{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"ibis","slug":"ibis","name":"Ibis","type":"repo","url":"https://github.com/ibis-project/ibis","page_url":"https://unfragile.ai/ibis","categories":["data-pipelines"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"ibis__cap_0","uri":"capability://data.processing.analysis.lazy.expression.construction.with.symbolic.dataframe.operations","name":"lazy expression construction with symbolic dataframe operations","description":"Builds an abstract syntax tree (AST) of dataframe operations without executing them, using Ibis's core expression system (ibis/expr/operations and ibis/expr/types) to represent table selections, projections, filters, and aggregations as composable symbolic objects. Expressions are constructed through method chaining on Table and Column types, with each operation creating a new immutable expression node that references its inputs, enabling deferred execution and optimization before compilation to backend-specific code.","intents":["I want to build complex data transformations in Python without immediately executing them so I can optimize the query plan","I need to compose multiple operations (filter, select, group by, join) in a readable, chainable syntax before sending to a database","I want to inspect the structure of my query before execution to understand what will actually run"],"best_for":["Data engineers building ETL pipelines who need query optimization","ML practitioners preparing datasets locally before scaling to cloud warehouses","Teams migrating from pandas to distributed backends without rewriting code"],"limitations":["Expressions are unbound until connected to a backend — cannot execute without calling .execute() or .to_pandas()","No automatic query optimization across all backends — optimization rules vary by backend implementation","Circular references in expression graphs are not supported; DAG structure is enforced"],"requires":["Python 3.9+","ibis package installed","At least one backend connection (DuckDB, Spark, BigQuery, etc.)"],"input_types":["Python method calls on Table/Column objects","SQL strings (via ibis.sql())","Pandas DataFrames (via ibis.memtable())"],"output_types":["Ibis Table expression","Ibis Column expression","Ibis Scalar expression"],"categories":["data-processing-analysis","query-abstraction"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ibis__cap_1","uri":"capability://data.processing.analysis.multi.backend.sql.compilation.with.sqlglot.integration","name":"multi-backend sql compilation with sqlglot integration","description":"Translates Ibis expression trees into backend-specific SQL dialects using SQLGlot as the compilation engine (ibis/backends/sql/compiler.py integration). Each backend registers its own SQL compiler that walks the expression DAG, applies backend-specific type mappings (via ibis/expr/operations type registry), and generates optimized SQL strings. The compilation layer handles dialect differences (e.g., window function syntax, string functions, date arithmetic) transparently, allowing a single Ibis expression to produce valid SQL for DuckDB, PostgreSQL, BigQuery, Snowflake, Spark SQL, and 15+ other engines.","intents":["I want to write one Ibis query and have it automatically compile to the correct SQL dialect for my target database","I need to inspect the generated SQL before execution to verify it's correct for my backend","I want to mix Ibis operations with raw SQL fragments without rewriting the entire query"],"best_for":["Teams using multiple data warehouses (BigQuery + Snowflake + Spark) who need code reuse","Data engineers who need to debug generated SQL and understand backend-specific behavior","Organizations migrating between warehouse vendors without rewriting pipelines"],"limitations":["Some advanced Ibis operations may not be supported on all backends — falls back to Python evaluation or raises NotImplementedError","SQL compilation adds ~50-200ms overhead per query depending on expression complexity","Backend-specific SQL functions (e.g., BigQuery ML functions) require explicit Ibis operation definitions","Dialect differences in NULL handling, string escaping, and numeric precision can cause subtle bugs"],"requires":["Python 3.9+","sqlglot package (installed as ibis dependency)","Backend-specific SQL dialect support in SQLGlot"],"input_types":["Ibis expression tree (Table, Column, Scalar)","Raw SQL strings (via ibis.sql() for SQL fragments)"],"output_types":["SQL string (backend-specific dialect)","Compiled query object (backend-dependent)"],"categories":["data-processing-analysis","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ibis__cap_10","uri":"capability://planning.reasoning.expression.optimization.and.rewriting.via.e.graph","name":"expression optimization and rewriting via e-graph","description":"Applies automated query optimization using an e-graph (equality graph) data structure (ibis/common/egraph.py) that represents equivalent expressions and enables rewriting rules to find more efficient query plans. The optimizer applies algebraic transformations (e.g., pushing filters down before joins, eliminating redundant projections, constant folding) to the expression DAG before compilation. Rewriting rules are defined declaratively and applied iteratively until a fixed point is reached, with cost-based selection to choose the most efficient equivalent expression.","intents":["I want my queries automatically optimized without manually reordering operations","I need to eliminate redundant operations (e.g., duplicate projections, unnecessary casts) from my queries","I want to push filters down to reduce data movement in joins and aggregations"],"best_for":["Data engineers building complex queries that benefit from algebraic optimization","Teams with performance-critical pipelines where query optimization matters","Organizations using backends with limited query optimization (e.g., SQLite)"],"limitations":["Optimization adds ~50-500ms overhead depending on expression complexity","Not all optimizations are beneficial for all backends — some backends have their own optimizers","Rewriting rules are generic and may not account for backend-specific statistics or indexes","No cost-based optimization based on table statistics — all rewrites are heuristic-based","Optimization can be disabled but is enabled by default, potentially hiding performance issues"],"requires":["Python 3.9+","ibis package"],"input_types":["Ibis expression DAG (Table, Column, Scalar)"],"output_types":["Optimized Ibis expression DAG (semantically equivalent but more efficient)"],"categories":["planning-reasoning","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ibis__cap_11","uri":"capability://automation.workflow.comprehensive.backend.test.suite.with.docker.environment","name":"comprehensive backend test suite with docker environment","description":"Provides a unified testing framework (ibis/backends/tests/) that runs the same test suite against all 20+ backends using Docker containers for database services. Tests are organized by feature (SQL, aggregation, window functions, etc.) and automatically skipped for backends that don't support a feature. The test infrastructure includes base test classes (e.g., BackendTestBase) that define test methods, and backend-specific test classes that override methods for backend-specific behavior. Docker Compose is used to spin up database services (PostgreSQL, MySQL, BigQuery emulator, etc.) for testing.","intents":["I want to verify that my Ibis code works correctly on all backends before deploying","I need to understand which backends support a particular feature (e.g., window functions) before using it","I want to add a new backend to Ibis and ensure it passes the full test suite"],"best_for":["Ibis contributors adding new features or backends","Teams building Ibis-based tools that need to support multiple backends","Organizations with strict testing requirements for data pipelines"],"limitations":["Running the full test suite against all backends takes hours — CI/CD pipelines are slow","Docker is required for testing — not suitable for environments without Docker support","Some backends (e.g., BigQuery) require credentials or emulators — setup is complex","Test coverage varies by backend — some backends have fewer tests than others","Tests are integration tests, not unit tests — they require actual database connections"],"requires":["Python 3.9+","ibis package with test dependencies","Docker and Docker Compose","Backend-specific credentials or emulators (for cloud backends)"],"input_types":["Test methods (defined in base test classes)","Backend-specific test configuration (database connection parameters)"],"output_types":["Test results (pass/fail/skip)","Coverage reports","Performance benchmarks"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ibis__cap_12","uri":"capability://data.processing.analysis.streaming.and.incremental.data.loading.from.multiple.sources","name":"streaming and incremental data loading from multiple sources","description":"Supports loading data incrementally from files (Parquet, CSV, JSON), databases (via SQL), and cloud storage (S3, GCS, Azure Blob) using backend-specific readers that stream data without loading it all into memory. Ibis abstracts the loading logic behind a unified API (ibis.read_parquet(), ibis.read_csv(), ibis.read_sql()) that returns a Table expression. For backends that support it (e.g., DuckDB), data is read lazily and only materialized when .execute() is called. For backends that don't support lazy reading, data is materialized locally and pushed to the backend.","intents":["I want to load data from Parquet/CSV files without loading them all into memory","I need to read data from cloud storage (S3, GCS) and process it with Ibis","I want to query data directly from a database without exporting it first"],"best_for":["Data engineers building ETL pipelines that process large files","ML engineers loading training data from cloud storage","Teams working with data lakes (Parquet, Iceberg, Delta Lake)"],"limitations":["Streaming support varies by backend — some backends (e.g., BigQuery) don't support lazy file reading","Cloud storage access requires credentials and network connectivity","File format support varies by backend — not all backends support all formats","Schema inference from files can be slow and unreliable — explicit schema specification is recommended","Partitioned datasets require explicit partition column specification"],"requires":["Python 3.9+","ibis package","Backend-specific reader libraries (e.g., pyarrow for Parquet)","Cloud credentials (for cloud storage access)"],"input_types":["File path (local or cloud)","SQL query string","Optional schema specification"],"output_types":["Ibis Table expression"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ibis__cap_13","uri":"capability://planning.reasoning.deferred.computation.with.expression.caching.and.reuse","name":"deferred computation with expression caching and reuse","description":"Caches expression objects to enable efficient reuse of intermediate results without recomputation. When the same expression is used multiple times in a query (e.g., a filtered table used in two different aggregations), Ibis detects the duplication and generates SQL that computes the expression once and reuses it (via CTEs or subqueries). The caching system uses expression hashing and structural equality to detect duplicates, and is transparent to the user — no explicit caching API is required.","intents":["I want to reuse intermediate results (e.g., a filtered table) in multiple downstream operations without recomputing","I need to avoid redundant computation when the same expression appears multiple times in a query","I want the generated SQL to use CTEs or subqueries to reuse intermediate results"],"best_for":["Data engineers building complex queries with repeated subexpressions","Teams optimizing query performance by reducing redundant computation","Organizations with performance-critical pipelines"],"limitations":["Caching is transparent and automatic — no control over when caching is applied","Not all backends support CTEs or subqueries efficiently — performance may vary","Caching adds overhead for simple queries that don't have repeated subexpressions","No user-facing API for explicit caching — caching decisions are made automatically"],"requires":["Python 3.9+","ibis package"],"input_types":["Ibis expression DAG with repeated subexpressions"],"output_types":["Optimized SQL with CTEs or subqueries"],"categories":["planning-reasoning","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ibis__cap_14","uri":"capability://data.processing.analysis.string.operations.and.text.manipulation.with.backend.specific.functions","name":"string operations and text manipulation with backend-specific functions","description":"Implements string operations (substring, length, upper, lower, replace, split, concatenate, regex matching) that compile to backend-specific string function syntax. The system abstracts over differences in string function names and behavior across backends (e.g., SUBSTR vs SUBSTRING, regex syntax differences), providing a unified API for text manipulation.","intents":["Extract substrings and manipulate text data","Normalize text (uppercase, lowercase, trimming)","Match and replace patterns using regular expressions"],"best_for":["Data cleaning pipelines requiring text normalization","Teams extracting structured data from unstructured text","Organizations with text-heavy data (logs, descriptions, etc.)"],"limitations":["String function support varies by backend; some backends lack certain functions (e.g., regex matching)","Regex syntax differs across backends (PCRE vs SQL regex); the same pattern may not work on all backends","String function performance varies; some backends optimize string operations, others don't","Unicode handling differs across backends; results may differ for non-ASCII text"],"requires":["String columns","Understanding of backend-specific string function support"],"input_types":["String column expressions","String literals and patterns"],"output_types":["Transformed string columns","Boolean results (for matching operations)"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ibis__cap_15","uri":"capability://data.processing.analysis.array.and.struct.operations.with.nested.data.type.support","name":"array and struct operations with nested data type support","description":"Supports operations on complex types (arrays, structs) including element access, flattening, unnesting, and aggregation of nested data. The system compiles array/struct operations to backend-specific syntax (UNNEST in SQL, explode in Spark, LATERAL FLATTEN in Snowflake), handling differences in nested data support across backends.","intents":["Work with nested data structures (JSON, arrays, structs) without flattening","Unnest arrays and structs to create multiple rows","Aggregate nested data (e.g., collect values into arrays)"],"best_for":["Data engineers working with semi-structured data (JSON, nested records)","Teams using modern data warehouses with native nested type support (BigQuery, Snowflake)","Organizations with complex data models using nested structures"],"limitations":["Array/struct support varies significantly across backends; some backends (older SQL databases) lack native support","Nested data operations are slower than flat data; unnesting creates many rows","Type inference for nested data is complex; schema must often be manually specified","Nested data operations may not be optimized on all backends"],"requires":["Backend support for nested data types (most modern data warehouses support them)","Understanding of nested data semantics (unnesting, aggregation)"],"input_types":["Array and struct column expressions","Nested data structures"],"output_types":["Unnested table expressions","Aggregated nested data"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ibis__cap_2","uri":"capability://tool.use.integration.backend.agnostic.connection.and.execution.abstraction","name":"backend-agnostic connection and execution abstraction","description":"Provides a unified connection interface (ibis.backends.Backend base class) that abstracts away backend-specific connection logic, authentication, and execution details. Developers call ibis.duckdb.connect(), ibis.bigquery.connect(), or ibis.snowflake.connect() with backend-specific credentials, which returns a Backend instance with a standard API (.sql(), .execute(), .to_pandas()). The Backend class handles query compilation, parameter binding, result fetching, and type conversion, allowing code to switch backends by changing a single line (e.g., from DuckDB to BigQuery) without modifying query logic.","intents":["I want to write code that works with DuckDB locally and BigQuery in production by only changing the connection line","I need a standard interface for executing queries, fetching results, and managing connections across different databases","I want to avoid learning backend-specific APIs (e.g., google-cloud-bigquery, pyspark) and use a unified Python API instead"],"best_for":["Data scientists prototyping locally and deploying to cloud without code changes","Teams managing multiple data warehouses with a single codebase","Developers building data apps (Streamlit, Dash) that need to support multiple backends"],"limitations":["Backend-specific features (e.g., BigQuery ML, Snowflake stored procedures) are not exposed through the unified API","Connection pooling and advanced authentication (e.g., OAuth, SSO) may require backend-specific configuration","Performance characteristics vary significantly by backend — local DuckDB is orders of magnitude faster than cloud warehouses for small data","Some backends require additional dependencies (e.g., google-cloud-bigquery for BigQuery, pyspark for Spark)"],"requires":["Python 3.9+","ibis package","Backend-specific client library (e.g., duckdb, google-cloud-bigquery, snowflake-connector-python)","Valid credentials/connection parameters for the target backend"],"input_types":["Connection parameters (host, port, database, credentials)","Ibis expressions (Table, Column, Scalar)"],"output_types":["Backend connection object","Pandas DataFrame","PyArrow Table","Raw result set (backend-dependent)"],"categories":["tool-use-integration","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ibis__cap_3","uri":"capability://data.processing.analysis.type.safe.schema.inference.and.validation","name":"type-safe schema inference and validation","description":"Automatically infers and validates data types for all expressions using Ibis's type system (ibis/expr/types/core.py, ibis/common/typing.py). When a table is created (via .sql(), .memtable(), or backend connection), Ibis introspects the schema and maps backend-specific types (e.g., BigQuery's BIGNUMERIC) to Ibis types (int64, float64, string, timestamp, etc.). All operations (filter, select, join, aggregate) validate that operands have compatible types at expression construction time, catching type errors before execution. Type coercion rules are applied automatically (e.g., int + float → float) and can be customized per backend.","intents":["I want type errors caught at query construction time, not at execution time on a remote warehouse","I need to understand the schema of my data and ensure operations are type-compatible before running them","I want automatic type mapping between Python types, Ibis types, and backend-specific types (e.g., BigQuery NUMERIC)"],"best_for":["Data engineers building production pipelines who need early error detection","Teams using statically-typed Python (mypy, pyright) who want type hints for dataframe operations","Organizations with strict data governance requiring schema validation"],"limitations":["Type inference relies on backend schema introspection — some backends (e.g., CSV files) may not have reliable schema information","Custom types or backend-specific types (e.g., BigQuery GEOGRAPHY) may not map cleanly to Ibis types","Type coercion rules are backend-specific and may differ from Python's implicit coercion","No support for union types or optional types in the core type system (nullable is a separate flag)"],"requires":["Python 3.9+","ibis package","Backend connection with schema introspection support"],"input_types":["Table schema (from backend introspection or explicit schema definition)","Ibis expressions (for type validation)"],"output_types":["Ibis type objects (int64, float64, string, timestamp, etc.)","Schema dictionary (column name → Ibis type)","Type validation errors (at expression construction time)"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ibis__cap_4","uri":"capability://data.processing.analysis.composable.table.operations.with.method.chaining","name":"composable table operations with method chaining","description":"Provides a fluent API for building complex queries through method chaining on Table objects, where each method (select, filter, join, group_by, order_by, limit) returns a new Table expression. Operations are composable and can be chained arbitrarily (e.g., t.filter(...).select(...).join(...).group_by(...).aggregate(...)), with each step creating a new expression node in the DAG. The API mirrors SQL semantics but uses Python idioms (e.g., filter instead of WHERE, select instead of SELECT), making it accessible to Python developers unfamiliar with SQL.","intents":["I want to build SQL-like queries using Python method chaining instead of writing raw SQL strings","I need to compose multiple operations (filter, select, join, group by) in a readable, step-by-step manner","I want to reuse intermediate results (e.g., filtered tables) in multiple downstream operations"],"best_for":["Python developers who prefer method chaining over SQL syntax","Data scientists building exploratory analyses with incremental refinement","Teams using IDEs with autocomplete that benefit from method discovery"],"limitations":["Some SQL constructs (e.g., window functions, CTEs, subqueries) require explicit method calls and may be less intuitive than SQL","Method chaining can lead to deeply nested expressions that are hard to debug","Performance depends on backend optimization — poorly written chains may not compile to efficient SQL","No support for imperative control flow (if/else, loops) — expressions are purely declarative"],"requires":["Python 3.9+","ibis package","Familiarity with method chaining pattern"],"input_types":["Ibis Table expression","Python functions (for filter, select predicates)","Column expressions (for grouping, ordering, aggregation)"],"output_types":["Ibis Table expression (result of chained operations)"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ibis__cap_5","uri":"capability://data.processing.analysis.cross.backend.join.and.set.operations.with.type.alignment","name":"cross-backend join and set operations with type alignment","description":"Enables joining tables from different backends (e.g., DuckDB table joined with BigQuery table) by materializing one side locally and performing the join in the target backend, with automatic type alignment and schema reconciliation. Implements set operations (union, intersection, difference) across heterogeneous backends by converting schemas to a common type representation and handling NULL semantics correctly. The join logic (ibis/expr/operations/relations.py) validates that join keys have compatible types and generates backend-specific join SQL with proper type casting.","intents":["I want to join a local DuckDB table with a BigQuery table without manually exporting/importing data","I need to combine datasets from multiple backends (e.g., Snowflake + Spark) in a single query","I want to perform set operations (union, intersection) on tables from different backends with automatic schema alignment"],"best_for":["Data engineers integrating data from multiple sources (data lake + data warehouse)","Teams using federated queries across heterogeneous backends","Organizations with data in multiple cloud providers (AWS + GCP + Azure)"],"limitations":["Cross-backend joins require materializing one table locally — can be slow and memory-intensive for large tables","Type alignment may require implicit casting, which can cause precision loss (e.g., float64 → int64)","Not all backends support all join types (e.g., FULL OUTER JOIN) — falls back to Python implementation","Performance is limited by the slowest backend and network latency between backends","Transactions are not supported across backends — no ACID guarantees for multi-backend operations"],"requires":["Python 3.9+","ibis package","Connections to both backends","Sufficient local memory to materialize one table"],"input_types":["Ibis Table expressions from different backends","Join keys (Column expressions with compatible types)","Join type (inner, left, right, outer, cross)"],"output_types":["Ibis Table expression (result of join/set operation)"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ibis__cap_6","uri":"capability://data.processing.analysis.aggregation.and.grouping.with.window.functions","name":"aggregation and grouping with window functions","description":"Provides aggregation operations (sum, mean, count, min, max, etc.) and window functions (row_number, rank, lag, lead, etc.) that compile to backend-specific SQL. Aggregations are applied via .aggregate() or .group_by() methods, which generate GROUP BY clauses with proper type handling for aggregate functions. Window functions are constructed via .over() method, specifying partition and order clauses, and compile to OVER (PARTITION BY ... ORDER BY ...) syntax. The implementation handles edge cases like NULL aggregation, empty groups, and frame specifications (ROWS BETWEEN ... AND ...) correctly across backends.","intents":["I want to compute aggregates (sum, count, mean) grouped by dimensions without writing GROUP BY SQL","I need to compute window functions (running totals, ranks, lag/lead) within groups or partitions","I want to combine multiple aggregates in a single operation and get results as a new table"],"best_for":["Data analysts computing summary statistics and KPIs","ML engineers preparing features with window functions (e.g., rolling averages)","Business intelligence teams building aggregated reports"],"limitations":["Window function support varies by backend — some backends (e.g., SQLite) have limited window function capabilities","Aggregating over very large groups can be slow — no automatic optimization for skewed data","NULL handling in aggregates is backend-specific (e.g., COUNT(*) vs COUNT(column))","Custom aggregate functions require backend-specific implementations","No support for approximate aggregates (e.g., HyperLogLog) in the core API"],"requires":["Python 3.9+","ibis package","Backend with aggregation and window function support"],"input_types":["Ibis Table expression","Column expressions (for grouping, aggregation)","Aggregate functions (sum, mean, count, min, max, etc.)","Window specifications (partition, order, frame)"],"output_types":["Ibis Table expression (aggregated result)","Ibis Column expression (aggregate value)"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ibis__cap_7","uri":"capability://data.processing.analysis.sql.fragment.embedding.and.mixed.mode.queries","name":"sql fragment embedding and mixed-mode queries","description":"Allows embedding raw SQL strings directly into Ibis expressions via ibis.sql() function, enabling developers to use backend-specific SQL features (e.g., BigQuery ML, Snowflake stored procedures) that aren't exposed through the Ibis API. SQL fragments are parsed and type-annotated, then composed with other Ibis operations in the expression DAG. The system validates that SQL fragments produce tables/columns with compatible schemas and types, and compiles them into the final backend-specific query without modification.","intents":["I want to use backend-specific SQL features (e.g., BigQuery ML, Snowflake UDFs) that aren't in the Ibis API","I need to embed raw SQL subqueries into Ibis expressions for performance or functionality reasons","I want to gradually migrate from raw SQL to Ibis without rewriting entire queries"],"best_for":["Teams using advanced backend features (ML functions, stored procedures) not exposed in Ibis","Organizations with existing SQL code that needs to be integrated with Ibis pipelines","Developers optimizing queries by hand-tuning SQL for specific backends"],"limitations":["SQL fragments are backend-specific — code using .sql() is not portable across backends","Type inference for SQL fragments requires explicit schema annotation or backend introspection","SQL injection is possible if fragments are constructed from untrusted input — no parameterization support","Debugging mixed Ibis/SQL queries is harder than pure Ibis or pure SQL","SQL fragments bypass Ibis optimization — may produce suboptimal query plans"],"requires":["Python 3.9+","ibis package","Backend connection","Knowledge of backend-specific SQL syntax"],"input_types":["SQL string (backend-specific dialect)","Optional schema annotation (column names and types)","Ibis expressions (for composition with SQL fragments)"],"output_types":["Ibis Table expression (result of SQL fragment)","Ibis Column expression (if SQL fragment returns a scalar or column)"],"categories":["data-processing-analysis","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ibis__cap_8","uri":"capability://data.processing.analysis.lazy.result.materialization.with.multiple.output.formats","name":"lazy result materialization with multiple output formats","description":"Defers execution until explicitly requested via .execute(), .to_pandas(), .to_pyarrow(), or .to_csv() methods, allowing developers to build complex queries without triggering computation. When materialization is requested, the expression DAG is compiled to backend-specific SQL, executed on the backend, and results are fetched and converted to the requested format (Pandas DataFrame, PyArrow Table, CSV file, etc.). The system handles result streaming for large datasets, type conversion between backend types and Python types, and NULL value representation correctly.","intents":["I want to build a query without executing it, then decide later whether to fetch results as Pandas, PyArrow, or CSV","I need to execute the same query multiple times with different output formats without recompiling","I want to stream large results without loading them all into memory at once"],"best_for":["Data scientists working with large datasets that don't fit in memory","ML engineers building pipelines that need to support multiple output formats","Teams integrating Ibis with different downstream tools (Pandas, PyArrow, DuckDB)"],"limitations":["Streaming is not supported for all backends — some backends (e.g., BigQuery) fetch all results at once","Type conversion overhead can be significant for large datasets — PyArrow is faster than Pandas","CSV export requires materializing results in memory first — not suitable for very large datasets","Result caching is not built-in — repeated .execute() calls re-run the query on the backend","No support for partial result fetching (e.g., LIMIT) without modifying the expression"],"requires":["Python 3.9+","ibis package","Backend connection","Optional: pandas, pyarrow, or other output format libraries"],"input_types":["Ibis expression (Table, Column, or Scalar)"],"output_types":["Pandas DataFrame","PyArrow Table","CSV file","Python scalar (for scalar expressions)","Iterator (for streaming results)"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ibis__cap_9","uri":"capability://data.processing.analysis.backend.specific.type.mapping.and.operation.registry","name":"backend-specific type mapping and operation registry","description":"Maintains a registry of backend-specific type mappings (e.g., BigQuery NUMERIC → Ibis decimal128) and operation implementations (e.g., string functions, date arithmetic) that vary across backends. Each backend registers its type mapper (ibis/backends/*/datatypes.py) and operation compiler (ibis/backends/*/compiler.py) that define how Ibis types and operations map to backend-specific SQL. When an operation is not supported by a backend, the registry falls back to Python evaluation or raises NotImplementedError, allowing graceful degradation or explicit error messages.","intents":["I want to understand how Ibis types map to my backend's types (e.g., what is BigQuery's equivalent of Ibis int64?)","I need to know which Ibis operations are supported on my backend before writing code","I want to add support for a new backend or operation without modifying core Ibis code"],"best_for":["Backend developers adding new backends to Ibis","Teams using backends with non-standard type systems (e.g., BigQuery GEOGRAPHY)","Organizations with custom backends or data sources"],"limitations":["Type mapping is not always bidirectional — some backend types don't have Ibis equivalents","Operation support varies significantly by backend — code may fail at execution time if an operation isn't supported","Custom type mappings require modifying backend-specific code — not user-configurable","No automatic type coercion across backends — explicit casting may be required","Documentation of supported operations per backend is incomplete"],"requires":["Python 3.9+","ibis package","Backend-specific knowledge (type system, SQL dialect, supported functions)"],"input_types":["Ibis type objects (int64, string, timestamp, etc.)","Ibis operations (filter, select, aggregate, etc.)"],"output_types":["Backend-specific type string (e.g., 'NUMERIC' for BigQuery)","Backend-specific SQL function call","Error message (if operation not supported)"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ibis__headline","uri":"capability://data.processing.analysis.portable.python.dataframe.library","name":"portable python dataframe library","description":"Ibis is a portable Python dataframe library that allows users to write data manipulation code once and execute it across 20+ different backend engines, making it ideal for both local and production environments.","intents":["best portable dataframe library","Python dataframe library for multiple backends","data manipulation tool for ML data prep","cross-platform dataframe operations","unified API for data processing"],"best_for":["data scientists","data engineers"],"limitations":["requires Python environment"],"requires":["Python 3.x"],"input_types":["dataframes"],"output_types":["executed queries"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":55,"verified":false,"data_access_risk":"high","permissions":["Python 3.9+","ibis package installed","At least one backend connection (DuckDB, Spark, BigQuery, etc.)","sqlglot package (installed as ibis dependency)","Backend-specific SQL dialect support in SQLGlot","ibis package","ibis package with test dependencies","Docker and Docker Compose","Backend-specific credentials or emulators (for cloud backends)","Backend-specific reader libraries (e.g., pyarrow for Parquet)"],"failure_modes":["Expressions are unbound until connected to a backend — cannot execute without calling .execute() or .to_pandas()","No automatic query optimization across all backends — optimization rules vary by backend implementation","Circular references in expression graphs are not supported; 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