Research on the Evolution of Python Frameworks and Their Impact on the Development of Information Technology

Тетяна Леонідівна Столяренко

Abstract


This article analyzes the development of the Python framework ecosystem and evaluates its impact on various fields of information technology. The study explores the transition from early CGI protocols to modern asynchronous architectures based on ASGI, which ensure high performance in high-load systems. It identifies patterns regarding the influence of architectural decisions (monolithic and microservices) on the stability, scalability, and security of information systems. The focus is placed on categories of web frameworks (Django, Flask, FastAPI, etc.), machine learning and deep learning frameworks (TensorFlow, PyTorch), test automation tools (Selenium, Robot Framework), DevOps tools (Ansible, Fabric), and toolsets for API development and graphical user interfaces.

The article details the key evolutionary stages of these frameworks, their application areas (software development, data science, cloud computing, automation, cybersecurity, system integration), and examples of their impact on the IT industry. According to Python community surveys for 2024–2025, the leading web frameworks are FastAPI (38%), Django (35%), and Flask (34%), while the top ML libraries are scikit-learn (68%), PyTorch (66%), and TensorFlow (49%). For instance, Django (created in 2003–2005) was designed for the rapid creation of complex, database-driven websites, whereas Flask (first released in 2010) became an immensely popular microframework used by companies like Pinterest and LinkedIn. TensorFlow (2015) and PyTorch (2016) facilitated the rapid expansion of artificial intelligence tools. In the field of automation, Selenium (since 2004) and Robot Framework (since 2005) became de facto standards for web and system interface testing, while DevOps tools such as Ansible (2012) and Fabric significantly simplified system configuration and software deployment. Furthermore, Python frameworks for scientific computing (Pandas, NumPy, SciPy) and ML tasks provided the core instruments for data science. Overall, the study examines current trends and practical examples of Python framework applications in IT, demonstrating their importance in accelerating development and implementing innovations.

Keywords


Python, frameworks, API evolution, WSGI, ASGI, microservices architecture, Django, Flask, TensorFlow, PyTorch, Selenium, Robot Framework, Ansible, Fabric, software development, data science, cloud computing, automation, cybersecurity

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DOI: https://doi.org/10.32620/oikit.2026.107.12

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