Data from: tableone: An open source Python package for producing summary statistics for research papers

Objectives: In quantitative research, understanding basic parameters of
the study population is key for interpretation of the results. As a
result, it is typical for the first table (“Table 1”) of a research paper
to include summary statistics for the study data. Our objectives are
2-fold. First, we seek to provide a simple, reproducible method for
providing summary statistics for research papers in the Python programming
language. Second, we seek to use the package to improve the quality of
summary statistics reported in research papers. Materials and Methods: The
tableone package is developed following good practice guidelines for
scientific computing and all code is made available under a permissive MIT
License. A testing framework runs on a continuous integration server,
helping to maintain code stability. Issues are tracked openly and public
contributions are encouraged. Results: The tableone software package
automatically compiles summary statistics into publishable formats such as
CSV, HTML, and LaTeX. An executable Jupyter Notebook demonstrates
application of the package to a subset of data from the MIMIC-III
database. Tests such as Tukey’s rule for outlier detection and Hartigan’s
Dip Test for modality are computed to highlight potential issues in
summarizing the data. Discussion and Conclusion: We present open source
software for researchers to facilitate carrying out reproducible studies
in Python, an increasingly popular language in scientific research. The
toolkit is intended to mature over time with community feedback and input.
Development of a common tool for summarizing data may help to promote good
practice when used as a supplement to existing guidelines and
recommendations. We encourage use of tableone alongside other methods of
descriptive statistics and, in particular, visualization to ensure
appropriate data handling. We also suggest seeking guidance from a
statistician when using tableone for a research study, especially prior to
submitting the study for publication.