Short Description : golf tee-time data based on user input date.
Detail Description : project scrapes golf course tee-time data from the Waterchase Golf website. Users provide a date, and the scraper fetches available tee-times for that date, processes the data, and stores it in structured JSON format for further use. This data is also converted into a CSV file for easier processing and analysis, where each row corresponds to a single tee-time rate
Structured JSON containing tee-time data such as date, time, minPlayers, maxPlayers, weekday, and rates (including green fees, holes, and transportation). This data is also converted into a CSV file for easier processing and analysis, where each row corresponds to a single tee-time rate.
Annual Summary Overview machine is the fifth machine in workflow for summarize the occupancy data in table. It’s provide Occupancy, RMS, ADR, Revenue and same things from the last year.
Choice PMS Res Standardization machine is the forth machine in workflow for making reservation and occupancy data to standard table structure which are used for all of the properties.
It seems you might be referring to Net Standard Last Year (LY), which is a common term in the hotel industry (and other industries) used to compare the financial performance or key metrics of the current year with the same period in the previous year. LY (Last Year) is typically used in reports and data analysis to assess growth, trends, and changes.
Annual Summary Overview machine is the fifth machine in workflow for summarize the occupancy data in table. It’s provide Occupancy, RMS, ADR, Revenue and same things from the last year.
Choice PMS Res Standardization machine is the forth machine in workflow for making reservation and occupancy data to standard table structure which are used for all of the properties.
It seems you might be referring to Net Standard Last Year (LY), which is a common term in the hotel industry (and other industries) used to compare the financial performance or key metrics of the current year with the same period in the previous year. LY (Last Year) is typically used in reports and data analysis to assess growth, trends, and changes.
Annual Summary Overview machine is the fifth machine in workflow for summarize the occupancy data in table. It’s provide Occupancy, RMS, ADR, Revenue and same things from the last year.
Choice PMS Res Standardization machine is the forth machine in workflow for making reservation and occupancy data to standard table structure which are used for all of the properties.
It seems you might be referring to Net Standard Last Year (LY), which is a common term in the hotel industry (and other industries) used to compare the financial performance or key metrics of the current year with the same period in the previous year. LY (Last Year) is typically used in reports and data analysis to assess growth, trends, and changes.
This scraper collects golf tee-time data from the site for a date entered by the user. The scraped data is then stored in JSON format either locally. If no data is available for the selected date, the scraper returns an empty JSON response along with a message indicating that no tee-times are available. This data is also converted into a CSV file for easier processing and analysis, where each row corresponds to a single tee-time rate.
Annual Summary Overview machine is the fifth machine in workflow for summarize the occupancy data in table. It’s provide Occupancy, RMS, ADR, Revenue and same things from the last year.
Choice PMS Res Standardization machine is the forth machine in workflow for making reservation and occupancy data to standard table structure which are used for all of the properties.
It seems you might be referring to Net Standard Last Year (LY), which is a common term in the hotel industry (and other industries) used to compare the financial performance or key metrics of the current year with the same period in the previous year. LY (Last Year) is typically used in reports and data analysis to assess growth, trends, and changes.
Structured JSON containing tee-time data such as date, time, minPlayers, maxPlayers, weekday, and rates (including green fees, holes, and transportation). This data is also converted into a CSV file for easier processing and analysis, where each row corresponds to a single tee-time rate.
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