Quickstart¶

Install¶

$ pip install tableone

See installation document for more information.

Run demo¶

The easiest way to understand what this package does is to evaluate it on data.

This Google Colaboratory Notebook is an executable document that allows you to explore tableone. At a high level, you can use the package as follows:

Import the data into a pandas DataFrame

Run tableone on this dataframe to output summary statistics

Specify your desired output format: text, latex, markdown, etc.

Additional options include:

Select a subset of columns.
Specify the data type (e.g. categorical, numerical, nonnormal).
Compute p-values, and adjust for multiple testing (e.g. with the Bonferroni correction).
Compute standardized mean differences (SMDs).
Provide a list of alternative labels for variables
Limit the output of categorical variables to the top N rows.
Display remarks relating to the appopriateness of summary measures (for example, computing tests for multimodality and normality).

Suggested citation¶

If you use tableone in your study, please cite the following paper:

Tom J Pollard, Alistair E W Johnson, Jesse D Raffa, Roger G Mark;
tableone: An open source Python package for producing summary statistics
for research papers, JAMIA Open, Volume 1, Issue 1, 1 July 2018, Pages 26–31,
https://doi.org/10.1093/jamiaopen/ooy012

Download the BibTex file from: https://academic.oup.com/jamiaopen/downloadcitation/5001910?format=bibtex

Example¶

Import libraries:

from tableone import TableOne
import pandas as pd

Load sample data into a pandas dataframe:

url="https://raw.githubusercontent.com/tompollard/data/master/primary-biliary-cirrhosis/pbc.csv"
data=pd.read_csv(url)

Optionally, a list of columns to be included in Table 1:

columns = ['age','bili','albumin','ast','platelet','protime',
       'ascites','hepato','spiders','edema','sex', 'trt']

Optionally, a list of columns containing categorical variables:

categorical = ['ascites','hepato','edema','sex','spiders','trt']

Optionally, a categorical variable for stratification and a list of non-normal variables:
```
groupby = 'trt'
nonnormal = ['bili']
```

Create an instance of TableOne with the input arguments:

mytable = TableOne(data, columns, categorical, groupby, nonnormal)

Display the table using the tabulate method. The tablefmt argument allows the table to be displayed in multiple formats, including “github”, “grid”, “fancy_grid”, “rst”, “html”, and “latex”.:
```
print(mytable.tabulate(tablefmt="github"))
```

Compute p values by setting the pval argument to True:

mytable = TableOne(data, columns, categorical, groupby, nonnormal, pval=True)

Tables can be exported to file in various formats, including LaTeX, CSV, and HTML. Files are exported by calling the to_format method on the DataFrame. For example, mytable can be exported to an Excel spreadsheet named ‘mytable.tex’ with the following command:
```
mytable.to_latex('mytable.tex')
```