Whether financial, political, or social -- data's true power lies in its ability to answer "What's the weather like as we approach the equator?"
Created a Python script to visualize the weather of 500+ cities across the world of varying distance from the equator. Utilized a Python library, the OpenWeatherMap API, to create a representative model of weather across world cities. Created 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
For the second requirement did a 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
Used jupyter-gmaps and the Google Places API for this part.
- Created a heat map that displays the humidity for every city from the part I.
- Narrow down the DataFrame to find the ideal weather condition.
- A max temperature lower than 80 degrees but higher than 70.
- Wind speed less than 10 mph.
- Zero cloudiness.
- Used Google Places API to find the first hotel for each city located within 5000 meters of the coordinates.
- Plotted the hotels on top of the humidity heatmap with each pin containing the Hotel Name, City, and Country.
Jupyter Notebook, Pandas, Matplotlib, API, Python libraries, Gmaps, Openweather API, Google Places API.