| 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.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'. |
