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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.