This activity is broken down into two deliverables, WeatherPy and VacationPy.
For WeatherPy, I employed Python scripts in a Jupyter notebook to visualize weather patterns across 500 cities at varying distances from the equator. Utilizing Citipy, I determined city locations based on latitude and longitude, and the OpenWeatherMap API for importing weather information.
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Started by generating random geographic coordinates and finding the nearest cities.
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Fetched weather data using the OpenWeatherMap API for these cities.
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Created scatter plots showing relationships like Latitude vs. Temperature, Humidity, Cloudiness, and Wind Speed. Example:
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Conducted linear regression analysis for each relationship. Splitting plots into Northern and Southern Hemispheres for better analysis.
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Explored relationships like Temperature, Humidity, Cloudiness, and Wind Speed against Latitude in both hemispheres. Example:
In VacationPy, I developed a code using Jupyter notebooks, the geoViews Python library, and the Geoapify API to analyze weather data and plan vacations based on specific weather characteristics.
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Loaded weather and coordinates data for cities from a CSV file.
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Utilized the Geoapify API and geoViews Python library to create map visualizations, with the point sizes indicating humidity levels.
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Narrowed down the DataFrame to identify ideal weather conditions: temperature between 21 and 28 degrees, low wind speed, and zero cloud cover.
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Created a new DataFrame, 'hotel_df,' to store city, country, coordinates, and humidity information.
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Leveraged the Geoapify API to find the closest hotel within 10,000 meters for each city's coordinates and added this information to the map's hover message.
