From 16d02621212cd7d83f89533265fc039df1d7be10 Mon Sep 17 00:00:00 2001
From: Will Ayd <william.ayd@icloud.com>
Date: Sun, 7 Jan 2018 20:01:10 -0800
Subject: [PATCH] Parametrized test_json_table_schema module

---
 .../tests/io/json/test_json_table_schema.py   | 274 ++++++++----------
 1 file changed, 125 insertions(+), 149 deletions(-)

diff --git a/pandas/tests/io/json/test_json_table_schema.py b/pandas/tests/io/json/test_json_table_schema.py
index 76748f30e639b..fc3790287d7da 100644
--- a/pandas/tests/io/json/test_json_table_schema.py
+++ b/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