Tableone的参数说明

# View the tableone docstring
TableOne??

Init signature:
TableOne(
    data: pandas.core.frame.DataFrame,
    columns: Optional[list] = None,
    categorical: Optional[list] = None,
    groupby: Optional[str] = None,
    nonnormal: Optional[list] = None,
    min_max: Optional[list] = None,
    pval: Optional[bool] = False,
    pval_adjust: Optional[str] = None,
    htest_name: bool = False,
    pval_test_name: bool = False,
    htest: Optional[dict] = None,
    isnull: Optional[bool] = None,
    missing: bool = True,
    ddof: int = 1,
    labels: Optional[dict] = None,
    rename: Optional[dict] = None,
    sort: Union[bool, str] = False,
    limit: Union[int, dict, NoneType] = None,
    order: Optional[dict] = None,
    remarks: bool = False,
    label_suffix: bool = True,
    decimals: Union[int, dict] = 1,
    smd: bool = False,
    overall: bool = True,
    row_percent: bool = False,
    display_all: bool = False,
    dip_test: bool = False,
    normal_test: bool = False,
    tukey_test: bool = False,
    pval_threshold: Optional[float] = None,
) -> None

Source:        
class TableOne:
    """

    If you use the tableone package, please cite:

    Pollard TJ, Johnson AEW, Raffa JD, Mark RG (2018). 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

    Create an instance of the tableone summary table.

    Parameters
    ----------
    data : pandas DataFrame
        The dataset to be summarised. Rows are observations, columns are
        variables.
    columns : list, optional
        List of columns in the dataset to be included in the final table.
    categorical : list, optional
        List of columns that contain categorical variables.
    groupby : str, optional
        Optional column for stratifying the final table (default: None).
    nonnormal : list, optional
        List of columns that contain non-normal variables (default: None).
    min_max: list, optional
        List of variables that should report minimum and maximum, instead of
        standard deviation (for normal) or Q1-Q3 (for non-normal).
    pval : bool, optional
        Display computed P-Values (default: False).
    pval_adjust : str, optional
        Method used to adjust P-Values for multiple testing.
        The P-values from the unadjusted table (default when pval=True)
        are adjusted to account for the number of total tests that were
        performed.
        These adjustments would be useful when many variables are being
        screened to assess if their distribution varies by the variable in the
        groupby argument.
        For a complete list of methods, see documentation for statsmodels
        multipletests.
        Available methods include ::

        `None` : no correction applied.
        `bonferroni` : one-step correction
        `sidak` : one-step correction
        `holm-sidak` : step down method using Sidak adjustments
        `simes-hochberg` : step-up method (independent)
        `hommel` : closed method based on Simes tests (non-negative)

    htest_name : bool, optional
        Display a column with the names of hypothesis tests (default: False).
    htest : dict, optional
        Dictionary of custom hypothesis tests. Keys are variable names and
        values are functions. Functions must take a list of Numpy Arrays as
        the input argument and must return a test result.
        e.g. htest = {'age': myfunc}
    missing : bool, optional
        Display a count of null values (default: True).
    ddof : int, optional
        Degrees of freedom for standard deviation calculations (default: 1).
    rename : dict, optional
        Dictionary of alternative names for variables.
        e.g. `rename = {'sex':'gender', 'trt':'treatment'}`
    sort : bool or str, optional
        If `True`, sort the variables alphabetically. If a string
        (e.g. `'P-Value'`), sort by the specified column in ascending order.
        Default (`False`) retains the sequence specified in the `columns`
        argument. Currently the only columns supported are: `'Missing'`,
        `'P-Value'`, `'P-Value (adjusted)'`, and `'Test'`.
    limit : int or dict, optional
        Limit to the top N most frequent categories. If int, apply to all
        categorical variables. If dict, apply to the key (e.g. {'sex': 1}).
    order : dict, optional
        Specify an order for categorical variables. Key is the variable, value
        is a list of values in order.  {e.g. 'sex': ['f', 'm', 'other']}
    label_suffix : bool, optional
        Append summary type (e.g. "mean (SD); median [Q1,Q3], n (%); ") to the
        row label (default: True).
    decimals : int or dict, optional
        Number of decimal places to display. An integer applies the rule to all
        variables (default: 1). A dictionary (e.g. `decimals = {'age': 0)`)
        applies the rule per variable, defaulting to 1 place for unspecified
        variables. For continuous variables, applies to all summary statistics
        (e.g. mean and standard deviation). For categorical variables, applies
        to percentage only.
    overall : bool, optional
        If True, add an "overall" column to the table. Smd and p-value
        calculations are performed only using stratified columns.
    row_percent : bool, optional
        If True, compute "n (%)" percentages for categorical variables across
        "groupby" rows rather than columns.
    display_all : bool, optional
        If True, set pd. display_options to display all columns and rows.
        (default: False)
    dip_test : bool, optional
        Run Hartigan's Dip Test for multimodality. If variables are found to
        have multimodal distributions, a remark will be added below the
        Table 1.
        (default: False)
    normal_test : bool, optional
        Test the null hypothesis that a sample come from a normal distribution.
        Uses scipy.stats.normaltest. If variables are found to have non-normal
        distributions, a remark will be added below the Table 1.
        (default: False)
    tukey_test : bool, optional
        Run Tukey's test for far outliers. If variables are found to
        have far outliers, a remark will be added below the Table 1.
        (default: False)

数据来源：https://github.com/tompollard/tableone