Dataset Card for The Stack

  Changelog

Release Description

v1.0 Initial release of the Stack. Included 30 programming languages and 18 permissive licenses. Note: Three included licenses (MPL/EPL/LGPL) are considered weak copyleft licenses. The resulting near-deduplicated dataset is 3TB in size.

v1.1 The three copyleft licenses ((MPL/EPL/LGPL) were excluded and the list of permissive licenses extended to 193 licenses in total. The list of programming languages… See the full description on the dataset page: https://huggingface.co/datasets/bigcode/the-stack.

In Dublin City University, students learn how to code by taking a variety of programming modules. Students develop code algorithms for problems proposed by Faculty. Many of these courses or modules are delivered through a custom Virtual Learning Environment (VLE) built for the purpose of teaching and learning computer programming. This custom VLE enables students to access course information, material and slides for each module. In addition, our system integrates an automatic grading platform where students can verify their code submissions for programming exercises. Students typically develop solutions locally for laboratory sheets for the computer programming courses. Then, they submit their programs online to the automatic grading platform which runs a number of testcases specified by the lecturer on each exercise. This provides instant feedback to students based on the suite of testcases run and ultimately tells the student whether the program is considered correct or incorrect if any of the testcases fail. This information is invaluable to their learning and such a platform is needed to verify their programs work as expected. The computer programming grading system has been used for several years on a variety of programming courses at our University. This allowed researchers and Faculty to gather a fine-grained digital footprint of students learning programming at our University. Recently, research in Learning Analytics has focused on Predictive Modelling and identifying those students having difficulties with course material, also in programming courses, and offering remediation, personalized feedback and interventions to students using Machine Learning techniques. Prior work has reported that customized notifications sent to students regarding their performance and offering resources such as further learning material, code solutions from peers in their class and university support services helped students to increase their differential performance and engagement on these programming courses. However, there is a limit to this prior work where most of the models use little or no programming work as features for the learning algorithms or feedback sent to students. In this work we explore different mechanisms to represent students’ code to predict its correctness and to better analyze students’ progress using their interactions which can be exploited to provide effective feedback and support better recommendations. Every time a student submits a code solution for verification, the system stores the code submission, the student identifier, the IP used on the network for the upload, the results of the testcases run with inputs and outputs, the course the submission belongs to, the exercise and the task name the student is attempting by using the submission’s filename. In total, we collected more than half a million programming submissions (591,707) for 666 students from 5 Python programming courses over 3 academic years.

Object Oriented Programming

## Overview

Object Oriented Programming is a dataset for object detection tasks - it contains Pedestrian annotations for 6,990 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Dataset Card for "programming-languages-keywords"

Structured version of https://github.com/e3b0c442/keywords Generated using: r = requests.get("https://raw.githubusercontent.com/e3b0c442/keywords/main/README.md") keywords = r.text.split("### ")[1:] keywords = [i for i in keywords if not i.startswith("Sources")] keywords = {i.split(" ")[0]:[j for j in re.findall("[a-zA-Z]*", i.split(" ",1)[1]) if j] for i in keywords} keywords =… See the full description on the dataset page: https://huggingface.co/datasets/bigcode/programming-languages-keywords.

About Dataset

This dataset was created during the Programming Language Ecosystem project from TU Wien using the code inside the repository https://github.com/ValentinFutterer/UsageOfProgramminglanguages2011-2023?tab=readme-ov-file.

The centerpiece of this repository is the usage_of_programming_languages_2011-2023.csv. This csv file shows the popularity of programming languages over the last 12 years in yearly increments. The repository also contains graphs created with the dataset. To get an accurate estimate on the popularity of programming languages, this dataset was created using 3 vastly different sources.

About Data collection methodology

The dataset was created using the github repository above. As input data, three public datasets where used.

github_metadata

Taken from https://www.kaggle.com/datasets/pelmers/github-repository-metadata-with-5-stars/ by Peter Elmers. It is licensed under CC BY 4.0 https://creativecommons.org/licenses/by/4.0/. It shows metadata information (no code) of all github repositories with more than 5 stars.

PYPL_survey_2004-2023

Taken from https://github.com/pypl/pypl.github.io/tree/master, put online by the user pcarbonn. It is licensed under CC BY 3.0 https://creativecommons.org/licenses/by/3.0/. It shows from 2004 to 2023 for each month the share of programming related google searches per language.

stack_overflow_developer_survey

Taken from https://insights.stackoverflow.com/survey. It is licensed under Open Data Commons Open Database License (ODbL) v1.0 https://opendatacommons.org/licenses/odbl/1-0/. It shows from 2011 to 2023 the results of the yearly stackoverflow developer survey.

All these datasets were downloaded on the 12.12.2023. The datasets are all in the github repository above

Description of the data

The dataset contains a column for the year and then many columns for the different languages, denoting their usage in percent. Additionally, vertical barcharts and piecharts for each year plus a line graph for each language over the whole timespan as png's are provided.

The languages that are going to be considered for the project can be seen here:

- Python

- C

- C++

- Java

- C#

- JavaScript

- PHP

- SQL

- Assembly

- Scratch

- Fortran

- Go

- Kotlin

- Delphi

- Swift

- Rust

- Ruby

- R

- COBOL

- F#

- Perl

- TypeScript

- Haskell

- Scala

License

This project is licensed under the Open Data Commons Open Database License (ODbL) v1.0 https://opendatacommons.org/licenses/odbl/1-0/ license.

TLDR: You are free to share, adapt, and create derivative works from this dataser as long as you attribute me, keep the database open (if you redistribute it), and continue to share-alike any adapted database under the ODbl.

Acknowledgments

Thanks go out to

- stackoverflow https://insights.stackoverflow.com/survey for providing the data from the yearly stackoverflow developer survey.

- the PYPL survey, https://github.com/pypl/pypl.github.io/tree/master for providing google search data.

- Peter Elmers, for crawling metadata on github repositories and providing the data https://www.kaggle.com/datasets/pelmers/github-repository-metadata-with-5-stars/.

programming languages statistics: The tech market which is also booming along with digital marketing is pretty good for a better income source. The tech market has many other things including programming languages. Programming languages are the basis for the formation of various websites, games, software, mobile applications, etc... There are nearly 9,000 programming languages around the world with each language with its own feature. In this most popular programming language statistics, we will have a look at statistical information and general knowledge about worldwide available various programming languages. Programming Languages Statistics (Editor’s Choice) There are 8,945 programming languages as stated by most popular Programming languages statistics. As of 2022, JavaScript is one of the most popular programming languages as around 47.86% of recruiters are demanding JavaScript language skills. A basic python developer earns between $70,000 to $1,00,00 a year. As per the most popular programming languages statistics Python has ranked number 1 in the United States of America, India, Germany, France, and the United Kingdom

The global Programming Language Learning Platform market is poised for significant expansion, projected to reach an estimated $15,000 million by 2025 with a compound annual growth rate (CAGR) of 18% through 2033. This robust growth is primarily fueled by the escalating demand for skilled professionals across various sectors, driven by rapid digital transformation and the ubiquitous integration of technology in daily life. The platform’s ability to cater to both individual skill enhancement for career advancement (Worker segment) and academic pursuit (Student segment) positions it as a crucial educational tool. The increasing adoption of in-demand languages like Python, JavaScript, and C further propels market expansion. Key players are investing heavily in innovative learning methodologies, personalized learning paths, and interactive content to enhance user engagement and learning outcomes, contributing to the market's upward trajectory. The market's growth is further augmented by several critical trends, including the rise of microlearning, gamification in education, and the increasing preference for flexible, self-paced online courses. The widespread availability of affordable internet access and personal computing devices globally democratizes access to quality programming education. However, the market also faces certain restraints, such as the high cost of developing sophisticated learning platforms and the challenge of maintaining learner engagement over extended periods. Despite these hurdles, the sustained need for digital literacy and specialized coding skills across industries, coupled with the continuous evolution of programming languages and tools, ensures a bright future for the Programming Language Learning Platform market. The Asia Pacific region, with its burgeoning tech industry and large student population, is expected to be a significant growth engine, closely followed by North America and Europe. This comprehensive report provides an in-depth analysis of the global Programming Language Learning Platform market, encompassing a study period from 2019 to 2033, with a base year of 2025. The analysis focuses on the forecast period of 2025-2033 and includes a detailed examination of the historical period from 2019-2024. The estimated market size for 2025 is projected to be in the millions, with significant growth anticipated throughout the forecast period.

In 2024 in Poland, among employees of Warsaw-based AI companies, over ** percent of possessed knowledge of the Python programming language.

Programming Education Market Outlook

The global programming education market size is anticipated to expand significantly, exhibiting a compound annual growth rate (CAGR) of 11.8% from 2024 to 2032. The market was valued at approximately USD 12.5 billion in 2023 and is projected to reach USD 29.5 billion by 2032. This growth is driven by several factors, including the increasing demand for coding proficiency across multiple sectors, the rise of digital transformation initiatives, and the growing availability of online learning platforms.

One of the critical growth factors in the programming education market is the burgeoning demand for digital skills across various industries. As businesses increasingly adopt digital technologies, there is a growing necessity for employees proficient in programming and coding. This demand is not confined to the IT sector alone but extends to healthcare, finance, manufacturing, and even creative industries. Moreover, the rise of emerging technologies such as artificial intelligence, machine learning, and big data analytics is further amplifying the need for skilled programmers who can develop, deploy, and maintain sophisticated software solutions.

Another significant driver is the increasing availability and sophistication of online learning platforms. Over the past decade, online education has revolutionized the way people acquire new skills, making learning more accessible, flexible, and affordable. Platforms such as Coursera, Udacity, and edX offer a plethora of programming courses catering to different skill levels, from beginners to advanced programmers. These platforms often collaborate with prestigious universities and tech companies, providing high-quality content that meets industry standards. Additionally, the advent of interactive and gamified learning experiences has made programming education more engaging and effective.

The emphasis on STEM (Science, Technology, Engineering, and Mathematics) education in the K-12 segment is also propelling the programming education market. Governments and educational institutions worldwide are increasingly recognizing the importance of integrating coding and programming into the school curriculum. Initiatives such as Hour of Code and Code.org are making significant strides in this direction. By introducing programming at an early age, these initiatives aim to equip students with critical problem-solving skills and prepare them for future careers in technology-driven fields.

In terms of regional outlook, North America leads the programming education market, driven by strong demand from the corporate sector and the presence of several leading educational technology companies. Europe is also witnessing substantial growth, supported by government initiatives to promote digital literacy. The Asia Pacific region is emerging as a lucrative market, owing to the increasing penetration of internet and mobile devices, coupled with a young, tech-savvy population. Latin America and the Middle East & Africa, though smaller in comparison, are showing promising growth, driven by increasing investments in educational infrastructure and a growing awareness of the importance of digital skills.

Component Analysis

In the programming education market, components are broadly categorized into software and services. The software segment comprises various tools and platforms used for coding education, including integrated development environments (IDEs), coding simulators, and gamified learning platforms. The services segment includes instructor-led training, online tutorials, workshops, and bootcamps. Both segments are critical to the overall growth and adoption of programming education.

The software segment is experiencing robust growth due to the increasing availability of sophisticated coding tools and platforms. Integrated Development Environments (IDEs) like Visual Studio Code, PyCharm, and Eclipse offer comprehensive environments where learners can write, test, and debug their code. These tools often come with built-in tutorials, sample projects, and community support, making them ideal for self-paced learning. Additionally, gamified learning platforms like Codecademy and CodeCombat are gaining traction for their interactive and engaging approach to coding education. These platforms use game mechanics to teach programming concepts, making learning fun and effective.

On the services side, instructor-led training and coding bootcamps are becoming increasingly popular, especially among professionals looking to upskill or switch careers. Coding bootcamps like Gen

This dataset contains 1 million Chinese programming questions with corresponding answers, detailed parses (explanations), and programming language labels. It includes a wide range of questions in C, C++, Python, Java, and JavaScript, making it ideal for training large language models (LLMs) on multilingual code understanding and generation. The questions cover fundamental to advanced topics, supporting AI applications such as code completion, bug fixing, and programming reasoning. This structured dataset enhances model performance in natural language programming tasks and helps reinforce code logic skills in AI systems. All data complies with international privacy regulations including GDPR, CCPA, and PIPL.

According to a survey conducted between late 2021 and early 2022, JavaScript is the most used programming language worldwide, with ** percent of respondents reporting that they use the language. Python was the second most used language at **** percent.

The online programming learning platform market is experiencing robust growth, driven by the increasing demand for skilled programmers across various industries and the accessibility of online learning resources. The market, estimated at $20 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033, reaching approximately $60 billion by 2033. This expansion is fueled by several key factors: the rising adoption of online learning models, particularly amongst younger demographics (teens and adults), the increasing affordability and accessibility of online courses compared to traditional education, and the diverse range of programming languages and specializations offered (Python, JavaScript, web development being particularly popular). The market is segmented by application (adult, child, teen learners) and course type (Python, JavaScript, Web Development, and other). Key players like Coursera, Udemy, and Codecademy are driving innovation through interactive learning environments, personalized learning paths, and project-based learning, enhancing the learning experience and attracting a broader learner base. However, market growth faces certain challenges. Competition among numerous platforms necessitates continuous innovation and adaptation to maintain a competitive edge. Ensuring the quality and relevance of the courses offered remains crucial, as does addressing the digital divide and ensuring equitable access to online learning opportunities across different regions and socioeconomic backgrounds. Furthermore, the evolution of technology and the emergence of new programming languages require platforms to consistently update their offerings to meet evolving industry demands. Geographical variations in market penetration exist; North America and Europe currently dominate market share, but significant growth potential lies in rapidly developing economies in Asia-Pacific and other regions. Therefore, strategic expansion into these regions, coupled with localized content and pricing strategies, will be vital for continued market growth.

According to the survey, Rust was the most desired language in 2022, with over ** percent of respondents that are not developing with it, but expressed interest in developing with it. Python ranked second, followed by TypeScript.

The programming education market is projected to witness significant growth in the coming years. In 2025, the market was valued at million, and it is estimated to reach million by 2033, exhibiting a CAGR of % during the forecast period. The growth of this market is primarily driven by the increasing demand for skilled programmers in various industries, the shift towards online learning, and the growing popularity of coding as a hobby. Key trends in the programming education market include the rise of online learning platforms such as Coursera, Udemy, and Pluralsight; the development of new programming languages and technologies; the increasing adoption of gamified learning approaches; and the growing focus on STEM education in schools and universities. The market is dominated by a few major players such as Coursera, Roblox, CSDN, Github, Udacity, Tynker, and Programming Hub. However, there are also a number of small and medium-sized companies offering programming education services. The market is expected to continue to grow in the coming years, as the demand for skilled programmers remains high.

In 2024 around ** percent of software development organizations used over ** programming languages. In the majority of companies with fewer than ***** employees and up to ***** employees, around *** languages were being utilized. More than 10,000 employees. Organizations with over 10,000 employees used a different number of programming languages with almost an equal share.

These files accompany the book entitled: An Introduction to Programming Languages: Simultaneous Learning in Multiple Coding Environments. This work is an introductory textbook in several computer languages. It describes the most well-known and popular programming environments such as: C#, C++, Java, JavaScript, PERL, PHP, Python, Ruby, and Visual Basic (VB) or Visual Basic for Applications (VBA). Therefore, the main objective of this unique guide is to provide code examples reflected in these nine computer languages. Readers can easily understand the connection and universality between the syntax of different environments and be adept at translating code. This learning experience can be ideal for upper-undergraduate introductory courses, researchers, doctoral students, and sociologists or engineers charged with implementing data analysis. Graphical illustrations are used for technical details about the computation examples to aid in an in-depth understanding of their inner workings. Moreover, the book contains original material that has been class-tested by the author and numerous cases are examined. Readers will also benefit from the inclusion of: a) Historical and philosophical perspectives on the past, present and future of computer languages. b) A total of 448 additional files freely available online, from which a total of 44 files are poster presentations (i.e. PowerPoint and PDF files). c) A total of 404 code examples reflected in nine computer languages, namely: C#, C++, Java, JavaScript, PERL, PHP, Python, Ruby and VB. This work first begins with a general introduction to history and presents the natural inevitable pathway from mechanical automatons to present electronic computers. Following this historical introduction, an in-detail look is made on philosophical questions, implementations, entropy and life. More often than not, there is a genuine amazement of the younger generations regarding the advancement of computer technology. Historical events that led to the development of technologies have been distilled down to the essence. However, the essence of any story is made with massive loss of detailed information. The essence of essences even more so. Over time, the lack of detail leads to a collective amnesia that can prevent us from understanding the naturalness by which technology has evolved. Thus, new constructs are always built upon older constructs to fit the evolutionary chain of technological progress, which boils down to the same fundamental rules as biological evolution. In the first stage, this book discusses the natural path of programming constructs by starting from time immemorial and ending with examples up to the present times. In the end, naturally driven constructs of all kinds also drive our society today. In the second part, the emphasis is made on the technical side where a total of nine computer languages are used simultaneously for mirrored examples. Simultaneous learning of multiple computer languages can be regarded as an asset in the world of science and technology. Thus, the reader can get used to the majority of known programming or scripting languages. Moreover, a basic knowledge of software implementation in several computer languages, even in an introductory way, helps the versatility and adaptability of the reader to new situations that may arise in industry, education, or research. Thus, this work is meant to bring a more concrete understanding of the similarities and differences between computer languages. Paul A. Gagniuc. An Introduction to Programming Languages: Simultaneous Learning in Multiple Coding Environments. Synthesis Lectures on Computer Science. Springer International Publishing, 2023, pp. 1-280.

The dataset was first revision was published in the paper "Matching Problem Statements to Editorials in Competitive Programming" - ICALT 2024 https://ieeexplore.ieee.org/abstract/document/10645920

The second revision which is 7 times bigger was published in the paper "Domain Adaptation for Automated Tag Prediction in Competitive Programming" - AIAI 2025

If you are interested in this dataset, cite one of the papers in your research.

The repository of papers can be found at 1. https://github.com/DinuGeorge0019/MatchingProblemStatementsToEditorialsInCP 2. https://github.com/DinuGeorge0019/MLCP

Competitive programming is a challenging task that demands proficiency in computer science concepts and strong problem-solving skills.

A significant limitation in the field of competitive programming, in the context of machine learning, is the lack of available datasets that include the problem statement, the editorial, and the source code for research purposes. This limitation hinders the development of new algorithms and techniques to improve the efficiency and accuracy of selecting or creating suitable editorials for given problems.

To address this problem, we have introduced a comprehensive series of over 7000 competitive programming problems that encompass editorial solutions, source code and other metadata.

Note: PSG named datasets from 01_TASK_DATASETS directory are provided from the paper https://arxiv.org/abs/2310.05791 with the public repository https://github.com/sronger/PSG_Predicting_Algorithm_Tags_and_Difficulty

The online programming courses market is experiencing robust growth, driven by the increasing demand for skilled software developers and the accessibility of online learning platforms. The market size in 2025 is estimated at $25 billion, exhibiting a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033. This significant growth is fueled by several factors: the rising adoption of online learning methodologies, particularly among professionals seeking upskilling or reskilling opportunities; the proliferation of diverse programming languages, creating a continuous need for specialized training; and the increasing affordability and accessibility of high-quality online courses. The market's segmentation reveals a strong preference for Java, Python, and C/C++ languages, reflecting their widespread industry applications. Web development and software development remain the dominant application segments, highlighting the substantial industry demand. Leading players like Coursera, Udemy, and edX leverage their established brand reputation and course quality to maintain their market share. However, new entrants and specialized platforms focusing on specific niche languages or technologies are also emerging, creating a competitive yet dynamic market landscape. The projected CAGR of 15% suggests a substantial market expansion, with the market size anticipated to reach approximately $70 billion by 2033. This growth trajectory is expected to be influenced by ongoing technological advancements, the expanding digital economy, and government initiatives promoting digital literacy. The regional distribution of the market is expected to be geographically diverse, with North America and Europe holding significant market shares initially, followed by a steady increase in demand from Asia-Pacific regions like India and China, driven by a burgeoning tech industry and a growing young population eager to embrace digital skills. However, challenges such as inconsistent internet access in certain regions and concerns about the quality and authenticity of online courses might pose some restraints on the market's overall growth. Nevertheless, the overall forecast points to sustained and significant growth in the online programming courses market over the next decade.

Programming Jokes Dataset

  Dataset Summary

This dataset contains programming-related jokes scraped from the website Punny Funny. The jokes are organized into different categories based on the structure of the original webpage. The dataset is intended for use in natural language processing tasks, such as fine-tuning language models to generate humor or analyze textual content in the programming domain. Number of Jokes: [220]

  Usage

This dataset is suitable for… See the full description on the dataset page: https://huggingface.co/datasets/asfandyarazhar/programming-jokes-dataset.

The global Online Programming Learn Platform market is projected for robust expansion, with an estimated market size of approximately $15,000 million in 2025, poised for significant growth at a Compound Annual Growth Rate (CAGR) of around 18% through 2033. This surge is primarily driven by the escalating demand for digital skills across all sectors, the increasing adoption of remote learning solutions, and the continuous evolution of technology necessitating constant upskilling. The platform offers a diverse range of applications, catering to adult learners seeking career advancement or pivots, as well as younger demographics like children and teens exploring early exposure to coding. Key types of learning encompass Python, JavaScript, and comprehensive Web Development, alongside "Other" specialized programming languages and frameworks. Leading companies such as Coursera, Pluralsight, Udemy, and Codecademy are actively shaping this landscape with their extensive course offerings, interactive learning modules, and flexible pricing models, fostering a competitive yet collaborative environment for learners worldwide. The market's trajectory is further bolstered by emerging trends like the integration of Artificial Intelligence (AI) in personalized learning paths, the growing emphasis on practical, project-based learning, and the proliferation of micro-credentialing and bootcamps designed for rapid skill acquisition. While the market enjoys strong growth, certain restraints exist, including the need for reliable internet access and a consistent learning environment, particularly for younger learners, and the challenge of ensuring the quality and relevance of rapidly evolving course content. Geographically, North America and Europe currently dominate the market share, driven by established tech industries and a strong culture of continuous learning. However, the Asia Pacific region, particularly China and India, is expected to witness the most rapid growth due to a burgeoning young population, increasing internet penetration, and a strong governmental push towards digital literacy and STEM education. This report provides an in-depth analysis of the global Online Programming Learn Platform market, encompassing a comprehensive study of its evolution from the historical period of 2019-2024, through the base year of 2025, and projecting its trajectory up to 2033. With a meticulous examination of market dynamics, industry trends, and strategic landscapes, this report offers invaluable insights for stakeholders seeking to capitalize on the burgeoning opportunities within this sector. The market size is projected to reach several million dollars by the end of the forecast period, driven by increasing demand for digital skills across various demographics and industries.