Suchergebnisse - (( (statement OR stat) python code analysis ) OR ( (statement OR state) python code analysis ))

Percolation theory using Python von Malthe-Sørenssen, Anders

“… Readers will learn how to generate, analyze, and comprehend data and models, with detailed theoretical discussions complemented by accessible computer codes …”
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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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Statistics Using Python von Campesato, Oswald

“… This book is designed to offer a fast-paced yet thorough introduction to essential statistical concepts using Python code samples, and aims to assist data scientists in their daily endeavors …”
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Assessing code readability in Python programming courses using eye‐tracking von Segedinac, Milan, Savić, Goran, Zeljković, Ivana, Slivka, Jelena, Konjović, Zora

“… This paper assesses the readability of Python code statements commonly used in undergraduate programming courses …”
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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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Mitigating the Uncertainty and Imprecision of Log-Based Code Coverage Without Requiring Additional Logging Statements von Xu, Xiaoyan, Cogo, Filipe R., McIntosh, Shane

“… Understanding code coverage is an important precursor to software maintenance activities (e.g., better testing …”
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Detecting Memory Errors in Python Native Code by Tracking Object Lifecycle with Reference Count von Ma, Xutong, Yan, Jiwei, Zhang, Hao, Yan, Jun, Zhang, Jian

“… To detect such problems, state-of-the-art approaches have made groundbreaking contributions to identifying inappropriate final refcount values before returning from native code to Python …”
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Evaluating Large Language Models in Class-Level Code Generation von Du, Xueying, Liu, Mingwei, Wang, Kaixin, Wang, Hanlin, Liu, Junwei, Chen, Yixuan, Feng, Jiayi, Sha, Chaofeng, Peng, Xin, Lou, Yiling

“… , function-level or statement-level code generation), which mainly asks LLMs to generate one single code unit (e.g …”
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GraphPyRec: A novel graph-based approach for fine-grained Python code recommendation von Zong, Xing, Zheng, Shang, Zou, Haitao, Yu, Hualong, Gao, Shang

“… Significant progress has been made in code recommendation for static languages in recent years, but it remains challenging for dynamic languages like Python as accurately determining data flows …”
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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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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

“… , using validated FEM commercial software. The methodology was validated by comparing the sensitivity analysis with the results obtained through finite differences and solving two benchmark problems with the following optimizers …”
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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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Quantifying the interpretation overhead of Python von Zhang, Qiang, Xu, Lei, Zhang, Xiangyu, Xu, Baowen

“… •Quantitative overhead analysis for the Python interpreter based on sampling.•Overhead decomposition from both opcode and project composition perspectives …”
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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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Robustness Analysis of Watermarking Techniques for LLM-Generated Code von Cao, Di, Liu, Shigang, Zhang, Jun, Xiang, Yang

“… This work systematically evaluates two state-of-the-art watermarking paradigms: SWEET, which embeds token-level statistical biases, and CodeMark, which leverages semantic-preserving transformations at the statement level …”
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Visualization of Aqueous Geochemical Data Using Python and WQChartPy von Yang, Jing, Liu, Honghua, Tang, Zhonghua, Peeters, Luk, Ye, Ming

“… This method note presents WQChartPy, an open‐source Python package developed to plot a total of 12 diagrams for analysis of aqueous geochemical data …”
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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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ProPy: Prolog-based Fault Localization Tool for Python von Morin, Janneke, Ghosh, Krishnendu

“… By examining which statements of the program were executed in successful versus unsuccessful test cases, then extract insight into the "suspiciousness" of areas of the program …”
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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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