Python API Challenge - What's the Weather Like?

Background

Whether financial, political, or social -- data's true power lies in its ability to answer questions definitively. So let's take what you've learned about Python requests, APIs, and JSON traversals to answer a fundamental question: "What's the weather like as we approach the equator?"

Part I - WeatherPy

Created a Python script to visualize the weather of 500+ cities across the world of varying distance from the equator. To accomplish this, I utilized (https://pypi.python.org/pypi/citipy) and the OpenWeatherMap API, and a little common sense to create a representative model of weather across world cities.

Then a series of scatter plots with color bars to showcase the following relationships:

• Temperature (F) vs. Latitude
• Humidity (%) vs. Latitude
• Cloudiness (%) vs. Latitude
• Wind Speed (mph) vs. Latitude

Then a linear regression was run on each Northern Hemisphere (greater than or equal to 0 degrees latitude) and Southern Hemisphere (less than 0 degrees latitude)relationship.:

• 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

Part II - VacationPy

• Created a heat map that displays the humidity for every city from Part I.

• Narrowed down the DataFrame to find appx ten cities with ideal weather conditions:

• A max temperature lower than 77 degrees but higher than 68.

• 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.

• Plot the hotels on top of the humidity heatmap with each pin containing the Hotel Name, City, and Country.