quinn
Pyspark helper methods to maximize developer efficiency
Decision gist · record as of 2026-08-14
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.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires a working Spark environment; Quinn is a library of helper functions, not a standalone tool.
- Low install friction with no runtime dependencies.
- Active maintenance with last commit on 2026-06-09 and 687 repository stars.
License · maintenance · safety
Apache-2.0 (permissive) — Licensed under Apache-2.0 (permissive), allowing use in commercial and proprietary projects with minimal restrictions.
last release 2024-02-13 (913 days) · last repo commit 2026-06-09 · 687 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 609,835 downloads/mo, #5,769 on PyPI
Alternatives
Verify before relying
pip install quinn
import quinn
# Validate required columns exist
quinn.validate_presence_of_columns(source_df, ["name", "age"])
# Transform column names to snake_case
df_clean = quinn.snake_case_col_names(source_df)
# Convert column to list
values_list = quinn.column_to_list(source_df, "name")- Performance characteristics of Quinn's transformations compared to native operations on large datasets.
- Whether helper functions are optimized for production workloads or primarily for development convenience.
- Specific compatibility with different PySpark versions beyond Python 3.7–3.11 support.
What it is and 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.
With 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.
Use it for
- 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.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
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.
Install
quinn on PyPI
Before you install
Low install friction with no runtime dependencies. Active maintenance with last commit on 2026-06-09 and 687 repository stars.
Requires a working Spark environment; Quinn is a library of helper functions, not a standalone tool.
License in practice
Licensed under Apache-2.0 (permissive), allowing use in commercial and proprietary projects with minimal restrictions.
Quickstart
pip install quinn
import quinn
# Validate required columns exist
quinn.validate_presence_of_columns(source_df, ["name", "age"])
# Transform column names to snake_case
df_clean = quinn.snake_case_col_names(source_df)
# Convert column to list
values_list = quinn.column_to_list(source_df, "name")Verify before relying
- Performance characteristics of Quinn's transformations compared to native operations on large datasets.
- Whether helper functions are optimized for production workloads or primarily for development convenience.
- Specific compatibility with different PySpark versions beyond Python 3.7–3.11 support.
Package facts
| License | Apache-2.0 permissive |
| Python support | Supports the current Python release >=3.7,<4.0 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | None |
| Maintenance | Actively maintained 913 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 609,835 / month, #5,769 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | License :: OSI Approved :: Apache Software LicenseProgramming Language :: Python :: 3Programming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.7Programming Language :: Python :: 3.8Programming Language :: Python :: 3.9 |
Evidence: quinn-0.10.3-py3-none-any.whl
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See also sparkaid · spark-expectations · chispa · pyspark-test · cuallee · tinsel · sparkdantic · dbl-tempo · dbldatagen · schematics