{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"polars","slug":"polars","name":"Polars","type":"repo","url":"https://github.com/pola-rs/polars","page_url":"https://unfragile.ai/polars","categories":["frameworks-sdks"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"polars__cap_0","uri":"capability://data.processing.analysis.lazy.expression.based.query.optimization.with.automatic.predicate.pushdown","name":"lazy expression-based query optimization with automatic predicate pushdown","description":"Polars defers execution of DataFrame operations by building an expression IR (intermediate representation) that is analyzed by a query optimizer before physical execution. The optimizer performs predicate pushdown (filtering before joins), column pruning (selecting only needed columns), and redundant computation elimination. This is implemented via the polars-plan crate which constructs a logical plan from the expression DSL, then converts it to an optimized physical plan executed by either the streaming or memory engine.","intents":["I want to write complex multi-step data transformations that execute efficiently without manually optimizing the order of operations","I need to process datasets larger than memory by streaming data through the query engine","I want to understand what operations will actually execute before running them"],"best_for":["data engineers building ETL pipelines with complex transformations","analysts processing multi-gigabyte datasets on resource-constrained machines","teams migrating from pandas and needing automatic query optimization"],"limitations":["Lazy evaluation adds complexity to debugging — errors surface at .collect() time, not at expression definition time","Some operations (e.g., custom Python functions via map_batches) cannot be optimized and break the optimization chain","Schema inference requires scanning data or explicit type hints for some I/O formats"],"requires":["Python 3.8+ or Node.js 14+","Rust 1.70+ if building from source","Understanding of expression-based DSL (different from pandas method chaining)"],"input_types":["LazyFrame (deferred computation graph)","expressions (pl.col(), pl.lit(), pl.when(), etc.)"],"output_types":["DataFrame (eager result after .collect())","optimized physical execution plan (internal)"],"categories":["data-processing-analysis","query-optimization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"polars__cap_1","uri":"capability://data.processing.analysis.apache.arrow.columnar.in.memory.format.with.zero.copy.data.sharing","name":"apache arrow columnar in-memory format with zero-copy data sharing","description":"Polars stores all data in Apache Arrow columnar format (via polars-arrow crate), organizing data by column rather than row. This enables SIMD vectorization, better CPU cache locality, and efficient compression. The columnar layout allows zero-copy data sharing between Polars and other Arrow-compatible libraries (DuckDB, Pandas with PyArrow backend, Apache Spark). Data is stored in ChunkedArray structures that can reference the same underlying memory buffers.","intents":["I want to share data between Polars and other data tools without serialization overhead","I need maximum performance for analytical operations that scan entire columns","I want to reduce memory footprint through columnar compression and efficient storage"],"best_for":["data scientists building multi-tool pipelines (Polars + DuckDB + Pandas)","teams processing analytical workloads with high cardinality columns","systems with memory constraints where compression matters"],"limitations":["Row-oriented operations (e.g., iterating row-by-row) are slower than in row-based systems like pandas","Modifying a single value in a column requires copying the entire column (immutable design)","Some legacy tools don't support Arrow format, requiring conversion overhead"],"requires":["Python 3.8+ or Node.js 14+","Understanding of columnar vs row-oriented data layouts"],"input_types":["Arrow-compatible data sources (Parquet, Arrow IPC, CSV)","NumPy arrays (converted to Arrow)","Pandas DataFrames (converted to Arrow)"],"output_types":["Arrow-compatible format (zero-copy to DuckDB, Pandas with PyArrow)","Parquet files","Arrow IPC format"],"categories":["data-processing-analysis","memory-management"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"polars__cap_10","uri":"capability://data.processing.analysis.temporal.operations.with.timezone.aware.datetime.and.date.arithmetic","name":"temporal operations with timezone-aware datetime and date arithmetic","description":"Polars implements comprehensive temporal operations (via polars-time crate) including timezone-aware datetime handling, date/time arithmetic, duration operations, and temporal component extraction (year, month, day, hour, etc.). Temporal types are stored efficiently as integer offsets from epoch, with timezone information preserved. Operations like date addition, duration calculation, and timezone conversion are vectorized.","intents":["I want to work with timezone-aware timestamps without manual conversion","I need to compute time differences and durations between dates","I want to extract temporal components (year, month, day) from datetime columns"],"best_for":["time series analysts working with global data","data engineers building temporal feature engineering pipelines","teams handling multi-timezone data"],"limitations":["Timezone conversion requires valid IANA timezone names; custom timezones not supported","Some temporal operations (e.g., business day calculations) require custom expressions","Daylight saving time transitions may cause unexpected behavior in some edge cases"],"requires":["Python 3.8+ or Node.js 14+","Understanding of timezone semantics and IANA timezone database"],"input_types":["Date columns (Date dtype)","Datetime columns (Datetime dtype with optional timezone)","Duration columns (Duration dtype)","temporal expressions (pl.col().dt.year(), etc.)"],"output_types":["Date/Datetime/Duration columns (transformed)","numeric columns (extracted components)","Boolean columns (temporal comparisons)"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"polars__cap_11","uri":"capability://tool.use.integration.pyo3.ffi.bridge.enabling.zero.copy.python.rust.data.exchange","name":"pyo3 ffi bridge enabling zero-copy python-rust data exchange","description":"Polars uses PyO3 (Python-Rust FFI framework) to expose the Rust core to Python without data copying. The FFI layer (in polars-python crate) marshals Python objects to Rust data structures and vice versa, leveraging Arrow's memory layout for zero-copy interop. Python DataFrames are thin wrappers around Rust DataFrame objects, with method calls dispatched to Rust implementations via PyO3 bindings.","intents":["I want to use Polars from Python with minimal overhead compared to native Rust","I need to integrate Polars with other Python libraries without serialization","I want to extend Polars with custom Python functions while maintaining performance"],"best_for":["Python developers needing high-performance data processing","teams building Python data pipelines with Rust performance requirements","data scientists integrating Polars with scikit-learn, TensorFlow, etc."],"limitations":["Custom Python functions (via map_batches) incur Python interpreter overhead and break optimization","PyO3 bindings add ~5-10% overhead compared to pure Rust","Some Rust-specific features (e.g., custom allocators) are not exposed to Python"],"requires":["Python 3.8+","Rust 1.70+ if building from source","PyO3 knowledge for extending Polars with custom functions"],"input_types":["Python objects (lists, dicts, numpy arrays, pandas DataFrames)","Arrow-compatible Python objects"],"output_types":["Python objects (Polars DataFrame/Series)","Arrow-compatible Python objects"],"categories":["tool-use-integration","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"polars__cap_12","uri":"capability://data.processing.analysis.categorical.dtype.with.dictionary.encoding.and.efficient.grouping","name":"categorical dtype with dictionary encoding and efficient grouping","description":"Polars implements categorical (enum) data type with dictionary encoding, storing unique values once and referencing them by integer index. This reduces memory usage for columns with many repeated values and accelerates grouping/joining operations. The categorical system (in polars-core crate) supports both ordered and unordered categories, with optional specification of category order.","intents":["I want to reduce memory usage for columns with many repeated string values","I need to preserve category order for ordinal data (e.g., education level)","I want to accelerate grouping and joining on categorical columns"],"best_for":["data engineers working with high-cardinality categorical data","analysts processing survey data with ordinal categories","teams with memory-constrained environments"],"limitations":["Adding new categories after creation requires reencoding (expensive operation)","Categorical operations may be slower than strings for low-cardinality columns","Interoperability with non-Polars tools requires converting back to strings"],"requires":["Python 3.8+ or Node.js 14+","Understanding of categorical semantics (ordered vs unordered)"],"input_types":["String columns (converted to categorical)","explicit category lists (for ordered categories)"],"output_types":["Categorical columns (dictionary-encoded)","integer indices (internal representation)"],"categories":["data-processing-analysis","memory-management"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"polars__cap_13","uri":"capability://data.processing.analysis.nested.data.types.struct.list.array.with.recursive.operations","name":"nested data types (struct, list, array) with recursive operations","description":"Polars supports nested data types including Struct (named fields), List (variable-length arrays), and Array (fixed-length arrays) with recursive operations that can navigate and transform nested structures. The type system (polars-core crate) allows operations like unnesting (flattening), extracting fields, and applying expressions to nested values. Nested types are stored efficiently in Arrow format with proper memory layout.","intents":["I want to work with JSON-like nested data without flattening","I need to extract fields from struct columns or elements from list columns","I want to apply transformations to nested values (e.g., map over list elements)"],"best_for":["data engineers processing JSON/API responses","analysts working with semi-structured data","teams handling complex nested data from databases"],"limitations":["Nested operations are less optimized than flat operations","Some operations (e.g., filtering nested lists) require explicit unnesting","Interoperability with non-Polars tools may require flattening"],"requires":["Python 3.8+ or Node.js 14+","Understanding of nested data structures"],"input_types":["JSON data (parsed to struct/list types)","Parquet files with nested schemas","explicit struct/list construction via pl.struct_(), pl.list_()"],"output_types":["Struct columns (named fields)","List columns (variable-length arrays)","Array columns (fixed-length arrays)","flattened/unnested columns"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"polars__cap_14","uri":"capability://tool.use.integration.node.js.bindings.with.async.await.support.for.javascript.environments","name":"node.js bindings with async/await support for javascript environments","description":"Polars provides Node.js bindings (via polars-python crate compiled to WASM or native modules) enabling JavaScript/TypeScript developers to use Polars with async/await semantics. The bindings expose the same API as Python but adapted for JavaScript conventions. Async operations allow non-blocking data processing in Node.js event loop.","intents":["I want to use Polars from JavaScript/TypeScript with async/await","I need to process data in Node.js servers without blocking the event loop","I want to share Polars code between Python and JavaScript environments"],"best_for":["JavaScript/TypeScript developers building data processing backends","full-stack teams using JavaScript across frontend and backend","Node.js applications requiring high-performance data operations"],"limitations":["Node.js bindings are less mature than Python bindings","Some features may lag behind Python API","WASM version has performance overhead compared to native modules","Async operations add complexity compared to synchronous Python API"],"requires":["Node.js 14+","TypeScript knowledge (optional but recommended)","Understanding of async/await patterns"],"input_types":["JavaScript objects (arrays, objects)","Arrow-compatible data","CSV/Parquet files"],"output_types":["JavaScript objects (Polars DataFrame/Series)","Arrow-compatible data","CSV/Parquet files"],"categories":["tool-use-integration","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"polars__cap_2","uri":"capability://data.processing.analysis.dual.execution.engine.streaming.and.memory.based.query.execution","name":"dual execution engine: streaming and memory-based query execution","description":"Polars implements two physical execution engines: a streaming engine that processes data in chunks without loading entire datasets into memory, and a memory engine for operations requiring full dataset visibility (e.g., sorts, joins). The query optimizer selects which engine to use based on operation type and available memory. The streaming engine processes data in configurable batch sizes (default 1M rows), enabling processing of datasets larger than RAM.","intents":["I need to process datasets that are larger than available RAM","I want predictable memory usage regardless of input size","I need to choose between speed (memory engine) and memory efficiency (streaming engine)"],"best_for":["data engineers processing multi-terabyte datasets on limited hardware","cloud environments with cost-sensitive memory allocation","real-time streaming pipelines that cannot buffer entire datasets"],"limitations":["Streaming engine cannot execute operations requiring full dataset visibility (e.g., global sort, cross joins) — falls back to memory engine","Some optimizations (e.g., predicate pushdown) work better with memory engine","Streaming adds ~5-10% overhead for small datasets that fit in memory"],"requires":["Python 3.8+ or Node.js 14+","Understanding of batch processing semantics","Explicit streaming mode enabled via .collect(streaming=True)"],"input_types":["LazyFrame with streaming-compatible operations","Parquet files with row group structure","CSV/NDJSON files"],"output_types":["DataFrame (collected in batches)","streaming results (internal batch processing)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"polars__cap_3","uri":"capability://data.processing.analysis.expression.dsl.with.schema.aware.type.coercion.and.validation","name":"expression dsl with schema-aware type coercion and validation","description":"Polars provides a Python/Node.js DSL for building expressions (pl.col(), pl.lit(), pl.when(), etc.) that are type-checked against the DataFrame schema before execution. The expression system (polars-plan crate) performs automatic type coercion (e.g., int to float for division) and validates that operations are valid for the column types. Schema inference happens during lazy frame construction, catching type errors before data is processed.","intents":["I want compile-time type checking for data transformations to catch errors before execution","I need automatic type coercion that respects analytical semantics (e.g., int division → float)","I want to understand what columns and types are available without inspecting data"],"best_for":["data teams building production pipelines where type safety matters","analysts who want IDE autocomplete for available columns and operations","teams migrating from SQL where schema validation is expected"],"limitations":["Type coercion rules may surprise users familiar with pandas (e.g., int / int → float, not int)","Custom Python functions in map_batches bypass type checking","Some complex type operations (e.g., nested struct manipulation) require explicit casting"],"requires":["Python 3.8+ or Node.js 14+","Understanding of Polars type system (distinct from NumPy/pandas dtypes)","IDE with Python language server for expression autocomplete"],"input_types":["DataFrame or LazyFrame schema","expressions built from pl.col(), pl.lit(), pl.when(), etc."],"output_types":["typed expressions (validated against schema)","type error messages (at lazy frame construction time)"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"polars__cap_4","uri":"capability://data.processing.analysis.multi.format.i.o.with.hive.partitioning.and.predicate.pushdown.to.storage","name":"multi-format i/o with hive partitioning and predicate pushdown to storage","description":"Polars implements format-specific I/O handlers (CSV, Parquet, NDJSON, JSON, Avro, ORC, database connectors) in the polars-io crate with intelligent predicate pushdown to the storage layer. For Parquet files with Hive partitioning, Polars can filter partitions before reading data. For database sources, filter predicates are pushed down as SQL WHERE clauses. The I/O layer integrates with the query optimizer to apply filters before data enters memory.","intents":["I want to read only relevant partitions from a partitioned dataset without scanning all files","I need to query databases efficiently by pushing filters down to SQL","I want to read large files in a streaming fashion without loading everything into memory"],"best_for":["data engineers working with Hive-partitioned data lakes (S3, HDFS)","teams querying databases and wanting to minimize data transfer","analysts processing multi-format data sources (CSV, Parquet, JSON, databases)"],"limitations":["Hive partitioning support requires specific directory structure (e.g., year=2024/month=01/)","Database pushdown only works for simple predicates; complex expressions may require client-side filtering","Some formats (e.g., JSON) don't support efficient predicate pushdown","Streaming I/O requires row group structure in Parquet (not all Parquet files support this)"],"requires":["Python 3.8+ or Node.js 14+","For database I/O: appropriate database driver (psycopg2 for PostgreSQL, etc.)","For cloud storage: cloud SDK (boto3 for S3, etc.)"],"input_types":["CSV files (local or cloud)","Parquet files with optional Hive partitioning","NDJSON, JSON, Avro, ORC files","Database connections (PostgreSQL, MySQL, SQLite, etc.)","Arrow IPC format"],"output_types":["DataFrame or LazyFrame","Parquet files with optional Hive partitioning","CSV, NDJSON, JSON files","database tables"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"polars__cap_5","uri":"capability://data.processing.analysis.sql.query.interface.with.full.polars.expression.translation","name":"sql query interface with full polars expression translation","description":"Polars includes a SQL parser (polars-sql crate) that translates SQL queries into the native expression DSL, enabling users to write SQL while benefiting from Polars' optimization and execution engines. The SQL interface supports standard SQL operations (SELECT, WHERE, JOIN, GROUP BY, window functions) and translates them to the same logical plan used by the expression API, ensuring identical performance and optimization.","intents":["I want to use SQL syntax for data transformations while leveraging Polars' performance","I need to migrate SQL queries from a database to Polars without rewriting in Python","I want to mix SQL and expression-based operations in the same pipeline"],"best_for":["SQL-fluent analysts transitioning to Polars","teams with existing SQL codebases wanting to migrate to Polars","organizations supporting both SQL and Python interfaces for the same data"],"limitations":["Not all SQL dialects are supported — some database-specific syntax (e.g., T-SQL) may not work","Complex window functions or recursive CTEs may have limited support","SQL interface is newer and less battle-tested than expression API","Some Polars-specific optimizations may not apply to SQL-generated plans"],"requires":["Python 3.8+","SQL knowledge","Polars 0.19.0+ (SQL interface added in recent versions)"],"input_types":["SQL query strings","DataFrame or LazyFrame (registered as tables in SQL context)"],"output_types":["DataFrame or LazyFrame (from SQL query result)","expression tree (internal translation)"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"polars__cap_6","uri":"capability://data.processing.analysis.grouped.aggregation.with.multiple.aggregation.functions.and.custom.expressions","name":"grouped aggregation with multiple aggregation functions and custom expressions","description":"Polars implements efficient grouped aggregation via the GroupBy API, which partitions data by key columns and applies aggregation expressions to each group. The aggregation engine (in polars-ops crate) supports both standard functions (sum, mean, count, etc.) and custom expressions, with automatic parallelization across groups. Multiple aggregations can be applied in a single pass over the data, reducing memory allocations.","intents":["I want to compute multiple aggregations (sum, mean, count) grouped by key columns in a single pass","I need to apply custom expressions within groups (e.g., top-N rows per group)","I want to compute aggregations efficiently without materializing intermediate results"],"best_for":["analysts computing summary statistics by category","data engineers building feature engineering pipelines","teams needing efficient multi-metric aggregations"],"limitations":["GroupBy requires materializing the grouping keys in memory (cannot stream groups)","Custom expressions in aggregation context have limited support compared to row context","Very high cardinality grouping keys (millions of unique values) may cause memory pressure"],"requires":["Python 3.8+ or Node.js 14+","Understanding of aggregation semantics (null handling, type coercion)"],"input_types":["DataFrame or LazyFrame","grouping key columns (string or expression)","aggregation expressions (pl.col().sum(), pl.col().mean(), etc.)"],"output_types":["DataFrame with one row per group","aggregated columns (sum, mean, count, etc.)"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"polars__cap_7","uri":"capability://data.processing.analysis.join.operations.with.automatic.join.type.selection.and.optimization","name":"join operations with automatic join type selection and optimization","description":"Polars implements multiple join algorithms (hash join, sort merge join, nested loop join) and automatically selects the optimal strategy based on data size and cardinality. The join optimizer (in polars-plan crate) can reorder joins to minimize intermediate result sizes and push predicates before joins. Supported join types include inner, left, right, full outer, cross, and anti/semi joins with optional coalesce of duplicate key columns.","intents":["I want to join two DataFrames efficiently without manually choosing join algorithms","I need to combine data from multiple sources with automatic optimization","I want to perform complex multi-table joins with predicate pushdown"],"best_for":["data engineers combining data from multiple sources","analysts performing relational operations on structured data","teams needing efficient joins on large datasets"],"limitations":["Cross joins require materializing the full Cartesian product (memory intensive)","Join order optimization is limited compared to database query planners","Some join types (e.g., asof joins) have specific requirements on key column ordering","Very large joins may require streaming mode to avoid memory overflow"],"requires":["Python 3.8+ or Node.js 14+","Understanding of join semantics (inner, left, right, full, cross, anti, semi)"],"input_types":["DataFrame or LazyFrame (left and right sides)","join key columns (string or expression)","join type (inner, left, right, full, cross, anti, semi)"],"output_types":["DataFrame with combined columns from both sides","optional coalesced key columns"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"polars__cap_8","uri":"capability://data.processing.analysis.window.functions.with.partitioning.and.ordering","name":"window functions with partitioning and ordering","description":"Polars implements window functions (row_number, rank, dense_rank, lag, lead, first, last, sum over window, etc.) via the expression system with support for partitioning (OVER clause) and ordering. Window functions are computed efficiently by the polars-ops crate, which partitions data, applies the window function within each partition, and maintains row order. Supports both unbounded and bounded windows (e.g., last 7 rows).","intents":["I want to compute row numbers or rankings within groups","I need to access previous/next row values (lag/lead) for time series analysis","I want to compute running sums or other aggregations over sliding windows"],"best_for":["time series analysts computing rolling statistics","data engineers building feature engineering pipelines","teams performing ranking or numbering operations within groups"],"limitations":["Window functions require materializing partitions in memory (cannot stream)","Complex window specifications (e.g., RANGE BETWEEN) have limited support","Performance degrades with very high cardinality partitions"],"requires":["Python 3.8+ or Node.js 14+","Understanding of window function semantics (partitioning, ordering, frame specification)"],"input_types":["DataFrame or LazyFrame","window function expressions (pl.col().rank(), pl.col().lag(), etc.)","partition columns (optional)","order columns (optional)"],"output_types":["DataFrame with window function results as new columns","computed values (ranks, row numbers, lag/lead values, etc.)"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"polars__cap_9","uri":"capability://data.processing.analysis.string.operations.with.regex.pattern.matching.and.unicode.support","name":"string operations with regex, pattern matching, and unicode support","description":"Polars provides a comprehensive string API (via polars-ops crate) supporting regex operations (match, extract, replace, split), pattern matching (contains, starts_with, ends_with), case conversion, trimming, and Unicode-aware operations. String operations are vectorized and can be applied to entire columns efficiently. Regex patterns are compiled once and reused across the column.","intents":["I want to extract patterns from text columns using regex","I need to clean text data (trim, case conversion, replace patterns)","I want to filter rows based on string patterns"],"best_for":["data engineers cleaning and preprocessing text data","analysts extracting information from unstructured text","teams performing text-based feature engineering"],"limitations":["Regex performance depends on pattern complexity; complex patterns may be slow","Some advanced regex features (e.g., lookahead/lookbehind) may not be supported","Unicode operations assume valid UTF-8 encoding; invalid UTF-8 causes errors"],"requires":["Python 3.8+ or Node.js 14+","Understanding of regex syntax","Valid UTF-8 encoded strings"],"input_types":["String columns (Utf8 or Categorical dtype)","regex patterns (as strings)","replacement strings"],"output_types":["String columns (extracted, replaced, or transformed)","Boolean columns (for pattern matching)","numeric columns (for extraction of numeric patterns)"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"polars__headline","uri":"capability://data.processing.analysis.high.performance.dataframe.library","name":"high-performance dataframe library","description":"Polars is a lightning-fast DataFrame library built in Rust, designed to outperform traditional libraries like pandas by 10-100x for data processing tasks, making it ideal for high-performance analytics.","intents":["best DataFrame library","DataFrame library for big data processing","fast DataFrame library for Python","Rust-based DataFrame library","high-performance analytics framework"],"best_for":["large datasets","complex data analysis"],"limitations":["requires Rust for optimal performance"],"requires":["Python or Node.js environment"],"input_types":["CSV","Parquet","NDJSON"],"output_types":["DataFrames","Series"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":55,"verified":false,"data_access_risk":"high","permissions":["Python 3.8+ or Node.js 14+","Rust 1.70+ if building from source","Understanding of expression-based DSL (different from pandas method chaining)","Understanding of columnar vs row-oriented data layouts","Understanding of timezone semantics and IANA timezone database","Python 3.8+","PyO3 knowledge for extending Polars with custom functions","Understanding of categorical semantics (ordered vs unordered)","Understanding of nested data structures","Node.js 14+"],"failure_modes":["Lazy evaluation adds complexity to debugging — errors surface at .collect() time, not at expression definition time","Some operations (e.g., custom Python functions via map_batches) cannot be optimized and break the optimization chain","Schema inference requires scanning data or explicit type hints for some I/O formats","Row-oriented operations (e.g., iterating row-by-row) are slower than in row-based systems like pandas","Modifying a single value in a column requires copying the entire column (immutable design)","Some legacy tools don't support Arrow format, requiring conversion overhead","Timezone conversion requires valid IANA timezone names; custom timezones not supported","Some temporal operations (e.g., business day calculations) require custom expressions","Daylight saving time transitions may cause unexpected behavior in some edge cases","Custom Python functions (via map_batches) incur Python interpreter overhead and break optimization","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7,"quality":0.9,"ecosystem":0.39999999999999997,"match_graph":0.25,"freshness":0.52,"weights":{"adoption":0.3,"quality":0.2,"ecosystem":0.15,"match_graph":0.3,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-06-17T09:51:05.295Z","last_scraped_at":null,"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=polars","compare_url":"https://unfragile.ai/compare?artifact=polars"}},"signature":"Ly6DxgcA5j/XuDm0EYgbMxSDlvGHu1dF90ozng7H2si/w7txBXx5XzgTyvzOP1Wu5ixt8U73vORQ8Pt/6H56BQ==","signedAt":"2026-06-22T07:54:40.556Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/polars","artifact":"https://unfragile.ai/polars","verify":"https://unfragile.ai/api/v1/verify?slug=polars","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"}}