VacationPy¶

Note¶

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.

In [32]:

# Dependencies and Setup
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import requests
import gmaps
import os

# Import API key
from api_keys import g_key

Store Part I results into DataFrame¶

Load the csv exported in Part I to a DataFrame

In [33]:

weather_csv_file = "../WeatherPy/output_data/city_weather_data.csv"

weather_df = pd.read_csv(weather_csv_file)

weather_df.head()

Out[33]:

	City	Lat	lng	Max Temp	Humidity	Cloudiness	Wind Speed	Country	Date
0	kapaa	22.08	-159.32	78.01	94.0	75.0	23.04	US	1.592599e+09
1	hilo	19.73	-155.09	80.60	61.0	75.0	7.25	US	1.592599e+09
2	bredasdorp	-34.53	20.04	50.00	87.0	0.0	5.23	ZA	1.592599e+09
3	albany	42.60	-73.97	87.01	52.0	56.0	4.65	US	1.592600e+09
4	itoman	26.12	127.67	82.40	94.0	75.0	11.41	JP	1.592600e+09

Humidity Heatmap¶

Configure gmaps.
Use the Lat and Lng as locations and Humidity as the weight.
Add Heatmap layer to map.

In [34]:

# Configure gmaps with API key.
gmaps.configure(api_key=g_key)

# Convert Humidity to float and store
# Also, handle NaN values
weather_df = weather_df.dropna()
humidity = weather_df["Humidity"].astype(float)

# Store 'Latitude' and 'Longitude' into  locations. 
locations = weather_df[["Lat", "lng"]].astype(float)

# Create a humidity Heatmap layer
# Plot Heatmap
fig = gmaps.figure(center = [0,0] ,zoom_level = 2)
# Create and add heat layer 
heat_layer = gmaps.heatmap_layer(locations, weights=humidity,
                               dissipating=False, max_intensity=100,
                               point_radius = 4)
fig.add_layer(heat_layer)
#Display figure
fig

Figure(layout=FigureLayout(height='420px'))

In [ ]:

Create new DataFrame fitting weather criteria¶

Narrow down the cities to fit weather conditions.
Drop any rows will null values.

In [35]:

# Narrow down the DataFrame to find your ideal weather condition.
filtered_weather_df = weather_df

# Drop any rows that don't contain all three conditions. Want to be sure the weather is ideal.

# A max temperature lower than 80 degrees but higher than 70.
filtered_weather_df = filtered_weather_df.loc[(filtered_weather_df["Max Temp"] < 80) & (filtered_weather_df["Max Temp"] > 70)]

# Wind speed less than 10 mph.
filtered_weather_df = filtered_weather_df.loc[filtered_weather_df["Wind Speed"] < 10]

# Zero cloudiness.
filtered_weather_df = filtered_weather_df.loc[filtered_weather_df["Cloudiness"] == 0]

# Drop any rows with null values
filtered_weather_df = filtered_weather_df.dropna()

filtered_weather_df

Out[35]:

	City	Lat	lng	Max Temp	Humidity	Wind Speed	Country	Date
54	sao felix do xingu	-6.64	-51.99	78.24	70.0	3.18	BR	1.592599e+09
99	deputatskiy	69.30	139.90	73.22	33.0	3.33	RU	1.592599e+09
174	ilebo	-4.32	20.58	70.11	51.0	1.45	CD	1.592600e+09
293	ilhabela	-23.78	-45.36	72.99	71.0	4.74	BR	1.592600e+09
337	astara	38.50	48.67	71.60	60.0	4.70	AZ	1.592600e+09
385	bisceglie	41.24	16.50	72.00	77.0	5.82	IT	1.592600e+09
386	darnah	32.77	22.64	73.83	57.0	8.32	LY	1.592600e+09
395	san nicolas	18.17	120.60	79.74	78.0	4.45	PH	1.592600e+09
412	soyo	-6.13	12.37	73.40	89.0	5.82	AO	1.592600e+09
427	ios	36.73	25.28	73.00	60.0	4.70	GR	1.592600e+09
429	kitob	39.08	66.83	72.97	21.0	5.57	UZ	1.592600e+09
441	santa isabel	-23.32	-46.22	78.80	44.0	8.05	BR	1.592600e+09

Hotel Map¶

Store into variable named hotel_df.
Add a "Hotel Name" column to the DataFrame.
Set parameters to search for hotels with 5000 meters.
Hit the Google Places API for each city's coordinates.
Store the first Hotel result into the DataFrame.
Plot markers on top of the heatmap.

In [36]:

hotel_df = filtered_weather_df

# params dictionary to update each iteration
params = {
    "radius": 5000,
    "types": "lodging",
    "key": g_key
}


for index, row in hotel_df.iterrows():
    # get lat, lng from df
    lat = row["Lat"]
    lng = row["lng"]

    # change location each iteration while leaving original params in place
    params["location"] = f"{lat},{lng}"
    
    base_url = "https://maps.googleapis.com/maps/api/place/nearbysearch/json"

    # assemble url and make API request
    print(f"Retrieving Results for Index {index}: {row['City']}.")
    response = requests.get(base_url, params=params).json()
    # print(json.dumps(response, indent=4, sort_keys=True))
    
    # extract results
    results = response['results']
    
    try:
        print(f"Closest hotel is {results[0]['name']}.")
        hotel_df.loc[index, 'Hotel Name'] = results[0]['name']
        
    except (KeyError, IndexError):
        print("Missing field/result... skipping.")
        
    print("------------")

Retrieving Results for Index 54: sao felix do xingu.
Missing field/result... skipping.
------------
Retrieving Results for Index 99: deputatskiy.
Closest hotel is Baza Otdykha.
------------
Retrieving Results for Index 174: ilebo.
Closest hotel is Hôtel des palmes.
------------
Retrieving Results for Index 293: ilhabela.
Missing field/result... skipping.
------------
Retrieving Results for Index 337: astara.
Missing field/result... skipping.
------------
Retrieving Results for Index 385: bisceglie.
Missing field/result... skipping.
------------
Retrieving Results for Index 386: darnah.
Missing field/result... skipping.
------------
Retrieving Results for Index 395: san nicolas.
Closest hotel is NORTHVIEW Hotel.
------------
Retrieving Results for Index 412: soyo.
Missing field/result... skipping.
------------
Retrieving Results for Index 427: ios.
Closest hotel is Ios Palace Hotel & Spa.
------------
Retrieving Results for Index 429: kitob.
Closest hotel is ULUG'BEK Hotel&Spa.
------------
Retrieving Results for Index 441: santa isabel.
Closest hotel is Chácara Sombra do Altíssimo.
------------

In [37]:

hotel_df

Out[37]:

	City	Lat	lng	Max Temp	Humidity	Wind Speed	Country	Date	Hotel Name
54	sao felix do xingu	-6.64	-51.99	78.24	70.0	3.18	BR	1.592599e+09	NaN
99	deputatskiy	69.30	139.90	73.22	33.0	3.33	RU	1.592599e+09	Baza Otdykha
174	ilebo	-4.32	20.58	70.11	51.0	1.45	CD	1.592600e+09	Hôtel des palmes
293	ilhabela	-23.78	-45.36	72.99	71.0	4.74	BR	1.592600e+09	NaN
337	astara	38.50	48.67	71.60	60.0	4.70	AZ	1.592600e+09	NaN
385	bisceglie	41.24	16.50	72.00	77.0	5.82	IT	1.592600e+09	NaN
386	darnah	32.77	22.64	73.83	57.0	8.32	LY	1.592600e+09	NaN
395	san nicolas	18.17	120.60	79.74	78.0	4.45	PH	1.592600e+09	NORTHVIEW Hotel
412	soyo	-6.13	12.37	73.40	89.0	5.82	AO	1.592600e+09	NaN
427	ios	36.73	25.28	73.00	60.0	4.70	GR	1.592600e+09	Ios Palace Hotel & Spa
429	kitob	39.08	66.83	72.97	21.0	5.57	UZ	1.592600e+09	ULUG'BEK Hotel&Spa
441	santa isabel	-23.32	-46.22	78.80	44.0	8.05	BR	1.592600e+09	Chácara Sombra do Altíssimo

In [38]:

# NOTE: Do not change any of the code in this cell
locations = hotel_df[["Lat", "lng"]]
markers = gmaps.marker_layer(locations)

# Using the template add the hotel marks to the heatmap
info_box_template = """
<dl>
<dt>Name</dt><dd>{Hotel Name}</dd>
<dt>City</dt><dd>{City}</dd>
<dt>Country</dt><dd>{Country}</dd>
</dl>
"""
# Store the DataFrame Row
# NOTE: be sure to update with your DataFrame name
hotel_info = [info_box_template.format(**row) for index, row in hotel_df.iterrows()]

In [39]:

# Add marker layer ontop of heat map
hotel_layer = gmaps.symbol_layer(
    locations, fill_color='rgba(0, 150, 0, 0.4)',
    stroke_color='rgba(0, 0, 150, 0.4)', scale=2,
    info_box_content=hotel_info
)

# Display figure
fig.add_layer(markers)
fig.add_layer(hotel_layer)
fig

Figure(layout=FigureLayout(height='420px'))

In [ ]: