Python API Challenge: WeatherPy & VacationPy

Overview

The primary objective of this project is to answer the question: "What is the weather like as we approach the equator?" Using Python requests, APIs, and JSON traversals, we aim to provide a comprehensive answer to this question.

Comparative Weather Analysis Across Latitudes

Part 1: WeatherPy

In this section, we analyze the weather of over 500 cities with varying distances from the equator using the citipy library and the OpenWeatherMap API.

Below is a snippet of the WeatherPy.ipynb file used in this project. For the complete code, please click here.

# Run an API request for each of the cities
    try:
        # Parse the JSON and retrieve data
        city_weather = requests.get(city_url).json()

        # Parse out latitude, longitude, max temp, humidity, cloudiness, wind speed, country, and date
        city_lat = city_weather["coord"]["lat"]
        city_lng = city_weather["coord"]["lon"]
        city_max_temp = city_weather["main"]["temp_max"]
        city_humidity = city_weather["main"]["humidity"]
        city_clouds = city_weather["clouds"]["all"]
        city_wind = city_weather["wind"]["speed"]
        city_country = city_weather["sys"]["country"]
        city_date = city_weather["dt"]

        # Append the City information into city_data list
        city_data.append({"City": city, 
                          "Lat": city_lat, 
                          "Lng": city_lng, 
                          "Max Temp": city_max_temp,
                          "Humidity": city_humidity,
                          "Cloudiness": city_clouds,
                          "Wind Speed": city_wind,
                          "Country": city_country,
                          "Date": city_date})

    # If an error is experienced, skip the city
    except:
        print("City not found. Skipping...")
        pass

Key Features:

• Scatter plots showcasing relationships between:
• Latitude vs. Temperature
• Latitude vs. Humidity
• Latitude vs. Cloudiness
• Latitude vs. Wind Speed
• Linear regression plots for the Northern and Southern Hemispheres for each of the above relationships.

Part 2: VacationPy

In this section, we use the weather data to plan future vacations using the geoViews library and the Geoapify API.

Hotel Locations in Global Cities Based on Humidity Levels

Below is a snippet of the VacationPy.ipynb file used in this project. For the complete code, please click here.

# Set parameters to search for a hotel
radius = 10000 # meters
limit = 1

params = {
    "radius": radius,
    "type": "accommodation.hotel",
    "apiKey": geoapify_key
}

# Print a message to follow up the hotel search
print("Starting hotel search")

# Iterate through the hotel_df DataFrame
for index, row in hotel_df.iterrows():
    # get latitude, longitude from the DataFrame
    lat = row["Lat"]
    lng = row["Lng"]
    
    # Add filter and bias parameters with the current city's latitude and longitude to the params dictionary
    params["filter"] = f"circle:{lng},{lat},{radius}"
    params["bias"] = f"proximity:{lng},{lat}"
    
    # Set base URL
    base_url = "https://api.geoapify.com/v2/places"


    # Make and API request using the params dictionary
    name_address = requests.get(base_url, params=params).json()
    # Convert the API response to JSON format
    
    
    # Grab the first hotel from the results and store the name in the hotel_df DataFrame
    try:
        hotel_df.loc[index, "Hotel Name"] = name_address["features"][0]["properties"]["name"]
    except (KeyError, IndexError):
        # If no hotel is found, set the hotel name as "No hotel found".
        hotel_df.loc[index, "Hotel Name"] = "No hotel found"
        
    # Log the search results
    print(f"{hotel_df.loc[index, 'City']} - nearest hotel: {hotel_df.loc[index, 'Hotel Name']}")

Key Features:

• Map displaying a point for every city based on humidity.
• Identification of cities with ideal weather conditions.
• Map showcasing hotels within 10,000 meters of the identified cities.

Acknowledgements

OpenWeatherMap API

Geoapify API

citipy Python library