def execute(
self,
expr: ir.Expr,
params: Mapping[ir.Expr, object] = None,
limit: str = 'default',
**kwargs: Any,
):
if isinstance(expr, ir.TableExpr):
frame = self.compile(expr, params, **kwargs)
table = _to_pyarrow_table(frame)
return table.to_pandas()
elif isinstance(expr, ir.ColumnExpr):
# expression must be named for the projection
expr = expr.name('tmp').to_projection()
frame = self.compile(expr, params, **kwargs)
table = _to_pyarrow_table(frame)
return table['tmp'].to_pandas()
elif isinstance(expr, ir.ScalarExpr):
if expr.op().root_tables():
# there are associated datafusion tables so convert the expr
# to a selection which we can directly convert to a datafusion
# plan
expr = expr.name('tmp').to_projection()
frame = self.compile(expr, params, **kwargs)
else:
# doesn't have any tables associated so create a plan from a
# dummy datafusion table
compiled = self.compile(expr, params, **kwargs)
frame = self._context.empty_table().select(compiled)
table = _to_pyarrow_table(frame)
return table[0][0].as_py()
else:
raise com.IbisError(
f"Cannot execute expression of type: {type(expr)}"
)
def execute(
self,
expr: ir.Expr,
timecontext: Mapping | None = None,
params: Mapping[ir.Scalar, Any] | None = None,
limit: str = 'default',
**kwargs: Any,
) -> Any:
"""Execute an expression."""
if isinstance(expr, types.Table):
return self.compile(expr, timecontext, params, **kwargs).toPandas()
elif isinstance(expr, types.Column):
# expression must be named for the projection
if not expr.has_name():
expr = expr.name("tmp")
return self.compile(
expr.to_projection(), timecontext, params, **kwargs
).toPandas()[expr.get_name()]
elif isinstance(expr, types.Scalar):
compiled = self.compile(expr, timecontext, params, **kwargs)
if isinstance(compiled, Column):
# attach result column to a fake DataFrame and
# select the result
compiled = self._session.range(0, 1).select(compiled)
return compiled.toPandas().iloc[0, 0]
else:
raise com.IbisError(
f"Cannot execute expression of type: {type(expr)}"
)