DS-1000

DatasetFree

1,000 data science problems across 7 Python libraries.

Open Source

/ 100

7 capabilities

Capabilities7 decomposed

realistic data science problem benchmarking with stackoverflow sourcing

Medium confidence

Provides 1,000 curated data science coding problems extracted directly from StackOverflow with real-world context, user intent, and accepted solutions. Problems are sourced from actual developer questions rather than synthetic algorithmic puzzles, ensuring they reflect genuine library usage patterns and edge cases encountered in production environments. Each problem includes the original question context, multiple solution approaches, and test cases derived from real-world validation.

Solves for

evaluate whether a code generation model understands practical library APIs in real-world scenariosbenchmark model performance on problems developers actually encounter rather than toy examplesassess a model's ability to handle ambiguous, context-dependent coding problems like those in productionmeasure improvement in data science coding capability across model iterations

Best for

ML researchers evaluating code generation models on practical data science tasks

teams building data science coding assistants who need realistic evaluation metrics

organizations assessing whether LLMs can handle production-grade data manipulation tasks

Requires

Python 3.7+

HuggingFace datasets library for loading

NumPy, Pandas, SciPy, Scikit-learn, PyTorch, TensorFlow, Matplotlib installed for running test cases

Limitations

limited to 7 Python libraries (NumPy, Pandas, SciPy, Scikit-learn, PyTorch, TensorFlow, Matplotlib) — does not cover other popular data science tools like Polars, DuckDB, or JAX

problems are static snapshots from StackOverflow at a specific point in time — library API evolution after dataset creation may affect relevance

StackOverflow sourcing introduces selection bias toward commonly-asked questions rather than edge cases or advanced techniques

What makes it unique

Uses StackOverflow as the source of truth for realistic problems rather than synthetic generation, capturing genuine developer intent, ambiguity, and multi-step reasoning patterns that synthetic benchmarks miss. Problems retain original context and discussion threads that provide implicit requirements.

vs alternatives

More representative of production data science work than algorithmic benchmarks (LeetCode-style) because it measures library API mastery and practical problem-solving rather than abstract algorithm knowledge

multi-library api coverage evaluation across 7 data science ecosystems

Medium confidence

Systematically covers 1,000 problems distributed across NumPy, Pandas, SciPy, Scikit-learn, PyTorch, TensorFlow, and Matplotlib, enabling evaluation of a model's breadth of knowledge across complementary data science libraries. The dataset structure allows filtering and analysis by library to identify which ecosystems a model handles well versus poorly. Problems test library-specific idioms, function signatures, parameter conventions, and integration patterns between libraries.

Solves for

identify which data science libraries a code generation model understands well and which need improvementmeasure cross-library integration capability (e.g., converting NumPy arrays to PyTorch tensors)evaluate whether a model generalizes library knowledge or memorizes specific APIsbenchmark library-specific code generation quality for different data science domains

Best for

model developers optimizing code generation for specific data science workflows

teams building domain-specific coding assistants for data science teams

researchers studying how LLMs acquire and generalize library-specific knowledge

Requires

Python 3.7+

All 7 target libraries installed: NumPy, Pandas, SciPy, Scikit-learn, PyTorch, TensorFlow, Matplotlib

HuggingFace datasets library for structured access

Limitations

equal problem distribution across libraries may not reflect real-world usage frequency (Pandas/NumPy are more common than TensorFlow in many workflows)

problems are static and do not evolve with library updates — TensorFlow 2.x and PyTorch API changes may make some problems outdated

does not cover library integration with external systems (databases, cloud APIs, distributed computing frameworks)

What makes it unique

Provides balanced coverage across 7 complementary libraries with explicit library tagging, enabling fine-grained analysis of model capability per ecosystem. Most benchmarks focus on a single library or generic coding; this isolates library-specific knowledge.

vs alternatives

Broader library coverage than domain-specific benchmarks (e.g., ML-specific) while remaining focused on practical data science, avoiding the dilution of generic code benchmarks that mix unrelated domains

test case-driven evaluation with automated pass/fail validation

Medium confidence

Each of the 1,000 problems includes executable test cases derived from real StackOverflow solutions, enabling automated evaluation of generated code without manual inspection. Test cases validate both correctness (output matches expected results) and robustness (handles edge cases, data types, and error conditions). The evaluation framework compares generated code execution against ground-truth test cases, producing binary pass/fail metrics and optional execution traces for debugging.

Solves for

automatically evaluate code generation models without manual code reviewmeasure pass rate and failure modes across a large problem setidentify which problem categories or library functions cause model failurestrack model improvement over iterations using consistent, reproducible metrics

Best for

ML researchers running large-scale model evaluations with minimal manual overhead

CI/CD pipelines for continuous evaluation of code generation models

teams comparing multiple model architectures on identical problem sets

Requires

Python 3.7+ with subprocess isolation or containerization for safe test execution

All 7 target libraries installed in test environment

HuggingFace datasets library for loading test cases

Limitations

test cases may not cover all edge cases or error conditions present in production code

pass/fail binary metric does not capture code quality, efficiency, or readability — a passing solution may be inefficient or unmaintainable

test case execution requires sandboxed environment to prevent malicious code execution; no built-in sandboxing provided

What makes it unique

Derives test cases from real StackOverflow accepted solutions rather than synthetic test generation, ensuring test cases reflect actual production requirements and edge cases that real developers encountered. Test cases are grounded in community-validated solutions.

vs alternatives

More reliable than hand-written test suites because they are extracted from real solutions; more comprehensive than simple output matching because they validate edge cases and error handling from actual StackOverflow discussions

data contamination avoidance through problem perturbation and deduplication

Medium confidence

Implements surface-level perturbations of original StackOverflow problems to prevent data leakage into model training sets while preserving semantic difficulty and real-world relevance. Perturbations include variable renaming, comment rewording, and minor structural changes that preserve the underlying algorithmic challenge. The dataset includes deduplication mechanisms to identify and remove near-duplicate problems that would inflate apparent model performance through memorization rather than generalization.

Solves for

ensure benchmark results reflect genuine model capability rather than training data memorizationcreate evaluation sets that are semantically equivalent to original problems but syntactically distinctdetect and remove duplicate or near-duplicate problems that would bias evaluation metricsmaintain benchmark integrity across multiple model training runs and dataset versions

Best for

researchers publishing model evaluations who need defensible claims about generalization

organizations building internal benchmarks derived from public data sources

teams concerned about data contamination in large-scale model training

Requires

Python 3.7+

HuggingFace datasets library

Optional: similarity detection tools (e.g., difflib, semantic similarity models) for custom deduplication analysis

Limitations

perturbation strategy is not disclosed in detail — unclear whether perturbations are sufficient to prevent sophisticated memorization attacks

deduplication is based on surface-level similarity; semantically equivalent problems with different implementations may not be detected

perturbations may inadvertently change problem difficulty or introduce ambiguity not present in originals

What makes it unique

Explicitly addresses data contamination risk through perturbation and deduplication rather than ignoring it, acknowledging that StackOverflow-sourced problems may appear in model training data. Perturbations preserve semantic difficulty while breaking surface-level memorization.

vs alternatives

More rigorous than benchmarks that ignore contamination risk; more practical than synthetic benchmarks because it retains real-world problem structure while mitigating memorization concerns

problem difficulty stratification and complexity analysis

Medium confidence

Organizes 1,000 problems into difficulty tiers based on solution complexity, required library knowledge, and algorithmic reasoning depth. Problems are tagged with metadata including required functions, data structure types, and reasoning patterns (e.g., 'requires understanding of broadcasting', 'multi-step data transformation'). This enables filtering evaluation sets by difficulty level and analyzing model performance across complexity gradients, from basic API usage to advanced multi-library integration.

Solves for

evaluate model performance separately on easy, medium, and hard problems to identify capability gapscreate difficulty-matched evaluation sets for comparing models at equivalent complexity levelsanalyze whether model performance degrades gracefully with problem complexity or fails abruptlyidentify which specific library features or reasoning patterns cause model failures

Best for

researchers analyzing model capability curves and identifying performance cliffs

teams building progressive evaluation frameworks that test increasing complexity

organizations benchmarking models across skill levels (junior vs senior developer equivalence)

Requires

Python 3.7+

HuggingFace datasets library with metadata filtering support

Optional: analysis tools for difficulty distribution visualization

Limitations

difficulty classification is subjective and may not align with actual model difficulty (some models may find 'easy' problems hard due to training data bias)

no explicit reasoning pattern taxonomy — difficulty tags are inferred from problem structure rather than explicit reasoning requirements

difficulty distribution across libraries is unknown — some libraries may have disproportionately hard or easy problems

What makes it unique

Provides explicit difficulty stratification with reasoning pattern tags, enabling fine-grained analysis of model capability across complexity dimensions. Most benchmarks treat all problems equally; this enables difficulty-aware evaluation.

vs alternatives

More diagnostic than flat benchmarks because it reveals whether model failures are due to fundamental capability gaps or just difficulty; enables fairer comparison between models with different training distributions

stackoverflow context preservation with solution diversity

Medium confidence

Retains original StackOverflow question context, discussion threads, and multiple accepted solutions for each problem, providing rich semantic information beyond the problem statement. Problems include not just the canonical solution but alternative approaches, edge case discussions, and performance trade-offs mentioned in comments. This multi-solution representation enables evaluation of whether models can discover multiple valid approaches or converge on a single memorized solution.

Solves for

evaluate whether a model can generate multiple valid solutions to the same problemassess model understanding of trade-offs between different approaches (e.g., performance vs readability)measure whether model solutions align with community-accepted best practices from StackOverflow discussionsprovide richer context for debugging model failures by examining alternative solutions

Best for

researchers studying solution diversity and generalization in code generation models

teams building coding assistants that should suggest multiple approaches

organizations evaluating whether models understand engineering trade-offs

Requires

Python 3.7+

HuggingFace datasets library

Storage for multiple solutions per problem (increased dataset size ~2-3x baseline)

Limitations

multiple solutions increase dataset size and evaluation complexity — no built-in mechanism to select which solution to evaluate against

StackOverflow solutions may not represent best practices in modern Python (e.g., deprecated NumPy patterns)

solution diversity is limited to what was discussed on StackOverflow — does not include all theoretically valid approaches

What makes it unique

Preserves full StackOverflow context including discussion threads and multiple solutions rather than extracting single canonical answers, capturing the reasoning and trade-off discussions that inform real-world coding decisions. This mirrors how developers actually use StackOverflow.

vs alternatives

Richer than single-solution benchmarks because it enables evaluation of solution diversity and trade-off understanding; more realistic than synthetic benchmarks because it includes actual community discussion and consensus

library-specific api signature and parameter validation

Medium confidence

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.

Solves for

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 functionsmeasure 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

Requires

Python 3.7+

All 7 target libraries installed with known versions

AST parsing library (built-in ast module)

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 latency and safety risks

library API changes after dataset creation may invalidate validation rules — no automatic update mechanism

What makes it unique

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.

vs alternatives

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.

Dataset48

APPS (Automated Programming Progress Standard)

10K coding problems across 3 difficulty levels with test suites.

comprehensive test suite curation and aggregationmulti-difficulty benchmark evaluation for code generation modelscross-platform problem sourcing and normalization

3 shared capabilities

Dataset48

CodeContests

13K competitive programming problems from AlphaCode research.

test-case-driven-code-evaluation-harnesscompetitive-programming-problem-corpus-with-multi-language-solutionsplatform-agnostic-problem-standardization

3 shared capabilities

Benchmark42

LiveCodeBench

Continuously updated coding benchmark — new competitive programming problems, prevents contamination.

competitive programming problem sourcing and curationpublic dataset and code repository access

2 shared capabilities

Benchmark42

Big Code Bench

Comprehensive code benchmark — 1,140 practical tasks with real library usage beyond HumanEval.

multi-split code generation task evaluation with pass@k metricsdetailed evaluation result inspection and analysis

2 shared capabilities

Benchmark42

SWE-bench

AI coding agent benchmark — real GitHub issues, end-to-end evaluation, the standard for code agents.

real-world github issue evaluation dataset construction

1 shared capability

Product16

varies

based on the model used by the agent.

software-engineering-task-benchmark-evaluation

1 shared capability

Best For

✓ML researchers evaluating code generation models on practical data science tasks
✓teams building data science coding assistants who need realistic evaluation metrics
✓organizations assessing whether LLMs can handle production-grade data manipulation tasks
✓model developers optimizing code generation for specific data science workflows
✓teams building domain-specific coding assistants for data science teams
✓researchers studying how LLMs acquire and generalize library-specific knowledge
✓ML researchers running large-scale model evaluations with minimal manual overhead
✓CI/CD pipelines for continuous evaluation of code generation models

Known Limitations

⚠limited to 7 Python libraries (NumPy, Pandas, SciPy, Scikit-learn, PyTorch, TensorFlow, Matplotlib) — does not cover other popular data science tools like Polars, DuckDB, or JAX
⚠problems are static snapshots from StackOverflow at a specific point in time — library API evolution after dataset creation may affect relevance
⚠StackOverflow sourcing introduces selection bias toward commonly-asked questions rather than edge cases or advanced techniques
⚠equal problem distribution across libraries may not reflect real-world usage frequency (Pandas/NumPy are more common than TensorFlow in many workflows)
⚠problems are static and do not evolve with library updates — TensorFlow 2.x and PyTorch API changes may make some problems outdated
⚠does not cover library integration with external systems (databases, cloud APIs, distributed computing frameworks)

Requirements

Python 3.7+HuggingFace datasets library for loadingNumPy, Pandas, SciPy, Scikit-learn, PyTorch, TensorFlow, Matplotlib installed for running test casesAll 7 target libraries installed: NumPy, Pandas, SciPy, Scikit-learn, PyTorch, TensorFlow, MatplotlibHuggingFace datasets library for structured accessPython 3.7+ with subprocess isolation or containerization for safe test executionAll 7 target libraries installed in test environmentHuggingFace datasets library for loading test cases

Input / Output

Accepts: problem statement (text), code snippet (Python), test case specifications (structured), problem statement with library context (text), input data specifications (structured), expected output format (structured), generated code (Python string), test case specifications (function signature, input data, expected output), execution context (imports, setup code), original StackOverflow problem (text, code), perturbation rules (variable name mappings, comment templates), similarity threshold for deduplication (float 0-1), problem statement with difficulty metadata (text, structured tags), difficulty filter criteria (easy/medium/hard or numeric complexity score), library and function tags (structured), StackOverflow discussion context (text, HTML), multiple solution implementations (Python code), library API specifications (function signatures, parameter types), validation rules (deprecated functions, parameter constraints)

Produces: code solution (Python), execution results (pass/fail), performance metrics (accuracy, latency), library-specific code (Python), per-library accuracy metrics, cross-library integration code, pass/fail boolean, execution time (milliseconds), error message (if failed), output comparison (generated vs expected), perturbed problem statement (text), deduplication report (similarity scores, flagged duplicates), dataset version with contamination metadata, filtered problem subset (list of problems matching criteria), difficulty distribution statistics (count per tier, library), complexity analysis report (required functions, reasoning patterns), list of valid solutions (Python code), solution metadata (upvotes, acceptance status, performance notes), solution comparison report (differences, trade-offs), API validation report (pass/fail per function call), signature mismatch details (expected vs actual parameters), deprecation warnings (if using outdated API)

UnfragileRank

Adoption70%(35% weight)

Quality28%(25% weight)

Ecosystem50%(20% weight)

Match Graph10%(15% weight)

Freshness100%(5% weight)

UnfragileRank is computed from adoption signals, documentation quality, ecosystem connectivity, match graph feedback, and freshness. No artifact can pay for a higher rank.

Type: Dataset

7 capabilities

Visit DS-1000→

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.

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Capabilities7 decomposed

realistic data science problem benchmarking with stackoverflow sourcing

Medium confidence

Solves for

Best for

ML researchers evaluating code generation models on practical data science tasks

teams building data science coding assistants who need realistic evaluation metrics

organizations assessing whether LLMs can handle production-grade data manipulation tasks

Requires

Python 3.7+

HuggingFace datasets library for loading

NumPy, Pandas, SciPy, Scikit-learn, PyTorch, TensorFlow, Matplotlib installed for running test cases

Limitations

limited to 7 Python libraries (NumPy, Pandas, SciPy, Scikit-learn, PyTorch, TensorFlow, Matplotlib) — does not cover other popular data science tools like Polars, DuckDB, or JAX

problems are static snapshots from StackOverflow at a specific point in time — library API evolution after dataset creation may affect relevance

StackOverflow sourcing introduces selection bias toward commonly-asked questions rather than edge cases or advanced techniques

What makes it unique

vs alternatives

multi-library api coverage evaluation across 7 data science ecosystems

Medium confidence

Solves for

Best for

model developers optimizing code generation for specific data science workflows

teams building domain-specific coding assistants for data science teams

researchers studying how LLMs acquire and generalize library-specific knowledge

Requires

Python 3.7+

All 7 target libraries installed: NumPy, Pandas, SciPy, Scikit-learn, PyTorch, TensorFlow, Matplotlib

HuggingFace datasets library for structured access

Limitations

equal problem distribution across libraries may not reflect real-world usage frequency (Pandas/NumPy are more common than TensorFlow in many workflows)

problems are static and do not evolve with library updates — TensorFlow 2.x and PyTorch API changes may make some problems outdated

does not cover library integration with external systems (databases, cloud APIs, distributed computing frameworks)

What makes it unique

vs alternatives

test case-driven evaluation with automated pass/fail validation

Medium confidence

Solves for

Best for

ML researchers running large-scale model evaluations with minimal manual overhead

CI/CD pipelines for continuous evaluation of code generation models

teams comparing multiple model architectures on identical problem sets

Requires

Python 3.7+ with subprocess isolation or containerization for safe test execution

All 7 target libraries installed in test environment

HuggingFace datasets library for loading test cases

Limitations

test cases may not cover all edge cases or error conditions present in production code

pass/fail binary metric does not capture code quality, efficiency, or readability — a passing solution may be inefficient or unmaintainable

test case execution requires sandboxed environment to prevent malicious code execution; no built-in sandboxing provided

What makes it unique

vs alternatives

data contamination avoidance through problem perturbation and deduplication

Medium confidence

Solves for

Best for

researchers publishing model evaluations who need defensible claims about generalization

organizations building internal benchmarks derived from public data sources

teams concerned about data contamination in large-scale model training

Requires

Python 3.7+

HuggingFace datasets library

Optional: similarity detection tools (e.g., difflib, semantic similarity models) for custom deduplication analysis

Limitations

perturbation strategy is not disclosed in detail — unclear whether perturbations are sufficient to prevent sophisticated memorization attacks

deduplication is based on surface-level similarity; semantically equivalent problems with different implementations may not be detected

perturbations may inadvertently change problem difficulty or introduce ambiguity not present in originals

What makes it unique

vs alternatives

More rigorous than benchmarks that ignore contamination risk; more practical than synthetic benchmarks because it retains real-world problem structure while mitigating memorization concerns

problem difficulty stratification and complexity analysis

Medium confidence

Solves for

Best for

researchers analyzing model capability curves and identifying performance cliffs

teams building progressive evaluation frameworks that test increasing complexity

organizations benchmarking models across skill levels (junior vs senior developer equivalence)

Requires

Python 3.7+

HuggingFace datasets library with metadata filtering support

Optional: analysis tools for difficulty distribution visualization

Limitations

difficulty classification is subjective and may not align with actual model difficulty (some models may find 'easy' problems hard due to training data bias)

no explicit reasoning pattern taxonomy — difficulty tags are inferred from problem structure rather than explicit reasoning requirements

difficulty distribution across libraries is unknown — some libraries may have disproportionately hard or easy problems

What makes it unique

vs alternatives

stackoverflow context preservation with solution diversity

Medium confidence

Solves for

Best for

researchers studying solution diversity and generalization in code generation models

teams building coding assistants that should suggest multiple approaches

organizations evaluating whether models understand engineering trade-offs

Requires

Python 3.7+

HuggingFace datasets library

Storage for multiple solutions per problem (increased dataset size ~2-3x baseline)

Limitations

multiple solutions increase dataset size and evaluation complexity — no built-in mechanism to select which solution to evaluate against

StackOverflow solutions may not represent best practices in modern Python (e.g., deprecated NumPy patterns)

solution diversity is limited to what was discussed on StackOverflow — does not include all theoretically valid approaches

What makes it unique

vs alternatives

library-specific api signature and parameter validation

Medium confidence

Solves for

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

Requires

Python 3.7+

All 7 target libraries installed with known versions

AST parsing library (built-in ast module)

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 latency and safety risks

library API changes after dataset creation may invalidate validation rules — no automatic update mechanism

What makes it unique

vs alternatives

Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.

About

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YOLOv846Model

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DS-1000

Capabilities7 decomposed

realistic data science problem benchmarking with stackoverflow sourcing

multi-library api coverage evaluation across 7 data science ecosystems

test case-driven evaluation with automated pass/fail validation

data contamination avoidance through problem perturbation and deduplication

problem difficulty stratification and complexity analysis

stackoverflow context preservation with solution diversity

library-specific api signature and parameter validation

Related Artifactssharing capabilities

APPS (Automated Programming Progress Standard)

CodeContests

LiveCodeBench

Big Code Bench

SWE-bench

varies

Best For

Known Limitations

Requirements

Input / Output

UnfragileRank

About

Categories

Alternatives to DS-1000

Are you the builder of DS-1000?

Get the weekly brief

Data Sources

DS-1000

Capabilities7 decomposed

realistic data science problem benchmarking with stackoverflow sourcing

multi-library api coverage evaluation across 7 data science ecosystems

test case-driven evaluation with automated pass/fail validation

data contamination avoidance through problem perturbation and deduplication

problem difficulty stratification and complexity analysis

stackoverflow context preservation with solution diversity

library-specific api signature and parameter validation

Related Artifactssharing capabilities

APPS (Automated Programming Progress Standard)

CodeContests

LiveCodeBench

Big Code Bench

SWE-bench

varies

Best For

Known Limitations

Requirements

Input / Output

UnfragileRank

About

Categories

Alternatives to DS-1000

Are you the builder of DS-1000?

Get the weekly brief

Data Sources