1 files changed, 0 insertions, 59 deletions
diff --git a/sqlglot/dataframe/sql/group.py b/sqlglot/dataframe/sql/group.py
deleted file mode 100644
index ba27c17..0000000
--- a/sqlglot/dataframe/sql/group.py
+++ /dev/null
@@ -1,59 +0,0 @@
-from __future__ import annotations
-
-import typing as t
-
-from sqlglot.dataframe.sql import functions as F
-from sqlglot.dataframe.sql.column import Column
-from sqlglot.dataframe.sql.operations import Operation, operation
-
-if t.TYPE_CHECKING:
-    from sqlglot.dataframe.sql.dataframe import DataFrame
-
-
-class GroupedData:
-    def __init__(self, df: DataFrame, group_by_cols: t.List[Column], last_op: Operation):
-        self._df = df.copy()
-        self.spark = df.spark
-        self.last_op = last_op
-        self.group_by_cols = group_by_cols
-
-    def _get_function_applied_columns(
-        self, func_name: str, cols: t.Tuple[str, ...]
-    ) -> t.List[Column]:
-        func_name = func_name.lower()
-        return [getattr(F, func_name)(name).alias(f"{func_name}({name})") for name in cols]
-
-    @operation(Operation.SELECT)
-    def agg(self, *exprs: t.Union[Column, t.Dict[str, str]]) -> DataFrame:
-        columns = (
-            [Column(f"{agg_func}({column_name})") for column_name, agg_func in exprs[0].items()]
-            if isinstance(exprs[0], dict)
-            else exprs
-        )
-        cols = self._df._ensure_and_normalize_cols(columns)
-
-        expression = self._df.expression.group_by(
-            *[x.expression for x in self.group_by_cols]
-        ).select(*[x.expression for x in self.group_by_cols + cols], append=False)
-        return self._df.copy(expression=expression)
-
-    def count(self) -> DataFrame:
-        return self.agg(F.count("*").alias("count"))
-
-    def mean(self, *cols: str) -> DataFrame:
-        return self.avg(*cols)
-
-    def avg(self, *cols: str) -> DataFrame:
-        return self.agg(*self._get_function_applied_columns("avg", cols))
-
-    def max(self, *cols: str) -> DataFrame:
-        return self.agg(*self._get_function_applied_columns("max", cols))
-
-    def min(self, *cols: str) -> DataFrame:
-        return self.agg(*self._get_function_applied_columns("min", cols))
-
-    def sum(self, *cols: str) -> DataFrame:
-        return self.agg(*self._get_function_applied_columns("sum", cols))
-
-    def pivot(self, *cols: str) -> DataFrame:
-        raise NotImplementedError("Sum distinct is not currently implemented")