Metadata-Version: 1.0
Name: tableone
Version: 0.5.5
Summary: TableOne
Home-page: https://github.com/tompollard/tableone
Author: Tom Pollard
Author-email: tpollard@mit.edu
License: MIT
Description-Content-Type: UNKNOWN
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
        -------------
        
        For documentation, see: 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
        
        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)
        
        #. Type the name of the instance in an interpreter::
        
            mytable
        
        #. ...which prints the following table to screen::
        
            Stratified by trt
                                   1.0                2.0                  isnull
            ---------------------  -----------------  -----------------  --------
            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. The name of the test that was used is also displayed::
        
            mytable = TableOne(data, columns, categorical, groupby, nonnormal, pval=True)
        
        #. ...which prints::
        
            Stratified by trt
                                   1.0                2.0                  isnull  pval    testname
            ---------------------  -----------------  -----------------  --------  ------  --------------
            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, Markdown, CSV, and HTML. Files are exported by calling the ``to_format`` method on the DataFrame. For example, mytable can be exported to a CSV named 'mytable.csv' with the following command::
        
            mytable.to_csv('mytable.csv')
        
Keywords: Table one Table 1 clinical research population cohort
Platform: UNKNOWN
