Part I - WeatherPy
In Part I, I used a Python script to visualize the weather of 500+ cities across the world of varying distance from the equator. To accomplish this, I used a simple Python library, and the OpenWeatherMap API to create a representative model of weather across world cities.
Your first requirement is to create 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
Next I ran 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
Observations:
According to the data, there is a correlation between latitude and maximum temperature. The closer a city is to the equator, the higher the max temperature will be.
Other factors tested (cloudiness, humidity and wind speed) do not have a correlation to latitude.
Part II - VacationPy
For Part II, I used the information found in WeatherPy to determine the ideal destination for a vacation. By narrowing down ideal temperature, cloudiness and wind speed I was able to plot a heat map with hotel information for locations that matched my dream vacation requirements.
Sample of final heat map:
