Scatter plots: Temperature (F) vs. Latitude Humidity (%) vs. Latitude Cloudiness (%) vs. Latitude Wind Speed (mph) vs. Latitude

Linear Regression: 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

Randomly select at least 500 unique (non-repeat) cities based on latitude and longitude. Perform a weather check on each of the cities using a series of successive API calls. Include a print log of each city as it's being processed with the city number and city name. Save a CSV of all retrieved data and a PNG image for each scatter plot.

Using jupyter-gmpas and google places API:

Narrow down the DataFrame to find your ideal weather condition. For example:

A max temperature lower than 80 degrees but higher than 70.

Wind speed less than 10 mph.

Zero cloudiness.

Drop any rows that don't contain all three conditions. You want to be sure the weather is ideal.

Note: Feel free to adjust to your specifications but be sure to limit the number of rows returned by your API requests to a reasonable number.

Using Google Places API to find the first hotel for each city located within 5000 meters of your coordinates.

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