Metadata-Version: 1.0
Name: tableone
Version: 0.5.9
Summary: TableOne
Home-page: https://github.com/tompollard/tableone
Author: Tom Pollard
Author-email: tpollard@mit.edu
License: MIT
Description: # tableone 
        
        tableone is a package for creating "Table 1" summary statistics for a patient 
        population. It was inspired by the R package of the same name by Yoshida and 
        Bohn.
        
        [![Build Status](https://travis-ci.org/tompollard/tableone.svg?branch=master)](https://travis-ci.org/tompollard/tableone) [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.837898.svg)](https://doi.org/10.5281/zenodo.837898) [![Documentation Status](https://readthedocs.org/projects/tableone/badge/?version=latest)](https://tableone.readthedocs.io/en/latest/?badge=latest) [![Anaconda-Server Badge](https://anaconda.org/conda-forge/tableone/badges/installer/conda.svg)](https://conda.anaconda.org/conda-forge) [![PyPI version](https://badge.fury.io/py/tableone.svg)](https://badge.fury.io/py/tableone)
        
        ## 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, [https://doi.org/10.1093/jamiaopen/ooy012](https://doi.org/10.1093/jamiaopen/ooy012)
        
        ## Documentation
        
        For documentation, see: [http://tableone.readthedocs.io/en/latest/](http://tableone.readthedocs.io/en/latest/). An executable demonstration of the package is available as a Jupyter Notebook: [https://github.com/tompollard/tableone/blob/master/tableone.ipynb](https://github.com/tompollard/tableone/blob/master/tableone.ipynb). A paper describing our motivations for creating the package is available at: [https://doi.org/10.1093/jamiaopen/ooy012](https://doi.org/10.1093/jamiaopen/ooy012).
        
        ## A note for users of `tableone`
        
        While we have tried to use best practices in creating this package, automation of even basic statistical tasks can be unsound if done without supervision. We encourage use of `tableone` alongside other methods of descriptive statistics and, in particular, visualization to ensure appropriate data handling. 
        
        It is beyond the scope of our documentation to provide detailed guidance on summary statistics, but as a primer we provide some considerations for choosing parameters when creating a summary table at: [http://tableone.readthedocs.io/en/latest/bestpractice.html](http://tableone.readthedocs.io/en/latest/bestpractice.html). 
        
        *Guidance should be sought from a statistician when using `tableone` for a research study, especially prior to submitting the study for publication*.
        
        ## Installation
        
        To install the package with pip, run:
        
        ```pip install tableone```
        
        To install this package with conda, run:
            
        ```conda install -c conda-forge tableone```
        
        ## Example usage
        
        1. Import libraries:
        
        ```python
        from tableone import TableOne
        import pandas as pd
        ```
        
        2. Load sample data into a pandas dataframe:
        
        ```python
        url="https://raw.githubusercontent.com/tompollard/tableone/master/data/pn2012_demo.csv"
        data=pd.read_csv(url)
        ```
        
        3. Optionally, a list of columns to be included in Table 1:
        
        ```python
        columns = ['Age', 'SysABP', 'Height', 'Weight', 'ICU', 'death']
        ```
        
        4. Optionally, a list of columns containing categorical variables:
        
        ```python
        categorical = ['ICU', 'death']
        ```
        
        5. Optionally, a categorical variable for stratification, a list of non-normal variables, and a dictionary of alternative labels:
        
        ```python
        groupby = ['death']
        nonnormal = ['Age']
        labels={'death': 'mortality'}
        ```
        
        6. Create an instance of TableOne with the input arguments:
        
        ```python
        mytable = TableOne(data, columns, categorical, groupby, nonnormal, labels=labels, pval=False)
        ```
        
        7. Type the name of the instance in an interpreter:
        
        ```python
        mytable
        ```
        
        8. ...which prints the following table to screen:
        
        Grouped by mortality:
        
        | variable  | level  | isnull |        0       |        1       | 
        | --------- | ------ | ------ | -------------- | -------------- | 
        | n         |        |        | 864            | 136            |
        | Age       |        |  0     | 66 [52,78]     | 75 [62,83]     |
        | SysABP    |        | 291    | 115.36 (38.34) | 107.57 (49.43) |
        | Height    |        | 475    | 170.33 (23.22) | 168.51 (11.31) |
        | Weight    |        | 302    | 83.04 (23.58)  | 82.29 (25.40)  | 
        | ICU       |  CCU   | 0      | 137 (15.86)    | 25 (18.38)     |
        |           |  CSRU  |        | 194 (22.45)    | 8 (5.88)       |  
        |           |  MICU  |        | 318 (36.81)    | 62 (45.59)     | 
        |           |  SICU  |        | 215 (24.88)    | 41 (30.15)     | 
        | mortality |  0     | 0      | 864 (100.0)    |                | 
        |           |  1     |        |                | 136 (100.0)    | 
        
        9. 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 tableone object. For example, mytable can be exported to a CSV named 'mytable.csv' with the following command:
        
        ```python
        mytable.to_csv('mytable.csv')
        ```
        
        
        
Keywords: Table one Table 1 clinical research population cohort
Platform: UNKNOWN
