https://creativecommons.org/publicdomain/zero/1.0/https://creativecommons.org/publicdomain/zero/1.0/
Data was imported from the BAK file found here into SQL Server, and then individual tables were exported as CSV. Jupyter Notebook containing the code used to clean the data can be found here
Version 6 has a some more cleaning and structuring that was noticed after importing in Power BI. Changes were made by adding code in python notebook to export new cleaned dataset, such as adding MonthNumber for sorting by month number, similar for WeekDayNumber.
Cleaning was done in python while also using SQL Server to quickly find things. Headers were added separately, ensuring no data loss.Data was cleaned for NaN, garbage values and other columns.
Attribution 3.0 (CC BY 3.0)https://creativecommons.org/licenses/by/3.0/
License information was derived automatically
The Department of the Prime Minister and Cabinet is no longer maintaining this dataset. If you would like to take ownership of this dataset for ongoing maintenance please contact us.
PLEASE READ BEFORE USING
The data format has been updated to align with a tidy data style (http://vita.had.co.nz/papers/tidy-data.html).
The data in this dataset is manually collected and combined in a csv format from the following state and territory portals:
The data API by default returns only the first 100 records. The JSON response will contain a key that shows the link for the next page of records. Alternatively you can view all records by updating the limit on the endpoint or using a query to select all records, i.e. /api/3/action/datastore_search_sql?sql=SELECT * from "{{resource_id}}".
ODC Public Domain Dedication and Licence (PDDL) v1.0http://www.opendatacommons.org/licenses/pddl/1.0/
License information was derived automatically
ISO 3166-1-alpha-2 English country names and code elements. This list states the country names (official short names in English) in alphabetical order as given in ISO 3166-1 and the corresponding ISO 3166-1-alpha-2 code elements.
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https://creativecommons.org/publicdomain/zero/1.0/https://creativecommons.org/publicdomain/zero/1.0/
Data was imported from the BAK file found here into SQL Server, and then individual tables were exported as CSV. Jupyter Notebook containing the code used to clean the data can be found here
Version 6 has a some more cleaning and structuring that was noticed after importing in Power BI. Changes were made by adding code in python notebook to export new cleaned dataset, such as adding MonthNumber for sorting by month number, similar for WeekDayNumber.
Cleaning was done in python while also using SQL Server to quickly find things. Headers were added separately, ensuring no data loss.Data was cleaned for NaN, garbage values and other columns.