def test_do_not_remove_totals_for_rollup_dimensions(self):
result = scrub_totals_from_share_results(dimx1_str_totals_df,
[Rollup(mock_dataset.fields.political_party)])
expected = dimx1_str_totals_df
pandas.testing.assert_frame_equal(result, expected)
def test_do_not_remove_totals_for_rollup_dimensions_with_multiindex_and_all_rolled_up(self):
result = scrub_totals_from_share_results(dimx2_date_str_totalsx2_df,
[Rollup(mock_dataset.fields.timestamp),
Rollup(mock_dataset.fields.political_party)])
expected = dimx2_date_str_totalsx2_df
pandas.testing.assert_frame_equal(result, expected)
def test_do_not_remove_totals_for_rollup_dimensions_with_multiindex_and_lower_dimension_totals(self):
result = scrub_totals_from_share_results(dimx2_date_str_totalsx2_df,
[mock_dataset.fields.timestamp,
Rollup(mock_dataset.fields.political_party)])
expected = dimx2_date_str_totalsx2_df.loc[:TIMESTAMP_UPPERBOUND]
pandas.testing.assert_frame_equal(result, expected)
def test_remove_totals_for_non_rollup_dimensions_with_multiindex_and_multiple_totals(self):
result = scrub_totals_from_share_results(dimx2_date_str_totalsx2_df,
[mock_dataset.fields.timestamp,
mock_dataset.fields.political_party])
expected = dimx2_date_str_df
pandas.testing.assert_frame_equal(result, expected)
def test_do_not_remove_totals_for_rollup_dimensions_with_multiindex_and_higher_dimension_totals(self):
result = scrub_totals_from_share_results(dimx2_date_str_totalsx2_df,
[Rollup(mock_dataset.fields.timestamp),
mock_dataset.fields.political_party])
expected = dimx2_date_str_totalsx2_df.loc[(slice(None),
slice('Democrat', 'Republican')), :] \
.append(dimx2_date_str_totalsx2_df.iloc[-1])
pandas.testing.assert_frame_equal(result, expected)
def fetch(self, hint=None) -> Iterable[Dict]:
"""
Fetch the data for this query and transform it into the widgets.
:param hint:
A query hint label used with database vendors which support it. Adds a label comment to the query.
:return:
A list of dict (JSON) objects containing the widget configurations.
"""
queries = add_hints(self.sql, hint)
operations = find_operations_for_widgets(self._widgets)
share_dimensions = find_share_dimensions(self._dimensions, operations)
data_frame = fetch_data(
self.dataset.database,
queries,
self._dimensions,
share_dimensions,
self.reference_groups,
)
# Apply operations
for operation in operations:
for reference in [None] + self._references:
df_key = alias_selector(reference_alias(operation, reference))
data_frame[df_key] = operation.apply(data_frame, reference)
data_frame = scrub_totals_from_share_results(data_frame,
self._dimensions)
data_frame = special_cases.apply_operations_to_data_frame(
operations, data_frame)
data_frame = paginate(
data_frame,
self._widgets,
orders=self.orders,
limit=self._limit,
offset=self._offset,
)
# Apply transformations
return [
widget.transform(data_frame, self.dataset, self._dimensions,
self._references) for widget in self._widgets
]
def fetch(self, hint=None) -> Union[Iterable[Dict], Dict]:
"""
Fetch the data for this query and transform it into the widgets.
:param hint:
A query hint label used with database vendors which support it. Adds a label comment to the query.
:return:
A list of dict (JSON) objects containing the widget configurations.
"""
queries = add_hints(self.sql, hint)
operations = find_operations_for_widgets(self._widgets)
dimensions = self.dimensions
share_dimensions = find_share_dimensions(dimensions, operations)
annotation_frame = None
if dimensions and self.dataset.annotation:
alignment_dimension_alias = self.dataset.annotation.dataset_alignment_field_alias
first_dimension = find_field_in_modified_field(dimensions[0])
if first_dimension.alias == alignment_dimension_alias:
annotation_frame = self.fetch_annotation()
max_rows_returned, data_frame = fetch_data(
self.dataset.database,
queries,
dimensions,
share_dimensions,
self.reference_groups,
)
# Apply reference filters
for reference in self._references:
data_frame = apply_reference_filters(data_frame, reference)
# Apply operations
for operation in operations:
for reference in [None] + self._references:
df_key = alias_selector(reference_alias(operation, reference))
data_frame[df_key] = operation.apply(data_frame, reference)
data_frame = scrub_totals_from_share_results(data_frame, dimensions)
data_frame = special_cases.apply_operations_to_data_frame(operations, data_frame)
data_frame = paginate(
data_frame,
self._widgets,
orders=self.orders,
limit=self._client_limit,
offset=self._client_offset,
)
# Apply transformations
widget_data = [
widget.transform(
data_frame,
dimensions,
self._references,
annotation_frame,
)
for widget in self._widgets
]
return self._transform_for_return(widget_data, max_rows_returned=max_rows_returned)
def ignore_dimensionless_result_sets(self):
result = scrub_totals_from_share_results(dimx0_metricx2_df, [])
expected = dimx0_metricx2_df
pandas.testing.assert_frame_equal(result, expected)