Metadata-Version: 1.0
Name: tableone
Version: 0.7.3
Summary: TableOne
Home-page: https://github.com/tompollard/tableone
Author: Tom Pollard
Author-email: tpollard@mit.edu
License: MIT
Description: TableOne
        =========
        
        .. image:: https://travis-ci.org/tompollard/tableone.svg?branch=master
            :target: https://travis-ci.org/tompollard/tableone
        
        .. image:: https://zenodo.org/badge/DOI/10.5281/zenodo.837898.svg
            :target: https://doi.org/10.5281/zenodo.837898
        
        .. image:: https://anaconda.org/conda-forge/tableone/badges/installer/conda.svg
            :target: https://conda.anaconda.org/conda-forge
        
        .. image:: https://readthedocs.org/projects/tableone/badge/?version=latest
            :target: http://tableone.readthedocs.io/en/latest/?badge=latest
            :alt: Documentation Status
                        
        
        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.
        
        Documentation
        -------------
        
        Documentation is available on `readthedocs <http://tableone.readthedocs.io/en/latest/>`_. An executable demonstration of the package is available `on GitHub <https://github.com/tompollard/tableone/blob/master/tableone.ipynb>`_ as a Jupyter Notebook. The easiest way to try out this notebook is to `open it in Google Colaboratory <https://colab.research.google.com/github/tompollard/tableone/blob/master/tableone.ipynb>`_.
        
        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
        
        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 in our `documentation <http://tableone.readthedocs.io/en/latest/>`_.
        
        *Guidance should be sought from a statistician when using `tableone` for a research study, especially prior to submitting the study for publication*.
        
        Overview
        ---------
        
        At a high level, you can use the package as follows:
        
        * Import the data into a pandas DataFrame
          
        .. image:: docs/images/input_data.png
          :width: 600
          :alt: Starting DataFrame 
        
        * Run tableone on this dataframe to output summary statistics
          
        .. image:: docs/images/table1.png
          :width: 600
          :alt: Table 1
        
        * Specify your desired output format: text, latex, markdown, etc.
          
        .. image:: docs/images/table1_latex.png
          :width: 600
          :alt: Export to LaTex
        
        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).
        
        
        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
        -------
        
        #. 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"))
        
        #. ...which prints the following table to screen::
        
            Stratified by trt
                                   1.0                2.0                 missing
            ---------------------  -----------------  -----------------  --------
            n                      158                154                     106
            time (mean (std))      2015.62 (1094.12)  1996.86 (1155.93)         0
            age (mean (std))       51.42 (11.01)      48.58 (9.96)              0
            bili (median [IQR])    1.40 [0.80,3.20]   1.30 [0.72,3.60]          0
            chol (mean (std))      365.01 (209.54)    373.88 (252.48)         134
            albumin (mean (std))   3.52 (0.44)        3.52 (0.40)               0
            copper (mean (std))    97.64 (90.59)      97.65 (80.49)           108
            alk.phos (mean (std))  2021.30 (2183.44)  1943.01 (2101.69)       106
            ast (mean (std))       120.21 (54.52)     124.97 (58.93)          106
            trig (mean (std))      124.14 (71.54)     125.25 (58.52)          136
            platelet (mean (std))  258.75 (100.32)    265.20 (90.73)           11
            protime (mean (std))   10.65 (0.85)       10.80 (1.14)              2
            status (n (%))                                                      0
            0                      83 (52.53)         85 (55.19)
            1                      10 (6.33)          9 (5.84)
            2                      65 (41.14)         60 (38.96)
            ascites (n (%))                                                   106
            0.0                    144 (91.14)        144 (93.51)
            1.0                    14 (8.86)          10 (6.49)
            hepato (n (%))                                                    106
            0.0                    85 (53.80)         67 (43.51)
            1.0                    73 (46.20)         87 (56.49)
            spiders (n (%))                                                   106
            0.0                    113 (71.52)        109 (70.78)
            1.0                    45 (28.48)         45 (29.22)
            edema (n (%))                                                       0
            0.0                    132 (83.54)        131 (85.06)
            0.5                    16 (10.13)         13 (8.44)
            1.0                    10 (6.33)          10 (6.49)
            stage (n (%))                                                       6
            1.0                    12 (7.59)          4 (2.60)
            2.0                    35 (22.15)         32 (20.78)
            3.0                    56 (35.44)         64 (41.56)
            4.0                    55 (34.81)         54 (35.06)
            sex (n (%))                                                         0
            f                      137 (86.71)        139 (90.26)
            m                      21 (13.29)         15 (9.74)    
        
        
        #. Compute p values by setting the ``pval`` argument to `True`::
        
            mytable = TableOne(data, columns, categorical, groupby, nonnormal, pval=True)
        
        #. ...which prints::
        
            Stratified by trt
                                   1.0                2.0                 missing  pval    test
            ---------------------  -----------------  -----------------  --------  ------  --------------
            n                      158                154                     106
            time (mean (std))      2015.62 (1094.12)  1996.86 (1155.93)         0  0.883   One_way_ANOVA
            age (mean (std))       51.42 (11.01)      48.58 (9.96)              0  0.018   One_way_ANOVA
            bili (median [IQR])    1.40 [0.80,3.20]   1.30 [0.72,3.60]          0  0.842   Kruskal-Wallis
            chol (mean (std))      365.01 (209.54)    373.88 (252.48)         134  0.748   One_way_ANOVA
            albumin (mean (std))   3.52 (0.44)        3.52 (0.40)               0  0.874   One_way_ANOVA
            copper (mean (std))    97.64 (90.59)      97.65 (80.49)           108  0.999   One_way_ANOVA
            alk.phos (mean (std))  2021.30 (2183.44)  1943.01 (2101.69)       106  0.747   One_way_ANOVA
            ast (mean (std))       120.21 (54.52)     124.97 (58.93)          106  0.460   One_way_ANOVA
            trig (mean (std))      124.14 (71.54)     125.25 (58.52)          136  0.886   One_way_ANOVA
            platelet (mean (std))  258.75 (100.32)    265.20 (90.73)           11  0.555   One_way_ANOVA
            protime (mean (std))   10.65 (0.85)       10.80 (1.14)              2  0.197   One_way_ANOVA
            status (n (%))                                                      0  0.894   Chi-squared
            0                      83 (52.53)         85 (55.19)
            1                      10 (6.33)          9 (5.84)
            2                      65 (41.14)         60 (38.96)
            ascites (n (%))                                                   106  0.567   Chi-squared
            0.0                    144 (91.14)        144 (93.51)
            1.0                    14 (8.86)          10 (6.49)
            hepato (n (%))                                                    106  0.088   Chi-squared
            0.0                    85 (53.80)         67 (43.51)
            1.0                    73 (46.20)         87 (56.49)
            spiders (n (%))                                                   106  0.985   Chi-squared
            0.0                    113 (71.52)        109 (70.78)
            1.0                    45 (28.48)         45 (29.22)
            edema (n (%))                                                       0  0.877   Chi-squared
            0.0                    132 (83.54)        131 (85.06)
            0.5                    16 (10.13)         13 (8.44)
            1.0                    10 (6.33)          10 (6.49)
            stage (n (%))                                                       6  0.201   Chi-squared
            1.0                    12 (7.59)          4 (2.60)
            2.0                    35 (22.15)         32 (20.78)
            3.0                    56 (35.44)         64 (41.56)
            4.0                    55 (34.81)         54 (35.06)
            sex (n (%))                                                         0  0.421   Chi-squared
            f                      137 (86.71)        139 (90.26)
            m                      21 (13.29)         15 (9.74)
        
        
        
        #. 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.xlsx' with the following command::
        
            mytable.to_excel('mytable.xlsx')
        
Keywords: Table one Table 1 clinical research population cohort
Platform: UNKNOWN
