Dataframe grouping functions#

Dataframe grouping methods#

class pystarburst.relational_grouped_dataframe.GroupingSets(*sets: Column | List[Column])#

Bases: object

Creates a GroupingSets object from a list of column/expression sets that you pass to DataFrame.group_by_grouping_sets(). See DataFrame.group_by_grouping_sets() for examples of how to use this class with a DataFrame. See GROUP BY GROUPING SETS for its counterpart in SQL (several examples are shown below).

Python interface	SQL interface
`GroupingSets([col("a")], [col("b")])`	`GROUPING SETS ((a), (b))`
`GroupingSets([col("a") , col("b")], [col("c"), col("d")])`	`GROUPING SETS ((a, b), (c, d))`
`GroupingSets([col("a"), col("b")])`	`GROUPING SETS ((a, b))`
`GroupingSets(col("a"), col("b"))`	`GROUPING SETS ((a, b))`

class pystarburst.relational_grouped_dataframe.RelationalGroupedDataFrame(df: DataFrame, grouping_exprs: List[Expression], group_type: _GroupType)#

Bases: object

Represents an underlying DataFrame with rows that are grouped by common values. Can be used to define aggregations on these grouped DataFrames.

agg(*exprs: Column | Tuple[ColumnOrName, str] | Dict[str, str]) → DataFrame#

Returns a DataFrame with computed aggregates. See examples in DataFrame.group_by().

Parameters:

exprs –

A variable length arguments list where every element is

a Column object
a tuple where the first element is a column object or a column name and the second element is the name of the aggregate function
a list of the above
a dict maps column names to aggregate function names.

Note

The name of the aggregate function to compute must be a valid Trino aggregate function.