This Project runs a Python Script which Visualizes the weather of 500+ cities across the world of varying distance from the equator; Randomly selects at least 500 unique (non-repeat) cities based on latitude and longitude. Performs a weather check on each of the cities using a series of successive API calls. Includes a print log of each city as it's being processed with the city number and city name. Saves a CSV of all retrieved data and a PNG image for each scatter plot below and showcases the following relationships:
- Temperature (F) vs. Latitude
- Humidity (%) vs. Latitude
- Cloudiness (%) vs. Latitude
- Wind Speed (mph) vs. Latitude
A run on linear regression on each relationship, only this time 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
From the Weather Data gathered in Part I , here's a fun exploration to plan future vacations . A heat map that displays the humidity for every city from the part I above. A narrowed down DataFrame to find your ideal weather condition. For example:
A max temperature lower than 80 degrees but higher than 70. Wind speed less than 10 mph. Zero cloudiness.
A Plot of hotels on top of the humidity heatmap with each pin containing the Hotel Name, City, and Country
Project is created with:
- Jupyter Notebook
- Python
- CitiPy
- MatPlotlib
- OpenWeatherMap API
- Jupyter-gmaps
- Google Places API
To run this project, Download both the folders locally and run the files with ipynb extension in VS code or Jupyter notebook.