{"categories":[{"label":"Application Frameworks","url":"https://skillfed.io/packages/category/software-development-libraries-application-frameworks/3"}],"enrichment":{"capability":"Quinn provides helper methods for PySpark DataFrame validation, column transformations, and data manipulation to reduce boilerplate in Spark applications.","skillfed_tags":["pyspark","data-transformation","dataframe-utilities"],"use_cases":["Validate that required columns exist in a DataFrame before processing to catch schema mismatches early.","Clean and normalize string columns by removing extra whitespace, non-word characters, or applying regex patterns.","Convert DataFrame columns to Python data structures (lists, dictionaries) for downstream processing or export.","Standardize DataFrame column naming conventions across a project by converting to snake_case.","Load and manage schemas from CSV files instead of defining them programmatically.","Sort DataFrame columns alphabetically to improve navigation of wide DataFrames."],"what_it_does":"Quinn is a utility library providing convenience functions for common DataFrame operations in Spark workflows. It offers DataFrame validation functions (checking column presence, schema conformance, column absence), column-level transformations (string cleaning, whitespace handling, pattern extraction, date calculations), and DataFrame utilities (converting columns to lists or dictionaries, parsing output strings back into DataFrames). The library also includes schema helpers for loading schemas from CSV files and printing schemas as code, plus transformations like converting column names to snake_case and sorting columns alphabetically.\n\nWith no external runtime dependencies and support for Python 3.7 through 3.11, Quinn integrates directly into existing workflows. It targets developer productivity by offering pre-built solutions for frequent data cleaning and validation tasks that would otherwise require repetitive boilerplate code.","worth_installing":"Yes. Quinn is actively maintained, has no external dependencies, carries a permissive Apache-2.0 license, and provides genuine convenience for common DataFrame tasks. It's well-suited for teams standardizing validation and transformation patterns. Install it if your workflows involve frequent data cleaning, validation, or schema management."},"id":"quinn","links":{"html":"https://skillfed.io/packages/quinn","md":"https://skillfed.io/packages/quinn.md","pypi":"https://pypi.org/project/quinn/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2024-02-13","license_spdx":null,"license_treatment":"permissive","name":"quinn","python_support":"supports_current","summary":"Pyspark helper methods to maximize developer efficiency"},"popularity":{"monthly_downloads":609835,"position":5769,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.10.3"}