def _compute_predicates(
table_op,
predicates,
data,
scope: Scope,
timecontext: Optional[TimeContext],
**kwargs,
):
"""Compute the predicates for a table operation.
Parameters
----------
table_op : TableNode
predicates : List[ir.ColumnExpr]
data : pd.DataFrame
scope : Scope
timecontext: Optional[TimeContext]
kwargs : dict
Returns
-------
computed_predicate : pd.Series[bool]
Notes
-----
This handles the cases where the predicates are computed columns, in
addition to the simple case of named columns coming directly from the input
table.
"""
for predicate in predicates:
# Map each root table of the predicate to the data so that we compute
# predicates on the result instead of any left or right tables if the
# Selection is on a Join. Project data to only inlude columns from
# the root table.
root_tables = predicate.op().root_tables()
# handle suffixes
data_columns = frozenset(data.columns)
additional_scope = Scope()
for root_table in root_tables:
mapping = remap_overlapping_column_names(
table_op, root_table, data_columns
)
if mapping is not None:
new_data = data.loc[:, mapping.keys()].rename(columns=mapping)
else:
new_data = data
additional_scope = additional_scope.merge_scope(
Scope({root_table: new_data}, timecontext)
)
scope = scope.merge_scope(additional_scope)
yield execute(predicate, scope=scope, **kwargs)
def test_scope_look_up():
# test if scope could lookup items properly
scope = Scope()
one_day = ibis.interval(days=1).op()
one_hour = ibis.interval(hours=1).op()
scope = scope.merge_scope(Scope({one_day: 1}, None))
assert scope.get_value(one_hour) is None
assert scope.get_value(one_day) is not None
def main_execute(
expr,
params=None,
scope=None,
timecontext: Optional[TimeContext] = None,
aggcontext=None,
**kwargs,
):
"""Execute an expression against data that are bound to it. If no data
are bound, raise an Exception.
Parameters
----------
expr : ibis.expr.types.Expr
The expression to execute
params : Mapping[ibis.expr.types.Expr, object]
The data that an unbound parameter in `expr` maps to
scope : Mapping[ibis.expr.operations.Node, object]
Additional scope, mapping ibis operations to data
timecontext : Optional[TimeContext]
timecontext needed for execution
aggcontext : Optional[ibis.backends.pandas.aggcontext.AggregationContext]
An object indicating how to compute aggregations. For example,
a rolling mean needs to be computed differently than the mean of a
column.
kwargs : Dict[str, object]
Additional arguments that can potentially be used by individual node
execution
Returns
-------
result : Union[
pandas.Series, pandas.DataFrame, ibis.backends.pandas.core.simple_types
]
Raises
------
ValueError
* If no data are bound to the input expression
"""
if scope is None:
scope = Scope()
if timecontext is not None:
# convert timecontext to datetime type, if time strings are provided
timecontext = canonicalize_context(timecontext)
if params is None:
params = {}
# TODO: make expresions hashable so that we can get rid of these .op()
# calls everywhere
params = {k.op() if hasattr(k, 'op') else k: v for k, v in params.items()}
scope = scope.merge_scope(Scope(params, timecontext))
return execute_with_scope(
expr, scope, timecontext=timecontext, aggcontext=aggcontext, **kwargs,
)
def execute_until_in_scope(
expr,
scope: Scope,
timecontext: Optional[TimeContext] = None,
aggcontext=None,
clients=None,
post_execute_=None,
**kwargs,
) -> Scope:
"""Execute until our op is in `scope`.
Parameters
----------
expr : ibis.expr.types.Expr
scope : Scope
timecontext : Optional[TimeContext]
aggcontext : Optional[AggregationContext]
clients : List[ibis.client.Client]
kwargs : Mapping
"""
# these should never be None
assert aggcontext is not None, 'aggcontext is None'
assert clients is not None, 'clients is None'
assert post_execute_ is not None, 'post_execute_ is None'
# base case: our op has been computed (or is a leaf data node), so
# return the corresponding value
op = expr.op()
if scope.get_value(op, timecontext) is not None:
return scope
if isinstance(op, ops.Literal):
# special case literals to avoid the overhead of dispatching
# execute_node
return Scope(
{
op:
execute_literal(
op, op.value, expr.type(), aggcontext=aggcontext, **kwargs)
},
timecontext,
)
# figure out what arguments we're able to compute on based on the
# expressions inputs. things like expressions, None, and scalar types are
# computable whereas ``list``s are not
computable_args = [arg for arg in op.inputs if is_computable_input(arg)]
# pre_executed_states is a list of states with same the length of
# computable_args, these states are passed to each arg
if timecontext:
arg_timecontexts = compute_time_context(
op,
num_args=len(computable_args),
timecontext=timecontext,
clients=clients,
)
else:
arg_timecontexts = [None] * len(computable_args)
pre_executed_scope = pre_execute(
op,
*clients,
scope=scope,
timecontext=timecontext,
aggcontext=aggcontext,
**kwargs,
)
new_scope = scope.merge_scope(pre_executed_scope)
# Short circuit: if pre_execute puts op in scope, then we don't need to
# execute its computable_args
if new_scope.get_value(op, timecontext) is not None:
return new_scope
# recursively compute each node's arguments until we've changed type.
# compute_time_context should return with a list with the same length
# as computable_args, the two lists will be zipping together for
# further execution
if len(arg_timecontexts) != len(computable_args):
raise com.IbisError(
'arg_timecontexts differ with computable_arg in length '
f'for type:\n{type(op).__name__}.')
scopes = [
execute_until_in_scope(
arg,
new_scope,
timecontext=timecontext,
aggcontext=aggcontext,
post_execute_=post_execute_,
clients=clients,
**kwargs,
) if hasattr(arg, 'op') else Scope({arg: arg}, timecontext)
for (arg, timecontext) in zip(computable_args, arg_timecontexts)
]
# if we're unable to find data then raise an exception
if not scopes and computable_args:
raise com.UnboundExpressionError(
'Unable to find data for expression:\n{}'.format(repr(expr)))
# there should be exactly one dictionary per computable argument
assert len(computable_args) == len(scopes)
new_scope = new_scope.merge_scopes(scopes)
# pass our computed arguments to this node's execute_node implementation
data = [
new_scope.get_value(arg.op(), timecontext)
if hasattr(arg, 'op') else arg for arg in computable_args
]
result = execute_node(
op,
*data,
scope=scope,
timecontext=timecontext,
aggcontext=aggcontext,
clients=clients,
**kwargs,
)
computed = post_execute_(op, result, timecontext=timecontext)
return Scope({op: computed}, timecontext)
def execute_with_scope(
expr,
scope: Scope,
timecontext: Optional[TimeContext] = None,
aggcontext=None,
clients=None,
**kwargs,
):
"""Execute an expression `expr`, with data provided in `scope`.
Parameters
----------
expr : ibis.expr.types.Expr
The expression to execute.
scope : Scope
A Scope class, with dictionary mapping
:class:`~ibis.expr.operations.Node` subclass instances to concrete
data such as a pandas DataFrame.
timecontext : Optional[TimeContext]
A tuple of (begin, end) that is passed from parent Node to children
see [timecontext.py](ibis/backends/pandas/execution/timecontext.py) for
detailed usage for this time context.
aggcontext : Optional[ibis.backends.pandas.aggcontext.AggregationContext]
Returns
-------
result : scalar, pd.Series, pd.DataFrame
"""
op = expr.op()
# Call pre_execute, to allow clients to intercept the expression before
# computing anything *and* before associating leaf nodes with data. This
# allows clients to provide their own data for each leaf.
if clients is None:
clients = list(find_backends(expr))
if aggcontext is None:
aggcontext = agg_ctx.Summarize()
pre_executed_scope = pre_execute(
op,
*clients,
scope=scope,
timecontext=timecontext,
aggcontext=aggcontext,
**kwargs,
)
new_scope = scope.merge_scope(pre_executed_scope)
result = execute_until_in_scope(
expr,
new_scope,
timecontext=timecontext,
aggcontext=aggcontext,
clients=clients,
# XXX: we *explicitly* pass in scope and not new_scope here so that
# post_execute sees the scope of execute_with_scope, not the scope of
# execute_until_in_scope
post_execute_=functools.partial(
post_execute,
scope=scope,
timecontext=timecontext,
aggcontext=aggcontext,
clients=clients,
**kwargs,
),
**kwargs,
).get_value(op, timecontext)
return result
def execute_window_op(
op,
data,
window,
scope: Scope = None,
timecontext: Optional[TimeContext] = None,
aggcontext=None,
clients=None,
**kwargs,
):
operand = op.expr
# pre execute "manually" here because otherwise we wouldn't pickup
# relevant scope changes from the child operand since we're managing
# execution of that by hand
operand_op = operand.op()
adjusted_timecontext = None
if timecontext:
arg_timecontexts = compute_time_context(
op, timecontext=timecontext, clients=clients
)
# timecontext is the original time context required by parent node
# of this WindowOp, while adjusted_timecontext is the adjusted context
# of this Window, since we are doing a manual execution here, use
# adjusted_timecontext in later execution phases
adjusted_timecontext = arg_timecontexts[0]
pre_executed_scope = pre_execute(
operand_op,
*clients,
scope=scope,
timecontext=adjusted_timecontext,
aggcontext=aggcontext,
**kwargs,
)
scope = scope.merge_scope(pre_executed_scope)
(root,) = op.root_tables()
root_expr = root.to_expr()
data = execute(
root_expr,
scope=scope,
timecontext=adjusted_timecontext,
clients=clients,
aggcontext=aggcontext,
**kwargs,
)
following = window.following
order_by = window._order_by
if (
order_by
and following != 0
and not isinstance(operand_op, ops.ShiftBase)
):
raise com.OperationNotDefinedError(
'Window functions affected by following with order_by are not '
'implemented'
)
group_by = window._group_by
grouping_keys = [
key_op.name
if isinstance(key_op, ops.TableColumn)
else execute(
key,
scope=scope,
clients=clients,
timecontext=adjusted_timecontext,
aggcontext=aggcontext,
**kwargs,
)
for key, key_op in zip(
group_by, map(operator.methodcaller('op'), group_by)
)
]
order_by = window._order_by
if not order_by:
ordering_keys = []
if group_by:
if order_by:
(
sorted_df,
grouping_keys,
ordering_keys,
) = util.compute_sorted_frame(
data,
order_by,
group_by=group_by,
timecontext=adjusted_timecontext,
**kwargs,
)
source = sorted_df.groupby(grouping_keys, sort=True)
post_process = _post_process_group_by_order_by
else:
source = data.groupby(grouping_keys, sort=False)
post_process = _post_process_group_by
else:
if order_by:
source, grouping_keys, ordering_keys = util.compute_sorted_frame(
data, order_by, timecontext=adjusted_timecontext, **kwargs
)
post_process = _post_process_order_by
else:
source = data
post_process = _post_process_empty
# Here groupby object should be add to the corresponding node in scope
# for execution, data will be overwrite to a groupby object, so we
# force an update regardless of time context
new_scope = scope.merge_scopes(
[
Scope({t: source}, adjusted_timecontext)
for t in operand.op().root_tables()
],
overwrite=True,
)
aggcontext = get_aggcontext(
window,
scope=scope,
operand=operand,
parent=source,
group_by=grouping_keys,
order_by=ordering_keys,
**kwargs,
)
result = execute(
operand,
scope=new_scope,
timecontext=adjusted_timecontext,
aggcontext=aggcontext,
clients=clients,
**kwargs,
)
series = post_process(
result, data, ordering_keys, grouping_keys, adjusted_timecontext,
)
assert len(data) == len(
series
), 'input data source and computed column do not have the same length'
# trim data to original time context
series = trim_with_timecontext(series, timecontext)
return series
