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Notebook and spreadsheet systems are currently the de-facto standard for data collection, preparation, and analysis. However, these systems have been criticized for their lack of reproducibility, versioning, and support for sharing. These shortcomings are particularly detrimental for data curation where data scientists iteratively build workflows to clean up and integrate data as a prerequisite for analysis. We present Vizier, an open-source tool that helps analysts to build and refine data pipelines. Vizier combines the flexibility of notebooks with the easy-to-use data manipulation interface of spreadsheets. Combined with advanced provenance tracking for both data and computational steps this enables reproducibility, versioning, and streamlined data exploration. Unique to Vizier is that it exposes potential issues with data, no matter whether they already exist in the input or are introduced by the operations of a notebook. We refer to such potential errors as data caveats. Caveats are propagated alongside data using principled techniques from uncertain data management. Vizier provides extensive user interface support for caveats, e.g., exposing them as summaries in a dedicated error view and highlighting cells with caveats in spreadsheets.
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Elevating Jupyter Notebook Maintenance Tooling by Identifying and Extracting Notebook Structures
Data analysis is an exploratory, interactive, and often collaborative process. Computational notebooks have become a popular tool to support this process, among others because of their ability to interleave code, narrative text, and results. However, notebooks in practice are often criticized as hard to maintain and being of low code quality, including problems such as unused or duplicated code and out-of-order code execution. Data scientists can benefit from better tool support when maintaining and evolving notebooks. We argue that central to such tool support is identifying the structure of notebooks. We present a lightweight and accurate approach to extract notebook structure and outline several ways such structure can be used to improve maintenance tooling for notebooks, including navigation and finding alternatives.
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- Award ID(s):
- 2131477
- PAR ID:
- 10444837
- Date Published:
- Journal Name:
- 2022 IEEE International Conference on Software Maintenance and Evolution (ICSME)
- Page Range / eLocation ID:
- 399 to 403
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/ICSME55016.2022.00047
