def setUpClass(cls):
db = TestDatabase()
t0 = Table("test0")
cls.dataset = DataSet(
table=t0,
database=db,
fields=[
Field(
"timestamp",
label="Timestamp",
definition=t0.timestamp,
data_type=DataType.date,
),
Field(
"metric0",
label="Metric0",
definition=t0.metric,
data_type=DataType.number,
),
Field(
"unused_metric",
label="Unused Metric",
definition=t0.unused_metric,
data_type=DataType.number,
),
],
)
cls.df = pd.DataFrame.from_dict({
"$metric0": [1, 2, 3, 4],
"$metric0_dod": [1, 5, 9, 12]
})
def test_converts_percentage_to_decimal_when_use_raw_value_True_and_suffix_is_percentage(self):
with self.subTest('when no precision'):
field = Field("number", None, data_type=DataType.number, suffix="%")
self.assertEqual("0.87123123131", formats.display_value(87.123123131, field, use_raw_value=True))
with self.subTest('when precision'):
field = Field("number", None, data_type=DataType.number, suffix="%", precision=2)
self.assertEqual("0.0739", formats.display_value(7.38652, field, use_raw_value=True))
def test_style_numbers_with_thousands_separator(self):
euro_field = Field("number",
None,
data_type=DataType.number,
thousands=",")
self.assertEqual("1,000,000",
formats.display_value(1000000, euro_field))
def test_raw_value_when_precision_specified(self):
field = Field("number", None, data_type=DataType.number, precision=4)
self.assertEqual(
"1244996.1138",
formats.display_value(1244996.1138000000000000,
field,
use_raw_value=True))
def test_envelopes_responses_if_return_additional_metadata_True(
self, mock_fetch_data):
mock_dataset = DataSet(
table=politicians_table,
database=test_database,
return_additional_metadata=True,
fields=[
Field(
"political_party",
label="Party",
definition=politicians_table.political_party,
data_type=DataType.text,
hyperlink_template="http://example.com/{political_party}",
)
],
)
df = pd.DataFrame({
'political_party': ['a', 'b', 'c']
}).set_index('political_party')
mock_fetch_data.return_value = 100, df
result = mock_dataset.fields.political_party.choices.fetch()
self.assertEqual(dict(max_rows_returned=100), result['metadata'])
self.assertTrue(
pd.Series(['a', 'b', 'c'],
index=['a', 'b', 'c'],
name='political_party').equals(result['data']))
def test_use_raw_value_int(self):
field = Field("number",
None,
data_type=DataType.number,
suffix="€",
prefix="$")
self.assertEqual(
"1234", formats.display_value(1234, field, use_raw_value=True))
def test_style_numbers_with_mixed(self):
euro_field = Field(
"number",
None,
data_type=DataType.number,
prefix="$",
thousands=",",
precision=2,
)
self.assertEqual("$-1,000,000.00", formats.display_value(-1000000, euro_field))
def test_reference_filters_are_applied(self, mock_fetch: Mock, mock_2: Mock, mock_apply_reference_filters: Mock):
db = TestDatabase()
t0 = Table("test0")
dataset = DataSet(
table=t0,
database=db,
fields=[
Field(
"timestamp",
label="Timestamp",
definition=t0.timestamp,
data_type=DataType.date,
),
Field(
"metric0",
label="Metric0",
definition=t0.metric,
data_type=DataType.number,
),
],
)
mock_widget = f.Widget(dataset.fields.metric0)
mock_widget.transform = Mock()
reference_filter = ReferenceFilter(
dataset.fields.metric0,
ComparisonOperator.gt,
5
)
reference = f.DayOverDay(dataset.fields.timestamp, filters=[reference_filter])
df = pd.DataFrame.from_dict({"$value": [1]})
mock_fetch.return_value = 100, df
mock_apply_reference_filters.return_value = df
(
dataset.query()
.dimension(dataset.fields.timestamp)
.widget(mock_widget)
.reference(reference)
).fetch()
mock_apply_reference_filters.assert_called_once_with(df, reference)
def test_pivoted_df_transformation_formats_totals_correctly(self):
test_table = Table('test')
ds = DataSet(
table=test_table,
database=test_database,
fields=[
Field('date', label='Date', definition=test_table.date, data_type=DataType.date),
Field('locale', label='Locale', definition=test_table.locale, data_type=DataType.text),
Field('company', label='Company', definition=test_table.text, data_type=DataType.text),
Field('metric1', label='Metric1', definition=Sum(test_table.number), data_type=DataType.number),
Field('metric2', label='Metric2', definition=Sum(test_table.number), data_type=DataType.number),
],
)
df = pd.DataFrame.from_dict(
{
'$metric1': {('~~totals', '~~totals'): 3, ('za', '~~totals'): 3, ('za', 'C1'): 2, ('za', 'C2'): 1},
'$metric2': {('~~totals', '~~totals'): 4, ('za', '~~totals'): 4, ('za', 'C1'): 2, ('za', 'C2'): 2},
}
)
df.index.names = [f(ds.fields.locale.alias), f(ds.fields.company.alias)]
result = Pandas(ds.fields.metric1, ds.fields.metric2, pivot=[ds.fields.company]).transform(
df, [Rollup(ds.fields.locale), Rollup(ds.fields.company)], [], use_raw_values=True
)
self.assertEqual(['Metrics', 'Company'], list(result.columns.names))
self.assertEqual(
[
('Metric1', 'C1'),
('Metric1', 'C2'),
('Metric1', 'Totals'),
('Metric2', 'C1'),
('Metric2', 'C2'),
('Metric2', 'Totals'),
],
result.columns.values.tolist(),
)
self.assertEqual(['Locale'], list(result.index.names))
self.assertEqual(['za', 'Totals'], result.index.values.tolist())
self.assertEqual([['2', '1', '3', '2', '2', '4'], ['', '', '3', '', '', '4']], result.values.tolist())
def test_with_empty_dataset_multiple_dimensions(self):
metric = Field("test", None, label="Test")
mi = pd.MultiIndex.from_product([[], []], names=("level0", "level1"))
frame = pd.DataFrame([], index=mi, columns=["$test"], dtype=np.float)
expected = pd.Series([], index=mi, name="$test", dtype=np.float)
for op in (CumSum, CumMean, CumProd):
with self.subTest(op.__name__):
op_cum = op(metric)
result = op_cum.apply(frame, None)
pd.testing.assert_series_equal(result, expected)
def test_with_empty_dataset_single_dimension(self):
metric = Field("test", None, label="Test")
idx = pd.Index([], name="level0")
frame = pd.DataFrame([], index=idx, columns=["$test"], dtype=np.float)
expected = pd.Series([], index=idx, name="$test", dtype=np.float)
for op in (CumSum, CumMean, CumProd):
with self.subTest(op.__name__):
op_cum = op(metric)
result = op_cum.apply(frame, None)
pd.testing.assert_series_equal(result, expected)
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('$dim-over')
result = share.apply(df, reference)
np.testing.assert_array_equal(([4, 2, -12, 6, 0]), result.values)
def test_apply_cummulative_for_delta_percent(self):
dataset = MagicMock()
dataset.table._table_name = "table"
field = Field("value", None)
cumsum = CumProd(field)
reference = Reference(field,
WeekOverWeek,
delta=True,
delta_percent=True)
df = pd.DataFrame.from_dict({
"$value": [55, 60, 108],
"$cumprod(value)": [55, 3300, 356400],
"$value_wow_delta_percent": [10, 20, 8],
})
result = cumsum.apply(df, reference)
pandas.testing.assert_series_equal(pd.Series([10.0, 32.0, 42.56]),
result)
def test_raw_value_does_not_trim_zero_value(self):
field = Field("number", None, data_type=DataType.number)
self.assertEqual("0",
formats.display_value(0, field, use_raw_value=True))
def test_raw_value_formats_integers_with_trailing_zeros(self):
field = Field("number", None, data_type=DataType.number)
self.assertEqual(
"126500", formats.display_value(126500, field, use_raw_value=True))
def test_style_numbers_with_precision(self):
euro_field = Field("number",
None,
data_type=DataType.number,
precision=2)
self.assertEqual("1.23", formats.display_value(1.234567, euro_field))
def test_style_numbers_with_suffix(self):
euro_field = Field("number",
None,
data_type=DataType.number,
suffix="€")
self.assertEqual("1€", formats.display_value(1, euro_field))
def test_style_negative_numbers_with_prefix(self):
dollar_field = Field("number",
None,
data_type=DataType.number,
prefix="$")
self.assertEqual("$-1", formats.display_value(-1, dollar_field))
Field,
day,
formats,
hour,
month,
quarter,
week,
year,
)
from fireant.dataset.totals import (
DATE_TOTALS,
NUMBER_TOTALS,
TEXT_TOTALS,
)
text_field = Field("text", None, data_type=DataType.text)
number_field = Field("number", None, data_type=DataType.number)
boolean_field = Field("boolean", None, data_type=DataType.boolean)
date_field = Field("date", None, data_type=DataType.date)
class SafeRawValueTests(TestCase):
def test_none_returned_as_null_label(self):
self.assertEqual("null", formats.safe_value(None))
def test_nan_returned_as_null_label(self):
self.assertEqual("null", formats.safe_value(np.nan))
def test_inf_returned_as_inf_label(self):
with self.subTest("positive inf"):
self.assertEqual("inf", formats.safe_value(np.inf))
