Suchergebnisse - (( (statement OR stat) python code analysis ) OR ( (stated OR state:new) python code analysis ))

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

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

“… 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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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 von Ross, Dylan H, Cho, Jang Ho, Zhang, Rutan, Hines, Kelly M, Xu, Libin

“… Existing solutions for these data analysis challenges (i.e., multivariate statistics and lipid identification …”
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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 von de Oliveira, Joseffe Barroso, Gino, Joao Vitor Santa Rosa, de Lima, Carlos Jose, Filho, Joao Inacio da Silva

“… This application enables the comparison of triggered alarms with the resultant logical states obtained during the analysis …”
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Mitigating the Uncertainty and Imprecision of Log-Based Code Coverage Without Requiring Additional Logging Statements von Xu, Xiaoyan, Cogo, Filipe R., McIntosh, Shane

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

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

“… Topology optimization has evidenced its capacity to provide new optimal designs in many different disciplines …”
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Python for Everyone: Learn and polish your coding skills in Python (English Edition) von Saurabh Chandrakar, Dr. Nilesh Bhaskarrao Bahadure

“… A hands-on guide that will help you to write clean and efficient code in Python Key Features …”
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Data Science Fundamentals with R, Python, and Open Data von Cremonini, Marco

“… 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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Statistical analysis of feature-based molecular networking results from non-targeted metabolomics data von 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

“… Especially users new to statistical analysis struggle to effectively handle and analyze complex data matrices …”
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Artificial intelligence programming with Python: from zero to hero von Xiao, Perry

“… A hands-on roadmap to using Python for artificial intelligence programming In Practical Artificial Intelligence Programming with Python …”
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The Python workshop: a practical, no-nonsense introduction to Python development von Andrew Bird, Bird, Dr Lau Cher Han, Han, Mario Corchero Jimenez, Jimenez, Graham Lee, Lee, Corey Wade, Wade

“… Cut through the noise and get real results with a step-by-step approach to learning Python 3 …”
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Understanding the Robustness of Transformer-Based Code Intelligence via Code Transformation: Challenges and Opportunities von Li, Yaoxian, Qi, Shiyi, Gao, Cuiyun, Peng, Yun, Lo, David, Lyu, Michael R., Xu, Zenglin

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

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

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

“… Pseudo-code written in natural language can aid the comprehension of source code in unfamiliar programming languages …”
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Towards Identifying Code Proficiency Through the Analysis of Python Textbooks von Rojpaisarnkit, Ruksit, Robles, Gregorio, Kula, Raula Gaikovina, Wang, Dong, Ragkhitwetsagul, Chaiyong, Gonzalez-Barahona, Jesus M., Matsumoto, Kenichi

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

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

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

“… 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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