vDataFrame[].agg / aggregate¶

In [ ]:

vDataFrame[].aggregate(func: list)

Aggregates the vcolumn using the input functions.

Name	Type	Optional	Description
func	list	❌	List of the different aggregation. aad : average absolute deviation 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 : min mode : most occurent element percent : percent of non-missing elements q% : q quantile (ex: 50% for the median) 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 std : standard deviation unique : cardinality (count distinct) var : variance Other aggregations could work if it is part of the DB version you are using.

tablesample : An object containing the result. For more information, see utilities.tablesample.

In [12]:

from verticapy.datasets import load_titanic
titanic = load_titanic()
display(titanic["embarked"])

Rows: 1-100 of 1234 | Column: embarked | Type: varchar(20)

In [13]:

titanic["embarked"].aggregate(func = ["unique", "top1", "top2"])

Out[13]: