def test_apply_to_two_dims_over_second(self):
share = Share(mock_dataset.fields.votes,
over=mock_dataset.fields.political_party)
result = share.apply(dimx2_date_str_totals_df, None)
f_metric_key = alias_selector(mock_dataset.fields.votes.alias)
expected = pd.Series(
[
49.79,
7.07,
43.12,
100.0,
49.78,
50.21,
100.0,
48.83,
51.16,
100.0,
55.42,
44.57,
100.0,
60.39,
39.60,
100.0,
26.60,
73.39,
100.0,
],
name=f_metric_key,
index=dimx2_date_str_totals_df.index,
)
pandas.testing.assert_series_equal(expected, result, rtol=0.5e-3)
def test_apply_to_zero_dims(self):
share = Share(mock_dataset.fields.votes)
result = share.apply(dimx0_metricx1_df, None)
f_metric_key = alias_selector(mock_dataset.fields.votes.alias)
expected = pd.Series([100.], name=f_metric_key)
pandas.testing.assert_series_equal(expected, result)
def test_apply_to_zero_dims(self):
share = Share(slicer.metrics.votes)
result = share.apply(single_metric_df, None)
f_metric_key = format_metric_key(slicer.metrics.votes.key)
expected = pd.Series([100.], name=f_metric_key)
pandas.testing.assert_series_equal(expected, result)
def test_apply_to_two_dims_over_first(self):
share = Share(mock_dataset.fields.votes,
over=mock_dataset.fields.timestamp)
result = share.apply(dimx2_date_str_totalsx2_df, None)
f_metric_key = alias_selector(mock_dataset.fields.votes.alias)
metric_series = dimx2_date_str_totalsx2_df[f_metric_key]
expected = 100 * metric_series / metric_series.iloc[-1]
pandas.testing.assert_series_equal(expected, result, rtol=0.5e-3)
def test_apply_to_one_dim_over_none(self):
share = Share(mock_dataset.fields.votes)
result = share.apply(dimx1_str_df, None)
f_metric_key = alias_selector(mock_dataset.fields.votes.alias)
expected = pd.Series([100.] * 3,
name=f_metric_key,
index=dimx1_str_df.index)
pandas.testing.assert_series_equal(expected, result, rtol=0.5e-3)
def test_apply_to_one_dim_over_first(self):
share = Share(mock_dataset.fields.votes,
over=mock_dataset.fields.political_party)
result = share.apply(dimx1_str_totals_df, None)
f_metric_key = alias_selector(mock_dataset.fields.votes.alias)
expected = pd.Series([48.8487, 0.9638, 50.1873, 100.0],
name=f_metric_key,
index=dimx1_str_totals_df.index)
pandas.testing.assert_series_equal(expected, result, rtol=0.5e-3)
def test_apply_to_two_dims_over_first(self):
share = Share(slicer.metrics.votes, over=slicer.dimensions.timestamp)
result = share.apply(cont_uni_dim_all_totals_df, None)
f_metric_key = format_metric_key(slicer.metrics.votes.key)
metric_series = cont_uni_dim_all_totals_df[f_metric_key]
expected = 100 * metric_series / metric_series.iloc[-1]
pandas.testing.assert_series_equal(expected,
result,
check_less_precise=True)
def test_apply_to_two_dims_over_none(self):
share = Share(mock_dataset.fields.votes)
result = share.apply(dimx2_date_str_df, None)
f_metric_key = alias_selector(mock_dataset.fields.votes.alias)
expected = pd.Series([100.] * 13,
name=f_metric_key,
index=dimx2_date_str_df.index)
pandas.testing.assert_series_equal(expected,
result,
check_less_precise=True)
def test_apply_to_one_dim_over_none(self):
share = Share(slicer.metrics.votes)
result = share.apply(cat_dim_df, None)
f_metric_key = format_metric_key(slicer.metrics.votes.key)
expected = pd.Series([100.] * 3,
name=f_metric_key,
index=cat_dim_df.index)
pandas.testing.assert_series_equal(expected,
result,
check_less_precise=True)
def test_find_share_dimensions_with_a_multiple_share_operations(self, mock_fetch_data: Mock, mock_paginate: Mock):
mock_widget = f.Widget(Share(slicer.metrics.votes, over=slicer.dimensions.state),
Share(slicer.metrics.wins, over=slicer.dimensions.state))
mock_widget.transform = Mock()
dimensions = slicer.dimensions.timestamp, slicer.dimensions.state, slicer.dimensions.political_party
slicer.data \
.widget(mock_widget) \
.dimension(*dimensions) \
.fetch()
mock_fetch_data.assert_called_once_with(ANY, ANY, ANY, DimensionMatcher(slicer.dimensions.state), ANY)
def test_apply_to_one_dim_over_first(self):
share = Share(slicer.metrics.votes,
over=slicer.dimensions.political_party)
result = share.apply(cat_dim_totals_df, None)
f_metric_key = format_metric_key(slicer.metrics.votes.key)
expected = pd.Series([48.849, 0.964, 50.187, 100.0],
name=f_metric_key,
index=cat_dim_totals_df.index)
pandas.testing.assert_series_equal(expected,
result,
check_less_precise=True)
def test_apply_to_two_dims_over_second_with_one_row_per_group(self):
raw_df = dimx2_date_str_totals_df.iloc[[0, 3, 4, 6]]
share = Share(mock_dataset.fields.votes,
over=mock_dataset.fields.political_party)
result = share.apply(raw_df, None)
f_metric_key = alias_selector(mock_dataset.fields.votes.alias)
expected = pd.Series([49.79, 100.0, 49.78, 100.0],
name=f_metric_key,
index=raw_df.index)
pandas.testing.assert_series_equal(expected, result, rtol=0.5e-3)
def test_apply_to_two_dims_over_second_with_one_row_per_group(self):
raw_df = cont_uni_dim_totals_df.iloc[[0, 2, 3, 5]]
share = Share(slicer.metrics.votes, over=slicer.dimensions.state)
result = share.apply(raw_df, None)
f_metric_key = format_metric_key(slicer.metrics.votes.key)
expected = pd.Series([36.624, 100., 37.411, 100.],
name=f_metric_key,
index=raw_df.index)
pandas.testing.assert_series_equal(expected,
result,
check_less_precise=True)
def test_apply_to_two_dims_over_second(self):
share = Share(slicer.metrics.votes, over=slicer.dimensions.state)
result = share.apply(cont_uni_dim_totals_df, None)
f_metric_key = format_metric_key(slicer.metrics.votes.key)
expected = pd.Series([
36.624, 63.376, 100., 37.411, 62.589, 100., 37.521, 62.479, 100.,
37.606, 62.394, 100., 38.294, 61.706, 100., 27.705, 72.295, 100.
],
name=f_metric_key,
index=cont_uni_dim_totals_df.index)
pandas.testing.assert_series_equal(expected,
result,
check_less_precise=True)
def test_find_share_dimensions_with_a_multiple_share_operations(
self, mock_fetch_data: Mock, mock_paginate: Mock):
mock_widget = f.Widget(
Share(mock_dataset.fields.votes, over=mock_dataset.fields.state),
Share(mock_dataset.fields.wins, over=mock_dataset.fields.state),
)
mock_widget.transform = Mock()
dimensions = (
mock_dataset.fields.timestamp,
mock_dataset.fields.state,
mock_dataset.fields.political_party,
)
mock_dataset.query.widget(mock_widget).dimension(*dimensions).fetch()
mock_fetch_data.assert_called_once_with(
ANY, ANY, ANY, FieldMatcher(mock_dataset.fields.state), ANY)
def test_share_for_references_with_delta(self):
dataset = MagicMock()
dataset.table._table_name = "table"
value_field = Field("value", None)
over_field = Field("dim-over", None)
share = Share(value_field, over_field)
reference = Reference(value_field,
WeekOverWeek,
delta=True,
delta_percent=False)
df = pd.DataFrame.from_dict({
"$value_wow": [10, 15, 20, 5, 50],
"$value": [12, 16, 14, 8, 50],
"$share(value,dim-over)": [24, 32, 28, 16, 100],
"$dim-over": ["A", "B", "C", "D", "~~totals"]
}).set_index('$dim-over')
result = share.apply(df, reference)
np.testing.assert_array_equal(([4, 2, -12, 6, 0]), result.values)