TST: Parametrized test_json_table_schema module (#19128)

Author: WillAyd

pandas/tests/io/json/test_json_table_schema.py

@@ -88,82 +88,82 @@ def test_multiindex(self):
 
 class TestTableSchemaType(object):
 
- def test_as_json_table_type_int_data(self):
+ @pytest.mark.parametrize('int_type', [
+ np.int, np.int16, np.int32, np.int64])
+ def test_as_json_table_type_int_data(self, int_type):
  int_data = [1, 2, 3]
- int_types = [np.int, np.int16, np.int32, np.int64]
- for t in int_types:
- assert as_json_table_type(np.array(
- int_data, dtype=t)) == 'integer'
+ assert as_json_table_type(np.array(
+ int_data, dtype=int_type)) == 'integer'
 
- def test_as_json_table_type_float_data(self):
+ @pytest.mark.parametrize('float_type', [
+ np.float, np.float16, np.float32, np.float64])
+ def test_as_json_table_type_float_data(self, float_type):
  float_data = [1., 2., 3.]
- float_types = [np.float, np.float16, np.float32, np.float64]
- for t in float_types:
- assert as_json_table_type(np.array(
- float_data, dtype=t)) == 'number'
+ assert as_json_table_type(np.array(
+ float_data, dtype=float_type)) == 'number'
 
- def test_as_json_table_type_bool_data(self):
+ @pytest.mark.parametrize('bool_type', [bool, np.bool])
+ def test_as_json_table_type_bool_data(self, bool_type):
  bool_data = [True, False]
- bool_types = [bool, np.bool]
- for t in bool_types:
- assert as_json_table_type(np.array(
- bool_data, dtype=t)) == 'boolean'
-
- def test_as_json_table_type_date_data(self):
- date_data = [pd.to_datetime(['2016']),
- pd.to_datetime(['2016'], utc=True),
- pd.Series(pd.to_datetime(['2016'])),
- pd.Series(pd.to_datetime(['2016'], utc=True)),
- pd.period_range('2016', freq='A', periods=3)]
- for arr in date_data:
- assert as_json_table_type(arr) == 'datetime'
-
- def test_as_json_table_type_string_data(self):
- strings = [pd.Series(['a', 'b']), pd.Index(['a', 'b'])]
- for t in strings:
- assert as_json_table_type(t) == 'string'
-
- def test_as_json_table_type_categorical_data(self):
- assert as_json_table_type(pd.Categorical(['a'])) == 'any'
- assert as_json_table_type(pd.Categorical([1])) == 'any'
- assert as_json_table_type(pd.Series(pd.Categorical([1]))) == 'any'
- assert as_json_table_type(pd.CategoricalIndex([1])) == 'any'
- assert as_json_table_type(pd.Categorical([1])) == 'any'
+ assert as_json_table_type(np.array(
+ bool_data, dtype=bool_type)) == 'boolean'
+
+ @pytest.mark.parametrize('date_data', [
+ pd.to_datetime(['2016']),
+ pd.to_datetime(['2016'], utc=True),
+ pd.Series(pd.to_datetime(['2016'])),
+ pd.Series(pd.to_datetime(['2016'], utc=True)),
+ pd.period_range('2016', freq='A', periods=3)
+ ])
+ def test_as_json_table_type_date_data(self, date_data):
+ assert as_json_table_type(date_data) == 'datetime'
+
+ @pytest.mark.parametrize('str_data', [
+ pd.Series(['a', 'b']), pd.Index(['a', 'b'])])
+ def test_as_json_table_type_string_data(self, str_data):
+ assert as_json_table_type(str_data) == 'string'
+
+ @pytest.mark.parametrize('cat_data', [
+ pd.Categorical(['a']),
+ pd.Categorical([1]),
+ pd.Series(pd.Categorical([1])),
+ pd.CategoricalIndex([1]),
+ pd.Categorical([1])])
+ def test_as_json_table_type_categorical_data(self, cat_data):
+ assert as_json_table_type(cat_data) == 'any'
 
  # ------
  # dtypes
  # ------
- def test_as_json_table_type_int_dtypes(self):
- integers = [np.int, np.int16, np.int32, np.int64]
- for t in integers:
- assert as_json_table_type(t) == 'integer'
-
- def test_as_json_table_type_float_dtypes(self):
- floats = [np.float, np.float16, np.float32, np.float64]
- for t in floats:
- assert as_json_table_type(t) == 'number'
-
- def test_as_json_table_type_bool_dtypes(self):
- bools = [bool, np.bool]
- for t in bools:
- assert as_json_table_type(t) == 'boolean'
-
- def test_as_json_table_type_date_dtypes(self):
+ @pytest.mark.parametrize('int_dtype', [
+ np.int, np.int16, np.int32, np.int64])
+ def test_as_json_table_type_int_dtypes(self, int_dtype):
+ assert as_json_table_type(int_dtype) == 'integer'
+
+ @pytest.mark.parametrize('float_dtype', [
+ np.float, np.float16, np.float32, np.float64])
+ def test_as_json_table_type_float_dtypes(self, float_dtype):
+ assert as_json_table_type(float_dtype) == 'number'
+
+ @pytest.mark.parametrize('bool_dtype', [bool, np.bool])
+ def test_as_json_table_type_bool_dtypes(self, bool_dtype):
+ assert as_json_table_type(bool_dtype) == 'boolean'
+
+ @pytest.mark.parametrize('date_dtype', [
+ np.datetime64, np.dtype("<M8[ns]"), PeriodDtype(),
+ DatetimeTZDtype('ns', 'US/Central')])
+ def test_as_json_table_type_date_dtypes(self, date_dtype):
  # TODO: datedate.date? datetime.time?
- dates = [np.datetime64, np.dtype("<M8[ns]"), PeriodDtype(),
- DatetimeTZDtype('ns', 'US/Central')]
- for t in dates:
- assert as_json_table_type(t) == 'datetime'
+ assert as_json_table_type(date_dtype) == 'datetime'
 
- def test_as_json_table_type_timedelta_dtypes(self):
- durations = [np.timedelta64, np.dtype("<m8[ns]")]
-  for t in durations:
-  assert as_json_table_type(t) == 'duration'
+ @pytest.mark.parametrize('td_dtype', [
+ np.timedelta64, np.dtype("<m8[ns]")])
+ def test_as_json_table_type_timedelta_dtypes(self, td_dtype):
+ assert as_json_table_type(td_dtype) == 'duration'
 
- def test_as_json_table_type_string_dtypes(self):
- strings = [object] # TODO
- for t in strings:
- assert as_json_table_type(t) == 'string'
+ @pytest.mark.parametrize('str_dtype', [object]) # TODO
+ def test_as_json_table_type_string_dtypes(self, str_dtype):
+ assert as_json_table_type(str_dtype) == 'string'
 
  def test_as_json_table_type_categorical_dtypes(self):
  # TODO: I think before is_categorical_dtype(Categorical)
@@ -336,61 +336,55 @@ def test_date_format_raises(self):
  self.df.to_json(orient='table', date_format='iso')
  self.df.to_json(orient='table')
 
- def test_convert_pandas_type_to_json_field_int(self):
+ @pytest.mark.parametrize('kind', [pd.Series, pd.Index])
+ def test_convert_pandas_type_to_json_field_int(self, kind):
  data = [1, 2, 3]
- kinds = [pd.Series(data, name='name'), pd.Index(data, name='name')]
- for kind in kinds:
- result = convert_pandas_type_to_json_field(kind)
- expected = {"name": "name", "type": 'integer'}
- assert result == expected
+ result = convert_pandas_type_to_json_field(kind(data, name='name'))
+ expected = {"name": "name", "type": "integer"}
+ assert result == expected
 
- def test_convert_pandas_type_to_json_field_float(self):
+ @pytest.mark.parametrize('kind', [pd.Series, pd.Index])
+ def test_convert_pandas_type_to_json_field_float(self, kind):
  data = [1., 2., 3.]
- kinds = [pd.Series(data, name='name'), pd.Index(data, name='name')]
- for kind in kinds:
- result = convert_pandas_type_to_json_field(kind)
- expected = {"name": "name", "type": 'number'}
- assert result == expected
+ result = convert_pandas_type_to_json_field(kind(data, name='name'))
+ expected = {"name": "name", "type": "number"}
+ assert result == expected
 
- def test_convert_pandas_type_to_json_field_datetime(self):
+ @pytest.mark.parametrize('dt_args,extra_exp', [
+ ({}, {}), ({'utc': True}, {'tz': 'UTC'})])
+ @pytest.mark.parametrize('wrapper', [None, pd.Series])
+ def test_convert_pandas_type_to_json_field_datetime(self, dt_args,
+ extra_exp, wrapper):
  data = [1., 2., 3.]
- kinds = [pd.Series(pd.to_datetime(data), name='values'),
- pd.to_datetime(data)]
- for kind in kinds:
- result = convert_pandas_type_to_json_field(kind)
- expected = {"name": "values", "type": 'datetime'}
- assert result == expected
-
- kinds = [pd.Series(pd.to_datetime(data, utc=True), name='values'),
- pd.to_datetime(data, utc=True)]
- for kind in kinds:
- result = convert_pandas_type_to_json_field(kind)
- expected = {"name": "values", "type": 'datetime', "tz": "UTC"}
- assert result == expected
+ data = pd.to_datetime(data, **dt_args)
+ if wrapper is pd.Series:
+ data = pd.Series(data, name='values')
+ result = convert_pandas_type_to_json_field(data)
+ expected = {"name": "values", "type": 'datetime'}
+ expected.update(extra_exp)
+ assert result == expected
 
+ def test_convert_pandas_type_to_json_period_range(self):
  arr = pd.period_range('2016', freq='A-DEC', periods=4)
  result = convert_pandas_type_to_json_field(arr)
  expected = {"name": "values", "type": 'datetime', "freq": "A-DEC"}
  assert result == expected
 
- def test_convert_pandas_type_to_json_field_categorical(self):
+ @pytest.mark.parametrize('kind', [pd.Categorical, pd.CategoricalIndex])
+ @pytest.mark.parametrize('ordered', [True, False])
+ def test_convert_pandas_type_to_json_field_categorical(self, kind,
+ ordered):
  data = ['a', 'b', 'c']
- ordereds = [True, False]
-
- for ordered in ordereds:
- arr = pd.Series(pd.Categorical(data, ordered=ordered), name='cats')
- result = convert_pandas_type_to_json_field(arr)
- expected = {"name": "cats", "type": "any",
- "constraints": {"enum": data},
- "ordered": ordered}
- assert result == expected
-
- arr = pd.CategoricalIndex(data, ordered=ordered, name='cats')
- result = convert_pandas_type_to_json_field(arr)
- expected = {"name": "cats", "type": "any",
- "constraints": {"enum": data},
- "ordered": ordered}
- assert result == expected
+ if kind is pd.Categorical:
+ arr = pd.Series(kind(data, ordered=ordered), name='cats')
+ elif kind is pd.CategoricalIndex:
+ arr = kind(data, ordered=ordered, name='cats')
+
+ result = convert_pandas_type_to_json_field(arr)
+ expected = {"name": "cats", "type": "any",
+ "constraints": {"enum": data},
+ "ordered": ordered}
+ assert result == expected
 
  @pytest.mark.parametrize("inp,exp", [
  ({'type': 'integer'}, 'int64'),
@@ -440,35 +434,22 @@ def test_categorical(self):
  OrderedDict([('idx', 2), ('values', 'a')])])])
  assert result == expected
 
- def test_set_default_names_unset(self):
- data = pd.Series(1, pd.Index([1]))
- result = set_default_names(data)
- assert result.index.name == 'index'
-
- def test_set_default_names_set(self):
- data = pd.Series(1, pd.Index([1], name='myname'))
- result = set_default_names(data)
- assert result.index.name == 'myname'
-
- def test_set_default_names_mi_unset(self):
- data = pd.Series(
- 1, pd.MultiIndex.from_product([('a', 'b'), ('c', 'd')]))
- result = set_default_names(data)
- assert result.index.names == ['level_0', 'level_1']
-
- def test_set_default_names_mi_set(self):
- data = pd.Series(
- 1, pd.MultiIndex.from_product([('a', 'b'), ('c', 'd')],
- names=['n1', 'n2']))
- result = set_default_names(data)
- assert result.index.names == ['n1', 'n2']
-
- def test_set_default_names_mi_partion(self):
- data = pd.Series(
- 1, pd.MultiIndex.from_product([('a', 'b'), ('c', 'd')],
- names=['n1', None]))
+ @pytest.mark.parametrize('idx,nm,prop', [
+ (pd.Index([1]), 'index', 'name'),
+ (pd.Index([1], name='myname'), 'myname', 'name'),
+ (pd.MultiIndex.from_product([('a', 'b'), ('c', 'd')]),
+ ['level_0', 'level_1'], 'names'),
+ (pd.MultiIndex.from_product([('a', 'b'), ('c', 'd')],
+ names=['n1', 'n2']),
+ ['n1', 'n2'], 'names'),
+ (pd.MultiIndex.from_product([('a', 'b'), ('c', 'd')],
+ names=['n1', None]),
+ ['n1', 'level_1'], 'names')
+ ])
+ def test_set_names_unset(self, idx, nm, prop):
+ data = pd.Series(1, idx)
  result = set_default_names(data)
- assert result.index.names == ['n1', 'level_1']
+ assert getattr(result.index, prop) == nm
 
  def test_timestamp_in_columns(self):
  df = pd.DataFrame([[1, 2]], columns=[pd.Timestamp('2016'),
@@ -478,20 +459,15 @@ def test_timestamp_in_columns(self):
  assert js['schema']['fields'][1]['name'] == 1451606400000
  assert js['schema']['fields'][2]['name'] == 10000
 
- def test_overlapping_names(self):
- cases = [
- pd.Series([1], index=pd.Index([1], name='a'), name='a'),
- pd.DataFrame({"A": [1]}, index=pd.Index([1], name="A")),
- pd.DataFrame({"A": [1]}, index=pd.MultiIndex.from_arrays([
- ['a'], [1]
- ], names=["A", "a"])),
- ]
-
- for data in cases:
- with pytest.raises(ValueError) as excinfo:
- data.to_json(orient='table')
-
- assert 'Overlapping' in str(excinfo.value)
+ @pytest.mark.parametrize('case', [
+ pd.Series([1], index=pd.Index([1], name='a'), name='a'),
+ pd.DataFrame({"A": [1]}, index=pd.Index([1], name="A")),
+ pd.DataFrame({"A": [1]}, index=pd.MultiIndex.from_arrays([
+ ['a'], [1]], names=["A", "a"]))
+ ])
+ def test_overlapping_names(self, case):
+ with tm.assert_raises_regex(ValueError, 'Overlapping'):
+ case.to_json(orient='table')
 
  def test_mi_falsey_name(self):
  # GH 16203