perspective-python 4.4.1

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#  ┃ Copyright (c) 2017, the Perspective Authors.                              ┃
#  ┃ ╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌ ┃
#  ┃ This file is part of the Perspective library, distributed under the terms ┃
#  ┃ of the [Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0). ┃
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import pandas as pd
import numpy as np
from perspective import PerspectiveError
from datetime import date, datetime
from pytest import approx, mark, raises

import perspective as psp

client = psp.Server().new_local_client()
Table = client.table


def date_timestamp(date):
    return int(datetime.combine(date, datetime.min.time()).timestamp()) * 1000


def compare_delta(received, expected):
    """Compare an arrow-serialized row delta by constructing a Table."""
    tbl = Table(received)
    assert tbl.view().to_columns() == expected


class TestView(object):
    def test_view_zero(self):
        data = [{"a": 1, "b": 2}, {"a": 3, "b": 4}]
        tbl = Table(data)
        view = tbl.view()
        dimms = view.dimensions()
        assert dimms["num_view_rows"] == 2
        assert dimms["num_view_columns"] == 2
        assert view.schema() == {"a": "integer", "b": "integer"}
        assert view.to_records() == data

    def test_view_one(self):
        data = [{"a": 1, "b": 2}, {"a": 3, "b": 4}]
        tbl = Table(data)
        view = tbl.view(group_by=["a"])
        dimms = view.dimensions()
        assert dimms["num_view_rows"] == 3
        assert dimms["num_view_columns"] == 2
        assert view.schema() == {"a": "integer", "b": "integer"}
        assert view.to_records() == [
            {"__ROW_PATH__": [], "a": 4, "b": 6},
            {"__ROW_PATH__": [1], "a": 1, "b": 2},
            {"__ROW_PATH__": [3], "a": 3, "b": 4},
        ]

    def test_view_two(self):
        data = [{"a": 1, "b": 2}, {"a": 3, "b": 4}]
        tbl = Table(data)
        view = tbl.view(group_by=["a"], split_by=["b"])
        dimms = view.dimensions()
        assert dimms["num_view_rows"] == 3
        assert dimms["num_view_columns"] == 4
        assert view.schema() == {"a": "integer", "b": "integer"}
        assert view.to_records() == [
            {"2|a": 1, "2|b": 2, "4|a": 3, "4|b": 4, "__ROW_PATH__": []},
            {"2|a": 1, "2|b": 2, "4|a": None, "4|b": None, "__ROW_PATH__": [1]},
            {"2|a": None, "2|b": None, "4|a": 3, "4|b": 4, "__ROW_PATH__": [3]},
        ]

    def test_view_two_column_only(self):
        data = [{"a": 1, "b": 2}, {"a": 3, "b": 4}]
        tbl = Table(data)
        view = tbl.view(split_by=["b"])
        dimms = view.dimensions()
        assert dimms["num_view_rows"] == 2
        assert dimms["num_view_columns"] == 4
        assert view.schema() == {"a": "integer", "b": "integer"}
        assert view.to_records() == [
            {"2|a": 1, "2|b": 2, "4|a": None, "4|b": None},
            {"2|a": None, "2|b": None, "4|a": 3, "4|b": 4},
        ]

    # column path

    def test_view_column_path_zero(self):
        data = {"a": [1, 2, 3], "b": [1.5, 2.5, 3.5]}
        tbl = Table(data)
        view = tbl.view()
        paths = view.column_paths()
        assert paths == ["a", "b"]

    def test_view_column_path_zero_schema(self):
        data = {"a": "integer", "b": "float"}
        tbl = Table(data)
        view = tbl.view()
        paths = view.column_paths()
        assert paths == ["a", "b"]

    def test_view_column_path_zero_hidden(self):
        data = {"a": [1, 2, 3], "b": [1.5, 2.5, 3.5]}
        tbl = Table(data)
        view = tbl.view(columns=["b"])
        paths = view.column_paths()
        assert paths == ["b"]

    def test_view_column_path_zero_respects_order(self):
        data = {"a": [1, 2, 3], "b": [1.5, 2.5, 3.5]}
        tbl = Table(data)
        view = tbl.view(columns=["b", "a"])
        paths = view.column_paths()
        assert paths == ["b", "a"]

    def test_view_column_path_one(self):
        data = {"a": [1, 2, 3], "b": [1.5, 2.5, 3.5]}
        tbl = Table(data)
        view = tbl.view(group_by=["a"])
        paths = view.column_paths()
        assert paths == ["a", "b"]

    def test_view_column_path_one_numeric_names(self):
        data = {"a": [1, 2, 3], "b": [1.5, 2.5, 3.5], "1234": [5, 6, 7]}
        tbl = Table(data)
        view = tbl.view(group_by=["a"], columns=["b", "1234", "a"])
        paths = view.column_paths()
        assert paths == ["b", "1234", "a"]

    def test_view_column_path_two(self):
        data = {"a": [1, 2, 3], "b": [1.5, 2.5, 3.5]}
        tbl = Table(data)
        view = tbl.view(group_by=["a"], split_by=["b"])
        paths = view.column_paths()
        assert paths == [
            "1.5|a",
            "1.5|b",
            "2.5|a",
            "2.5|b",
            "3.5|a",
            "3.5|b",
        ]

    def test_view_column_path_two_column_only(self):
        data = {"a": [1, 2, 3], "b": [1.5, 2.5, 3.5]}
        tbl = Table(data)
        view = tbl.view(split_by=["b"])
        paths = view.column_paths()
        assert paths == ["1.5|a", "1.5|b", "2.5|a", "2.5|b", "3.5|a", "3.5|b"]

    def test_view_column_path_hidden_sort(self):
        data = {"a": [1, 2, 3], "b": [1.5, 2.5, 3.5], "c": [3, 2, 1]}
        tbl = Table(data)
        view = tbl.view(columns=["a", "b"], sort=[["c", "desc"]])
        paths = view.column_paths()
        assert paths == ["a", "b"]

    def test_view_column_path_hidden_col_sort(self):
        data = {"a": [1, 2, 3], "b": [1.5, 2.5, 3.5], "c": [3, 2, 1]}
        tbl = Table(data)
        view = tbl.view(split_by=["a"], columns=["a", "b"], sort=[["c", "col desc"]])
        paths = view.column_paths()
        assert paths == ["1|a", "1|b", "2|a", "2|b", "3|a", "3|b"]

    def test_view_column_path_pivot_by_bool(self):
        data = {"a": [1, 2, 3], "b": [True, False, True], "c": [3, 2, 1]}
        tbl = Table(data)
        view = tbl.view(split_by=["b"], columns=["a", "b", "c"])
        paths = view.column_paths()
        assert paths == ["false|a", "false|b", "false|c", "true|a", "true|b", "true|c"]

    # schema correctness

    def test_string_view_schema(self):
        data = [{"a": 1, "b": 2}, {"a": 3, "b": 4}]
        tbl = Table(data)
        view = tbl.view()
        assert view.schema() == {"a": "integer", "b": "integer"}

    def test_zero_view_schema(self):
        data = [{"a": 1, "b": 2}, {"a": 3, "b": 4}]
        tbl = Table(data)
        view = tbl.view()
        assert view.schema() == {"a": "integer", "b": "integer"}

    def test_one_view_schema(self):
        data = [{"a": "abc", "b": 2}, {"a": "abc", "b": 4}]
        tbl = Table(data)
        view = tbl.view(group_by=["a"], aggregates={"a": "distinct count"})
        assert view.schema() == {"a": "integer", "b": "integer"}

    def test_two_view_schema(self):
        data = [{"a": "abc", "b": "def"}, {"a": "abc", "b": "def"}]
        tbl = Table(data)
        view = tbl.view(
            group_by=["a"], split_by=["b"], aggregates={"a": "count", "b": "count"}
        )
        assert view.schema() == {"a": "integer", "b": "integer"}

    # aggregates and column specification

    def test_view_no_columns(self):
        data = [{"a": 1, "b": 2}, {"a": 3, "b": 4}]
        tbl = Table(data)
        view = tbl.view(columns=[])
        assert view.dimensions()["num_view_columns"] == 0
        assert view.to_records() == []

    def test_view_no_columns_pivoted(self):
        data = [{"a": 1, "b": 2}, {"a": 3, "b": 4}]
        tbl = Table(data)
        view = tbl.view(group_by=["a"], columns=[])
        assert view.dimensions()["num_view_columns"] == 0
        assert view.to_records() == [
            {"__ROW_PATH__": []},
            {"__ROW_PATH__": [1]},
            {"__ROW_PATH__": [3]},
        ]

    def test_view_specific_column(self):
        data = [{"a": 1, "b": 2, "c": 3, "d": 4}, {"a": 3, "b": 4, "c": 5, "d": 6}]
        tbl = Table(data)
        view = tbl.view(columns=["a", "c", "d"])
        assert view.dimensions()["num_view_columns"] == 3
        assert view.to_records() == [{"a": 1, "c": 3, "d": 4}, {"a": 3, "c": 5, "d": 6}]

    def test_view_column_order(self):
        data = [{"a": 1, "b": 2}, {"a": 3, "b": 4}]
        tbl = Table(data)
        view = tbl.view(columns=["b", "a"])
        assert view.to_records() == [{"b": 2, "a": 1}, {"b": 4, "a": 3}]

    def test_view_dataframe_column_order(self):
        table = Table(
            pd.DataFrame(
                {
                    "0.1": [5, 6, 7, 8],
                    "-0.05": [5, 6, 7, 8],
                    "0.0": [1, 2, 3, 4],
                    "-0.1": [1, 2, 3, 4],
                    "str": ["a", "b", "c", "d"],
                }
            )
        )
        view = table.view(columns=["-0.1", "-0.05", "0.0", "0.1"], group_by=["str"])
        assert view.column_paths() == ["-0.1", "-0.05", "0.0", "0.1"]

    def test_view_aggregate_order_with_columns(self):
        """If `columns` is provided, order is always guaranteed."""
        data = [{"a": 1, "b": 2, "c": 3, "d": 4}, {"a": 3, "b": 4, "c": 5, "d": 6}]
        tbl = Table(data)
        view = tbl.view(
            group_by=["a"],
            columns=["a", "b", "c", "d"],
            aggregates={"d": "avg", "c": "avg", "b": "last", "a": "last"},
        )

        order = ["a", "b", "c", "d"]
        assert view.column_paths() == order

    def test_view_df_aggregate_order_with_columns(self):
        """If `columns` is provided, order is always guaranteed."""
        data = pd.DataFrame(
            {"a": [1, 2, 3], "b": [2, 3, 4], "c": [3, 4, 5], "d": [4, 5, 6]},
            columns=["d", "a", "c", "b"],
        )
        tbl = Table(data)
        view = tbl.view(
            group_by=["a"],
            aggregates={"d": "avg", "c": "avg", "b": "last", "a": "last"},
        )

        order = ["index", "d", "a", "c", "b"]
        assert view.column_paths() == order

    def test_view_aggregates_with_no_columns(self):
        data = [{"a": 1, "b": 2, "c": 3, "d": 4}, {"a": 3, "b": 4, "c": 5, "d": 6}]
        tbl = Table(data)
        view = tbl.view(
            group_by=["a"], aggregates={"c": "avg", "a": "last"}, columns=[]
        )
        assert view.column_paths() == []
        assert view.to_records() == [
            {"__ROW_PATH__": []},
            {"__ROW_PATH__": [1]},
            {"__ROW_PATH__": [3]},
        ]

    def test_view_aggregates_default_column_order(self):
        """Order of columns are entirely determined by the `columns` kwarg. If
        it is not provided, order of columns is default based on the order
        of table.columns()."""
        data = [{"a": 1, "b": 2, "c": 3, "d": 4}, {"a": 3, "b": 4, "c": 5, "d": 6}]
        tbl = Table(data)
        cols = tbl.columns()
        view = tbl.view(group_by=["a"], aggregates={"c": "avg", "a": "last"})

        assert view.column_paths() == cols

        # check that default aggregates have been applied
        result = view.to_columns()
        assert result["b"] == [6, 2, 4]
        assert result["d"] == [10, 4, 6]

        # and that specified aggregates are applied
        assert result["a"] == [3, 1, 3]
        assert result["c"] == [4, 3, 5]

    # row and split by paths
    def test_view_group_by_datetime_row_paths_are_same_as_data(self, util):
        """Tests row paths for datetimes in UTC. Timezone-related tests are
        in the `test_table_datetime` file."""
        data = {"a": [datetime(2019, 7, 11, 12, 30)], "b": [1]}
        tbl = Table(data)
        view = tbl.view(group_by=["a"])
        data = view.to_columns()

        for rp in data["__ROW_PATH__"]:
            if len(rp) > 0:
                assert rp[0] == util.to_timestamp(datetime(2019, 7, 11, 12, 30))

        assert tbl.view().to_columns() == {
            "a": [util.to_timestamp(datetime(2019, 7, 11, 12, 30))],
            "b": [1],
        }

    def test_view_split_by_datetime_names_utc(self):
        """Tests column paths for datetimes in UTC. Timezone-related tests are
        in the `test_table_datetime` file."""
        data = {"a": [datetime(2019, 7, 11, 12, 30)], "b": [1]}
        tbl = Table(data)
        view = tbl.view(split_by=["a"])
        cols = view.column_paths()
        assert cols == ["2019-07-11 12:30:00.000|a", "2019-07-11 12:30:00.000|b"]

    # TODO: time slightly off! thinks its NYE 1969
    @mark.skip  # We do not support python datetimes.
    def test_view_split_by_datetime_names_min(self):
        """Tests column paths for datetimes in UTC. Timezone-related tests are
        in the `test_table_datetime` file."""
        import os

        os.environ["TZ"] = "UTC"
        data = {"a": [datetime.min], "b": [1]}
        tbl = Table({"a": "datetime", "b": "integer"})
        tbl.update(data)
        view = tbl.view(split_by=["a"])
        cols = view.column_paths()
        assert cols == ["1970-01-01 00:00:00.000|a", "1970-01-01 00:00:00.000|b"]

    @mark.skip  # We dont support python datetimes.
    def test_view_split_by_datetime_names_max(self):
        """Tests column paths for datetimes in UTC. Timezone-related tests are
        in the `test_table_datetime` file."""
        data = {"a": [datetime.max], "b": [1]}
        tbl = Table(data)
        view = tbl.view(split_by=["a"])
        cols = view.column_paths()
        assert cols == ["10000-01-01 00:00:00.000|a", "10000-01-01 00:00:00.000|b"]

    # aggregate

    def test_view_aggregate_int(self):
        data = [{"a": 1, "b": 2}, {"a": 3, "b": 4}]
        tbl = Table(data)
        view = tbl.view(aggregates={"a": "avg"}, group_by=["a"])
        assert view.to_records() == [
            {"__ROW_PATH__": [], "a": 2.0, "b": 6},
            {"__ROW_PATH__": [1], "a": 1.0, "b": 2},
            {"__ROW_PATH__": [3], "a": 3.0, "b": 4},
        ]

    def test_view_aggregate_str(self):
        data = [{"a": "abc", "b": 2}, {"a": "def", "b": 4}]
        tbl = Table(data)
        view = tbl.view(aggregates={"a": "count"}, group_by=["a"])
        assert view.to_records() == [
            {"__ROW_PATH__": [], "a": 2, "b": 6},
            {"__ROW_PATH__": ["abc"], "a": 1, "b": 2},
            {"__ROW_PATH__": ["def"], "a": 1, "b": 4},
        ]

    def test_view_aggregate_datetime(self, util):
        data = [
            {"a": datetime(2019, 10, 1, 11, 30)},
            {"a": datetime(2019, 10, 1, 11, 30)},
        ]
        tbl = Table(data)
        view = tbl.view(aggregates={"a": "distinct count"}, group_by=["a"])
        assert view.to_records() == [
            {"__ROW_PATH__": [], "a": 1},
            {
                "__ROW_PATH__": [util.to_timestamp(datetime(2019, 10, 1, 11, 30))],
                "a": 1,
            },
        ]

    def test_view_aggregate_datetime_leading_zeroes(self, util):
        data = [
            {"a": datetime(2019, 1, 1, 5, 5, 5)},
            {"a": datetime(2019, 1, 1, 5, 5, 5)},
        ]
        tbl = Table(data)
        view = tbl.view(aggregates={"a": "distinct count"}, group_by=["a"])
        assert view.to_records() == [
            {"__ROW_PATH__": [], "a": 1},
            {
                "__ROW_PATH__": [util.to_timestamp(datetime(2019, 1, 1, 5, 5, 5))],
                "a": 1,
            },
        ]

    def test_view_aggregate_mean(self):
        data = [
            {"a": "a", "x": 1, "y": 200},
            {"a": "a", "x": 2, "y": 100},
            {"a": "a", "x": 3, "y": None},
        ]
        tbl = Table(data)
        view = tbl.view(aggregates={"y": "mean"}, group_by=["a"], columns=["y"])
        assert view.to_records() == [
            {"__ROW_PATH__": [], "y": 300 / 2},
            {"__ROW_PATH__": ["a"], "y": 300 / 2},
        ]

    def test_view_aggregate_mean_from_schema(self):
        data = [
            {"a": "a", "x": 1, "y": 200},
            {"a": "a", "x": 2, "y": 100},
            {"a": "a", "x": 3, "y": None},
        ]
        tbl = Table({"a": "string", "x": "integer", "y": "float"})
        view = tbl.view(aggregates={"y": "mean"}, group_by=["a"], columns=["y"])
        tbl.update(data)
        assert view.to_records() == [
            {"__ROW_PATH__": [], "y": 300 / 2},
            {"__ROW_PATH__": ["a"], "y": 300 / 2},
        ]

    def test_view_aggregate_weighted_mean(self):
        data = [
            {"a": "a", "x": 1, "y": 200},
            {"a": "a", "x": 2, "y": 100},
            {"a": "a", "x": 3, "y": None},
        ]
        tbl = Table(data)
        view = tbl.view(
            aggregates={"y": ("weighted mean", ["x"])},
            group_by=["a"],
            columns=["y"],
        )
        assert view.to_records() == [
            {"__ROW_PATH__": [], "y": (1.0 * 200 + 2 * 100) / (1.0 + 2)},
            {"__ROW_PATH__": ["a"], "y": (1.0 * 200 + 2 * 100) / (1.0 + 2)},
        ]

    def test_view_aggregate_weighted_mean_with_negative_weights(self):
        data = [
            {"a": "a", "x": 1, "y": 200},
            {"a": "a", "x": -2, "y": 100},
            {"a": "a", "x": 3, "y": None},
        ]
        tbl = Table(data)
        view = tbl.view(
            aggregates={"y": ("weighted mean", ["x"])},
            group_by=["a"],
            columns=["y"],
        )
        assert view.to_records() == [
            {"__ROW_PATH__": [], "y": (1 * 200 + (-2) * 100) / (1 - 2)},
            {"__ROW_PATH__": ["a"], "y": (1 * 200 + (-2) * 100) / (1 - 2)},
        ]

    def test_view_variance(self):
        data = {"x": list(np.random.rand(10)), "y": ["a" for _ in range(10)]}

        table = Table(data)
        view = table.view(aggregates={"x": "var"}, group_by=["y"])

        result = view.to_columns()
        expected = np.var(data["x"])

        assert result["x"] == approx([expected, expected])

    def test_view_variance_multi(self):
        data = {
            "a": [
                91.96,
                258.576,
                29.6,
                243.16,
                36.24,
                25.248,
                79.99,
                206.1,
                31.5,
                55.6,
            ],
            "b": [1 if i % 2 == 0 else 0 for i in range(10)],
        }
        table = Table(data)
        view = table.view(aggregates={"a": "var"}, group_by=["b"])

        result = view.to_columns()
        expected_total = np.var(data["a"])
        expected_zero = np.var(
            [data["a"][1], data["a"][3], data["a"][5], data["a"][7], data["a"][9]]
        )
        expected_one = np.var(
            [data["a"][0], data["a"][2], data["a"][4], data["a"][6], data["a"][8]]
        )

        assert result["a"] == approx([expected_total, expected_zero, expected_one])

    def test_view_variance_update_none(self):
        data = {"a": [0.1, 0.5, None, 0.8], "b": [0, 1, 0, 1], "c": [1, 2, 3, 4]}
        table = Table(data, index="c")
        view = table.view(columns=["a"], group_by=["b"], aggregates={"a": "var"})
        result = view.to_columns()
        assert result["a"][0] == approx(np.var([0.1, 0.5, 0.8]))
        assert result["a"][1] is None
        assert result["a"][2] == approx(np.var([0.5, 0.8]))

        table.update({"a": [0.3], "c": [3]})

        result = view.to_columns()
        assert result["a"] == approx(
            [np.var([0.1, 0.5, 0.3, 0.8]), np.var([0.1, 0.3]), np.var([0.5, 0.8])]
        )

        table.update({"a": [None], "c": [1]})

        result = view.to_columns()
        assert result["a"][0] == approx(np.var([0.5, 0.3, 0.8]))
        assert result["a"][1] is None
        assert result["a"][2] == approx(np.var([0.5, 0.8]))

    def test_view_variance_multi_update(self):
        data = {
            "a": [
                91.96,
                258.576,
                29.6,
                243.16,
                36.24,
                25.248,
                79.99,
                206.1,
                31.5,
                55.6,
            ],
            "b": [1 if i % 2 == 0 else 0 for i in range(10)],
        }
        table = Table(data)
        view = table.view(aggregates={"a": "var"}, group_by=["b"])

        result = view.to_columns()
        expected_total = data["a"]
        expected_zero = [
            data["a"][1],
            data["a"][3],
            data["a"][5],
            data["a"][7],
            data["a"][9],
        ]
        expected_one = [
            data["a"][0],
            data["a"][2],
            data["a"][4],
            data["a"][6],
            data["a"][8],
        ]

        assert result["a"] == approx(
            [np.var(expected_total), np.var(expected_zero), np.var(expected_one)]
        )

        # 2 here should result in null var because the group size is 1
        update_data = {"a": [15.12, 9.102, 0.99, 12.8], "b": [1, 0, 1, 2]}
        table.update(update_data)

        result = view.to_columns()
        expected_total += update_data["a"]
        expected_zero += [update_data["a"][1]]
        expected_one += [update_data["a"][0], update_data["a"][2]]

        assert result["__ROW_PATH__"] == [[], [0], [1], [2]]
        assert result["a"][:-1] == approx(
            [np.var(expected_total), np.var(expected_zero), np.var(expected_one)]
        )
        assert result["a"][-1] is None

    def test_view_variance_multi_update_delta(self):
        data = {
            "a": [
                91.96,
                258.576,
                29.6,
                243.16,
                36.24,
                25.248,
                79.99,
                206.1,
                31.5,
                55.6,
            ],
            "b": [1 if i % 2 == 0 else 0 for i in range(10)],
        }
        table = Table(data)
        view = table.view(aggregates={"a": "var"}, group_by=["b"])

        result = view.to_columns()
        expected_total = data["a"]
        expected_zero = [
            data["a"][1],
            data["a"][3],
            data["a"][5],
            data["a"][7],
            data["a"][9],
        ]
        expected_one = [
            data["a"][0],
            data["a"][2],
            data["a"][4],
            data["a"][6],
            data["a"][8],
        ]

        assert result["a"] == approx(
            [np.var(expected_total), np.var(expected_zero), np.var(expected_one)]
        )

        # 2 here should result in null var because the group size is 1
        update_data = {"a": [15.12, 9.102, 0.99, 12.8], "b": [1, 0, 1, 2]}

        def cb1(port_id, delta):
            table2 = Table(delta)
            view2 = table2.view()
            result = view2.to_columns()

            flat_view = table.view()
            flat_data = flat_view.to_columns()
            result = view.to_columns()

            expected_total = flat_data["a"]
            expected_zero = []
            expected_one = []

            for i, num in enumerate(expected_total):
                if flat_data["b"][i] == 1:
                    expected_one.append(num)
                elif flat_data["b"][i] == 0:
                    expected_zero.append(num)

            assert result["a"][0] == approx(np.var(expected_total))
            assert result["a"][1] == approx(np.var(expected_zero))
            assert result["a"][2] == approx(np.var(expected_one))
            assert result["a"][3] is None

        view.on_update(cb1, mode="row")

        table.update(update_data)

    def test_view_variance_multi_update_indexed(self):
        data = {
            "a": [
                91.96,
                258.576,
                29.6,
                243.16,
                36.24,
                25.248,
                79.99,
                206.1,
                31.5,
                55.6,
            ],
            "b": [1 if i % 2 == 0 else 0 for i in range(10)],
            "c": [i for i in range(10)],
        }
        table = Table(data, index="c")
        view = table.view(aggregates={"a": "var"}, group_by=["b"])

        result = view.to_columns()
        expected_total = data["a"]
        expected_zero = [
            data["a"][1],
            data["a"][3],
            data["a"][5],
            data["a"][7],
            data["a"][9],
        ]
        expected_one = [
            data["a"][0],
            data["a"][2],
            data["a"][4],
            data["a"][6],
            data["a"][8],
        ]

        assert result["a"] == approx(
            [np.var(expected_total), np.var(expected_zero), np.var(expected_one)]
        )

        # "b" = 2 here should result in null var because the group size is 1
        update_data = {
            "a": [15.12, 9.102, 0.99, 12.8],
            "b": [1, 0, 1, 2],
            "c": [1, 5, 2, 7],
        }

        table.update(update_data)

        result = view.to_columns()

        view2 = table.view()
        flat_data = view2.to_columns()
        expected_total = flat_data["a"]

        expected_zero = []
        expected_one = []

        for i, val in enumerate(flat_data["a"]):
            if flat_data["b"][i] == 1:
                expected_one.append(val)
            elif flat_data["b"][i] == 0:
                expected_zero.append(val)

        assert result["__ROW_PATH__"] == [[], [0], [1], [2]]
        assert result["a"][:-1] == approx(
            [np.var(expected_total), np.var(expected_zero), np.var(expected_one)]
        )
        assert result["a"][-1] is None

    def test_view_variance_multi_update_indexed_delta(self):
        data = {
            "a": [
                91.96,
                258.576,
                29.6,
                243.16,
                36.24,
                25.248,
                79.99,
                206.1,
                31.5,
                55.6,
            ],
            "b": [1 if i % 2 == 0 else 0 for i in range(10)],
            "c": [i for i in range(10)],
        }
        table = Table(data, index="c")
        view = table.view(
            aggregates={"a": "var", "b": "last", "c": "last"}, group_by=["b"]
        )

        result = view.to_columns()
        expected_total = data["a"]
        expected_zero = [
            data["a"][1],
            data["a"][3],
            data["a"][5],
            data["a"][7],
            data["a"][9],
        ]
        expected_one = [
            data["a"][0],
            data["a"][2],
            data["a"][4],
            data["a"][6],
            data["a"][8],
        ]

        assert result["a"] == approx(
            [np.var(expected_total), np.var(expected_zero), np.var(expected_one)]
        )

        # 2 here should result in null var because the group size is 1
        update_data = {
            "a": [15.12, 9.102, 0.99, 12.8],
            "b": [1, 0, 1, 2],
            "c": [0, 4, 1, 6],
        }

        def cb1(port_id, delta):
            table2 = Table(delta)
            view2 = table2.view()
            result = view2.to_columns()

            flat_view = table.view()
            flat_result = flat_view.to_columns()

            new_a = flat_result["a"]
            b = flat_result["b"]
            expected_zero = []
            expected_one = []

            for i, num in enumerate(new_a):
                if b[i] == 0:
                    expected_zero.append(num)
                elif b[i] == 1:
                    expected_one.append(num)

            assert result["a"][0] == approx(np.var(new_a))
            assert result["a"][1] == approx(np.var(expected_zero))
            assert result["a"][2] == approx(np.var(expected_one))
            assert result["a"][3] is None
            assert result["b"] == [2, 0, 1, 2]
            assert result["c"] == [6, 9, 8, 6]

        view.on_update(cb1, mode="row")

        table.update(update_data)

    def test_view_variance_less_than_two(self):
        data = {"a": list(np.random.rand(10)), "b": [i for i in range(10)]}

        table = Table(data)
        view = table.view(aggregates={"a": "var"}, group_by=["b"])

        result = view.to_columns()
        assert result["a"][0] == approx(np.var(data["a"]))
        assert result["a"][1:] == [None] * 10

    def test_view_variance_normal_distribution(self):
        data = {"a": list(np.random.standard_normal(100)), "b": [1] * 100}

        table = Table(data)
        view = table.view(aggregates={"a": "var"}, group_by=["b"])

        result = view.to_columns()
        assert result["a"] == approx([np.var(data["a"]), np.var(data["a"])])

    def test_view_standard_deviation(self):
        data = {"x": list(np.random.rand(10)), "y": ["a" for _ in range(10)]}

        table = Table(data)
        view = table.view(aggregates={"x": "stddev"}, group_by=["y"])

        result = view.to_columns()
        expected = np.std(data["x"])

        assert result["x"] == approx([expected, expected])

    def test_view_standard_deviation_multi(self):
        data = {
            "a": [
                91.96,
                258.576,
                29.6,
                243.16,
                36.24,
                25.248,
                79.99,
                206.1,
                31.5,
                55.6,
            ],
            "b": [1 if i % 2 == 0 else 0 for i in range(10)],
        }
        table = Table(data)
        view = table.view(aggregates={"a": "stddev"}, group_by=["b"])

        result = view.to_columns()
        expected_total = np.std(data["a"])
        expected_zero = np.std(
            [data["a"][1], data["a"][3], data["a"][5], data["a"][7], data["a"][9]]
        )
        expected_one = np.std(
            [data["a"][0], data["a"][2], data["a"][4], data["a"][6], data["a"][8]]
        )

        assert result["a"] == approx([expected_total, expected_zero, expected_one])

    def test_view_standard_deviation_update_none(self):
        data = {"a": [0.1, 0.5, None, 0.8], "b": [0, 1, 0, 1], "c": [1, 2, 3, 4]}
        table = Table(data, index="c")
        view = table.view(columns=["a"], group_by=["b"], aggregates={"a": "stddev"})
        result = view.to_columns()
        assert result["a"][0] == approx(np.std([0.1, 0.5, 0.8]))
        assert result["a"][1] is None
        assert result["a"][2] == approx(np.std([0.5, 0.8]))

        table.update({"a": [0.3], "c": [3]})

        result = view.to_columns()
        assert result["a"] == approx(
            [np.std([0.1, 0.5, 0.3, 0.8]), np.std([0.1, 0.3]), np.std([0.5, 0.8])]
        )

        table.update({"a": [None], "c": [1]})

        result = view.to_columns()
        assert result["a"][0] == approx(np.std([0.5, 0.3, 0.8]))
        assert result["a"][1] is None
        assert result["a"][2] == approx(np.std([0.5, 0.8]))

    def test_view_standard_deviation_multi_update(self):
        data = {
            "a": [
                91.96,
                258.576,
                29.6,
                243.16,
                36.24,
                25.248,
                79.99,
                206.1,
                31.5,
                55.6,
            ],
            "b": [1 if i % 2 == 0 else 0 for i in range(10)],
        }
        table = Table(data)
        view = table.view(aggregates={"a": "stddev"}, group_by=["b"])

        result = view.to_columns()
        expected_total = data["a"]
        expected_zero = [
            data["a"][1],
            data["a"][3],
            data["a"][5],
            data["a"][7],
            data["a"][9],
        ]
        expected_one = [
            data["a"][0],
            data["a"][2],
            data["a"][4],
            data["a"][6],
            data["a"][8],
        ]

        assert result["a"] == approx(
            [np.std(expected_total), np.std(expected_zero), np.std(expected_one)]
        )

        # 2 here should result in null stddev because the group size is 1
        update_data = {"a": [15.12, 9.102, 0.99, 12.8], "b": [1, 0, 1, 2]}
        table.update(update_data)

        result = view.to_columns()
        expected_total += update_data["a"]
        expected_zero += [update_data["a"][1]]
        expected_one += [update_data["a"][0], update_data["a"][2]]

        assert result["__ROW_PATH__"] == [[], [0], [1], [2]]
        assert result["a"][:-1] == approx(
            [np.std(expected_total), np.std(expected_zero), np.std(expected_one)]
        )
        assert result["a"][-1] is None

    def test_view_standard_deviation_multi_update_delta(self):
        data = {
            "a": [
                91.96,
                258.576,
                29.6,
                243.16,
                36.24,
                25.248,
                79.99,
                206.1,
                31.5,
                55.6,
            ],
            "b": [1 if i % 2 == 0 else 0 for i in range(10)],
        }
        table = Table(data)
        view = table.view(aggregates={"a": "stddev"}, group_by=["b"])

        result = view.to_columns()
        expected_total = data["a"]
        expected_zero = [
            data["a"][1],
            data["a"][3],
            data["a"][5],
            data["a"][7],
            data["a"][9],
        ]
        expected_one = [
            data["a"][0],
            data["a"][2],
            data["a"][4],
            data["a"][6],
            data["a"][8],
        ]

        assert result["a"] == approx(
            [np.std(expected_total), np.std(expected_zero), np.std(expected_one)]
        )

        # 2 here should result in null stddev because the group size is 1
        update_data = {"a": [15.12, 9.102, 0.99, 12.8], "b": [1, 0, 1, 2]}

        def cb1(port_id, delta):
            table2 = Table(delta)
            view2 = table2.view()
            result = view2.to_columns()

            flat_view = table.view()
            flat_data = flat_view.to_columns()
            result = view.to_columns()

            expected_total = flat_data["a"]
            expected_zero = []
            expected_one = []

            for i, num in enumerate(expected_total):
                if flat_data["b"][i] == 1:
                    expected_one.append(num)
                elif flat_data["b"][i] == 0:
                    expected_zero.append(num)

            assert result["a"][0] == approx(np.std(expected_total))
            assert result["a"][1] == approx(np.std(expected_zero))
            assert result["a"][2] == approx(np.std(expected_one))
            assert result["a"][3] is None

        view.on_update(cb1, mode="row")

        table.update(update_data)

    def test_view_standard_deviation_multi_update_indexed(self):
        data = {
            "a": [
                91.96,
                258.576,
                29.6,
                243.16,
                36.24,
                25.248,
                79.99,
                206.1,
                31.5,
                55.6,
            ],
            "b": [1 if i % 2 == 0 else 0 for i in range(10)],
            "c": [i for i in range(10)],
        }
        table = Table(data, index="c")
        view = table.view(aggregates={"a": "stddev"}, group_by=["b"])

        result = view.to_columns()
        expected_total = data["a"]
        expected_zero = [
            data["a"][1],
            data["a"][3],
            data["a"][5],
            data["a"][7],
            data["a"][9],
        ]
        expected_one = [
            data["a"][0],
            data["a"][2],
            data["a"][4],
            data["a"][6],
            data["a"][8],
        ]

        assert result["a"] == approx(
            [np.std(expected_total), np.std(expected_zero), np.std(expected_one)]
        )

        # "b" = 2 here should result in null stddev because the group size is 1
        update_data = {
            "a": [15.12, 9.102, 0.99, 12.8],
            "b": [1, 0, 1, 2],
            "c": [1, 5, 2, 7],
        }

        table.update(update_data)

        result = view.to_columns()

        view2 = table.view()
        flat_data = view2.to_columns()
        expected_total = flat_data["a"]

        expected_zero = []
        expected_one = []

        for i, val in enumerate(flat_data["a"]):
            if flat_data["b"][i] == 1:
                expected_one.append(val)
            elif flat_data["b"][i] == 0:
                expected_zero.append(val)

        assert result["__ROW_PATH__"] == [[], [0], [1], [2]]
        assert result["a"][:-1] == approx(
            [np.std(expected_total), np.std(expected_zero), np.std(expected_one)]
        )
        assert result["a"][-1] is None

    def test_view_standard_deviation_multi_update_indexed_delta(self):
        data = {
            "a": [
                91.96,
                258.576,
                29.6,
                243.16,
                36.24,
                25.248,
                79.99,
                206.1,
                31.5,
                55.6,
            ],
            "b": [1 if i % 2 == 0 else 0 for i in range(10)],
            "c": [i for i in range(10)],
        }
        table = Table(data, index="c")
        view = table.view(
            aggregates={"a": "stddev", "b": "last", "c": "last"}, group_by=["b"]
        )

        result = view.to_columns()
        expected_total = data["a"]
        expected_zero = [
            data["a"][1],
            data["a"][3],
            data["a"][5],
            data["a"][7],
            data["a"][9],
        ]
        expected_one = [
            data["a"][0],
            data["a"][2],
            data["a"][4],
            data["a"][6],
            data["a"][8],
        ]

        assert result["a"] == approx(
            [np.std(expected_total), np.std(expected_zero), np.std(expected_one)]
        )

        # 2 here should result in null stddev because the group size is 1
        update_data = {
            "a": [15.12, 9.102, 0.99, 12.8],
            "b": [1, 0, 1, 2],
            "c": [0, 4, 1, 6],
        }

        def cb1(port_id, delta):
            table2 = Table(delta)
            view2 = table2.view()
            result = view2.to_columns()

            flat_view = table.view()
            flat_result = flat_view.to_columns()

            new_a = flat_result["a"]
            b = flat_result["b"]
            expected_zero = []
            expected_one = []

            for i, num in enumerate(new_a):
                if b[i] == 0:
                    expected_zero.append(num)
                elif b[i] == 1:
                    expected_one.append(num)

            assert result["a"][0] == approx(np.std(new_a))
            assert result["a"][1] == approx(np.std(expected_zero))
            assert result["a"][2] == approx(np.std(expected_one))
            assert result["a"][3] is None
            assert result["b"] == [2, 0, 1, 2]
            assert result["c"] == [6, 9, 8, 6]

        view.on_update(cb1, mode="row")

        table.update(update_data)

    def test_view_standard_deviation_less_than_two(self):
        data = {"a": list(np.random.rand(10)), "b": [i for i in range(10)]}

        table = Table(data)
        view = table.view(aggregates={"a": "stddev"}, group_by=["b"])

        result = view.to_columns()
        assert result["a"][0] == approx(np.std(data["a"]))
        assert result["a"][1:] == [None] * 10

    def test_view_standard_deviation_normal_distribution(self):
        data = {"a": list(np.random.standard_normal(100)), "b": [1] * 100}

        table = Table(data)
        view = table.view(aggregates={"a": "stddev"}, group_by=["b"])

        result = view.to_columns()
        assert result["a"] == approx([np.std(data["a"]), np.std(data["a"])])

    # sort

    def test_view_sort_int(self):
        data = [{"a": 1, "b": 2}, {"a": 3, "b": 4}]
        tbl = Table(data)
        view = tbl.view(sort=[["a", "desc"]])
        assert view.to_records() == [{"a": 3, "b": 4}, {"a": 1, "b": 2}]

    def test_view_sort_float(self):
        data = [{"a": 1.1, "b": 2}, {"a": 1.2, "b": 4}]
        tbl = Table(data)
        view = tbl.view(sort=[["a", "desc"]])
        assert view.to_records() == [{"a": 1.2, "b": 4}, {"a": 1.1, "b": 2}]

    def test_view_sort_string(self):
        data = [{"a": "abc", "b": 2}, {"a": "def", "b": 4}]
        tbl = Table(data)
        view = tbl.view(sort=[["a", "desc"]])
        assert view.to_records() == [{"a": "def", "b": 4}, {"a": "abc", "b": 2}]

    def test_view_sort_date(self, util):
        data = [{"a": date(2019, 7, 11), "b": 2}, {"a": date(2019, 7, 12), "b": 4}]
        tbl = Table(data)
        view = tbl.view(sort=[["a", "desc"]])
        assert view.to_records() == [
            {"a": util.to_timestamp(datetime(2019, 7, 12)), "b": 4},
            {"a": util.to_timestamp(datetime(2019, 7, 11)), "b": 2},
        ]

    def test_view_sort_datetime(self, util):
        data = [
            {"a": datetime(2019, 7, 11, 8, 15), "b": 2},
            {"a": datetime(2019, 7, 11, 8, 16), "b": 4},
        ]
        tbl = Table(data)
        view = tbl.view(sort=[["a", "desc"]])
        assert view.to_records() == [
            {"a": util.to_timestamp(datetime(2019, 7, 11, 8, 16)), "b": 4},
            {"a": util.to_timestamp(datetime(2019, 7, 11, 8, 15)), "b": 2},
        ]

    def test_view_sort_hidden(self):
        data = [{"a": 1.1, "b": 2}, {"a": 1.2, "b": 4}]
        tbl = Table(data)
        view = tbl.view(sort=[["a", "desc"]], columns=["b"])
        assert view.to_records() == [{"b": 4}, {"b": 2}]

    def test_view_sort_avg_nan(self):
        data = {
            "w": [3.5, 4.5, None, None, None, None, 1.5, 2.5],
            "x": [1, 2, 3, 4, 4, 3, 2, 1],
            "y": ["a", "b", "c", "d", "e", "f", "g", "h"],
        }
        tbl = Table(data)
        view = tbl.view(
            columns=["x", "w"],
            group_by=["y"],
            sort=[["w", "asc"]],
            aggregates={"w": "avg", "x": "unique"},
        )
        assert view.to_columns() == {
            "__ROW_PATH__": [
                [],
                ["c"],
                ["d"],
                ["e"],
                ["f"],
                ["g"],
                ["h"],
                ["a"],
                ["b"],
            ],
            "w": [3, None, None, None, None, 1.5, 2.5, 3.5, 4.5],
            "x": [None, 3, 4, 4, 3, 2, 1, 1, 2],
        }

    def test_view_sort_sum_nan(self):
        data = {
            "w": [3.5, 4.5, None, None, None, None, 1.5, 2.5],
            "x": [1, 2, 3, 4, 4, 3, 2, 1],
            "y": ["a", "b", "c", "d", "e", "f", "g", "h"],
        }
        tbl = Table(data)
        view = tbl.view(
            columns=["x", "w"],
            group_by=["y"],
            sort=[["w", "asc"]],
            aggregates={"w": "sum", "x": "unique"},
        )
        assert view.to_columns() == {
            "__ROW_PATH__": [
                [],
                ["c"],
                ["d"],
                ["e"],
                ["f"],
                ["g"],
                ["h"],
                ["a"],
                ["b"],
            ],
            "w": [12, 0, 0, 0, 0, 1.5, 2.5, 3.5, 4.5],
            "x": [None, 3, 4, 4, 3, 2, 1, 1, 2],
        }

    def test_view_sort_unique_nan(self):
        data = {
            "w": [3.5, 4.5, None, None, None, None, 1.5, 2.5],
            "x": [1, 2, 3, 4, 4, 3, 2, 1],
            "y": ["a", "b", "c", "d", "e", "f", "g", "h"],
        }
        tbl = Table(data)
        view = tbl.view(
            columns=["x", "w"],
            group_by=["y"],
            sort=[["w", "asc"]],
            aggregates={"w": "unique", "x": "unique"},
        )
        assert view.to_columns() == {
            "__ROW_PATH__": [
                [],
                ["c"],
                ["d"],
                ["e"],
                ["f"],
                ["g"],
                ["h"],
                ["a"],
                ["b"],
            ],
            "w": [None, None, None, None, None, 1.5, 2.5, 3.5, 4.5],
            "x": [None, 3, 4, 4, 3, 2, 1, 1, 2],
        }

    # filter

    def test_view_filter_int_eq(self):
        data = [{"a": 1, "b": 2}, {"a": 3, "b": 4}]
        tbl = Table(data)
        view = tbl.view(filter=[["a", "==", 1]])
        assert view.to_records() == [{"a": 1, "b": 2}]

    def test_view_filter_int_neq(self):
        data = [{"a": 1, "b": 2}, {"a": 3, "b": 4}]
        tbl = Table(data)
        view = tbl.view(filter=[["a", "!=", 1]])
        assert view.to_records() == [{"a": 3, "b": 4}]

    def test_view_filter_int_gt(self):
        data = [{"a": 1, "b": 2}, {"a": 3, "b": 4}]
        tbl = Table(data)
        view = tbl.view(filter=[["a", ">", 1]])
        assert view.to_records() == [{"a": 3, "b": 4}]

    def test_view_filter_int_lt(self):
        data = [{"a": 1, "b": 2}, {"a": 3, "b": 4}]
        tbl = Table(data)
        view = tbl.view(filter=[["a", "<", 3]])
        assert view.to_records() == [{"a": 1, "b": 2}]

    def test_view_filter_float_eq(self):
        data = [{"a": 1.1, "b": 2}, {"a": 1.2, "b": 4}]
        tbl = Table(data)
        view = tbl.view(filter=[["a", "==", 1.2]])
        assert view.to_records() == [{"a": 1.2, "b": 4}]

    def test_view_filter_float_neq(self):
        data = [{"a": 1.1, "b": 2}, {"a": 1.2, "b": 4}]
        tbl = Table(data)
        view = tbl.view(filter=[["a", "!=", 1.2]])
        assert view.to_records() == [{"a": 1.1, "b": 2}]

    def test_view_filter_string_eq(self):
        data = [{"a": "abc", "b": 2}, {"a": "def", "b": 4}]
        tbl = Table(data)
        view = tbl.view(filter=[["a", "==", "def"]])
        assert view.to_records() == [{"a": "def", "b": 4}]

    def test_view_filter_string_neq(self):
        data = [{"a": "abc", "b": 2}, {"a": "def", "b": 4}]
        tbl = Table(data)
        view = tbl.view(filter=[["a", "!=", "def"]])
        assert view.to_records() == [{"a": "abc", "b": 2}]

    def test_view_filter_string_gt(self):
        data = [{"a": "abc", "b": 2}, {"a": "def", "b": 4}]
        tbl = Table(data)
        view = tbl.view(filter=[["a", ">", "abc"]])
        assert view.to_records() == [{"a": "def", "b": 4}]

    def test_view_filter_string_lt(self):
        data = [{"a": "abc", "b": 2}, {"a": "def", "b": 4}]
        tbl = Table(data)
        view = tbl.view(filter=[["a", "<", "def"]])
        assert view.to_records() == [{"a": "abc", "b": 2}]

    # @mark.skip # We do not support using `datetime.date` in operators.
    def test_view_filter_date_eq(self, util):
        data = [{"a": date(2019, 7, 11), "b": 2}, {"a": date(2019, 7, 12), "b": 4}]
        tbl = Table(data)
        view = tbl.view(filter=[["a", "==", str(date(2019, 7, 12))]])
        assert view.to_records() == [
            {"a": util.to_timestamp(datetime(2019, 7, 12)), "b": 4}
        ]

    # @mark.skip # We do not support using `datetime.date` in operators.
    def test_view_filter_date_neq(self, util):
        data = [{"a": date(2019, 7, 11), "b": 2}, {"a": date(2019, 7, 12), "b": 4}]
        tbl = Table(data)
        view = tbl.view(filter=[["a", "!=", str(date(2019, 7, 12))]])
        assert view.to_records() == [
            {"a": util.to_timestamp(datetime(2019, 7, 11)), "b": 2}
        ]

    def test_view_filter_date_str_eq(self, util):
        data = [{"a": date(2019, 7, 11), "b": 2}, {"a": date(2019, 7, 12), "b": 4}]
        tbl = Table(data)
        view = tbl.view(filter=[["a", "==", "2019/7/12"]])
        assert view.to_records() == [
            {"a": util.to_timestamp(datetime(2019, 7, 12)), "b": 4}
        ]

    def test_view_filter_date_str_neq(self, util):
        data = [{"a": date(2019, 7, 11), "b": 2}, {"a": date(2019, 7, 12), "b": 4}]
        tbl = Table(data)
        view = tbl.view(filter=[["a", "!=", "2019/7/12"]])
        assert view.to_records() == [
            {"a": util.to_timestamp(datetime(2019, 7, 11)), "b": 2}
        ]

    @mark.skip  # We do not support using `datetime.datetime` in operators
    def test_view_filter_datetime_eq(self, util):
        data = [
            {"a": datetime(2019, 7, 11, 8, 15), "b": 2},
            {"a": datetime(2019, 7, 11, 8, 16), "b": 4},
        ]
        tbl = Table(data)
        view = tbl.view(filter=[["a", "==", datetime(2019, 7, 11, 8, 15)]])
        assert view.to_records() == [
            {"a": util.to_timestamp(datetime(2019, 7, 11, () * 1000)), "b": 2}
        ]

    @mark.skip  # We do not support using `datetime.datetime` in operators
    def test_view_filter_datetime_neq(self, util):
        data = [
            {"a": datetime(2019, 7, 11, 8, 15), "b": 2},
            {"a": datetime(2019, 7, 11, 8, 16), "b": 4},
        ]
        tbl = Table(data)
        view = tbl.view(filter=[["a", "!=", datetime(2019, 7, 11, 8, 15)]])
        assert view.to_records() == [
            {"a": util.to_timestamp(datetime(2019, 7, 11, () * 1000)), "b": 4}
        ]

    @mark.skip  # We do not support numpy anymore
    def test_view_filter_datetime_np_eq(self, util):
        data = [
            {"a": datetime(2019, 7, 11, 8, 15), "b": 2},
            {"a": datetime(2019, 7, 11, 8, 16), "b": 4},
        ]
        tbl = Table(data)
        view = tbl.view(
            filter=[["a", "==", np.datetime64(datetime(2019, 7, 11, 8, 15))]]
        )
        assert view.to_records() == [
            {"a": util.to_timestamp(datetime(2019, 7, 11, () * 1000)), "b": 2}
        ]

    @mark.skip  # We do not support numpy anymore.
    def test_view_filter_datetime_np_neq(self, util):
        data = [
            {"a": datetime(2019, 7, 11, 8, 15), "b": 2},
            {"a": datetime(2019, 7, 11, 8, 16), "b": 4},
        ]
        tbl = Table(data)
        view = tbl.view(
            filter=[["a", "!=", np.datetime64(datetime(2019, 7, 11, 8, 15))]]
        )
        assert view.to_records() == [
            {"a": util.to_timestamp(datetime(2019, 7, 11, () * 1000)), "b": 4}
        ]

    def test_view_filter_datetime_as_number_eq(self, util):
        data = [
            {"a": datetime(2019, 7, 11, 8, 15).timestamp(), "b": 2},
            {"a": datetime(2019, 7, 11, 8, 16).timestamp(), "b": 4},
        ]

        tbl = Table({"a": "datetime", "b": "integer"})
        tbl.update(data)
        view = tbl.view(filter=[["a", "==", datetime(2019, 7, 11, 8, 15).timestamp()]])
        assert view.to_records() == [
            {"a": datetime(2019, 7, 11, 8, 15).timestamp(), "b": 2}
        ]

    def test_view_filter_datetime_as_number_neq(self, util):
        data = [
            {"a": datetime(2019, 7, 11, 8, 15).timestamp(), "b": 2},
            {"a": datetime(2019, 7, 11, 8, 16).timestamp(), "b": 4},
        ]
        tbl = Table({"a": "datetime", "b": "integer"})
        tbl.update(data)
        view = tbl.view(filter=[["a", "!=", datetime(2019, 7, 11, 8, 15).timestamp()]])
        assert view.to_records() == [
            {"a": datetime(2019, 7, 11, 8, 16).timestamp(), "b": 4}
        ]

    def test_view_filter_datetime_str_eq(self, util):
        data = [
            {"a": datetime(2019, 7, 11, 8, 15), "b": 2},
            {"a": datetime(2019, 7, 11, 8, 16), "b": 4},
        ]
        tbl = Table(data)
        view = tbl.view(filter=[["a", "==", "2019/7/11 8:15"]])
        assert view.to_records() == [
            {"a": util.to_timestamp(datetime(2019, 7, 11, 8, 15)), "b": 2}
        ]

    def test_view_filter_datetime_str_neq(self, util):
        data = [
            {"a": datetime(2019, 7, 11, 8, 15), "b": 2},
            {"a": datetime(2019, 7, 11, 8, 16), "b": 4},
        ]
        tbl = Table(data)
        view = tbl.view(filter=[["a", "!=", "2019/7/11 8:15"]])
        assert view.to_records() == [
            {"a": util.to_timestamp(datetime(2019, 7, 11, 8, 16)), "b": 4}
        ]

    def test_view_filter_string_is_none(self):
        data = [{"a": None, "b": 2}, {"a": "abc", "b": 4}]
        tbl = Table(data)
        view = tbl.view(
            filter=[["a", "is null", ""]]
        )  # XXX: Having to add this "" is not soooo great.
        assert view.to_records() == [{"a": None, "b": 2}]

    def test_view_filter_string_is_not_none(self):
        data = [{"a": None, "b": 2}, {"a": "abc", "b": 4}]
        tbl = Table(data)
        view = tbl.view(
            filter=[["a", "is not null", ""]]
        )  # XXX: Having to add this "" is not soooo great.
        assert view.to_records() == [{"a": "abc", "b": 4}]

    # on_update
    def test_view_on_update(self, sentinel):
        s = sentinel(False)

        def callback(port_id):
            s.set(True)

        data = [{"a": 1, "b": 2}, {"a": 3, "b": 4}]
        tbl = Table(data)
        view = tbl.view()
        view.on_update(callback)
        tbl.update(data)
        assert s.get() is True

    def test_view_on_update_multiple_callback(self, sentinel):
        s = sentinel(0)

        def callback(port_id):
            s.set(s.get() + 1)

        def callback1(port_id):
            s.set(s.get() - 1)

        data = [{"a": 1, "b": 2}, {"a": 3, "b": 4}]
        tbl = Table(data)
        view = tbl.view()
        view.on_update(callback)
        view.on_update(callback1)
        tbl.update(data)
        assert s.get() == 0

    # on_delete

    def test_view_on_delete(self, sentinel):
        s = sentinel(False)

        def callback():
            s.set(True)

        data = [{"a": 1, "b": 2}, {"a": 3, "b": 4}]
        tbl = Table(data)
        view = tbl.view()
        view.on_delete(callback)
        view.delete()
        assert s.get() is True

    # delete

    def test_view_delete(self):
        data = [{"a": 1, "b": 2}, {"a": 3, "b": 4}]
        tbl = Table(data)
        view = tbl.view()
        with raises(PerspectiveError):
            tbl.delete()
        view.delete()
        tbl.delete()

    def test_view_delete_multiple_callbacks(self, sentinel):
        # make sure that callbacks on views get filtered
        s1 = sentinel(0)
        s2 = sentinel(0)

        def cb1(port_id):
            s1.set(s1.get() + 1)

        def cb2(port_id):
            s2.set(s2.get() + 1)

        data = [{"a": 1, "b": 2}, {"a": 3, "b": 4}]
        tbl = Table(data)
        v1 = tbl.view()
        v2 = tbl.view()
        v1.on_update(cb1)
        v2.on_update(cb2)
        tbl.update(data)
        assert s1.get() == 1
        assert s2.get() == 1
        v1.delete()
        tbl.update(data)
        assert s1.get() == 1
        assert s2.get() == 2

    def test_view_delete_full_cleanup(self, sentinel):
        s = sentinel(0)

        def cb1(port_id):
            s.set(s.get() + 1)

        def cb2(port_id):
            s.set(s.get() + 2)

        data = [{"a": 1, "b": 2}, {"a": 3, "b": 4}]
        tbl = Table(data)
        v1 = tbl.view()
        v2 = tbl.view()
        v1.on_update(cb1)
        v2.on_update(cb2)
        v1.delete()
        v2.delete()
        tbl.update(data)
        assert s.get() == 0

    # remove_update

    def test_view_remove_update(self, sentinel):
        s = sentinel(0)

        def cb1(port_id):
            s.set(s.get() + 1)

        def cb2(port_id):
            s.set(s.get() + 2)

        data = [{"a": 1, "b": 2}, {"a": 3, "b": 4}]
        tbl = Table(data)
        view = tbl.view()
        t1 = view.on_update(cb1)
        view.on_update(cb2)
        view.remove_update(t1)
        tbl.update(data)
        assert s.get() == 2

    def test_view_remove_multiple_update(self, sentinel):
        s1 = sentinel(0)
        s2 = sentinel(0)

        def cb1(port_id):
            s1.set(s1.get() + 1)

        def cb2(port_id):
            s2.set(s2.get() + 1)

        data = [{"a": 1, "b": 2}, {"a": 3, "b": 4}]
        tbl = Table(data)
        view = tbl.view()
        t1 = view.on_update(cb1)
        view.on_update(cb2)
        view.on_update(cb1)
        tbl.update(data)
        assert s1.get() == 2
        assert s2.get() == 1
        view.remove_update(t1)
        tbl.update(data)
        assert s1.get() == 3
        assert s2.get() == 2

    # row delta

    def test_view_row_delta_zero(self, util):
        data = [{"a": 1, "b": 2}, {"a": 3, "b": 4}]
        update_data = {"a": [5], "b": [6]}

        def cb1(port_id, delta):
            compare_delta(delta, update_data)

        tbl = Table(data)
        view = tbl.view()
        view.on_update(cb1, mode="row")
        tbl.update(update_data)

    def test_view_row_delta_zero_column_subset(self, util):
        data = [{"a": 1, "b": 2}, {"a": 3, "b": 4}]
        update_data = {"a": [5], "b": [6]}

        def cb1(port_id, delta):
            compare_delta(delta, {"b": [6]})

        tbl = Table(data)
        view = tbl.view(columns=["b"])
        view.on_update(cb1, mode="row")
        tbl.update(update_data)

    def test_view_row_delta_zero_from_schema(self, util):
        update_data = {"a": [5], "b": [6]}

        def cb1(port_id, delta):
            compare_delta(delta, update_data)

        tbl = Table({"a": "integer", "b": "integer"})
        view = tbl.view()
        view.on_update(cb1, mode="row")
        tbl.update(update_data)

    def test_view_row_delta_zero_from_schema_column_subset(self, util):
        update_data = {"a": [5], "b": [6]}

        def cb1(port_id, delta):
            compare_delta(delta, {"b": [6]})

        tbl = Table({"a": "integer", "b": "integer"})

        view = tbl.view(columns=["b"])
        view.on_update(cb1, mode="row")
        tbl.update(update_data)

    def test_view_row_delta_zero_from_schema_filtered(self, util):
        update_data = {"a": [8, 9, 10, 11], "b": [1, 2, 3, 4]}

        def cb1(port_id, delta):
            compare_delta(delta, {"a": [11], "b": [4]})

        tbl = Table({"a": "integer", "b": "integer"})
        view = tbl.view(filter=[["a", ">", 10]])
        view.on_update(cb1, mode="row")
        tbl.update(update_data)

    def test_view_row_delta_zero_from_schema_indexed(self, util):
        update_data = {"a": ["a", "b", "a"], "b": [1, 2, 3]}

        def cb1(port_id, delta):
            compare_delta(delta, {"a": ["a", "b"], "b": [3, 2]})

        tbl = Table({"a": "string", "b": "integer"}, index="a")

        view = tbl.view()
        view.on_update(cb1, mode="row")

        tbl.update(update_data)

    def test_view_row_delta_zero_from_schema_indexed_filtered(self, util):
        update_data = {"a": [8, 9, 10, 11, 11], "b": [1, 2, 3, 4, 5]}

        def cb1(port_id, delta):
            compare_delta(delta, {"a": [11], "b": [5]})

        tbl = Table({"a": "integer", "b": "integer"}, index="a")
        view = tbl.view(filter=[["a", ">", 10]])
        view.on_update(cb1, mode="row")
        tbl.update(update_data)

    def test_view_row_delta_one(self, util):
        data = [{"a": 1, "b": 2}, {"a": 3, "b": 4}]
        update_data = {"a": [5], "b": [6]}

        def cb1(port_id, delta):
            compare_delta(delta, {"a": [9, 5], "b": [12, 6]})

        tbl = Table(data)
        view = tbl.view(group_by=["a"])
        assert view.to_columns() == {
            "__ROW_PATH__": [[], [1], [3]],
            "a": [4, 1, 3],
            "b": [6, 2, 4],
        }
        view.on_update(cb1, mode="row")
        tbl.update(update_data)

    def test_view_row_delta_one_from_schema(self, util):
        update_data = {"a": [1, 2, 3, 4, 5], "b": [6, 7, 8, 9, 10]}

        def cb1(port_id, delta):
            compare_delta(delta, {"a": [15, 1, 2, 3, 4, 5], "b": [40, 6, 7, 8, 9, 10]})

        tbl = Table({"a": "integer", "b": "integer"})
        view = tbl.view(group_by=["a"])
        view.on_update(cb1, mode="row")
        tbl.update(update_data)

    def test_view_row_delta_one_from_schema_sorted(self, util):
        update_data = {"a": [1, 2, 3, 4, 5], "b": [6, 7, 8, 9, 10]}

        def cb1(port_id, delta):
            compare_delta(delta, {"a": [15, 5, 4, 3, 2, 1], "b": [40, 10, 9, 8, 7, 6]})

        tbl = Table({"a": "integer", "b": "integer"})
        view = tbl.view(group_by=["a"], sort=[["a", "desc"]])
        view.on_update(cb1, mode="row")
        tbl.update(update_data)

    def test_view_row_delta_one_from_schema_filtered(self, util):
        update_data = {"a": [1, 2, 3, 4, 5], "b": [6, 7, 8, 9, 10]}

        def cb1(port_id, delta):
            compare_delta(delta, {"a": [9, 4, 5], "b": [19, 9, 10]})

        tbl = Table({"a": "integer", "b": "integer"})
        view = tbl.view(group_by=["a"], filter=[["a", ">", 3]])
        view.on_update(cb1, mode="row")
        tbl.update(update_data)

    def test_view_row_delta_one_from_schema_sorted_filtered(self, util):
        update_data = {"a": [1, 2, 3, 4, 5], "b": [6, 7, 8, 9, 10]}

        def cb1(port_id, delta):
            compare_delta(delta, {"a": [9, 5, 4], "b": [19, 10, 9]})

        tbl = Table({"a": "integer", "b": "integer"})
        view = tbl.view(group_by=["a"], sort=[["a", "desc"]], filter=[["a", ">", 3]])
        view.on_update(cb1, mode="row")
        tbl.update(update_data)

    def test_view_row_delta_one_from_schema_indexed(self, util):
        update_data = {"a": [1, 2, 3, 4, 5, 5, 4], "b": [6, 7, 8, 9, 10, 11, 12]}

        def cb1(port_id, delta):
            compare_delta(delta, {"a": [15, 1, 2, 3, 4, 5], "b": [44, 6, 7, 8, 12, 11]})

        tbl = Table({"a": "integer", "b": "integer"}, index="a")

        view = tbl.view(group_by=["a"])
        view.on_update(cb1, mode="row")

        tbl.update(update_data)

    def test_view_row_delta_one_from_schema_sorted_indexed(self, util):
        update_data = {"a": [1, 2, 3, 4, 5, 5, 4], "b": [6, 7, 8, 9, 10, 11, 12]}

        def cb1(port_id, delta):
            compare_delta(delta, {"a": [15, 4, 5, 3, 2, 1], "b": [44, 12, 11, 8, 7, 6]})

        tbl = Table({"a": "integer", "b": "integer"}, index="a")

        view = tbl.view(group_by=["a"], sort=[["b", "desc"]])
        view.on_update(cb1, mode="row")

        tbl.update(update_data)

    def test_view_row_delta_one_from_schema_filtered_indexed(self, util):
        update_data = {"a": [1, 2, 3, 4, 5, 5, 4], "b": [6, 7, 8, 9, 10, 11, 12]}

        def cb1(port_id, delta):
            compare_delta(delta, {"a": [9, 4, 5], "b": [23, 12, 11]})

        tbl = Table({"a": "integer", "b": "integer"}, index="a")

        view = tbl.view(group_by=["a"], filter=[["a", ">", 3]])
        view.on_update(cb1, mode="row")

        tbl.update(update_data)

    def test_view_row_delta_two(self, util):
        data = [{"a": 1, "b": 2}, {"a": 3, "b": 4}]
        update_data = {"a": [5], "b": [6]}

        def cb1(port_id, delta):
            compare_delta(
                delta,
                {
                    "2|a": [1, None],
                    "2|b": [2, None],
                    "4|a": [3, None],
                    "4|b": [4, None],
                    "6|a": [5, 5],
                    "6|b": [6, 6],
                },
            )

        tbl = Table(data)
        view = tbl.view(group_by=["a"], split_by=["b"])
        assert view.to_columns() == {
            "__ROW_PATH__": [[], [1], [3]],
            "2|a": [1, 1, None],
            "2|b": [2, 2, None],
            "4|a": [3, None, 3],
            "4|b": [4, None, 4],
        }
        view.on_update(cb1, mode="row")
        tbl.update(update_data)

    def test_view_row_delta_two_from_schema(self, util):
        data = [{"a": 1, "b": 2}, {"a": 3, "b": 4}]

        def cb1(port_id, delta):
            compare_delta(
                delta,
                {
                    "2|a": [1, 1, None],
                    "2|b": [2, 2, None],
                    "4|a": [3, None, 3],
                    "4|b": [4, None, 4],
                },
            )

        tbl = Table({"a": "integer", "b": "integer"})
        view = tbl.view(group_by=["a"], split_by=["b"])
        view.on_update(cb1, mode="row")
        tbl.update(data)

    def test_view_row_delta_two_from_schema_indexed(self, util):
        data = [{"a": 1, "b": 2}, {"a": 3, "b": 4}, {"a": 3, "b": 5}]

        def cb1(port_id, delta):
            compare_delta(
                delta,
                {
                    "2|a": [1, 1, None],
                    "2|b": [2, 2, None],
                    "5|a": [3, None, 3],
                    "5|b": [5, None, 5],
                },
            )

        tbl = Table({"a": "integer", "b": "integer"}, index="a")
        view = tbl.view(group_by=["a"], split_by=["b"])
        view.on_update(cb1, mode="row")
        tbl.update(data)

    def test_view_row_delta_two_column_only(self, util):
        data = [{"a": 1, "b": 2}, {"a": 3, "b": 4}]
        update_data = {"a": [5], "b": [6]}

        def cb1(port_id, delta):
            compare_delta(
                delta,
                {
                    "2|a": [1, None],
                    "2|b": [2, None],
                    "4|a": [3, None],
                    "4|b": [4, None],
                    "6|a": [5, 5],
                    "6|b": [6, 6],
                },
            )

        tbl = Table(data)
        view = tbl.view(split_by=["b"])
        assert view.to_columns() == {
            "2|a": [1, None],
            "2|b": [2, None],
            "4|a": [None, 3],
            "4|b": [None, 4],
        }
        view.on_update(cb1, mode="row")
        tbl.update(update_data)

    def test_view_row_delta_two_column_only_indexed(self, util):
        data = [{"a": 1, "b": 2}, {"a": 3, "b": 4}, {"a": 3, "b": 5}]
        update_data = {"a": [5], "b": [6]}

        def cb1(port_id, delta):
            compare_delta(
                delta,
                {
                    "2|a": [1, None],
                    "2|b": [2, None],
                    "5|a": [3, None],
                    "5|b": [5, None],
                    "6|a": [5, 5],
                    "6|b": [6, 6],
                },
            )

        tbl = Table(data, index="a")
        view = tbl.view(split_by=["b"])
        assert view.to_columns() == {
            "2|a": [1, None],
            "2|b": [2, None],
            "5|a": [None, 3],
            "5|b": [None, 5],
        }
        view.on_update(cb1, mode="row")
        tbl.update(update_data)

    def test_view_row_delta_two_column_only_from_schema(self, util):
        data = [{"a": 1, "b": 2}, {"a": 3, "b": 4}]

        def cb1(port_id, delta):
            compare_delta(
                delta,
                {
                    "2|a": [1, 1, None],
                    "2|b": [2, 2, None],
                    "4|a": [3, None, 3],
                    "4|b": [4, None, 4],
                },
            )

        tbl = Table({"a": "integer", "b": "integer"})
        view = tbl.view(split_by=["b"])
        view.on_update(cb1, mode="row")
        tbl.update(data)

    def test_view_row_delta_two_column_only_from_schema_indexed(self, util):
        data = [{"a": 1, "b": 2}, {"a": 3, "b": 4}, {"a": 3, "b": 5}]

        def cb1(port_id, delta):
            compare_delta(
                delta,
                {
                    "2|a": [1, 1, None],
                    "2|b": [2, 2, None],
                    "5|a": [3, None, 3],
                    "5|b": [5, None, 5],
                },
            )

        tbl = Table({"a": "integer", "b": "integer"}, index="a")
        view = tbl.view(split_by=["b"])
        view.on_update(cb1, mode="row")
        tbl.update(data)

    # hidden cols

    def test_view_num_hidden_cols(self):
        data = [{"a": 1, "b": 2}, {"a": 3, "b": 4}]
        tbl = Table(data)
        view = tbl.view(columns=["a"], sort=[["b", "desc"]])
        cols = view.to_columns()
        assert cols == {"a": [3, 1]}

    def test_view_context_two_update_clears_column_regression(self, util):
        """Tests that, when a 2-sided View() is updated to a state where one of
        the column groups is empty, an infinite loop is not encountered.
        """
        data = [
            {"a": "a", "b": 1, "c": 1.5, "i": 0},
            {"a": "a", "b": 2, "c": 2.5, "i": 1},
            {"a": "a", "b": 3, "c": 3.5, "i": 2},
            {"a": "b", "b": 1, "c": 4.5, "i": 3},
            {"a": "b", "b": 2, "c": 5.5, "i": 4},
            {"a": "b", "b": 3, "c": 6.5, "i": 5},
        ]

        tbl = Table(data, index="i")
        view = tbl.view(
            group_by=["b"],
            split_by=["a"],
            columns=["c"],
            filter=[["c", ">", 0]],
            sort=[["c", "asc"], ["a", "col asc"]],
        )

        assert view.to_records() == [
            {"__ROW_PATH__": [], "a|c": 7.5, "b|c": 16.5},
            {"__ROW_PATH__": [1], "a|c": 1.5, "b|c": 4.5},
            {"__ROW_PATH__": [2], "a|c": 2.5, "b|c": 5.5},
            {"__ROW_PATH__": [3], "a|c": 3.5, "b|c": 6.5},
        ]

        tbl.update(
            [
                {"c": -1, "i": 0},
                {"c": -1, "i": 1},
                {"c": -1, "i": 2},
            ]
        )

        assert view.to_records() == [
            {"__ROW_PATH__": [], "b|c": 16.5},
            {"__ROW_PATH__": [1], "b|c": 4.5},
            {"__ROW_PATH__": [2], "b|c": 5.5},
            {"__ROW_PATH__": [3], "b|c": 6.5},
        ]

        tbl.update(
            [
                {"a": "a", "b": 1, "c": 1.5, "i": 6},
                {"a": "a", "b": 2, "c": 2.5, "i": 7},
                {"a": "a", "b": 3, "c": 3.5, "i": 8},
            ]
        )

        assert view.to_records() == [
            {"__ROW_PATH__": [], "a|c": 7.5, "b|c": 16.5},
            {"__ROW_PATH__": [1], "a|c": 1.5, "b|c": 4.5},
            {"__ROW_PATH__": [2], "a|c": 2.5, "b|c": 5.5},
            {"__ROW_PATH__": [3], "a|c": 3.5, "b|c": 6.5},
        ]

        assert tbl.size() == 9

    # expand/collapse

    def test_view_collapse_one(self):
        data = [{"a": 1, "b": 2}, {"a": 3, "b": 4}]
        tbl = Table(data)
        view = tbl.view(group_by=["a"])
        assert view.collapse(0) == 2

    def test_view_collapse_two(self):
        data = [{"a": 1, "b": 2, "c": "a"}, {"a": 3, "b": 4, "c": "b"}]
        tbl = Table(data)
        view = tbl.view(group_by=["a"], split_by=["c"])
        assert view.collapse(0) == 2

    # TODO collapse/espand should be no-ops on column only contexts, but
    # the concept of "column only" is not yet implemented in C++
    @mark.skip
    def test_view_collapse_two_column_only(self):
        data = [{"a": 1, "b": 2, "c": "a"}, {"a": 3, "b": 4, "c": "b"}]
        tbl = Table(data)
        view = tbl.view(split_by=["c"])
        assert view.collapse(0) == 0

    def test_view_expand_one(self):
        data = [{"a": 1, "b": 2}, {"a": 3, "b": 4}]
        tbl = Table(data)
        view = tbl.view(group_by=["a"])
        assert view.expand(0) == 0

    def test_view_expand_two(self):
        data = [{"a": 1, "b": 2, "c": "a"}, {"a": 3, "b": 4, "c": "b"}]
        tbl = Table(data)
        view = tbl.view(group_by=["a"], split_by=["c"])
        assert view.expand(1) == 1

    def test_view_expand_two_column_only(self):
        data = [{"a": 1, "b": 2, "c": "a"}, {"a": 3, "b": 4, "c": "b"}]
        tbl = Table(data)
        view = tbl.view(split_by=["c"])
        assert view.expand(0) == 0

    # view config validation

    def test_invalid_column_should_throw(self):
        data = [{"a": 1, "b": 2, "c": "a"}, {"a": 3, "b": 4, "c": "b"}]
        tbl = Table(data)
        with raises(PerspectiveError) as ex:
            tbl.view(columns=["x"])
        assert str(ex.value) == "Abort(): Invalid column 'x' found in View columns.\n"

    def test_invalid_column_should_throw_and_updates_should_work(self):
        data = [{"a": 1, "b": 2, "c": "a"}, {"a": 3, "b": 4, "c": "b"}]
        tbl = Table(data)

        with raises(PerspectiveError) as ex:
            tbl.view(columns=["x"])
        assert str(ex.value) == "Abort(): Invalid column 'x' found in View columns.\n"

        for i in range(100):
            tbl.update(data)
            # force call to _process which should shake out invalid column ptrs
            tbl.size()

        view2 = tbl.view()
        assert view2.num_rows() == 202

    def test_invalid_column_aggregate_should_throw(self):
        data = [{"a": 1, "b": 2, "c": "a"}, {"a": 3, "b": 4, "c": "b"}]
        tbl = Table(data)

        with raises(PerspectiveError) as ex:
            tbl.view(columns=["x"], aggregates={"x": "sum"})

        assert str(ex.value) == "Abort(): Invalid column 'x' found in View columns.\n"

    def test_invalid_column_aggregate_should_throw_and_updates_should_work(self):
        data = [{"a": 1, "b": 2, "c": "a"}, {"a": 3, "b": 4, "c": "b"}]
        tbl = Table(data)

        with raises(PerspectiveError) as ex:
            tbl.view(columns=["x"], aggregates={"x": "sum"})

        assert str(ex.value) == "Abort(): Invalid column 'x' found in View columns.\n"

        for i in range(100):
            tbl.update(data)
            # force call to _process which should shake out invalid column ptrs
            tbl.size()

        view2 = tbl.view()
        assert view2.num_rows() == 202

    def test_invalid_group_by_should_throw(self):
        data = [{"a": 1, "b": 2, "c": "a"}, {"a": 3, "b": 4, "c": "b"}]
        tbl = Table(data)
        with raises(PerspectiveError) as ex:
            tbl.view(group_by=["x"])
        assert str(ex.value) == "Abort(): Invalid column 'x' found in View group_by.\n"

    def test_invalid_split_by_should_throw(self):
        data = [{"a": 1, "b": 2, "c": "a"}, {"a": 3, "b": 4, "c": "b"}]
        tbl = Table(data)
        with raises(PerspectiveError) as ex:
            tbl.view(split_by=["x"])
        assert str(ex.value) == "Abort(): Invalid column 'x' found in View split_by.\n"

    def test_invalid_filters_should_throw(self):
        data = [{"a": 1, "b": 2, "c": "a"}, {"a": 3, "b": 4, "c": "b"}]
        tbl = Table(data)
        with raises(PerspectiveError) as ex:
            tbl.view(filter=[["x", "==", "abc"]])
        assert str(ex.value) == "Abort(): Filter column not in schema: x"

    def test_invalid_sorts_should_throw(self):
        data = [{"a": 1, "b": 2, "c": "a"}, {"a": 3, "b": 4, "c": "b"}]
        tbl = Table(data)
        with raises(PerspectiveError) as ex:
            tbl.view(sort=[["x", "desc"]])
        assert str(ex.value) == "Abort(): Invalid column 'x' found in View sorts.\n"

    def test_should_throw_on_first_invalid(self):
        data = [{"a": 1, "b": 2, "c": "a"}, {"a": 3, "b": 4, "c": "b"}]
        tbl = Table(data)
        with raises(PerspectiveError) as ex:
            tbl.view(
                group_by=["a"],
                split_by=["c"],
                filter=[["a", ">", 1]],
                aggregates={"a": "avg"},
                sort=[["x", "desc"]],
            )
        assert str(ex.value) == "Abort(): Invalid column 'x' found in View sorts.\n"

    def test_invalid_columns_not_in_expression_should_throw(self):
        data = [{"a": 1, "b": 2, "c": "a"}, {"a": 3, "b": 4, "c": "b"}]
        tbl = Table(data)
        with raises(PerspectiveError) as ex:
            tbl.view(columns=["abc", "x"], expressions={"abc": "1 + 2"})
        assert str(ex.value) == "Abort(): Invalid column 'x' found in View columns.\n"

    def test_should_not_throw_valid_expression(self):
        data = [{"a": 1, "b": 2, "c": "a"}, {"a": 3, "b": 4, "c": "b"}]
        tbl = Table(data)
        view = tbl.view(columns=["abc"], expressions={"abc": "'hello!'"})

        assert view.schema() == {"abc": "string"}

    def test_should_not_throw_valid_expression_config(self):
        data = [{"a": 1, "b": 2, "c": "a"}, {"a": 3, "b": 4, "c": "b"}]
        tbl = Table(data)
        view = tbl.view(
            aggregates={"abc": "dominant"},
            columns=["abc"],
            sort=[["abc", "desc"]],
            filter=[["abc", "==", "A"]],
            group_by=["abc"],
            split_by=["abc"],
            expressions={"abc": "'hello!'"},
        )

        assert view.schema() == {"abc": "string"}