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DC Session 4 Python
Thursday Feb 6, 16:00 UK = 17:00 CET
Convenors: Paula Granados García (Open University), Matteo Romanello (Lausanne)
YouTube link: https://youtu.be/JDxRd-RYkXA
Presentation (Jupyter notebook): tba
This session will begin with a general discussion of programming for the humanities with an specific focus on how programming languages can be useful to humanists, followed by a general introduction to the Python programming language. We will then look at two key Python libraries (collections of code that enhance Python funtionality for specific purposes): Pandas (for structuring and analysing data), and Beautiful Soup (for parsing HTML and XML). These skills will then be illustrated with specific examples and exercises, all of which will be available for your use and adaptation in the Jupyter notebook linked from this session page.
- tba (two open access articles/chapters please)
- Büchler, Marco, et al. (2013), "Measuring the Influence of a Work by Text-Reuse." In ed. Dunn/Mahony, The Digital Classicist 2013. Bulletin of the Institute of Classical Studies, Supplement 122. Pp. 63–79.
- Hawkins, Laura F. 'Computational Models for Analyzing Data Collected from Reconstructed Cuneiform Syllabaries.' Digital Humanities Quarterly 12.1 (2018). Available: http://digitalhumanities.org:8081/dhq/vol/12/1/000368/000368.html (Wayback Machine)
- Kestermont, Mike & Justin A. Stover (2016), "The Authorship of the Historia Augusta: Two new computational studies." Bulletin of the Institute of Classical Studies 59.2. Pp. 140–157. Available: https://onlinelibrary.wiley.com/doi/epdf/10.1111/j.2041-5370.2016.12043.x
- McKinney, W. (2011). "pandas: a foundational Python library for data analysis and statistics.@ Python for High Performance and Scientific Computing 14. Available: https://www.dlr.de/sc/portaldata/15/resources/dokumente/pyhpc2011/submissions/pyhpc2011_submission_9.pdf
- Teodora Petkova (2017). "Semantic Information Extraction: From Data Bits to Knowledge Bytes." Ontotext blog, 22 June 2017. Available: https://ontotext.com/semantic-information-extraction-data-bits-knowledge-bytes/
- Python Programming for the Humanities http://www.karsdorp.io/python-course/
- Programming Historian: https://programminghistorian.org/en/lessons/
- Python for Everybody: https://www.py4e.com/
- Pandas: https://pandas.pydata.org/pandas-docs/version/0.15/tutorials.html
- Charlie Harper (2018). "Visualizing Data with Bokeh and Pandas." Programming Historian. Available: https://programminghistorian.org/en/lessons/visualizing-with-bokeh
- Jeri Wieringa (2012), "Intro to Beautiful Soup." Programming Historian. Available: https://programminghistorian.org/en/lessons/intro-to-beautiful-soup
- To set up your own Jupyter Notebook environment:
- Install Jupyter on your desktop (easiest as part of the Anaconda package) (getting started with Jupyter)
- Set up a Microsoft Azure Notebooks instance online (if you have 365 account)
- Set up a Google Colab notebooks instance online (if you have Gmail account) (getting started with Colab)
- tba