Python API Challenge - What's the Weather Like?

Where is the data from?

The data is provided by Monash University Data Analytics Bootcamp.

Create a Python script to visualize the weather of 500+ cities across the world of varying distance from the equator, utilizing a simple Python library and the OpenWeatherMap API.
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
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

Use jupyter-gmaps and the Google Places API for this part.

Create a heat map that displays the humidity for every city from part I.
Narrow down the DataFrame to find my ideal weather condition.
Using 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

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