75.99  38.89  -76.51           "
      ]
     },
     "execution_count": 14,
     "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": 15,
   "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": 19,
   "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",
      "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": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>2</th>\n",
       "      <td>Kavieng</td>\n",
       "      <td>PG</td>\n",
       "      <td>85.86</td>\n",
       "      <td>-2.57</td>\n",
       "      <td>150.80</td>\n",
       "      <td>Nusa Island Retreat</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Bambanglipuro</td>\n",
       "      <td>ID</td>\n",
       "      <td>83.44</td>\n",
       "      <td>-7.95</td>\n",
       "      <td>110.28</td>\n",
       "      <td>Puri Brata</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Avarua</td>\n",
       "      <td>CK</td>\n",
       "      <td>75.20</td>\n",
       "      <td>-21.21</td>\n",
       "      <td>-159.78</td>\n",
       "      <td>Paradise Inn</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Vaini</td>\n",
       "      <td>TO</td>\n",
       "      <td>80.60</td>\n",
       "      <td>-21.20</td>\n",
       "      <td>-175.20</td>\n",
       "      <td>Keleti Beach Resort</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Nhulunbuy</td>\n",
       "      <td>AU</td>\n",
       "      <td>80.08</td>\n",
       "      <td>-12.23</td>\n",
       "      <td>136.77</td>\n",
       "      <td>East Arnhem Real Estate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>558</th>\n",
       "      <td>Pandharpur</td>\n",
       "      <td>IN</td>\n",
       "      <td>79.70</td>\n",
       "      <td>17.67</td>\n",
       "      <td>75.33</td>\n",
       "      <td>Hotel Vitthal Inn Pandharpur</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>559</th>\n",
       "      <td>Harper</td>\n",
       "      <td>LR</td>\n",
       "      <td>76.14</td>\n",
       "      <td>4.38</td>\n",
       "      <td>-7.72</td>\n",
       "      <td>Screensaver</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>562</th>\n",
       "      <td>Lanxi</td>\n",
       "      <td>CN</td>\n",
       "      <td>82.06</td>\n",
       "      <td>29.22</td>\n",
       "      <td>119.48</td>\n",
       "      <td>Shangri-La Hotel</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>564</th>\n",
       "      <td>Jiaojiang</td>\n",
       "      <td>CN</td>\n",
       "      <td>86.00</td>\n",
       "      <td>28.68</td>\n",
       "      <td>121.44</td>\n",
       "      <td>Fangyuan Hotel</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>566</th>\n",
       "      <td>Mackay</td>\n",
       "      <td>AU</td>\n",
       "      <td>78.01</td>\n",
       "      <td>-21.15</td>\n",
       "      <td>149.20</td>\n",
       "      <td>International Lodge Motel</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>185 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              City Country  Max Temp    Lat     Lng  \\\n",
       "2          Kavieng      PG     85.86  -2.57  150.80   \n",
       "6    Bambanglipuro      ID     83.44  -7.95  110.28   \n",
       "7           Avarua      CK     75.20 -21.21 -159.78   \n",
       "8            Vaini      TO     80.60 -21.20 -175.20   \n",
       "9        Nhulunbuy      AU     80.08 -12.23  136.77   \n",
       "..             ...     ...       ...    ...     ...   \n",
       "558     Pandharpur      IN     79.70  17.67   75.33   \n",
       "559         Harper      LR     76.14   4.38   -7.72   \n",
       "562          Lanxi      CN     82.06  29.22  119.48   \n",
       "564      Jiaojiang      CN     86.00  28.68  121.44   \n",
       "566         Mackay      AU     78.01 -21.15  149.20   \n",
       "\n",
       "                       Hotel Name  \n",
       "2             Nusa Island Retreat  \n",
       "6                      Puri Brata  \n",
       "7                    Paradise Inn  \n",
       "8             Keleti Beach Resort  \n",
       "9         East Arnhem Real Estate  \n",
       "..                            ...  \n",
       "558  Hotel Vitthal Inn Pandharpur  \n",
       "559                   Screensaver  \n",
       "562              Shangri-La Hotel  \n",
       "564                Fangyuan Hotel  \n",
       "566     International Lodge Motel  \n",
       "\n",
       "[185 rows x 6 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hotel_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "02ab8769bfec423fbfa11495d94b6a8f",
       "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",
    "marker_layer = gmaps.marker_layer(locations)\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": 23,
   "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",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Store the DataFrame Row.\n",
    "hotel_info = [info_box_template.format(**row) for index, row in hotel_df.iterrows()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c9c0239371674de7b6eefa9be56c37e6",
       "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,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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