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  1. 1

    Python Data Analyst's Toolkit - Learn Python and Python-Based Libraries with Applications in Data Analysis and Statistics by Rajagopalan, Gayathri

    ISBN: 9781484263983, 1484263987, 9781484263990, 1484263995
    Published: Berkeley, CA Apress, an imprint of Springer Nature 2021
    “… The code is presented in Jupyter notebooks that can further be adapted and extended. This book is divided into three parts - programming with Python, data analysis and visualization, and statistics…”
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    eBook
  2. 2

    Introduction to computational causal inference using reproducible Stata, R, and Python code: A tutorial by Smith, Matthew J., Mansournia, Mohammad A., Maringe, Camille, Zivich, Paul N., Cole, Stephen R., Leyrat, Clémence, Belot, Aurélien, Rachet, Bernard, Luque‐Fernandez, Miguel A.

    ISSN: 0277-6715, 1097-0258, 1097-0258
    Published: England Wiley Subscription Services, Inc 30.01.2022
    Published in Statistics in medicine (30.01.2022)
    “…The main purpose of many medical studies is to estimate the effects of a treatment or exposure on an outcome. However, it is not always possible to randomize…”
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    Journal Article
  3. 3

    LiPydomics: A Python Package for Comprehensive Prediction of Lipid Collision Cross Sections and Retention Times and Analysis of Ion Mobility-Mass Spectrometry-Based Lipidomics Data by Ross, Dylan H, Cho, Jang Ho, Zhang, Rutan, Hines, Kelly M, Xu, Libin

    ISSN: 1520-6882
    Published: United States 17.11.2020
    Published in Analytical chemistry (Washington) (17.11.2020)
    “… Existing solutions for these data analysis challenges (i.e., multivariate statistics and lipid identification…”
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    Journal Article
  4. 4

    Mitigating the Uncertainty and Imprecision of Log-Based Code Coverage Without Requiring Additional Logging Statements by Xu, Xiaoyan, Cogo, Filipe R., McIntosh, Shane

    ISSN: 0098-5589, 1939-3520
    Published: New York IEEE 01.09.2024
    Published in IEEE transactions on software engineering (01.09.2024)
    “… proposed LogCoCo - a tool that implements log-based code coverage for Java . While LogCoCo breaks important new ground, it has fundamental limitations, namely…”
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    Journal Article
  5. 5

    Decoding the JAK-STAT Axis in Colorectal Cancer with AI-HOPE-JAK-STAT: A Conversational Artificial Intelligence Approach to Clinical–Genomic Integration by Yang, Ei-Wen, Waldrup, Brigette, Velazquez-Villarreal, Enrique

    ISSN: 2072-6694, 2072-6694
    Published: Switzerland MDPI AG 17.07.2025
    Published in Cancers (17.07.2025)
    “…: AI-HOPE-JAK-STAT combines large language models (LLMs), a natural language-to-code engine, and harmonized public CRC datasets from cBioPortal…”
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    Journal Article
  6. 6

    Stress-Constrained Topology Optimization for Commercial Software: A Python Implementation for ABAQUS by Fernandes, Pedro, Ferrer, Àlex, Gonçalves, Paulo, Parente, Marco, Pinto, Ricardo, Correia, Nuno

    ISSN: 2076-3417, 2076-3417
    Published: Basel MDPI AG 01.12.2023
    Published in Applied sciences (01.12.2023)
    “…Topology optimization has evidenced its capacity to provide new optimal designs in many different disciplines…”
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    Journal Article
  7. 7

    Python for Everyone: Learn and polish your coding skills in Python (English Edition) by Saurabh Chandrakar, Dr. Nilesh Bhaskarrao Bahadure

    ISBN: 9789355518170, 935551817X
    Published: Los Angeles BPB Publications 2023
    “…A hands-on guide that will help you to write clean and efficient code in Python Key Features…”
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    eBook
  8. 8

    Data Science Fundamentals with R, Python, and Open Data by Cremonini, Marco

    ISBN: 9781394213245, 1394213247
    Published: Hoboken, New Jersey John Wiley & Sons 2024
    “…Introduction to essential concepts and techniques of the fundamentals of R and Python needed to start data science projectsOrganized with a strong focus on open data, This book discusses concepts…”
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    eBook Book
  9. 9

    Statistical analysis of feature-based molecular networking results from non-targeted metabolomics data by Pakkir Shah, Abzer K., Walter, Axel, Ottosson, Filip, Russo, Francesco, Navarro-Diaz, Marcelo, Boldt, Judith, Kalinski, Jarmo-Charles J., Kontou, Eftychia Eva, Elofson, James, Polyzois, Alexandros, González-Marín, Carolina, Farrell, Shane, Aggerbeck, Marie R., Pruksatrakul, Thapanee, Chan, Nathan, Wang, Yunshu, Pöchhacker, Magdalena, Brungs, Corinna, Cámara, Beatriz, Caraballo-Rodríguez, Andrés Mauricio, Cumsille, Andres, de Oliveira, Fernanda, Dührkop, Kai, El Abiead, Yasin, Geibel, Christian, Graves, Lana G., Hansen, Martin, Heuckeroth, Steffen, Knoblauch, Simon, Kostenko, Anastasiia, Kuijpers, Mirte C. M., Mildau, Kevin, Papadopoulos Lambidis, Stilianos, Portal Gomes, Paulo Wender, Schramm, Tilman, Steuer-Lodd, Karoline, Stincone, Paolo, Tayyab, Sibgha, Vitale, Giovanni Andrea, Wagner, Berenike C., Xing, Shipei, Yazzie, Marquis T., Zuffa, Simone, de Kruijff, Martinus, Beemelmanns, Christine, Link, Hannes, Mayer, Christoph, van der Hooft, Justin J. J., Damiani, Tito, Pluskal, Tomáš, Dorrestein, Pieter, Stanstrup, Jan, Schmid, Robin, Wang, Mingxun, Aron, Allegra, Ernst, Madeleine, Petras, Daniel

    ISSN: 1754-2189, 1750-2799, 1750-2799
    Published: London Nature Publishing Group UK 01.01.2025
    Published in Nature protocols (01.01.2025)
    “… Especially users new to statistical analysis struggle to effectively handle and analyze complex data matrices…”
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    Journal Article
  10. 10

    From code to reliability: Python-powered Paraconsistent Logic for alarm detection in the power gris/ Do codigo a confiabilidade: Logica Paraconsistente impulsionada por Python para a deteccao de alarmes na rede de energia by de Oliveira, Joseffe Barroso, Gino, Joao Vitor Santa Rosa, de Lima, Carlos Jose, Filho, Joao Inacio da Silva

    ISSN: 2178-9010, 2178-9010
    Published: Sindicato das Secretarias e Secretarios do Estado de Sao Paulo 01.10.2023
    Published in GeSec : Revista de Gestão e Secretariado (01.10.2023)
    “… This application enables the comparison of triggered alarms with the resultant logical states obtained during the analysis…”
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    Journal Article
  11. 11

    Artificial intelligence programming with Python: from zero to hero by Xiao, Perry

    ISBN: 1119820863, 9781119820864, 1119820944, 9781119820949
    Published: Hoboken Wiley 2022
    “…A hands-on roadmap to using Python for artificial intelligence programming In Practical Artificial Intelligence Programming with Python…”
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    eBook Book
  12. 12

    The Python workshop: a practical, no-nonsense introduction to Python development by Andrew Bird, Bird, Dr Lau Cher Han, Han, Mario Corchero Jimenez, Jimenez, Graham Lee, Lee, Corey Wade, Wade

    Published: Packt Publishing 06.11.2019
    “…Cut through the noise and get real results with a step-by-step approach to learning Python 3…”
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  13. 13

    Understanding the Robustness of Transformer-Based Code Intelligence via Code Transformation: Challenges and Opportunities by Li, Yaoxian, Qi, Shiyi, Gao, Cuiyun, Peng, Yun, Lo, David, Lyu, Michael R., Xu, Zenglin

    ISSN: 0098-5589, 1939-3520
    Published: New York IEEE Computer Society 01.02.2025
    Published in IEEE transactions on software engineering (01.02.2025)
    “…Transformer-based models have demonstrated state-of-the-art performance in various intelligent coding tasks such as code comment generation and code completion…”
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    Journal Article
  14. 14

    NAS Parallel Benchmarks with Python: a performance and programming effort analysis focusing on GPUs by Di Domenico, Daniel, Lima, João V. F., Cavalheiro, Gerson G. H.

    ISSN: 0920-8542, 1573-0484, 1573-0484
    Published: New York Springer US 01.05.2023
    Published in The Journal of supercomputing (01.05.2023)
    “… As a counterpoint to that trend, this paper presents a performance and programming effort analysis with Python, an interpreted and high-level language, which was applied to develop the kernels…”
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    Journal Article
  15. 15

    Semi-Supervised Code Translation Overcoming the Scarcity of Parallel Code Data by Zhu, Ming, Karim, Mohimenul, Lourentzou, Ismini, Yao, Danfeng Daphne

    ISSN: 2643-1572
    Published: ACM 27.10.2024
    “… MIRACLE leverages static analysis and compilation to generate synthetic parallel code datasets with enhanced quality and alignment to address the challenge of data scarcity…”
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    Conference Proceeding
  16. 16

    Learning to Generate Pseudo-Code from Source Code Using Statistical Machine Translation by Oda, Yusuke, Fudaba, Hiroyuki, Neubig, Graham, Hata, Hideaki, Sakti, Sakriani, Toda, Tomoki, Nakamura, Satoshi

    Published: IEEE 01.11.2015
    “…Pseudo-code written in natural language can aid the comprehension of source code in unfamiliar programming languages…”
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    Conference Proceeding
  17. 17

    Towards Identifying Code Proficiency Through the Analysis of Python Textbooks by Rojpaisarnkit, Ruksit, Robles, Gregorio, Kula, Raula Gaikovina, Wang, Dong, Ragkhitwetsagul, Chaiyong, Gonzalez-Barahona, Jesus M., Matsumoto, Kenichi

    ISSN: 2576-3148
    Published: IEEE 06.10.2024
    “… This method involves assigning levels of competence to Python constructs-for instance, placing simple 'print' statements at the most basic level and abstract base classes at the most…”
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    Conference Proceeding
  18. 18

    A Large-Scale Study of Test Coverage Evolution by Hilton, Michael, Bell, Jonathan, Marinov, Darko

    ISSN: 2643-1572
    Published: ACM 01.09.2018
    “…Statement coverage is commonly used as a measure of test suite quality. Coverage is often used as a part of a code review process…”
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    Conference Proceeding
  19. 19

    Enhancing LLMs Interactions for Python: A Smart API Framework for Extracting and Utilizing Semantic Code Information by Ahmad, Nouman, Zhang, Changsheng

    Published: IEEE 20.06.2025
    “… In order to overcome this difficulty, we introduce a brand-new system that uses FastAPI to automatically extract semantic data from Python codebases…”
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    Conference Proceeding
  20. 20

    Towards Identifying Code Proficiency through the Analysis of Python Textbooks by Rojpaisarnkit, Ruksit, Robles, Gregorio, Kula, Raula Gaikovina, Wang, Dong, Ragkhitwetsagul, Chaiyong, Gonzalez-Barahona, Jesus M, Matsumoto, Kenichi

    ISSN: 2331-8422
    Published: Ithaca Cornell University Library, arXiv.org 05.08.2024
    Published in arXiv.org (05.08.2024)
    “… This method involves assigning levels of competence to Python constructs, for instance, placing simple 'print' statements at the most basic level and abstract base classes at the most…”
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    Paper