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\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>Namibe</td>\n",
       "      <td>AO</td>\n",
       "      <td>2020-11-03 18:21:57</td>\n",
       "      <td>-15.20</td>\n",
       "      <td>12.15</td>\n",
       "      <td>71.11</td>\n",
       "      <td>66</td>\n",
       "      <td>1</td>\n",
       "      <td>9.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Labuan</td>\n",
       "      <td>MY</td>\n",
       "      <td>2020-11-03 18:21:58</td>\n",
       "      <td>5.28</td>\n",
       "      <td>115.24</td>\n",
       "      <td>78.80</td>\n",
       "      <td>94</td>\n",
       "      <td>40</td>\n",
       "      <td>3.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>Ushuaia</td>\n",
       "      <td>AR</td>\n",
       "      <td>2020-11-03 18:21:58</td>\n",
       "      <td>-54.80</td>\n",
       "      <td>-68.30</td>\n",
       "      <td>51.80</td>\n",
       "      <td>66</td>\n",
       "      <td>75</td>\n",
       "      <td>4.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>Sassandra</td>\n",
       "      <td>CI</td>\n",
       "      <td>2020-11-03 18:21:58</td>\n",
       "      <td>4.95</td>\n",
       "      <td>-6.08</td>\n",
       "      <td>80.19</td>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "      <td>7.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Sumbe</td>\n",
       "      <td>AO</td>\n",
       "      <td>2020-11-03 18:21:58</td>\n",
       "      <td>-11.21</td>\n",
       "      <td>13.84</td>\n",
       "      <td>77.83</td>\n",
       "      <td>86</td>\n",
       "      <td>24</td>\n",
       "      <td>8.03</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   City_ID       City Country                 Date    Lat     Lng  Max Temp  \\\n",
       "0        0     Namibe      AO  2020-11-03 18:21:57 -15.20   12.15     71.11   \n",
       "1        1     Labuan      MY  2020-11-03 18:21:58   5.28  115.24     78.80   \n",
       "2        2    Ushuaia      AR  2020-11-03 18:21:58 -54.80  -68.30     51.80   \n",
       "3        3  Sassandra      CI  2020-11-03 18:21:58   4.95   -6.08     80.19   \n",
       "4        4      Sumbe      AO  2020-11-03 18:21:58 -11.21   13.84     77.83   \n",
       "\n",
       "   Humidity  Cloudiness  Wind Speed  \n",
       "0        66           1        9.19  \n",
       "1        94          40        3.53  \n",
       "2        66          75        4.70  \n",
       "3        80           0        7.34  \n",
       "4        86          24        8.03  "
      ]
     },
     "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()\n"
   ]
  },
  {
   "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": [
    "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": 13,
   "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, 25.0), zoom_level=1.1)\n",
    "# Assign the heatmap variable.\n",
    "heat_layer = gmaps.heatmap_layer(locations, weights=temps, dissipating=False, max_intensity=300, point_radius=7)\n",
    "# Add the heatmap layer.\n",
    "fig.add_layer(heat_layer)\n",
    "# Call the figure to plot the data.\n",
    "fig\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
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       "Figure(layout=FigureLayout(height='420px'))"
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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, 25.0), zoom_level=1.1)\n",
    "heat_layer = gmaps.heatmap_layer(locations, weights=humidity, dissipating=False, max_intensity=300, point_radius=7)\n",
    "\n",
    "fig.add_layer(heat_layer)\n",
    "# Call the figure to plot the data.\n",
    "fig\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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, 25.0), zoom_level=1.1)\n",
    "heat_layer = gmaps.heatmap_layer(locations, weights=clouds, dissipating=False, max_intensity=300, point_radius=7)\n",
    "\n",
    "fig.add_layer(heat_layer)\n",
    "# Call the figure to plot the data.\n",
    "fig\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
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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, 25.0), zoom_level=1.1)\n",
    "heat_layer = gmaps.heatmap_layer(locations, weights=wind, dissipating=False, max_intensity=300, point_radius=7)\n",
    "\n",
    "fig.add_layer(heat_layer)\n",
    "# Call the figure to plot the data.\n",
    "fig\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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": 23,
   "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>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Labuan</td>\n",
       "      <td>MY</td>\n",
       "      <td>2020-11-03 18:21:58</td>\n",
       "      <td>5.28</td>\n",
       "      <td>115.24</td>\n",
       "      <td>78.80</td>\n",
       "      <td>94</td>\n",
       "      <td>40</td>\n",
       "      <td>3.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>Sassandra</td>\n",
       "      <td>CI</td>\n",
       "      <td>2020-11-03 18:21:58</td>\n",
       "      <td>4.95</td>\n",
       "      <td>-6.08</td>\n",
       "      <td>80.19</td>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "      <td>7.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Sumbe</td>\n",
       "      <td>AO</td>\n",
       "      <td>2020-11-03 18:21:58</td>\n",
       "      <td>-11.21</td>\n",
       "      <td>13.84</td>\n",
       "      <td>77.83</td>\n",
       "      <td>86</td>\n",
       "      <td>24</td>\n",
       "      <td>8.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>Hithadhoo</td>\n",
       "      <td>MV</td>\n",
       "      <td>2020-11-03 18:21:58</td>\n",
       "      <td>-0.60</td>\n",
       "      <td>73.08</td>\n",
       "      <td>81.81</td>\n",
       "      <td>77</td>\n",
       "      <td>100</td>\n",
       "      <td>10.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>Georgetown</td>\n",
       "      <td>MY</td>\n",
       "      <td>2020-11-03 18:21:59</td>\n",
       "      <td>5.41</td>\n",
       "      <td>100.34</td>\n",
       "      <td>77.00</td>\n",
       "      <td>94</td>\n",
       "      <td>20</td>\n",
       "      <td>4.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>Trincomalee</td>\n",
       "      <td>LK</td>\n",
       "      <td>2020-11-03 18:21:59</td>\n",
       "      <td>8.57</td>\n",
       "      <td>81.23</td>\n",
       "      <td>81.36</td>\n",
       "      <td>79</td>\n",
       "      <td>79</td>\n",
       "      <td>3.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>14</td>\n",
       "      <td>Cayenne</td>\n",
       "      <td>GF</td>\n",
       "      <td>2020-11-03 18:19:20</td>\n",
       "      <td>4.93</td>\n",
       "      <td>-52.33</td>\n",
       "      <td>86.00</td>\n",
       "      <td>62</td>\n",
       "      <td>75</td>\n",
       "      <td>11.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>16</td>\n",
       "      <td>Hilo</td>\n",
       "      <td>US</td>\n",
       "      <td>2020-11-03 18:22:01</td>\n",
       "      <td>19.73</td>\n",
       "      <td>-155.09</td>\n",
       "      <td>78.80</td>\n",
       "      <td>74</td>\n",
       "      <td>20</td>\n",
       "      <td>4.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>21</td>\n",
       "      <td>Jiwani</td>\n",
       "      <td>PK</td>\n",
       "      <td>2020-11-03 18:22:02</td>\n",
       "      <td>25.05</td>\n",
       "      <td>61.74</td>\n",
       "      <td>75.99</td>\n",
       "      <td>74</td>\n",
       "      <td>0</td>\n",
       "      <td>9.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>25</td>\n",
       "      <td>Avarua</td>\n",
       "      <td>CK</td>\n",
       "      <td>2020-11-03 18:22:03</td>\n",
       "      <td>-21.21</td>\n",
       "      <td>-159.78</td>\n",
       "      <td>75.20</td>\n",
       "      <td>83</td>\n",
       "      <td>96</td>\n",
       "      <td>10.29</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    City_ID         City Country                 Date    Lat     Lng  \\\n",
       "1         1       Labuan      MY  2020-11-03 18:21:58   5.28  115.24   \n",
       "3         3    Sassandra      CI  2020-11-03 18:21:58   4.95   -6.08   \n",
       "4         4        Sumbe      AO  2020-11-03 18:21:58 -11.21   13.84   \n",
       "5         5    Hithadhoo      MV  2020-11-03 18:21:58  -0.60   73.08   \n",
       "6         6   Georgetown      MY  2020-11-03 18:21:59   5.41  100.34   \n",
       "9         9  Trincomalee      LK  2020-11-03 18:21:59   8.57   81.23   \n",
       "14       14      Cayenne      GF  2020-11-03 18:19:20   4.93  -52.33   \n",
       "16       16         Hilo      US  2020-11-03 18:22:01  19.73 -155.09   \n",
       "21       21       Jiwani      PK  2020-11-03 18:22:02  25.05   61.74   \n",
       "25       25       Avarua      CK  2020-11-03 18:22:03 -21.21 -159.78   \n",
       "\n",
       "    Max Temp  Humidity  Cloudiness  Wind Speed  \n",
       "1      78.80        94          40        3.53  \n",
       "3      80.19        80           0        7.34  \n",
       "4      77.83        86          24        8.03  \n",
       "5      81.81        77         100       10.38  \n",
       "6      77.00        94          20        4.70  \n",
       "9      81.36        79          79        3.67  \n",
       "14     86.00        62          75       11.41  \n",
       "16     78.80        74          20        4.70  \n",
       "21     75.99        74           0        9.69  \n",
       "25     75.20        83          96       10.29  "
      ]
     },
     "execution_count": 23,
     "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)].dropna()\n",
    "preferred_cities_df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "City_ID       161\n",
       "City          161\n",
       "Country       161\n",
       "Date          161\n",
       "Lat           161\n",
       "Lng           161\n",
       "Max Temp      161\n",
       "Humidity      161\n",
       "Cloudiness    161\n",
       "Wind Speed    161\n",
       "dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preferred_cities_df.count()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "City_ID       0\n",
       "City          0\n",
       "Country       0\n",
       "Date          0\n",
       "Lat           0\n",
       "Lng           0\n",
       "Max Temp      0\n",
       "Humidity      0\n",
       "Cloudiness    0\n",
       "Wind Speed    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preferred_cities_df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>1</th>\n",
       "      <td>Labuan</td>\n",
       "      <td>MY</td>\n",
       "      <td>78.80</td>\n",
       "      <td>5.28</td>\n",
       "      <td>115.24</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Sassandra</td>\n",
       "      <td>CI</td>\n",
       "      <td>80.19</td>\n",
       "      <td>4.95</td>\n",
       "      <td>-6.08</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Sumbe</td>\n",
       "      <td>AO</td>\n",
       "      <td>77.83</td>\n",
       "      <td>-11.21</td>\n",
       "      <td>13.84</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Hithadhoo</td>\n",
       "      <td>MV</td>\n",
       "      <td>81.81</td>\n",
       "      <td>-0.60</td>\n",
       "      <td>73.08</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Georgetown</td>\n",
       "      <td>MY</td>\n",
       "      <td>77.00</td>\n",
       "      <td>5.41</td>\n",
       "      <td>100.34</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Trincomalee</td>\n",
       "      <td>LK</td>\n",
       "      <td>81.36</td>\n",
       "      <td>8.57</td>\n",
       "      <td>81.23</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Cayenne</td>\n",
       "      <td>GF</td>\n",
       "      <td>86.00</td>\n",
       "      <td>4.93</td>\n",
       "      <td>-52.33</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Hilo</td>\n",
       "      <td>US</td>\n",
       "      <td>78.80</td>\n",
       "      <td>19.73</td>\n",
       "      <td>-155.09</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Jiwani</td>\n",
       "      <td>PK</td>\n",
       "      <td>75.99</td>\n",
       "      <td>25.05</td>\n",
       "      <td>61.74</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           City Country  Max Temp    Lat     Lng Hotel Name\n",
       "1        Labuan      MY     78.80   5.28  115.24           \n",
       "3     Sassandra      CI     80.19   4.95   -6.08           \n",
       "4         Sumbe      AO     77.83 -11.21   13.84           \n",
       "5     Hithadhoo      MV     81.81  -0.60   73.08           \n",
       "6    Georgetown      MY     77.00   5.41  100.34           \n",
       "9   Trincomalee      LK     81.36   8.57   81.23           \n",
       "14      Cayenne      GF     86.00   4.93  -52.33           \n",
       "16         Hilo      US     78.80  19.73 -155.09           \n",
       "21       Jiwani      PK     75.99  25.05   61.74           \n",
       "25       Avarua      CK     75.20 -21.21 -159.78           "
      ]
     },
     "execution_count": 26,
     "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": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set parameters to search for a hotel.\n",
    "params = {\n",
    "    \"radius\": 5000,\n",
    "    \"type\": \"lodging\",\n",
    "    \"key\": g_key\n",
    "}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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"
     ]
    }
   ],
   "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": 34,
   "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>1</th>\n",
       "      <td>Labuan</td>\n",
       "      <td>MY</td>\n",
       "      <td>78.80</td>\n",
       "      <td>5.28</td>\n",
       "      <td>115.24</td>\n",
       "      <td>Tiara Labuan Hotel</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Sassandra</td>\n",
       "      <td>CI</td>\n",
       "      <td>80.19</td>\n",
       "      <td>4.95</td>\n",
       "      <td>-6.08</td>\n",
       "      <td>la terrasse</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Sumbe</td>\n",
       "      <td>AO</td>\n",
       "      <td>77.83</td>\n",
       "      <td>-11.21</td>\n",
       "      <td>13.84</td>\n",
       "      <td>Hotel Ritz Sumbe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Hithadhoo</td>\n",
       "      <td>MV</td>\n",
       "      <td>81.81</td>\n",
       "      <td>-0.60</td>\n",
       "      <td>73.08</td>\n",
       "      <td>Scoop Guest House</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Georgetown</td>\n",
       "      <td>MY</td>\n",
       "      <td>77.00</td>\n",
       "      <td>5.41</td>\n",
       "      <td>100.34</td>\n",
       "      <td>Cititel Penang</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Trincomalee</td>\n",
       "      <td>LK</td>\n",
       "      <td>81.36</td>\n",
       "      <td>8.57</td>\n",
       "      <td>81.23</td>\n",
       "      <td>Welcombe Hotel</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Cayenne</td>\n",
       "      <td>GF</td>\n",
       "      <td>86.00</td>\n",
       "      <td>4.93</td>\n",
       "      <td>-52.33</td>\n",
       "      <td>Hôtel Le Dronmi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Hilo</td>\n",
       "      <td>US</td>\n",
       "      <td>78.80</td>\n",
       "      <td>19.73</td>\n",
       "      <td>-155.09</td>\n",
       "      <td>Hilo Hawaiian Hotel</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Jiwani</td>\n",
       "      <td>PK</td>\n",
       "      <td>75.99</td>\n",
       "      <td>25.05</td>\n",
       "      <td>61.74</td>\n",
       "      <td>Al Mumtaz Transport Jiwani</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           City Country  Max Temp    Lat     Lng                  Hotel Name\n",
       "1        Labuan      MY     78.80   5.28  115.24          Tiara Labuan Hotel\n",
       "3     Sassandra      CI     80.19   4.95   -6.08                 la terrasse\n",
       "4         Sumbe      AO     77.83 -11.21   13.84            Hotel Ritz Sumbe\n",
       "5     Hithadhoo      MV     81.81  -0.60   73.08           Scoop Guest House\n",
       "6    Georgetown      MY     77.00   5.41  100.34              Cititel Penang\n",
       "9   Trincomalee      LK     81.36   8.57   81.23              Welcombe Hotel\n",
       "14      Cayenne      GF     86.00   4.93  -52.33             Hôtel Le Dronmi\n",
       "16         Hilo      US     78.80  19.73 -155.09         Hilo Hawaiian Hotel\n",
       "21       Jiwani      PK     75.99  25.05   61.74  Al Mumtaz Transport Jiwani\n",
       "25       Avarua      CK     75.20 -21.21 -159.78                Paradise Inn"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hotel_df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9723ddffa1a140c9bc1d79adf2c463a6",
       "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 marker for each city.\n",
    "locations = hotel_df[[\"Lat\", \"Lng\"]]\n",
    "max_temp = hotel_df[\"Max Temp\"]\n",
    "fig = gmaps.figure(center=(30.0, 25.0), zoom_level=1.1)\n",
    "heat_layer = gmaps.heatmap_layer(locations, weights=max_temp,\n",
    "             dissipating=False, max_intensity=300, point_radius=7)\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": 39,
   "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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Store the DataFrame Row.\n",
    "hotel_info = [info_box_template.format(**row) for index, row in hotel_df.iterrows()]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5f20a42f83c64634b575894dfd35bcaa",
       "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, 25.0), zoom_level=1.1)\n",
    "heat_layer = gmaps.heatmap_layer(locations, weights=max_temp,dissipating=False,\n",
    "             max_intensity=300, point_radius=7)\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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