DS-1000
DatasetFree1,000 data science problems across 7 Python libraries.
Capabilities7 decomposed
stackoverflow-sourced data science problem benchmark evaluation
Medium confidenceProvides a curated dataset of 1,000 real-world data science coding problems extracted directly from StackOverflow questions, preserving authentic problem context, user intent, and practical constraints. Each problem includes the original question text, expected outputs, and test cases derived from accepted answers. Enables evaluation of LLM and developer performance on problems that reflect actual library usage patterns rather than synthetic algorithmic puzzles.
Directly sources problems from StackOverflow's accepted answers rather than synthetic problem generation, preserving authentic developer context, error patterns, and multi-step workflows that reflect real-world data science work. Uses surface-level perturbations to avoid data contamination while maintaining semantic equivalence to original problems.
More representative of actual developer workflows than algorithmic benchmarks like LeetCode or HumanEval, because it captures library API usage patterns and domain-specific data manipulation tasks that practitioners encounter daily
multi-library api coverage evaluation across 7 data science frameworks
Medium confidenceSystematically evaluates code generation model capability across NumPy, Pandas, SciPy, Scikit-learn, PyTorch, TensorFlow, and Matplotlib by distributing problems across these libraries and their common interaction patterns. Problems test both single-library operations and cross-library workflows (e.g., Pandas data preparation → Scikit-learn model training → Matplotlib visualization). Enables fine-grained analysis of which libraries and API patterns models struggle with most.
Explicitly structures problems to test cross-library workflows and interactions (e.g., Pandas → Scikit-learn → Matplotlib pipelines) rather than isolated single-library tasks, reflecting how data scientists actually compose multiple libraries in real workflows. Enables per-library performance breakdown and interaction pattern analysis.
Provides library-specific performance metrics that general code generation benchmarks like HumanEval or MBPP cannot offer, allowing targeted optimization for data science workflows rather than generic programming tasks
test case-driven correctness validation with stackoverflow-derived ground truth
Medium confidenceEach of the 1,000 problems includes executable test cases derived from accepted StackOverflow answers, enabling automated validation of generated code against expected outputs. Test cases cover normal cases, edge cases, and error conditions extracted from real problem discussions. Validation harness executes generated code in isolated environments and compares outputs (numerical arrays, DataFrames, model metrics, plots) against ground truth with configurable tolerance for floating-point comparisons.
Test cases are derived from real StackOverflow accepted answers rather than synthetic test generation, capturing authentic edge cases and error conditions that actual developers encountered. Includes tolerance-aware numerical comparison for floating-point outputs and multi-type validation (arrays, DataFrames, model objects, plots).
More robust than simple output matching because it handles floating-point precision, data structure variations, and multiple valid solution formats, while being more realistic than synthetic test suites because it reflects actual problem-solving discussions
data contamination avoidance through surface-level problem perturbation
Medium confidenceApplies controlled perturbations to original StackOverflow problems to prevent data leakage and contamination in model training/evaluation pipelines. Perturbations modify surface-level aspects (variable names, constant values, data shapes, problem wording) while preserving semantic equivalence and solution logic. Enables safe use of the dataset for both training and evaluation without risk of models memorizing exact problem text from their training data.
Explicitly addresses data contamination risk through controlled perturbations rather than ignoring the problem or using completely synthetic data. Preserves authentic problem semantics and solution logic while modifying surface text, enabling safe evaluation of models trained on web-scale data.
More practical than synthetic benchmarks because it maintains real-world problem characteristics, while being more rigorous than unperturbed StackOverflow data because it mitigates contamination risks for models trained on web-scale corpora
practical data science workflow evaluation beyond algorithmic puzzle-solving
Medium confidenceEvaluates code generation models on realistic data science workflows that emphasize library API mastery, data manipulation patterns, and practical problem-solving over algorithmic complexity. Problems require understanding of data transformation pipelines, statistical operations, model training workflows, and visualization patterns rather than algorithmic puzzle-solving or complex mathematical derivations. Reflects the actual distribution of tasks data scientists encounter (80% data wrangling, 10% modeling, 10% visualization) rather than academic algorithm problems.
Deliberately avoids algorithmic puzzle-solving and focuses on library API mastery and data manipulation patterns that dominate real data science work. Problems are sourced from actual StackOverflow questions where practitioners asked for help, ensuring relevance to real-world tasks rather than academic exercises.
More predictive of real-world code generation model utility than algorithmic benchmarks like LeetCode or HumanEval because it measures practical library knowledge and workflow understanding rather than algorithmic problem-solving ability
hugging face datasets integration for streamlined benchmark access and evaluation
Medium confidenceDataset is hosted and distributed through Hugging Face Datasets platform, enabling one-line loading via the datasets library with automatic caching, versioning, and metadata management. Provides standardized dataset schema with problem descriptions, code solutions, test cases, and metadata organized in a structured format. Integrates with Hugging Face ecosystem tools for evaluation, model comparison, and leaderboard tracking, enabling researchers to benchmark models and share results without custom data loading infrastructure.
Leverages Hugging Face Datasets infrastructure for distribution, versioning, and community integration rather than requiring custom hosting or download mechanisms. Enables seamless integration with Hugging Face evaluation tools, leaderboards, and model comparison frameworks.
Reduces friction for researchers already in the Hugging Face ecosystem by eliminating custom data loading code and enabling direct integration with evaluation tools and leaderboards, while providing automatic caching and versioning
library-specific api signature and parameter validation
Medium confidenceValidates generated code against the correct function signatures, parameter names, and type hints for each of the 7 supported libraries, catching common errors like incorrect parameter order, deprecated function names, or wrong argument types. Validation is performed through static analysis (AST parsing) and dynamic execution, comparing generated code against library documentation and actual library behavior. This enables detection of subtle API misuse that would pass basic output matching but fail in production.
Combines static AST analysis with dynamic execution to validate API correctness beyond output matching, catching subtle misuse that would pass functional tests. Validation is library-specific rather than generic.
More rigorous than output-only evaluation because it catches API misuse that happens to produce correct results; more practical than linting because it validates against actual library behavior rather than style rules
Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.
Related Artifactssharing capabilities
Artifacts that share capabilities with DS-1000, ranked by overlap. Discovered automatically through the match graph.
APPS (Automated Programming Progress Standard)
10K coding problems across 3 difficulty levels with test suites.
SWE-bench
AI coding agent benchmark — real GitHub issues, end-to-end evaluation, the standard for code agents.
Aider Polyglot
Multi-language AI coding benchmark — tests code editing ability across 10+ languages.
CodeContests
13K competitive programming problems from AlphaCode research.
LiveCodeBench
Continuously updated coding benchmark — new competitive programming problems, prevents contamination.
Open LLM Leaderboard
Hugging Face open-source LLM leaderboard — standardized benchmarks, automatic evaluation.
Best For
- ✓ML researchers evaluating code generation models on practical data science tasks
- ✓Teams building data science coding assistants who need realistic evaluation benchmarks
- ✓Organizations assessing LLM capability for data engineering and analysis workflows
- ✓Researchers studying library API comprehension and multi-library problem-solving
- ✓LLM developers optimizing models for data science code generation
- ✓Data science tool builders identifying which libraries need better training data or fine-tuning
- ✓Researchers studying transfer learning across different library ecosystems
- ✓Teams building domain-specific code assistants for data engineering workflows
Known Limitations
- ⚠Limited to Python ecosystem — does not cover R, Julia, or other data science languages
- ⚠Focused on 7 specific libraries — does not include newer libraries like Polars, DuckDB, or JAX
- ⚠Problems are static snapshots from StackOverflow — does not evolve with library API changes or new versions
- ⚠No built-in support for evaluating code efficiency or performance optimization — only correctness
- ⚠Test cases may have edge cases or ambiguities inherited from original StackOverflow answers
- ⚠Coverage is fixed to 7 libraries — does not scale to emerging or niche libraries without dataset extension
Requirements
Input / Output
UnfragileRank
UnfragileRank is computed from adoption signals, documentation quality, ecosystem connectivity, match graph feedback, and freshness. No artifact can pay for a higher rank.
About
Benchmark of 1,000 realistic data science coding problems spanning 7 popular Python libraries: NumPy, Pandas, SciPy, Scikit-learn, PyTorch, TensorFlow, and Matplotlib. Problems sourced from StackOverflow with real-world context and test cases. Evaluates practical data science coding ability rather than algorithmic puzzle-solving. Tests understanding of library APIs, data manipulation, model training, and visualization. Designed to avoid data contamination through surface-level perturbations of original problems.
Categories
Alternatives to DS-1000
Open-source image generation — SD3, SDXL, massive ecosystem of LoRAs, ControlNets, runs locally.
Compare →Are you the builder of DS-1000?
Claim this artifact to get a verified badge, access match analytics, see which intents users search for, and manage your listing.
Get the weekly brief
New tools, rising stars, and what's actually worth your time. No spam.
Data Sources
Looking for something else?
Search →