In this project the climate was thoroughly analyzed in order to find patterns. This is divided into two:
- WeatherPy
- VacationPy
Each chart of each project have the conclusions in the jupyter notebooks.
The data was provided by OpenWeatherMap
In this part of the project a python scripts were created to visualize the weather of 500+ cities across the world of varying distance from the equator. To accomplish this simple Python libraries were used, the OpenWeatherMap API, and a little common sense to create a representative model of weather across world cities.
The objective is to build a series of scatter plots to showcase the following relationships:
- Temperature (F) vs. Latitude
- Humidity (%) vs. Latitude
- Cloudiness (%) vs. Latitude
- Wind Speed (mph) vs. Latitude
After that the next step was run linear regression on each relationship and then separating them into Northern Hemisphere (greater than or equal to 0 degrees latitude) and Southern Hemisphere (less than 0 degrees latitude):
Northern Hemisphere - Temperature (F) vs. Latitude Southern Hemisphere - Temperature (F) vs. Latitude Northern Hemisphere - Humidity (%) vs. Latitude Southern Hemisphere - Humidity (%) vs. Latitude Northern Hemisphere - Cloudiness (%) vs. Latitude Southern Hemisphere - Cloudiness (%) vs. Latitude Northern Hemisphere - Wind Speed (mph) vs. Latitude Southern Hemisphere - Wind Speed (mph) vs. Latitude
At the end this jupyter notebook have:
- A selection of randomly 500 unique (non-repeat) cities based on latitude and longitude.
- A weather check on each of the cities using a series of successive API calls.
- A print log of each city as it's being processed with the city number and city name.
- A PNG image for each scatter plot.
In this part a heat map was displayed showing the humidity for every city.
At the end it shows a plot showing the hotels on top of the humidity heatmap with each pin containing the Hotel Name, City, and Country.