Data literacy with Python

Unless otherwise stated all content in this workflow is COPYRIGHT © Curtin University 2024. Curtin branding and trademarks are COPYRIGHT © Curtin University.

The text, images, data, documentation and general web content presented in this Jupyter notebook, unless stated otherwise, is licensed Creative Commons Attribution-ShareAlike 4.0 International. The Python code presented in this Jupyter notebook is licensed BSD-3-clause. The maps demonstration (Demonstration 1) is produced using Python’s Geopandas.explore and folium, which in turn uses the Leaflet.js library. Demonstrations 1 and 3 include content from OpenStreetMap and CartoDB.

As mentioned in the text, the data used here is licenced as follow.

• The dataset for Australian Taxation Statistics 2021-2022, which is an Excel formatted xlsx file, is published under a Creative Commons Attribution 2.5 Australia licence.
• The dataset for Australian Taxation Statistics 2020-2021, which is an Excel formatted xlsx file, is published under a Creative Commons Attribution 2.5 Australia licence.
• The specific dataset from the Socio-Economic Indexes for Areas (SEIFA), which is an Excel formatted xls file, is published under a Creative Commons Licence.
• The map data for Postcodes, which is an Shapefile, is published under a Creative Commons Licence.

The various software tools used here are licenced as follows.

• Jupyter Notebook is provided under the BSD-3-Clause license
• Python is provided under the PSF-2.0 licence.
• Python packages
  • Pandas, statsmodels, GeoPandas, geojson and topojson all use the BSD-3-Clause licence
  • Matplotlib uses this open source style licence