Descriptive Statistics¶

Help on function aggregate in module verticapy.vdataframe:

aggregate(self, func:list, columns:list=[], ncols_block:int=20, processes:int=1)
    ---------------------------------------------------------------------------
    Aggregates the vDataFrame using the input functions.
    
    Parameters
    ----------
    func: list
        List of the different aggregations.
            aad            : average absolute deviation
            approx_median  : approximate median
            approx_q%      : approximate q quantile 
                             (ex: approx_50% for the approximate median)
            approx_unique  : approximative cardinality
            count          : number of non-missing elements
            cvar           : conditional value at risk
            dtype          : virtual column type
            iqr            : interquartile range
            kurtosis       : kurtosis
            jb             : Jarque-Bera index 
            mad            : median absolute deviation
            max            : maximum
            mean           : average
            median         : median
            min            : minimum
            mode           : most occurent element
            percent        : percent of non-missing elements 
            q%             : q quantile (ex: 50% for the median)
                             Use the 'approx_q%' (approximate quantile) 
                             aggregation to get better performances.
            prod           : product
            range          : difference between the max and the min
            sem            : standard error of the mean
            skewness       : skewness
            sum            : sum
            std            : standard deviation
            topk           : kth most occurent element (ex: top1 for the mode)
            topk_percent   : kth most occurent element density
            unique         : cardinality (count distinct)
            var            : variance
                Other aggregations will work if supported by your version of 
                the database.
    columns: list, optional
        List of the vColumn's names. If empty, depending on the aggregations,
        all or only numerical vColumns will be used.
    ncols_block: int, optional
        Number of columns used per query. Setting this parameter divides
        what would otherwise be one large query into many smaller queries called
        "blocks." The size of each block is determined by the ncols_block parameter.
    processes: int, optional
        Number of child processes to create. Setting this with the ncols_block parameter
        lets you parallelize a single query into many smaller queries, where each child 
        process creates its own connection to the database and sends one query. This can 
        improve query performance, but consumes more resources. If processes is set to 1, 
        the queries are sent iteratively from a single process.
    
    Returns
    -------
    tablesample
        An object containing the result. For more information, see
        utilities.tablesample.
    
    See Also
    --------
    vDataFrame.analytic : Adds a new vColumn to the vDataFrame by using an advanced 
        analytical function on a specific vColumn.