Welcome to greater_tables’s documentation!
Contents:
Greater Tables is a Python tool for producing high-quality, static display tables—intended for use in journal articles, books, formal reports, and printed financial statements. It turns your pandas DataFrame into a clean, black-and-white table—ready for print, PDF, or web. It produces consistent, typographically sound output in HTML, LaTeX (via TikZ), and plain text.
It’s opinionated but flexible, with many options and sensible defaults. Designed for use in Jupyter Lab, Quarto, and scripting environments, it auto-detects the output format and renders accordingly. Display tables are small and focused—the end result of your analysis, after selecting rows and columns, ordering, and labeling. Greater Tables helps you get those raw materials onto the page, cleanly and consistently.
`python
from greater_tables import GT
GT(df)
`
Or use display(GT(df)) in notebooks and Quarto documents. Once created, a GT(df) object is immutable; re-create it to apply new options. Arguments can be passed directly or loaded from a YAML config file—validated using pydantic.
Greater Tables offers similar functionality to pandas.DataFrame.to_html, to_latex, and to_markdown, but with tighter control, better defaults, and no reliance on pandas internals. The LaTeX backend uses TikZ for precise control over layout and grid lines.
This is a tool for serious tables—no sparklines, colors, or shading. Just your data, rendered cleanly.
Also included: Fabricator, a flexible test DataFrame generator—specify row count, index and column hierarchies, data types, missing values, and more.
Installation
pip install greater-tables
Documentation
Licence
MIT.
Examples
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()
end = pd.Timestamp(f"{start.year}-12-31") # End of the year
df = pd.DataFrame(
{'year': 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('year')
df.columns = multi_index
gtc.GT(df, caption='A simple GT table.',
year_cols='year',
vrule_widths=(1,.5, 0))
The output illustrates:
Quarto or Jupyter automatically calls the class’s
_repr_html_method (or_repr_latex_for pdf/TeX/Beamer output), providing seamless integration across different output formats.print()produces fixed-pitch text output.Text is left-aligned, numbers are right-aligned, and dates are centered.
The index is displayed, and formatted without a comma separator, being specified in
year_cols. Columns specified inratio_coluse % formatting. Explicit control provided over all columns; these are just helpers.The first column of integers with a comma thousands separator and no decimals.
The second column of floats spans several orders of magnitude and is formatted using Engineering format, n for nano through k for kilo.
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.
Text, in the last column, is sensibly wrapped and can include TeX.
The vertical lines separate the levels of the column multiindex.