graph LR
Core_Data_Structures_Schema["Core Data Structures & Schema"]
Expression_Function_Engine["Expression & Function Engine"]
Lazy_Execution_Framework["Lazy Execution Framework"]
Input_Output_Data_Connectors["Input-Output & Data Connectors"]
Data_Type_System["Data Type System"]
Interoperability["Interoperability"]
SQL_Interface["SQL Interface"]
Selection_Filtering["Selection & Filtering"]
Core_Utilities_Configuration["Core Utilities & Configuration"]
Extension_Plugin_System["Extension & Plugin System"]
Core_Data_Structures_Schema -- "uses" --> Expression_Function_Engine
Core_Data_Structures_Schema -- "uses" --> Lazy_Execution_Framework
Core_Data_Structures_Schema -- "uses" --> Input_Output_Data_Connectors
Core_Data_Structures_Schema -- "uses" --> Interoperability
Core_Data_Structures_Schema -- "uses" --> Core_Utilities_Configuration
Expression_Function_Engine -- "uses" --> Core_Data_Structures_Schema
Expression_Function_Engine -- "uses" --> Data_Type_System
Expression_Function_Engine -- "uses" --> Core_Utilities_Configuration
Expression_Function_Engine -- "uses" --> Selection_Filtering
Expression_Function_Engine -- "uses" --> Extension_Plugin_System
Lazy_Execution_Framework -- "uses" --> Expression_Function_Engine
Lazy_Execution_Framework -- "uses" --> Core_Data_Structures_Schema
Lazy_Execution_Framework -- "uses" --> Input_Output_Data_Connectors
Lazy_Execution_Framework -- "uses" --> Core_Utilities_Configuration
Lazy_Execution_Framework -- "uses" --> SQL_Interface
Input_Output_Data_Connectors -- "uses" --> Core_Data_Structures_Schema
Input_Output_Data_Connectors -- "uses" --> Lazy_Execution_Framework
Input_Output_Data_Connectors -- "uses" --> Data_Type_System
Input_Output_Data_Connectors -- "uses" --> Core_Utilities_Configuration
Input_Output_Data_Connectors -- "uses" --> Expression_Function_Engine
Input_Output_Data_Connectors -- "uses" --> Selection_Filtering
Data_Type_System -- "uses" --> Core_Data_Structures_Schema
Data_Type_System -- "uses" --> Core_Utilities_Configuration
Interoperability -- "uses" --> Core_Data_Structures_Schema
Interoperability -- "uses" --> Data_Type_System
Interoperability -- "uses" --> Core_Utilities_Configuration
SQL_Interface -- "uses" --> Lazy_Execution_Framework
SQL_Interface -- "uses" --> Core_Data_Structures_Schema
SQL_Interface -- "uses" --> Core_Utilities_Configuration
Selection_Filtering -- "uses" --> Core_Data_Structures_Schema
Selection_Filtering -- "uses" --> Data_Type_System
Selection_Filtering -- "uses" --> Expression_Function_Engine
Selection_Filtering -- "uses" --> Core_Utilities_Configuration
Core_Utilities_Configuration -- "uses" --> Data_Type_System
Core_Utilities_Configuration -- "uses" --> Core_Data_Structures_Schema
Core_Utilities_Configuration -- "uses" --> Lazy_Execution_Framework
Core_Utilities_Configuration -- "uses" --> Input_Output_Data_Connectors
Extension_Plugin_System -- "uses" --> Expression_Function_Engine
Extension_Plugin_System -- "uses" --> Core_Utilities_Configuration
click Core_Data_Structures_Schema href "https://github.com/CodeBoarding/GeneratedOnBoardings/blob/main/polars/Core Data Structures & Schema.md" "Details"
click Expression_Function_Engine href "https://github.com/CodeBoarding/GeneratedOnBoardings/blob/main/polars/Expression & Function Engine.md" "Details"
click Lazy_Execution_Framework href "https://github.com/CodeBoarding/GeneratedOnBoardings/blob/main/polars/Lazy Execution Framework.md" "Details"
click Input_Output_Data_Connectors href "https://github.com/CodeBoarding/GeneratedOnBoardings/blob/main/polars/Input-Output & Data Connectors.md" "Details"
click Data_Type_System href "https://github.com/CodeBoarding/GeneratedOnBoardings/blob/main/polars/Data Type System.md" "Details"
click Interoperability href "https://github.com/CodeBoarding/GeneratedOnBoardings/blob/main/polars/Interoperability.md" "Details"
click SQL_Interface href "https://github.com/CodeBoarding/GeneratedOnBoardings/blob/main/polars/SQL Interface.md" "Details"
click Selection_Filtering href "https://github.com/CodeBoarding/GeneratedOnBoardings/blob/main/polars/Selection & Filtering.md" "Details"
click Core_Utilities_Configuration href "https://github.com/CodeBoarding/GeneratedOnBoardings/blob/main/polars/Core Utilities & Configuration.md" "Details"
click Extension_Plugin_System href "https://github.com/CodeBoarding/GeneratedOnBoardings/blob/main/polars/Extension & Plugin System.md" "Details"
The Polars library is designed for efficient in-memory and out-of-core data manipulation, leveraging a columnar data model and lazy execution. Its core functionality revolves around DataFrames and Series, which are optimized for performance through Rust-based backend operations. The architecture supports a rich Expression API for complex transformations, a Lazy Execution Framework for query optimization, and extensive I/O capabilities for various data formats and sources. It also includes a robust data type system, interoperability with other data science tools, a SQL interface, and a flexible plugin system for extensibility.
This component forms the bedrock of Polars, defining the fundamental data structures (DataFrame and Series) and their associated schemas. It handles the creation, manipulation, and structural integrity of data, ensuring type consistency and efficient memory layout. This component is central to all data operations within Polars.
Related Classes/Methods:
polars.dataframe.frame.DataFrame(201:12391)polars.series.series.Series(180:8011)polars.schema.Schema(52:216)polars.datatypes.classes.DataType(112:254)
This component provides the powerful and flexible Expression API, allowing users to define complex data transformations and aggregations. It includes various expression types for different data operations (list, array, categorical, string, datetime, etc.) and a rich set of functions for both eager and lazy computations. This is where the core logic for data manipulation resides.
Related Classes/Methods:
polars.expr.expr.Expr(118:11133)polars.functions.col.Col(120:375)polars.functions.lit(30:202)polars.functions.aggregation.horizontal(full file reference)
This component is dedicated to Polars' lazy execution model, enabling deferred computation and query optimization. It defines the LazyFrame, which allows chaining operations without immediate execution, leading to highly efficient processing of large datasets. It includes functionalities for query planning, optimization flags, and schema collection in a lazy context.
Related Classes/Methods:
polars.lazyframe.frame.LazyFrame(235:8350)polars.lazyframe.group_by.LazyGroupBy(24:667)polars.lazyframe.opt_flags.QueryOptFlags(24:265)
This component manages all data ingress and egress for Polars, supporting a wide array of file formats (CSV, Parquet, IPC, JSON, Excel, etc.) and external data sources like databases and cloud storage. It also includes integration with data catalogs for managing external table metadata and credentials.
Related Classes/Methods:
polars.io.csv.functions(full file reference)polars.io.parquet.functions(full file reference)polars.io.database.functions(full file reference)polars.catalog.unity.client.Catalog(45:687)
This component is responsible for the definition, parsing, conversion, and overall management of data types within the Polars ecosystem. It provides the necessary utilities to ensure data integrity and compatibility across various operations and data sources.
Related Classes/Methods:
polars.datatypes.convert(full file reference)polars.datatypes._parse(full file reference)
This component facilitates seamless data exchange and integration between Polars and other data processing and machine learning libraries. It implements the DataFrame Interchange Protocol and provides utilities for converting Polars data structures to formats compatible with frameworks like PyTorch and NumPy.
Related Classes/Methods:
polars.interchange.dataframe.PolarsDataFrame(18:230)polars.interchange.from_dataframe(25:50)polars.ml.torch.PolarsDataset(35:213)
This component provides a SQL layer within Polars, allowing users to execute SQL queries directly against registered DataFrames. It bridges the gap between traditional SQL-based data analysis and Polars' native API, enabling flexible data querying.
Related Classes/Methods:
This component offers a specialized API for selecting and filtering columns within DataFrames and LazyFrames based on various criteria, such as names, data types, or regular expressions. It provides a powerful and concise way to specify column subsets for operations.
Related Classes/Methods:
polars.selectors(full file reference)
This component encompasses a collection of foundational utility functions, global configurations, and dependency management mechanisms used throughout the Polars library. It handles aspects like deprecation warnings, type conversions, asynchronous operations, and CPU feature checks, ensuring the library's stability and adaptability.
Related Classes/Methods:
polars._utils.deprecation(full file reference)polars._utils.convert(full file reference)polars.config.Config(148:1513)polars.dependencies._LazyModule(28:102)
This component provides a robust mechanism for extending Polars' functionality through external plugins. It allows developers to register and integrate custom operations and data sources, enhancing the library's versatility and adaptability to diverse use cases.
Related Classes/Methods:
polars.plugins(full file reference)