{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# VacationPy\n",
"----\n",
"\n",
"#### Note\n",
"* Instructions have been included for each segment. You do not have to follow them exactly, but they are included to help you think through the steps."
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [],
"source": [
"# Dependencies and Setup\n",
"import matplotlib.pyplot as plt\n",
"import pandas as pd\n",
"import numpy as np\n",
"import requests\n",
"import gmaps\n",
"import os\n",
"\n",
"# Import API key\n",
"from api_keys import g_key"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Store Part I results into DataFrame\n",
"* Load the csv exported in Part I to a DataFrame"
]
},
{
"cell_type": "code",
"execution_count": 33,
"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>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>kapaa</td>\n",
" <td>22.08</td>\n",
" <td>-159.32</td>\n",
" <td>78.01</td>\n",
" <td>94.0</td>\n",
" <td>75.0</td>\n",
" <td>23.04</td>\n",
" <td>US</td>\n",
" <td>1.592599e+09</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>hilo</td>\n",
" <td>19.73</td>\n",
" <td>-155.09</td>\n",
" <td>80.60</td>\n",
" <td>61.0</td>\n",
" <td>75.0</td>\n",
" <td>7.25</td>\n",
" <td>US</td>\n",
" <td>1.592599e+09</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>bredasdorp</td>\n",
" <td>-34.53</td>\n",
" <td>20.04</td>\n",
" <td>50.00</td>\n",
" <td>87.0</td>\n",
" <td>0.0</td>\n",
" <td>5.23</td>\n",
" <td>ZA</td>\n",
" <td>1.592599e+09</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>albany</td>\n",
" <td>42.60</td>\n",
" <td>-73.97</td>\n",
" <td>87.01</td>\n",
" <td>52.0</td>\n",
" <td>56.0</td>\n",
" <td>4.65</td>\n",
" <td>US</td>\n",
" <td>1.592600e+09</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>itoman</td>\n",
" <td>26.12</td>\n",
" <td>127.67</td>\n",
" <td>82.40</td>\n",
" <td>94.0</td>\n",
" <td>75.0</td>\n",
" <td>11.41</td>\n",
" <td>JP</td>\n",
" <td>1.592600e+09</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" City Lat lng Max Temp Humidity Cloudiness Wind Speed \\\n",
"0 kapaa 22.08 -159.32 78.01 94.0 75.0 23.04 \n",
"1 hilo 19.73 -155.09 80.60 61.0 75.0 7.25 \n",
"2 bredasdorp -34.53 20.04 50.00 87.0 0.0 5.23 \n",
"3 albany 42.60 -73.97 87.01 52.0 56.0 4.65 \n",
"4 itoman 26.12 127.67 82.40 94.0 75.0 11.41 \n",
"\n",
" Country Date \n",
"0 US 1.592599e+09 \n",
"1 US 1.592599e+09 \n",
"2 ZA 1.592599e+09 \n",
"3 US 1.592600e+09 \n",
"4 JP 1.592600e+09 "
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"weather_csv_file = \"../WeatherPy/output_data/city_weather_data.csv\"\n",
"\n",
"weather_df = pd.read_csv(weather_csv_file)\n",
"\n",
"weather_df.head()\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Humidity Heatmap\n",
"* Configure gmaps.\n",
"* Use the Lat and Lng as locations and Humidity as the weight.\n",
"* Add Heatmap layer to map."
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "9fe2951cbba0450081e620aa924b3fc3",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Figure(layout=FigureLayout(height='420px'))"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Configure gmaps with API key.\n",
"gmaps.configure(api_key=g_key)\n",
"\n",
"# Convert Humidity to float and store\n",
"# Also, handle NaN values\n",
"weather_df = weather_df.dropna()\n",
"humidity = weather_df[\"Humidity\"].astype(float)\n",
"\n",
"# Store 'Latitude' and 'Longitude' into locations. \n",
"locations = weather_df[[\"Lat\", \"lng\"]].astype(float)\n",
"\n",
"# Create a humidity Heatmap layer\n",
"# Plot Heatmap\n",
"fig = gmaps.figure(center = [0,0] ,zoom_level = 2)\n",
"# Create and add heat layer \n",
"heat_layer = gmaps.heatmap_layer(locations, weights=humidity,\n",
" dissipating=False, max_intensity=100,\n",
" point_radius = 4)\n",
"fig.add_layer(heat_layer)\n",
"#Display figure\n",
"fig"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Create new DataFrame fitting weather criteria\n",
"* Narrow down the cities to fit weather conditions.\n",
"* Drop any rows will null values."
]
},
{
"cell_type": "code",
"execution_count": 35,
"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>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>54</th>\n",
" <td>sao felix do xingu</td>\n",
" <td>-6.64</td>\n",
" <td>-51.99</td>\n",
" <td>78.24</td>\n",
" <td>70.0</td>\n",
" <td>0.0</td>\n",
" <td>3.18</td>\n",
" <td>BR</td>\n",
" <td>1.592599e+09</td>\n",
" </tr>\n",
" <tr>\n",
" <th>99</th>\n",
" <td>deputatskiy</td>\n",
" <td>69.30</td>\n",
" <td>139.90</td>\n",
" <td>73.22</td>\n",
" <td>33.0</td>\n",
" <td>0.0</td>\n",
" <td>3.33</td>\n",
" <td>RU</td>\n",
" <td>1.592599e+09</td>\n",
" </tr>\n",
" <tr>\n",
" <th>174</th>\n",
" <td>ilebo</td>\n",
" <td>-4.32</td>\n",
" <td>20.58</td>\n",
" <td>70.11</td>\n",
" <td>51.0</td>\n",
" <td>0.0</td>\n",
" <td>1.45</td>\n",
" <td>CD</td>\n",
" <td>1.592600e+09</td>\n",
" </tr>\n",
" <tr>\n",
" <th>293</th>\n",
" <td>ilhabela</td>\n",
" <td>-23.78</td>\n",
" <td>-45.36</td>\n",
" <td>72.99</td>\n",
" <td>71.0</td>\n",
" <td>0.0</td>\n",
" <td>4.74</td>\n",
" <td>BR</td>\n",
" <td>1.592600e+09</td>\n",
" </tr>\n",
" <tr>\n",
" <th>337</th>\n",
" <td>astara</td>\n",
" <td>38.50</td>\n",
" <td>48.67</td>\n",
" <td>71.60</td>\n",
" <td>60.0</td>\n",
" <td>0.0</td>\n",
" <td>4.70</td>\n",
" <td>AZ</td>\n",
" <td>1.592600e+09</td>\n",
" </tr>\n",
" <tr>\n",
" <th>385</th>\n",
" <td>bisceglie</td>\n",
" <td>41.24</td>\n",
" <td>16.50</td>\n",
" <td>72.00</td>\n",
" <td>77.0</td>\n",
" <td>0.0</td>\n",
" <td>5.82</td>\n",
" <td>IT</td>\n",
" <td>1.592600e+09</td>\n",
" </tr>\n",
" <tr>\n",
" <th>386</th>\n",
" <td>darnah</td>\n",
" <td>32.77</td>\n",
" <td>22.64</td>\n",
" <td>73.83</td>\n",
" <td>57.0</td>\n",
" <td>0.0</td>\n",
" <td>8.32</td>\n",
" <td>LY</td>\n",
" <td>1.592600e+09</td>\n",
" </tr>\n",
" <tr>\n",
" <th>395</th>\n",
" <td>san nicolas</td>\n",
" <td>18.17</td>\n",
" <td>120.60</td>\n",
" <td>79.74</td>\n",
" <td>78.0</td>\n",
" <td>0.0</td>\n",
" <td>4.45</td>\n",
" <td>PH</td>\n",
" <td>1.592600e+09</td>\n",
" </tr>\n",
" <tr>\n",
" <th>412</th>\n",
" <td>soyo</td>\n",
" <td>-6.13</td>\n",
" <td>12.37</td>\n",
" <td>73.40</td>\n",
" <td>89.0</td>\n",
" <td>0.0</td>\n",
" <td>5.82</td>\n",
" <td>AO</td>\n",
" <td>1.592600e+09</td>\n",
" </tr>\n",
" <tr>\n",
" <th>427</th>\n",
" <td>ios</td>\n",
" <td>36.73</td>\n",
" <td>25.28</td>\n",
" <td>73.00</td>\n",
" <td>60.0</td>\n",
" <td>0.0</td>\n",
" <td>4.70</td>\n",
" <td>GR</td>\n",
" <td>1.592600e+09</td>\n",
" </tr>\n",
" <tr>\n",
" <th>429</th>\n",
" <td>kitob</td>\n",
" <td>39.08</td>\n",
" <td>66.83</td>\n",
" <td>72.97</td>\n",
" <td>21.0</td>\n",
" <td>0.0</td>\n",
" <td>5.57</td>\n",
" <td>UZ</td>\n",
" <td>1.592600e+09</td>\n",
" </tr>\n",
" <tr>\n",
" <th>441</th>\n",
" <td>santa isabel</td>\n",
" <td>-23.32</td>\n",
" <td>-46.22</td>\n",
" <td>78.80</td>\n",
" <td>44.0</td>\n",
" <td>0.0</td>\n",
" <td>8.05</td>\n",
" <td>BR</td>\n",
" <td>1.592600e+09</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" City Lat lng Max Temp Humidity Cloudiness \\\n",
"54 sao felix do xingu -6.64 -51.99 78.24 70.0 0.0 \n",
"99 deputatskiy 69.30 139.90 73.22 33.0 0.0 \n",
"174 ilebo -4.32 20.58 70.11 51.0 0.0 \n",
"293 ilhabela -23.78 -45.36 72.99 71.0 0.0 \n",
"337 astara 38.50 48.67 71.60 60.0 0.0 \n",
"385 bisceglie 41.24 16.50 72.00 77.0 0.0 \n",
"386 darnah 32.77 22.64 73.83 57.0 0.0 \n",
"395 san nicolas 18.17 120.60 79.74 78.0 0.0 \n",
"412 soyo -6.13 12.37 73.40 89.0 0.0 \n",
"427 ios 36.73 25.28 73.00 60.0 0.0 \n",
"429 kitob 39.08 66.83 72.97 21.0 0.0 \n",
"441 santa isabel -23.32 -46.22 78.80 44.0 0.0 \n",
"\n",
" Wind Speed Country Date \n",
"54 3.18 BR 1.592599e+09 \n",
"99 3.33 RU 1.592599e+09 \n",
"174 1.45 CD 1.592600e+09 \n",
"293 4.74 BR 1.592600e+09 \n",
"337 4.70 AZ 1.592600e+09 \n",
"385 5.82 IT 1.592600e+09 \n",
"386 8.32 LY 1.592600e+09 \n",
"395 4.45 PH 1.592600e+09 \n",
"412 5.82 AO 1.592600e+09 \n",
"427 4.70 GR 1.592600e+09 \n",
"429 5.57 UZ 1.592600e+09 \n",
"441 8.05 BR 1.592600e+09 "
]
},
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Narrow down the DataFrame to find your ideal weather condition.\n",
"filtered_weather_df = weather_df\n",
"\n",
"# Drop any rows that don't contain all three conditions. Want to be sure the weather is ideal.\n",
"\n",
"# A max temperature lower than 80 degrees but higher than 70.\n",
"filtered_weather_df = filtered_weather_df.loc[(filtered_weather_df[\"Max Temp\"] < 80) & (filtered_weather_df[\"Max Temp\"] > 70)]\n",
"\n",
"# Wind speed less than 10 mph.\n",
"filtered_weather_df = filtered_weather_df.loc[filtered_weather_df[\"Wind Speed\"] < 10]\n",
"\n",
"# Zero cloudiness.\n",
"filtered_weather_df = filtered_weather_df.loc[filtered_weather_df[\"Cloudiness\"] == 0]\n",
"\n",
"# Drop any rows with null values\n",
"filtered_weather_df = filtered_weather_df.dropna()\n",
"\n",
"filtered_weather_df\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Hotel Map\n",
"* Store into variable named `hotel_df`.\n",
"* Add a \"Hotel Name\" column to the DataFrame.\n",
"* Set parameters to search for hotels with 5000 meters.\n",
"* Hit the Google Places API for each city's coordinates.\n",
"* Store the first Hotel result into the DataFrame.\n",
"* Plot markers on top of the heatmap."
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Retrieving Results for Index 54: sao felix do xingu.\n",
"Missing field/result... skipping.\n",
"------------\n",
"Retrieving Results for Index 99: deputatskiy.\n",
"Closest hotel is Baza Otdykha.\n",
"------------\n",
"Retrieving Results for Index 174: ilebo.\n",
"Closest hotel is Hôtel des palmes.\n",
"------------\n",
"Retrieving Results for Index 293: ilhabela.\n",
"Missing field/result... skipping.\n",
"------------\n",
"Retrieving Results for Index 337: astara.\n",
"Missing field/result... skipping.\n",
"------------\n",
"Retrieving Results for Index 385: bisceglie.\n",
"Missing field/result... skipping.\n",
"------------\n",
"Retrieving Results for Index 386: darnah.\n",
"Missing field/result... skipping.\n",
"------------\n",
"Retrieving Results for Index 395: san nicolas.\n",
"Closest hotel is NORTHVIEW Hotel.\n",
"------------\n",
"Retrieving Results for Index 412: soyo.\n",
"Missing field/result... skipping.\n",
"------------\n",
"Retrieving Results for Index 427: ios.\n",
"Closest hotel is Ios Palace Hotel & Spa.\n",
"------------\n",
"Retrieving Results for Index 429: kitob.\n",
"Closest hotel is ULUG'BEK Hotel&Spa.\n",
"------------\n",
"Retrieving Results for Index 441: santa isabel.\n",
"Closest hotel is Chácara Sombra do Altíssimo.\n",
"------------\n"
]
}
],
"source": [
"hotel_df = filtered_weather_df\n",
"\n",
"# params dictionary to update each iteration\n",
"params = {\n",
" \"radius\": 5000,\n",
" \"types\": \"lodging\",\n",
" \"key\": g_key\n",
"}\n",
"\n",
"\n",
"for index, row in hotel_df.iterrows():\n",
" # get lat, lng from df\n",
" lat = row[\"Lat\"]\n",
" lng = row[\"lng\"]\n",
"\n",
" # change location each iteration while leaving original params in place\n",
" params[\"location\"] = f\"{lat},{lng}\"\n",
" \n",
" base_url = \"https://maps.googleapis.com/maps/api/place/nearbysearch/json\"\n",
"\n",
" # assemble url and make API request\n",
" print(f\"Retrieving Results for Index {index}: {row['City']}.\")\n",
" response = requests.get(base_url, params=params).json()\n",
" # print(json.dumps(response, indent=4, sort_keys=True))\n",
" \n",
" # extract results\n",
" results = response['results']\n",
" \n",
" try:\n",
" print(f\"Closest hotel is {results[0]['name']}.\")\n",
" hotel_df.loc[index, 'Hotel Name'] = results[0]['name']\n",
" \n",
" except (KeyError, IndexError):\n",
" print(\"Missing field/result... skipping.\")\n",
" \n",
" print(\"------------\")"
]
},
{
"cell_type": "code",
"execution_count": 37,
"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>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",
" <th>Hotel Name</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>54</th>\n",
" <td>sao felix do xingu</td>\n",
" <td>-6.64</td>\n",
" <td>-51.99</td>\n",
" <td>78.24</td>\n",
" <td>70.0</td>\n",
" <td>0.0</td>\n",
" <td>3.18</td>\n",
" <td>BR</td>\n",
" <td>1.592599e+09</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>99</th>\n",
" <td>deputatskiy</td>\n",
" <td>69.30</td>\n",
" <td>139.90</td>\n",
" <td>73.22</td>\n",
" <td>33.0</td>\n",
" <td>0.0</td>\n",
" <td>3.33</td>\n",
" <td>RU</td>\n",
" <td>1.592599e+09</td>\n",
" <td>Baza Otdykha</td>\n",
" </tr>\n",
" <tr>\n",
" <th>174</th>\n",
" <td>ilebo</td>\n",
" <td>-4.32</td>\n",
" <td>20.58</td>\n",
" <td>70.11</td>\n",
" <td>51.0</td>\n",
" <td>0.0</td>\n",
" <td>1.45</td>\n",
" <td>CD</td>\n",
" <td>1.592600e+09</td>\n",
" <td>Hôtel des palmes</td>\n",
" </tr>\n",
" <tr>\n",
" <th>293</th>\n",
" <td>ilhabela</td>\n",
" <td>-23.78</td>\n",
" <td>-45.36</td>\n",
" <td>72.99</td>\n",
" <td>71.0</td>\n",
" <td>0.0</td>\n",
" <td>4.74</td>\n",
" <td>BR</td>\n",
" <td>1.592600e+09</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>337</th>\n",
" <td>astara</td>\n",
" <td>38.50</td>\n",
" <td>48.67</td>\n",
" <td>71.60</td>\n",
" <td>60.0</td>\n",
" <td>0.0</td>\n",
" <td>4.70</td>\n",
" <td>AZ</td>\n",
" <td>1.592600e+09</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>385</th>\n",
" <td>bisceglie</td>\n",
" <td>41.24</td>\n",
" <td>16.50</td>\n",
" <td>72.00</td>\n",
" <td>77.0</td>\n",
" <td>0.0</td>\n",
" <td>5.82</td>\n",
" <td>IT</td>\n",
" <td>1.592600e+09</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>386</th>\n",
" <td>darnah</td>\n",
" <td>32.77</td>\n",
" <td>22.64</td>\n",
" <td>73.83</td>\n",
" <td>57.0</td>\n",
" <td>0.0</td>\n",
" <td>8.32</td>\n",
" <td>LY</td>\n",
" <td>1.592600e+09</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>395</th>\n",
" <td>san nicolas</td>\n",
" <td>18.17</td>\n",
" <td>120.60</td>\n",
" <td>79.74</td>\n",
" <td>78.0</td>\n",
" <td>0.0</td>\n",
" <td>4.45</td>\n",
" <td>PH</td>\n",
" <td>1.592600e+09</td>\n",
" <td>NORTHVIEW Hotel</td>\n",
" </tr>\n",
" <tr>\n",
" <th>412</th>\n",
" <td>soyo</td>\n",
" <td>-6.13</td>\n",
" <td>12.37</td>\n",
" <td>73.40</td>\n",
" <td>89.0</td>\n",
" <td>0.0</td>\n",
" <td>5.82</td>\n",
" <td>AO</td>\n",
" <td>1.592600e+09</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>427</th>\n",
" <td>ios</td>\n",
" <td>36.73</td>\n",
" <td>25.28</td>\n",
" <td>73.00</td>\n",
" <td>60.0</td>\n",
" <td>0.0</td>\n",
" <td>4.70</td>\n",
" <td>GR</td>\n",
" <td>1.592600e+09</td>\n",
" <td>Ios Palace Hotel & Spa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>429</th>\n",
" <td>kitob</td>\n",
" <td>39.08</td>\n",
" <td>66.83</td>\n",
" <td>72.97</td>\n",
" <td>21.0</td>\n",
" <td>0.0</td>\n",
" <td>5.57</td>\n",
" <td>UZ</td>\n",
" <td>1.592600e+09</td>\n",
" <td>ULUG'BEK Hotel&Spa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>441</th>\n",
" <td>santa isabel</td>\n",
" <td>-23.32</td>\n",
" <td>-46.22</td>\n",
" <td>78.80</td>\n",
" <td>44.0</td>\n",
" <td>0.0</td>\n",
" <td>8.05</td>\n",
" <td>BR</td>\n",
" <td>1.592600e+09</td>\n",
" <td>Chácara Sombra do Altíssimo</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" City Lat lng Max Temp Humidity Cloudiness \\\n",
"54 sao felix do xingu -6.64 -51.99 78.24 70.0 0.0 \n",
"99 deputatskiy 69.30 139.90 73.22 33.0 0.0 \n",
"174 ilebo -4.32 20.58 70.11 51.0 0.0 \n",
"293 ilhabela -23.78 -45.36 72.99 71.0 0.0 \n",
"337 astara 38.50 48.67 71.60 60.0 0.0 \n",
"385 bisceglie 41.24 16.50 72.00 77.0 0.0 \n",
"386 darnah 32.77 22.64 73.83 57.0 0.0 \n",
"395 san nicolas 18.17 120.60 79.74 78.0 0.0 \n",
"412 soyo -6.13 12.37 73.40 89.0 0.0 \n",
"427 ios 36.73 25.28 73.00 60.0 0.0 \n",
"429 kitob 39.08 66.83 72.97 21.0 0.0 \n",
"441 santa isabel -23.32 -46.22 78.80 44.0 0.0 \n",
"\n",
" Wind Speed Country Date Hotel Name \n",
"54 3.18 BR 1.592599e+09 NaN \n",
"99 3.33 RU 1.592599e+09 Baza Otdykha \n",
"174 1.45 CD 1.592600e+09 Hôtel des palmes \n",
"293 4.74 BR 1.592600e+09 NaN \n",
"337 4.70 AZ 1.592600e+09 NaN \n",
"385 5.82 IT 1.592600e+09 NaN \n",
"386 8.32 LY 1.592600e+09 NaN \n",
"395 4.45 PH 1.592600e+09 NORTHVIEW Hotel \n",
"412 5.82 AO 1.592600e+09 NaN \n",
"427 4.70 GR 1.592600e+09 Ios Palace Hotel & Spa \n",
"429 5.57 UZ 1.592600e+09 ULUG'BEK Hotel&Spa \n",
"441 8.05 BR 1.592600e+09 Chácara Sombra do Altíssimo "
]
},
"execution_count": 37,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"hotel_df"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [],
"source": [
"# NOTE: Do not change any of the code in this cell\n",
"locations = hotel_df[[\"Lat\", \"lng\"]]\n",
"markers = gmaps.marker_layer(locations)\n",
"\n",
"# Using the template add the hotel marks to the heatmap\n",
"info_box_template = \"\"\"\n",
"<dl>\n",
"<dt>Name</dt><dd>{Hotel Name}</dd>\n",
"<dt>City</dt><dd>{City}</dd>\n",
"<dt>Country</dt><dd>{Country}</dd>\n",
"</dl>\n",
"\"\"\"\n",
"# Store the DataFrame Row\n",
"# NOTE: be sure to update with your DataFrame name\n",
"hotel_info = [info_box_template.format(**row) for index, row in hotel_df.iterrows()]\n"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "9fe2951cbba0450081e620aa924b3fc3",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Figure(layout=FigureLayout(height='420px'))"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Add marker layer ontop of heat map\n",
"hotel_layer = gmaps.symbol_layer(\n",
" locations, fill_color='rgba(0, 150, 0, 0.4)',\n",
" stroke_color='rgba(0, 0, 150, 0.4)', scale=2,\n",
" info_box_content=hotel_info\n",
")\n",
"\n",
"# Display figure\n",
"fig.add_layer(markers)\n",
"fig.add_layer(hotel_layer)\n",
"fig\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
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"source": []
}
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