Results 271 to 280 of about 221,251 (313)

Selections from the prison notebooks

The Applied Theatre Reader, 2020
ion, but it leads nobody to think that one fly equals one elephant. The rules of formal logic are abstractions of the same kind, they are like the grammar of normal thought; but they still need to be studied, since they are not something innate, but have
Antonio Gramsci
semanticscholar   +1 more source

What's Wrong with Computational Notebooks? Pain Points, Needs, and Design Opportunities

International Conference on Human Factors in Computing Systems, 2020
Computational notebooks - such as Azure, Databricks, and Jupyter - are a popular, interactive paradigm for data scientists to author code, analyze data, and interleave visualizations, all within a single document.
Souti Chattopadhyay, I. Prasad, Austin Z. Henley, A. Sarma, Titus Barik   +4 more
semanticscholar   +1 more source

A Large-Scale Study About Quality and Reproducibility of Jupyter Notebooks

IEEE Working Conference on Mining Software Repositories, 2019
Jupyter Notebooks have been widely adopted by many different communities, both in science and industry. They support the creation of literate programming documents that combine code, text, and execution results with visualizations and all sorts of rich ...
J. F. Pimentel, Leonardo Gresta Paulino Murta, V. Braganholo, J. Freire   +3 more
semanticscholar   +1 more source

Interactive Python Notebooks for Physical Chemistry

Journal of Chemical Education, 2022
Chemistry simulations using interactive graphic user interfaces (GUIs) represent uniquely effective and safe tools to support multidimensional learning. Computer literacy and coding skills have become increasingly important in the chemical sciences.
A. Bravenec, Karen D. Ward
semanticscholar   +1 more source

DistilKaggle: A Distilled Dataset of Kaggle Jupyter Notebooks

IEEE Working Conference on Mining Software Repositories
Jupyter notebooks have become indispensable tools for data analysis and processing in various domains. However, despite their widespread use, there is a notable research gap in understanding and analyzing the contents and code metrics of these notebooks.
Mojtaba Mostafavi Ghahfarokhi, Arash Asgari, Mohammad Abolnejadian, Abbas Heydarnoori   +3 more
semanticscholar   +1 more source

Bolt-on, Compact, and Rapid Program Slicing for Notebooks [Scalable Data Science]

Proceedings of the VLDB Endowment, 2022
Computational notebooks are commonly used for iterative workflows, such as in exploratory data analysis. This process lends itself to the accumulation of old code and hidden state, making it hard for users to reason about the lineage of, e.g., plots ...
Shreya Shankar, Stephen Macke, Sarah E. Chasins, Andrew Head, Aditya G. Parameswaran   +4 more
semanticscholar   +1 more source

Notebook: Science notebook

Science News, 2008
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Contextualized Data-Wrangling Code Generation in Computational Notebooks

International Conference on Automated Software Engineering
Data wrangling, the process of preparing raw data for further analysis in computational notebooks, is a crucial yet time-consuming step in data science. Code generation has the potential to automate the data wrangling process to reduce analysts’ overhead
Junjie Huang, Daya Guo, Chenglong Wang, Jiazhen Gu, Shuai Lu, J. Inala, Cong Yan, Jianfeng Gao, Nan Duan, Michael R. Lyu   +9 more
semanticscholar   +1 more source

Auto-Suggest: Learning-to-Recommend Data Preparation Steps Using Data Science Notebooks

SIGMOD Conference, 2020
Data preparation is widely recognized as the most time-consuming process in modern business intelligence (BI) and machine learning (ML) projects. Automating complex data preparation steps (e.g., Pivot, Unpivot, Normalize-JSON, etc.)holds the potential to
Cong Yan, Yeye He
semanticscholar   +1 more source