def test_clean_multichartseries_non_number_is_prompting_error(self):
context = RenderContext(
None, None,
TableShape(3, [
Column('A', ColumnType.TEXT()),
Column('B', ColumnType.DATETIME()),
]), None, None)
value = [
{
'column': 'A',
'color': '#aaaaaa'
},
{
'column': 'B',
'color': '#cccccc'
},
]
with self.assertRaises(PromptingError) as cm:
clean_value(ParamDType.Multichartseries(), value, context)
self.assertEqual(cm.exception.errors, [
PromptingError.WrongColumnType(['A'], 'text', frozenset({'number'
})),
PromptingError.WrongColumnType(['B'], 'datetime',
frozenset({'number'})),
])
def test_clean_multichartseries_non_number_is_prompting_error(self):
context = RenderContext(
None,
None,
TableShape(3, [
Column("A", ColumnType.TEXT()),
Column("B", ColumnType.DATETIME())
]),
None,
None,
)
value = [
{
"column": "A",
"color": "#aaaaaa"
},
{
"column": "B",
"color": "#cccccc"
},
]
with self.assertRaises(PromptingError) as cm:
clean_value(ParamDType.Multichartseries(), value, context)
self.assertEqual(
cm.exception.errors,
[
PromptingError.WrongColumnType(["A"], "text",
frozenset({"number"})),
PromptingError.WrongColumnType(["B"], "datetime",
frozenset({"number"})),
],
)
def test_dict_prompting_error_concatenate_different_types(self):
context = RenderContext(
None,
None,
TableShape(3, [
Column("A", ColumnType.TEXT()),
Column("B", ColumnType.DATETIME())
]),
None,
None,
)
schema = ParamDType.Dict({
"x":
ParamDType.Column(column_types=frozenset({"number"})),
"y":
ParamDType.Column(column_types=frozenset({"number"})),
})
with self.assertRaises(PromptingError) as cm:
clean_value(schema, {"x": "A", "y": "B"}, context)
self.assertEqual(
cm.exception.errors,
[
PromptingError.WrongColumnType(["A"], "text",
frozenset({"number"})),
PromptingError.WrongColumnType(["B"], "datetime",
frozenset({"number"})),
],
)
def test_clean_multicolumn_prompting_error_convert_to_text(self):
# TODO make this _automatic_ instead of quick-fix?
# ... but for now: prompt for a Quick Fix.
context = RenderContext(
None,
None,
TableShape(
3,
[
Column("A", ColumnType.NUMBER()),
Column("B", ColumnType.DATETIME()),
Column("C", ColumnType.TEXT()),
],
),
None,
None,
)
with self.assertRaises(PromptingError) as cm:
schema = ParamDType.Multicolumn(column_types=frozenset({"text"}))
clean_value(schema, "A,B", context)
self.assertEqual(
cm.exception.errors,
[
PromptingError.WrongColumnType(["A", "B"], None,
frozenset({"text"}))
],
)
def test_list_prompting_error_concatenate_different_type_to_text(self):
context = RenderContext(
None,
None,
TableShape(
3,
[
Column("A", ColumnType.NUMBER()),
Column("B", ColumnType.DATETIME())
],
),
None,
None,
)
schema = ParamDType.List(inner_dtype=ParamDType.Column(
column_types=frozenset({"text"})))
with self.assertRaises(PromptingError) as cm:
clean_value(schema, ["A", "B"], context)
self.assertEqual(
cm.exception.errors,
[
PromptingError.WrongColumnType(["A", "B"], None,
frozenset({"text"}))
],
)
def test_metadata_comes_from_db_columns(self):
columns = [
Column("A", ColumnType.NUMBER(format="{:,d}")),
Column("B", ColumnType.DATETIME()),
Column("C", ColumnType.TEXT()),
Column("D", ColumnType.TEXT()),
]
result = ProcessResult(
dataframe=pandas.DataFrame(
{
"A": [1], # int64
"B": [datetime.datetime(2018, 8, 20)], # datetime64[ns]
"C": ["foo"], # str
"D": pandas.Series(["cat"], dtype="category"),
}
),
columns=columns,
)
cached_result = self.wf_module.cache_render_result(self.delta.id, result)
# cache_render_result() keeps its `result` parameter in memory, so we
# can avoid disk entirely. Prove it by deleting from disk.
minio.remove(minio.CachedRenderResultsBucket, cached_result.parquet_key)
# Load _new_ CachedRenderResult -- from DB columns, not memory
fresh_wf_module = WfModule.objects.get(id=self.wf_module.id)
cached_result = fresh_wf_module.cached_render_result
self.assertFalse(hasattr(cached_result, "_result"))
self.assertEqual(cached_result.nrows, 1)
self.assertEqual(cached_result.columns, columns)
def test_result_and_metadata_come_from_memory_when_available(self):
columns = [
Column("A", ColumnType.NUMBER(format="{:,d}")),
Column("B", ColumnType.DATETIME()),
Column("C", ColumnType.TEXT()),
Column("D", ColumnType.TEXT()),
]
result = ProcessResult(
dataframe=pandas.DataFrame(
{
"A": [1], # int64
"B": [datetime.datetime(2018, 8, 20)], # datetime64[ns]
"C": ["foo"], # str
"D": pandas.Series(["cat"], dtype="category"),
}
),
columns=columns,
)
cached_result = self.wf_module.cache_render_result(self.delta.id, result)
# cache_render_result() keeps its `result` parameter in memory, so we
# can avoid disk entirely. Prove it by deleting from disk.
minio.remove(minio.CachedRenderResultsBucket, cached_result.parquet_key)
self.assertFalse(cached_result._result is None)
self.assertEqual(cached_result.result, result)
self.assertEqual(cached_result.nrows, 1)
self.assertEqual(cached_result.columns, columns)
def test_format(self):
series = pd.Series([dt(1999, 2, 3, 4, 5, 6, 7), np.nan,
dt(2000, 3, 4, 5, 6, 7, 8)])
column_type = ColumnType.DATETIME()
result = column_type.format_series(series)
assert_series_equal(
result,
pd.Series(['1999-02-03T04:05:06.000007Z', np.nan,
'2000-03-04T05:06:07.000008Z'])
)
def test_ctor_infer_columns(self):
result = ProcessResult(pd.DataFrame({
'A': [1, 2],
'B': ['x', 'y'],
'C': [ np.nan, dt(2019, 3, 3, 4, 5, 6, 7) ],
}))
self.assertEqual(result.columns, [
Column('A', ColumnType.NUMBER()),
Column('B', ColumnType.TEXT()),
Column('C', ColumnType.DATETIME()),
])
def test_columns(self):
df = pd.DataFrame({
'A': [1], # number
'B': ['foo'], # str
'C': dt(2018, 8, 20), # datetime64
})
df['D'] = pd.Series(['cat'], dtype='category')
result = ProcessResult(df)
self.assertEqual(result.column_names, ['A', 'B', 'C', 'D'])
self.assertEqual(result.columns, [
Column('A', ColumnType.NUMBER()),
Column('B', ColumnType.TEXT()),
Column('C', ColumnType.DATETIME()),
Column('D', ColumnType.TEXT()),
])
def test_ctor_infer_columns(self):
result = ProcessResult(
pd.DataFrame({
"A": [1, 2],
"B": ["x", "y"],
"C": [np.nan, dt(2019, 3, 3, 4, 5, 6, 7)],
}))
self.assertEqual(
result.columns,
[
Column("A", ColumnType.NUMBER()),
Column("B", ColumnType.TEXT()),
Column("C", ColumnType.DATETIME()),
],
)
def test_list_prompting_error_concatenate_different_type(self):
context = RenderContext(None, None, TableShape(3, [
Column('A', ColumnType.TEXT()),
Column('B', ColumnType.DATETIME()),
]), None, None)
schema = ParamDType.List(
inner_dtype=ParamDType.Column(column_types=frozenset({'number'}))
)
with self.assertRaises(PromptingError) as cm:
clean_value(schema, ['A', 'B'], context)
self.assertEqual(cm.exception.errors, [
PromptingError.WrongColumnType(['A'], 'text',
frozenset({'number'})),
PromptingError.WrongColumnType(['B'], 'datetime',
frozenset({'number'})),
])
def test_clean_multicolumn_prompting_error_convert_to_text(self):
# TODO make this _automatic_ instead of quick-fix?
# ... but for now: prompt for a Quick Fix.
input_shape = TableShape(3, [
Column('A', ColumnType.NUMBER()),
Column('B', ColumnType.DATETIME()),
Column('C', ColumnType.TEXT()),
])
with self.assertRaises(PromptingError) as cm:
schema = ParamDType.Multicolumn(column_types=frozenset({'text'}))
clean_value(schema, 'A,B', input_shape)
self.assertEqual(cm.exception.errors, [
PromptingError.WrongColumnType(['A'], 'number', frozenset({'text'
})),
PromptingError.WrongColumnType(['B'], 'datetime',
frozenset({'text'})),
])
def test_columns(self):
df = pd.DataFrame({
"A": [1], # number
"B": ["foo"], # str
"C": dt(2018, 8, 20), # datetime64
})
df["D"] = pd.Series(["cat"], dtype="category")
result = ProcessResult(df)
self.assertEqual(result.column_names, ["A", "B", "C", "D"])
self.assertEqual(
result.columns,
[
Column("A", ColumnType.NUMBER()),
Column("B", ColumnType.TEXT()),
Column("C", ColumnType.DATETIME()),
Column("D", ColumnType.TEXT()),
],
)
