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Enables evaluation of LLM and developer performance on problems that reflect actual library usage patterns rather than synthetic algorithmic puzzles.","intents":["Evaluate how well code generation models handle real-world data science tasks from actual developer questions","Benchmark LLM performance on practical library API usage across NumPy, Pandas, SciPy, Scikit-learn, PyTorch, TensorFlow, and Matplotlib","Test whether models can solve problems that require understanding of domain-specific workflows and data manipulation patterns","Measure generalization capability on problems sourced from authentic developer pain points rather than curated algorithmic challenges"],"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"],"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"],"requires":["Python 3.7+ environment","NumPy, Pandas, SciPy, Scikit-learn, PyTorch, TensorFlow, and Matplotlib installed for execution","Hugging Face Datasets library for loading the benchmark","Test harness or evaluation framework to execute generated code and validate outputs"],"input_types":["Natural language problem descriptions (from StackOverflow questions)","Code snippets (partial solutions or context code)","Structured data specifications (input shapes, dtypes, ranges)"],"output_types":["Python code solutions","Numerical arrays or DataFrames","Model objects or trained weights","Visualization outputs (plots, figures)","Boolean pass/fail evaluation results"],"categories":["data-processing-analysis","testing-quality"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ds-1000__cap_1","uri":"capability://data.processing.analysis.multi.library.api.coverage.evaluation.across.7.data.science.frameworks","name":"multi-library api coverage evaluation across 7 data science frameworks","description":"Systematically 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.","intents":["Identify which data science libraries are well-understood by code generation models vs. which have API comprehension gaps","Measure model performance on cross-library workflows that require understanding multiple APIs in sequence","Benchmark capability on library-specific idioms and design patterns (e.g., Pandas method chaining, PyTorch tensor operations)","Detect systematic weaknesses in handling specific library versions or deprecated API patterns"],"best_for":["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"],"limitations":["Coverage is fixed to 7 libraries — does not scale to emerging or niche libraries without dataset extension","Problem distribution across libraries may not reflect real-world usage frequency or complexity distribution","Does not measure performance on library version compatibility issues or deprecation handling","No built-in capability to measure code style adherence or idiomatic usage patterns"],"requires":["All 7 target libraries installed with compatible versions","Understanding of each library's API surface and common usage patterns","Execution environment with sufficient memory for PyTorch and TensorFlow model training"],"input_types":["Problem descriptions with implicit library requirements","Data specifications (shapes, dtypes, ranges)","Model training parameters or visualization requirements"],"output_types":["Library-specific code (NumPy array operations, Pandas DataFrames, PyTorch models, etc.)","Execution results validated against expected outputs","Per-library performance metrics and error analysis"],"categories":["data-processing-analysis","testing-quality"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ds-1000__cap_2","uri":"capability://data.processing.analysis.test.case.driven.correctness.validation.with.stackoverflow.derived.ground.truth","name":"test case-driven correctness validation with stackoverflow-derived ground truth","description":"Each 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.","intents":["Automatically evaluate whether generated code produces correct outputs without manual inspection","Measure pass rates and identify systematic failure modes in code generation models","Validate edge case handling and robustness of generated solutions","Enable continuous benchmarking and regression testing as models evolve"],"best_for":["Researchers running large-scale model evaluations requiring automated correctness checking","ML engineers building evaluation pipelines for code generation models","Teams implementing CI/CD for data science code generation systems","Organizations tracking model performance improvements over time"],"limitations":["Test cases may not cover all edge cases or error conditions present in real-world usage","Floating-point tolerance thresholds require manual tuning for different problem types","Test execution is sequential and can be slow for large-scale evaluations with heavy computations (model training, large data processing)","No built-in support for measuring code efficiency, memory usage, or runtime performance","Test cases are static — do not adapt to new library versions or API changes"],"requires":["Isolated execution environment (Docker, virtual machine, or sandboxed process) for safety","Timeout mechanisms to prevent infinite loops or resource exhaustion","Numerical comparison libraries (numpy.allclose, pandas.testing) for floating-point validation","Memory and compute resources sufficient for executing all test cases (especially PyTorch/TensorFlow)"],"input_types":["Generated Python code (as strings)","Problem context and input data specifications","Expected output specifications (shapes, dtypes, value ranges)"],"output_types":["Boolean pass/fail results per test case","Numerical comparison metrics (absolute/relative error)","Execution logs and error messages","Per-problem and aggregate performance statistics"],"categories":["data-processing-analysis","testing-quality"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ds-1000__cap_3","uri":"capability://data.processing.analysis.data.contamination.avoidance.through.surface.level.problem.perturbation","name":"data contamination avoidance through surface-level problem perturbation","description":"Applies 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.","intents":["Ensure benchmark problems are not identical to problems in model training data, preventing inflated evaluation scores","Create multiple variants of the same underlying problem to test generalization","Maintain semantic equivalence while avoiding surface-level memorization","Enable safe benchmarking of models trained on web-scale data that may include StackOverflow"],"best_for":["Researchers evaluating models trained on web-scale data including StackOverflow","Teams building benchmarks that need to avoid data contamination risks","Organizations conducting rigorous model evaluation with contamination-aware methodology","Researchers studying generalization vs. memorization in code generation models"],"limitations":["Perturbations are surface-level only — do not guarantee semantic independence if models learn deep structural patterns","Perturbation strategy is fixed — does not adapt to new model architectures or training approaches","No quantitative measure of contamination risk or semantic equivalence validation","Perturbations may introduce subtle biases or artifacts that affect problem difficulty"],"requires":["Original StackOverflow problem text and solutions","Perturbation rules and constraints (which aspects can be modified)","Validation that perturbations preserve problem semantics and solution correctness"],"input_types":["Original StackOverflow problem descriptions","Solution code and test cases","Perturbation parameters (variable name patterns, value ranges, etc.)"],"output_types":["Perturbed problem descriptions","Adjusted test cases and expected outputs","Mapping between original and perturbed problems"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ds-1000__cap_4","uri":"capability://data.processing.analysis.practical.data.science.workflow.evaluation.beyond.algorithmic.puzzle.solving","name":"practical data science workflow evaluation beyond algorithmic puzzle-solving","description":"Evaluates 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.","intents":["Measure code generation model capability on practical data science tasks that reflect real-world work","Evaluate whether models understand data transformation patterns and library idioms used in production","Test capability on multi-step workflows that require chaining operations across libraries","Assess readiness of code generation models for deployment in data science teams"],"best_for":["Data science teams evaluating code generation tools for productivity gains","ML engineers building data science-specific code assistants","Organizations assessing whether LLMs can handle real data engineering workflows","Researchers studying the gap between algorithmic benchmarks and practical capability"],"limitations":["Does not evaluate code efficiency, optimization, or performance — only correctness","Problems are limited to 7 libraries — does not cover full modern data science stack (Polars, DuckDB, Spark, etc.)","Does not measure code quality, readability, or adherence to best practices","Problems are static — do not evolve with changing data science practices or library versions","No evaluation of model's ability to explain or document generated code"],"requires":["Understanding of data science workflows and common patterns","Familiarity with NumPy, Pandas, SciPy, Scikit-learn, PyTorch, TensorFlow, Matplotlib APIs","Execution environment with sufficient compute for model training and data processing"],"input_types":["Natural language problem descriptions from data science practitioners","Input data specifications (shapes, dtypes, distributions)","Desired output specifications (transformed data, model metrics, visualizations)"],"output_types":["Python code implementing data transformations, model training, or visualizations","Transformed datasets or model objects","Numerical results or visualization outputs","Pass/fail correctness validation"],"categories":["data-processing-analysis","testing-quality"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ds-1000__cap_5","uri":"capability://tool.use.integration.hugging.face.datasets.integration.for.streamlined.benchmark.access.and.evaluation","name":"hugging face datasets integration for streamlined benchmark access and evaluation","description":"Dataset 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.","intents":["Load the benchmark dataset with a single line of code without manual downloading or parsing","Access standardized problem metadata and test cases in a consistent format","Integrate with Hugging Face evaluation tools and leaderboards for model comparison","Share evaluation results and model performance metrics with the research community"],"best_for":["Researchers using Hugging Face ecosystem tools and models","Teams building evaluation pipelines that leverage Hugging Face infrastructure","Organizations wanting to participate in community benchmarking and leaderboards","Developers familiar with Hugging Face Datasets API and conventions"],"limitations":["Requires Hugging Face Datasets library — adds dependency for non-Hugging Face workflows","Dataset schema is fixed — customization requires downloading and modifying locally","Leaderboard and evaluation infrastructure is managed by Hugging Face — limited control over evaluation methodology","Caching and versioning are automatic but may consume significant disk space for large-scale evaluations"],"requires":["Python 3.7+","Hugging Face Datasets library (pip install datasets)","Internet connection for initial download and metadata fetching","Hugging Face account for leaderboard submission (optional)"],"input_types":["Dataset identifier (xlangai/DS-1000)","Optional configuration parameters (split, subset)"],"output_types":["Hugging Face Dataset object with standardized schema","Problem descriptions, solutions, test cases, and metadata","Evaluation results compatible with Hugging Face leaderboards"],"categories":["tool-use-integration","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ds-1000__cap_6","uri":"capability://code.generation.editing.library.specific.api.signature.and.parameter.validation","name":"library-specific api signature and parameter validation","description":"Validates 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.","intents":["detect API misuse errors that produce correct outputs by accident (e.g., wrong parameter order but correct result)","validate that generated code uses current library APIs rather than deprecated functions","measure model understanding of library-specific conventions (e.g., NumPy broadcasting rules, Pandas method chaining)","identify which library functions are frequently misused by models"],"best_for":["teams building production-grade code generation where API correctness is critical","researchers analyzing model understanding of library semantics beyond output correctness","organizations evaluating whether models can write maintainable, idiomatic code"],"limitations":["validation requires all 7 libraries installed — cannot validate individual libraries in isolation","static analysis (AST parsing) cannot detect all API misuse; dynamic execution is required but adds 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