"""Tests for Table Schema integration."""
from collections import OrderedDict
import json
import sys

import numpy as np
import pytest

from pandas.core.dtypes.dtypes import CategoricalDtype, DatetimeTZDtype, PeriodDtype

import pandas as pd
from pandas import DataFrame
import pandas._testing as tm

from pandas.io.json._table_schema import (
    as_json_table_type,
    build_table_schema,
    convert_json_field_to_pandas_type,
    convert_pandas_type_to_json_field,
    set_default_names,
)


class TestBuildSchema:
    def setup_method(self, method):
        self.df = DataFrame(
            {
                "A": [1, 2, 3, 4],
                "B": ["a", "b", "c", "c"],
                "C": pd.date_range("2016-01-01", freq="d", periods=4),
                "D": pd.timedelta_range("1H", periods=4, freq="T"),
            },
            index=pd.Index(range(4), name="idx"),
        )

    def test_build_table_schema(self):
        result = build_table_schema(self.df, version=False)
        expected = {
            "fields": [
                {"name": "idx", "type": "integer"},
                {"name": "A", "type": "integer"},
                {"name": "B", "type": "string"},
                {"name": "C", "type": "datetime"},
                {"name": "D", "type": "duration"},
            ],
            "primaryKey": ["idx"],
        }
        assert result == expected
        result = build_table_schema(self.df)
        assert "pandas_version" in result

    def test_series(self):
        s = pd.Series([1, 2, 3], name="foo")
        result = build_table_schema(s, version=False)
        expected = {
            "fields": [
                {"name": "index", "type": "integer"},
                {"name": "foo", "type": "integer"},
            ],
            "primaryKey": ["index"],
        }
        assert result == expected
        result = build_table_schema(s)
        assert "pandas_version" in result

    def test_series_unnamed(self):
        result = build_table_schema(pd.Series([1, 2, 3]), version=False)
        expected = {
            "fields": [
                {"name": "index", "type": "integer"},
                {"name": "values", "type": "integer"},
            ],
            "primaryKey": ["index"],
        }
        assert result == expected

    def test_multiindex(self):
        df = self.df.copy()
        idx = pd.MultiIndex.from_product([("a", "b"), (1, 2)])
        df.index = idx

        result = build_table_schema(df, version=False)
        expected = {
            "fields": [
                {"name": "level_0", "type": "string"},
                {"name": "level_1", "type": "integer"},
                {"name": "A", "type": "integer"},
                {"name": "B", "type": "string"},
                {"name": "C", "type": "datetime"},
                {"name": "D", "type": "duration"},
            ],
            "primaryKey": ["level_0", "level_1"],
        }
        assert result == expected

        df.index.names = ["idx0", None]
        expected["fields"][0]["name"] = "idx0"
        expected["primaryKey"] = ["idx0", "level_1"]
        result = build_table_schema(df, version=False)
        assert result == expected


class TestTableSchemaType:
    @pytest.mark.parametrize("int_type", [int, np.int16, np.int32, np.int64])
    def test_as_json_table_type_int_data(self, int_type):
        int_data = [1, 2, 3]
        assert as_json_table_type(np.array(int_data, dtype=int_type).dtype) == "integer"

    @pytest.mark.parametrize("float_type", [float, np.float16, np.float32, np.float64])
    def test_as_json_table_type_float_data(self, float_type):
        float_data = [1.0, 2.0, 3.0]
        assert (
            as_json_table_type(np.array(float_data, dtype=float_type).dtype) == "number"
        )

    @pytest.mark.parametrize("bool_type", [bool, np.bool_])
    def test_as_json_table_type_bool_data(self, bool_type):
        bool_data = [True, False]
        assert (
            as_json_table_type(np.array(bool_data, dtype=bool_type).dtype) == "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.dtype) == "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.dtype) == "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.dtype) == "any"

    # ------
    # dtypes
    # ------
    @pytest.mark.parametrize("int_dtype", [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", [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("D"),
            DatetimeTZDtype("ns", "US/Central"),
        ],
    )
    def test_as_json_table_type_date_dtypes(self, date_dtype):
        # TODO: datedate.date? datetime.time?
        assert as_json_table_type(date_dtype) == "datetime"

    @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"

    @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)
        # returned True, but now it's False. Figure out why or
        # if it matters
        assert as_json_table_type(pd.Categorical(["a"]).dtype) == "any"
        assert as_json_table_type(CategoricalDtype()) == "any"


class TestTableOrient:
    def setup_method(self, method):
        self.df = DataFrame(
            {
                "A": [1, 2, 3, 4],
                "B": ["a", "b", "c", "c"],
                "C": pd.date_range("2016-01-01", freq="d", periods=4),
                "D": pd.timedelta_range("1H", periods=4, freq="T"),
                "E": pd.Series(pd.Categorical(["a", "b", "c", "c"])),
                "F": pd.Series(pd.Categorical(["a", "b", "c", "c"], ordered=True)),
                "G": [1.0, 2.0, 3, 4.0],
                "H": pd.date_range("2016-01-01", freq="d", periods=4, tz="US/Central"),
            },
            index=pd.Index(range(4), name="idx"),
        )

    def test_build_series(self):
        s = pd.Series([1, 2], name="a")
        s.index.name = "id"
        result = s.to_json(orient="table", date_format="iso")
        result = json.loads(result, object_pairs_hook=OrderedDict)

        assert "pandas_version" in result["schema"]
        result["schema"].pop("pandas_version")

        fields = [{"name": "id", "type": "integer"}, {"name": "a", "type": "integer"}]

        schema = {"fields": fields, "primaryKey": ["id"]}

        expected = OrderedDict(
            [
                ("schema", schema),
                (
                    "data",
                    [
                        OrderedDict([("id", 0), ("a", 1)]),
                        OrderedDict([("id", 1), ("a", 2)]),
                    ],
                ),
            ]
        )

        assert result == expected

    def test_read_json_from_to_json_results(self):
        # GH32383
        df = DataFrame(
            {
                "_id": {"row_0": 0},
                "category": {"row_0": "Goods"},
                "recommender_id": {"row_0": 3},
                "recommender_name_jp": {"row_0": "浦田"},
                "recommender_name_en": {"row_0": "Urata"},
                "name_jp": {"row_0": "博多人形（松尾吉将まつお よしまさ）"},
                "name_en": {"row_0": "Hakata Dolls Matsuo"},
            }
        )
        result1 = pd.read_json(df.to_json())
        result2 = DataFrame.from_dict(json.loads(df.to_json()))
        tm.assert_frame_equal(result1, df)
        tm.assert_frame_equal(result2, df)

    @pytest.mark.filterwarnings(
        "ignore:an integer is required (got type float)*:DeprecationWarning"
    )
    def test_to_json(self):
        df = self.df.copy()
        df.index.name = "idx"
        result = df.to_json(orient="table", date_format="iso")
        result = json.loads(result, object_pairs_hook=OrderedDict)

        assert "pandas_version" in result["schema"]
        result["schema"].pop("pandas_version")

        fields = [
            {"name": "idx", "type": "integer"},
            {"name": "A", "type": "integer"},
            {"name": "B", "type": "string"},
            {"name": "C", "type": "datetime"},
            {"name": "D", "type": "duration"},
            {
                "constraints": {"enum": ["a", "b", "c"]},
                "name": "E",
                "ordered": False,
                "type": "any",
            },
            {
                "constraints": {"enum": ["a", "b", "c"]},
                "name": "F",
                "ordered": True,
                "type": "any",
            },
            {"name": "G", "type": "number"},
            {"name": "H", "type": "datetime", "tz": "US/Central"},
        ]

        schema = {"fields": fields, "primaryKey": ["idx"]}
        data = [
            OrderedDict(
                [
                    ("idx", 0),
                    ("A", 1),
                    ("B", "a"),
                    ("C", "2016-01-01T00:00:00.000Z"),
                    ("D", "P0DT1H0M0S"),
                    ("E", "a"),
                    ("F", "a"),
                    ("G", 1.0),
                    ("H", "2016-01-01T06:00:00.000Z"),
                ]
            ),
            OrderedDict(
                [
                    ("idx", 1),
                    ("A", 2),
                    ("B", "b"),
                    ("C", "2016-01-02T00:00:00.000Z"),
                    ("D", "P0DT1H1M0S"),
                    ("E", "b"),
                    ("F", "b"),
                    ("G", 2.0),
                    ("H", "2016-01-02T06:00:00.000Z"),
                ]
            ),
            OrderedDict(
                [
                    ("idx", 2),
                    ("A", 3),
                    ("B", "c"),
                    ("C", "2016-01-03T00:00:00.000Z"),
                    ("D", "P0DT1H2M0S"),
                    ("E", "c"),
                    ("F", "c"),
                    ("G", 3.0),
                    ("H", "2016-01-03T06:00:00.000Z"),
                ]
            ),
            OrderedDict(
                [
                    ("idx", 3),
                    ("A", 4),
                    ("B", "c"),
                    ("C", "2016-01-04T00:00:00.000Z"),
                    ("D", "P0DT1H3M0S"),
                    ("E", "c"),
                    ("F", "c"),
                    ("G", 4.0),
                    ("H", "2016-01-04T06:00:00.000Z"),
                ]
            ),
        ]
        expected = OrderedDict([("schema", schema), ("data", data)])

        assert result == expected

    def test_to_json_float_index(self):
        data = pd.Series(1, index=[1.0, 2.0])
        result = data.to_json(orient="table", date_format="iso")
        result = json.loads(result, object_pairs_hook=OrderedDict)
        result["schema"].pop("pandas_version")

        expected = OrderedDict(
            [
                (
                    "schema",
                    {
                        "fields": [
                            {"name": "index", "type": "number"},
                            {"name": "values", "type": "integer"},
                        ],
                        "primaryKey": ["index"],
                    },
                ),
                (
                    "data",
                    [
                        OrderedDict([("index", 1.0), ("values", 1)]),
                        OrderedDict([("index", 2.0), ("values", 1)]),
                    ],
                ),
            ]
        )

        assert result == expected

    def test_to_json_period_index(self):
        idx = pd.period_range("2016", freq="Q-JAN", periods=2)
        data = pd.Series(1, idx)
        result = data.to_json(orient="table", date_format="iso")
        result = json.loads(result, object_pairs_hook=OrderedDict)
        result["schema"].pop("pandas_version")

        fields = [
            {"freq": "Q-JAN", "name": "index", "type": "datetime"},
            {"name": "values", "type": "integer"},
        ]

        schema = {"fields": fields, "primaryKey": ["index"]}
        data = [
            OrderedDict([("index", "2015-11-01T00:00:00.000Z"), ("values", 1)]),
            OrderedDict([("index", "2016-02-01T00:00:00.000Z"), ("values", 1)]),
        ]
        expected = OrderedDict([("schema", schema), ("data", data)])

        assert result == expected

    def test_to_json_categorical_index(self):
        data = pd.Series(1, pd.CategoricalIndex(["a", "b"]))
        result = data.to_json(orient="table", date_format="iso")
        result = json.loads(result, object_pairs_hook=OrderedDict)
        result["schema"].pop("pandas_version")

        expected = OrderedDict(
            [
                (
                    "schema",
                    {
                        "fields": [
                            {
                                "name": "index",
                                "type": "any",
                                "constraints": {"enum": ["a", "b"]},
                                "ordered": False,
                            },
                            {"name": "values", "type": "integer"},
                        ],
                        "primaryKey": ["index"],
                    },
                ),
                (
                    "data",
                    [
                        OrderedDict([("index", "a"), ("values", 1)]),
                        OrderedDict([("index", "b"), ("values", 1)]),
                    ],
                ),
            ]
        )

        assert result == expected

    @pytest.mark.filterwarnings(
        "ignore:an integer is required (got type float)*:DeprecationWarning"
    )
    def test_date_format_raises(self):
        with pytest.raises(ValueError):
            self.df.to_json(orient="table", date_format="epoch")

        # others work
        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, index_or_series):
        kind = index_or_series
        data = [1, 2, 3]
        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, index_or_series):
        kind = index_or_series
        data = [1.0, 2.0, 3.0]
        result = convert_pandas_type_to_json_field(kind(data, name="name"))
        expected = {"name": "name", "type": "number"}
        assert result == expected

    @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 = [1.0, 2.0, 3.0]
        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

    @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"]
        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"),
            ({"type": "number"}, "float64"),
            ({"type": "boolean"}, "bool"),
            ({"type": "duration"}, "timedelta64"),
            ({"type": "datetime"}, "datetime64[ns]"),
            ({"type": "datetime", "tz": "US/Hawaii"}, "datetime64[ns, US/Hawaii]"),
            ({"type": "any"}, "object"),
            (
                {
                    "type": "any",
                    "constraints": {"enum": ["a", "b", "c"]},
                    "ordered": False,
                },
                CategoricalDtype(categories=["a", "b", "c"], ordered=False),
            ),
            (
                {
                    "type": "any",
                    "constraints": {"enum": ["a", "b", "c"]},
                    "ordered": True,
                },
                CategoricalDtype(categories=["a", "b", "c"], ordered=True),
            ),
            ({"type": "string"}, "object"),
        ],
    )
    def test_convert_json_field_to_pandas_type(self, inp, exp):
        field = {"name": "foo"}
        field.update(inp)
        assert convert_json_field_to_pandas_type(field) == exp

    @pytest.mark.parametrize("inp", ["geopoint", "geojson", "fake_type"])
    def test_convert_json_field_to_pandas_type_raises(self, inp):
        field = {"type": inp}
        with pytest.raises(
            ValueError, match=f"Unsupported or invalid field type: {inp}"
        ):
            convert_json_field_to_pandas_type(field)

    def test_categorical(self):
        s = pd.Series(pd.Categorical(["a", "b", "a"]))
        s.index.name = "idx"
        result = s.to_json(orient="table", date_format="iso")
        result = json.loads(result, object_pairs_hook=OrderedDict)
        result["schema"].pop("pandas_version")

        fields = [
            {"name": "idx", "type": "integer"},
            {
                "constraints": {"enum": ["a", "b"]},
                "name": "values",
                "ordered": False,
                "type": "any",
            },
        ]

        expected = OrderedDict(
            [
                ("schema", {"fields": fields, "primaryKey": ["idx"]}),
                (
                    "data",
                    [
                        OrderedDict([("idx", 0), ("values", "a")]),
                        OrderedDict([("idx", 1), ("values", "b")]),
                        OrderedDict([("idx", 2), ("values", "a")]),
                    ],
                ),
            ]
        )

        assert result == expected

    @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 getattr(result.index, prop) == nm

    @pytest.mark.parametrize(
        "idx",
        [
            pd.Index([], name="index"),
            pd.MultiIndex.from_arrays([["foo"], ["bar"]], names=("level_0", "level_1")),
            pd.MultiIndex.from_arrays([["foo"], ["bar"]], names=("foo", "level_1")),
        ],
    )
    def test_warns_non_roundtrippable_names(self, idx):
        # GH 19130
        df = DataFrame(index=idx)
        df.index.name = "index"
        with tm.assert_produces_warning():
            set_default_names(df)

    def test_timestamp_in_columns(self):
        df = DataFrame(
            [[1, 2]], columns=[pd.Timestamp("2016"), pd.Timedelta(10, unit="s")]
        )
        result = df.to_json(orient="table")
        js = json.loads(result)
        assert js["schema"]["fields"][1]["name"] == "2016-01-01T00:00:00.000Z"
        assert js["schema"]["fields"][2]["name"] == "P0DT0H0M10S"

    @pytest.mark.parametrize(
        "case",
        [
            pd.Series([1], index=pd.Index([1], name="a"), name="a"),
            DataFrame({"A": [1]}, index=pd.Index([1], name="A")),
            DataFrame(
                {"A": [1]},
                index=pd.MultiIndex.from_arrays([["a"], [1]], names=["A", "a"]),
            ),
        ],
    )
    def test_overlapping_names(self, case):
        with pytest.raises(ValueError, match="Overlapping"):
            case.to_json(orient="table")

    def test_mi_falsey_name(self):
        # GH 16203
        df = DataFrame(
            np.random.randn(4, 4),
            index=pd.MultiIndex.from_product([("A", "B"), ("a", "b")]),
        )
        result = [x["name"] for x in build_table_schema(df)["fields"]]
        assert result == ["level_0", "level_1", 0, 1, 2, 3]


class TestTableOrientReader:
    @pytest.mark.parametrize(
        "index_nm",
        [None, "idx", pytest.param("index", marks=pytest.mark.xfail), "level_0"],
    )
    @pytest.mark.parametrize(
        "vals",
        [
            {"ints": [1, 2, 3, 4]},
            {"objects": ["a", "b", "c", "d"]},
            {"objects": ["1", "2", "3", "4"]},
            {"date_ranges": pd.date_range("2016-01-01", freq="d", periods=4)},
            {"categoricals": pd.Series(pd.Categorical(["a", "b", "c", "c"]))},
            {
                "ordered_cats": pd.Series(
                    pd.Categorical(["a", "b", "c", "c"], ordered=True)
                )
            },
            {"floats": [1.0, 2.0, 3.0, 4.0]},
            {"floats": [1.1, 2.2, 3.3, 4.4]},
            {"bools": [True, False, False, True]},
            {
                "timezones": pd.date_range(
                    "2016-01-01", freq="d", periods=4, tz="US/Central"
                )  # added in # GH 35973
            },
        ],
    )
    @pytest.mark.skipif(sys.version_info[:3] == (3, 7, 0), reason="GH-35309")
    def test_read_json_table_orient(self, index_nm, vals, recwarn):
        df = DataFrame(vals, index=pd.Index(range(4), name=index_nm))
        out = df.to_json(orient="table")
        result = pd.read_json(out, orient="table")
        tm.assert_frame_equal(df, result)

    @pytest.mark.parametrize("index_nm", [None, "idx", "index"])
    @pytest.mark.parametrize(
        "vals",
        [{"timedeltas": pd.timedelta_range("1H", periods=4, freq="T")}],
    )
    def test_read_json_table_orient_raises(self, index_nm, vals, recwarn):
        df = DataFrame(vals, index=pd.Index(range(4), name=index_nm))
        out = df.to_json(orient="table")
        with pytest.raises(NotImplementedError, match="can not yet read "):
            pd.read_json(out, orient="table")

    @pytest.mark.parametrize(
        "idx",
        [
            pd.Index(range(4)),
            pd.Index(
                pd.date_range(
                    "2020-08-30",
                    freq="d",
                    periods=4,
                ),
                freq=None,
            ),
            pd.Index(
                pd.date_range("2020-08-30", freq="d", periods=4, tz="US/Central"),
                freq=None,
            ),
            pd.MultiIndex.from_product(
                [
                    pd.date_range("2020-08-30", freq="d", periods=2, tz="US/Central"),
                    ["x", "y"],
                ],
            ),
        ],
    )
    @pytest.mark.parametrize(
        "vals",
        [
            {"floats": [1.1, 2.2, 3.3, 4.4]},
            {"dates": pd.date_range("2020-08-30", freq="d", periods=4)},
            {
                "timezones": pd.date_range(
                    "2020-08-30", freq="d", periods=4, tz="Europe/London"
                )
            },
        ],
    )
    @pytest.mark.skipif(sys.version_info[:3] == (3, 7, 0), reason="GH-35309")
    def test_read_json_table_timezones_orient(self, idx, vals, recwarn):
        # GH 35973
        df = DataFrame(vals, index=idx)
        out = df.to_json(orient="table")
        result = pd.read_json(out, orient="table")
        tm.assert_frame_equal(df, result)

    def test_comprehensive(self):
        df = DataFrame(
            {
                "A": [1, 2, 3, 4],
                "B": ["a", "b", "c", "c"],
                "C": pd.date_range("2016-01-01", freq="d", periods=4),
                # 'D': pd.timedelta_range('1H', periods=4, freq='T'),
                "E": pd.Series(pd.Categorical(["a", "b", "c", "c"])),
                "F": pd.Series(pd.Categorical(["a", "b", "c", "c"], ordered=True)),
                "G": [1.1, 2.2, 3.3, 4.4],
                # 'H': pd.date_range('2016-01-01', freq='d', periods=4,
                #                   tz='US/Central'),
                "I": [True, False, False, True],
            },
            index=pd.Index(range(4), name="idx"),
        )

        out = df.to_json(orient="table")
        result = pd.read_json(out, orient="table")
        tm.assert_frame_equal(df, result)

    @pytest.mark.parametrize(
        "index_names",
        [[None, None], ["foo", "bar"], ["foo", None], [None, "foo"], ["index", "foo"]],
    )
    def test_multiindex(self, index_names):
        # GH 18912
        df = DataFrame(
            [["Arr", "alpha", [1, 2, 3, 4]], ["Bee", "Beta", [10, 20, 30, 40]]],
            index=[["A", "B"], ["Null", "Eins"]],
            columns=["Aussprache", "Griechisch", "Args"],
        )
        df.index.names = index_names
        out = df.to_json(orient="table")
        result = pd.read_json(out, orient="table")
        tm.assert_frame_equal(df, result)

    def test_empty_frame_roundtrip(self):
        # GH 21287
        df = DataFrame(columns=["a", "b", "c"])
        expected = df.copy()
        out = df.to_json(orient="table")
        result = pd.read_json(out, orient="table")
        tm.assert_frame_equal(expected, result)
