mirror of
https://github.com/wassname/greater_tables_project.git
synced 2026-06-27 17:30:44 +08:00
82 lines
3.4 KiB
Markdown
82 lines
3.4 KiB
Markdown
# 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](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".
|
|
|
|
## Installation
|
|
|
|
``` python
|
|
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.
|
|
|
|
``` {.python .cell-code}
|
|
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.')
|
|
```
|
|
|
|
{width=66%}
|
|
|
|
{width=66%}
|
|
|
|
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](https://greater-tables-project.readthedocs.io/en/latest).
|