Descriptive Statistics

Method Definition
vDataFrame.aad Aggregates the vDataFrame using 'aad' (Average Absolute Deviation).
vDataFrame[].aad Aggregates the vcolumn using 'aad' (Average Absolute Deviation).
vDataFrame.agg / aggregate Aggregates the vDataFrame using the input functions.
vDataFrame[].agg / aggregate Aggregates the vcolumn using the input functions.
vDataFrame.all Aggregates the vDataFrame using 'bool_and'.
vDataFrame.any Aggregates the vDataFrame using 'bool_or'.
vDataFrame.avg / mean Aggregates the vDataFrame using 'avg' (Average).
vDataFrame[].avg / mean Aggregates the vcolumn using 'avg' (Average).
vDataFrame.count Aggregates the vDataFrame using a list of 'count' (Number of missing values).
vDataFrame[].count Aggregates the vcolumn using 'count' (Number of Missing elements).
vDataFrame.describe Aggregates the vDataFrame using multiple statistical aggregations.
vDataFrame[].describe Aggregates the vcolumn using multiple statistical aggregations.
vDataFrame[].distinct Returns the vcolumn distinct categories.
vDataFrame.duplicated Returns the duplicated values.
vDataFrame.groupby Aggregates the vDataFrame by grouping the elements.
vDataFrame.isin Looks if some specific records are in the vDataFrame.
vDataFrame[].isin Looks if some specific records are in the vcolumn.
vDataFrame.kurt / kurtosis Aggregates the vDataFrame using 'kurtosis'.
vDataFrame[].kurt / kurtosis Aggregates the vcolumn using 'kurtosis'.
vDataFrame.mad Aggregates the vDataFrame using 'mad' (Median Absolute Deviation).
vDataFrame[].mad Aggregates the vcolumn using 'mad' (Median Absolute Deviation).
vDataFrame.max Aggregates the vDataFrame using 'max' (Maximum).
vDataFrame[].max Aggregates the vcolumn using 'max' (Maximum).
vDataFrame.median Aggregates the vDataFrame using 'median'.
vDataFrame[].median Aggregates the vcolumn using 'median'.
vDataFrame.min Aggregates the vDataFrame using 'min' (Minimum).
vDataFrame[].min Aggregates the vcolumn using 'min' (Minimum).
vDataFrame[].mode Returns the nth most occurent element.
vDataFrame[].nlargest Returns the n largest vcolumn elements.
vDataFrame[].nsmallest Returns the n smallest vcolumn elements.
vDataFrame.nunique Aggregates the vDataFrame using 'unique' (cardinality).
vDataFrame[].numh Computes the optimal vcolumn bar width.
vDataFrame[].nunique Aggregates the vcolumn using 'unique' (cardinality).
vDataFrame.prod /product Aggregates the vDataFrame using 'product'.
vDataFrame[].prod /product Aggregates the vcolumn using 'product'.
vDataFrame.quantile Aggregates the vDataFrame using a list of 'quantiles'.
vDataFrame[].quantile Aggregates the vcolumn using an input 'quantile'.
vDataFrame.score Computes the score using the input columns and the input method.
vDataFrame.sem Aggregates the vDataFrame using 'sem' (Standard Error of the Mean).
vDataFrame[].sem Aggregates the vcolumn using 'sem' (Standard Error of the Mean).
vDataFrame.shape Returns the number of rows and columns of the vDataFrame.
vDataFrame.skew / skewness Aggregates the vDataFrame using 'skewness'.
vDataFrame[].skew / skewness Aggregates the vcolumn using 'skewness'.
vDataFrame.std Aggregates the vDataFrame using 'std' (Standard Deviation).
vDataFrame[].std Aggregates the vcolumn using 'std' (Standard Deviation).
vDataFrame.sum Aggregates the vDataFrame using 'sum'.
vDataFrame[].sum Aggregates the vcolumn using 'sum'.
vDataFrame[].topk Returns the top-k most occurent elements and their percentages of the distribution.
vDataFrame[].value_counts Returns the top-k most frequent elements and how often they appear.
vDataFrame.var Aggregates the vDataFrame using 'variance'.
vDataFrame[].var Aggregates the vcolumn using 'variance'.