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Journal articles on the topic 'Langage Python'

Author: Grafiati

Published: 7 July 2024

Last updated: 31 July 2025

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1

Laurent Bloch. "Un langage pour enseigner la programmation, Scheme ou Python ?" Bulletin 1024, no. 20 (November 2022): 85–95. http://dx.doi.org/10.48556/sif.1024.20.85.

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Philippot, Alexandre, Stéphane Lecasse, Bernard Riera, and François Gellot. "Développement d’un connecteur logiciel pour l’apprentissage de l’automatisme." J3eA 21 (2022): 2056. http://dx.doi.org/10.1051/j3ea/20222056.

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L’apprentissage de l’automatisme s’adresse aujourd’hui à un public d’étudiants ayant pour la plupart avant tout un background informatique. Ils/elles passent par des phases d’apprentissage de langages compilés et/ou interprétés. Il est compliqué alors pour eux de passer d’une réflexion informatique avec un langage venant du monde de l’IT (Information Technology) vers la programmation d’Automates Programmable Industriel (API), au comportement cyclique, synchrone et aux langages normalisés (IEC 61131-3) issu du monde de l’OT (Operational Technology). Ce papier présente une proposition de mise en

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KENOUFI, Abdelouahab. "Probabilist Set Inversion using Pseudo-Intervals Arithmetic." TEMA (São Carlos) 15, no. 1 (2014): 097. http://dx.doi.org/10.5540/tema.2014.015.01.0097.

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<pre>In this paper, we present how to use an interval arithmetic framework based on free algebra construction, in order to build better defined inclusion function for interval semi-group and for its associated vector space. One introduces the <span>psi</span>-algorithm, which performs set inversion of functions and exhibits some numerical examples developed with the python programming langage</pre>.

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Jovanović, S., and S. Weber. "Modélisation et accélération de réseaux de neurones profonds (CNN) en Python/VHDL/C++ et leur vérification et test à l’aide de l’environnement Pynq sur les FPGA Xilinx." J3eA 21 (2022): 1028. http://dx.doi.org/10.1051/j3ea/20220028.

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Nous présentons un ensemble de travaux pratiques qui seront dispensés au sein du Master EEA - Électronique Embarquée à l’université de Lorraine dans le cadre des modules Modélisation SystemC et Conception VLSI. Ces TP sont destinés à initier les étudiants à la compréhension, modélisation et conception des réseaux de neurones convolutifs dans des langages de description de matériel au niveau RTL (VHDL, le module Conception VLSI) et dans un langage de haut niveau (C++/SystemC, le module Modélisation SystemC). Ils sont organisés autour d’un ensemble d’outils de modélisation et de synthèse de Ment

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Graillet, Olivia, Frédéric Alicalapa, Pierre-Olivier Lucas de Peslouan, Denis Genon-Catalot, and Jean-Pierre Chabriat. "Approche pédagogique pour l’étude d’autoconsommation photovoltaïque au niveau Master avec utilisation de l’API de SolarIO en langage Python." J3eA 23 (2024): 0002. http://dx.doi.org/10.1051/j3ea/20240002.

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L’île de La Réunion, qui fait partie des Zones Non Interconnectées (ZNI), dépend actuellement à 62% des importations d’énergies fossiles pour la production d’électricité. Afin de contribuer à son autonomie énergétique, il est nécessaire de développer les sources d’énergies renouvelables locales. Dans ce contexte, l’unité de recherche ENERGY-Lab et la Faculté des Sciences et Technologies de l’Université de La Réunion proposent le cursus « Master Energie ». L’un des objectifs du Master est de permettre aux étudiants d’acquérir des compétences pouvant répondre aux problématiques énergétiques actu

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Akeel Hussein Alaasam, Hussein, Ahmed Ali Talib Al-Khazaali, Ali Hussein Aleiwi, and Doaa Wahhab Ibrahim. "Learn Land Features Using Python Language." BIO Web of Conferences 97 (2024): 00111. http://dx.doi.org/10.1051/bioconf/20249700111.

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Python has emerged as an essential programming language for research due to continuous technological advancements that emphasize its role in streamlining scientific workflows. This article elucidates Python's burgeoning impact on researchers across disciplines. Tracing Python's origins and applications within the earth sciences contextualizes its versatility. While acquiring proficiency in Python exceeds this article's scope, discussions detail its utilities for earth science data analysis, visualization, management, and rapid computations. With Python expertise, researchers can engineer custo

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Tom, Tiji. "Python Language Libraries." International Journal of Science and Research (IJSR) 10, no. 2 (2021): 1225–27. https://doi.org/10.21275/sr21220134218.

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Zhang, Zejun, Zhenchang Xing, Xiaoxue Ren, Qinghua Lu, and Xiwei Xu. "Refactoring to Pythonic Idioms: A Hybrid Knowledge-Driven Approach Leveraging Large Language Models." Proceedings of the ACM on Software Engineering 1, FSE (2024): 1107–28. http://dx.doi.org/10.1145/3643776.

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Pythonic idioms are highly valued and widely used in the Python programming community. However, many Python users find it challenging to use Pythonic idioms. Adopting rule-based approach or LLM-only approach is not sufficient to overcome three persistent challenges of code idiomatization including code miss, wrong detection and wrong refactoring. Motivated by the determinism of rules and adaptability of LLMs, we propose a hybrid approach consisting of three modules. We not only write prompts to instruct LLMs to complete tasks, but we also invoke Analytic Rule Interfaces (ARIs) to accomplish ta

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Gujar, Advait. "C vs Python: A Cursory Look with Industry Opinion." International Journal for Research in Applied Science and Engineering Technology 11, no. 10 (2023): 55–64. http://dx.doi.org/10.22214/ijraset.2023.56446.

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In this paper, the author explores the characteristics and applications of C & Python programming languages across various industries, drawing insights from interviews with professionals. The study includes a comparative analysis of C and Python based on execution time, code readability, and length. C, favored for its speed and applicability in game development and embedded systems, has complexities such as large code size and lack of cross-platform support. In contrast, Python excels in artificial intelligence, machine learning, and web scraping due to its simplicity and extensive librari

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Peta, Saphalya. "Python- An Appetite for the Software Industry." International Journal of Programming Languages and Applications 12, no. 4 (2022): 1–14. http://dx.doi.org/10.5121/ijpla.2022.12401.

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Python is a scripting language that's high- positioned, interpreted, interactive, and object- oriented. Python is intended to be a veritably accessible programming language. It generally uses English terms rather than punctuation, and it has smaller syntactical structures than other languages. Python is a must-have skill for scholars and working professionals who want to become exceptional software masterminds, especially if they work in the web development field. It's a freshman-friendly scripting language. Some of the crucial features of Python programming language are- It supports OOP as we

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Patel, Aryan. "Mojo: A Python-based Language for High-Performance AI Models and Deployment." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 07, no. 10 (2023): 1–11. http://dx.doi.org/10.55041/ijsrem26529.

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Python has become a popular language for AI model development due to its elegant and flexible programming capabilities, extensive tool ecosystem, and high-performance libraries like Numpy and PyTorch. However, Python's execution speed remains a challenge, especially for performance-critical inner loops. To address this, Python programmers often rely on wrappers for C, FORTRAN, or Rust code, leading to a "two-language" approach that introduces complexities in deployment and debugging. This research paper introduces Mojo, a promising solution to the Python performance issue, which is essentially

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Lazebna, Nataliia. "ENGLISH-LANGUAGE BASIS OF PYTHON PROGRAMMING LANGUAGE." Research Bulletin Series Philological Sciences 1, no. 193 (2021): 371–76. http://dx.doi.org/10.36550/2522-4077-2021-1-193-371-376.

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The dynamic nature of the Python programming language, the accumulation of a certain linguosemiotic basis indicates the similarity of this language with the English language, which is the international one and mediates human communication in both real and virtual worlds. In this study, the English language is positioned as the linguistic basis of Python language of programming, which is widely used in industry, research, natural language processing, textual information retrieval, textual data processing, texts corpora, and more. English language, its lexical features, text representation and i

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Alder, Denis, José Natalino Silva, João Olegário Pereira de Carvalho, Jose Do Carmo Lopes, and Ademir R. Ruschel. "La stratégie de modélisation empirique « cohort » et son application pour l¿aménagement de la forêt de Tapajós, Pará, Amazonie brésilienne." BOIS & FORETS DES TROPIQUES 314, no. 314 (2012): 17. http://dx.doi.org/10.19182/bft2012.314.a20486.

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La stratégie de modélisation empirique est ici revue et présentée ainsi que son application à l'Amazonie orientale. Le modèle de croissance Cafogrom élaboré au cours de la période 1994-1998 a pu être testé grâce aux récentes mesures de 2003 et 2007 en Forêt nationale de Tapajós dans deux zones expérimentales dénommées km67 et km114 au long de l'autoroute BR 163 reliant Santarém à Cuiabá. Le modèle montre un accroissement annuel de la forêt avec un écart annuel de moins de 15 % au cours de la période 1981-2007 sur le km67 et avec la même précision sur km114, un site moins productif, mais avec u

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Aravind Ayyagiri, Dr. Arpit Jain, and Om Goel. "Utilizing Python for Scalable Data Processing in Cloud Environments." Darpan International Research Analysis 12, no. 2 (2024): 183–98. http://dx.doi.org/10.36676/dira.v12.i2.78.

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In the age of big data and cloud computing, enterprises need to effectively analyze enormous datasets to get meaningful insights and stay ahead. Python, a popular programming language, is a strong tool for cloud-scale data processing. This research study examines Python's integration with cloud platforms and its effects on performance and efficiency in scalable data processing. The study introduces scalable data processing and cloud computing. It then discusses Python's ecosystem, including Dask, Apache Spark with PySpark, TensorFlow, and PyTorch for data processing and machine learning. The s

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Researcher. "PYTHON IN FINANCE: REVOLUTIONIZING FINANCIAL SERVICES." International Journal of Research In Computer Applications and Information Technology (IJRCAIT) 7, no. 2 (2024): 973–81. https://doi.org/10.5281/zenodo.14055211.

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This article explores the transformative impact of Python programming language on the financial services industry, focusing on its applications in algorithmic trading, risk management, and fraud detection. It traces the historical context of programming in finance and examines the reasons behind Python's widespread adoption, including its versatility, extensive library ecosystem, and ease of use. The article delves into Python's role in developing sophisticated trading algorithms, highlighting the use of libraries such as pandas, NumPy, and QuantLib, and discusses how Python-based backtesting

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Galli, Massimiliano, Enric Tejedor, and Stefan Wunsch. "A New PyROOT: Modern, Interoperable and More Pythonic." EPJ Web of Conferences 245 (2020): 06004. http://dx.doi.org/10.1051/epjconf/202024506004.

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Python is nowadays one of the most widely-used languages for data science. Its rich ecosystem of libraries together with its simplicity and readability are behind its popularity. HEP is also embracing that trend, often using Python as an interface language to access C++ libraries for the sake of performance. PyROOT, the Python bindings of the ROOT software toolkit, plays a key role here, since it allows to automatically and dynamically invoke C++ code from Python without the generation of any static wrappers beforehand. In that sense, this paper presents the efforts to create a new PyROOT with

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Dhruvitkumar V. Talati. "Python: The alchemist behind AI’s intelligent evolution." International Journal of Science and Research Archive 3, no. 1 (2021): 235–48. https://doi.org/10.30574/ijsra.2021.3.1.0169.

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In the constantly evolving landscape of Artificial Intelligence (AI), programming language selection has a defining role in the generation of innovation. This paper examines the harmonious dance between Python and AI, a language that has emerged to be the spine of intelligent systems. In a comprehensive review, we bring forth Python's heterogeneity and dominance in AI design, with applications in real life in machine learning, deep learning, natural language processing, robotics, and more. A deeper look into the strengths of Python—simplicity, rich libraries, and supportive community—offers a

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Dr., K. Sreekala, N. Musrat Sultana Ms., and B.Thirumala. "LANGUAGE TRANSLATOR USING PYTHON." Journal of Advancement in Software Engineering and Testing 7, no. 3 (2024): 49–56. https://doi.org/10.5281/zenodo.12634752.

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<em>In today's interconnected world, effective language translation systems are essential for communication across linguistic boundaries. This project presents the development of an advanced language translator using Python, which harnesses the capabilities of cutting-edge natural language processing (NLP) techniques and machine learning algorithms. </em> <em> The translation system begins with a robust data preprocessing stage, where text data is cleaned, tokenized, and normalized to ensure optimal performance. Next, the core translation engine is implemented, employing state-of-the-art

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Wibowo, Firmansyah Rekso, and Muhammad Faisal. "Comparative Analysis of Sorting Algorithms: TimSort Python and Classical Sorting Methods." JISA(Jurnal Informatika dan Sains) 7, no. 1 (2024): 11–18. http://dx.doi.org/10.31326/jisa.v7i1.1785.

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The sorted() function within the Python programming language has emerged as the primary choice among developers for sorting operations. Consequently, this study offers a comparative analysis of various classical sorting algorithms and Python's built-in sorting mechanisms, with the objective of identifying the most time-efficient sorting algorithm. The analysis involves assessing the time complexity of each algorithm while handling data arrays ranging from 10 to 1,000,000 elements using Python. These arrays are populated with randomly generated numeric values falling within the range of 1 to 10

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Satvoldiev, Abrorjon. "THE COLLABORATION OF PYTHON AND DATABASES: A MODERN APPROACH TO DATA-DRIVEN APPLICATIONS." INTERNATIONAL JOURNAL OF SCIENCE AND TECHNOLOGY 2, no. 06 (2025): 88–90. https://doi.org/10.70728/tech.v2.i06.032.

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In the era of data-driven decision-making, the synergy between programming languages and database systems has become paramount. This study explores the integration of Python—a versatile and widely-used programming language—with relational databases such as SQLite, MySQL, and PostgreSQL. Employing libraries like sqlite3, psycopg2, and Object-Relational Mapping (ORM) tools such as SQLAlchemy and Django ORM, the research demonstrates how Python facilitates efficient database interactions. Through practical implementations and performance benchmarks, the study highlights Python's strengths in simp

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Bronshteyn, I. E. "Type inference for Python programming language." Proceedings of the Institute for System Programming of RAS 24 (2013): 161–90. http://dx.doi.org/10.15514/ispras-2013-24-9.

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Ye, Chengze, Zhuoyang Shen, Yue Wu, and Pavel Loskot. "Reconsidering Python Syntax to Enhance Programming Productivity." International Journal for Research in Applied Science and Engineering Technology 12, no. 3 (2024): 776–85. http://dx.doi.org/10.22214/ijraset.2024.58903.

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Abstract: Data analytics plays a crucial role in today's society across various domains, driven by technological advancements and exponential data growth. Handling large-scale data poses a challenge due to increased computational and storage requirements. The heterogeneity of tasks in data analytics programming languages complicates integration and interaction, necessitating effective cross-language integration for productivity and extended capabilities. This paper proposes a generalized interpreter accepting various language syntaxes, primarily based on Python and MATLAB, with comparisons to

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Galimullin, Niyaz R. "PYTHON: USING PYTHON TO AUTOMATE EVERYDAY TASKS." EKONOMIKA I UPRAVLENIE: PROBLEMY, RESHENIYA 9/9, no. 150 (2024): 69–76. http://dx.doi.org/10.36871/ek.up.p.r.2024.09.09.010.

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The article discusses the possibilities of using the Python programming language to automate everyday tasks in various fields. The main automation scenarios are presented, such as data processing and analysis, file and document management, interaction with web APIs and cloud services. The advantages of using Python for automation are described, including increased productivity, reduced time costs and reduced likelihood of errors. The limitations and challenges that user face when imple-menting automation are discussed, as well as prospects for further development in this area.

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Sibiya, Malusi. "Pattern Matching in Python: Expanding the Horizons of Engineering Applications." International Conference on Artificial Intelligence and its Applications 2023 (November 9, 2023): 80–86. http://dx.doi.org/10.59200/icarti.2023.011.

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Pattern matching is a powerful programming construct that simplifies code, enhances readability, and enables efficient handling of complex data structures. This paper introduces the pattern matching feature newly introduced in Python 3.10 and explores its applications in various engineering domains. The aim of this research is to showcase how Python's pattern matching capability can be leveraged for parsing and analyzing data, structural matching in data analysis, model and system validation, and signal processing. Through illustrative examples and case studies, we demonstrate the versatility

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Tripathi, Ramesh Chandra. "Python: The future programming language." Asian Journal of Multidimensional Research 10, no. 11 (2021): 105–9. http://dx.doi.org/10.5958/2278-4853.2021.01067.3.

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Goto, Isao. "Python for Natural Language Processing." Journal of The Institute of Image Information and Television Engineers 72, no. 11 (2018): 909–12. http://dx.doi.org/10.3169/itej.72.909.

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Sharma, Mrs Anush, Ankit Choudhary, Himanshu Himanshu, and Aman Chaudhary. "Exploring Python: A Comprehensive Guide for Data Science, Machine Learning, and IoT." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 10 (2024): 1–3. http://dx.doi.org/10.55041/ijsrem37816.

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Python is a versatile, high-level programming language that has gained immense popularity in various domains, especially in data science, machine learning, and the Internet of Things (IoT). Originally created by Guido van Rossum, Python's simplicity and extensive libraries make it an ideal choice for both beginners and experienced programmers. This paper aims to provide an overview of Python's application in these fields, highlighting essential tools and libraries while showcasing practical examples. By delving into Python's features and capabilities, we aim to demonstrate its pivotal role in

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Patil, Mr Vrushab, Mr Pradeep Parit, Miss Ruchita Yadav, Mr Aniruddha Yalgudre, Mr Prathamesh Gurav, and Prof P. R. Desai. "Deaf Helper Using Python." International Journal for Research in Applied Science and Engineering Technology 11, no. 11 (2023): 244–48. http://dx.doi.org/10.22214/ijraset.2023.56432.

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Abstract: The Deaf helper using machine learning project represents a pivotal endeavor aimed at bridging communication gaps and enhancing accessibility for the deaf and hard of hearing community. In a world where spoken language dominates, this project harnesses the power of machine learning to facilitate seamless communication for individuals who rely on sign language as their primary mode of expression. At its core, this project leverages state-of-the-art machine learning techniques, including computer vision and natural language processing, to recognize and translate sign language gestures

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Sahani, Sweety, and Sushmitha Mary. "Chatbot Using Python." International Journal for Research in Applied Science and Engineering Technology 10, no. 5 (2022): 3565–68. http://dx.doi.org/10.22214/ijraset.2022.43045.

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Abstract: A chatbot enables a user to simply ask questions in the same manner that they would respond to humans. The most well-known chatbots currently are voices chatbots: SIRI and Alexa. However, chatbots have been adopted and brought into the daily application at a high rate on the computer chat platform. NLP also allows computers and algorithms to understand human interactions through various languages. Recent advances in machine learning have greatly improved the accurate and effective of natural language processing, making chatbots a viable option for many organizations. This improvement

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Munawar, Kashif, and Muhammad Shumail Naveed. "The Impact of Language Syntax on the Complexity of Programs: A Case Study of Java and Python." Vol 4 Issue 3 4, no. 3 (2022): 683–95. http://dx.doi.org/10.33411/ijist/2022040310.

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Programming is the cornerstone of computer science, yet it is difficult to learn and program. The syntax of a programming language is particularly challenging to comprehend, which makes learning arduous and affects the program's testability. There is currently no literature that definitively gives quantitative evidence about the effect of programming language complex syntax. The main purpose of this article was to examine the effects of programming syntax on the complexity of their source programs. During the study, 298 algorithms were selected and their implementations in Java and Python were

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Islam, Md Tohidul, Md Rakibul Islam, Md Sabbir Faruque, Syed Mohammed Daiam Ullah Daiam, and Md Minhajul Islam. "Comparative Stock Performance Analysis of Leading Electric Vehicle Brands: Tesla, BYD, and NIO Using Python Programming Language." European Journal of Theoretical and Applied Sciences 2, no. 4 (2024): 327–38. http://dx.doi.org/10.59324/ejtas.2024.2(4).27.

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This research paper aims to perform a comparative stock performance analysis of three leading electric vehicle brands: Tesla, BYD, and NIO, utilizing the Python programming language. The primary objective is to examine the financial trajectories of these companies by analyzing their historical stock prices, volatility, and return on investment over a defined period. Python programming language is also a key part of data analysis and data visualization. Methodologically, the study employs various Python libraries for data collection, preprocessing, and analysis, ensuring a robust and efficient

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Md, Tohidul Islam, Rakibul Islam Md, Sabbir Faruque Md, Mohammed Daiam Ullah Daiam Syed, and Minhajul Islam Md. "Comparative Stock Performance Analysis of Leading Electric Vehicle Brands: Tesla, BYD, and NIO Using Python Programming Language." European Journal of Theoretical and Applied Sciences 2, no. 4 (2024): 327–38. https://doi.org/10.59324/ejtas.2024.2(4).27.

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This research paper aims to perform a comparative stock performance analysis of three leading electric vehicle brands: Tesla, BYD, and NIO, utilizing the Python programming language. The primary objective is to examine the financial trajectories of these companies by analyzing their historical stock prices, volatility, and return on investment over a defined period. Python programming language is also a key part of data analysis and data visualization. Methodologically, the study employs various Python libraries for data collection, preprocessing, and analysis, ensuring a robust and efficient

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Li, Yang, Yanchen Zou, and Yanda Qian. "Practice of Python in Programming and Optimization of Quantitative Analysis Model of Fixed Income." Mathematical Modeling and Algorithm Application 3, no. 1 (2024): 23–26. http://dx.doi.org/10.54097/z441c084.

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Python has become an indispensable tool in fixed income trading. This paper introduces the principles and general process of data analysis visualisation by analysing the basic operation and performance of personal investment and finance, combined with data analysis in the era of big data. The library of data analysis tools using Python has become an indispensable tool in fixed income trading. In fixed income trading, it supports fixed income traders to handle tasks such as bond pricing, interest rate modelling, and credit risk analysis Using Python's powerful data processing capabilities, trad

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Maheswara, Reddy Basireddy. "Developing Tools to Compare Databases using Python." European Journal of Advances in Engineering and Technology 10, no. 2 (2023): 56–61. https://doi.org/10.5281/zenodo.13253397.

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Organizations frequently use several databases to store and manage their data in today's data-driven environment. It can be difficult to guarantee data integrity and consistency among various databases, though. This work investigates the creation of tools for database comparisons using Python, an effective and adaptable programming language. With the help of Python's vast library ecosystem, programmers may construct powerful tools that can connect to many kinds of databases, extract data, and carry out in-depth comparisons. In order to provide developers with a thorough manual for streamlining

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Maheswara, Reddy Basireddy. "Developing Tools to Compare Databases using Python." European Journal of Advances in Engineering and Technology 10, no. 2 (2023): 56–61. https://doi.org/10.5281/zenodo.13325121.

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Organizations frequently use several databases to store and manage their data in today's data-driven environment. It can be difficult to guarantee data integrity and consistency among various databases, though. This work investigates the creation of tools for database comparisons using Python, an effective and adaptable programming language. With the help of Python's vast library ecosystem, programmers may construct powerful tools that can connect to many kinds of databases, extract data, and carry out in-depth comparisons. In order to provide developers with a thorough manual for streamlining

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Thaker, Nimit, and Abhilash Shukla. "Python as Multi Paradigm Programming Language." International Journal of Computer Applications 177, no. 31 (2020): 38–42. http://dx.doi.org/10.5120/ijca2020919775.

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Турдубаева, Кандалатхан Ташполотовна, and Гүлжамал Максатовна Маатова. "WRITING CODE IN PYTHON PROGRAMMING LANGUAGE." Илимий-маалыматтык журналы 23, no. 7 (2023): 99–104. http://dx.doi.org/10.58494/esai.23(7).2023.20.

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To teach the Python programming language in schools, it is necessary to teach code examples. Code examples provide students with a visual representation of programming concepts. Python’s clean, readable syntax helps students understand the basic building blocks of the language and see the structure of the code. These examples cover basic programming concepts and can serve as a starting point for learning Python. This article answers this question by revealing the main benefits of Python, its applications in various fields, and resources for learning the language.

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Borcherds, P. H. "Python: a language for computational physics." Computer Physics Communications 177, no. 1-2 (2007): 199–201. http://dx.doi.org/10.1016/j.cpc.2007.02.019.

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Chodarev, Sergej, and Sharoon Ilyas. "Metamodel-based Language Definition with Python." IPSI Transactions on Internet Research 19, no. 01 (2023): 32–38. http://dx.doi.org/10.58245/ipsi.tir.2301.06.

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Most of the parser tools are concentrated on concrete syntax and grammar definition. This paper describes a language definition tool that uses a metamodel specification instead of grammar as the basis of the language definition. Inspired by a similar Java tool known as YAJCo, the metamodel is defined using usual object-oriented techniques—as classes in the Python programming language, and the result of the parsing process is a graph of objects. The tool is demonstrated in a case study of a simple imperative programming language. We explain our design decisions and also demonstrate the suitabil

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Taiirbekova, R., B. Kochkonbaeva, and D. Kalbaeva. "Data Analysis using Python Programming Language." Bulletin of Science and Practice 11, no. 5 (2025): 139–44. https://doi.org/10.33619/2414-2948/114/20.

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Today, Python is the most powerful programming language for processing and analyzing big data. This is achieved through many useful language libraries, which are updated every day with new features. This article discusses the analysis of the demographic state of the population of Kyrgyzstan for 2019-2023 using the Python programming language and its libraries for processing and visualizing data. Using Python to analyze demographic data allows you to effectively process large amounts of information, identify patterns and present the results in a visual form. This approach can be useful for gove

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Chen, Junqiao. "Model Algorithm Research based on Python Fast API." Frontiers in Science and Engineering 3, no. 9 (2023): 7–10. http://dx.doi.org/10.54691/fse.v3i9.5591.

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In recent years, the application of Python programming language in developing web services has gained significant attention, with FASTAPI emerging as a prominent framework for its rapid development and efficient performance. This paper delves into the realm of model algorithm research, leveraging the capabilities of Python's FASTAPI framework. Through this study, we explore the integration of advanced algorithms within the context of web-based applications. By focusing on the seamless amalgamation of algorithmic processes with FASTAPI's structure, we aim to demonstrate the feasibility and adva

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Joshi, Vivek. "Virtual Assistant Using Python." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 05 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem33777.

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Today there is huge Advancement in the Technical field which is increasing day by day. In early days there were only computer systems where we were able to perform only few tasks, but today new technologies like machine learning, artificial intelligence, deep learning, and few some others have made computer systems so advance that we can perform any type of task with them. In recent years, Artificial Intelligence (AI) have done remarkable progress and its Capability is increasing day by day. One of the application Area of AI is Natural Language Processing (NLP). Natural Language Processing (NL

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Xiong, Taisong, and Yuanyuan Huang. "Research on Python Language Teaching Based on Case." Scholars Journal of Arts, Humanities and Social Sciences 9, no. 10 (2021): 513–15. http://dx.doi.org/10.36347/sjahss.2021.v09i10.005.

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Python language is a more and more widely used programming language. It becomes an inevitable choice to chose Python language as an undergraduate programming language teaching. Aiming at the current Python language teaching, focusing on basic grammar explanations, lack of case-based and comprehensive application of knowledge points. We propose a case-based python teaching plan, and these cases include comprehensively Python grammar knowledge to effectively enhance students' learning interest and learning effect.

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Sahu, Chiranjeev, and Kranti Kumar Dewangan. "Stock Market Prediction using Twitter." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 07, no. 10 (2023): 1–11. http://dx.doi.org/10.55041/ijsrem26020.

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This study presents an assessment of monetary trade discussions on Twitter using Python. The fast improvement of online diversion has uncovered it a critical focal point for getting a handle on feeling towards stocks. We preprocess an enormous dataset of tweets connected with explicit stock images by using Python's strong elements. We use feeling assessment strategies to gauge the assessment (great, negative, or unprejudiced) imparted in these tweets. Additionally, we are able to identify potential correlations between changes in the stock market and patterns and trends in Twitter sentiment by

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shi, Dongzhe. "Simulating strong gravity-lensing effect using python with 10 source and 20 lensing galaxies." Theoretical and Natural Science 14, no. 1 (2023): 85–90. http://dx.doi.org/10.54254/2753-8818/14/20240883.

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This report explores using Python, a coding language, to create simulated images of a gravitational lens system, using the Hubble Space Telescope (HST) parameters. With Pythons helpful tools, like NumPy for math operations and Astropy for astronomy tasks, we build algorithms that recreate the interactions within our chosen group of galaxies and take into account HSTs unique imaging capabilities. Our method combines theory of gravitational lensing with practical coding strategies to make simulations show these complex light-bending interactions. The report walks through how the algorithms are d

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Kumar*1,, Mr Alok, Mr Pradeep Kumar Sharma *2,, and Mr Mohit Kumar Tyagi*3. "The High Demanding Programming Language for Data Science-Python." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 07 (2024): 1–9. http://dx.doi.org/10.55041/ijsrem34913.

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Python is a suitable language for both learning and real world programming. Python is a powerful high-level, object-oriented programming language created by Guido van Rossum. In this paper we first introduce you to the python programming characteristics and features. This paper also discusses about the reasons behind python being credited as the fastest growing programming language in the recent times supported by research done over the articles procured from various magazines and popular websites. This paper features about the characteristics and most important features of python language, th

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Mishra, Pawan, ShubhamKumar Singh, Sonu Mishra, and Siddharth Singh. "User Authentication System Using Python." International Journal of Innovative Research in Advanced Engineering 11, no. 12 (2024): 957–63. https://doi.org/10.26562/ijirae.2024.v1112.11.

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User authentication is a critical component in ensuring the security and privacy of digital systems. This paper User authentication is a critical component in ensuring the security and privacy of digital systems. This paper explores the implementation of user authentication explores the implementation of user authentication systems using Python, a versatile and widely-used systems using Python, a versatile and widely used programming language. With its robust libraries and frame works, Python provides a comprehensive eco system for designing, developing, and deploying secure authentication mec

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V C, Dr Mahavishnu, Roopakumar R, Vikhas S G, and Abivishvas A. "Standalone Chatbot Application in Python." International Journal for Research in Applied Science and Engineering Technology 10, no. 7 (2022): 1244–50. http://dx.doi.org/10.22214/ijraset.2022.45445.

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Abstract: A chatbot is an artificial intelligence (AI) software that can simulate a conversation (or a chat) with a user in natural language through messaging applications, websites, mobile apps or through the telephone. This software is used to do duties such as replying swiftly to users, informing them, assisting with product purchases, and delivering better customer support. Business groups are increasingly using chatbots because they may minimize customer support costs and handle several consumers at once. However, in order to complete various jobs, chatbots must be as efficient as possibl

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Jyoti Singh, Sarita Passey, Anjali, and Hemant Verma. "COMPUTATIONAL VISUALIZATION OF 3D BRAVAIS LATTICES USING PYTHON." RASAYAN Journal of Chemistry 18, no. 02 (2025): 678–96. https://doi.org/10.31788/rjc.2025.1829028.

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The Bravais lattice is a fundamental concept in crystallography that provides a mathematical framework to describe the periodic arrangement of points in a crystal. The significance of Bravais lattices lies in representing the fundamental symmetry and shape of crystals, making accurate visualization crucial for chemists and material scientists. Traditional visualization methods often rely on specialized software that may lack flexibility or customization options. This paper presents a novel approach for visualizing Bravais lattices in both 2D and 3D, using Python, an increasingly popular progra

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Kuznetsova, А. А. "APPLYING OF PYTHON TOOLS IN A STATISTICS COURSE." CURRENT PROBLEMS OF TEACHING MATHEMATICS AT TECHNICAL UNIVERSITY 10 (2023): 64–68. http://dx.doi.org/10.25206/2307-5430-2023-10-64-68.

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The methodological aspects of teaching mathematical statistics in a technical university using laboratory work are considered. Examples of tasks that can be solved using the SciPy and NumPy libraries of the Python language are given. The advantages of this programming language over other computer mathematical systems are substantiated. The problems of generating data from a given distribution, constructing confidence intervals, testing hypotheses, correlation analysis, and some others are considered.

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