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 list of columns containing continuous variables:

continuous = ['age','bili','albumin','ast','platelet','protime']

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, continuous, 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')
```