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": {
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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>Jamestown</td>\n",
       "      <td>US</td>\n",
       "      <td>2020-08-18 23:08:25</td>\n",
       "      <td>42.10</td>\n",
       "      <td>-79.24</td>\n",
       "      <td>70.00</td>\n",
       "      <td>56</td>\n",
       "      <td>1</td>\n",
       "      <td>10.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Ribeira Grande</td>\n",
       "      <td>PT</td>\n",
       "      <td>2020-08-18 23:08:54</td>\n",
       "      <td>38.52</td>\n",
       "      <td>-28.70</td>\n",
       "      <td>73.08</td>\n",
       "      <td>91</td>\n",
       "      <td>54</td>\n",
       "      <td>23.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>Rikitea</td>\n",
       "      <td>PF</td>\n",
       "      <td>2020-08-18 23:07:16</td>\n",
       "      <td>-23.12</td>\n",
       "      <td>-134.97</td>\n",
       "      <td>71.96</td>\n",
       "      <td>79</td>\n",
       "      <td>0</td>\n",
       "      <td>10.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>Taoudenni</td>\n",
       "      <td>ML</td>\n",
       "      <td>2020-08-18 23:08:54</td>\n",
       "      <td>22.68</td>\n",
       "      <td>-3.98</td>\n",
       "      <td>101.86</td>\n",
       "      <td>24</td>\n",
       "      <td>2</td>\n",
       "      <td>17.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Georgetown</td>\n",
       "      <td>MY</td>\n",
       "      <td>2020-08-18 23:06:25</td>\n",
       "      <td>5.41</td>\n",
       "      <td>100.34</td>\n",
       "      <td>80.60</td>\n",
       "      <td>94</td>\n",
       "      <td>20</td>\n",
       "      <td>2.24</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   City_ID            City Country                 Date    Lat     Lng  \\\n",
       "0        0       Jamestown      US  2020-08-18 23:08:25  42.10  -79.24   \n",
       "1        1  Ribeira Grande      PT  2020-08-18 23:08:54  38.52  -28.70   \n",
       "2        2         Rikitea      PF  2020-08-18 23:07:16 -23.12 -134.97   \n",
       "3        3       Taoudenni      ML  2020-08-18 23:08:54  22.68   -3.98   \n",
       "4        4      Georgetown      MY  2020-08-18 23:06:25   5.41  100.34   \n",
       "\n",
       "   Max Temp  Humidity  Cloudiness  Wind Speed  \n",
       "0     70.00        56           1       10.29  \n",
       "1     73.08        91          54       23.06  \n",
       "2     71.96        79           0       10.29  \n",
       "3    101.86        24           2       17.72  \n",
       "4     80.60        94          20        2.24  "
      ]
     },
     "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": [
    "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": [
    "# 1. Assign the locations to an array of latitude and longitude pairs.\n",
    "locations = city_data_df[[\"Lat\", \"Lng\"]]\n",
    "# 2. Assign the weights variable to some values.\n",
    "max_temp= city_data_df[\"Max Temp\"]\n",
    "# max_temp = temps\n",
    "# 3. Assign the figure variable to the gmaps.figure() attribute.\n",
    "fig = gmaps.figure()\n",
    "fig = gmaps.figure(center=(30.0, 31.0), zoom_level=1.75)\n",
    "# 4. Assign the heatmap_layer variable to the heatmap_layer attribute and add in the locations.\n",
    "# heat_layer = gmaps.heatmap_layer(locations, weights=max_temp)\n",
    "# heat_layer = gmaps.heatmap_layer(locations, weights=[max(temp, 0) for temp in max_temp])\n",
    "heat_layer = gmaps.heatmap_layer(locations, weights=temps, dissipating=False, max_intensity=300, point_radius=4)\n",
    "# 5. Add the heatmap layer.\n",
    "fig.add_layer(heat_layer)\n",
    "# 6. Call the figure to plot the data.\n",
    "fig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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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=1.75)\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": 8,
   "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=1.75)\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": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "model_id": "b36b8b50be294369a87f802b88f2f747",
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       "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=1.75)\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": 10,
   "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": 11,
   "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>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Georgetown</td>\n",
       "      <td>MY</td>\n",
       "      <td>2020-08-18 23:06:25</td>\n",
       "      <td>5.41</td>\n",
       "      <td>100.34</td>\n",
       "      <td>80.60</td>\n",
       "      <td>94</td>\n",
       "      <td>20</td>\n",
       "      <td>2.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>Bethel</td>\n",
       "      <td>US</td>\n",
       "      <td>2020-08-18 23:08:55</td>\n",
       "      <td>41.37</td>\n",
       "      <td>-73.41</td>\n",
       "      <td>80.60</td>\n",
       "      <td>39</td>\n",
       "      <td>1</td>\n",
       "      <td>6.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>Hilo</td>\n",
       "      <td>US</td>\n",
       "      <td>2020-08-18 23:08:55</td>\n",
       "      <td>19.73</td>\n",
       "      <td>-155.09</td>\n",
       "      <td>82.40</td>\n",
       "      <td>65</td>\n",
       "      <td>90</td>\n",
       "      <td>7.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>13</td>\n",
       "      <td>Atuona</td>\n",
       "      <td>PF</td>\n",
       "      <td>2020-08-18 23:07:26</td>\n",
       "      <td>-9.80</td>\n",
       "      <td>-139.03</td>\n",
       "      <td>78.57</td>\n",
       "      <td>76</td>\n",
       "      <td>1</td>\n",
       "      <td>21.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>14</td>\n",
       "      <td>Johor Bahru</td>\n",
       "      <td>MY</td>\n",
       "      <td>2020-08-18 23:05:42</td>\n",
       "      <td>1.47</td>\n",
       "      <td>103.76</td>\n",
       "      <td>77.00</td>\n",
       "      <td>94</td>\n",
       "      <td>71</td>\n",
       "      <td>1.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>15</td>\n",
       "      <td>Albany</td>\n",
       "      <td>US</td>\n",
       "      <td>2020-08-18 23:08:57</td>\n",
       "      <td>42.60</td>\n",
       "      <td>-73.97</td>\n",
       "      <td>75.99</td>\n",
       "      <td>51</td>\n",
       "      <td>8</td>\n",
       "      <td>7.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>17</td>\n",
       "      <td>Hirara</td>\n",
       "      <td>JP</td>\n",
       "      <td>2020-08-18 23:08:58</td>\n",
       "      <td>24.80</td>\n",
       "      <td>125.28</td>\n",
       "      <td>82.40</td>\n",
       "      <td>94</td>\n",
       "      <td>75</td>\n",
       "      <td>3.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>27</td>\n",
       "      <td>Santa Marta</td>\n",
       "      <td>CO</td>\n",
       "      <td>2020-08-18 23:07:11</td>\n",
       "      <td>11.24</td>\n",
       "      <td>-74.20</td>\n",
       "      <td>84.00</td>\n",
       "      <td>80</td>\n",
       "      <td>79</td>\n",
       "      <td>12.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>31</td>\n",
       "      <td>Conakry</td>\n",
       "      <td>GN</td>\n",
       "      <td>2020-08-18 23:09:01</td>\n",
       "      <td>9.54</td>\n",
       "      <td>-13.68</td>\n",
       "      <td>80.60</td>\n",
       "      <td>88</td>\n",
       "      <td>75</td>\n",
       "      <td>6.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>32</td>\n",
       "      <td>Sarankhola</td>\n",
       "      <td>BD</td>\n",
       "      <td>2020-08-18 23:09:01</td>\n",
       "      <td>22.31</td>\n",
       "      <td>89.79</td>\n",
       "      <td>79.09</td>\n",
       "      <td>92</td>\n",
       "      <td>99</td>\n",
       "      <td>2.64</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    City_ID         City Country                 Date    Lat     Lng  \\\n",
       "4         4   Georgetown      MY  2020-08-18 23:06:25   5.41  100.34   \n",
       "6         6       Bethel      US  2020-08-18 23:08:55  41.37  -73.41   \n",
       "7         7         Hilo      US  2020-08-18 23:08:55  19.73 -155.09   \n",
       "13       13       Atuona      PF  2020-08-18 23:07:26  -9.80 -139.03   \n",
       "14       14  Johor Bahru      MY  2020-08-18 23:05:42   1.47  103.76   \n",
       "15       15       Albany      US  2020-08-18 23:08:57  42.60  -73.97   \n",
       "17       17       Hirara      JP  2020-08-18 23:08:58  24.80  125.28   \n",
       "27       27  Santa Marta      CO  2020-08-18 23:07:11  11.24  -74.20   \n",
       "31       31      Conakry      GN  2020-08-18 23:09:01   9.54  -13.68   \n",
       "32       32   Sarankhola      BD  2020-08-18 23:09:01  22.31   89.79   \n",
       "\n",
       "    Max Temp  Humidity  Cloudiness  Wind Speed  \n",
       "4      80.60        94          20        2.24  \n",
       "6      80.60        39           1        6.93  \n",
       "7      82.40        65          90        7.11  \n",
       "13     78.57        76           1       21.14  \n",
       "14     77.00        94          71        1.12  \n",
       "15     75.99        51           8        7.16  \n",
       "17     82.40        94          75        3.36  \n",
       "27     84.00        80          79       12.41  \n",
       "31     80.60        88          75        6.93  \n",
       "32     79.09        92          99        2.64  "
      ]
     },
     "execution_count": 11,
     "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": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "City_ID       206\n",
       "City          206\n",
       "Country       206\n",
       "Date          206\n",
       "Lat           206\n",
       "Lng           206\n",
       "Max Temp      206\n",
       "Humidity      206\n",
       "Cloudiness    206\n",
       "Wind Speed    206\n",
       "dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preferred_cities_df.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>4</th>\n",
       "      <td>Georgetown</td>\n",
       "      <td>MY</td>\n",
       "      <td>80.60</td>\n",
       "      <td>5.41</td>\n",
       "      <td>100.34</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Bethel</td>\n",
       "      <td>US</td>\n",
       "      <td>80.60</td>\n",
       "      <td>41.37</td>\n",
       "      <td>-73.41</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Hilo</td>\n",
       "      <td>US</td>\n",
       "      <td>82.40</td>\n",
       "      <td>19.73</td>\n",
       "      <td>-155.09</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Atuona</td>\n",
       "      <td>PF</td>\n",
       "      <td>78.57</td>\n",
       "      <td>-9.80</td>\n",
       "      <td>-139.03</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Johor Bahru</td>\n",
       "      <td>MY</td>\n",
       "      <td>77.00</td>\n",
       "      <td>1.47</td>\n",
       "      <td>103.76</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Albany</td>\n",
       "      <td>US</td>\n",
       "      <td>75.99</td>\n",
       "      <td>42.60</td>\n",
       "      <td>-73.97</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Hirara</td>\n",
       "      <td>JP</td>\n",
       "      <td>82.40</td>\n",
       "      <td>24.80</td>\n",
       "      <td>125.28</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Santa Marta</td>\n",
       "      <td>CO</td>\n",
       "      <td>84.00</td>\n",
       "      <td>11.24</td>\n",
       "      <td>-74.20</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Conakry</td>\n",
       "      <td>GN</td>\n",
       "      <td>80.60</td>\n",
       "      <td>9.54</td>\n",
       "      <td>-13.68</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Sarankhola</td>\n",
       "      <td>BD</td>\n",
       "      <td>79.09</td>\n",
       "      <td>22.31</td>\n",
       "      <td>89.79</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           City Country  Max Temp    Lat     Lng Hotel Name\n",
       "4    Georgetown      MY     80.60   5.41  100.34           \n",
       "6        Bethel      US     80.60  41.37  -73.41           \n",
       "7          Hilo      US     82.40  19.73 -155.09           \n",
       "13       Atuona      PF     78.57  -9.80 -139.03           \n",
       "14  Johor Bahru      MY     77.00   1.47  103.76           \n",
       "15       Albany      US     75.99  42.60  -73.97           \n",
       "17       Hirara      JP     82.40  24.80  125.28           \n",
       "27  Santa Marta      CO     84.00  11.24  -74.20           \n",
       "31      Conakry      GN     80.60   9.54  -13.68           \n",
       "32   Sarankhola      BD     79.09  22.31   89.79           "
      ]
     },
     "execution_count": 13,
     "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": 14,
   "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": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "#params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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.\")\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>4</th>\n",
       "      <td>Georgetown</td>\n",
       "      <td>MY</td>\n",
       "      <td>80.60</td>\n",
       "      <td>5.41</td>\n",
       "      <td>100.34</td>\n",
       "      <td>Cititel Penang</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Bethel</td>\n",
       "      <td>US</td>\n",
       "      <td>80.60</td>\n",
       "      <td>41.37</td>\n",
       "      <td>-73.41</td>\n",
       "      <td>Courtyard by Marriott Danbury</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Hilo</td>\n",
       "      <td>US</td>\n",
       "      <td>82.40</td>\n",
       "      <td>19.73</td>\n",
       "      <td>-155.09</td>\n",
       "      <td>Hilo Hawaiian Hotel</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Atuona</td>\n",
       "      <td>PF</td>\n",
       "      <td>78.57</td>\n",
       "      <td>-9.80</td>\n",
       "      <td>-139.03</td>\n",
       "      <td>Villa Enata</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Johor Bahru</td>\n",
       "      <td>MY</td>\n",
       "      <td>77.00</td>\n",
       "      <td>1.47</td>\n",
       "      <td>103.76</td>\n",
       "      <td>KSL Resort Johor Bahru City Centre</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Albany</td>\n",
       "      <td>US</td>\n",
       "      <td>75.99</td>\n",
       "      <td>42.60</td>\n",
       "      <td>-73.97</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Hirara</td>\n",
       "      <td>JP</td>\n",
       "      <td>82.40</td>\n",
       "      <td>24.80</td>\n",
       "      <td>125.28</td>\n",
       "      <td>Hotel Atoll Emerald Miyakojima</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Santa Marta</td>\n",
       "      <td>CO</td>\n",
       "      <td>84.00</td>\n",
       "      <td>11.24</td>\n",
       "      <td>-74.20</td>\n",
       "      <td>Hotel La Ballena Azul</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Conakry</td>\n",
       "      <td>GN</td>\n",
       "      <td>80.60</td>\n",
       "      <td>9.54</td>\n",
       "      <td>-13.68</td>\n",
       "      <td>Riviera Royal Hotel</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Sarankhola</td>\n",
       "      <td>BD</td>\n",
       "      <td>79.09</td>\n",
       "      <td>22.31</td>\n",
       "      <td>89.79</td>\n",
       "      <td>ইসা ষ্টোর</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           City Country  Max Temp    Lat     Lng  \\\n",
       "4    Georgetown      MY     80.60   5.41  100.34   \n",
       "6        Bethel      US     80.60  41.37  -73.41   \n",
       "7          Hilo      US     82.40  19.73 -155.09   \n",
       "13       Atuona      PF     78.57  -9.80 -139.03   \n",
       "14  Johor Bahru      MY     77.00   1.47  103.76   \n",
       "15       Albany      US     75.99  42.60  -73.97   \n",
       "17       Hirara      JP     82.40  24.80  125.28   \n",
       "27  Santa Marta      CO     84.00  11.24  -74.20   \n",
       "31      Conakry      GN     80.60   9.54  -13.68   \n",
       "32   Sarankhola      BD     79.09  22.31   89.79   \n",
       "\n",
       "                            Hotel Name  \n",
       "4                       Cititel Penang  \n",
       "6        Courtyard by Marriott Danbury  \n",
       "7                  Hilo Hawaiian Hotel  \n",
       "13                         Villa Enata  \n",
       "14  KSL Resort Johor Bahru City Centre  \n",
       "15                                      \n",
       "17      Hotel Atoll Emerald Miyakojima  \n",
       "27               Hotel La Ballena Azul  \n",
       "31                 Riviera Royal Hotel  \n",
       "32                           ইসা ষ্টোর  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hotel_df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a751082520574911a19338d49e22b281",
       "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",
    "fig.add_layer(heat_layer)\n",
    "# Call the figure to plot the data.\n",
    "fig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "75bc5af3d307481988ed8aebf51735c6",
       "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, 31.0), zoom_level=1.5)\n",
    "heat_layer = gmaps.heatmap_layer(locations, weights=max_temp,\n",
    "             dissipating=False, 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",
    "# Call the figure to plot the data.\n",
    "fig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "info_box_template = \"\"\"\n",
    "<dl>\n",
    "<dt>Name</dt><dd>{column1}</dd>\n",
    "<dt>Another name</dt><dd>{column2}</dd>\n",
    "</dl>\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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": 22,
   "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": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "89a7e89480214d89ac59f1acec2283e4",
       "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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