Welcome to an exceptional dataset meticulously crafted for training state-of-the-art language models such as Gemma, Llama 2, Orca, and more.

Dataset Highlights - Challenging Questions : Immerse your language models in various Python programming questions designed to stimulate cognitive growth. - Real-world Inputs : Provide your models with authentic input scenarios, ensuring they are well-equipped to handle practical coding challenges. - Accurate Answers : Sharpen the precision of your language models by exposing them to meticulously crafted Python code solutions.

How to Get Started - Download : Grab a copy of the dataset and inject new life into your language models. - Build Brilliance : Watch your LLMs evolve as they engage with the challenging questions and nuanced coding scenarios. - Share & Collaborate : Join the Kaggle community to discuss, share insights, and collaborate with fellow enthusiasts.

Unleash the full potential of your language models with this dataset. Elevate your LLM training experience and witness unprecedented growth in language understanding and coding prowess. Happy coding !

🧠 ProgrammingDataset

A high-quality, production-grade dataset of programming code snippets across multiple languages, collected and curated manually to support research in code generation, analysis, and educational tools.

  📌 Dataset Summary

Field Description

Rows 100+ code samples

Languages Python, JavaScript, C++, Java, etc.

Tasks Data structures, algorithms, system utilities

Format Excel (.xlsx) and CSV

License MIT

Each entry includes:

id:… See the full description on the dataset page: https://huggingface.co/datasets/kaiiddo/ProgrammingDataset.

As of 2025, JavaScript and HTML/CSS are the most commonly used programming languages among software developers around the world, with more than 66 percent of respondents stating that they used JavaScript and just around 61.9 percent using HTML/CSS. Python, SQL, and Bash/Shell rounded out the top five most widely used programming languages around the world. Programming languages At a very basic level, programming languages serve as sets of instructions that direct computers on how to behave and carry out tasks. Thanks to the increased prevalence of, and reliance on, computers and electronic devices in today’s society, these languages play a crucial role in the everyday lives of people around the world. An increasing number of people are interested in furthering their understanding of these tools through courses and bootcamps, while current developers are constantly seeking new languages and resources to learn to add to their skills. Furthermore, programming knowledge is becoming an important skill to possess within various industries throughout the business world. Job seekers with skills in Python, R, and SQL will find their knowledge to be among the most highly desirable data science skills and likely assist in their search for employment.

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.

Vulnerable Programming Dataset Overview The Vulnerable Programming Dataset is a comprehensive collection of 550 unique code vulnerabilities across 10 programming languages: Python, JavaScript, PHP, Java, Ruby, Go, TypeScript, C++, SQL, and C. Designed for cybersecurity professionals, red teamers, pentesters, and developers, this dataset highlights unconventional vulnerabilities such as insecure interprocess communication, misconfigured rate limiting, insecure dependency pinning, and logic… See the full description on the dataset page: https://huggingface.co/datasets/darkknight25/Vulnerable_Programming_Dataset.

This dataset contains code snippets from 22 different programming languages, with 100 code files per language (total 2,200 samples). Each entry includes metadata such as file name, number of lines, number of characters, a code preview, and the source URLs from GitHub repositories.

Columns Descriptor:

id: Unique identifier language: Programming language name file_name: Name of the file num_lines: Number of lines in the code num_chars: Total number of characters code_preview: Partial preview of the code repo_url: GitHub repository link source_url: Raw file link from GitHub

This dataset is useful for programming language detection and related code analysis tasks.

SWE-Bench Dataset - 8,712 files

The dataset comprises 8,712 files across 6 programming languages, featuring verified tasks and benchmarks for evaluating coding agents and language models. It supports coding agents, language models, and developer tools with verified benchmark scores and multi-language test sets. - Get the data

Dataset characteristics:

Here's a sample dataset to check out. For full access, go here

Dataset structure

• Go - Files in Go
• Scala - Files in Scala

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A table listing common programming languages used in data science, their purpose, and key capabilities.

The most popular programming language used in the past 12 months by software developers worldwide is JavaScript as of 2024, according to ** percent of the software developers surveyed. This is followed by Python at ** percent of the respondents surveyed.

JavaScript and Java were some of the most tested programming languages on the DevSkiller platform as of 2024. SQL and Python ranked second and fourth, with ** percent and ** percent of respondents testing this language in 2024, respectively. Nevertheless, the tech skill developers wanted to learn the most in 2024 was related to artificial intelligence, machine learning, and deep learning. At the same time, the fastest growing IT skills among DevSkiller customers were C/C++ and data science, while cybersecurity ranked third. Software skills When it came to the most used programming language among developers worldwide, JavaScript took the top spot, chosen by 62 percent of surveyed respondents. Most software developers learn how to code between 11 and 17 years old, with some of them writing their first line of code by the age of 5. Moreover, seven out of 10 developers learned how to program by accessing online resources such as videos and blogs. Software skills pay In 2024, the average annual software developer’s salary in the U.S. amounted to nearly ** thousand U.S. dollars, while in Germany, it totaled above ** thousand U.S. dollars. The programming languages associated with the highest salaries worldwide in 2024 were Clojure and Erlang.

SomAnon/awesome-programming-dataset dataset hosted on Hugging Face and contributed by the HF Datasets community

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/.

The GitHub Code dataest consists of 115M code files from GitHub in 32 programming languages with 60 extensions totalling in 1TB of text data. The dataset was created from the GitHub dataset on BiqQuery.

snapshot-tab-pane Programming Language Training Market Size 2024-2028The programming language training market size is forecast to increase by USD 8.53 billion, at a CAGR of 19.31% between 2023 and 2028. Market growth hinges on several factors: rising adoption of boot camps in developing economies, heightened emphasis on blended learning methods, and integration of programming languages into school curricula. Meanwhile, online language learning tools leverage learning management systems and smart devices to promote language fluency and proficiency. These trends reflect a global shift towards practical, skills-based education tailored to meet evolving industry demands. Boot camps offer intensive, hands-on training that accelerates career readiness, particularly in the tech and digital sectors. Blended learning combines online and in-person instruction, catering to diverse learning styles and enhancing accessibility. Meanwhile, embedding programming languages in school curricula equip students with essential computational skills early on, fostering a future-ready workforce. Together, these factors drive innovation in education, preparing individuals and industries alike for the challenges and opportunities of a digitally-driven world.What will be the size of the Market During the Forecast Period?To learn more about this report, Download Report SampleMarket DynamicIn the digital era, the landscape of software programs and scripts is enriched by versatile languages like C, Python, Ruby, PHP, and Java. These languages serve as foundational tools for developing everything from basic scripts to sophisticated applications. With the rise of machine learning and AI technologies, these languages play pivotal roles in building intelligent systems that automate processes and enhance decision-making. Cloud solutions further enable scalability and accessibility, supporting seamless deployment and management of applications globally. From computer-assisted learning to mobile-assisted language learning via cell phones, these tools offer personalized learning experiences. Self-assessment modules gauge progress, while software-enabled technology enhances interactivity and engagement. This integrated approach not only empowers learners but also caters to diverse educational needs, bridging language barriers and fostering global communication. As demand grows for flexible, tech-driven learning solutions, this market continues to innovate, shaping the future of language education worldwide.Key Market DriverThe increased emphasis on blended learning is notably driving market growth. Blended learning has many benefits both in academic and corporate training. It helps companies and colleges cut their training costs. It also provides learners with real-time access and allows them the benefits of live learning. It gives more control to learners and allows them to learn at their own pace. It provides the training institutes with a consistent medium of training; in the corporate training scenario, it can lead to higher employee retention. As a result, many companies have significant scope to develop various learning technology solutions that can be effectively implemented in the blended learning framework.Moreover, the blended learning model involves learning from traditional activities in the classroom along with web-enabled courses. Blended learning blends face-to-face learning with online learning and is becoming an attractive model in the higher education sector, especially in programming. Increasing emphasis on online training is one of the factors driving the growth of this model. Corporate companies are adopting the blended learning model to train their employees. The major reason for the adoption of the blended model of programming language training is the flexibility and convenience that the model provides to learners. As programming language training involves extensive practical learning methods, integrating new learning technologies has only made the overall learning process more effective. With these new learning frameworks, students understand concepts faster, providing them the flexibility to learn more advanced levels in the same subjects. The blended learning model is expected to grow during the forecast period due to its increased adoption in the academic as well as non-academic sectors leading to market growth.Significant Market TrendIncreased integration of e-learning is a major trend in the market. In its early stages, e-learning relied heavily on desktop computers and networks. However, currently, it has evolved into systems that encompass a variety of channels, such as wireless communications and technologies, such as smartphones, AR, VR, and wearables. E-learning provides organizations with the convenience of flexible timings for trai

Python Coding Dataset

This dataset contains high-quality Python function examples designed for fine-tuning coding-focused language models. It includes carefully curated samples covering common programming tasks, bug fixes, refactoring, and code completions.

  Dataset Details

Number of samples: 732 (and growing)
Purpose: Fine-tuning LLMs to generate accurate and idiomatic Python code
Content: Functions, bug fixes, refactors, completions
License: MIT License… See the full description on the dataset page: https://huggingface.co/datasets/Hoglet-33/python-coding-dataset.

Comprehensive programming language and framework data including use cases, job market demand, learning difficulty, and comparisons. Perfect for developer blogs, coding bootcamp pages, and tech comparison content.

A huge dataset of 500+ programming questions with step-by-step AI-generated solutions across multiple domains. Each entry includes: ✅ Question (Problem statement) ✅ Difficulty Level (Easy, Medium, Hard) ✅ Programming Language (Python, Java, C++, etc.) ✅ AI-Generated Solution (Optimized code) ✅ Time Complexity & Explanation ✅ Topic Tag (DP, Graphs, OOP, etc.)

🔹 Ideal for developers, coding interview prep, and machine learning models for auto code generation!

JavaScript was the most frequently used coding language in Russia, used by around ********** of the surveyed software companies in 2024. Furthermore, over ******** of the companies reported to use Python and Java.

Discover the booming market for online programming language learning platforms! This in-depth analysis reveals market size, growth projections (CAGR 15%), key players (Coursera, Udemy, Udacity), and future trends, helping you understand this rapidly expanding sector.

The most demanded programming languages by recruiters in 2025 were Python, JavaScript, and Java, with around ** percent of recruiters looking to hire people with these programming skills.