--- title: "data-juicer vs DS-1000" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/datajuicer-data-juicer-vs-xlang-ai-ds-1000" tools: ["datajuicer-data-juicer", "xlang-ai-ds-1000"] --- # data-juicer vs DS-1000 *GraphCanon updated Aug 17, 2026* ## Verdict Pick data-juicer if a Python library for foundational AI model data processing, offering a pipeline for tasks like instruction tuning and synthetic data generation; pick DS-1000 if the DS-1000 benchmark evaluates the code generation capabilities of large language models for data science tasks across Python libraries like Matplotlib, Numpy, Pandas, etc. [data-juicer](https://datajuicer.github.io/data-juicer/) reports 6.9k GitHub stars, 404 forks, and 59 open issues, last pushed Aug 13, 2026. [DS-1000](https://ds1000-code-gen.github.io) has 276 stars, 31 forks, and 2 open issues, last pushed Oct 30, 2024. Figures are from public GitHub metadata via [data-juicer's repository](https://github.com/datajuicer/data-juicer) and [DS-1000's repository](https://github.com/xlang-ai/DS-1000). | | [data-juicer](/tools/datajuicer-data-juicer.md) | [DS-1000](/tools/xlang-ai-ds-1000.md) | | --- | --- | --- | | Tagline | Data processing for and with foundation models | Benchmark and code for evaluating large language models on data science tasks | | Stars | 6,897 | 276 | | Forks | 404 | 31 | | Open issues | 59 | 2 | | Language | Python | Python | | Adopt for | A Python library for foundational AI model data processing, offering a pipeline for tasks like instruction tuning and synthetic data generation. | The DS-1000 benchmark evaluates the code generation capabilities of large language models for data science tasks across Python libraries like Matplotlib, Numpy, Pandas, etc. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | CC-BY-SA-4.0 | | Categories | Data & Retrieval, Model Training | Data & Retrieval, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [data-juicer](/tools/datajuicer-data-juicer.md) | [DS-1000](/tools/xlang-ai-ds-1000.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Dormant (18%) | | Days since push | 4d | 644d | | Open issues (now) | 59 | 2 | | Stars delta | +166 (30d) | Unknown | | Open issues delta | -3 (30d) | Unknown | | Full report | [trust report](/tools/datajuicer-data-juicer/trust.md) | [trust report](/tools/xlang-ai-ds-1000/trust.md) | ## Shared compatibility - **Python**: [data-juicer](/tools/datajuicer-data-juicer.md) - Python runtime; [DS-1000](/tools/xlang-ai-ds-1000.md) - Python runtime ## Decision facts: data-juicer - **Adopt for:** A Python library for foundational AI model data processing, offering a pipeline for tasks like instruction tuning and synthetic data generation. ## Decision facts: DS-1000 - **Adopt for:** The DS-1000 benchmark evaluates the code generation capabilities of large language models for data science tasks across Python libraries like Matplotlib, Numpy, Pandas, etc. ## Choose when ### Choose data-juicer if… - License: data-juicer is Apache-2.0, DS-1000 is CC-BY-SA-4.0. - Tags unique to data-juicer: foundation-models, instruction-tuning, llm, synthetic-data. - data-juicer ships Docker support for self-hosted deployment. - When you need to preprocess large datasets specifically for training large language models (LLMs) with pipelines that support sophisticated processes like instruction tuning. ### Choose DS-1000 if… - License: DS-1000 is CC-BY-SA-4.0, data-juicer is Apache-2.0. - Tags unique to DS-1000: benchmark, code generation, data-science, semantic-parsing. - When you want to assess how well a large language model can generate reliable and accurate code for data science projects involving popular Python libraries. ## When NOT to use data-juicer - If your project does not involve foundational AI model training or if you do not require advanced data processing capabilities such as synthetic data generation. ## When NOT to use DS-1000 - Avoid using DS-1000 if your project does not involve data science or if the models do not generate code in Python. - It is unsuitable for evaluating text generation abilities unrelated to coding, such as natural language processing tasks. ## Common questions ### What is the difference between data-juicer and DS-1000? data-juicer: Data processing for and with foundation models. DS-1000: Benchmark and code for evaluating large language models on data science tasks. See the comparison table for live GitHub stats and shared categories. ### When should I choose data-juicer over DS-1000? Choose data-juicer over DS-1000 when License: data-juicer is Apache-2.0, DS-1000 is CC-BY-SA-4.0; Tags unique to data-juicer: foundation-models, instruction-tuning, llm, synthetic-data; data-juicer ships Docker support for self-hosted deployment; When you need to preprocess large datasets specifically for training large language models (LLMs) with pipelines that support sophisticated processes like instruction tuning. ### When should I choose DS-1000 over data-juicer? Choose DS-1000 over data-juicer when License: DS-1000 is CC-BY-SA-4.0, data-juicer is Apache-2.0; Tags unique to DS-1000: benchmark, code generation, data-science, semantic-parsing; When you want to assess how well a large language model can generate reliable and accurate code for data science projects involving popular Python libraries. ### When should I avoid data-juicer? If your project does not involve foundational AI model training or if you do not require advanced data processing capabilities such as synthetic data generation. ### When should I avoid DS-1000? Avoid using DS-1000 if your project does not involve data science or if the models do not generate code in Python. It is unsuitable for evaluating text generation abilities unrelated to coding, such as natural language processing tasks. ### Is data-juicer or DS-1000 more popular on GitHub? data-juicer has more GitHub stars (6,897 vs 276). Stars measure visibility, not whether either tool fits your constraints. ### Are data-juicer and DS-1000 open source? Yes - both are open-source projects on GitHub (data-juicer: Apache-2.0, DS-1000: CC-BY-SA-4.0). ### Where can I find alternatives to data-juicer or DS-1000? GraphCanon lists graph-backed alternatives at [data-juicer alternatives](/tools/datajuicer-data-juicer/alternatives) and [DS-1000 alternatives](/tools/xlang-ai-ds-1000/alternatives) ([data-juicer markdown twin](/tools/datajuicer-data-juicer/alternatives.md), [DS-1000 markdown twin](/tools/xlang-ai-ds-1000/alternatives.md)), ranked by typed relationship edges rather than popularity votes. ### Is there a machine-readable version of this comparison? Yes. The markdown twin at [this comparison](/compare/datajuicer-data-juicer-vs-xlang-ai-ds-1000.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, data-juicer or DS-1000? data-juicer: Very active. DS-1000: Dormant. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance. ### Where are the full trust reports for data-juicer and DS-1000? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [data-juicer trust report](/tools/datajuicer-data-juicer/trust); [DS-1000 trust report](/tools/xlang-ai-ds-1000/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=datajuicer-data-juicer`](/api/graphcanon/graph?tool=datajuicer-data-juicer) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_