sort out the variety of readmes... this is the main one

v 3.0 update

• config files
• unified col width and info dataframe
• de-texing
• cli for config and writeout a csv etc.
• tests

TODO

• Ratio cols with multi index columns
• % in tex output - never allow comments?
• center / left / right table output -> CSS
• ?Option to hide index
• Bring over the roll your own logger

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from GPT

Absolutely—here’s a structured summary of everything we’ve covered, organized by topic.

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🧱 Project Structure & Philosophy

• Your GreaterTables class formats a pandas.DataFrame to HTML, text, or LaTeX.
• The class is immutable: formatting is fixed at construction time, like a pure value object.
• You avoid branchy, incremental APIs (like ggplot) and prefer creating fresh objects.
• You wanted a way to handle growing config complexity—→ led to a YAML config + Pydantic schema design.

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📁 Project Layout

greater_tables_project/
├── greater_tables/
│   ├── __init__.py
│   ├── gtconfig.py          ← config model + loader
│   ├── gtcore.py            ← GreaterTables class
│   └── defaults/
│       └── config_template.yaml
├── tests/
├── pyproject.toml

• GTConfigModel = schema + default source of truth
• GTConfig = singleton loader and validator
• config_template.yaml = editable fallback + documentation base

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🔧 Config Management

• All defaults and types are declared in GTConfigModel (Pydantic).
• Config is loaded from YAML, validated by GTConfigModel.
• You can generate a valid config file from the model using .model_dump() → YAML.
• Singleton pattern (GTConfig.__new__) caches the config at runtime.

Helpers

• GTConfig().get(overrides=...) gives a safe, override-able config
• write_template(path) writes a default config YAML for user to edit

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🛠 Git Workflow (Solo Dev, Linear)

• Use tags (git tag v0.2.0) to label stable versions
• Use git reset --hard <tag> to roll back and discard later commits
• Avoid branches entirely—keep a single linear history
• Tags let you bounce around safely, with names instead of hashes
• Releases on GitHub are tags + metadata, optional for publishing

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⚙️ CLI Tool

• Built with click, with subcommands:

  • gt render data.csv --format html
  • gt write-template

• Reads any Pandas-supported file (.csv, .feather, .pkl, etc.)

• Outputs to console or to file

• Uses current config by default, or override with --config path.yaml

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🧠 Design Principles You’re Following

Principle	Your Approach
Immutability	GT(df, config) is fixed once created
Separation of concerns	GTConfigModel holds defaults/types
Config as code/documentation	config_template.yaml generated from model
CLI-first mindset	click used to expose functionality
Linear Git workflow	Tags for rollback, no branches

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Let me know if you want me to generate:

• a Markdown doc for contributors
• a .bat script to roll back to a tag
• test scaffolding or release automation

You're in great shape. Gum-level perfection achieved.

---

OLD

Greater Tables

Creating presentation quality tables from pandas dataframes is frustrating. It is hard to left-align text and right-align numbers using pandas display or df.to_html. The great_tables package does a really nice job with pandas and polars dataframes but does not support indexes or TeX output.

This package provides consistent HTML and TeX table output with flexible type-based formatting, and table rules. Neither output relies on the pandas to_html or to_latex functions. TeX output uses Tikz tables for very tight control over layout and grid lines. The package is designed for use in Jupyter Lab notebooks Quarto documents.

Usage: the main class GT should be subclassed to set appropriate defaults for your project. sGT provides an example.

The project is currently in beta status. HTML output is better developed than TeX.

The Name

Obviously, the name is a play on the great_tables package. But, I have been maintaining a set of macros called GREATools (generalized, reusable, extensible actuarial tools) in VBA and Python since the late 1990s, and call all my macro packages "GREAT".

Installation

pip install greater-tables

Examples

The following example shows quite a hard table. It is formatted using the sGT class, which is a subclass of GT with a few defaults set.

import pandas as pd
import numpy as np
from greater_tables import sGT
level_1 = ["Group A", "Group A", "Group B", "Group B", 'Group C']
level_2 = ['Sub 1', 'Sub 2', 'Sub 2', 'Sub 3', 'Sub 3']

multi_index = pd.MultiIndex.from_arrays([level_1, level_2])

start = pd.Timestamp.today().normalize()  # Today's date, normalized to midnight
end = pd.Timestamp(f"{start.year}-12-31")  # End of the year

hard = pd.DataFrame(
{'x': np.arange(2020, 2025, dtype=int), 
'a': np.array((100, 105, 2000, 2025, 100000), dtype=int),
'b': 10. ** np.linspace(-9, 9, 5),
'c': np.linspace(601, 4000, 5),
'd': pd.date_range(start=start, end=end, periods=5),
'e': 'once upon a time, risk is hard to define, not in Kansas anymore, neutrinos are hard to detect,  $\\int_\\infty^\\infty e^{-x^2/2}dx$ is a hard integral'.split(',')
}).set_index('x')
hard.columns = multi_index
sGT(hard, 'A hard table.')

The output illustrates:

• Quarto or Jupyter automatically the class's _repr_html_ method (or _repr_latex_ for pdf/TeX/Beamer output), providing seamless integration across different output formats.
• Text is left-aligned, numbers are right-aligned.
• The index is displayed, was detected as likely years, and formatted without a comma separator.
• The first column of integers does have a comma thousands separator.
• The second column of floats spans several orders of magnitude and is formatted using Engineering format, n for nano through G for giga.
• The third column of floats is formatted with a comma separator and two decimals, based on the average absolute value.
• The fourth column of date times is formatted as ISO standard dates (not date times).
• The vertical lines separate the levels of the column multiindex. The subgroups are a little tricky.

More coming soon.

Documentation

Available on readthedocs.

Versions

1.1.1

• Added logo, updated docs.

1.1.0