VacationPy.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "502a4617",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "191530ba",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import the dependencies.\n",
    "import pandas as pd\n",
    "import gmaps\n",
    "import requests\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Import the API key.\n",
    "import os\n",
    "from dotenv import load_dotenv\n",
    "load_dotenv()\n",
    "g_key = os.getenv(\"g_key\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "96b0d4b5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "str"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(g_key)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b8bce488",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a408352f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\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_ID</th>\n",
       "      <th>City</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",
       "      <th>Country</th>\n",
       "      <th>Date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Jaisinghnagar</td>\n",
       "      <td>23.7000</td>\n",
       "      <td>81.3833</td>\n",
       "      <td>76.23</td>\n",
       "      <td>84</td>\n",
       "      <td>92</td>\n",
       "      <td>7.74</td>\n",
       "      <td>IN</td>\n",
       "      <td>2022-07-18 23:17:44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Hot Springs</td>\n",
       "      <td>34.5037</td>\n",
       "      <td>-93.0552</td>\n",
       "      <td>82.40</td>\n",
       "      <td>83</td>\n",
       "      <td>27</td>\n",
       "      <td>1.99</td>\n",
       "      <td>US</td>\n",
       "      <td>2022-07-19 04:58:10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>Rikitea</td>\n",
       "      <td>-23.1203</td>\n",
       "      <td>-134.9692</td>\n",
       "      <td>74.48</td>\n",
       "      <td>74</td>\n",
       "      <td>4</td>\n",
       "      <td>12.80</td>\n",
       "      <td>PF</td>\n",
       "      <td>2022-07-19 04:58:11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>Mahebourg</td>\n",
       "      <td>-20.4081</td>\n",
       "      <td>57.7000</td>\n",
       "      <td>71.26</td>\n",
       "      <td>68</td>\n",
       "      <td>75</td>\n",
       "      <td>19.57</td>\n",
       "      <td>MU</td>\n",
       "      <td>2022-07-19 04:58:11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Saskylakh</td>\n",
       "      <td>71.9167</td>\n",
       "      <td>114.0833</td>\n",
       "      <td>44.37</td>\n",
       "      <td>64</td>\n",
       "      <td>100</td>\n",
       "      <td>15.39</td>\n",
       "      <td>RU</td>\n",
       "      <td>2022-07-19 04:58:12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   City_ID           City      Lat       Lng  Max Temp  Humidity  Cloudiness  \\\n",
       "0        0  Jaisinghnagar  23.7000   81.3833     76.23        84          92   \n",
       "1        1    Hot Springs  34.5037  -93.0552     82.40        83          27   \n",
       "2        2        Rikitea -23.1203 -134.9692     74.48        74           4   \n",
       "3        3      Mahebourg -20.4081   57.7000     71.26        68          75   \n",
       "4        4      Saskylakh  71.9167  114.0833     44.37        64         100   \n",
       "\n",
       "   Wind Speed Country                 Date  \n",
       "0        7.74      IN  2022-07-18 23:17:44  \n",
       "1        1.99      US  2022-07-19 04:58:10  \n",
       "2       12.80      PF  2022-07-19 04:58:11  \n",
       "3       19.57      MU  2022-07-19 04:58:11  \n",
       "4       15.39      RU  2022-07-19 04:58:12  "
      ]
     },
     "execution_count": 4,
     "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": 5,
   "id": "271fe44c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "City_ID         int64\n",
       "City           object\n",
       "Lat           float64\n",
       "Lng           float64\n",
       "Max Temp      float64\n",
       "Humidity        int64\n",
       "Cloudiness      int64\n",
       "Wind Speed    float64\n",
       "Country        object\n",
       "Date           object\n",
       "dtype: object"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "city_data_df.dtypes\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "4fca6b56",
   "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))\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "740d7828",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "model_id": "9017496612194e7eabf53e445dc8dc1c",
       "version_major": 2,
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      },
      "text/plain": [
       "Figure(layout=FigureLayout(height='420px'))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Configure gmaps to use your Google API key.\n",
    "gmaps.configure(api_key=g_key)\n",
    "\n",
    "# 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=1.5)\n",
    "# Assign the heatmap variable.\n",
    "heat_layer = gmaps.heatmap_layer(locations, weights=[max(temp, 0) for temp in max_temp], dissipating=False, max_intensity=300, point_radius=4)\n",
    "# Add the heatmap layer.\n",
    "fig.add_layer(heat_layer)\n",
    "# Call the figure to plot the data.\n",
    "fig\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "3f8ab235",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "model_id": "6aaa3800213b4f319b75f12799e36f9a",
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      "text/plain": [
       "Figure(layout=FigureLayout(height='420px'))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Heatmap of percent humidity\n",
    "locations = city_data_df[[\"Lat\", \"Lng\"]]\n",
    "humidity = city_data_df[\"Humidity\"]\n",
    "fig = gmaps.figure(center=(30.0, 31.0), zoom_level=1.5)\n",
    "heat_layer = gmaps.heatmap_layer(locations, weights=humidity, dissipating=False, max_intensity=300, point_radius=4)\n",
    "\n",
    "fig.add_layer(heat_layer)\n",
    "# Call the figure to plot the data.\n",
    "fig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a5da3622",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3ae2bc94d3dc4d2ea78f02825e0d5614",
       "version_major": 2,
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      },
      "text/plain": [
       "Figure(layout=FigureLayout(height='420px'))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# Heatmap of percent cloudiness\n",
    "locations = city_data_df[[\"Lat\", \"Lng\"]]\n",
    "wind = city_data_df[\"Wind Speed\"]\n",
    "fig = gmaps.figure(center=(30.0, 31.0), zoom_level=1.5)\n",
    "heat_layer = gmaps.heatmap_layer(locations, weights=wind, dissipating=False, max_intensity=300, point_radius=4)\n",
    "\n",
    "fig.add_layer(heat_layer)\n",
    "# Call the figure to plot the data.\n",
    "\n",
    "fig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "eb92d105",
   "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,
   "id": "c52f6c70",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>City_ID</th>\n",
       "      <th>City</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",
       "      <th>Country</th>\n",
       "      <th>Date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Jaisinghnagar</td>\n",
       "      <td>23.7000</td>\n",
       "      <td>81.3833</td>\n",
       "      <td>76.23</td>\n",
       "      <td>84</td>\n",
       "      <td>92</td>\n",
       "      <td>7.74</td>\n",
       "      <td>IN</td>\n",
       "      <td>2022-07-18 23:17:44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Hot Springs</td>\n",
       "      <td>34.5037</td>\n",
       "      <td>-93.0552</td>\n",
       "      <td>82.40</td>\n",
       "      <td>83</td>\n",
       "      <td>27</td>\n",
       "      <td>1.99</td>\n",
       "      <td>US</td>\n",
       "      <td>2022-07-19 04:58:10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>13</td>\n",
       "      <td>Kapaa</td>\n",
       "      <td>22.0752</td>\n",
       "      <td>-159.3190</td>\n",
       "      <td>77.11</td>\n",
       "      <td>81</td>\n",
       "      <td>15</td>\n",
       "      <td>4.00</td>\n",
       "      <td>US</td>\n",
       "      <td>2022-07-19 04:58:16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>15</td>\n",
       "      <td>Sao Filipe</td>\n",
       "      <td>14.8961</td>\n",
       "      <td>-24.4956</td>\n",
       "      <td>75.07</td>\n",
       "      <td>83</td>\n",
       "      <td>97</td>\n",
       "      <td>9.46</td>\n",
       "      <td>CV</td>\n",
       "      <td>2022-07-19 04:58:17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>16</td>\n",
       "      <td>Charleston</td>\n",
       "      <td>32.7766</td>\n",
       "      <td>-79.9309</td>\n",
       "      <td>83.01</td>\n",
       "      <td>85</td>\n",
       "      <td>0</td>\n",
       "      <td>9.22</td>\n",
       "      <td>US</td>\n",
       "      <td>2022-07-19 04:55:25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>22</td>\n",
       "      <td>Bato</td>\n",
       "      <td>13.3528</td>\n",
       "      <td>123.3677</td>\n",
       "      <td>84.09</td>\n",
       "      <td>79</td>\n",
       "      <td>65</td>\n",
       "      <td>4.99</td>\n",
       "      <td>PH</td>\n",
       "      <td>2022-07-19 04:58:20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>24</td>\n",
       "      <td>Tautira</td>\n",
       "      <td>-17.7333</td>\n",
       "      <td>-149.1500</td>\n",
       "      <td>76.24</td>\n",
       "      <td>72</td>\n",
       "      <td>18</td>\n",
       "      <td>2.71</td>\n",
       "      <td>PF</td>\n",
       "      <td>2022-07-19 04:58:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>25</td>\n",
       "      <td>Itarema</td>\n",
       "      <td>-2.9248</td>\n",
       "      <td>-39.9167</td>\n",
       "      <td>76.14</td>\n",
       "      <td>82</td>\n",
       "      <td>6</td>\n",
       "      <td>13.15</td>\n",
       "      <td>BR</td>\n",
       "      <td>2022-07-19 04:58:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>30</td>\n",
       "      <td>Isangel</td>\n",
       "      <td>-19.5500</td>\n",
       "      <td>169.2667</td>\n",
       "      <td>79.30</td>\n",
       "      <td>75</td>\n",
       "      <td>74</td>\n",
       "      <td>9.01</td>\n",
       "      <td>VU</td>\n",
       "      <td>2022-07-19 04:58:23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>40</td>\n",
       "      <td>Luganville</td>\n",
       "      <td>-15.5333</td>\n",
       "      <td>167.1667</td>\n",
       "      <td>81.37</td>\n",
       "      <td>73</td>\n",
       "      <td>20</td>\n",
       "      <td>9.17</td>\n",
       "      <td>VU</td>\n",
       "      <td>2022-07-19 04:58:28</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    City_ID           City      Lat       Lng  Max Temp  Humidity  Cloudiness  \\\n",
       "0         0  Jaisinghnagar  23.7000   81.3833     76.23        84          92   \n",
       "1         1    Hot Springs  34.5037  -93.0552     82.40        83          27   \n",
       "13       13          Kapaa  22.0752 -159.3190     77.11        81          15   \n",
       "15       15     Sao Filipe  14.8961  -24.4956     75.07        83          97   \n",
       "16       16     Charleston  32.7766  -79.9309     83.01        85           0   \n",
       "22       22           Bato  13.3528  123.3677     84.09        79          65   \n",
       "24       24        Tautira -17.7333 -149.1500     76.24        72          18   \n",
       "25       25        Itarema  -2.9248  -39.9167     76.14        82           6   \n",
       "30       30        Isangel -19.5500  169.2667     79.30        75          74   \n",
       "40       40     Luganville -15.5333  167.1667     81.37        73          20   \n",
       "\n",
       "    Wind Speed Country                 Date  \n",
       "0         7.74      IN  2022-07-18 23:17:44  \n",
       "1         1.99      US  2022-07-19 04:58:10  \n",
       "13        4.00      US  2022-07-19 04:58:16  \n",
       "15        9.46      CV  2022-07-19 04:58:17  \n",
       "16        9.22      US  2022-07-19 04:55:25  \n",
       "22        4.99      PH  2022-07-19 04:58:20  \n",
       "24        2.71      PF  2022-07-19 04:58:21  \n",
       "25       13.15      BR  2022-07-19 04:58:21  \n",
       "30        9.01      VU  2022-07-19 04:58:23  \n",
       "40        9.17      VU  2022-07-19 04:58:28  "
      ]
     },
     "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,
   "id": "0d1495a7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "City_ID       0\n",
       "City          0\n",
       "Lat           0\n",
       "Lng           0\n",
       "Max Temp      0\n",
       "Humidity      0\n",
       "Cloudiness    0\n",
       "Wind Speed    0\n",
       "Country       0\n",
       "Date          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preferred_cities_df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "5ca67111",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "City_ID       179\n",
       "City          179\n",
       "Lat           179\n",
       "Lng           179\n",
       "Max Temp      179\n",
       "Humidity      179\n",
       "Cloudiness    179\n",
       "Wind Speed    179\n",
       "Country       179\n",
       "Date          179\n",
       "dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "preferred_cities_df.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "d2ab970f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "City_ID       179\n",
       "City          179\n",
       "Lat           179\n",
       "Lng           179\n",
       "Max Temp      179\n",
       "Humidity      179\n",
       "Cloudiness    179\n",
       "Wind Speed    179\n",
       "Country       179\n",
       "Date          179\n",
       "dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preferred_cities_df.notnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "77e5487b",
   "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>0</th>\n",
       "      <td>Jaisinghnagar</td>\n",
       "      <td>IN</td>\n",
       "      <td>76.23</td>\n",
       "      <td>23.7000</td>\n",
       "      <td>81.3833</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Hot Springs</td>\n",
       "      <td>US</td>\n",
       "      <td>82.40</td>\n",
       "      <td>34.5037</td>\n",
       "      <td>-93.0552</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Kapaa</td>\n",
       "      <td>US</td>\n",
       "      <td>77.11</td>\n",
       "      <td>22.0752</td>\n",
       "      <td>-159.3190</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Sao Filipe</td>\n",
       "      <td>CV</td>\n",
       "      <td>75.07</td>\n",
       "      <td>14.8961</td>\n",
       "      <td>-24.4956</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Charleston</td>\n",
       "      <td>US</td>\n",
       "      <td>83.01</td>\n",
       "      <td>32.7766</td>\n",
       "      <td>-79.9309</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Bato</td>\n",
       "      <td>PH</td>\n",
       "      <td>84.09</td>\n",
       "      <td>13.3528</td>\n",
       "      <td>123.3677</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Tautira</td>\n",
       "      <td>PF</td>\n",
       "      <td>76.24</td>\n",
       "      <td>-17.7333</td>\n",
       "      <td>-149.1500</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Itarema</td>\n",
       "      <td>BR</td>\n",
       "      <td>76.14</td>\n",
       "      <td>-2.9248</td>\n",
       "      <td>-39.9167</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Isangel</td>\n",
       "      <td>VU</td>\n",
       "      <td>79.30</td>\n",
       "      <td>-19.5500</td>\n",
       "      <td>169.2667</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>Luganville</td>\n",
       "      <td>VU</td>\n",
       "      <td>81.37</td>\n",
       "      <td>-15.5333</td>\n",
       "      <td>167.1667</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             City Country  Max Temp      Lat       Lng Hotel Name\n",
       "0   Jaisinghnagar      IN     76.23  23.7000   81.3833           \n",
       "1     Hot Springs      US     82.40  34.5037  -93.0552           \n",
       "13          Kapaa      US     77.11  22.0752 -159.3190           \n",
       "15     Sao Filipe      CV     75.07  14.8961  -24.4956           \n",
       "16     Charleston      US     83.01  32.7766  -79.9309           \n",
       "22           Bato      PH     84.09  13.3528  123.3677           \n",
       "24        Tautira      PF     76.24 -17.7333 -149.1500           \n",
       "25        Itarema      BR     76.14  -2.9248  -39.9167           \n",
       "30        Isangel      VU     79.30 -19.5500  169.2667           \n",
       "40     Luganville      VU     81.37 -15.5333  167.1667           "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# MODULE 6.5.4\n",
    "\n",
    "# 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)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "1026499f",
   "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"
     ]
    },
    {
     "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>0</th>\n",
       "      <td>Jaisinghnagar</td>\n",
       "      <td>IN</td>\n",
       "      <td>76.23</td>\n",
       "      <td>23.7000</td>\n",
       "      <td>81.3833</td>\n",
       "      <td>Sonu Up Dhawa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Hot Springs</td>\n",
       "      <td>US</td>\n",
       "      <td>82.40</td>\n",
       "      <td>34.5037</td>\n",
       "      <td>-93.0552</td>\n",
       "      <td>Embassy Suites by Hilton Hot Springs Hotel &amp; Spa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Kapaa</td>\n",
       "      <td>US</td>\n",
       "      <td>77.11</td>\n",
       "      <td>22.0752</td>\n",
       "      <td>-159.3190</td>\n",
       "      <td>Sheraton Kauai Coconut Beach Resort</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Sao Filipe</td>\n",
       "      <td>CV</td>\n",
       "      <td>75.07</td>\n",
       "      <td>14.8961</td>\n",
       "      <td>-24.4956</td>\n",
       "      <td>Hotel Xaguate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Charleston</td>\n",
       "      <td>US</td>\n",
       "      <td>83.01</td>\n",
       "      <td>32.7766</td>\n",
       "      <td>-79.9309</td>\n",
       "      <td>The Mills House Wyndham Grand Hotel</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Bato</td>\n",
       "      <td>PH</td>\n",
       "      <td>84.09</td>\n",
       "      <td>13.3528</td>\n",
       "      <td>123.3677</td>\n",
       "      <td>Casa de Piedra Pension House</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Tautira</td>\n",
       "      <td>PF</td>\n",
       "      <td>76.24</td>\n",
       "      <td>-17.7333</td>\n",
       "      <td>-149.1500</td>\n",
       "      <td>Pension Oaoa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Itarema</td>\n",
       "      <td>BR</td>\n",
       "      <td>76.14</td>\n",
       "      <td>-2.9248</td>\n",
       "      <td>-39.9167</td>\n",
       "      <td>Sea Lounge! Bar, Club &amp; Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Isangel</td>\n",
       "      <td>VU</td>\n",
       "      <td>79.30</td>\n",
       "      <td>-19.5500</td>\n",
       "      <td>169.2667</td>\n",
       "      <td>Tanna Lodge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>Luganville</td>\n",
       "      <td>VU</td>\n",
       "      <td>81.37</td>\n",
       "      <td>-15.5333</td>\n",
       "      <td>167.1667</td>\n",
       "      <td>The Espiritu Hotel</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             City Country  Max Temp      Lat       Lng  \\\n",
       "0   Jaisinghnagar      IN     76.23  23.7000   81.3833   \n",
       "1     Hot Springs      US     82.40  34.5037  -93.0552   \n",
       "13          Kapaa      US     77.11  22.0752 -159.3190   \n",
       "15     Sao Filipe      CV     75.07  14.8961  -24.4956   \n",
       "16     Charleston      US     83.01  32.7766  -79.9309   \n",
       "22           Bato      PH     84.09  13.3528  123.3677   \n",
       "24        Tautira      PF     76.24 -17.7333 -149.1500   \n",
       "25        Itarema      BR     76.14  -2.9248  -39.9167   \n",
       "30        Isangel      VU     79.30 -19.5500  169.2667   \n",
       "40     Luganville      VU     81.37 -15.5333  167.1667   \n",
       "\n",
       "                                          Hotel Name  \n",
       "0                                      Sonu Up Dhawa  \n",
       "1   Embassy Suites by Hilton Hot Springs Hotel & Spa  \n",
       "13               Sheraton Kauai Coconut Beach Resort  \n",
       "15                                     Hotel Xaguate  \n",
       "16               The Mills House Wyndham Grand Hotel  \n",
       "22                      Casa de Piedra Pension House  \n",
       "24                                      Pension Oaoa  \n",
       "25                Sea Lounge! Bar, Club & Restaurant  \n",
       "30                                       Tanna Lodge  \n",
       "40                                The Espiritu Hotel  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Set parameters to search for a hotel.\n",
    "params = {\n",
    "    \"radius\": 5000,\n",
    "    \"type\": \"lodging\",\n",
    "    \"key\": g_key\n",
    "}\n",
    "\n",
    "# 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",
    "    \n",
    "    # Make request and get the JSON data from the search.\n",
    "    hotels = requests.get(base_url, params=params).json()\n",
    "    \n",
    "    # Grab the first hotel from the results and store the name.\n",
    "    #hotel_df[index, \"Hotel Name\"] = hotels[\"results\"][0][\"name\"]\n",
    "    \n",
    "    try:\n",
    "        hotel_df.loc[index, \"Hotel Name\"] = hotels[\"results\"][0][\"name\"]\n",
    "    except (IndexError):\n",
    "        print(\"Hotel not found... skipping.\")\n",
    "\n",
    "hotel_df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "d2c5d25e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f5d7b575847944daab4426b48e5c134e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Figure(layout=FigureLayout(height='420px'))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "info_box_template = \"\"\"\n",
    "<dl>\n",
    "<dt>Hotel Name</dt><dd>{Hotel Name}</dd>\n",
    "<dt>City</dt><dd>{City}</dd>\n",
    "<dt>Country</dt><dd>{Country}</dd>\n",
    "<dt>Max Temp</dt><dd>{Max Temp} °F</dd>\n",
    "</dl>\n",
    "\"\"\"\n",
    "\n",
    "# Store the DataFrame Row.\n",
    "hotel_info = [info_box_template.format(**row) for index, row in hotel_df.iterrows()]\n",
    "\n",
    "# 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, info_box_content=hotel_info)\n",
    "fig.add_layer(heat_layer)\n",
    "fig.add_layer(marker_layer)\n",
    "# Call the figure to plot the data.\n",
    "fig\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "39e04bad",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "637d7049",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "cbfbe168",
   "metadata": {},
   "source": [
    "##### "
   ]
  }
 ],
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