Part I - WeatherPy

• Randomly select >500 unique 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.

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

• Aa written description of three observable trends based on the data.

Part II - VacationPy

• Create a heat map that displays the humidity for every city from the part I of the homework.

• Narrow down the DataFrame to find ideal weather condition:

  • A max temperature lower than 80 degrees but higher than 70.
  • Wind speed less than 10 mph.
  • Zero cloudiness.

• 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.