# Licensed to Modin Development Team under one or more contributor license agreements.
# See the NOTICE file distributed with this work for additional information regarding
# copyright ownership.  The Modin Development Team licenses this file to you under the
# Apache License, Version 2.0 (the "License"); you may not use this file except in
# compliance with the License.  You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software distributed under
# the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF
# ANY KIND, either express or implied. See the License for the specific language
# governing permissions and limitations under the License.

import contextlib
import pandas
import pytest
import modin.pandas as pd
import numpy as np
from numpy.testing import assert_array_equal
from modin.utils import get_current_execution, to_pandas
from modin.test.test_utils import warns_that_defaulting_to_pandas
from pandas.testing import assert_frame_equal

from .utils import (
    test_data_values,
    test_data_keys,
    df_equals,
    sort_index_for_equal_values,
)


@pytest.mark.parametrize("data", test_data_values, ids=test_data_keys)
def test_isna(data):
    pandas_df = pandas.DataFrame(data)
    modin_df = pd.DataFrame(data)

    pandas_result = pandas.isna(pandas_df)
    modin_result = pd.isna(modin_df)
    df_equals(modin_result, pandas_result)

    modin_result = pd.isna(pd.Series([1, np.nan, 2]))
    pandas_result = pandas.isna(pandas.Series([1, np.nan, 2]))
    df_equals(modin_result, pandas_result)

    assert pd.isna(np.nan) == pandas.isna(np.nan)


@pytest.mark.parametrize("data", test_data_values, ids=test_data_keys)
def test_isnull(data):
    pandas_df = pandas.DataFrame(data)
    modin_df = pd.DataFrame(data)

    pandas_result = pandas.isnull(pandas_df)
    modin_result = pd.isnull(modin_df)
    df_equals(modin_result, pandas_result)

    modin_result = pd.isnull(pd.Series([1, np.nan, 2]))
    pandas_result = pandas.isnull(pandas.Series([1, np.nan, 2]))
    df_equals(modin_result, pandas_result)

    assert pd.isna(np.nan) == pandas.isna(np.nan)


@pytest.mark.parametrize("data", test_data_values, ids=test_data_keys)
def test_notna(data):
    pandas_df = pandas.DataFrame(data)
    modin_df = pd.DataFrame(data)

    pandas_result = pandas.notna(pandas_df)
    modin_result = pd.notna(modin_df)
    df_equals(modin_result, pandas_result)

    modin_result = pd.notna(pd.Series([1, np.nan, 2]))
    pandas_result = pandas.notna(pandas.Series([1, np.nan, 2]))
    df_equals(modin_result, pandas_result)

    assert pd.isna(np.nan) == pandas.isna(np.nan)


@pytest.mark.parametrize("data", test_data_values, ids=test_data_keys)
def test_notnull(data):
    pandas_df = pandas.DataFrame(data)
    modin_df = pd.DataFrame(data)

    pandas_result = pandas.notnull(pandas_df)
    modin_result = pd.notnull(modin_df)
    df_equals(modin_result, pandas_result)

    modin_result = pd.notnull(pd.Series([1, np.nan, 2]))
    pandas_result = pandas.notnull(pandas.Series([1, np.nan, 2]))
    df_equals(modin_result, pandas_result)

    assert pd.isna(np.nan) == pandas.isna(np.nan)


def test_merge():
    frame_data = {
        "col1": [0, 1, 2, 3],
        "col2": [4, 5, 6, 7],
        "col3": [8, 9, 0, 1],
        "col4": [2, 4, 5, 6],
    }

    modin_df = pd.DataFrame(frame_data)
    pandas_df = pandas.DataFrame(frame_data)

    frame_data2 = {"col1": [0, 1, 2], "col2": [1, 5, 6]}
    modin_df2 = pd.DataFrame(frame_data2)
    pandas_df2 = pandas.DataFrame(frame_data2)

    join_types = ["outer", "inner"]
    for how in join_types:
        with warns_that_defaulting_to_pandas() if how == "outer" else contextlib.nullcontext():
            modin_result = pd.merge(modin_df, modin_df2, how=how)
        pandas_result = pandas.merge(pandas_df, pandas_df2, how=how)
        df_equals(modin_result, pandas_result)

        # left_on and right_index
        with warns_that_defaulting_to_pandas():
            modin_result = pd.merge(
                modin_df, modin_df2, how=how, left_on="col1", right_index=True
            )
        pandas_result = pandas.merge(
            pandas_df, pandas_df2, how=how, left_on="col1", right_index=True
        )
        df_equals(modin_result, pandas_result)

        # left_index and right_on
        with warns_that_defaulting_to_pandas():
            modin_result = pd.merge(
                modin_df, modin_df2, how=how, left_index=True, right_on="col1"
            )
        pandas_result = pandas.merge(
            pandas_df, pandas_df2, how=how, left_index=True, right_on="col1"
        )
        df_equals(modin_result, pandas_result)

        # left_on and right_on col1
        if how == "outer":
            warning_catcher = warns_that_defaulting_to_pandas()
        else:
            warning_catcher = contextlib.nullcontext()
        with warning_catcher:
            modin_result = pd.merge(
                modin_df, modin_df2, how=how, left_on="col1", right_on="col1"
            )
        pandas_result = pandas.merge(
            pandas_df, pandas_df2, how=how, left_on="col1", right_on="col1"
        )
        df_equals(modin_result, pandas_result)

        # left_on and right_on col2
        if how == "outer":
            warning_catcher = warns_that_defaulting_to_pandas()
        else:
            warning_catcher = contextlib.nullcontext()
        with warning_catcher:
            modin_result = pd.merge(
                modin_df, modin_df2, how=how, left_on="col2", right_on="col2"
            )
        pandas_result = pandas.merge(
            pandas_df, pandas_df2, how=how, left_on="col2", right_on="col2"
        )
        df_equals(modin_result, pandas_result)

        # left_index and right_index
        modin_result = pd.merge(
            modin_df, modin_df2, how=how, left_index=True, right_index=True
        )
        pandas_result = pandas.merge(
            pandas_df, pandas_df2, how=how, left_index=True, right_index=True
        )
        df_equals(modin_result, pandas_result)

    s = pd.Series(frame_data.get("col1"))
    with pytest.raises(ValueError):
        pd.merge(s, modin_df2)

    with pytest.raises(TypeError):
        pd.merge("Non-valid type", modin_df2)


def test_merge_ordered():
    data_a = {
        "key": list("aceace"),
        "lvalue": [1, 2, 3, 1, 2, 3],
        "group": list("aaabbb"),
    }
    data_b = {"key": list("bcd"), "rvalue": [1, 2, 3]}

    modin_df_a = pd.DataFrame(data_a)
    modin_df_b = pd.DataFrame(data_b)

    with warns_that_defaulting_to_pandas():
        df = pd.merge_ordered(
            modin_df_a, modin_df_b, fill_method="ffill", left_by="group"
        )
        assert isinstance(df, pd.DataFrame)

    with pytest.raises(ValueError):
        pd.merge_ordered(data_a, data_b, fill_method="ffill", left_by="group")


@pytest.mark.parametrize("right_index", [None, [0] * 5], ids=["default", "non_unique"])
def test_merge_asof(right_index):
    left = pd.DataFrame({"a": [1, 5, 10], "left_val": ["a", "b", "c"]})
    right = pd.DataFrame(
        {"a": [1, 2, 3, 6, 7], "right_val": [1, 2, 3, 6, 7]}, index=right_index
    )

    with warns_that_defaulting_to_pandas():
        df = pd.merge_asof(left, right, on="a")
        assert isinstance(df, pd.DataFrame)

    with warns_that_defaulting_to_pandas():
        df = pd.merge_asof(left, right, on="a", allow_exact_matches=False)
        assert isinstance(df, pd.DataFrame)

    with warns_that_defaulting_to_pandas():
        df = pd.merge_asof(left, right, on="a", direction="forward")
        assert isinstance(df, pd.DataFrame)

    with warns_that_defaulting_to_pandas():
        df = pd.merge_asof(left, right, on="a", direction="nearest")
        assert isinstance(df, pd.DataFrame)

    left = pd.DataFrame({"left_val": ["a", "b", "c"]}, index=[1, 5, 10])
    right = pd.DataFrame({"right_val": [1, 2, 3, 6, 7]}, index=[1, 2, 3, 6, 7])

    with warns_that_defaulting_to_pandas():
        df = pd.merge_asof(left, right, left_index=True, right_index=True)
        assert isinstance(df, pd.DataFrame)

    with pytest.raises(ValueError):
        pd.merge_asof(
            {"left_val": ["a", "b", "c"]},
            {"right_val": [1, 2, 3, 6, 7]},
            left_index=True,
            right_index=True,
        )


def test_merge_asof_on_variations():
    """on=,left_on=,right_on=,right_index=,left_index= options match Pandas."""
    left = {"a": [1, 5, 10], "left_val": ["a", "b", "c"]}
    left_index = [6, 8, 12]
    right = {"a": [1, 2, 3, 6, 7], "right_val": ["d", "e", "f", "g", "h"]}
    right_index = [6, 7, 8, 9, 15]
    pandas_left, pandas_right = (
        pandas.DataFrame(left, index=left_index),
        pandas.DataFrame(right, index=right_index),
    )
    modin_left, modin_right = (
        pd.DataFrame(left, index=left_index),
        pd.DataFrame(right, index=right_index),
    )
    for on_arguments in [
        {"on": "a"},
        {"left_on": "a", "right_on": "a"},
        {"left_on": "a", "right_index": True},
        {"left_index": True, "right_on": "a"},
        {"left_index": True, "right_index": True},
    ]:
        pandas_merged = pandas.merge_asof(pandas_left, pandas_right, **on_arguments)
        with warns_that_defaulting_to_pandas():
            modin_merged = pd.merge_asof(modin_left, modin_right, **on_arguments)
        df_equals(pandas_merged, modin_merged)


def test_merge_asof_suffixes():
    """Suffix variations are handled the same as Pandas."""
    left = {"a": [1, 5, 10]}
    right = {"a": [2, 3, 6]}
    pandas_left, pandas_right = (pandas.DataFrame(left), pandas.DataFrame(right))
    modin_left, modin_right = pd.DataFrame(left), pd.DataFrame(right)
    for suffixes in [("a", "b"), (False, "c"), ("d", False)]:
        pandas_merged = pandas.merge_asof(
            pandas_left,
            pandas_right,
            left_index=True,
            right_index=True,
            suffixes=suffixes,
        )
        with warns_that_defaulting_to_pandas():
            modin_merged = pd.merge_asof(
                modin_left,
                modin_right,
                left_index=True,
                right_index=True,
                suffixes=suffixes,
            )
        df_equals(pandas_merged, modin_merged)

    with pytest.raises(ValueError):
        pandas.merge_asof(
            pandas_left,
            pandas_right,
            left_index=True,
            right_index=True,
            suffixes=(False, False),
        )
    with pytest.raises(ValueError), warns_that_defaulting_to_pandas():
        modin_merged = pd.merge_asof(
            modin_left,
            modin_right,
            left_index=True,
            right_index=True,
            suffixes=(False, False),
        )


def test_merge_asof_bad_arguments():
    left = {"a": [1, 5, 10], "b": [5, 7, 9]}
    right = {"a": [2, 3, 6], "b": [6, 5, 20]}
    pandas_left, pandas_right = (pandas.DataFrame(left), pandas.DataFrame(right))
    modin_left, modin_right = pd.DataFrame(left), pd.DataFrame(right)

    # Can't mix by with left_by/right_by
    with pytest.raises(ValueError), warns_that_defaulting_to_pandas():
        pandas.merge_asof(
            pandas_left, pandas_right, on="a", by="b", left_by="can't do with by"
        )
    with pytest.raises(ValueError), warns_that_defaulting_to_pandas():
        pd.merge_asof(
            modin_left, modin_right, on="a", by="b", left_by="can't do with by"
        )
    with pytest.raises(ValueError), warns_that_defaulting_to_pandas():
        pandas.merge_asof(
            pandas_left, pandas_right, by="b", on="a", right_by="can't do with by"
        )
    with pytest.raises(ValueError), warns_that_defaulting_to_pandas():
        pd.merge_asof(
            modin_left, modin_right, by="b", on="a", right_by="can't do with by"
        )

    # Can't mix on with left_on/right_on
    with pytest.raises(ValueError), warns_that_defaulting_to_pandas():
        pandas.merge_asof(pandas_left, pandas_right, on="a", left_on="can't do with by")
    with pytest.raises(ValueError), warns_that_defaulting_to_pandas():
        pd.merge_asof(modin_left, modin_right, on="a", left_on="can't do with by")
    with pytest.raises(ValueError), warns_that_defaulting_to_pandas():
        pandas.merge_asof(
            pandas_left, pandas_right, on="a", right_on="can't do with by"
        )
    with pytest.raises(ValueError), warns_that_defaulting_to_pandas():
        pd.merge_asof(modin_left, modin_right, on="a", right_on="can't do with by")

    # Can't mix left_index with left_on or on, similarly for right.
    with pytest.raises(ValueError), warns_that_defaulting_to_pandas():
        pd.merge_asof(modin_left, modin_right, on="a", right_index=True)
    with pytest.raises(ValueError), warns_that_defaulting_to_pandas():
        pd.merge_asof(
            modin_left, modin_right, left_on="a", right_on="a", right_index=True
        )
    with pytest.raises(ValueError), warns_that_defaulting_to_pandas():
        pd.merge_asof(modin_left, modin_right, on="a", left_index=True)
    with pytest.raises(ValueError), warns_that_defaulting_to_pandas():
        pd.merge_asof(
            modin_left, modin_right, left_on="a", right_on="a", left_index=True
        )

    # Need both left and right
    with pytest.raises(Exception):  # Pandas bug, didn't validate inputs sufficiently
        pandas.merge_asof(pandas_left, pandas_right, left_on="a")
    with pytest.raises(ValueError), warns_that_defaulting_to_pandas():
        pd.merge_asof(modin_left, modin_right, left_on="a")
    with pytest.raises(Exception):  # Pandas bug, didn't validate inputs sufficiently
        pandas.merge_asof(pandas_left, pandas_right, right_on="a")
    with pytest.raises(ValueError), warns_that_defaulting_to_pandas():
        pd.merge_asof(modin_left, modin_right, right_on="a")
    with pytest.raises(ValueError), warns_that_defaulting_to_pandas():
        pandas.merge_asof(pandas_left, pandas_right)
    with pytest.raises(ValueError), warns_that_defaulting_to_pandas():
        pd.merge_asof(modin_left, modin_right)


def test_merge_asof_merge_options():
    modin_quotes = pd.DataFrame(
        {
            "time": [
                pd.Timestamp("2016-05-25 13:30:00.023"),
                pd.Timestamp("2016-05-25 13:30:00.023"),
                pd.Timestamp("2016-05-25 13:30:00.030"),
                pd.Timestamp("2016-05-25 13:30:00.041"),
                pd.Timestamp("2016-05-25 13:30:00.048"),
                pd.Timestamp("2016-05-25 13:30:00.049"),
                pd.Timestamp("2016-05-25 13:30:00.072"),
                pd.Timestamp("2016-05-25 13:30:00.075"),
            ],
            "ticker": ["GOOG", "MSFT", "MSFT", "MSFT", "GOOG", "AAPL", "GOOG", "MSFT"],
            "bid": [720.50, 51.95, 51.97, 51.99, 720.50, 97.99, 720.50, 52.01],
            "ask": [720.93, 51.96, 51.98, 52.00, 720.93, 98.01, 720.88, 52.03],
        }
    )
    modin_trades = pd.DataFrame(
        {
            "time": [
                pd.Timestamp("2016-05-25 13:30:00.023"),
                pd.Timestamp("2016-05-25 13:30:00.038"),
                pd.Timestamp("2016-05-25 13:30:00.048"),
                pd.Timestamp("2016-05-25 13:30:00.048"),
                pd.Timestamp("2016-05-25 13:30:00.048"),
            ],
            "ticker2": ["MSFT", "MSFT", "GOOG", "GOOG", "AAPL"],
            "price": [51.95, 51.95, 720.77, 720.92, 98.0],
            "quantity": [75, 155, 100, 100, 100],
        }
    )
    pandas_quotes, pandas_trades = to_pandas(modin_quotes), to_pandas(modin_trades)

    # left_by + right_by
    with warns_that_defaulting_to_pandas():
        modin_result = pd.merge_asof(
            modin_quotes,
            modin_trades,
            on="time",
            left_by="ticker",
            right_by="ticker2",
        )
    df_equals(
        pandas.merge_asof(
            pandas_quotes,
            pandas_trades,
            on="time",
            left_by="ticker",
            right_by="ticker2",
        ),
        modin_result,
    )

    # Just by:
    pandas_trades["ticker"] = pandas_trades["ticker2"]
    modin_trades["ticker"] = modin_trades["ticker2"]
    with warns_that_defaulting_to_pandas():
        modin_result = pd.merge_asof(
            modin_quotes,
            modin_trades,
            on="time",
            by="ticker",
        )
    df_equals(
        pandas.merge_asof(
            pandas_quotes,
            pandas_trades,
            on="time",
            by="ticker",
        ),
        modin_result,
    )

    # Tolerance
    with warns_that_defaulting_to_pandas():
        modin_result = pd.merge_asof(
            modin_quotes,
            modin_trades,
            on="time",
            by="ticker",
            tolerance=pd.Timedelta("2ms"),
        )
    df_equals(
        pandas.merge_asof(
            pandas_quotes,
            pandas_trades,
            on="time",
            by="ticker",
            tolerance=pd.Timedelta("2ms"),
        ),
        modin_result,
    )

    # Direction
    with warns_that_defaulting_to_pandas():
        modin_result = pd.merge_asof(
            modin_quotes,
            modin_trades,
            on="time",
            by="ticker",
            direction="forward",
        )
    df_equals(
        pandas.merge_asof(
            pandas_quotes,
            pandas_trades,
            on="time",
            by="ticker",
            direction="forward",
        ),
        modin_result,
    )

    # Allow exact matches
    with warns_that_defaulting_to_pandas():
        modin_result = pd.merge_asof(
            modin_quotes,
            modin_trades,
            on="time",
            by="ticker",
            tolerance=pd.Timedelta("10ms"),
            allow_exact_matches=False,
        )
    df_equals(
        pandas.merge_asof(
            pandas_quotes,
            pandas_trades,
            on="time",
            by="ticker",
            tolerance=pd.Timedelta("10ms"),
            allow_exact_matches=False,
        ),
        modin_result,
    )


def test_pivot():
    test_df = pd.DataFrame(
        {
            "foo": ["one", "one", "one", "two", "two", "two"],
            "bar": ["A", "B", "C", "A", "B", "C"],
            "baz": [1, 2, 3, 4, 5, 6],
            "zoo": ["x", "y", "z", "q", "w", "t"],
        }
    )

    df = pd.pivot(test_df, index="foo", columns="bar", values="baz")
    assert isinstance(df, pd.DataFrame)

    with pytest.raises(ValueError):
        pd.pivot(test_df["bar"], index="foo", columns="bar", values="baz")


def test_pivot_table():
    test_df = pd.DataFrame(
        {
            "A": ["foo", "foo", "foo", "foo", "foo", "bar", "bar", "bar", "bar"],
            "B": ["one", "one", "one", "two", "two", "one", "one", "two", "two"],
            "C": [
                "small",
                "large",
                "large",
                "small",
                "small",
                "large",
                "small",
                "small",
                "large",
            ],
            "D": [1, 2, 2, 3, 3, 4, 5, 6, 7],
            "E": [2, 4, 5, 5, 6, 6, 8, 9, 9],
        }
    )

    df = pd.pivot_table(
        test_df, values="D", index=["A", "B"], columns=["C"], aggfunc=np.sum
    )
    assert isinstance(df, pd.DataFrame)

    with pytest.raises(ValueError):
        pd.pivot_table(
            test_df["C"], values="D", index=["A", "B"], columns=["C"], aggfunc=np.sum
        )


def test_unique():
    modin_result = pd.unique([2, 1, 3, 3])
    pandas_result = pandas.unique([2, 1, 3, 3])
    assert_array_equal(modin_result, pandas_result)
    assert modin_result.shape == pandas_result.shape

    modin_result = pd.unique(pd.Series([2] + [1] * 5))
    pandas_result = pandas.unique(pandas.Series([2] + [1] * 5))
    assert_array_equal(modin_result, pandas_result)
    assert modin_result.shape == pandas_result.shape

    modin_result = pd.unique(
        pd.Series([pd.Timestamp("20160101"), pd.Timestamp("20160101")])
    )
    pandas_result = pandas.unique(
        pandas.Series([pandas.Timestamp("20160101"), pandas.Timestamp("20160101")])
    )
    assert_array_equal(modin_result, pandas_result)
    assert modin_result.shape == pandas_result.shape

    modin_result = pd.unique(
        pd.Series(
            [
                pd.Timestamp("20160101", tz="US/Eastern"),
                pd.Timestamp("20160101", tz="US/Eastern"),
            ]
        )
    )
    pandas_result = pandas.unique(
        pandas.Series(
            [
                pandas.Timestamp("20160101", tz="US/Eastern"),
                pandas.Timestamp("20160101", tz="US/Eastern"),
            ]
        )
    )
    assert_array_equal(modin_result, pandas_result)
    assert modin_result.shape == pandas_result.shape

    modin_result = pd.unique(
        pd.Index(
            [
                pd.Timestamp("20160101", tz="US/Eastern"),
                pd.Timestamp("20160101", tz="US/Eastern"),
            ]
        )
    )
    pandas_result = pandas.unique(
        pandas.Index(
            [
                pandas.Timestamp("20160101", tz="US/Eastern"),
                pandas.Timestamp("20160101", tz="US/Eastern"),
            ]
        )
    )
    assert_array_equal(modin_result, pandas_result)
    assert modin_result.shape == pandas_result.shape

    modin_result = pd.unique(pd.Series(pd.Categorical(list("baabc"))))
    pandas_result = pandas.unique(pandas.Series(pandas.Categorical(list("baabc"))))
    assert_array_equal(modin_result, pandas_result)
    assert modin_result.shape == pandas_result.shape


@pytest.mark.parametrize("normalize, bins, dropna", [(True, 3, False)])
def test_value_counts(normalize, bins, dropna):
    # We sort indices for Modin and pandas result because of issue #1650
    values = np.array([3, 1, 2, 3, 4, np.nan])
    with warns_that_defaulting_to_pandas():
        modin_result = sort_index_for_equal_values(
            pd.value_counts(values, normalize=normalize, ascending=False), False
        )
    pandas_result = sort_index_for_equal_values(
        pandas.value_counts(values, normalize=normalize, ascending=False), False
    )
    df_equals(modin_result, pandas_result)

    with warns_that_defaulting_to_pandas():
        modin_result = sort_index_for_equal_values(
            pd.value_counts(values, bins=bins, ascending=False), False
        )
    pandas_result = sort_index_for_equal_values(
        pandas.value_counts(values, bins=bins, ascending=False), False
    )
    df_equals(modin_result, pandas_result)

    with warns_that_defaulting_to_pandas():
        modin_result = sort_index_for_equal_values(
            pd.value_counts(values, dropna=dropna, ascending=True), True
        )
    pandas_result = sort_index_for_equal_values(
        pandas.value_counts(values, dropna=dropna, ascending=True), True
    )
    df_equals(modin_result, pandas_result)


def test_to_datetime():
    # DataFrame input for to_datetime
    modin_df = pd.DataFrame({"year": [2015, 2016], "month": [2, 3], "day": [4, 5]})
    pandas_df = pandas.DataFrame({"year": [2015, 2016], "month": [2, 3], "day": [4, 5]})
    df_equals(pd.to_datetime(modin_df), pandas.to_datetime(pandas_df))

    # Series input for to_datetime
    modin_s = pd.Series(["3/11/2000", "3/12/2000", "3/13/2000"] * 1000)
    pandas_s = pandas.Series(["3/11/2000", "3/12/2000", "3/13/2000"] * 1000)
    df_equals(pd.to_datetime(modin_s), pandas.to_datetime(pandas_s))

    # Other inputs for to_datetime
    value = 1490195805
    assert pd.to_datetime(value, unit="s") == pandas.to_datetime(value, unit="s")
    value = 1490195805433502912
    assert pd.to_datetime(value, unit="ns") == pandas.to_datetime(value, unit="ns")
    value = [1, 2, 3]
    assert pd.to_datetime(value, unit="D", origin=pd.Timestamp("2000-01-01")).equals(
        pandas.to_datetime(value, unit="D", origin=pandas.Timestamp("2000-01-01"))
    )


def test_to_datetime_inplace_side_effect():
    # See GH#3063
    times = list(range(1617993360, 1618193360))
    values = list(range(215441, 415441))
    modin_df = pd.DataFrame({"time": times, "value": values})
    pandas_df = pandas.DataFrame({"time": times, "value": values})
    df_equals(
        pd.to_datetime(modin_df["time"], unit="s"),
        pandas.to_datetime(pandas_df["time"], unit="s"),
    )


@pytest.mark.parametrize(
    "data, errors, downcast",
    [
        (["1.0", "2", -3], "raise", None),
        (["1.0", "2", -3], "raise", "float"),
        (["1.0", "2", -3], "raise", "signed"),
        (["apple", "1.0", "2", -3], "ignore", None),
        (["apple", "1.0", "2", -3], "coerce", None),
    ],
)
def test_to_numeric(data, errors, downcast):
    modin_series = pd.Series(data)
    pandas_series = pandas.Series(data)
    modin_result = pd.to_numeric(modin_series, errors=errors, downcast=downcast)
    pandas_result = pandas.to_numeric(pandas_series, errors=errors, downcast=downcast)
    df_equals(modin_result, pandas_result)


@pytest.mark.parametrize(
    "data", [test_data_values[0], []], ids=["test_data_values[0]", "[]"]
)
def test_to_pandas_indices(data):
    md_df = pd.DataFrame(data)
    index = pandas.MultiIndex.from_tuples(
        [(i, i * 2) for i in np.arange(len(md_df) + 1)], names=["A", "B"]
    ).drop(0)
    columns = pandas.MultiIndex.from_tuples(
        [(i, i * 2) for i in np.arange(len(md_df.columns) + 1)], names=["A", "B"]
    ).drop(0)

    md_df.index = index
    md_df.columns = columns

    pd_df = md_df._to_pandas()

    for axis in [0, 1]:
        assert md_df.axes[axis].equals(
            pd_df.axes[axis]
        ), f"Indices at axis {axis} are different!"
        assert md_df.axes[axis].equal_levels(
            pd_df.axes[axis]
        ), f"Levels of indices at axis {axis} are different!"


def test_create_categorical_dataframe_with_duplicate_column_name():
    # This tests for https://github.com/modin-project/modin/issues/4312
    pd_df = pandas.DataFrame(
        {
            "a": pandas.Categorical([1, 2]),
            "b": [4, 5],
            "c": pandas.Categorical([7, 8]),
        }
    )
    pd_df.columns = ["a", "b", "a"]
    md_df = pd.DataFrame(pd_df)
    # Use assert_frame_equal instead of the common modin util df_equals because
    # we should check dtypes of the new categorical with check_dtype=True.
    # TODO(https://github.com/modin-project/modin/issues/3804): Make
    # df_equals set check_dtype=True and use df_equals instead.
    assert_frame_equal(
        md_df._to_pandas(),
        pd_df,
        check_dtype=True,
        check_index_type=True,
        check_column_type=True,
        check_names=True,
        check_categorical=True,
    )


@pytest.mark.skipif(
    get_current_execution() != "BaseOnPython",
    reason="This test make sense only on BaseOnPython execution.",
)
@pytest.mark.parametrize(
    "func, regex",
    [
        (lambda df: df.mean(level=0), r"DataFrame\.mean"),
        (lambda df: df + df, r"DataFrame\.add"),
        (lambda df: df.index, r"DataFrame\.get_axis\(0\)"),
        (
            lambda df: df.drop(columns="col1").squeeze().repeat(2),
            r"Series\.repeat",
        ),
        (lambda df: df.groupby("col1").prod(), r"GroupBy\.prod"),
        (lambda df: df.rolling(1).count(), r"Rolling\.count"),
    ],
)
def test_default_to_pandas_warning_message(func, regex):
    data = {"col1": [1, 2, 3], "col2": [4, 5, 6]}
    df = pd.DataFrame(data)

    with pytest.warns(UserWarning, match=regex):
        func(df)


def test_empty_dataframe():
    df = pd.DataFrame(columns=["a", "b"])
    with warns_that_defaulting_to_pandas():
        df[(df.a == 1) & (df.b == 2)]


def test_empty_series():
    s = pd.Series([])
    pd.to_numeric(s)