2025 | Andreas Koenzen, M.Sc. | Data Engineer @ Loka, Inc

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K-Zen
Hey hey visitor... 🤖 welcome to my website..! Let me give you the intro...
  • My name is Andreas and I work as a Senior Data Engineer at Loka, Inc ...
  • I am a professional (M.Sc. / B.Sc.) data engineer with expertise into Data Lakes, Data Warehouses and Data Lakehouses. I'm also proficient in Apache Airflow, PySpark, ETLs and ELTs, which makes it quite handy when you need to move data around 🙂 ...
  • I work building data solutions mostly for companies in the US using the AWS infrastructure, and ...
  • When I'm not doing that I like to spent quality time with my family 🙂 ...
  • I live in South America and speak Spanish 🇪🇸, English 🇬🇧 and now dusting off my German 🇩🇪 ...
  • I also did some research into computational notebooks while in grad school at the University of Victoria in beautiful Victoria, British Columbia. At the bottom of this page you can find a section about this. And finally, ...
  • I don't use any kind of social media, except for LinkedIn 🎉

How to contact me?

My Certifications

SAA-C03

Research / University of Victoria

Code Duplication and Reuse in Jupyter Notebooks (M.Sc. Thesis) | [UVicSpace]

BibTeX

@mastersthesis { 
  Koenzen:2020, 
  author  = {Andreas Koenzen}, 
  title   = {Code Duplication and Reuse in Jupyter Notebooks},
  school  = {University of Victoria},
  year    = {2020},
  address = {Victoria, BC, Canada},
  month   = {9}
}

Code Duplication and Reuse in Jupyter Notebooks | [Pre-print] | [Presentation Slides]

Duplicating one's own code makes it faster to write software. This expediency is particularly valuable for users of computational notebooks. Duplication allows notebook users to quickly test hypotheses and iterate over data. In this paper, we explore how much, how and from where code duplication occurs in computational notebooks, and identify potential barriers to code reuse. Previous work in the area of computational notebooks describes developers' motivations for reuse and duplication but does not show how much reuse occurs or which barriers they face when reusing code. To address this gap, we first analyzed GitHub repositories for code duplicates contained in a repository's Jupyter notebooks, and then conducted an observational user study of code reuse, where participants solved specific tasks using notebooks. Our findings reveal that repositories in our sample have a mean self-duplication rate of 7.6%. However, in our user study, few participants duplicated their own code, preferring to reuse code from online sources.

Accepted as a full paper at the IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC) 2020
BibTeX 

@inproceedings{
  Koenzen:2020,
  author    = {A. P. {Koenzen} and N. A. {Ernst} and M.-A. D. {Storey}},
  booktitle = {2020 IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC)}, 
  title     = {Code Duplication and Reuse in Jupyter Notebooks}, 
  year      = {2020},
  volume    = {},
  number    = {},
  pages     = {1-9}
}

Plain

A. P. Koenzen, N. A. Ernst and M.-A. D. Storey, "Code Duplication and Reuse in Jupyter Notebooks" 2020 IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC), Dunedin, New Zealand, 2020, pp. 1-9, doi: 10.1109/VL/HCC50065.2020.9127202.

DOI