1. Clone repository to folder on computer

2. Open jupyter lab

3. Navigate to Python API's HW.ipynb and run all cells

A Python script is created to visualize the weather of 500+ cities across the world of varying distance from the equator. I utilized simple Python library, the OpenWeatherMap API, and a little common sense to create a representative model of weather across world cities.

My first objective was 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

My next objective was to run 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

My findings were as follows:

-There is an inverse relationship between latitude and temperature -A strong relationship was not found between humidity and latitude -A strong relationship was not found between cloudiness and latitude -There are not many locations in the world where wind speed reaches over 10 mph

Temp_Latitude

The relationship between temperature and latitude in the northern hemisphere was quantified using a scatter plot and an r-squared value.

Wind_Latitude

The relationship between wind speed and latitude in the southern hemisphere was quantified using a scatter plot and an r-squared value.