---
id: quinn
version: "0.10.3"
license: Apache-2.0
license_treatment: permissive
maintenance: active
---

# quinn — Pyspark helper methods to maximize developer efficiency

License: permissive · Maintenance: active · Downloads: 609.8K/mo

## 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 above — verify before relying.

Quinn provides helper methods for PySpark DataFrame validation, column transformations, and data manipulation to reduce boilerplate in Spark applications.

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

pip install quinn
uv add quinn
poetry add quinn

## Installing quinn

Before you install: Low install friction with no runtime dependencies. Active maintenance with last commit on 2026-06-09 and 687 repository stars.

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")

Requires a working Spark environment; Quinn is a library of helper functions, not a standalone tool.

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_current
- Install friction: low
- Maintenance: active
- Downloads: 609.8K/month (top 15,000 on PyPI)
- Known vulnerabilities: none known

## Tags

pyspark dataframe helpers, spark column transformations, pyspark validation functions, spark data cleaning utilities, pyspark schema helpers, spark dataframe utilities, pyspark productivity tools, pyspark, data-transformation, dataframe-utilities

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