This repo uses existing Python Libraries and public APIs to create maps visualizing a relationship between hotel locations and recorded weather data.
500+ cities were randomly generated using the CityPy Python Library. Using this list of cities and the OpenWeatherMap API, I generated a Pandas dataframe of each cities' weather. Following this was an analysis of the data.
The analysis focused on:
- Temperature (Farenheit)
- Humidity
- Cloudiness
- Wind Speed (mph)
Separating the weather data by Northern Hemisphere cities and Southern Hemisphere cities allowed for potential observable trends unique to each.
In the WeatherPy notebook, this analysis takes the form of scatterplots and linear regression models generated using the Matplotlib Python Library. Trends and observations for each analysis included in the notebook.
| Northern Hemisphere Latitudes v. Temperature | Southern Hemisphere Latitudes v. Temperature |
|---|---|
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The VacationPy notebook uses the previous weather data to generate a Google heat map as a visualization.
Conditions were passed through the weather data to create a data frame of cities suitable for vacations. These cities were passed through the Google Places API to find suitable hotels. Markers were placed on the heatmap to display hotel locations.
LinkedIn | https://www.linkedin.com/in/niko-elvambuena/
Email | niko.elvambuena95@gmail.com