mirror of
https://github.com/wassname/greater_tables_project.git
synced 2026-06-27 17:48:45 +08:00
Stephen Mildenhall
100 lines
3.8 KiB
Plaintext
100 lines
3.8 KiB
Plaintext
---
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format:
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pdf:
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include-in-header: prefobnicate.tex
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html:
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table-processing: false
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jupyter:
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keep-ipynb: true
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jupytext:
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formats: ipynb,qmd
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text_representation:
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extension: .qmd
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format_name: quarto
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format_version: '1.0'
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jupytext_version: 1.16.4
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kernelspec:
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display_name: Python 3 (ipykernel)
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language: python
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name: python3
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---
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# Greater Tables
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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.
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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.
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Usage: the main class `GT` should be subclassed to set appropriate defaults for your project. `sGT` provides an example.
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The project is currently in **beta** status. HTML output is better developed than TeX.
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## The Name
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Obviously, the name is a play on the `great_tables` package. But, I have been maintaining a set of macros called [GREATools](https://www.mynl.com/old/GREAT/home.html) (generalized, reusable, extensible actuarial tools) in VBA and Python since the late 1990s, and call all my macro packages "GREAT".
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## Installation
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```python
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pip install greater-tables
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```
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## Examples
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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.
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```{python}
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#| echo: true
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#| output: asis
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import pandas as pd
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import numpy as np
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from greater_tables import sGT
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level_1 = ["Group A", "Group A", "Group B", "Group B", 'Group C']
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level_2 = ['Sub 1', 'Sub 2', 'Sub 2', 'Sub 3', 'Sub 3']
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multi_index = pd.MultiIndex.from_arrays([level_1, level_2])
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start = pd.Timestamp.today().normalize() # Today's date, normalized to midnight
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end = pd.Timestamp(f"{start.year}-12-31") # End of the year
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hard = pd.DataFrame(
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{'x': np.arange(2020, 2025, dtype=int),
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'a': np.array((100, 105, 2000, 2025, 100000), dtype=int),
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'b': 10. ** np.linspace(-9, 9, 5),
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'c': np.linspace(601, 4000, 5),
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'd': pd.date_range(start=start, end=end, periods=5),
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'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(',')
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}).set_index('x')
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hard.columns = multi_index
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sGT(hard, 'A hard table.')
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```
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The output illustrates:
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* 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.
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* Text is left-aligned, numbers are right-aligned.
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* The index is displayed, was detected as likely years, and formatted without a comma separator.
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* The first column of integers does have a comma thousands separator.
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* The second column of floats spans several orders of magnitude and is formatted using Engineering format, n for nano through G for giga.
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* The third column of floats is formatted with a comma separator and two decimals, based on the average absolute value.
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* The fourth column of date times is formatted as ISO standard dates (not date times).
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* The vertical lines separate the levels of the column multiindex. The subgroups are a little tricky.
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More coming soon.
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## Documentation
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Available on [readthedocs](https://greater-tables-project.readthedocs.io/en/latest).
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## Versions
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### 1.1.1
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* Added logo, updated docs.
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### 1.1.0
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