great-tables
Easily generate information-rich, publication-quality tables from Python.
Decision gist · record as of 2026-08-14
Yes. Great Tables is actively maintained, has low install friction, carries a permissive MIT license, and solves a clear problem—turning DataFrames into polished, formatted tables. It's well-suited for anyone doing data reporting, analysis, or documentation in Python. No known security vulnerabilities as of 2026-08-14.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.10 or later.
- Low friction: pure Python wheel with 8 runtime dependencies (multimark, faicons, htmltools, importlib-metadata, nokap, typing_extensions, Babel, importlib-resources).
- Active maintenance with a release 18 days ago; last commit 2026-08-14.
License · maintenance · safety
permissive license (permissive) — MIT License (permissive): you can use, modify, and distribute Great Tables freely in commercial and private projects with minimal restrictions, provided you retain the copyright notice and license text.
last release 2026-07-27 (18 days) · last repo commit 2026-08-14 · 2,716 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 683,169 downloads/mo, #5,360 on PyPI
Alternatives
Verify before relying
pip install great_tables
from great_tables import GT
from great_tables.data import sp500
sp500_mini = sp500[(sp500["date"] >= "2010-06-07") & (sp500["date"] <= "2010-06-14")]
GT(sp500_mini).tab_header(title="S&P 500").fmt_currency(columns=["open", "high", "low", "close"])- Whether image rendering requires additional system dependencies beyond the listed Python packages.
- Performance characteristics with large DataFrames (row/column count limits).
- Compatibility with Jupyter, Quarto, and other notebook environments beyond the documented examples.
What it is and what it does
Great Tables is a Python library for creating formatted, visually polished tables from DataFrame data. It takes Pandas or Polars DataFrames as input and lets you compose tables with headers, footers, row labels (stubs), column spanners, and cell-level formatting—such as currency, dates, and numbers—before rendering to HTML or image output. The library emphasizes a composable, method-chaining API where you build tables incrementally by applying formatting and layout operations.
The package is designed for both everyday table display needs and publication-quality output. It comes with 16 built-in datasets for experimentation and is commonly used in notebook environments and Quarto documents. The active maintenance, low install friction, and permissive MIT license make it accessible for data analysis, reporting, and documentation workflows across scientific, financial, healthcare, and general software development contexts.
Use it for
- Generate formatted financial or statistical reports from DataFrames with currency and number formatting applied.
- Create publication-ready tables for research papers, dashboards, or documentation with headers, footers, and styled columns.
- Build interactive HTML tables in Jupyter notebooks or Quarto documents with custom cell formatting and layout.
- Display date-based data with locale-aware formatting using Babel integration.
- Render large datasets as styled HTML or image tables for web or print distribution.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
Great Tables is actively maintained, has low install friction, carries a permissive MIT license, and solves a clear problem—turning DataFrames into polished, formatted tables. It's well-suited for anyone doing data reporting, analysis, or documentation in Python. No known security vulnerabilities as of 2026-08-14.
Install
great-tables on PyPI
Before you install
Low friction: pure Python wheel with 8 runtime dependencies (multimark, faicons, htmltools, importlib-metadata, nokap, typing_extensions, Babel, importlib-resources). Active maintenance with a release 18 days ago; last commit 2026-08-14.
Requires Python 3.10 or later.
License in practice
MIT License (permissive): you can use, modify, and distribute Great Tables freely in commercial and private projects with minimal restrictions, provided you retain the copyright notice and license text.
Quickstart
pip install great_tables
from great_tables import GT
from great_tables.data import sp500
sp500_mini = sp500[(sp500["date"] >= "2010-06-07") & (sp500["date"] <= "2010-06-14")]
GT(sp500_mini).tab_header(title="S&P 500").fmt_currency(columns=["open", "high", "low", "close"])Verify before relying
- Whether image rendering requires additional system dependencies beyond the listed Python packages.
- Performance characteristics with large DataFrames (row/column count limits).
- Compatibility with Jupyter, Quarto, and other notebook environments beyond the documented examples.
Package facts
| License | permissive license permissive |
| Python support | Supports the current Python release >=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 8 packagesmultimarkfaiconshtmltoolsimportlib-metadatanokaptyping_extensionsBabelimportlib-resources |
| Maintenance | Actively maintained 18 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 683,169 / month, #5,360 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Intended Audience :: DevelopersIntended Audience :: End Users/DesktopIntended Audience :: Financial and Insurance IndustryIntended Audience :: Healthcare IndustryIntended Audience :: Science/ResearchLicense :: OSI Approved :: MIT LicenseProgramming Language :: PythonProgramming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14Programming Language :: Python :: Implementation :: PyPyTopic :: Internet :: WWW/HTTP :: Dynamic ContentTopic :: Scientific/Engineering :: VisualizationTopic :: Software Development :: Libraries :: Python ModulesTopic :: Text Processing :: Markup :: HTML |
Evidence: great_tables-0.23.0-py3-none-any.whl
Tags
Let your AI agent find packages like this
Example. Real query, live index.
You found this page by searching. An agent finds it by wishing: SkillFed indexes 14,416 PyPI packages by what they can do, searchable in plain language.
wish › “python table formatting”
- great-tablesGreat Tables transforms Pandas or Polars DataFrames into formatted,…
- PTablePTable formats tabular data into visually appealing ASCII tables,…
- markdown-tableGenerates Markdown-formatted tables from Python data structures.
Give your agent the search over MCP, or paste the wish link into any chat.
More Python Modules packages
Converts domain names between Unicode and ASCII-compatible encoding (Punycode) according to IDNA 2008 and Unicode Technical Standard 46, with security validation and broader script coverage than the standard library.
Install it if you work with internationalized domain names, need to validate domains, or use HTTP clients that depend on it transitively.
Setuptools is a Python build backend and package management tool that handles building, distributing, and installing Python packages, including support for C/C++ extension modules.
PyYAML parses and emits YAML 1.1 data format, enabling serialization and deserialization of configuration files and Python objects to and from human-readable YAML text.
Pydantic validates Python data structures against type hints, coercing and checking input at runtime to ensure it matches a declared schema.
Provides reusable metadata objects for use with PEP-593 `typing.Annotated` to express common constraints like bounds, collection sizes, and predicates on types.
Install it if you use or build libraries that need to express type constraints in a standardized, inspectable way—or if you want to annotate your own types with…
Provides runtime tools to inspect and introspect Python type annotations, enabling programmatic examination of type hints at execution time.
See also itables · pretty-html-table · pyreadstat · texttable · narwhals · percentify · treetable · datacompy · sklearn-pandas · patito