Siga este link para ver outros tipos de publicações sobre o tema: Langage Python.
Artigos de revistas sobre o tema "Langage Python"
Crie uma referência precisa em APA, MLA, Chicago, Harvard, e outros estilos
Veja os 50 melhores artigos de revistas para estudos sobre o assunto "Langage Python".
Ao lado de cada fonte na lista de referências, há um botão "Adicionar à bibliografia". Clique e geraremos automaticamente a citação bibliográfica do trabalho escolhido no estilo de citação de que você precisa: APA, MLA, Harvard, Chicago, Vancouver, etc.
Você também pode baixar o texto completo da publicação científica em formato .pdf e ler o resumo do trabalho online se estiver presente nos metadados.
Veja os artigos de revistas das mais diversas áreas científicas e compile uma bibliografia correta.
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.
Texto completo da fonte
2
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.
Texto completo da fonteResumo:
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 place d’un connecteur logiciel entre ces deux mondes aux travers de l’utilisation d’un logiciel de simulation de Parties Opératives Factory I/O (realgames.co) et du langage Python.
3
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.
Texto completo da fonteResumo:
<pre><!--StartFragment-->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<!--EndFragment--></pre>.
4
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.
Texto completo da fonteResumo:
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 Mentor Graphics (Modelsim, Catapult HLS) et spécifiques aux plateformes FPGA Xilinx et à l’environnement Pynq pour la simulation, test et vérification.
5
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.
Texto completo da fonteResumo:
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 actuelles. A La Réunion, le secteur des installations photovoltaïques est particulièrement actif en raison d’un fort potentiel solaire, lié à son climat subtropical. La démarche pédagogique présentée dans ce document est ainsi composée d’activités en lien avec l’optimisation de l’autoconsommation d’une centrale photovoltaïque : établir un plan de sobriété énergétique, affiner la précision du dimensionnement de la centrale PV et simuler des flux de puissances. Les différents outils et technologies utilisés (tableurs, programmation Python et G LabVIEW, API, bases de données) ont été choisis pour s’adapter à la fois à un contexte scientifique et industriel.
6
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.
Texto completo da fonteResumo:
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 customized software with domain-specific tools to advance all earth science spheres. Ultimately, this article underscores Python's position as a vital programming language for contemporary academic research through its flexibility and specialization for scientific use cases.
7
Tom, Tiji. "Python Language Libraries." International Journal of Science and Research (IJSR) 10, no. 2 (2021): 1225–27. https://doi.org/10.21275/sr21220134218.
Texto completo da fonte
8
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.
Texto completo da fonteResumo:
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 tasks. The ARIs are Python code generated by prompting LLMs to generate code. We first construct a knowledge module with three elements including ASTscenario, ASTcomponent and Condition, and prompt LLMs to generate Python code for incorporation into an ARI library for subsequent use. After that, for any syntax-error-free Python code, we invoke ARIs from the ARI library to extract ASTcomponent from the ASTscenario, and then filter out ASTcomponent that does not meet the condition. Finally, we design prompts to instruct LLMs to abstract and idiomatize code, and then invoke ARIs from the ARI library to rewrite non-idiomatic code into the idiomatic code. Next, we conduct a comprehensive evaluation of our approach, RIdiom, and Prompt-LLM on nine established Pythonic idioms in RIdiom. Our approach exhibits superior accuracy, F1 score, and recall, while maintaining precision levels comparable to RIdiom, all of which consistently exceed or come close to 90% for each metric of each idiom. Lastly, we extend our evaluation to encompass four new Pythonic idioms. Our approach consistently outperforms Prompt-LLM, achieving metrics with values consistently exceeding 90% for accuracy, F1-score, precision, and recall.
9
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.
Texto completo da fonteResumo:
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 libraries. The article emphasizes the influence of programming communities on language popularity, noting Python's widespread adoption due to its concise syntax and strong community support. Industry experts concur on C's complexity and time-intensive nature but acknowledge its effectiveness. Python's ease of learning has made it the world's most widely used language, prompting non-coding sectors to encourage Python education.
10
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.
Texto completo da fonteResumo:
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 well as functional and structured programming methodologies. It can be used as a scripting language or collected into bytecode for large-scale operations. It allows dynamic type verification and provides veritably high-position dynamic data types. It facilitates scrap collection by itself. Numerous different programming languages have been impacted by Python's design and gospel. Some of those languages are Boo, Cobra, CoffeeScript, Go, Swift, Ruby, etc. Some of the advantages of Python programming language are straightforward, free, simple to use, and largely compatible, object- acquainted, has multitudinous libraries, has erected in data structures, has a wide range of uses, boosts productivity and speed, and simple to understand. One of the most extensively used programming languages is Python. It's an open- source language. Python's demand is growing, and its operations are expanding in virtually every assiduity. It's abundant in every way. It has a wide range of capabilities. Python is a popular programming language. It's also developing a strong request in the IT sector. Python is in high demand across the globe. Python helps you negotiate more in lower time. Python has a large community that supports and meets the requirements of inventors. Python is therefore one of the most popular programming languages. It's a veritably reliable and effective programming language. Python programmers are in high demand because Python is being used in a variety of sectors. Python is an extensively used computer language that was created nearly 25 years ago. Python is useful in a variety of fields, including web development, desktop app development, machine literacy, big data, data analysis, and robotics. Clean syntax, extremely clear law, a wide range of uses, packages that help apply features, and a cool community that helps grow this excellent language are just a many of the reasons why people like this language and why it's well suited for different tasks. The Python programming language has a bright future. The advanced technologies like Artificial Intelligence, Machine Learning, Big Data, Cloud Computing, Data Science, etc and world-notorious companies similar as Amazon, Google, Apple, Deloitte, Microsoft, Netflix, and Accenture have the Python programming language as their backbone which states that Python is in demand and AN APPETITE FOR THE SOFTWARE INDUSTRY! A standard and scientific procedure of an Empirical Exploration Methodology (Survey) was conducted to check the statement stated by the author where 900 repliers from colourful corridors of the globe shared their thoughts. From the check, it was concluded that 99.8% of the respondents feel that Python is one of the in- demand programming languages for the digital assiduity in the present time.
11
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.
Texto completo da fonteResumo:
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 Python++ and built on top of MLIR (Multi-Level Intermediate Representation). Mojo is a rigorously designed superset of Python that allows seamless integration of high-performance implementations by switching to a faster "mode." This paper discusses the key features of Mojo, its deployment advantages, and its comparison with other alternatives in the AI and ML development landscape.
12
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.
Texto completo da fonteResumo:
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 interaction with logical and functional basis in the context of Python programming language are considered further in this research. Thus, the unity of verbal units and symbols in the modern English-language digital discourse indicates both the order and variability of the constituents therein. The functionality of linguosemiotic elements produces a network of relationships, where each of these integrated elements can produce from a word or symbol a holistic set of units, which are extrapolated in the English-language digital discourse and mediates human communication with a machine. An overview of the basic properties of Python language, such as values, types, expressions, and operations are in focus of the study. Though users understand the responses of Python interpreter, there is a need to follow certain instructions and codes. To facilitate work with this programming language and prescribed English-language commands, it is necessary to involve linguists to cooperate with programmers to invent a certain logical and reasonable principle of Python commands operation.
13
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.
Texto completo da fonteResumo:
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 un biais accru de sous-estimation d'environ 32 % en 26 ans. L'accroissement moyen annuel du volume des arbres de plus de 50 cm de diamètre (DBH) a été de 2,2 m3/ha/an en 26 ans, dont 1,2 m3/ha/an (54 %) pour les essences commerciales. Les parcelles étudiées sur le site km114, le moins productif, ont eu un accroissement moyen de 1,07 m3/ha/an au cours de vingt ans couvrant la période 1983-2003. En considérant les règles du gouvernement brésilien dont l'intensité maximale d'exploitation est de 30 m3/ha avec une rotation de passage en coupe de 35 ans (0,86 m3/ha/an), la viabilité de ce régime conservateur est confirmée à condition que l'exploitation comprenne une gamme variée d'espèces commerciales. La stratégie de mise à jour de Cafogrom est détaillée, elle devra être réécrite sous la forme d'une application en langage Python dans le cadre contextuel Myrlin/ Fmt (www.myrlin.org, www.eofmt.com).
14
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.
Texto completo da fonteResumo:
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 study also examines Python's interoperability with cloud services like AWS, Google Cloud Platform, and Microsoft Azure in data input, transformation, and analysis. Many case studies and real-world applications demonstrate how Python has been used in banking, healthcare, and e-commerce. Python is useful for managing massive amounts of data, streamlining processing processes, and scaling cloud applications, as shown in the case studies. The report also analyzes Python-based cloud systems' performance indicators and cost consequences, revealing best practices and possible issues. The article explores Python's involvement in cloud computing trends and technology. Serverless architectures, Docker and Kubernetes, and Python interaction with cloud-native tools and services are examples. These patterns show how data processing is changing and how Python is improving to meet current data needs. This study concludes that Python is a reliable and scalable cloud data processing option. The language's strengths, alignment with cloud technologies, and practical applications in many areas are covered in detail. The results indicate that Python's versatility and cloud scalability provide a robust foundation for handling and analyzing massive datasets, enabling better decision-making and innovation across fields.
15
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.
Texto completo da fonteResumo:
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 methodologies have improved strategy development. In risk management, the article analyzes Python's contributions to financial forecasting, emphasizing the use of SciPy for statistical analysis and PyMC3 for Bayesian modeling. The article also investigates Python's application in fraud detection, showcasing how machine learning libraries like scikit-learn and TensorFlow have significantly enhanced detection accuracy. Through case studies and comparative analyses, the article demonstrates the tangible benefits of Python-based solutions in finance, including improved trading efficiency, more accurate risk assessments, and substantial cost savings in fraud prevention. Finally, the article addresses current limitations of Python in finance and explores future prospects, including the integration of AI and blockchain technologies. This comprehensive review underscores Python's growing importance in shaping the future of quantitative finance and driving innovation in the financial industry.
16
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.
Texto completo da fonteResumo:
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 three main qualities: modern, able to exploit the latest C++ features from Python; pythonic, providing Python syntax to use C++ classes; interoperable, able to interact with the most important libraries of the Python data science toolset.
17
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.
Texto completo da fonteResumo:
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 sufficient reason why it is now the choice of AI researchers and developers. No technology is flawless, though; hence, we also mention the shortcomings of Python, such as performance bottlenecks and runtime issues, to present an overall picture. Since a tremendous amount of programming languages are used in AI, the paper also provides a comparative summary of popular AI-guided languages in terms of their efficiency, scalability, and usability for various AI applications. By providing an overview of Python's prevalence and placement among other AI languages, the research seeks to offer developers, researchers, and professionals decision-making information for AI-project deployment.
18
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.
Texto completo da fonteResumo:
<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 machine translation models such as neural machine translation (NMT) or transformer-based architectures like BERT or GPT. These models are trained on large multilingual corpora to learn the intricacies of language structures and semantic nuances.</em> <em>To enhance translation accuracy and versatility, the system integrates with external APIs such as Google Translate, Microsoft Translator, or DeepL, thereby leveraging their vast language databases and continuous updates. Additionally, the system incorporates techniques for handling language ambiguities, idiomatic expressions, and context-dependent translations to improve overall linguistic fidelity.</em> <em>The user interface is designed to be intuitive and user-friendly, allowing users to input text in their desired language and select the target language for translation. The interface also provides options for customizing translation settings, such as choosing between literal or contextual translation modes, adjusting translation quality, and enabling bilingual text alignment for comparative analysis</em> <em>Furthermore, the system supports various text formats including plain text, documents, web pages, and multimedia content, ensuring versatility across different communication mediums. Real-time translation capabilities are also explored, enabling instant translation of spoken language through speech recognition and synthesis technologies.</em> <em>Through this comprehensive approach, the language translation system aims to address the growing demands for accurate, efficient, and accessible translation services, facilitating seamless communication and cultural exchange on a global scale.</em>
19
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.
Texto completo da fonteResumo:
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 1000. The benchmark algorithms utilized encompass Heap Sort, Shell Sort, Quick Sort, and Merge Sort. A looping mechanism is applied to each algorithm, and their execution speeds are gauged utilizing the Python 'time.perf_counter()' library. The findings of this study collectively indicate that Python's standard algorithm, surpasses classic sorting algorithms, including Heapsort, Shellsort, Quicksort, and Mergesort, in terms of execution.
20
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.
Texto completo da fonteResumo:
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 simplicity, scalability, and a rich ecosystem, while also addressing limitations like concurrency handling and performance constraints in high-load scenarios. The findings underscore Python's efficacy as a tool for students and researchers in developing robust, data-centric applications.
21
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.
Texto completo da fonte
22
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.
Texto completo da fonteResumo:
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 R and Julia. Findings reveal Python's beginner-friendly learning curve and rich resources, Julia's high-performance computing, MATLAB’s numerical prowess and specialized toolbox, and Python and R's focus on flexibility. Both Python and R boast active communities, while Python offers extensive portability, and Julia emphasizes interoperability. Despite syntactic differences, a common interpreter offers flexibility and efficiency, benefiting developers by enabling language selection based on project needs. Challenges can be mitigated through good design and technical solutions. Encouragement for research and innovation in universal interpreter development fosters collaboration, enhancing opportunities in data analysis and scientific computing. Active participation from developers and researchers is encouraged for continual improvement and advancement in the field.
23
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.
Texto completo da fonteResumo:
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.
24
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.
Texto completo da fonteResumo:
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 and effectiveness of Python pattern matching in solving real-world engineering problems. By introducing pattern matching in Python, this research opens new avenues for engineers and scientists to tackle complex data processing tasks, enhance system validation techniques, and streamline algorithmic implementations. With the integration of pattern matching into Python's ecosystem, the language becomes even more powerful and expressive, empowering practitioners to write cleaner, more concise, and efficient code. This research lays the foundation for the adoption and exploration of pattern matching techniques in Python, highlighting its potential impact on engineering applications and providing a roadmap for future research and development in this field.
25
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.
Texto completo da fonte
26
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.
Texto completo da fonte
27
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.
Texto completo da fonteResumo:
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 advancing technology and research.
28
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.
Texto completo da fonteResumo:
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 into written or spoken language and vice versa. By fusing these technologies, the project endeavors to create an inclusive and accessible communication tool.
29
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.
Texto completo da fonteResumo:
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 in NLP is firing a great deal of additional research which should lead to continued improvement in the effective of chatbots in the years to come.A bot is trained on and according to the training, based on some rules on which it is trained, it answers questions. It is called ruled based approach. The language by which these bots can be created is Artificial Intelligence Markup Language (AIML). It is a language based on XML which allows the developer to write the rules which the bot will follow. In this research paper, We are trying to understand these chatbots and understanding their shortcomings. question or statement submitted by a user and allow the user to control over the content to be displayed Keywords: AI; ML; Wordnet; Chatbot; NLP
30
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.
Texto completo da fonteResumo:
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 analyzed with the cyclomatic complexity matrix. The results of the study show that Python's syntax is less complex than Java's, and thus coding in Python is more comprehensive and less difficult than Java coding. The Mann-Whitney U test was performed on the results of a statistical analysis that showed a significant difference between Java and Python, indicating that the syntax of a programming language has a major impact on program complexity. The novelty of this article lies in the formulation of new knowledge and study patterns that can be used primarily to compare and analyze other programming languages.
31
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.
Texto completo da fonteResumo:
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 analytical process. The key findings reveal distinct performance patterns and market behaviors for each company. Tesla demonstrated high volatility but significant long-term returns, while BYD showed consistent growth with moderate volatility. NIO, as a newer entrant, exhibited rapid growth with higher short-term risks. The conclusions drawn from this study provide valuable insights into the financial health and market positioning of these EV giants. By leveraging Python's powerful data analysis capabilities, this research not only enhances understanding of stock performance in the EV sector but also offers a practical framework for investors and analysts.
32
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.
Texto completo da fonteResumo:
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 analytical process. The key findings reveal distinct performance patterns and market behaviors for each company. Tesla demonstrated high volatility but significant long-term returns, while BYD showed consistent growth with moderate volatility. NIO, as a newer entrant, exhibited rapid growth with higher short-term risks. The conclusions drawn from this study provide valuable insights into the financial health and market positioning of these EV giants. By leveraging Python's powerful data analysis capabilities, this research not only enhances understanding of stock performance in the EV sector but also offers a practical framework for investors and analysts.
33
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.
Texto completo da fonteResumo:
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, traders can simplify data collection, analysis, and reporting to make better decisions. Based on the Python language, financial data is acquired from different platforms, processed and analysed step-by-step, ensuring that the model remains accurate and stable in different market conditions. Backtesting and ongoing performance evaluations have revealed that Python can be a valuable asset in the dynamic world of fixed income trading by refining trading strategies and effectively managing risk.
34
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.
Texto completo da fonteResumo:
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 database comparison procedures, the article explores the usage of Python database libraries, text comparison modules, and data manipulation tools.
35
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.
Texto completo da fonteResumo:
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 database comparison procedures, the article explores the usage of Python database libraries, text comparison modules, and data manipulation tools.
36
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.
Texto completo da fonte
37
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.
Texto completo da fonteResumo:
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.
38
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.
Texto completo da fonte
39
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.
Texto completo da fonteResumo:
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 suitability of a dynamic language such, as Python, for this task.
40
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.
Texto completo da fonteResumo:
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 government agencies and researchers when planning demographic policy.
41
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.
Texto completo da fonteResumo:
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 advantages of utilizing this combination in various research and practical scenarios. Coupled with illustrative examples, this paper highlights the potential of Python FASTAPI as a robust platform for driving model algorithm research across diverse domains.
42
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.
Texto completo da fonteResumo:
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 (NLP) helps Humans to communicate with the computer system in their own Language. For example, Voice Assistant. Various voice assistants were developed and they are still being improved more for better performance to overcome struggling of humans to interact with their machine. we are trying to develop a voice assistant using python which will help user to perform any type of task without interaction with keyboard. The aim of this paper is to study how voice assistants behaves smartly and can be used to get everyday work done and also be used for educational purpose also. Keywords: Virtual Assistant, UI, Artificial Intelligent, Python Library Key Features of the system are: ❖ Natural Language Understanding (NLU) ❖ Speech Recognition ❖ Text-to-Speech (TTS) Conversion ❖ User Interface ❖ Context Management
43
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.
Texto completo da fonteResumo:
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.
44
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.
Texto completo da fonteResumo:
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 employing tools for statistical analysis and visualization. This examination exhibits how to really utilize Python to investigate Twitter information and gives financial backers valuable data for going with informed securities exchange choices. In the present speedy monetary scene, information driven direction is fundamental for financial backers and merchants. This theoretical presents an extensive examination of Twitter's financial exchange execution utilizing Python, a flexible and strong programming language for information investigation and representation. The review starts by social event authentic stock cost information for Twitter (NYSE: TWTR) utilizing well known monetary APIs or web scratching strategies. Python libraries, for example, Pandas and NumPy are utilized to control and clean the information, guaranteeing its reasonableness for examination. Different information perception instruments like Matplotlib and Seaborn are saddled to make shrewd outlines and diagrams that give a visual portrayal of Twitter's stock presentation over the long haul. To acquire further experiences, the investigation integrates factual and monetary measurements, for example, moving midpoints, relative strength file (RSI), and beta coefficient. These measurements are International Journal of Scientific Research in Engineering and Management (IJSREM) Volume: 07 Issue: 10 | October - 2023 SJIF Rating: 8.176 ISSN: 2582-3930 © 2023, IJSREM | www.ijsrem.com DOI: 10.55041/IJSREM26020 | Page 2 determined utilizing Python's numerical libraries and are critical in surveying the stock's unpredictability, energy, and market risk. Opinion examination likewise assumes a huge part in understanding what Twitter's stock is meant for by web-based entertainment. Regular Language Handling (NLP) libraries like NLTK or spaCy are used to dissect tweets and news stories connected with Twitter. Feeling scores are processed to measure the public's opinion towards the organization, and this information is connected with stock cost developments. Moreover, AI models can be carried out utilizing Python's Scikit-Learn or TensorFlow libraries to anticipate future stock cost patterns in view of authentic information and opinion examination results. Techniques for time series forecasting like ARIMA and LSTM can offer useful insights into potential price movements. All in all, this Twitter Securities exchange Examination utilizing Python exhibits the force of information driven dynamic in the monetary world. Investors and traders can use Python's data manipulation, visualization, and machine learning capabilities to make better decisions, reduce risks, and possibly take advantage of market opportunities in Twitter's stock. The study demonstrates how Python's adaptability and the stock market's dynamic nature complement one another. Key Words: Twitter, Stock Market
45
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.
Texto completo da fonteResumo:
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 developed with specific scientific simulation models like Sersic profile and point-spread function (PSF), showcasing the important role of computer simulations in deepening our understanding of space. In this report, I will introduce how we can use python code to create simulation images of a gravitational lens system. This system involves with 10 source galaxies ,20 lensing galaxies and with consideration of dark matter halo.
46
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.
Texto completo da fonteResumo:
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, the types of programming supported by python and its users and its applications. Key words: Python, Programming languages, Real world programming, Data, Flask, Django
47
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.
Texto completo da fonteResumo:
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 mechanisms. The study focuses on the practical implementation of authentication methods, including password-based systems, two-factor authentication (2FA), and biometrics integration. Python libraries such as Flask and Django are employed to create backend systems, while libraries liked crypt and pass lib are utilized for hashing and securing passwords. The integration of 2FA is demonstrated using PyOTP, which facilitates one-time password generation to enhance security. This paper also highlights the role of JSON Web Tokens (JWT) in enabling secure, stateless user sessions, making them suitable for modern web and mobile applications. Python's inter operability with biometric authentication systems is explored through the use of APIs and machine learning libraries, providing a foundation for advanced authentication mechanisms. The proposed authentication system is evaluated for its security, scalability, and ease of implementation, offering insights into best practices for minimizing vulnerabilities such as brute force attacks and data breaches. Additionally, the paper emphasizes the importance of encrypting sensitive user data during transmission using libraries like cryptography and SSL/TLS protocols. In conclusion, the paper demonstrates Python's potential to implement secure, flexible, and scalable user authentication systems suitable for diverse applications. It serves as a guide for developers and researchers aiming to enhance the security of their systems through robust authentication techniques. Programming language with its robust libraries and frameworks, Python provides a comprehensive eco system for designing, developing, and deploying secure authentication mechanisms. The study focuses on the practical implementation of authentication methods, including password-based systems, two-factor authentication (2FA), and biometrics integration. Python libraries such as Flask and Django are employed to create backend systems, while libraries like bcrypt and passlib are utilized for hashing and securing passwords. The integration of 2FA is demonstrated using PyOTP, which facilitates one-time password generation to enhance security.
48
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.
Texto completo da fonteResumo:
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 possible. In this project, we provide the architecture of a chatbot, which provides a human like and accurate response for any questions raised by users using Natural Language ToolKit(NLTK) and PyTorch with python language.
49
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.
Texto completo da fonteResumo:
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 programming language in scientific research. This method covers all five 2D lattices and fourteen 3D crystal structures, leveraging Python's robust libraries such as the Matplotlib and NumPy to generate precise and customizable lattice representations. Incorporating Python into crystallographic visualization demonstrates its flexibility, accessibility and versatility showcasing its ability to integrate seamlessly with other scientific computing jobs. This approach exemplifies the fusion of Python programming with chemistry enhancing detailed analysis and educational capabilities. By using the Python code, this paper aims to equip chemists to perform the above-mentioned operations theoretically from their desks, eliminating the need for tedious manual drawing of crystal lattices, and providing access to practical computation tools relevant in today's research environment.
50
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.
Texto completo da fonteResumo:
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.