- Citipy libary code already provided to obtain a random selection of Longitude and Latitude values and the nearest City.
- Using the 'OpenWeatherMap API' iterate through the cities identified by the Citipy:
    - Through the JSON result for each city obtain:
        - Latitude
        - Longitude
        - Max Temp
        - Humidity
        - Cloud cover
        - Wind speed
        - Country code
        - Date
    - Append the obtained data to the 'city_data' list
- Create a dataframe from the 'city_data' list
    - Save the dataframe to CSV ready for the 'VacationPy' task

- Using the data collected, generate scatter plots for:
    - Latitude vs Temperature (c)
    - Latitude vs. Humidity (%)
    - Latitude vs. Cloudiness (%)
    - Latitude vs. Wind Speed (m/s)
- Note: To save time/repetitve coding, I created a function to generate the base scatter plots and then just added additional lines for the titles/labels

- Separate the Citie's from the previous dataframe into Northern and Southern Hemisphere dataframes
- Create a function to compute the linear regression for the previously created plots, broken down into Northern and Southern Hemisphere dataframes
- Create plots, linear regression and Line Equation for the following relationships:
    - Northern Hemisphere: Temperature vs. Latitude
    - Southern Hemisphere: Temperature 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 vs. Latitude
    - Southern Hemisphere: Wind Speed vs. Latitude

- Using the CSV from the previous 'city_data_df' plot each City on a hvplot world map
- The size of the marker should be determined by the 'Humidity' value of each City
- And each City point should be coloured differently

- I added three conditions to reducde the number of cities down:
    - Temperature should be <= 30
    - Temperature should be >= 20
    - Cloudiness should be == 0
- This reduced dataset was then renamed to 'hotel_df' consisting of the following columns:
    - City
    - Country
    - Longitude
    - Latitude
    - Humidity
    - Hotel Name - This was an empty column added to the dataframe ready for the next task

- Using the 'Geoapify API' iterate through each City within the 'hotel_df' and find:
    - The nearest Hotel
    - Within 10km of the Latitude and Longitude of the city
    - Append the name of the hotel to the 'Hotel Name' column of the DataFrame
- I added an additional step to filter out any Cities where a hotel was not found within the search area
- Using the 'hotel_df' generate another world map to show each City which met the previous conditions set
    - Add the 'Hotel Name' and 'Country' to the hover message for each point

- Code for converting Unix timestamp from 'Date' column to a Date Format - https://stackoverflow.com/a/75222939/21871037
- Code used in an attempt to stop the labels/titles being cut off with the 'savefig' function - https://ecoagi.ai/topics/Matplotlib/matplotlib-savefig-cuts-off-labels#saving-the-figure-with-bbox_inchestight
- Initial coding - edX Boot Camps LLC
- Weather data - OpenWeatherMap API
- Hotel Data - Geoapify API