polartableone 0.1.1

A polars-based implementation of the `tableone` library for generating summary statistics tables, often referred to as a 'Table 1'.

polartableone

A polars-based implementation of the tableone library for generating summary statistics tables, often referred to as a 'Table 1'.

Features

• Fast summary statistics for a 'Table 1' using polars
• Automatic detection of categorical, continuous, and nonnormal variables
• Support for stratified tables and p-values
• Beautiful table formatting via Great Tables
• Export to HTML, CSV, Excel, LaTeX, and many tabular formats

Differences with tableone Library

• polars rather than pandas
• Use of great_tables to display nicer tables.
• In some cases, use of polars operations rather than numpy, though these are almost always identical.
• Automatic determination of nonnormal columns and will display q1, median, q3 (whereas tableone defaults to showing mean even in these cases).
• Specify number of bins for histograms using show_histograms=8; if show_histograms is set to True, the default is 10 bins (tableone defaults to 8)

But I Prefer tableone

Great! Just convert your polars dataframe to pandas (df.to_pandas()) and then use tableone!

Installation

pip install polartableone

Usage

import polars as pl
from polartableone import TableOne

# load your data
df = pl.read_csv('tests/datasets/pn2012.csv')

# columns to summarize
columns = ['Age', 'SysABP', 'Height', 'Weight', 'ICU', 'death']
# columns containing categorical variables
categorical = ['ICU']
# columns containing categorical variables
continuous = ['Age', 'SysABP', 'Height', 'Weight']
# non-normal variables
nonnormal = ['Age']
# limit the binary variable "death" to a single row
limit = {"death": 1}
# set the order of the categorical variables
order = {"ICU": ["MICU", "SICU", "CSRU", "CCU"]}
# set decimal places for age to 0
decimals = {"Age": 0}
# optionally, a categorical variable for stratification
groupby = 'death'
# rename the death column
labels = {'death': 'Mortality'}

# display minimum and maximum for listed variables
min_max = ['Height']

table = TableOne(df, columns=columns, categorical=categorical, continuous=continuous,
                  groupby=groupby, nonnormal=nonnormal, rename=labels, label_suffix=True,
                  decimals=decimals, limit=limit, min_max=min_max, show_histograms=True,
                  dip_test=True, normal_test=True, tukey_test=True)


# Export to CSV
table.write_csv('table1.csv')

# Export to Great Tables object for further styling
gt_table = table.to_gt()
gt_table.show()

	Level	Missing	Overall	Histogram	0	1
n			1000		864	136
Age, median [Q1,Q3]		0	68 [53,79]	▂▁▂▃▅▆▆▇▇█	66 [53,78]	75 [62,83]
SysABP, median [Q1,Q3]		291	119.0 [102.0,137.0]	▂▁▁▁▄█▇▅▂▁	119.0 [104.0,137.0]	115.0 [90.0,136.0]
Height, min – max		475	13.0 – 406.4	▁▁▆▅▅▆█▅▁▁	13.0 – 406.4	144.8 – 188.0
Weight, median [Q1,Q3]		302	80.0 [66.1,96.0]	▂▅▇█▆▃▃▂▁▁	80.5 [66.3,96.0]	77.0 [64.8,96.7]
ICU, n (%)	CCU		162 (16.2)		137 (15.9)	25 (18.4)
ICU, n (%)	CSRU		202 (20.2)		194 (22.5)	8 (5.9)
ICU, n (%)	MICU		380 (38.0)		318 (36.8)	62 (45.6)
ICU, n (%)	SICU		256 (25.6)		215 (24.9)	41 (30.1)