{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"vscode-ms-toolsai-python-ds-extension-pack","slug":"python-data-science","name":"Python Data Science","type":"extension","url":"https://marketplace.visualstudio.com/items?itemName=ms-toolsai.python-ds-extension-pack","page_url":"https://unfragile.ai/python-data-science","categories":["code-editors"],"tags":["__web_extension","AI","copilot","data analysis","data cleaning","data exploration","data visualization","data wrangling","datascience","interactive","jupyter","machine learning","notebook","python"],"pricing":{"model":"freemium","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"vscode-ms-toolsai-python-ds-extension-pack__cap_0","uri":"capability://code.generation.editing.ai.assisted.python.code.completion.and.generation","name":"ai-assisted python code completion and generation","description":"Leverages GitHub Copilot (OpenAI-based model) integrated into VS Code to provide real-time code suggestions, function generation, and multi-line code completion for Python scripts and notebooks. The extension pack bundles Copilot directly, enabling context-aware suggestions based on the current file, project structure, and open tabs without requiring separate authentication setup beyond GitHub login.","intents":["Generate boilerplate Python code for data science workflows without manual typing","Get AI suggestions for completing partially-written functions or data processing pipelines","Accelerate prototyping of machine learning model training loops and evaluation code","Receive alternative code suggestions when the default completion doesn't match intent"],"best_for":["Python developers building data science projects in VS Code","Data scientists prototyping ML models who want reduced boilerplate writing","Teams using GitHub for version control seeking integrated AI pair programming"],"limitations":["Requires active GitHub Copilot subscription (paid tier after trial period)","Context window limited to current file, project structure, and open tabs — cannot access external documentation or custom domain knowledge","Suggestions may require manual review and testing; no built-in validation that generated code is syntactically correct or logically sound","Performance depends on network latency to GitHub Copilot API; offline mode not supported"],"requires":["VS Code 1.60 or later","GitHub account with Copilot subscription or active trial","Python 3.6+ installed locally for syntax validation","Internet connectivity for API calls to GitHub Copilot service"],"input_types":["Python source code (partial or complete)","Jupyter notebook cells","Code comments describing intent","Function signatures and type hints"],"output_types":["Python code suggestions (single or multi-line)","Complete function implementations","Code snippets for common patterns"],"categories":["code-generation-editing","ai-pair-programming"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-ms-toolsai-python-ds-extension-pack__cap_1","uri":"capability://code.generation.editing.interactive.jupyter.notebook.creation.and.execution","name":"interactive jupyter notebook creation and execution","description":"Provides native Jupyter notebook support within VS Code via the bundled Jupyter extension, enabling cell-based code execution, inline visualization rendering, and kernel management without leaving the editor. Cells execute against local or remote Python kernels, with output (text, plots, tables) rendered directly in the notebook interface.","intents":["Create and edit Jupyter notebooks (.ipynb files) directly in VS Code without switching to Jupyter Lab or Notebook","Execute Python cells interactively and iterate on data analysis code with immediate feedback","Render matplotlib, seaborn, and plotly visualizations inline within notebook cells","Manage multiple kernel connections and switch between local and remote Python environments"],"best_for":["Data scientists and analysts prototyping analyses in notebook format","Teams preferring VS Code as a unified IDE for both scripts and notebooks","Researchers documenting computational workflows with mixed code and markdown"],"limitations":["Kernel execution is synchronous per cell — long-running computations block the UI until completion","No built-in notebook version control or diff visualization (requires external tools like nbdime)","Large notebooks (>100MB) may experience performance degradation in rendering and cell execution","Remote kernel connections require manual SSH tunneling or Jupyter server configuration; no built-in remote kernel discovery"],"requires":["VS Code 1.60 or later","Python 3.6+ with Jupyter package installed (pip install jupyter)","Local or remote Jupyter kernel accessible via standard Jupyter protocol","ipykernel or equivalent kernel implementation for Python execution"],"input_types":["Jupyter notebook files (.ipynb)","Python code in notebook cells","Markdown cells for documentation","Raw cell content"],"output_types":["Cell execution results (stdout, stderr)","Rendered plots and visualizations (PNG, SVG, interactive HTML)","Structured data tables (pandas DataFrames rendered as HTML)","Jupyter output MIME types (images, LaTeX, JSON)"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-ms-toolsai-python-ds-extension-pack__cap_10","uri":"capability://code.generation.editing.code.formatting.and.auto.formatting.on.save","name":"code formatting and auto-formatting on save","description":"The Python extension integrates code formatters (Black, autopep8, yapf) that automatically reformat Python code to match style standards. Formatting can be triggered manually or automatically on file save, ensuring consistent code style across the project without manual formatting effort.","intents":["Automatically format Python code to PEP 8 or Black style standards on save","Reformat selected code blocks without affecting the rest of the file","Enforce consistent code style across team projects by configuring formatters in workspace settings","Resolve style conflicts before code review by auto-formatting on save"],"best_for":["Teams standardizing code style across data science projects","Developers using Black or autopep8 for consistent formatting","Projects where code style is enforced in CI/CD pipelines"],"limitations":["Formatting can change code semantics in rare cases (e.g., string literal formatting affecting regex patterns)","Formatter configuration requires manual setup in workspace settings or pyproject.toml","Auto-formatting on save adds latency (typically 500ms-2s per file)","Some formatters (Black) have limited customization options, which may conflict with team preferences"],"requires":["VS Code 1.60 or later","Python extension (bundled in this pack)","Formatter package installed (black, autopep8, yapf, etc.)","Optional: formatter configuration files (pyproject.toml, .black, setup.cfg)"],"input_types":["Python source files (.py)","Code selections for partial formatting","Formatter configuration files"],"output_types":["Reformatted Python code","Formatting change summaries","Formatted file with consistent style"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-ms-toolsai-python-ds-extension-pack__cap_11","uri":"capability://automation.workflow.test.discovery.and.execution.within.the.editor","name":"test discovery and execution within the editor","description":"The Python extension discovers and runs unit tests (pytest, unittest) directly from VS Code, displaying test results in the Test Explorer sidebar. Users can run individual tests, test classes, or entire test suites without leaving the editor, with inline test status indicators and failure details.","intents":["Discover and run unit tests for data processing functions without terminal commands","View test results and failure details directly in VS Code","Run specific tests or test suites during development for rapid feedback","Debug failing tests by setting breakpoints in test code"],"best_for":["Data science teams building testable ML pipelines with unit tests","Developers practicing test-driven development in Python","Projects requiring continuous test execution during development"],"limitations":["Test discovery requires standard test naming conventions (test_*.py or *_test.py)","Slow or long-running tests block the UI during execution","No built-in test coverage reporting (requires separate coverage.py integration)","Parametrized tests may not display clearly in the Test Explorer UI"],"requires":["VS Code 1.60 or later","Python extension (bundled in this pack)","Test framework installed (pytest or unittest)","Test files in workspace following naming conventions"],"input_types":["Python test files (test_*.py or *_test.py)","Test functions and classes","Test configuration files (pytest.ini, setup.cfg)"],"output_types":["Test results (pass/fail/skip)","Test execution logs","Failure stack traces","Test execution time"],"categories":["automation-workflow","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-ms-toolsai-python-ds-extension-pack__cap_12","uri":"capability://text.generation.language.docstring.generation.and.documentation.assistance","name":"docstring generation and documentation assistance","description":"The Python extension can generate docstring templates for functions and classes, helping developers document code with standardized formats (Google, NumPy, Sphinx styles). This reduces documentation boilerplate and encourages consistent documentation practices across projects.","intents":["Generate docstring templates for functions and classes to reduce manual documentation effort","Ensure consistent docstring format across the project (Google, NumPy, Sphinx)","Provide type information in docstrings for functions without explicit type hints","Improve code documentation for data science functions and ML model classes"],"best_for":["Data science teams building documented ML pipelines and libraries","Developers learning documentation best practices","Projects requiring comprehensive API documentation"],"limitations":["Generated docstrings are templates and require manual content completion","Docstring generation does not infer parameter descriptions or return value documentation","Limited support for complex type hints (Union, Optional, generics)","No automatic docstring validation or completeness checking"],"requires":["VS Code 1.60 or later","Python extension (bundled in this pack)","Optional: docstring formatter extension for automatic formatting"],"input_types":["Function definitions","Class definitions","Type hints and annotations"],"output_types":["Docstring templates (Google, NumPy, Sphinx format)","Parameter documentation stubs","Return value documentation stubs"],"categories":["text-generation-language","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-ms-toolsai-python-ds-extension-pack__cap_2","uri":"capability://data.processing.analysis.tabular.data.exploration.and.interactive.cleaning","name":"tabular data exploration and interactive cleaning","description":"The bundled Data Wrangler extension provides a visual interface for exploring, profiling, and cleaning tabular data (CSV, Parquet, Excel) directly within VS Code. It generates Python code for data transformations (filtering, sorting, deduplication, type conversion) that users can apply and export, bridging visual data exploration with reproducible code-based workflows.","intents":["Visually inspect and profile CSV or Parquet files to understand data distributions, missing values, and data types","Apply data cleaning operations (remove duplicates, handle nulls, rename columns) through a GUI without writing pandas code","Generate Python code for applied transformations to maintain reproducibility and integrate into pipelines","Export cleaned datasets in multiple formats (CSV, Parquet, Excel) with transformation history"],"best_for":["Data analysts and non-technical stakeholders exploring datasets without writing code","Data engineers building reproducible ETL workflows who want visual feedback before committing transformations","Teams collaborating on data cleaning where some members prefer GUI-based tools"],"limitations":["Limited to tabular data formats (CSV, Parquet, Excel, JSON) — no support for unstructured data or images","Performance degrades on large datasets (>1GB); no built-in sampling or chunking for memory-efficient exploration","Generated Python code uses pandas; no support for alternative data processing libraries (Polars, DuckDB, Spark)","No built-in data validation rules or schema enforcement — transformations are applied without constraint checking","Visual interface is read-only for data preview; editing requires code generation and re-execution"],"requires":["VS Code 1.60 or later","Python 3.6+ with pandas installed (pip install pandas)","Tabular data file (CSV, Parquet, Excel, JSON) in workspace or accessible path","Sufficient RAM to load dataset into memory (no streaming support)"],"input_types":["CSV files","Parquet files","Excel files (.xlsx, .xls)","JSON files with tabular structure"],"output_types":["Python code (pandas-based transformations)","Cleaned CSV, Parquet, or Excel files","Data profile reports (column statistics, missing value counts)","Transformation history as reproducible code"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-ms-toolsai-python-ds-extension-pack__cap_3","uri":"capability://code.generation.editing.python.language.intelligence.and.type.checking","name":"python language intelligence and type checking","description":"The bundled Python extension with Pylance language server provides real-time code analysis, type checking, and intelligent code completion for Python files. Pylance uses static analysis and type inference to detect errors, suggest fixes, and provide IDE features (go-to-definition, refactoring, hover documentation) without executing code, leveraging Microsoft's Pylance engine which supports Python 3.6+.","intents":["Catch type errors and undefined variables in Python code before execution","Navigate large codebases using go-to-definition, find-all-references, and symbol search","Refactor code safely with rename-all-occurrences and extract-function operations","Receive intelligent code completion suggestions based on type information and imported modules"],"best_for":["Python developers building production data science code who want static analysis","Teams enforcing type safety in Python projects using type hints and mypy-compatible checks","Large projects where codebase navigation and refactoring tools reduce manual effort"],"limitations":["Type checking requires explicit type hints (PEP 484) — untyped code receives limited analysis","Pylance's inference engine may not catch all runtime errors; dynamic Python features (eval, getattr) are not fully analyzed","Performance can degrade on very large codebases (>10k files) due to indexing overhead","Some third-party libraries lack type stubs, reducing code completion accuracy for those dependencies"],"requires":["VS Code 1.60 or later","Python 3.6+ installed and discoverable by VS Code","Pylance extension (bundled in this pack)","Optional: type stubs for third-party libraries (typeshed or library-specific stubs)"],"input_types":["Python source files (.py)","Type hint annotations (PEP 484, PEP 585, PEP 604)","Docstrings with type information","Imported modules and packages"],"output_types":["Diagnostic messages (errors, warnings, hints)","Code completion suggestions with type information","Hover documentation and type signatures","Refactoring operations (rename, extract, organize imports)"],"categories":["code-generation-editing","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-ms-toolsai-python-ds-extension-pack__cap_4","uri":"capability://automation.workflow.integrated.python.environment.and.kernel.management","name":"integrated python environment and kernel management","description":"The extension pack automatically discovers and manages Python interpreters and Jupyter kernels installed on the system, allowing users to select different environments (virtual environments, conda, system Python) for script execution and notebook kernels. The Python extension handles environment detection, package management integration, and kernel switching without manual configuration.","intents":["Switch between multiple Python environments (venv, conda, system) for different projects without manual PATH configuration","Select which Jupyter kernel to use for notebook execution when multiple Python versions are installed","Automatically detect and use the correct Python interpreter for linting, type checking, and code execution","Manage and activate virtual environments directly from VS Code without terminal commands"],"best_for":["Data scientists managing multiple projects with different dependency versions","Teams using conda for environment management who want seamless VS Code integration","Developers switching between Python 3.8, 3.9, 3.10+ versions for compatibility testing"],"limitations":["Environment detection relies on standard installation paths; custom or non-standard Python installations may not be auto-discovered","No built-in environment creation UI — users must create venv/conda environments via terminal first","Kernel switching requires manual selection from dropdown; no automatic kernel selection based on project configuration","Virtual environment activation is VS Code-scoped; does not affect system PATH or other applications"],"requires":["VS Code 1.60 or later","Python 3.6+ installed in a discoverable location (system PATH, standard venv/conda directories)","Python extension (bundled in this pack)","Optional: conda or venv for environment management"],"input_types":["System Python installations","Virtual environment paths (venv, conda)","requirements.txt or environment.yml files for dependency detection"],"output_types":["Selected Python interpreter path","Active Jupyter kernel identifier","Environment variables for subprocess execution","Package list for selected environment"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-ms-toolsai-python-ds-extension-pack__cap_5","uri":"capability://image.visual.data.visualization.rendering.in.notebooks","name":"data visualization rendering in notebooks","description":"The Jupyter extension renders inline visualizations (matplotlib, seaborn, plotly, altair) directly in notebook cells, displaying plots as embedded images or interactive HTML widgets. This enables exploratory data analysis workflows where visualization output is immediately visible alongside code without requiring separate plotting windows or external tools.","intents":["Display matplotlib and seaborn plots inline within notebook cells for iterative visualization development","Render interactive plotly and altair charts with hover tooltips and zoom capabilities","Compare multiple visualizations side-by-side by executing cells in sequence","Export notebook with embedded visualizations as HTML or PDF for sharing with stakeholders"],"best_for":["Data scientists building exploratory data analysis notebooks with heavy visualization","Analysts creating presentation-ready notebooks with embedded charts and plots","Teams collaborating on data insights where visualization is central to communication"],"limitations":["Large or complex visualizations (>10MB) may slow notebook rendering and increase file size","Interactive widgets (plotly, altair) require JavaScript execution; some corporate environments may restrict this","3D visualizations and real-time streaming plots have limited support","Exported HTML files may not render correctly in all email clients or document viewers"],"requires":["VS Code 1.60 or later","Jupyter extension (bundled in this pack)","Python visualization library (matplotlib, seaborn, plotly, altair, etc.)","Jupyter kernel with ipywidgets support for interactive visualizations"],"input_types":["matplotlib Figure objects","seaborn plot outputs","plotly Graph objects","altair Chart objects","pandas DataFrame.plot() outputs"],"output_types":["Embedded PNG/SVG images in notebook cells","Interactive HTML widgets (plotly, altair)","Notebook file (.ipynb) with embedded visualization data","Exported HTML or PDF with rendered visualizations"],"categories":["image-visual","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-ms-toolsai-python-ds-extension-pack__cap_6","uri":"capability://code.generation.editing.multi.file.python.project.context.awareness","name":"multi-file python project context awareness","description":"Pylance and the Python extension maintain a full project index, enabling code intelligence features (go-to-definition, find-references, refactoring) that work across multiple files and modules. This allows developers to navigate and refactor large codebases without manual file switching, with symbol resolution working across imports and package boundaries.","intents":["Navigate to function or class definitions across multiple files using go-to-definition","Find all references to a function or variable throughout the entire project","Refactor function names or class names and automatically update all call sites across files","Understand module dependencies and import relationships visually"],"best_for":["Data science teams building modular ML pipelines with code split across multiple files","Developers maintaining large data processing codebases with complex interdependencies","Teams refactoring legacy data science code to improve maintainability"],"limitations":["Project indexing adds startup latency (typically 5-30 seconds for large projects)","Dynamic imports (importlib, __import__) are not fully resolved by static analysis","Circular imports may cause incomplete symbol resolution","Very large projects (>50k files) may experience memory pressure and slower indexing"],"requires":["VS Code 1.60 or later","Python extension with Pylance (bundled in this pack)","Python project with standard package structure (directories with __init__.py or PEP 420 namespace packages)","Project folder opened in VS Code workspace"],"input_types":["Python source files (.py) in workspace","Import statements and module references","Type hints and docstrings with type information"],"output_types":["Symbol locations (file path, line number)","Reference lists across multiple files","Refactoring change summaries","Dependency graphs (implicit from import analysis)"],"categories":["code-generation-editing","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-ms-toolsai-python-ds-extension-pack__cap_7","uri":"capability://code.generation.editing.integrated.debugging.for.python.scripts.and.notebooks","name":"integrated debugging for python scripts and notebooks","description":"The Python extension provides integrated debugging capabilities for both .py scripts and Jupyter notebooks, allowing users to set breakpoints, step through code, inspect variables, and evaluate expressions in the debugger console. The debugger works with local Python interpreters and supports conditional breakpoints and logpoints for non-intrusive debugging.","intents":["Set breakpoints in Python scripts and step through execution to understand control flow","Inspect variable values and data structures at runtime without print statements","Debug Jupyter notebook cells by setting breakpoints and stepping through cell execution","Evaluate expressions in the debugger console to test hypotheses during debugging sessions"],"best_for":["Python developers debugging complex data processing pipelines","Data scientists troubleshooting ML model training code with runtime inspection","Teams building production data science code that requires robust debugging"],"limitations":["Remote debugging requires manual SSH tunneling or debugpy server configuration","Debugging large DataFrames in the variable inspector can be slow (>100MB objects)","Some Jupyter magic commands and shell commands cannot be debugged","Conditional breakpoints with complex expressions may impact performance"],"requires":["VS Code 1.60 or later","Python extension (bundled in this pack)","Python 3.6+ with debugpy installed (pip install debugpy)","Local Python interpreter or remote debugpy server"],"input_types":["Python source code (.py files)","Jupyter notebook cells","Breakpoint locations (line numbers)","Conditional breakpoint expressions"],"output_types":["Variable inspection data (values, types, nested structures)","Call stack traces","Debugger console output","Expression evaluation results"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-ms-toolsai-python-ds-extension-pack__cap_8","uri":"capability://automation.workflow.package.and.dependency.management.integration","name":"package and dependency management integration","description":"The Python extension integrates with pip and conda to provide package management features, including dependency installation, environment-specific package lists, and import error detection. When imports fail, the extension can suggest installing missing packages and execute installation commands directly from VS Code.","intents":["Install missing Python packages directly from VS Code when import errors are detected","View installed packages and their versions in the current environment","Manage project dependencies by installing packages from requirements.txt or environment.yml","Detect and suggest fixes for unresolved imports in code"],"best_for":["Python developers managing project dependencies without leaving VS Code","Data scientists setting up new environments and installing data science libraries","Teams standardizing dependency management across projects"],"limitations":["Package installation is synchronous and blocks the editor during installation","No built-in dependency conflict resolution — pip/conda conflicts must be resolved manually","Complex dependency specifications (git URLs, local paths) require manual pip/conda commands","No built-in package version pinning or lock file generation"],"requires":["VS Code 1.60 or later","Python extension (bundled in this pack)","pip or conda installed and accessible from Python environment","Internet connectivity for package downloads"],"input_types":["Import statements in Python code","requirements.txt files","environment.yml files (for conda)","Package names and versions"],"output_types":["Installation status messages","Installed package list","Dependency resolution results","Error messages for failed installations"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-ms-toolsai-python-ds-extension-pack__cap_9","uri":"capability://safety.moderation.linting.and.code.quality.analysis","name":"linting and code quality analysis","description":"The Python extension supports multiple linters (Pylint, Flake8, mypy) that run on-save or on-demand to detect code quality issues, style violations, and potential bugs. Linting results are displayed as inline diagnostics in the editor, with quick-fix suggestions for common issues like unused imports or undefined variables.","intents":["Automatically detect code style violations (PEP 8) and fix them with quick actions","Identify unused imports and variables to keep code clean","Catch potential bugs like undefined variables or type mismatches before runtime","Enforce team coding standards by running configured linters on all Python files"],"best_for":["Teams enforcing code quality standards across data science projects","Developers learning Python best practices through linter feedback","Projects requiring code review preparation with pre-checked quality issues"],"limitations":["Linting adds latency to file saves (typically 1-5 seconds per file)","Some linters (Pylint) produce false positives for dynamic code patterns","Linter configuration requires manual setup in workspace settings or .pylintrc files","Conflicting linter rules may require manual resolution (e.g., Pylint vs Flake8 line length)"],"requires":["VS Code 1.60 or later","Python extension (bundled in this pack)","Linter package installed (pylint, flake8, mypy, etc.)","Optional: linter configuration files (.pylintrc, .flake8, setup.cfg)"],"input_types":["Python source files (.py)","Linter configuration files","Code style rules (PEP 8, custom rules)"],"output_types":["Diagnostic messages (errors, warnings, hints)","Quick-fix suggestions","Code quality reports","Linting summary statistics"],"categories":["safety-moderation","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":44,"verified":false,"data_access_risk":"high","permissions":["VS Code 1.60 or later","GitHub account with Copilot subscription or active trial","Python 3.6+ installed locally for syntax validation","Internet connectivity for API calls to GitHub Copilot service","Python 3.6+ with Jupyter package installed (pip install jupyter)","Local or remote Jupyter kernel accessible via standard Jupyter protocol","ipykernel or equivalent kernel implementation for Python execution","Python extension (bundled in this pack)","Formatter package installed (black, autopep8, yapf, etc.)","Optional: formatter configuration files (pyproject.toml, .black, setup.cfg)"],"failure_modes":["Requires active GitHub Copilot subscription (paid tier after trial period)","Context window limited to current file, project structure, and open tabs — cannot access external documentation or custom domain knowledge","Suggestions may require manual review and testing; no built-in validation that generated code is syntactically correct or logically sound","Performance depends on network latency to GitHub Copilot API; offline mode not supported","Kernel execution is synchronous per cell — long-running computations block the UI until completion","No built-in notebook version control or diff visualization (requires external tools like nbdime)","Large notebooks (>100MB) may experience performance degradation in rendering and cell execution","Remote kernel connections require manual SSH tunneling or Jupyter server configuration; no built-in remote kernel discovery","Formatting can change code semantics in rare cases (e.g., string literal formatting affecting regex patterns)","Formatter configuration requires manual setup in workspace settings or pyproject.toml","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.52,"quality":0.35,"ecosystem":0.35000000000000003,"match_graph":0.25,"freshness":0.9,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.15,"match_graph":0.23,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:34.803Z","last_scraped_at":"2026-05-03T15:20:36.253Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=python-data-science","compare_url":"https://unfragile.ai/compare?artifact=python-data-science"}},"signature":"W1yOrR3nVzN+FyUvPao3VdD2tSes4p7hm2+Vnx1W6vg07XQh05rTf3e0IXRAK0hmr+f6pjjRISNysLLPuUPpDg==","signedAt":"2026-06-15T07:59:27.300Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/python-data-science","artifact":"https://unfragile.ai/python-data-science","verify":"https://unfragile.ai/api/v1/verify?slug=python-data-science","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}