test_dataframe.py

# Copyright 2023 Google LLC
#
# Licensed 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 io
import operator
import sys
import tempfile
import typing
from typing import Dict, List, Tuple

import geopandas as gpd  # type: ignore
import numpy as np
import pandas as pd
import pandas.testing
import pyarrow as pa  # type: ignore
import pytest

import bigframes
import bigframes._config.display_options as display_options
import bigframes.core.indexes as bf_indexes
import bigframes.dataframe as dataframe
import bigframes.dtypes as dtypes
import bigframes.pandas as bpd
import bigframes.series as series
from bigframes.testing.utils import (
    assert_dfs_equivalent,
    assert_pandas_df_equal,
    assert_series_equal,
    assert_series_equivalent,
)


def test_df_construct_copy(scalars_dfs):
    columns = ["int64_col", "string_col", "float64_col"]
    scalars_df, scalars_pandas_df = scalars_dfs
    # Make the mapping from label to col_id non-trivial
    bf_df = scalars_df.copy()
    bf_df["int64_col"] = bf_df["int64_col"] / 2
    pd_df = scalars_pandas_df.copy()
    pd_df["int64_col"] = pd_df["int64_col"] / 2

    bf_result = dataframe.DataFrame(bf_df, columns=columns).to_pandas()

    pd_result = pd.DataFrame(pd_df, columns=columns)
    pandas.testing.assert_frame_equal(bf_result, pd_result)


def test_df_construct_pandas_default(scalars_dfs):
    # This should trigger the inlined codepath
    columns = [
        "int64_too",
        "int64_col",
        "float64_col",
        "bool_col",
        "string_col",
        "date_col",
        "datetime_col",
        "numeric_col",
        "float64_col",
        "time_col",
        "timestamp_col",
    ]
    _, scalars_pandas_df = scalars_dfs
    bf_result = dataframe.DataFrame(scalars_pandas_df, columns=columns).to_pandas()
    pd_result = pd.DataFrame(scalars_pandas_df, columns=columns)
    pandas.testing.assert_frame_equal(bf_result, pd_result)


@pytest.mark.parametrize(
    ("write_engine"),
    [
        ("bigquery_inline"),
        ("bigquery_load"),
        ("bigquery_streaming"),
        ("bigquery_write"),
    ],
)
def test_read_pandas_all_nice_types(
    session: bigframes.Session, scalars_pandas_df_index: pd.DataFrame, write_engine
):
    bf_result = session.read_pandas(
        scalars_pandas_df_index, write_engine=write_engine
    ).to_pandas()
    pandas.testing.assert_frame_equal(bf_result, scalars_pandas_df_index)


def test_df_construct_large_strings():
    data = [["hello", "w" + "o" * 50000 + "rld"]]
    bf_result = dataframe.DataFrame(data).to_pandas()
    pd_result = pd.DataFrame(data, dtype=pd.StringDtype(storage="pyarrow"))
    pandas.testing.assert_frame_equal(bf_result, pd_result, check_index_type=False)


def test_df_construct_pandas_load_job(scalars_dfs_maybe_ordered):
    # This should trigger the inlined codepath
    columns = [
        "int64_too",
        "int64_col",
        "float64_col",
        "bool_col",
        "string_col",
        "date_col",
        "datetime_col",
        "numeric_col",
        "float64_col",
        "time_col",
        "timestamp_col",
        "geography_col",
    ]
    _, scalars_pandas_df = scalars_dfs_maybe_ordered
    bf_result = dataframe.DataFrame(scalars_pandas_df, columns=columns)
    pd_result = pd.DataFrame(scalars_pandas_df, columns=columns)
    assert_dfs_equivalent(pd_result, bf_result)


def test_df_construct_structs(session):
    pd_frame = pd.Series(
        [
            {"version": 1, "project": "pandas"},
            {"version": 2, "project": "pandas"},
            {"version": 1, "project": "numpy"},
        ]
    ).to_frame()
    bf_series = session.read_pandas(pd_frame)
    pd.testing.assert_frame_equal(
        bf_series.to_pandas(), pd_frame, check_index_type=False, check_dtype=False
    )


def test_df_construct_pandas_set_dtype(scalars_dfs):
    columns = [
        "int64_too",
        "int64_col",
        "float64_col",
        "bool_col",
    ]
    _, scalars_pandas_df = scalars_dfs
    bf_result = dataframe.DataFrame(
        scalars_pandas_df, columns=columns, dtype="Float64"
    ).to_pandas()
    pd_result = pd.DataFrame(scalars_pandas_df, columns=columns, dtype="Float64")
    pandas.testing.assert_frame_equal(bf_result, pd_result)


def test_df_construct_from_series(scalars_dfs_maybe_ordered):
    scalars_df, scalars_pandas_df = scalars_dfs_maybe_ordered
    bf_result = dataframe.DataFrame(
        {"a": scalars_df["int64_col"], "b": scalars_df["string_col"]},
        dtype="string[pyarrow]",
    )
    pd_result = pd.DataFrame(
        {"a": scalars_pandas_df["int64_col"], "b": scalars_pandas_df["string_col"]},
        dtype="string[pyarrow]",
    )
    assert_dfs_equivalent(pd_result, bf_result)


def test_df_construct_from_dict():
    input_dict = {
        "Animal": ["Falcon", "Falcon", "Parrot", "Parrot"],
        # With a space in column name. We use standardized SQL schema ids to solve the problem that BQ schema doesn't support column names with spaces. b/296751058
        "Max Speed": [380.0, 370.0, 24.0, 26.0],
    }
    bf_result = dataframe.DataFrame(input_dict).to_pandas()
    pd_result = pd.DataFrame(input_dict)

    pandas.testing.assert_frame_equal(
        bf_result, pd_result, check_dtype=False, check_index_type=False
    )


@pytest.mark.parametrize(
    ("json_type"),
    [
        pytest.param(dtypes.JSON_DTYPE),
        pytest.param("json"),
    ],
)
def test_df_construct_w_json_dtype(json_type):
    data = [
        "1",
        "false",
        '["a", {"b": 1}, null]',
        None,
    ]
    df = dataframe.DataFrame({"json_col": data}, dtype=json_type)

    assert df["json_col"].dtype == dtypes.JSON_DTYPE
    assert df["json_col"][1] == "false"


def test_df_construct_inline_respects_location(reset_default_session_and_location):
    # Note: This starts a thread-local session.
    with bpd.option_context("bigquery.location", "europe-west1"):
        df = bpd.DataFrame([[1, 2, 3], [4, 5, 6]])
        df.to_gbq()
        assert df.query_job is not None
        table = bpd.get_global_session().bqclient.get_table(df.query_job.destination)

        assert table.location == "europe-west1"


def test_df_construct_dtype():
    data = {
        "int_col": [1, 2, 3],
        "string_col": ["1.1", "2.0", "3.5"],
        "float_col": [1.0, 2.0, 3.0],
    }
    dtype = pd.StringDtype(storage="pyarrow")
    bf_result = dataframe.DataFrame(data, dtype=dtype)
    pd_result = pd.DataFrame(data, dtype=dtype)
    pd_result.index = pd_result.index.astype("Int64")
    pandas.testing.assert_frame_equal(bf_result.to_pandas(), pd_result)


def test_get_column(scalars_dfs):
    scalars_df, scalars_pandas_df = scalars_dfs
    col_name = "int64_col"
    series = scalars_df[col_name]
    bf_result = series.to_pandas()
    pd_result = scalars_pandas_df[col_name]
    assert_series_equal(bf_result, pd_result)


def test_get_column_nonstring(scalars_dfs):
    scalars_df, scalars_pandas_df = scalars_dfs
    series = scalars_df.rename(columns={"int64_col": 123.1})[123.1]
    bf_result = series.to_pandas()
    pd_result = scalars_pandas_df.rename(columns={"int64_col": 123.1})[123.1]
    assert_series_equal(bf_result, pd_result)


@pytest.mark.parametrize(
    "row_slice",
    [
        (slice(1, 7, 2)),
        (slice(1, 7, None)),
        (slice(None, -3, None)),
    ],
)
def test_get_rows_with_slice(scalars_dfs, row_slice):
    scalars_df, scalars_pandas_df = scalars_dfs
    bf_result = scalars_df[row_slice].to_pandas()
    pd_result = scalars_pandas_df[row_slice]
    assert_pandas_df_equal(bf_result, pd_result)


def test_hasattr(scalars_dfs):
    scalars_df, _ = scalars_dfs
    assert hasattr(scalars_df, "int64_col")
    assert hasattr(scalars_df, "head")
    assert not hasattr(scalars_df, "not_exist")


@pytest.mark.parametrize(
    ("ordered"),
    [
        (True),
        (False),
    ],
)
def test_head_with_custom_column_labels(
    scalars_df_index, scalars_pandas_df_index, ordered
):
    rename_mapping = {
        "int64_col": "Integer Column",
        "string_col": "言語列",
    }
    bf_df = scalars_df_index.rename(columns=rename_mapping).head(3)
    bf_result = bf_df.to_pandas(ordered=ordered)
    pd_result = scalars_pandas_df_index.rename(columns=rename_mapping).head(3)
    assert_pandas_df_equal(bf_result, pd_result, ignore_order=not ordered)


def test_tail_with_custom_column_labels(scalars_df_index, scalars_pandas_df_index):
    rename_mapping = {
        "int64_col": "Integer Column",
        "string_col": "言語列",
    }
    bf_df = scalars_df_index.rename(columns=rename_mapping).tail(3)
    bf_result = bf_df.to_pandas()
    pd_result = scalars_pandas_df_index.rename(columns=rename_mapping).tail(3)
    pandas.testing.assert_frame_equal(bf_result, pd_result)


@pytest.mark.parametrize(
    ("keep",),
    [
        ("first",),
        ("last",),
        ("all",),
    ],
)
def test_df_nlargest(scalars_df_index, scalars_pandas_df_index, keep):
    bf_result = scalars_df_index.nlargest(3, ["bool_col", "int64_too"], keep=keep)
    pd_result = scalars_pandas_df_index.nlargest(
        3, ["bool_col", "int64_too"], keep=keep
    )

    pd.testing.assert_frame_equal(
        bf_result.to_pandas(),
        pd_result,
    )


@pytest.mark.parametrize(
    ("keep",),
    [
        ("first",),
        ("last",),
        ("all",),
    ],
)
def test_df_nsmallest(scalars_df_index, scalars_pandas_df_index, keep):
    bf_result = scalars_df_index.nsmallest(6, ["bool_col"], keep=keep)
    pd_result = scalars_pandas_df_index.nsmallest(6, ["bool_col"], keep=keep)

    pd.testing.assert_frame_equal(
        bf_result.to_pandas(),
        pd_result,
    )


def test_get_column_by_attr(scalars_dfs):
    scalars_df, scalars_pandas_df = scalars_dfs
    series = scalars_df.int64_col
    bf_result = series.to_pandas()
    pd_result = scalars_pandas_df.int64_col
    assert_series_equal(bf_result, pd_result)


def test_get_columns(scalars_dfs):
    scalars_df, scalars_pandas_df = scalars_dfs
    col_names = ["bool_col", "float64_col", "int64_col"]
    df_subset = scalars_df.get(col_names)
    df_pandas = df_subset.to_pandas()
    pd.testing.assert_index_equal(
        df_pandas.columns, scalars_pandas_df[col_names].columns
    )


def test_get_columns_default(scalars_dfs):
    scalars_df, _ = scalars_dfs
    col_names = ["not", "column", "names"]
    result = scalars_df.get(col_names, "default_val")
    assert result == "default_val"


@pytest.mark.parametrize(
    ("loc", "column", "value", "allow_duplicates"),
    [
        (0, 666, 2, False),
        (5, "float64_col", 2.2, True),
        (13, "rowindex_2", [8, 7, 6, 5, 4, 3, 2, 1, 0], True),
        pytest.param(
            14,
            "test",
            2,
            False,
            marks=pytest.mark.xfail(
                raises=IndexError,
            ),
        ),
        pytest.param(
            12,
            "int64_col",
            2,
            False,
            marks=pytest.mark.xfail(
                raises=ValueError,
            ),
        ),
    ],
)
def test_insert(scalars_dfs, loc, column, value, allow_duplicates):
    scalars_df, scalars_pandas_df = scalars_dfs
    # insert works inplace, so will influence other tests.
    # make a copy to avoid inplace changes.
    bf_df = scalars_df.copy()
    pd_df = scalars_pandas_df.copy()
    bf_df.insert(loc, column, value, allow_duplicates)
    pd_df.insert(loc, column, value, allow_duplicates)

    pd.testing.assert_frame_equal(bf_df.to_pandas(), pd_df, check_dtype=False)


def test_mask_series_cond(scalars_df_index, scalars_pandas_df_index):
    cond_bf = scalars_df_index["int64_col"] > 0
    cond_pd = scalars_pandas_df_index["int64_col"] > 0

    bf_df = scalars_df_index[["int64_too", "int64_col", "float64_col"]]
    pd_df = scalars_pandas_df_index[["int64_too", "int64_col", "float64_col"]]
    bf_result = bf_df.mask(cond_bf, bf_df + 1).to_pandas()
    pd_result = pd_df.mask(cond_pd, pd_df + 1)
    pandas.testing.assert_frame_equal(bf_result, pd_result)


def test_where_multi_column(scalars_df_index, scalars_pandas_df_index):
    # Test when a dataframe has multi-columns.
    columns = ["int64_col", "float64_col"]
    dataframe_bf = scalars_df_index[columns]

    dataframe_bf.columns = pd.MultiIndex.from_tuples(
        [("str1", 1), ("str2", 2)], names=["STR", "INT"]
    )
    cond_bf = dataframe_bf["str1"] > 0

    with pytest.raises(NotImplementedError) as context:
        dataframe_bf.where(cond_bf).to_pandas()
    assert (
        str(context.value)
        == "The dataframe.where() method does not support multi-column."
    )


def test_where_series_cond(scalars_df_index, scalars_pandas_df_index):
    # Condition is dataframe, other is None (as default).
    cond_bf = scalars_df_index["int64_col"] > 0
    cond_pd = scalars_pandas_df_index["int64_col"] > 0
    bf_result = scalars_df_index.where(cond_bf).to_pandas()
    pd_result = scalars_pandas_df_index.where(cond_pd)
    pandas.testing.assert_frame_equal(bf_result, pd_result)


def test_where_series_cond_const_other(scalars_df_index, scalars_pandas_df_index):
    # Condition is a series, other is a constant.
    columns = ["int64_col", "float64_col"]
    dataframe_bf = scalars_df_index[columns]
    dataframe_pd = scalars_pandas_df_index[columns]
    dataframe_bf.columns.name = "test_name"
    dataframe_pd.columns.name = "test_name"

    cond_bf = dataframe_bf["int64_col"] > 0
    cond_pd = dataframe_pd["int64_col"] > 0
    other = 0

    bf_result = dataframe_bf.where(cond_bf, other).to_pandas()
    pd_result = dataframe_pd.where(cond_pd, other)
    pandas.testing.assert_frame_equal(bf_result, pd_result)


def test_where_series_cond_dataframe_other(scalars_df_index, scalars_pandas_df_index):
    # Condition is a series, other is a dataframe.
    columns = ["int64_col", "float64_col"]
    dataframe_bf = scalars_df_index[columns]
    dataframe_pd = scalars_pandas_df_index[columns]

    cond_bf = dataframe_bf["int64_col"] > 0
    cond_pd = dataframe_pd["int64_col"] > 0
    other_bf = -dataframe_bf
    other_pd = -dataframe_pd

    bf_result = dataframe_bf.where(cond_bf, other_bf).to_pandas()
    pd_result = dataframe_pd.where(cond_pd, other_pd)
    pandas.testing.assert_frame_equal(bf_result, pd_result)


def test_where_dataframe_cond(scalars_df_index, scalars_pandas_df_index):
    # Condition is a dataframe, other is None.
    columns = ["int64_col", "float64_col"]
    dataframe_bf = scalars_df_index[columns]
    dataframe_pd = scalars_pandas_df_index[columns]

    cond_bf = dataframe_bf > 0
    cond_pd = dataframe_pd > 0

    bf_result = dataframe_bf.where(cond_bf, None).to_pandas()
    pd_result = dataframe_pd.where(cond_pd, None)
    pandas.testing.assert_frame_equal(bf_result, pd_result)


def test_where_dataframe_cond_const_other(scalars_df_index, scalars_pandas_df_index):
    # Condition is a dataframe, other is a constant.
    columns = ["int64_col", "float64_col"]
    dataframe_bf = scalars_df_index[columns]
    dataframe_pd = scalars_pandas_df_index[columns]

    cond_bf = dataframe_bf > 0
    cond_pd = dataframe_pd > 0
    other_bf = 10
    other_pd = 10

    bf_result = dataframe_bf.where(cond_bf, other_bf).to_pandas()
    pd_result = dataframe_pd.where(cond_pd, other_pd)
    pandas.testing.assert_frame_equal(bf_result, pd_result)


def test_where_dataframe_cond_dataframe_other(
    scalars_df_index, scalars_pandas_df_index
):
    # Condition is a dataframe, other is a dataframe.
    columns = ["int64_col", "float64_col"]
    dataframe_bf = scalars_df_index[columns]
    dataframe_pd = scalars_pandas_df_index[columns]

    cond_bf = dataframe_bf > 0
    cond_pd = dataframe_pd > 0
    other_bf = dataframe_bf * 2
    other_pd = dataframe_pd * 2

    bf_result = dataframe_bf.where(cond_bf, other_bf).to_pandas()
    pd_result = dataframe_pd.where(cond_pd, other_pd)
    pandas.testing.assert_frame_equal(bf_result, pd_result)


def test_where_callable_cond_constant_other(scalars_df_index, scalars_pandas_df_index):
    # Condition is callable, other is a constant.
    columns = ["int64_col", "float64_col"]
    dataframe_bf = scalars_df_index[columns]
    dataframe_pd = scalars_pandas_df_index[columns]

    other = 10

    bf_result = dataframe_bf.where(lambda x: x > 0, other).to_pandas()
    pd_result = dataframe_pd.where(lambda x: x > 0, other)
    pandas.testing.assert_frame_equal(bf_result, pd_result)


def test_where_dataframe_cond_callable_other(scalars_df_index, scalars_pandas_df_index):
    # Condition is a dataframe, other is callable.
    columns = ["int64_col", "float64_col"]
    dataframe_bf = scalars_df_index[columns]
    dataframe_pd = scalars_pandas_df_index[columns]

    cond_bf = dataframe_bf > 0
    cond_pd = dataframe_pd > 0

    def func(x):
        return x * 2

    bf_result = dataframe_bf.where(cond_bf, func).to_pandas()
    pd_result = dataframe_pd.where(cond_pd, func)
    pandas.testing.assert_frame_equal(bf_result, pd_result)


def test_where_callable_cond_callable_other(scalars_df_index, scalars_pandas_df_index):
    # Condition is callable, other is callable too.
    columns = ["int64_col", "float64_col"]
    dataframe_bf = scalars_df_index[columns]
    dataframe_pd = scalars_pandas_df_index[columns]

    def func(x):
        return x["int64_col"] > 0

    bf_result = dataframe_bf.where(func, lambda x: x * 2).to_pandas()
    pd_result = dataframe_pd.where(func, lambda x: x * 2)
    pandas.testing.assert_frame_equal(bf_result, pd_result)


def test_drop_column(scalars_dfs):
    scalars_df, scalars_pandas_df = scalars_dfs
    col_name = "int64_col"
    df_pandas = scalars_df.drop(columns=col_name).to_pandas()
    pd.testing.assert_index_equal(
        df_pandas.columns, scalars_pandas_df.drop(columns=col_name).columns
    )


def test_drop_columns(scalars_dfs):
    scalars_df, scalars_pandas_df = scalars_dfs
    col_names = ["int64_col", "geography_col", "time_col"]
    df_pandas = scalars_df.drop(columns=col_names).to_pandas()
    pd.testing.assert_index_equal(
        df_pandas.columns, scalars_pandas_df.drop(columns=col_names).columns
    )


def test_drop_labels_axis_1(scalars_dfs):
    scalars_df, scalars_pandas_df = scalars_dfs
    labels = ["int64_col", "geography_col", "time_col"]

    pd_result = scalars_pandas_df.drop(labels=labels, axis=1)
    bf_result = scalars_df.drop(labels=labels, axis=1).to_pandas()

    pd.testing.assert_frame_equal(pd_result, bf_result)


def test_drop_with_custom_column_labels(scalars_dfs):
    scalars_df, scalars_pandas_df = scalars_dfs
    rename_mapping = {
        "int64_col": "Integer Column",
        "string_col": "言語列",
    }
    dropped_columns = [
        "言語列",
        "timestamp_col",
    ]
    bf_df = scalars_df.rename(columns=rename_mapping).drop(columns=dropped_columns)
    bf_result = bf_df.to_pandas()
    pd_result = scalars_pandas_df.rename(columns=rename_mapping).drop(
        columns=dropped_columns
    )
    assert_pandas_df_equal(bf_result, pd_result)


def test_df_memory_usage(scalars_dfs):
    scalars_df, scalars_pandas_df = scalars_dfs

    pd_result = scalars_pandas_df.memory_usage()
    bf_result = scalars_df.memory_usage()

    pd.testing.assert_series_equal(pd_result, bf_result, rtol=1.5)


def test_df_info(scalars_dfs):
    expected = (
        "<class 'bigframes.dataframe.DataFrame'>\n"
        "Index: 9 entries, 0 to 8\n"
        "Data columns (total 14 columns):\n"
        "  #  Column         Non-Null Count    Dtype\n"
        "---  -------------  ----------------  ------------------------------\n"
        "  0  bool_col       8 non-null        boolean\n"
        "  1  bytes_col      6 non-null        binary[pyarrow]\n"
        "  2  date_col       7 non-null        date32[day][pyarrow]\n"
        "  3  datetime_col   6 non-null        timestamp[us][pyarrow]\n"
        "  4  geography_col  4 non-null        geometry\n"
        "  5  int64_col      8 non-null        Int64\n"
        "  6  int64_too      9 non-null        Int64\n"
        "  7  numeric_col    6 non-null        decimal128(38, 9)[pyarrow]\n"
        "  8  float64_col    7 non-null        Float64\n"
        "  9  rowindex_2     9 non-null        Int64\n"
        " 10  string_col     8 non-null        string\n"
        " 11  time_col       6 non-null        time64[us][pyarrow]\n"
        " 12  timestamp_col  6 non-null        timestamp[us, tz=UTC][pyarrow]\n"
        " 13  duration_col   7 non-null        duration[us][pyarrow]\n"
        "dtypes: Float64(1), Int64(3), binary[pyarrow](1), boolean(1), date32[day][pyarrow](1), decimal128(38, 9)[pyarrow](1), duration[us][pyarrow](1), geometry(1), string(1), time64[us][pyarrow](1), timestamp[us, tz=UTC][pyarrow](1), timestamp[us][pyarrow](1)\n"
        "memory usage: 1341 bytes\n"
    )

    scalars_df, _ = scalars_dfs
    bf_result = io.StringIO()

    scalars_df.info(buf=bf_result)

    assert expected == bf_result.getvalue()


@pytest.mark.parametrize(
    ("include", "exclude"),
    [
        ("Int64", None),
        (["int"], None),
        ("number", None),
        ([pd.Int64Dtype(), pd.BooleanDtype()], None),
        (None, [pd.Int64Dtype(), pd.BooleanDtype()]),
        ("Int64", ["boolean"]),
    ],
)
def test_select_dtypes(scalars_dfs, include, exclude):
    scalars_df, scalars_pandas_df = scalars_dfs

    pd_result = scalars_pandas_df.select_dtypes(include=include, exclude=exclude)
    bf_result = scalars_df.select_dtypes(include=include, exclude=exclude).to_pandas()

    pd.testing.assert_frame_equal(pd_result, bf_result)


def test_drop_index(scalars_dfs):
    scalars_df, scalars_pandas_df = scalars_dfs

    pd_result = scalars_pandas_df.drop(index=[4, 1, 2])
    bf_result = scalars_df.drop(index=[4, 1, 2]).to_pandas()

    pd.testing.assert_frame_equal(pd_result, bf_result)


def test_drop_pandas_index(scalars_dfs):
    scalars_df, scalars_pandas_df = scalars_dfs
    drop_index = scalars_pandas_df.iloc[[4, 1, 2]].index

    pd_result = scalars_pandas_df.drop(index=drop_index)
    bf_result = scalars_df.drop(index=drop_index).to_pandas()

    pd.testing.assert_frame_equal(pd_result, bf_result)


def test_drop_bigframes_index(scalars_dfs):
    scalars_df, scalars_pandas_df = scalars_dfs
    drop_index = scalars_df.loc[[4, 1, 2]].index
    drop_pandas_index = scalars_pandas_df.loc[[4, 1, 2]].index

    pd_result = scalars_pandas_df.drop(index=drop_pandas_index)
    bf_result = scalars_df.drop(index=drop_index).to_pandas()

    pd.testing.assert_frame_equal(pd_result, bf_result)


def test_drop_bigframes_index_with_na(scalars_dfs):
    scalars_df, scalars_pandas_df = scalars_dfs
    scalars_df = scalars_df.copy()
    scalars_pandas_df = scalars_pandas_df.copy()
    scalars_df = scalars_df.set_index("bytes_col")
    scalars_pandas_df = scalars_pandas_df.set_index("bytes_col")
    drop_index = scalars_df.iloc[[3, 5]].index
    drop_pandas_index = scalars_pandas_df.iloc[[3, 5]].index

    pd_result = scalars_pandas_df.drop(index=drop_pandas_index)  # drop_pandas_index)
    bf_result = scalars_df.drop(index=drop_index).to_pandas()

    pd.testing.assert_frame_equal(pd_result, bf_result)


def test_drop_bigframes_multiindex(scalars_dfs):
    # TODO: supply a reason why this isn't compatible with pandas 1.x
    pytest.importorskip("pandas", minversion="2.0.0")
    scalars_df, scalars_pandas_df = scalars_dfs
    scalars_df = scalars_df.copy()
    scalars_pandas_df = scalars_pandas_df.copy()
    sub_df = scalars_df.iloc[[4, 1, 2]]
    sub_pandas_df = scalars_pandas_df.iloc[[4, 1, 2]]
    sub_df = sub_df.set_index(["bytes_col", "numeric_col"])
    sub_pandas_df = sub_pandas_df.set_index(["bytes_col", "numeric_col"])
    drop_index = sub_df.index
    drop_pandas_index = sub_pandas_df.index

    scalars_df = scalars_df.set_index(["bytes_col", "numeric_col"])
    scalars_pandas_df = scalars_pandas_df.set_index(["bytes_col", "numeric_col"])
    bf_result = scalars_df.drop(index=drop_index).to_pandas()
    pd_result = scalars_pandas_df.drop(index=drop_pandas_index)

    pd.testing.assert_frame_equal(pd_result, bf_result)


def test_drop_labels_axis_0(scalars_dfs):
    scalars_df, scalars_pandas_df = scalars_dfs

    pd_result = scalars_pandas_df.drop(labels=[4, 1, 2], axis=0)
    bf_result = scalars_df.drop(labels=[4, 1, 2], axis=0).to_pandas()

    pd.testing.assert_frame_equal(pd_result, bf_result)


def test_drop_index_and_columns(scalars_dfs):
    scalars_df, scalars_pandas_df = scalars_dfs

    pd_result = scalars_pandas_df.drop(index=[4, 1, 2], columns="int64_col")
    bf_result = scalars_df.drop(index=[4, 1, 2], columns="int64_col").to_pandas()

    pd.testing.assert_frame_equal(pd_result, bf_result)


def test_rename(scalars_dfs):
    scalars_df, scalars_pandas_df = scalars_dfs
    col_name_dict = {"bool_col": 1.2345}
    df_pandas = scalars_df.rename(columns=col_name_dict).to_pandas()
    pd.testing.assert_index_equal(
        df_pandas.columns, scalars_pandas_df.rename(columns=col_name_dict).columns
    )


def test_df_peek(scalars_dfs_maybe_ordered):
    scalars_df, scalars_pandas_df = scalars_dfs_maybe_ordered

    peek_result = scalars_df.peek(n=3, force=False, allow_large_results=True)

    pd.testing.assert_index_equal(scalars_pandas_df.columns, peek_result.columns)
    assert len(peek_result) == 3


def test_df_peek_with_large_results_not_allowed(scalars_dfs_maybe_ordered):
    scalars_df, scalars_pandas_df = scalars_dfs_maybe_ordered

    peek_result = scalars_df.peek(n=3, force=False, allow_large_results=False)

    pd.testing.assert_index_equal(scalars_pandas_df.columns, peek_result.columns)
    assert len(peek_result) == 3


def test_df_peek_filtered(scalars_dfs):
    scalars_df, scalars_pandas_df = scalars_dfs
    peek_result = scalars_df[scalars_df.int64_col != 0].peek(n=3, force=False)
    pd.testing.assert_index_equal(scalars_pandas_df.columns, peek_result.columns)
    assert len(peek_result) == 3


def test_df_peek_exception(scalars_dfs):
    scalars_df, _ = scalars_dfs

    with pytest.raises(ValueError):
        # Window ops aren't compatible with efficient peeking
        scalars_df[["int64_col", "int64_too"]].cumsum().peek(n=3, force=False)


def test_df_peek_force_default(scalars_dfs):
    scalars_df, scalars_pandas_df = scalars_dfs
    peek_result = scalars_df[["int64_col", "int64_too"]].cumsum().peek(n=3)
    pd.testing.assert_index_equal(
        scalars_pandas_df[["int64_col", "int64_too"]].columns, peek_result.columns
    )
    assert len(peek_result) == 3


def test_df_peek_reset_index(scalars_dfs):
    scalars_df, scalars_pandas_df = scalars_dfs
    peek_result = (
        scalars_df[["int64_col", "int64_too"]].reset_index(drop=True).peek(n=3)
    )
    pd.testing.assert_index_equal(
        scalars_pandas_df[["int64_col", "int64_too"]].columns, peek_result.columns
    )
    assert len(peek_result) == 3


def test_repr_w_all_rows(scalars_dfs):
    scalars_df, scalars_pandas_df = scalars_dfs

    # Remove columns with flaky formatting, like NUMERIC columns (which use the
    # object dtype). Also makes a copy so that mutating the index name doesn't
    # break other tests.
    scalars_df = scalars_df.drop(columns=["numeric_col"])
    scalars_pandas_df = scalars_pandas_df.drop(columns=["numeric_col"])

    # When there are 10 or fewer rows, the outputs should be identical.
    actual = repr(scalars_df.head(10))

    with display_options.pandas_repr(bigframes.options.display):
        expected = repr(scalars_pandas_df.head(10))

    assert actual == expected


def test_join_repr(scalars_dfs_maybe_ordered):
    scalars_df, scalars_pandas_df = scalars_dfs_maybe_ordered

    scalars_df = (
        scalars_df[["int64_col"]]
        .join(scalars_df.set_index("int64_col")[["int64_too"]])
        .sort_index()
    )
    scalars_pandas_df = (
        scalars_pandas_df[["int64_col"]]
        .join(scalars_pandas_df.set_index("int64_col")[["int64_too"]])
        .sort_index()
    )
    # Pandas join result index name seems to depend on the index values in a way that bigframes can't match exactly
    scalars_pandas_df.index.name = None

    actual = repr(scalars_df)

    with display_options.pandas_repr(bigframes.options.display):
        expected = repr(scalars_pandas_df)

    assert actual == expected


def test_repr_html_w_all_rows(scalars_dfs, session):
    metrics = session._metrics
    scalars_df, _ = scalars_dfs
    # get a pandas df of the expected format
    df, _ = scalars_df._block.to_pandas()
    pandas_df = df.set_axis(scalars_df._block.column_labels, axis=1)
    pandas_df.index.name = scalars_df.index.name

    executions_pre = metrics.execution_count
    # When there are 10 or fewer rows, the outputs should be identical except for the extra note.
    actual = scalars_df.head(10)._repr_html_()
    executions_post = metrics.execution_count

    with display_options.pandas_repr(bigframes.options.display):
        pandas_repr = pandas_df.head(10)._repr_html_()

    expected = (
        pandas_repr
        + f"[{len(pandas_df.index)} rows x {len(pandas_df.columns)} columns in total]"
    )
    assert actual == expected
    assert (executions_post - executions_pre) <= 3


def test_df_column_name_with_space(scalars_dfs):
    scalars_df, scalars_pandas_df = scalars_dfs
    col_name_dict = {"bool_col": "bool  col"}
    df_pandas = scalars_df.rename(columns=col_name_dict).to_pandas()
    pd.testing.assert_index_equal(
        df_pandas.columns, scalars_pandas_df.rename(columns=col_name_dict).columns
    )


def test_df_column_name_duplicate(scalars_dfs):
    scalars_df, scalars_pandas_df = scalars_dfs
    col_name_dict = {"int64_too": "int64_col"}
    df_pandas = scalars_df.rename(columns=col_name_dict).to_pandas()
    pd.testing.assert_index_equal(
        df_pandas.columns, scalars_pandas_df.rename(columns=col_name_dict).columns
    )


def test_get_df_column_name_duplicate(scalars_dfs):
    scalars_df, scalars_pandas_df = scalars_dfs
    col_name_dict = {"int64_too": "int64_col"}

    bf_result = scalars_df.rename(columns=col_name_dict)["int64_col"].to_pandas()
    pd_result = scalars_pandas_df.rename(columns=col_name_dict)["int64_col"]
    pd.testing.assert_index_equal(bf_result.columns, pd_result.columns)


@pytest.mark.parametrize(
    ("indices", "axis"),
    [
        ([1, 3, 5], 0),
        ([2, 4, 6], 1),
        ([1, -3, -5, -6], "index"),
        ([-2, -4, -6], "columns"),
    ],
)
def test_take_df(scalars_dfs, indices, axis):
    scalars_df, scalars_pandas_df = scalars_dfs

    bf_result = scalars_df.take(indices, axis=axis).to_pandas()
    pd_result = scalars_pandas_df.take(indices, axis=axis)

    assert_pandas_df_equal(bf_result, pd_result)


def test_filter_df(scalars_dfs):
    scalars_df, scalars_pandas_df = scalars_dfs

    bf_bool_series = scalars_df["bool_col"]
    bf_result = scalars_df[bf_bool_series].to_pandas()

    pd_bool_series = scalars_pandas_df["bool_col"]
    pd_result = scalars_pandas_df[pd_bool_series]

    assert_pandas_df_equal(bf_result, pd_result)


def test_df_to_pandas_batches(scalars_dfs):
    scalars_df, scalars_pandas_df = scalars_dfs

    capped_unfiltered_batches = scalars_df.to_pandas_batches(page_size=2, max_results=6)
    bf_bool_series = scalars_df["bool_col"]
    filtered_batches = scalars_df[bf_bool_series].to_pandas_batches()

    pd_bool_series = scalars_pandas_df["bool_col"]
    pd_result = scalars_pandas_df[pd_bool_series]

    assert 6 == capped_unfiltered_batches.total_rows
    assert len(pd_result) == filtered_batches.total_rows
    assert_pandas_df_equal(pd.concat(filtered_batches), pd_result)


@pytest.mark.parametrize(
    ("literal", "expected_dtype"),
    (
        pytest.param(
            2,
            dtypes.INT_DTYPE,
            id="INT64",
        ),
        # ====================================================================
        # NULL values
        #
        # These are regression tests for b/428999884. It needs to be possible to
        # set a column to NULL with a desired type (not just the pandas default
        # of float64).
        # ====================================================================
        pytest.param(None, dtypes.FLOAT_DTYPE, id="NULL-None"),
        pytest.param(
            pa.scalar(None, type=pa.int64()),
            dtypes.INT_DTYPE,
            id="NULL-pyarrow-TIMESTAMP",
        ),
        pytest.param(
            pa.scalar(None, type=pa.timestamp("us", tz="UTC")),
            dtypes.TIMESTAMP_DTYPE,
            id="NULL-pyarrow-TIMESTAMP",
        ),
        pytest.param(
            pa.scalar(None, type=pa.timestamp("us")),
            dtypes.DATETIME_DTYPE,
            id="NULL-pyarrow-DATETIME",
        ),
    ),
)
def test_assign_new_column_w_literal(scalars_dfs, literal, expected_dtype):
    scalars_df, scalars_pandas_df = scalars_dfs
    df = scalars_df.assign(new_col=literal)
    bf_result = df.to_pandas()

    new_col_pd = literal
    if isinstance(literal, pa.Scalar):
        # PyArrow integer scalars aren't yet supported in pandas Int64Dtype.
        new_col_pd = literal.as_py()

    # Pandas might not pick the same dtype as BigFrames, but it should at least
    # be castable to it.
    pd_result = scalars_pandas_df.assign(new_col=new_col_pd)
    pd_result["new_col"] = pd_result["new_col"].astype(expected_dtype)

    assert_pandas_df_equal(bf_result, pd_result)


def test_assign_new_column_w_loc(scalars_dfs):
    scalars_df, scalars_pandas_df = scalars_dfs
    bf_df = scalars_df.copy()
    pd_df = scalars_pandas_df.copy()
    bf_df.loc[:, "new_col"] = 2
    pd_df.loc[:, "new_col"] = 2
    bf_result = bf_df.to_pandas()
    pd_result = pd_df

    # Convert default pandas dtypes `int64` to match BigQuery DataFrames dtypes.
    pd_result["new_col"] = pd_result["new_col"].astype("Int64")

    pd.testing.assert_frame_equal(bf_result, pd_result)


@pytest.mark.parametrize(
    ("scalar",),
    [
        (2.1,),
        (None,),
    ],
)
def test_assign_new_column_w_setitem(scalars_dfs, scalar):
    scalars_df, scalars_pandas_df = scalars_dfs
    bf_df = scalars_df.copy()
    pd_df = scalars_pandas_df.copy()
    bf_df["new_col"] = scalar
    pd_df["new_col"] = scalar
    bf_result = bf_df.to_pandas()
    pd_result = pd_df

    # Convert default pandas dtypes `float64` to match BigQuery DataFrames dtypes.
    pd_result["new_col"] = pd_result["new_col"].astype("Float64")

    pd.testing.assert_frame_equal(bf_result, pd_result)


def test_assign_new_column_w_setitem_dataframe(scalars_dfs):
    scalars_df, scalars_pandas_df = scalars_dfs
    bf_df = scalars_df.copy()
    pd_df = scalars_pandas_df.copy()
    bf_df["int64_col"] = bf_df["int64_too"].to_frame()
    pd_df["int64_col"] = pd_df["int64_too"].to_frame()

    # Convert default pandas dtypes `int64` to match BigQuery DataFrames dtypes.
    pd_df["int64_col"] = pd_df["int64_col"].astype("Int64")

    pd.testing.assert_frame_equal(bf_df.to_pandas(), pd_df)


def test_assign_new_column_w_setitem_dataframe_error(scalars_dfs):
    scalars_df, scalars_pandas_df = scalars_dfs
    bf_df = scalars_df.copy()
    pd_df = scalars_pandas_df.copy()

    with pytest.raises(ValueError):
        bf_df["impossible_col"] = bf_df[["int64_too", "string_col"]]
    with pytest.raises(ValueError):
        pd_df["impossible_col"] = pd_df[["int64_too", "string_col"]]


def test_assign_new_column_w_setitem_list(scalars_dfs):
    scalars_df, scalars_pandas_df = scalars_dfs
    bf_df = scalars_df.copy()
    pd_df = scalars_pandas_df.copy()
    bf_df["new_col"] = [9, 8, 7, 6, 5, 4, 3, 2, 1]
    pd_df["new_col"] = [9, 8, 7, 6, 5, 4, 3, 2, 1]
    bf_result = bf_df.to_pandas()
    pd_result = pd_df

    # Convert default pandas dtypes `int64` to match BigQuery DataFrames dtypes.
    pd_result["new_col"] = pd_result["new_col"].astype("Int64")

    pd.testing.assert_frame_equal(bf_result, pd_result)


def test_assign_new_column_w_setitem_list_repeated(scalars_dfs):
    scalars_df, scalars_pandas_df = scalars_dfs
    bf_df = scalars_df.copy()
    pd_df = scalars_pandas_df.copy()
    bf_df["new_col"] = [9, 8, 7, 6, 5, 4, 3, 2, 1]
    pd_df["new_col"] = [9, 8, 7, 6, 5, 4, 3, 2, 1]
    bf_df["new_col_2"] = [1, 3, 2, 5, 4, 7, 6, 9, 8]
    pd_df["new_col_2"] = [1, 3, 2, 5, 4, 7, 6, 9, 8]
    bf_result = bf_df.to_pandas()
    pd_result = pd_df

    # Convert default pandas dtypes `int64` to match BigQuery DataFrames dtypes.
    pd_result["new_col"] = pd_result["new_col"].astype("Int64")
    pd_result["new_col_2"] = pd_result["new_col_2"].astype("Int64")

    pd.testing.assert_frame_equal(bf_result, pd_result)


def test_assign_new_column_w_setitem_list_custom_index(scalars_dfs):
    scalars_df, scalars_pandas_df = scalars_dfs
    bf_df = scalars_df.copy()
    pd_df = scalars_pandas_df.copy()

    # set the custom index
    pd_df = pd_df.set_index(["string_col", "int64_col"])
    bf_df = bf_df.set_index(["string_col", "int64_col"])

    bf_df["new_col"] = [9, 8, 7, 6, 5, 4, 3, 2, 1]
    pd_df["new_col"] = [9, 8, 7, 6, 5, 4, 3, 2, 1]
    bf_result = bf_df.to_pandas()
    pd_result = pd_df

    # Convert default pandas dtypes `int64` to match BigQuery DataFrames dtypes.
    pd_result["new_col"] = pd_result["new_col"].astype("Int64")

    pd.testing.assert_frame_equal(bf_result, pd_result)


def test_assign_new_column_w_setitem_list_error(scalars_dfs):
    scalars_df, scalars_pandas_df = scalars_dfs
    bf_df = scalars_df.copy()
    pd_df = scalars_pandas_df.copy()

    with pytest.raises(ValueError):
        pd_df["new_col"] = [1, 2, 3]  # should be len 9, is 3
    with pytest.raises(ValueError):
        bf_df["new_col"] = [1, 2, 3]


def test_assign_existing_column(scalars_dfs):
    scalars_df, scalars_pandas_df = scalars_dfs
    kwargs = {"int64_col": 2}
    df = scalars_df.assign(**kwargs)
    bf_result = df.to_pandas()
    pd_result = scalars_pandas_df.assign(**kwargs)

    # Convert default pandas dtypes `int64` to match BigQuery DataFrames dtypes.
    pd_result["int64_col"] = pd_result["int64_col"].astype("Int64")

    assert_pandas_df_equal(bf_result, pd_result)


def test_assign_listlike_to_empty_df(session):
    empty_df = dataframe.DataFrame(session=session)
    empty_pandas_df = pd.DataFrame()

    bf_result = empty_df.assign(new_col=[1, 2, 3])
    pd_result = empty_pandas_df.assign(new_col=[1, 2, 3])

    pd_result["new_col"] = pd_result["new_col"].astype("Int64")
    pd_result.index = pd_result.index.astype("Int64")
    assert_pandas_df_equal(bf_result.to_pandas(), pd_result)


def test_assign_to_empty_df_multiindex_error(session):
    empty_df = dataframe.DataFrame(session=session)
    empty_pandas_df = pd.DataFrame()

    empty_df["empty_col_1"] = typing.cast(series.Series, [])
    empty_df["empty_col_2"] = typing.cast(series.Series, [])
    empty_pandas_df["empty_col_1"] = []
    empty_pandas_df["empty_col_2"] = []
    empty_df = empty_df.set_index(["empty_col_1", "empty_col_2"])
    empty_pandas_df = empty_pandas_df.set_index(["empty_col_1", "empty_col_2"])

    with pytest.raises(ValueError):
        empty_df.assign(new_col=[1, 2, 3, 4, 5, 6, 7, 8, 9])
    with pytest.raises(ValueError):
        empty_pandas_df.assign(new_col=[1, 2, 3, 4, 5, 6, 7, 8, 9])


@pytest.mark.parametrize(
    ("ordered"),
    [
        (True),
        (False),
    ],
)
def test_assign_series(scalars_dfs, ordered):
    scalars_df, scalars_pandas_df = scalars_dfs
    column_name = "int64_col"
    df = scalars_df.assign(new_col=scalars_df[column_name])
    bf_result = df.to_pandas(ordered=ordered)
    pd_result = scalars_pandas_df.assign(new_col=scalars_pandas_df[column_name])

    assert_pandas_df_equal(bf_result, pd_result, ignore_order=not ordered)


def test_assign_series_overwrite(scalars_dfs):
    scalars_df, scalars_pandas_df = scalars_dfs
    column_name = "int64_col"
    df = scalars_df.assign(**{column_name: scalars_df[column_name] + 3})
    bf_result = df.to_pandas()
    pd_result = scalars_pandas_df.assign(
        **{column_name: scalars_pandas_df[column_name] + 3}
    )

    assert_pandas_df_equal(bf_result, pd_result)


def test_assign_sequential(scalars_dfs):
    scalars_df, scalars_pandas_df = scalars_dfs
    kwargs = {"int64_col": 2, "new_col": 3, "new_col2": 4}
    df = scalars_df.assign(**kwargs)
    bf_result = df.to_pandas()
    pd_result = scalars_pandas_df.assign(**kwargs)

    # Convert default pandas dtypes `int64` to match BigQuery DataFrames dtypes.
    pd_result["int64_col"] = pd_result["int64_col"].astype("Int64")
    pd_result["new_col"] = pd_result["new_col"].astype("Int64")
    pd_result["new_col2"] = pd_result["new_col2"].astype("Int64")

    assert_pandas_df_equal(bf_result, pd_result)


# Require an index so that the self-join is consistent each time.
def test_assign_same_table_different_index_performs_self_join(
    scalars_df_index, scalars_pandas_df_index
):
    column_name = "int64_col"
    bf_df = scalars_df_index.assign(
        alternative_index=scalars_df_index["rowindex_2"] + 2
    )
    pd_df = scalars_pandas_df_index.assign(
        alternative_index=scalars_pandas_df_index["rowindex_2"] + 2
    )
    bf_df_2 = bf_df.set_index("alternative_index")
    pd_df_2 = pd_df.set_index("alternative_index")
    bf_result = bf_df.assign(new_col=bf_df_2[column_name] * 10).to_pandas()
    pd_result = pd_df.assign(new_col=pd_df_2[column_name] * 10)

    pandas.testing.assert_frame_equal(bf_result, pd_result)


# Different table expression must have Index
def test_assign_different_df(
    scalars_df_index, scalars_df_2_index, scalars_pandas_df_index
):
    column_name = "int64_col"
    df = scalars_df_index.assign(new_col=scalars_df_2_index[column_name])
    bf_result = df.to_pandas()
    # Doesn't matter to pandas if it comes from the same DF or a different DF.
    pd_result = scalars_pandas_df_index.assign(
        new_col=scalars_pandas_df_index[column_name]
    )

    assert_pandas_df_equal(bf_result, pd_result)


def test_assign_different_df_w_loc(
    scalars_df_index, scalars_df_2_index, scalars_pandas_df_index
):
    bf_df = scalars_df_index.copy()
    bf_df2 = scalars_df_2_index.copy()
    pd_df = scalars_pandas_df_index.copy()
    assert "int64_col" in bf_df.columns
    assert "int64_col" in pd_df.columns
    bf_df.loc[:, "int64_col"] = bf_df2.loc[:, "int64_col"] + 1
    pd_df.loc[:, "int64_col"] = pd_df.loc[:, "int64_col"] + 1
    bf_result = bf_df.to_pandas()
    pd_result = pd_df

    # Convert default pandas dtypes `int64` to match BigQuery DataFrames dtypes.
    pd_result["int64_col"] = pd_result["int64_col"].astype("Int64")

    pd.testing.assert_frame_equal(bf_result, pd_result)


def test_assign_different_df_w_setitem(
    scalars_df_index, scalars_df_2_index, scalars_pandas_df_index
):
    bf_df = scalars_df_index.copy()
    bf_df2 = scalars_df_2_index.copy()
    pd_df = scalars_pandas_df_index.copy()
    assert "int64_col" in bf_df.columns
    assert "int64_col" in pd_df.columns
    bf_df["int64_col"] = bf_df2["int64_col"] + 1
    pd_df["int64_col"] = pd_df["int64_col"] + 1
    bf_result = bf_df.to_pandas()
    pd_result = pd_df

    # Convert default pandas dtypes `int64` to match BigQuery DataFrames dtypes.
    pd_result["int64_col"] = pd_result["int64_col"].astype("Int64")

    pd.testing.assert_frame_equal(bf_result, pd_result)


def test_assign_callable_lambda(scalars_dfs):
    scalars_df, scalars_pandas_df = scalars_dfs
    kwargs = {"new_col": lambda x: x["int64_col"] + x["int64_too"]}
    df = scalars_df.assign(**kwargs)
    bf_result = df.to_pandas()
    pd_result = scalars_pandas_df.assign(**kwargs)

    # Convert default pandas dtypes `int64` to match BigQuery DataFrames dtypes.
    pd_result["new_col"] = pd_result["new_col"].astype("Int64")

    assert_pandas_df_equal(bf_result, pd_result)


@pytest.mark.parametrize(
    ("axis", "how", "ignore_index", "subset"),
    [
        (0, "any", False, None),
        (0, "any", True, None),
        (0, "all", False, ["bool_col", "time_col"]),
        (0, "any", False, ["bool_col", "time_col"]),
        (0, "all", False, "time_col"),
        (1, "any", False, None),
        (1, "all", False, None),
    ],
)
def test_df_dropna_by_how(scalars_dfs, axis, how, ignore_index, subset):
    # TODO: supply a reason why this isn't compatible with pandas 1.x
    pytest.importorskip("pandas", minversion="2.0.0")
    scalars_df, scalars_pandas_df = scalars_dfs
    df = scalars_df.dropna(axis=axis, how=how, ignore_index=ignore_index, subset=subset)
    bf_result = df.to_pandas()
    pd_result = scalars_pandas_df.dropna(
        axis=axis, how=how, ignore_index=ignore_index, subset=subset
    )

    # Pandas uses int64 instead of Int64 (nullable) dtype.
    pd_result.index = pd_result.index.astype(pd.Int64Dtype())
    pandas.testing.assert_frame_equal(bf_result, pd_result)


@pytest.mark.parametrize(
    ("axis", "ignore_index", "subset", "thresh"),
    [
        (0, False, None, 2),
        (0, True, None, 3),
        (1, False, None, 2),
    ],
)
def test_df_dropna_by_thresh(scalars_dfs, axis, ignore_index, subset, thresh):
    """
    Tests that dropna correctly keeps rows/columns with a minimum number
    of non-null values.
    """
    # TODO: supply a reason why this isn't compatible with pandas 1.x
    pytest.importorskip("pandas", minversion="2.0.0")
    scalars_df, scalars_pandas_df = scalars_dfs

    df_result = scalars_df.dropna(
        axis=axis, thresh=thresh, ignore_index=ignore_index, subset=subset
    )
    pd_result = scalars_pandas_df.dropna(
        axis=axis, thresh=thresh, ignore_index=ignore_index, subset=subset
    )

    bf_result = df_result.to_pandas()
    # Pandas uses int64 instead of Int64 (nullable) dtype.
    pd_result.index = pd_result.index.astype(pd.Int64Dtype())
    pd.testing.assert_frame_equal(bf_result, pd_result)


def test_df_dropna_range_columns(scalars_dfs):
    # TODO: supply a reason why this isn't compatible with pandas 1.x
    pytest.importorskip("pandas", minversion="2.0.0")
    scalars_df, scalars_pandas_df = scalars_dfs
    scalars_df = scalars_df.copy()
    scalars_pandas_df = scalars_pandas_df.copy()
    scalars_df.columns = pandas.RangeIndex(0, len(scalars_df.columns))
    scalars_pandas_df.columns = pandas.RangeIndex(0, len(scalars_pandas_df.columns))

    df = scalars_df.dropna()
    bf_result = df.to_pandas()
    pd_result = scalars_pandas_df.dropna()

    pandas.testing.assert_frame_equal(bf_result, pd_result)


def test_df_interpolate(scalars_dfs):
    scalars_df, scalars_pandas_df = scalars_dfs
    columns = ["int64_col", "int64_too", "float64_col"]
    bf_result = scalars_df[columns].interpolate().to_pandas()
    # Pandas can only interpolate on "float64" columns
    # https://github.com/pandas-dev/pandas/issues/40252
    pd_result = scalars_pandas_df[columns].astype("float64").interpolate()

    pandas.testing.assert_frame_equal(
        bf_result,
        pd_result,
        check_index_type=False,
        check_dtype=False,
    )


@pytest.mark.parametrize(
    "col, fill_value",
    [
        (["int64_col", "float64_col"], 3),
        (["string_col"], "A"),
        (["datetime_col"], pd.Timestamp("2023-01-01")),
    ],
)
def test_df_fillna(scalars_dfs, col, fill_value):
    scalars_df, scalars_pandas_df = scalars_dfs
    bf_result = scalars_df[col].fillna(fill_value).to_pandas()
    pd_result = scalars_pandas_df[col].fillna(fill_value)

    pd.testing.assert_frame_equal(bf_result, pd_result, check_dtype=False)


def test_df_replace_scalar_scalar(scalars_dfs):
    scalars_df, scalars_pandas_df = scalars_dfs
    bf_result = scalars_df.replace(555.555, 3).to_pandas()
    pd_result = scalars_pandas_df.replace(555.555, 3)

    # pandas has narrower result types as they are determined dynamically
    pd.testing.assert_frame_equal(pd_result, bf_result, check_dtype=False)


def test_df_replace_regex_scalar(scalars_dfs):
    scalars_df, scalars_pandas_df = scalars_dfs
    bf_result = scalars_df.replace("^H.l", "Howdy, Planet!", regex=True).to_pandas()
    pd_result = scalars_pandas_df.replace("^H.l", "Howdy, Planet!", regex=True)

    pd.testing.assert_frame_equal(
        pd_result,
        bf_result,
    )


def test_df_replace_list_scalar(scalars_dfs):
    scalars_df, scalars_pandas_df = scalars_dfs
    bf_result = scalars_df.replace([555.555, 3.2], 3).to_pandas()
    pd_result = scalars_pandas_df.replace([555.555, 3.2], 3)

    # pandas has narrower result types as they are determined dynamically
    pd.testing.assert_frame_equal(
        pd_result,
        bf_result,
        check_dtype=False,
    )


def test_df_replace_value_dict(scalars_dfs):
    scalars_df, scalars_pandas_df = scalars_dfs
    bf_result = scalars_df.replace(1, {"int64_col": 100, "int64_too": 200}).to_pandas()
    pd_result = scalars_pandas_df.replace(1, {"int64_col": 100, "int64_too": 200})

    pd.testing.assert_frame_equal(
        pd_result,
        bf_result,
    )


def test_df_ffill(scalars_dfs):
    scalars_df, scalars_pandas_df = scalars_dfs
    bf_result = scalars_df[["int64_col", "float64_col"]].ffill(limit=1).to_pandas()
    pd_result = scalars_pandas_df[["int64_col", "float64_col"]].ffill(limit=1)

    pandas.testing.assert_frame_equal(bf_result, pd_result)


def test_df_bfill(scalars_dfs):
    scalars_df, scalars_pandas_df = scalars_dfs
    bf_result = scalars_df[["int64_col", "float64_col"]].bfill().to_pandas()
    pd_result = scalars_pandas_df[["int64_col", "float64_col"]].bfill()

    pandas.testing.assert_frame_equal(bf_result, pd_result)


def test_apply_series_series_callable(
    scalars_df_index,
    scalars_pandas_df_index,
):
    columns = ["int64_too", "int64_col"]

    def foo(series, arg1, arg2, *, kwarg1=0, kwarg2=0):
        return series**2 + (arg1 * arg2 % 4) + (kwarg1 * kwarg2 % 7)

    bf_result = (
        scalars_df_index[columns]
        .apply(foo, args=(33, 61), kwarg1=52, kwarg2=21)
        .to_pandas()
    )

    pd_result = scalars_pandas_df_index[columns].apply(
        foo, args=(33, 61), kwarg1=52, kwarg2=21
    )

    pandas.testing.assert_frame_equal(bf_result, pd_result)


def test_apply_series_listlike_callable(
    scalars_df_index,
    scalars_pandas_df_index,
):
    columns = ["int64_too", "int64_col"]
    bf_result = (
        scalars_df_index[columns].apply(lambda x: [len(x), x.min(), 24]).to_pandas()
    )

    pd_result = scalars_pandas_df_index[columns].apply(lambda x: [len(x), x.min(), 24])

    # Convert default pandas dtypes `int64` to match BigQuery DataFrames dtypes.
    pd_result.index = pd_result.index.astype("Int64")
    pd_result = pd_result.astype("Int64")
    pandas.testing.assert_frame_equal(bf_result, pd_result)


def test_apply_series_scalar_callable(
    scalars_df_index,
    scalars_pandas_df_index,
):
    columns = ["int64_too", "int64_col"]
    bf_result = scalars_df_index[columns].apply(lambda x: x.sum())

    pd_result = scalars_pandas_df_index[columns].apply(lambda x: x.sum())

    pandas.testing.assert_series_equal(bf_result, pd_result)


def test_df_pipe(
    scalars_df_index,
    scalars_pandas_df_index,
):
    columns = ["int64_too", "int64_col"]

    def foo(x: int, y: int, df):
        return (df + x) % y

    bf_result = (
        scalars_df_index[columns]
        .pipe((foo, "df"), x=7, y=9)
        .pipe(lambda x: x**2)
        .to_pandas()
    )

    pd_result = (
        scalars_pandas_df_index[columns]
        .pipe((foo, "df"), x=7, y=9)
        .pipe(lambda x: x**2)
    )

    pandas.testing.assert_frame_equal(bf_result, pd_result)


def test_df_keys(
    scalars_df_index,
    scalars_pandas_df_index,
):
    pandas.testing.assert_index_equal(
        scalars_df_index.keys(), scalars_pandas_df_index.keys()
    )


def test_df_iter(
    scalars_df_index,
    scalars_pandas_df_index,
):
    for bf_i, df_i in zip(scalars_df_index, scalars_pandas_df_index):
        assert bf_i == df_i


def test_iterrows(
    scalars_df_index,
    scalars_pandas_df_index,
):
    # TODO: supply a reason why this isn't compatible with pandas 1.x
    pytest.importorskip("pandas", minversion="2.0.0")
    scalars_df_index = scalars_df_index.add_suffix("_suffix", axis=1)
    scalars_pandas_df_index = scalars_pandas_df_index.add_suffix("_suffix", axis=1)
    for (bf_index, bf_series), (pd_index, pd_series) in zip(
        scalars_df_index.iterrows(), scalars_pandas_df_index.iterrows()
    ):
        assert bf_index == pd_index
        pandas.testing.assert_series_equal(bf_series, pd_series)


@pytest.mark.parametrize(
    (
        "index",
        "name",
    ),
    [
        (
            True,
            "my_df",
        ),
        (False, None),
    ],
)
def test_itertuples(scalars_df_index, index, name):
    # Numeric has slightly different representation as a result of conversions.
    bf_tuples = scalars_df_index.itertuples(index, name)
    pd_tuples = scalars_df_index.to_pandas().itertuples(index, name)
    for bf_tuple, pd_tuple in zip(bf_tuples, pd_tuples):
        assert bf_tuple == pd_tuple


def test_df_isin_list(scalars_dfs):
    scalars_df, scalars_pandas_df = scalars_dfs
    values = ["Hello, World!", 55555, 2.51, pd.NA, True]
    bf_result = (
        scalars_df[["int64_col", "float64_col", "string_col", "bool_col"]]
        .isin(values)
        .to_pandas()
    )
    pd_result = scalars_pandas_df[
        ["int64_col", "float64_col", "string_col", "bool_col"]
    ].isin(values)

    pandas.testing.assert_frame_equal(bf_result, pd_result.astype("boolean"))


def test_df_isin_dict(scalars_dfs):
    scalars_df, scalars_pandas_df = scalars_dfs
    values = {
        "string_col": ["Hello, World!", 55555, 2.51, pd.NA, True],
        "int64_col": [5555, 2.51],
        "bool_col": [pd.NA],
    }
    bf_result = (
        scalars_df[["int64_col", "float64_col", "string_col", "bool_col"]]
        .isin(values)
        .to_pandas()
    )
    pd_result = scalars_pandas_df[
        ["int64_col", "float64_col", "string_col", "bool_col"]
    ].isin(values)

    pandas.testing.assert_frame_equal(bf_result, pd_result.astype("boolean"))


def test_df_cross_merge(scalars_dfs):
    scalars_df, scalars_pandas_df = scalars_dfs
    left_columns = ["int64_col", "float64_col", "rowindex_2"]
    right_columns = ["int64_col", "bool_col", "string_col", "rowindex_2"]

    left = scalars_df[left_columns]
    # Offset the rows somewhat so that outer join can have an effect.
    right = scalars_df[right_columns].assign(rowindex_2=scalars_df["rowindex_2"] + 2)

    bf_result = left.merge(right, "cross").to_pandas()

    pd_result = scalars_pandas_df[left_columns].merge(
        scalars_pandas_df[right_columns].assign(
            rowindex_2=scalars_pandas_df["rowindex_2"] + 2
        ),
        "cross",
    )
    pd.testing.assert_frame_equal(bf_result, pd_result, check_index_type=False)


@pytest.mark.parametrize(
    ("merge_how",),
    [
        ("inner",),
        ("outer",),
        ("left",),
        ("right",),
    ],
)
def test_df_merge(scalars_dfs, merge_how):
    scalars_df, scalars_pandas_df = scalars_dfs
    on = "rowindex_2"
    left_columns = ["int64_col", "float64_col", "rowindex_2"]
    right_columns = ["int64_col", "bool_col", "string_col", "rowindex_2"]

    left = scalars_df[left_columns]
    # Offset the rows somewhat so that outer join can have an effect.
    right = scalars_df[right_columns].assign(rowindex_2=scalars_df["rowindex_2"] + 2)

    df = left.merge(right, merge_how, on, sort=True)
    bf_result = df.to_pandas()

    pd_result = scalars_pandas_df[left_columns].merge(
        scalars_pandas_df[right_columns].assign(
            rowindex_2=scalars_pandas_df["rowindex_2"] + 2
        ),
        merge_how,
        on,
        sort=True,
    )

    assert_pandas_df_equal(
        bf_result, pd_result, ignore_order=True, check_index_type=False
    )


@pytest.mark.parametrize(
    ("left_on", "right_on"),
    [
        (["int64_col", "rowindex_2"], ["int64_col", "rowindex_2"]),
        (["rowindex_2", "int64_col"], ["int64_col", "rowindex_2"]),
        (["rowindex_2", "float64_col"], ["int64_col", "rowindex_2"]),
    ],
)
def test_df_merge_multi_key(scalars_dfs, left_on, right_on):
    scalars_df, scalars_pandas_df = scalars_dfs
    left_columns = ["int64_col", "float64_col", "rowindex_2"]
    right_columns = ["int64_col", "bool_col", "string_col", "rowindex_2"]

    left = scalars_df[left_columns]
    # Offset the rows somewhat so that outer join can have an effect.
    right = scalars_df[right_columns].assign(rowindex_2=scalars_df["rowindex_2"] + 2)

    df = left.merge(right, "outer", left_on=left_on, right_on=right_on, sort=True)
    bf_result = df.to_pandas()

    pd_result = scalars_pandas_df[left_columns].merge(
        scalars_pandas_df[right_columns].assign(
            rowindex_2=scalars_pandas_df["rowindex_2"] + 2
        ),
        "outer",
        left_on=left_on,
        right_on=right_on,
        sort=True,
    )

    assert_pandas_df_equal(
        bf_result, pd_result, ignore_order=True, check_index_type=False
    )


@pytest.mark.parametrize(
    ("merge_how",),
    [
        ("inner",),
        ("outer",),
        ("left",),
        ("right",),
    ],
)
def test_merge_custom_col_name(scalars_dfs, merge_how):
    scalars_df, scalars_pandas_df = scalars_dfs
    left_columns = ["int64_col", "float64_col"]
    right_columns = ["int64_col", "bool_col", "string_col"]
    on = "int64_col"
    rename_columns = {"float64_col": "f64_col"}

    left = scalars_df[left_columns]
    left = left.rename(columns=rename_columns)
    right = scalars_df[right_columns]
    df = left.merge(right, merge_how, on, sort=True)
    bf_result = df.to_pandas()

    pandas_left_df = scalars_pandas_df[left_columns]
    pandas_left_df = pandas_left_df.rename(columns=rename_columns)
    pandas_right_df = scalars_pandas_df[right_columns]
    pd_result = pandas_left_df.merge(pandas_right_df, merge_how, on, sort=True)

    assert_pandas_df_equal(
        bf_result, pd_result, ignore_order=True, check_index_type=False
    )


@pytest.mark.parametrize(
    ("merge_how",),
    [
        ("inner",),
        ("outer",),
        ("left",),
        ("right",),
    ],
)
def test_merge_left_on_right_on(scalars_dfs, merge_how):
    scalars_df, scalars_pandas_df = scalars_dfs
    left_columns = ["int64_col", "float64_col", "int64_too"]
    right_columns = ["int64_col", "bool_col", "string_col", "rowindex_2"]

    left = scalars_df[left_columns]
    right = scalars_df[right_columns]

    df = left.merge(
        right, merge_how, left_on="int64_too", right_on="rowindex_2", sort=True
    )
    bf_result = df.to_pandas()

    pd_result = scalars_pandas_df[left_columns].merge(
        scalars_pandas_df[right_columns],
        merge_how,
        left_on="int64_too",
        right_on="rowindex_2",
        sort=True,
    )

    assert_pandas_df_equal(
        bf_result, pd_result, ignore_order=True, check_index_type=False
    )


def test_self_merge_self_w_on_args():
    data = {
        "A": pd.Series([1, 2, 3], dtype="Int64"),
        "B": pd.Series([1, 2, 3], dtype="Int64"),
        "C": pd.Series([100, 200, 300], dtype="Int64"),
        "D": pd.Series(["alpha", "beta", "gamma"], dtype="string[pyarrow]"),
    }
    df = pd.DataFrame(data)

    df1 = df[["A", "C"]]
    df2 = df[["B", "C", "D"]]
    pd_result = df1.merge(df2, left_on=["A", "C"], right_on=["B", "C"], how="inner")

    bf_df = bpd.DataFrame(data)

    bf_df1 = bf_df[["A", "C"]]
    bf_df2 = bf_df[["B", "C", "D"]]
    bf_result = bf_df1.merge(
        bf_df2, left_on=["A", "C"], right_on=["B", "C"], how="inner"
    ).to_pandas()
    pd.testing.assert_frame_equal(bf_result, pd_result, check_index_type=False)


@pytest.mark.parametrize(
    ("decimals",),
    [
        (2,),
        ({"float64_col": 0, "bool_col": 1, "int64_too": -3},),
        ({},),
    ],
)
def test_dataframe_round(scalars_dfs, decimals):
    if pd.__version__.startswith("1."):
        pytest.skip("Rounding doesn't work as expected in pandas 1.x")
    scalars_df, scalars_pandas_df = scalars_dfs

    bf_result = scalars_df.round(decimals).to_pandas()
    pd_result = scalars_pandas_df.round(decimals)

    assert_pandas_df_equal(bf_result, pd_result)


def test_get_dtypes(scalars_df_default_index):
    dtypes = scalars_df_default_index.dtypes
    dtypes_dict: Dict[str, bigframes.dtypes.Dtype] = {
        "bool_col": pd.BooleanDtype(),
        "bytes_col": pd.ArrowDtype(pa.binary()),
        "date_col": pd.ArrowDtype(pa.date32()),
        "datetime_col": pd.ArrowDtype(pa.timestamp("us")),
        "geography_col": gpd.array.GeometryDtype(),
        "int64_col": pd.Int64Dtype(),
        "int64_too": pd.Int64Dtype(),
        "numeric_col": pd.ArrowDtype(pa.decimal128(38, 9)),
        "float64_col": pd.Float64Dtype(),
        "rowindex": pd.Int64Dtype(),
        "rowindex_2": pd.Int64Dtype(),
        "string_col": pd.StringDtype(storage="pyarrow"),
        "time_col": pd.ArrowDtype(pa.time64("us")),
        "timestamp_col": pd.ArrowDtype(pa.timestamp("us", tz="UTC")),
        "duration_col": pd.ArrowDtype(pa.duration("us")),
    }
    pd.testing.assert_series_equal(
        dtypes,
        pd.Series(dtypes_dict),
    )


def test_get_dtypes_array_struct_query(session):
    df = session.read_gbq(
        """SELECT
        [1, 3, 2] AS array_column,
        STRUCT(
            "a" AS string_field,
            1.2 AS float_field) AS struct_column"""
    )

    dtypes = df.dtypes
    pd.testing.assert_series_equal(
        dtypes,
        pd.Series(
            {
                "array_column": pd.ArrowDtype(pa.list_(pa.int64())),
                "struct_column": pd.ArrowDtype(
                    pa.struct(
                        [
                            ("string_field", pa.string()),
                            ("float_field", pa.float64()),
                        ]
                    )
                ),
            }
        ),
    )


def test_get_dtypes_array_struct_table(nested_df):
    dtypes = nested_df.dtypes
    pd.testing.assert_series_equal(
        dtypes,
        pd.Series(
            {
                "customer_id": pd.StringDtype(storage="pyarrow"),
                "day": pd.ArrowDtype(pa.date32()),
                "flag": pd.Int64Dtype(),
                "label": pd.ArrowDtype(
                    pa.struct(
                        [
                            ("key", pa.string()),
                            ("value", pa.string()),
                        ]
                    ),
                ),
                "event_sequence": pd.ArrowDtype(
                    pa.list_(
                        pa.struct(
                            [
                                pa.field(
                                    "data",
                                    pa.list_(
                                        pa.struct(
                                            [
                                                ("value", pa.float64()),
                                                ("key", pa.string()),
                                            ],
                                        ),
                                    ),
                                    nullable=False,
                                ),
                                ("timestamp", pa.timestamp("us", "UTC")),
                                ("category", pa.string()),
                            ]
                        ),
                    ),
                ),
                "address": pd.ArrowDtype(
                    pa.struct(
                        [
                            ("street", pa.string()),
                            ("city", pa.string()),
                        ]
                    ),
                ),
            }
        ),
    )


def test_shape(scalars_dfs):
    scalars_df, scalars_pandas_df = scalars_dfs
    bf_result = scalars_df.shape
    pd_result = scalars_pandas_df.shape

    assert bf_result == pd_result


@pytest.mark.parametrize(
    "reference_table, test_table",
    [
        (
            "bigframes-dev.bigframes_tests_sys.base_table",
            "bigframes-dev.bigframes_tests_sys.base_table_mat_view",
        ),
        (
            "bigframes-dev.bigframes_tests_sys.base_table",
            "bigframes-dev.bigframes_tests_sys.base_table_view",
        ),
        (
            "bigframes-dev.bigframes_tests_sys.csv_native_table",
            "bigframes-dev.bigframes_tests_sys.csv_external_table",
        ),
    ],
)
def test_view_and_external_table_shape(session, reference_table, test_table):
    reference_df = session.read_gbq(reference_table)
    test_df = session.read_gbq(test_table)

    assert test_df.shape == reference_df.shape


def test_len(scalars_dfs):
    scalars_df, scalars_pandas_df = scalars_dfs
    bf_result = len(scalars_df)
    pd_result = len(scalars_pandas_df)

    assert bf_result == pd_result


@pytest.mark.parametrize(
    ("n_rows",),
    [
        (50,),
        (10000,),
    ],
)
@pytest.mark.parametrize(
    "write_engine",
    ["bigquery_load", "bigquery_streaming", "bigquery_write"],
)
def test_df_len_local(session, n_rows, write_engine):
    assert (
        len(
            session.read_pandas(
                pd.DataFrame(np.random.randint(1, 7, n_rows), columns=["one"]),
                write_engine=write_engine,
            )
        )
        == n_rows
    )


def test_size(scalars_dfs):
    scalars_df, scalars_pandas_df = scalars_dfs
    bf_result = scalars_df.size
    pd_result = scalars_pandas_df.size

    assert bf_result == pd_result


def test_ndim(scalars_dfs):
    scalars_df, scalars_pandas_df = scalars_dfs
    bf_result = scalars_df.ndim
    pd_result = scalars_pandas_df.ndim

    assert bf_result == pd_result


def test_empty_false(scalars_dfs):
    scalars_df, scalars_pandas_df = scalars_dfs

    bf_result = scalars_df.empty
    pd_result = scalars_pandas_df.empty

    assert bf_result == pd_result


def test_empty_true_column_filter(scalars_dfs):
    scalars_df, scalars_pandas_df = scalars_dfs

    bf_result = scalars_df[[]].empty
    pd_result = scalars_pandas_df[[]].empty

    assert bf_result == pd_result


def test_empty_true_row_filter(scalars_dfs: Tuple[dataframe.DataFrame, pd.DataFrame]):
    scalars_df, scalars_pandas_df = scalars_dfs
    bf_bool: series.Series = typing.cast(series.Series, scalars_df["bool_col"])
    pd_bool: pd.Series = scalars_pandas_df["bool_col"]
    bf_false = bf_bool.notna() & (bf_bool != bf_bool)
    pd_false = pd_bool.notna() & (pd_bool != pd_bool)

    bf_result = scalars_df[bf_false].empty
    pd_result = scalars_pandas_df[pd_false].empty

    assert pd_result
    assert bf_result == pd_result


def test_empty_true_memtable(session: bigframes.Session):
    bf_df = dataframe.DataFrame(session=session)
    pd_df = pd.DataFrame()

    bf_result = bf_df.empty
    pd_result = pd_df.empty

    assert pd_result
    assert bf_result == pd_result


@pytest.mark.parametrize(
    ("drop",),
    ((True,), (False,)),
)
def test_reset_index(scalars_df_index, scalars_pandas_df_index, drop):
    df = scalars_df_index.reset_index(drop=drop)
    assert df.index.name is None

    bf_result = df.to_pandas()
    pd_result = scalars_pandas_df_index.reset_index(drop=drop)

    # Pandas uses int64 instead of Int64 (nullable) dtype.
    pd_result.index = pd_result.index.astype(pd.Int64Dtype())

    # reset_index should maintain the original ordering.
    pandas.testing.assert_frame_equal(bf_result, pd_result)


@pytest.mark.parametrize(
    ("drop",),
    ((True,), (False,)),
)
def test_reset_index_inplace(scalars_df_index, scalars_pandas_df_index, drop):
    df = scalars_df_index.copy()
    df.reset_index(drop=drop, inplace=True)
    assert df.index.name is None

    bf_result = df.to_pandas()
    pd_result = scalars_pandas_df_index.copy()
    pd_result.reset_index(drop=drop, inplace=True)

    # Pandas uses int64 instead of Int64 (nullable) dtype.
    pd_result.index = pd_result.index.astype(pd.Int64Dtype())

    # reset_index should maintain the original ordering.
    pandas.testing.assert_frame_equal(bf_result, pd_result)


def test_reset_index_then_filter(
    scalars_df_index,
    scalars_pandas_df_index,
):
    bf_filter = scalars_df_index["bool_col"].fillna(True)
    bf_df = scalars_df_index.reset_index()[bf_filter]
    bf_result = bf_df.to_pandas()
    pd_filter = scalars_pandas_df_index["bool_col"].fillna(True)
    pd_result = scalars_pandas_df_index.reset_index()[pd_filter]

    # Pandas uses int64 instead of Int64 (nullable) dtype.
    pd_result.index = pd_result.index.astype(pd.Int64Dtype())

    # reset_index should maintain the original ordering and index keys
    # post-filter will have gaps.
    pandas.testing.assert_frame_equal(bf_result, pd_result)


def test_reset_index_with_unnamed_index(
    scalars_df_index,
    scalars_pandas_df_index,
):
    scalars_df_index = scalars_df_index.copy()
    scalars_pandas_df_index = scalars_pandas_df_index.copy()

    scalars_df_index.index.name = None
    scalars_pandas_df_index.index.name = None
    df = scalars_df_index.reset_index(drop=False)
    assert df.index.name is None

    # reset_index(drop=False) creates a new column "index".
    assert df.columns[0] == "index"

    bf_result = df.to_pandas()
    pd_result = scalars_pandas_df_index.reset_index(drop=False)

    # Pandas uses int64 instead of Int64 (nullable) dtype.
    pd_result.index = pd_result.index.astype(pd.Int64Dtype())

    # reset_index should maintain the original ordering.
    pandas.testing.assert_frame_equal(bf_result, pd_result)


def test_reset_index_with_unnamed_multiindex(
    scalars_df_index,
    scalars_pandas_df_index,
):
    bf_df = dataframe.DataFrame(
        ([1, 2, 3], [2, 5, 7]),
        index=pd.MultiIndex.from_tuples([("a", "aa"), ("a", "aa")]),
    )
    pd_df = pd.DataFrame(
        ([1, 2, 3], [2, 5, 7]),
        index=pd.MultiIndex.from_tuples([("a", "aa"), ("a", "aa")]),
    )

    bf_df = bf_df.reset_index()
    pd_df = pd_df.reset_index()

    assert pd_df.columns[0] == "level_0"
    assert bf_df.columns[0] == "level_0"
    assert pd_df.columns[1] == "level_1"
    assert bf_df.columns[1] == "level_1"


def test_reset_index_with_unnamed_index_and_index_column(
    scalars_df_index,
    scalars_pandas_df_index,
):
    scalars_df_index = scalars_df_index.copy()
    scalars_pandas_df_index = scalars_pandas_df_index.copy()

    scalars_df_index.index.name = None
    scalars_pandas_df_index.index.name = None
    df = scalars_df_index.assign(index=scalars_df_index["int64_col"]).reset_index(
        drop=False
    )
    assert df.index.name is None

    # reset_index(drop=False) creates a new column "level_0" if the "index" column already exists.
    assert df.columns[0] == "level_0"

    bf_result = df.to_pandas()
    pd_result = scalars_pandas_df_index.assign(
        index=scalars_pandas_df_index["int64_col"]
    ).reset_index(drop=False)

    # Pandas uses int64 instead of Int64 (nullable) dtype.
    pd_result.index = pd_result.index.astype(pd.Int64Dtype())

    # reset_index should maintain the original ordering.
    pandas.testing.assert_frame_equal(bf_result, pd_result)


@pytest.mark.parametrize(
    ("drop",),
    (
        (True,),
        (False,),
    ),
)
@pytest.mark.parametrize(
    ("append",),
    (
        (True,),
        (False,),
    ),
)
@pytest.mark.parametrize(
    ("index_column",),
    (("int64_too",), ("string_col",), ("timestamp_col",)),
)
def test_set_index(scalars_dfs, index_column, drop, append):
    scalars_df, scalars_pandas_df = scalars_dfs
    df = scalars_df.set_index(index_column, append=append, drop=drop)
    bf_result = df.to_pandas()
    pd_result = scalars_pandas_df.set_index(index_column, append=append, drop=drop)

    # Sort to disambiguate when there are duplicate index labels.
    # Note: Doesn't use assert_pandas_df_equal_ignore_ordering because we get
    # "ValueError: 'timestamp_col' is both an index level and a column label,
    # which is ambiguous" when trying to sort by a column with the same name as
    # the index.
    bf_result = bf_result.sort_values("rowindex_2")
    pd_result = pd_result.sort_values("rowindex_2")

    pandas.testing.assert_frame_equal(bf_result, pd_result)


def test_set_index_key_error(scalars_dfs):
    scalars_df, scalars_pandas_df = scalars_dfs
    with pytest.raises(KeyError):
        scalars_pandas_df.set_index(["not_a_col"])
    with pytest.raises(KeyError):
        scalars_df.set_index(["not_a_col"])


@pytest.mark.parametrize(
    ("ascending",),
    ((True,), (False,)),
)
@pytest.mark.parametrize(
    ("na_position",),
    (("first",), ("last",)),
)
def test_sort_index(scalars_dfs, ascending, na_position):
    index_column = "int64_col"
    scalars_df, scalars_pandas_df = scalars_dfs
    df = scalars_df.set_index(index_column)
    bf_result = df.sort_index(ascending=ascending, na_position=na_position).to_pandas()
    pd_result = scalars_pandas_df.set_index(index_column).sort_index(
        ascending=ascending, na_position=na_position
    )
    pandas.testing.assert_frame_equal(bf_result, pd_result)


def test_dataframe_sort_index_inplace(scalars_dfs):
    index_column = "int64_col"
    scalars_df, scalars_pandas_df = scalars_dfs
    df = scalars_df.copy().set_index(index_column)
    df.sort_index(ascending=False, inplace=True)
    bf_result = df.to_pandas()

    pd_result = scalars_pandas_df.set_index(index_column).sort_index(ascending=False)
    pandas.testing.assert_frame_equal(bf_result, pd_result)


def test_df_abs(scalars_dfs_maybe_ordered):
    scalars_df, scalars_pandas_df = scalars_dfs_maybe_ordered
    columns = ["int64_col", "int64_too", "float64_col"]

    bf_result = scalars_df[columns].abs()
    pd_result = scalars_pandas_df[columns].abs()

    assert_dfs_equivalent(pd_result, bf_result)


def test_df_pos(scalars_dfs):
    scalars_df, scalars_pandas_df = scalars_dfs
    bf_result = (+scalars_df[["int64_col", "numeric_col"]]).to_pandas()
    pd_result = +scalars_pandas_df[["int64_col", "numeric_col"]]

    assert_pandas_df_equal(pd_result, bf_result)


def test_df_neg(scalars_dfs):
    scalars_df, scalars_pandas_df = scalars_dfs
    bf_result = (-scalars_df[["int64_col", "numeric_col"]]).to_pandas()
    pd_result = -scalars_pandas_df[["int64_col", "numeric_col"]]

    assert_pandas_df_equal(pd_result, bf_result)


def test_df_invert(scalars_dfs):
    scalars_df, scalars_pandas_df = scalars_dfs
    columns = ["int64_col", "bool_col"]

    bf_result = (~scalars_df[columns]).to_pandas()
    pd_result = ~scalars_pandas_df[columns]

    assert_pandas_df_equal(bf_result, pd_result)


def test_df_isnull(scalars_dfs):
    scalars_df, scalars_pandas_df = scalars_dfs

    columns = ["int64_col", "int64_too", "string_col", "bool_col"]
    bf_result = scalars_df[columns].isnull().to_pandas()
    pd_result = scalars_pandas_df[columns].isnull()

    # One of dtype mismatches to be documented. Here, the `bf_result.dtype` is
    # `BooleanDtype` but the `pd_result.dtype` is `bool`.
    pd_result["int64_col"] = pd_result["int64_col"].astype(pd.BooleanDtype())
    pd_result["int64_too"] = pd_result["int64_too"].astype(pd.BooleanDtype())
    pd_result["string_col"] = pd_result["string_col"].astype(pd.BooleanDtype())
    pd_result["bool_col"] = pd_result["bool_col"].astype(pd.BooleanDtype())

    assert_pandas_df_equal(bf_result, pd_result)


def test_df_notnull(scalars_dfs):
    scalars_df, scalars_pandas_df = scalars_dfs

    columns = ["int64_col", "int64_too", "string_col", "bool_col"]
    bf_result = scalars_df[columns].notnull().to_pandas()
    pd_result = scalars_pandas_df[columns].notnull()

    # One of dtype mismatches to be documented. Here, the `bf_result.dtype` is
    # `BooleanDtype` but the `pd_result.dtype` is `bool`.
    pd_result["int64_col"] = pd_result["int64_col"].astype(pd.BooleanDtype())
    pd_result["int64_too"] = pd_result["int64_too"].astype(pd.BooleanDtype())
    pd_result["string_col"] = pd_result["string_col"].astype(pd.BooleanDtype())
    pd_result["bool_col"] = pd_result["bool_col"].astype(pd.BooleanDtype())

    assert_pandas_df_equal(bf_result, pd_result)


@pytest.mark.parametrize(
    ("left_labels", "right_labels", "overwrite", "fill_value"),
    [
        (["a", "b", "c"], ["c", "a", "b"], True, None),
        (["a", "b", "c"], ["c", "a", "b"], False, None),
        (["a", "b", "c"], ["a", "b", "c"], False, 2),
    ],
    ids=[
        "one_one_match_overwrite",
        "one_one_match_no_overwrite",
        "exact_match",
    ],
)
def test_combine(
    scalars_df_index,
    scalars_df_2_index,
    scalars_pandas_df_index,
    left_labels,
    right_labels,
    overwrite,
    fill_value,
):
    if pd.__version__.startswith("1."):
        pytest.skip("pd.NA vs NaN not handled well in pandas 1.x.")
    columns = ["int64_too", "int64_col", "float64_col"]

    bf_df_a = scalars_df_index[columns]
    bf_df_a.columns = left_labels
    bf_df_b = scalars_df_2_index[columns]
    bf_df_b.columns = right_labels
    bf_result = bf_df_a.combine(
        bf_df_b,
        lambda x, y: x**2 + 2 * x * y + y**2,
        overwrite=overwrite,
        fill_value=fill_value,
    ).to_pandas()

    pd_df_a = scalars_pandas_df_index[columns]
    pd_df_a.columns = left_labels
    pd_df_b = scalars_pandas_df_index[columns]
    pd_df_b.columns = right_labels
    pd_result = pd_df_a.combine(
        pd_df_b,
        lambda x, y: x**2 + 2 * x * y + y**2,
        overwrite=overwrite,
        fill_value=fill_value,
    )

    # Some dtype inconsistency for all-NULL columns
    pd.testing.assert_frame_equal(bf_result, pd_result, check_dtype=False)


@pytest.mark.parametrize(
    ("overwrite", "filter_func"),
    [
        (True, None),
        (False, None),
        (True, lambda x: x.isna() | (x % 2 == 0)),
    ],
    ids=[
        "default",
        "overwritefalse",
        "customfilter",
    ],
)
def test_df_update(overwrite, filter_func):
    if pd.__version__.startswith("1."):
        pytest.skip("dtype handled differently in pandas 1.x.")

    index1: pandas.Index = pandas.Index([1, 2, 3, 4], dtype="Int64")

    index2: pandas.Index = pandas.Index([1, 2, 4, 5], dtype="Int64")
    pd_df1 = pandas.DataFrame(
        {"a": [1, None, 3, 4], "b": [5, 6, None, 8]}, dtype="Int64", index=index1
    )
    pd_df2 = pandas.DataFrame(
        {"a": [None, 20, 30, 40], "c": [90, None, 110, 120]},
        dtype="Int64",
        index=index2,
    )

    bf_df1 = dataframe.DataFrame(pd_df1)
    bf_df2 = dataframe.DataFrame(pd_df2)

    bf_df1.update(bf_df2, overwrite=overwrite, filter_func=filter_func)
    pd_df1.update(pd_df2, overwrite=overwrite, filter_func=filter_func)

    pd.testing.assert_frame_equal(bf_df1.to_pandas(), pd_df1)


def test_df_idxmin():
    pd_df = pd.DataFrame(
        {"a": [1, 2, 3], "b": [7, None, 3], "c": [4, 4, 4]}, index=["x", "y", "z"]
    )
    bf_df = dataframe.DataFrame(pd_df)

    bf_result = bf_df.idxmin().to_pandas()
    pd_result = pd_df.idxmin()

    pd.testing.assert_series_equal(
        bf_result, pd_result, check_index_type=False, check_dtype=False
    )


def test_df_idxmax():
    pd_df = pd.DataFrame(
        {"a": [1, 2, 3], "b": [7, None, 3], "c": [4, 4, 4]}, index=["x", "y", "z"]
    )
    bf_df = dataframe.DataFrame(pd_df)

    bf_result = bf_df.idxmax().to_pandas()
    pd_result = pd_df.idxmax()

    pd.testing.assert_series_equal(
        bf_result, pd_result, check_index_type=False, check_dtype=False
    )


@pytest.mark.parametrize(
    ("join", "axis"),
    [
        ("outer", None),
        ("outer", 0),
        ("outer", 1),
        ("left", 0),
        ("right", 1),
        ("inner", None),
        ("inner", 1),
    ],
)
def test_df_align(join, axis):

    index1: pandas.Index = pandas.Index([1, 2, 3, 4], dtype="Int64")

    index2: pandas.Index = pandas.Index([1, 2, 4, 5], dtype="Int64")
    pd_df1 = pandas.DataFrame(
        {"a": [1, None, 3, 4], "b": [5, 6, None, 8]}, dtype="Int64", index=index1
    )
    pd_df2 = pandas.DataFrame(
        {"a": [None, 20, 30, 40], "c": [90, None, 110, 120]},
        dtype="Int64",
        index=index2,
    )

    bf_df1 = dataframe.DataFrame(pd_df1)
    bf_df2 = dataframe.DataFrame(pd_df2)

    bf_result1, bf_result2 = bf_df1.align(bf_df2, join=join, axis=axis)
    pd_result1, pd_result2 = pd_df1.align(pd_df2, join=join, axis=axis)

    # Don't check dtype as pandas does unnecessary float conversion
    assert isinstance(bf_result1, dataframe.DataFrame) and isinstance(
        bf_result2, dataframe.DataFrame
    )
    pd.testing.assert_frame_equal(bf_result1.to_pandas(), pd_result1, check_dtype=False)
    pd.testing.assert_frame_equal(bf_result2.to_pandas(), pd_result2, check_dtype=False)


def test_combine_first(
    scalars_df_index,
    scalars_df_2_index,
    scalars_pandas_df_index,
):
    if pd.__version__.startswith("1."):
        pytest.skip("pd.NA vs NaN not handled well in pandas 1.x.")
    columns = ["int64_too", "int64_col", "float64_col"]

    bf_df_a = scalars_df_index[columns].iloc[0:6]
    bf_df_a.columns = ["a", "b", "c"]
    bf_df_b = scalars_df_2_index[columns].iloc[2:8]
    bf_df_b.columns = ["b", "a", "d"]
    bf_result = bf_df_a.combine_first(bf_df_b).to_pandas()

    pd_df_a = scalars_pandas_df_index[columns].iloc[0:6]
    pd_df_a.columns = ["a", "b", "c"]
    pd_df_b = scalars_pandas_df_index[columns].iloc[2:8]
    pd_df_b.columns = ["b", "a", "d"]
    pd_result = pd_df_a.combine_first(pd_df_b)

    # Some dtype inconsistency for all-NULL columns
    pd.testing.assert_frame_equal(bf_result, pd_result, check_dtype=False)


@pytest.mark.parametrize(
    ("columns", "numeric_only"),
    [
        (["bool_col", "int64_col", "float64_col"], True),
        (["bool_col", "int64_col", "float64_col"], False),
        (["bool_col", "int64_col", "float64_col", "string_col"], True),
        pytest.param(
            ["bool_col", "int64_col", "float64_col", "string_col"],
            False,
            marks=pytest.mark.xfail(
                raises=NotImplementedError,
            ),
        ),
    ],
)
def test_df_corr_w_numeric_only(scalars_dfs_maybe_ordered, columns, numeric_only):
    scalars_df, scalars_pandas_df = scalars_dfs_maybe_ordered

    bf_result = scalars_df[columns].corr(numeric_only=numeric_only).to_pandas()
    pd_result = scalars_pandas_df[columns].corr(numeric_only=numeric_only)

    # BigFrames and Pandas differ in their data type handling:
    # - Column types: BigFrames uses Float64, Pandas uses float64.
    # - Index types: BigFrames uses strign, Pandas uses object.
    pd.testing.assert_index_equal(bf_result.columns, pd_result.columns)
    # Only check row order in ordered mode.
    pd.testing.assert_frame_equal(
        bf_result,
        pd_result,
        check_dtype=False,
        check_index_type=False,
        check_like=~scalars_df._block.session._strictly_ordered,
    )


def test_df_corr_w_invalid_parameters(scalars_dfs):
    columns = ["int64_too", "int64_col", "float64_col"]
    scalars_df, _ = scalars_dfs

    with pytest.raises(NotImplementedError):
        scalars_df[columns].corr(method="kendall")

    with pytest.raises(NotImplementedError):
        scalars_df[columns].corr(min_periods=1)


@pytest.mark.parametrize(
    ("columns", "numeric_only"),
    [
        (["bool_col", "int64_col", "float64_col"], True),
        (["bool_col", "int64_col", "float64_col"], False),
        (["bool_col", "int64_col", "float64_col", "string_col"], True),
        pytest.param(
            ["bool_col", "int64_col", "float64_col", "string_col"],
            False,
            marks=pytest.mark.xfail(
                raises=NotImplementedError,
            ),
        ),
    ],
)
def test_cov_w_numeric_only(scalars_dfs_maybe_ordered, columns, numeric_only):
    scalars_df, scalars_pandas_df = scalars_dfs_maybe_ordered
    bf_result = scalars_df[columns].cov(numeric_only=numeric_only).to_pandas()
    pd_result = scalars_pandas_df[columns].cov(numeric_only=numeric_only)
    # BigFrames and Pandas differ in their data type handling:
    # - Column types: BigFrames uses Float64, Pandas uses float64.
    # - Index types: BigFrames uses strign, Pandas uses object.
    pd.testing.assert_index_equal(bf_result.columns, pd_result.columns)
    # Only check row order in ordered mode.
    pd.testing.assert_frame_equal(
        bf_result,
        pd_result,
        check_dtype=False,
        check_index_type=False,
        check_like=~scalars_df._block.session._strictly_ordered,
    )


def test_df_corrwith_df(scalars_dfs_maybe_ordered):
    scalars_df, scalars_pandas_df = scalars_dfs_maybe_ordered

    l_cols = ["int64_col", "float64_col", "int64_too"]
    r_cols = ["int64_too", "float64_col"]

    bf_result = scalars_df[l_cols].corrwith(scalars_df[r_cols]).to_pandas()
    pd_result = scalars_pandas_df[l_cols].corrwith(scalars_pandas_df[r_cols])

    # BigFrames and Pandas differ in their data type handling:
    # - Column types: BigFrames uses Float64, Pandas uses float64.
    # - Index types: BigFrames uses strign, Pandas uses object.
    pd.testing.assert_series_equal(
        bf_result, pd_result, check_dtype=False, check_index_type=False
    )


def test_df_corrwith_df_numeric_only(scalars_dfs):
    scalars_df, scalars_pandas_df = scalars_dfs

    l_cols = ["int64_col", "float64_col", "int64_too", "string_col"]
    r_cols = ["int64_too", "float64_col", "bool_col"]

    bf_result = (
        scalars_df[l_cols].corrwith(scalars_df[r_cols], numeric_only=True).to_pandas()
    )
    pd_result = scalars_pandas_df[l_cols].corrwith(
        scalars_pandas_df[r_cols], numeric_only=True
    )

    # BigFrames and Pandas differ in their data type handling:
    # - Column types: BigFrames uses Float64, Pandas uses float64.
    # - Index types: BigFrames uses strign, Pandas uses object.
    pd.testing.assert_series_equal(
        bf_result, pd_result, check_dtype=False, check_index_type=False
    )


def test_df_corrwith_df_non_numeric_error(scalars_dfs):
    scalars_df, _ = scalars_dfs

    l_cols = ["int64_col", "float64_col", "int64_too", "string_col"]
    r_cols = ["int64_too", "float64_col", "bool_col"]

    with pytest.raises(NotImplementedError):
        scalars_df[l_cols].corrwith(scalars_df[r_cols], numeric_only=False)


def test_df_corrwith_series(scalars_dfs_maybe_ordered):
    # TODO: supply a reason why this isn't compatible with pandas 1.x
    pytest.importorskip("pandas", minversion="2.0.0")
    scalars_df, scalars_pandas_df = scalars_dfs_maybe_ordered

    l_cols = ["int64_col", "float64_col", "int64_too"]
    r_col = "float64_col"

    bf_result = scalars_df[l_cols].corrwith(scalars_df[r_col]).to_pandas()
    pd_result = scalars_pandas_df[l_cols].corrwith(scalars_pandas_df[r_col])

    # BigFrames and Pandas differ in their data type handling:
    # - Column types: BigFrames uses Float64, Pandas uses float64.
    # - Index types: BigFrames uses strign, Pandas uses object.
    pd.testing.assert_series_equal(
        bf_result, pd_result, check_dtype=False, check_index_type=False
    )


@pytest.mark.parametrize(
    ("op"),
    [
        operator.add,
        operator.sub,
        operator.mul,
        operator.truediv,
        operator.floordiv,
        operator.eq,
        operator.ne,
        operator.gt,
        operator.ge,
        operator.lt,
        operator.le,
    ],
    ids=[
        "add",
        "subtract",
        "multiply",
        "true_divide",
        "floor_divide",
        "eq",
        "ne",
        "gt",
        "ge",
        "lt",
        "le",
    ],
)
# TODO(garrettwu): deal with NA values
@pytest.mark.parametrize(("other_scalar"), [1, 2.5, 0, 0.0])
@pytest.mark.parametrize(("reverse_operands"), [True, False])
def test_scalar_binop(scalars_dfs, op, other_scalar, reverse_operands):
    scalars_df, scalars_pandas_df = scalars_dfs
    columns = ["int64_col", "float64_col"]

    maybe_reversed_op = (lambda x, y: op(y, x)) if reverse_operands else op

    bf_result = maybe_reversed_op(scalars_df[columns], other_scalar).to_pandas()
    pd_result = maybe_reversed_op(scalars_pandas_df[columns], other_scalar)

    assert_pandas_df_equal(bf_result, pd_result)


def test_dataframe_string_radd_const(scalars_dfs):
    pytest.importorskip(
        "pandas",
        minversion="2.0.0",
        reason="PyArrow string addition requires pandas 2.0+",
    )

    scalars_df, scalars_pandas_df = scalars_dfs
    columns = ["string_col", "string_col"]

    bf_result = ("prefix" + scalars_df[columns]).to_pandas()
    pd_result = "prefix" + scalars_pandas_df[columns]

    assert_pandas_df_equal(bf_result, pd_result)


@pytest.mark.parametrize(("other_scalar"), [1, -2])
def test_mod(scalars_dfs, other_scalar):
    # Zero case excluded as pandas produces 0 result for Int64 inputs rather than NA/NaN.
    # This is likely a pandas bug as mod 0 is undefined in other dtypes, and most programming languages.
    scalars_df, scalars_pandas_df = scalars_dfs

    bf_result = (scalars_df[["int64_col", "int64_too"]] % other_scalar).to_pandas()
    pd_result = scalars_pandas_df[["int64_col", "int64_too"]] % other_scalar

    assert_pandas_df_equal(bf_result, pd_result)


def test_scalar_binop_str_exception(scalars_dfs):
    scalars_df, _ = scalars_dfs
    columns = ["string_col"]
    with pytest.raises(TypeError, match="Cannot add dtypes"):
        (scalars_df[columns] + 1).to_pandas()


@pytest.mark.parametrize(
    ("op"),
    [
        (lambda x, y: x.add(y, axis="index")),
        (lambda x, y: x.radd(y, axis="index")),
        (lambda x, y: x.sub(y, axis="index")),
        (lambda x, y: x.rsub(y, axis="index")),
        (lambda x, y: x.mul(y, axis="index")),
        (lambda x, y: x.rmul(y, axis="index")),
        (lambda x, y: x.truediv(y, axis="index")),
        (lambda x, y: x.rtruediv(y, axis="index")),
        (lambda x, y: x.floordiv(y, axis="index")),
        (lambda x, y: x.floordiv(y, axis="index")),
        (lambda x, y: x.gt(y, axis="index")),
        (lambda x, y: x.ge(y, axis="index")),
        (lambda x, y: x.lt(y, axis="index")),
        (lambda x, y: x.le(y, axis="index")),
    ],
    ids=[
        "add",
        "radd",
        "sub",
        "rsub",
        "mul",
        "rmul",
        "truediv",
        "rtruediv",
        "floordiv",
        "rfloordiv",
        "gt",
        "ge",
        "lt",
        "le",
    ],
)
def test_series_binop_axis_index(
    scalars_dfs,
    op,
):
    scalars_df, scalars_pandas_df = scalars_dfs
    df_columns = ["int64_col", "float64_col"]
    series_column = "int64_too"

    bf_result = op(scalars_df[df_columns], scalars_df[series_column]).to_pandas()
    pd_result = op(scalars_pandas_df[df_columns], scalars_pandas_df[series_column])

    assert_pandas_df_equal(bf_result, pd_result)


@pytest.mark.parametrize(
    ("input"),
    [
        ((1000, 2000, 3000)),
        (pd.Index([1000, 2000, 3000])),
        (pd.Series((1000, 2000), index=["int64_too", "float64_col"])),
    ],
    ids=[
        "tuple",
        "pd_index",
        "pd_series",
    ],
)
def test_listlike_binop_axis_1_in_memory_data(scalars_dfs, input):
    # TODO: supply a reason why this isn't compatible with pandas 1.x
    pytest.importorskip("pandas", minversion="2.0.0")
    scalars_df, scalars_pandas_df = scalars_dfs

    df_columns = ["int64_col", "float64_col", "int64_too"]

    bf_result = scalars_df[df_columns].add(input, axis=1).to_pandas()
    if hasattr(input, "to_pandas"):
        input = input.to_pandas()
    pd_result = scalars_pandas_df[df_columns].add(input, axis=1)

    assert_pandas_df_equal(bf_result, pd_result, check_dtype=False)


def test_df_reverse_binop_pandas(scalars_dfs):
    # TODO: supply a reason why this isn't compatible with pandas 1.x
    pytest.importorskip("pandas", minversion="2.0.0")
    scalars_df, scalars_pandas_df = scalars_dfs

    pd_series = pd.Series([100, 200, 300])

    df_columns = ["int64_col", "float64_col", "int64_too"]

    bf_result = pd_series + scalars_df[df_columns].to_pandas()
    pd_result = pd_series + scalars_pandas_df[df_columns]

    assert_pandas_df_equal(bf_result, pd_result, check_dtype=False)


def test_listlike_binop_axis_1_bf_index(scalars_dfs):
    scalars_df, scalars_pandas_df = scalars_dfs

    df_columns = ["int64_col", "float64_col", "int64_too"]

    bf_result = (
        scalars_df[df_columns]
        .add(bf_indexes.Index([1000, 2000, 3000]), axis=1)
        .to_pandas()
    )
    pd_result = scalars_pandas_df[df_columns].add(pd.Index([1000, 2000, 3000]), axis=1)

    assert_pandas_df_equal(bf_result, pd_result, check_dtype=False)


def test_binop_with_self_aggregate(scalars_dfs_maybe_ordered):
    scalars_df, scalars_pandas_df = scalars_dfs_maybe_ordered

    df_columns = ["int64_col", "float64_col", "int64_too"]

    # Ensure that this takes the optimized single-query path by counting executions
    execution_count_before = scalars_df._session._metrics.execution_count
    bf_df = scalars_df[df_columns]
    bf_result = (bf_df - bf_df.mean()).to_pandas()
    execution_count_after = scalars_df._session._metrics.execution_count

    pd_df = scalars_pandas_df[df_columns]
    pd_result = pd_df - pd_df.mean()

    executions = execution_count_after - execution_count_before

    assert executions == 1
    assert_pandas_df_equal(bf_result, pd_result, check_dtype=False)


def test_binop_with_self_aggregate_w_index_reset(scalars_dfs_maybe_ordered):
    scalars_df, scalars_pandas_df = scalars_dfs_maybe_ordered

    df_columns = ["int64_col", "float64_col", "int64_too"]

    # Ensure that this takes the optimized single-query path by counting executions
    execution_count_before = scalars_df._session._metrics.execution_count
    bf_df = scalars_df[df_columns].reset_index(drop=True)
    bf_result = (bf_df - bf_df.mean()).to_pandas()
    execution_count_after = scalars_df._session._metrics.execution_count

    pd_df = scalars_pandas_df[df_columns].reset_index(drop=True)
    pd_result = pd_df - pd_df.mean()

    executions = execution_count_after - execution_count_before

    assert executions == 1
    pd_result.index = pd_result.index.astype("Int64")
    assert_pandas_df_equal(
        bf_result, pd_result, check_dtype=False, check_index_type=False
    )


@pytest.mark.parametrize(
    ("left_labels", "right_labels"),
    [
        (["a", "a", "b"], ["c", "c", "d"]),
        (["a", "b", "c"], ["c", "a", "b"]),
        (["a", "c", "c"], ["c", "a", "c"]),
        (["a", "b", "c"], ["a", "b", "c"]),
    ],
    ids=[
        "no_overlap",
        "one_one_match",
        "multi_match",
        "exact_match",
    ],
)
def test_binop_df_df_binary_op(
    scalars_df_index,
    scalars_df_2_index,
    scalars_pandas_df_index,
    left_labels,
    right_labels,
):
    if pd.__version__.startswith("1."):
        pytest.skip("pd.NA vs NaN not handled well in pandas 1.x.")
    columns = ["int64_too", "int64_col", "float64_col"]

    bf_df_a = scalars_df_index[columns]
    bf_df_a.columns = left_labels
    bf_df_b = scalars_df_2_index[columns]
    bf_df_b.columns = right_labels
    bf_result = (bf_df_a - bf_df_b).to_pandas()

    pd_df_a = scalars_pandas_df_index[columns]
    pd_df_a.columns = left_labels
    pd_df_b = scalars_pandas_df_index[columns]
    pd_df_b.columns = right_labels
    pd_result = pd_df_a - pd_df_b

    # Some dtype inconsistency for all-NULL columns
    pd.testing.assert_frame_equal(bf_result, pd_result, check_dtype=False)


# Differnt table will only work for explicit index, since default index orders are arbitrary.
@pytest.mark.parametrize(
    ("ordered"),
    [
        (True),
        (False),
    ],
)
def test_series_binop_add_different_table(
    scalars_df_index, scalars_pandas_df_index, scalars_df_2_index, ordered
):
    df_columns = ["int64_col", "float64_col"]
    series_column = "int64_too"

    bf_result = (
        scalars_df_index[df_columns]
        .add(scalars_df_2_index[series_column], axis="index")
        .to_pandas(ordered=ordered)
    )
    pd_result = scalars_pandas_df_index[df_columns].add(
        scalars_pandas_df_index[series_column], axis="index"
    )

    assert_pandas_df_equal(bf_result, pd_result, ignore_order=not ordered)


# TODO(garrettwu): Test series binop with different index

all_joins = pytest.mark.parametrize(
    ("how",),
    (("outer",), ("left",), ("right",), ("inner",), ("cross",)),
)


@all_joins
def test_join_same_table(scalars_dfs_maybe_ordered, how):
    bf_df, pd_df = scalars_dfs_maybe_ordered
    if not bf_df._session._strictly_ordered and how == "cross":
        pytest.skip("Cross join not supported in partial ordering mode.")

    bf_df_a = bf_df.set_index("int64_too")[["string_col", "int64_col"]]
    bf_df_a = bf_df_a.sort_index()

    bf_df_b = bf_df.set_index("int64_too")[["float64_col"]]
    bf_df_b = bf_df_b[bf_df_b.float64_col > 0]
    bf_df_b = bf_df_b.sort_values("float64_col")

    bf_result = bf_df_a.join(bf_df_b, how=how).to_pandas()

    pd_df_a = pd_df.set_index("int64_too")[["string_col", "int64_col"]].sort_index()
    pd_df_a = pd_df_a.sort_index()

    pd_df_b = pd_df.set_index("int64_too")[["float64_col"]]
    pd_df_b = pd_df_b[pd_df_b.float64_col > 0]
    pd_df_b = pd_df_b.sort_values("float64_col")

    pd_result = pd_df_a.join(pd_df_b, how=how)

    assert_pandas_df_equal(bf_result, pd_result, ignore_order=True)


@all_joins
def test_join_different_table(
    scalars_df_index, scalars_df_2_index, scalars_pandas_df_index, how
):
    bf_df_a = scalars_df_index[["string_col", "int64_col"]]
    bf_df_b = scalars_df_2_index.dropna()[["float64_col"]]
    bf_result = bf_df_a.join(bf_df_b, how=how).to_pandas()
    pd_df_a = scalars_pandas_df_index[["string_col", "int64_col"]]
    pd_df_b = scalars_pandas_df_index.dropna()[["float64_col"]]
    pd_result = pd_df_a.join(pd_df_b, how=how)
    assert_pandas_df_equal(bf_result, pd_result, ignore_order=True)


@all_joins
def test_join_different_table_with_duplicate_column_name(
    scalars_df_index, scalars_pandas_df_index, how
):
    bf_df_a = scalars_df_index[["string_col", "int64_col", "int64_too"]].rename(
        columns={"int64_too": "int64_col"}
    )
    bf_df_b = scalars_df_index.dropna()[
        ["string_col", "int64_col", "int64_too"]
    ].rename(columns={"int64_too": "int64_col"})
    bf_result = bf_df_a.join(bf_df_b, how=how, lsuffix="_l", rsuffix="_r").to_pandas()
    pd_df_a = scalars_pandas_df_index[["string_col", "int64_col", "int64_too"]].rename(
        columns={"int64_too": "int64_col"}
    )
    pd_df_b = scalars_pandas_df_index.dropna()[
        ["string_col", "int64_col", "int64_too"]
    ].rename(columns={"int64_too": "int64_col"})
    pd_result = pd_df_a.join(pd_df_b, how=how, lsuffix="_l", rsuffix="_r")

    # Ensure no inplace changes
    pd.testing.assert_index_equal(bf_df_a.columns, pd_df_a.columns)
    pd.testing.assert_index_equal(bf_df_b.index.to_pandas(), pd_df_b.index)
    pd.testing.assert_frame_equal(bf_result, pd_result, check_index_type=False)


@all_joins
def test_join_param_on_with_duplicate_column_name_not_on_col(
    scalars_df_index, scalars_pandas_df_index, how
):
    # This test is for duplicate column names, but the 'on' column is not duplicated.
    if how == "cross":
        return
    bf_df_a = scalars_df_index[
        ["string_col", "datetime_col", "timestamp_col", "int64_too"]
    ].rename(columns={"timestamp_col": "datetime_col"})
    bf_df_b = scalars_df_index.dropna()[
        ["string_col", "datetime_col", "timestamp_col"]
    ].rename(columns={"timestamp_col": "datetime_col"})
    bf_result = bf_df_a.join(
        bf_df_b, on="int64_too", how=how, lsuffix="_l", rsuffix="_r"
    ).to_pandas()
    pd_df_a = scalars_pandas_df_index[
        ["string_col", "datetime_col", "timestamp_col", "int64_too"]
    ].rename(columns={"timestamp_col": "datetime_col"})
    pd_df_b = scalars_pandas_df_index.dropna()[
        ["string_col", "datetime_col", "timestamp_col"]
    ].rename(columns={"timestamp_col": "datetime_col"})
    pd_result = pd_df_a.join(
        pd_df_b, on="int64_too", how=how, lsuffix="_l", rsuffix="_r"
    )
    pd.testing.assert_frame_equal(
        bf_result.sort_index(),
        pd_result.sort_index(),
        check_like=True,
        check_index_type=False,
        check_names=False,
    )
    pd.testing.assert_index_equal(bf_result.columns, pd_result.columns)


@pytest.mark.skipif(
    pandas.__version__.startswith("1."), reason="bad left join in pandas 1.x"
)
@all_joins
def test_join_param_on_with_duplicate_column_name_on_col(
    scalars_df_index, scalars_pandas_df_index, how
):
    # This test is for duplicate column names, and the 'on' column is duplicated.
    if how == "cross":
        return
    bf_df_a = scalars_df_index[
        ["string_col", "datetime_col", "timestamp_col", "int64_too"]
    ].rename(columns={"timestamp_col": "datetime_col"})
    bf_df_b = scalars_df_index.dropna()[
        ["string_col", "datetime_col", "timestamp_col", "int64_too"]
    ].rename(columns={"timestamp_col": "datetime_col"})
    bf_result = bf_df_a.join(
        bf_df_b, on="int64_too", how=how, lsuffix="_l", rsuffix="_r"
    ).to_pandas()
    pd_df_a = scalars_pandas_df_index[
        ["string_col", "datetime_col", "timestamp_col", "int64_too"]
    ].rename(columns={"timestamp_col": "datetime_col"})
    pd_df_b = scalars_pandas_df_index.dropna()[
        ["string_col", "datetime_col", "timestamp_col", "int64_too"]
    ].rename(columns={"timestamp_col": "datetime_col"})
    pd_result = pd_df_a.join(
        pd_df_b, on="int64_too", how=how, lsuffix="_l", rsuffix="_r"
    )
    pd.testing.assert_frame_equal(
        bf_result.sort_index(),
        pd_result.sort_index(),
        check_like=True,
        check_index_type=False,
        check_names=False,
    )
    pd.testing.assert_index_equal(bf_result.columns, pd_result.columns)


@all_joins
def test_join_param_on(scalars_dfs, how):
    bf_df, pd_df = scalars_dfs

    bf_df_a = bf_df[["string_col", "int64_col", "rowindex_2"]]
    bf_df_a = bf_df_a.assign(rowindex_2=bf_df_a["rowindex_2"] + 2)
    bf_df_b = bf_df[["float64_col"]]

    if how == "cross":
        with pytest.raises(ValueError):
            bf_df_a.join(bf_df_b, on="rowindex_2", how=how)
    else:
        bf_result = bf_df_a.join(bf_df_b, on="rowindex_2", how=how).to_pandas()

        pd_df_a = pd_df[["string_col", "int64_col", "rowindex_2"]]
        pd_df_a = pd_df_a.assign(rowindex_2=pd_df_a["rowindex_2"] + 2)
        pd_df_b = pd_df[["float64_col"]]
        pd_result = pd_df_a.join(pd_df_b, on="rowindex_2", how=how)
        assert_pandas_df_equal(bf_result, pd_result, ignore_order=True)


@all_joins
def test_df_join_series(scalars_dfs, how):
    bf_df, pd_df = scalars_dfs

    bf_df_a = bf_df[["string_col", "int64_col", "rowindex_2"]]
    bf_df_a = bf_df_a.assign(rowindex_2=bf_df_a["rowindex_2"] + 2)
    bf_series_b = bf_df["float64_col"]

    if how == "cross":
        with pytest.raises(ValueError):
            bf_df_a.join(bf_series_b, on="rowindex_2", how=how)
    else:
        bf_result = bf_df_a.join(bf_series_b, on="rowindex_2", how=how).to_pandas()

        pd_df_a = pd_df[["string_col", "int64_col", "rowindex_2"]]
        pd_df_a = pd_df_a.assign(rowindex_2=pd_df_a["rowindex_2"] + 2)
        pd_series_b = pd_df["float64_col"]
        pd_result = pd_df_a.join(pd_series_b, on="rowindex_2", how=how)
        assert_pandas_df_equal(bf_result, pd_result, ignore_order=True)


@pytest.mark.parametrize(
    ("by", "ascending", "na_position"),
    [
        ("int64_col", True, "first"),
        (["bool_col", "int64_col"], True, "last"),
        ("int64_col", False, "first"),
        (["bool_col", "int64_col"], [False, True], "last"),
        (["bool_col", "int64_col"], [True, False], "first"),
    ],
)
def test_dataframe_sort_values(
    scalars_df_index, scalars_pandas_df_index, by, ascending, na_position
):
    # Test needs values to be unique
    bf_result = scalars_df_index.sort_values(
        by, ascending=ascending, na_position=na_position
    ).to_pandas()
    pd_result = scalars_pandas_df_index.sort_values(
        by, ascending=ascending, na_position=na_position
    )

    pandas.testing.assert_frame_equal(
        bf_result,
        pd_result,
    )


@pytest.mark.parametrize(
    ("by", "ascending", "na_position"),
    [
        ("int64_col", True, "first"),
        (["bool_col", "int64_col"], True, "last"),
    ],
)
def test_dataframe_sort_values_inplace(
    scalars_df_index, scalars_pandas_df_index, by, ascending, na_position
):
    # Test needs values to be unique
    bf_sorted = scalars_df_index.copy()
    bf_sorted.sort_values(
        by, ascending=ascending, na_position=na_position, inplace=True
    )
    bf_result = bf_sorted.to_pandas()
    pd_result = scalars_pandas_df_index.sort_values(
        by, ascending=ascending, na_position=na_position
    )

    pandas.testing.assert_frame_equal(
        bf_result,
        pd_result,
    )


def test_dataframe_sort_values_invalid_input(scalars_df_index):
    with pytest.raises(KeyError):
        scalars_df_index.sort_values(by=scalars_df_index["int64_col"])


def test_dataframe_sort_values_stable(scalars_df_index, scalars_pandas_df_index):
    bf_result = (
        scalars_df_index.sort_values("int64_col", kind="stable")
        .sort_values("bool_col", kind="stable")
        .to_pandas()
    )
    pd_result = scalars_pandas_df_index.sort_values(
        "int64_col", kind="stable"
    ).sort_values("bool_col", kind="stable")

    pandas.testing.assert_frame_equal(
        bf_result,
        pd_result,
    )


@pytest.mark.parametrize(
    ("operator", "columns"),
    [
        pytest.param(lambda x: x.cumsum(), ["float64_col", "int64_too"]),
        pytest.param(lambda x: x.cumprod(), ["float64_col", "int64_too"]),
        pytest.param(
            lambda x: x.cumprod(),
            ["string_col"],
            marks=pytest.mark.xfail(
                raises=ValueError,
            ),
        ),
    ],
    ids=[
        "cumsum",
        "cumprod",
        "non-numeric",
    ],
)
def test_dataframe_numeric_analytic_op(
    scalars_df_index, scalars_pandas_df_index, operator, columns
):
    # TODO: Add nullable ints (pandas 1.x has poor behavior on these)
    bf_series = operator(scalars_df_index[columns])
    pd_series = operator(scalars_pandas_df_index[columns])
    bf_result = bf_series.to_pandas()
    pd.testing.assert_frame_equal(pd_series, bf_result, check_dtype=False)


@pytest.mark.parametrize(
    ("operator"),
    [
        (lambda x: x.cummin()),
        (lambda x: x.cummax()),
        (lambda x: x.shift(2)),
        (lambda x: x.shift(-2)),
    ],
    ids=[
        "cummin",
        "cummax",
        "shiftpostive",
        "shiftnegative",
    ],
)
def test_dataframe_general_analytic_op(
    scalars_df_index, scalars_pandas_df_index, operator
):
    col_names = ["int64_too", "float64_col", "int64_col", "bool_col"]
    bf_series = operator(scalars_df_index[col_names])
    pd_series = operator(scalars_pandas_df_index[col_names])
    bf_result = bf_series.to_pandas()
    pd.testing.assert_frame_equal(
        pd_series,
        bf_result,
    )


@pytest.mark.parametrize(
    ("periods",),
    [
        (1,),
        (2,),
        (-1,),
    ],
)
def test_dataframe_diff(scalars_df_index, scalars_pandas_df_index, periods):
    col_names = ["int64_too", "float64_col", "int64_col"]
    bf_result = scalars_df_index[col_names].diff(periods=periods).to_pandas()
    pd_result = scalars_pandas_df_index[col_names].diff(periods=periods)
    pd.testing.assert_frame_equal(
        pd_result,
        bf_result,
    )


@pytest.mark.parametrize(
    ("periods",),
    [
        (1,),
        (2,),
        (-1,),
    ],
)
def test_dataframe_pct_change(scalars_df_index, scalars_pandas_df_index, periods):
    col_names = ["int64_too", "float64_col", "int64_col"]
    bf_result = scalars_df_index[col_names].pct_change(periods=periods).to_pandas()
    pd_result = scalars_pandas_df_index[col_names].pct_change(periods=periods)
    pd.testing.assert_frame_equal(
        pd_result,
        bf_result,
    )


def test_dataframe_agg_single_string(scalars_dfs):
    numeric_cols = ["int64_col", "int64_too", "float64_col"]
    scalars_df, scalars_pandas_df = scalars_dfs

    bf_result = scalars_df[numeric_cols].agg("sum").to_pandas()
    pd_result = scalars_pandas_df[numeric_cols].agg("sum")

    assert bf_result.dtype == "Float64"
    pd.testing.assert_series_equal(
        pd_result, bf_result, check_dtype=False, check_index_type=False
    )


@pytest.mark.parametrize(
    ("agg",),
    (
        ("sum",),
        ("size",),
    ),
)
def test_dataframe_agg_int_single_string(scalars_dfs, agg):
    numeric_cols = ["int64_col", "int64_too", "bool_col"]
    scalars_df, scalars_pandas_df = scalars_dfs

    bf_result = scalars_df[numeric_cols].agg(agg).to_pandas()
    pd_result = scalars_pandas_df[numeric_cols].agg(agg)

    assert bf_result.dtype == "Int64"
    pd.testing.assert_series_equal(
        pd_result, bf_result, check_dtype=False, check_index_type=False
    )


def test_dataframe_agg_multi_string(scalars_dfs_maybe_ordered):
    numeric_cols = ["int64_col", "int64_too", "float64_col"]
    aggregations = [
        "sum",
        "mean",
        "median",
        "std",
        "var",
        "min",
        "max",
        "nunique",
        "count",
    ]
    scalars_df, scalars_pandas_df = scalars_dfs_maybe_ordered
    bf_result = scalars_df[numeric_cols].agg(aggregations)
    pd_result = scalars_pandas_df[numeric_cols].agg(aggregations)

    # Pandas may produce narrower numeric types, but bigframes always produces Float64
    pd_result = pd_result.astype("Float64")

    # Drop median, as it's an approximation.
    bf_median = bf_result.loc["median", :]
    bf_result = bf_result.drop(labels=["median"])
    pd_result = pd_result.drop(labels=["median"])

    assert_dfs_equivalent(pd_result, bf_result, check_index_type=False)

    # Double-check that median is at least plausible.
    assert (
        (bf_result.loc["min", :] <= bf_median) & (bf_median <= bf_result.loc["max", :])
    ).all()


def test_dataframe_agg_int_multi_string(scalars_dfs):
    numeric_cols = ["int64_col", "int64_too", "bool_col"]
    aggregations = [
        "sum",
        "nunique",
        "count",
        "size",
    ]
    scalars_df, scalars_pandas_df = scalars_dfs
    bf_result = scalars_df[numeric_cols].agg(aggregations).to_pandas()
    pd_result = scalars_pandas_df[numeric_cols].agg(aggregations)

    for dtype in bf_result.dtypes:
        assert dtype == "Int64"

    # Pandas may produce narrower numeric types
    # Pandas has object index type
    pd.testing.assert_frame_equal(
        pd_result, bf_result, check_dtype=False, check_index_type=False
    )


def test_df_transpose():
    # Include some floats to ensure type coercion
    values = [[0, 3.5, True], [1, 4.5, False], [2, 6.5, None]]
    # Test complex case of both axes being multi-indices with non-unique elements

    columns: pandas.Index = pd.Index(
        ["A", "B", "A"], dtype=pd.StringDtype(storage="pyarrow")
    )
    columns_multi = pd.MultiIndex.from_arrays([columns, columns], names=["c1", "c2"])

    index: pandas.Index = pd.Index(
        ["b", "a", "a"], dtype=pd.StringDtype(storage="pyarrow")
    )
    rows_multi = pd.MultiIndex.from_arrays([index, index], names=["r1", "r2"])

    pd_df = pandas.DataFrame(values, index=rows_multi, columns=columns_multi)
    bf_df = dataframe.DataFrame(values, index=rows_multi, columns=columns_multi)

    pd_result = pd_df.T
    bf_result = bf_df.T.to_pandas()

    pd.testing.assert_frame_equal(pd_result, bf_result, check_dtype=False)


def test_df_transpose_error():
    with pytest.raises(TypeError, match="Cannot coerce.*to a common type."):
        dataframe.DataFrame([[1, "hello"], [2, "world"]]).transpose()


def test_df_transpose_repeated_uses_cache():
    bf_df = dataframe.DataFrame([[1, 2.5], [2, 3.5]])
    pd_df = pandas.DataFrame([[1, 2.5], [2, 3.5]])
    # Transposing many times so that operation will fail from complexity if not using cache
    for i in range(10):
        # Cache still works even with simple scalar binop
        bf_df = bf_df.transpose() + i
        pd_df = pd_df.transpose() + i

    pd.testing.assert_frame_equal(
        pd_df, bf_df.to_pandas(), check_dtype=False, check_index_type=False
    )


@pytest.mark.parametrize(
    ("ordered"),
    [
        (True),
        (False),
    ],
)
def test_df_stack(scalars_dfs, ordered):
    if pandas.__version__.startswith("1.") or pandas.__version__.startswith("2.0"):
        pytest.skip("pandas <2.1 uses different stack implementation")
    scalars_df, scalars_pandas_df = scalars_dfs
    # To match bigquery dataframes
    scalars_pandas_df = scalars_pandas_df.copy()
    scalars_pandas_df.columns = scalars_pandas_df.columns.astype("string[pyarrow]")
    # Can only stack identically-typed columns
    columns = ["int64_col", "int64_too", "rowindex_2"]

    bf_result = scalars_df[columns].stack().to_pandas(ordered=ordered)
    pd_result = scalars_pandas_df[columns].stack(future_stack=True)

    # Pandas produces NaN, where bq dataframes produces pd.NA
    assert_series_equal(
        bf_result, pd_result, check_dtype=False, ignore_order=not ordered
    )


def test_df_melt_default(scalars_dfs):
    scalars_df, scalars_pandas_df = scalars_dfs
    # To match bigquery dataframes
    scalars_pandas_df = scalars_pandas_df.copy()
    scalars_pandas_df.columns = scalars_pandas_df.columns.astype("string[pyarrow]")
    # Can only stack identically-typed columns
    columns = ["int64_col", "int64_too", "rowindex_2"]

    bf_result = scalars_df[columns].melt().to_pandas()
    pd_result = scalars_pandas_df[columns].melt()

    # Pandas produces int64 index, Bigframes produces Int64 (nullable)
    pd.testing.assert_frame_equal(
        bf_result,
        pd_result,
        check_index_type=False,
        check_dtype=False,
    )


def test_df_melt_parameterized(scalars_dfs):
    scalars_df, scalars_pandas_df = scalars_dfs
    # To match bigquery dataframes
    scalars_pandas_df = scalars_pandas_df.copy()
    scalars_pandas_df.columns = scalars_pandas_df.columns.astype("string[pyarrow]")
    # Can only stack identically-typed columns

    bf_result = scalars_df.melt(
        var_name="alice",
        value_name="bob",
        id_vars=["string_col"],
        value_vars=["int64_col", "int64_too"],
    ).to_pandas()
    pd_result = scalars_pandas_df.melt(
        var_name="alice",
        value_name="bob",
        id_vars=["string_col"],
        value_vars=["int64_col", "int64_too"],
    )

    # Pandas produces int64 index, Bigframes produces Int64 (nullable)
    pd.testing.assert_frame_equal(
        bf_result, pd_result, check_index_type=False, check_dtype=False
    )


@pytest.mark.parametrize(
    ("ordered"),
    [
        (True),
        (False),
    ],
)
def test_df_unstack(scalars_dfs, ordered):
    scalars_df, scalars_pandas_df = scalars_dfs
    # To match bigquery dataframes
    scalars_pandas_df = scalars_pandas_df.copy()
    scalars_pandas_df.columns = scalars_pandas_df.columns.astype("string[pyarrow]")
    # Can only stack identically-typed columns
    columns = [
        "rowindex_2",
        "int64_col",
        "int64_too",
    ]

    # unstack on mono-index produces series
    bf_result = scalars_df[columns].unstack().to_pandas(ordered=ordered)
    pd_result = scalars_pandas_df[columns].unstack()

    # Pandas produces NaN, where bq dataframes produces pd.NA
    assert_series_equal(
        bf_result, pd_result, check_dtype=False, ignore_order=not ordered
    )


@pytest.mark.parametrize(
    ("values", "index", "columns"),
    [
        ("int64_col", "int64_too", ["string_col"]),
        (["int64_col"], "int64_too", ["string_col"]),
        (["int64_col", "float64_col"], "int64_too", ["string_col"]),
    ],
)
def test_df_pivot(scalars_dfs, values, index, columns):
    scalars_df, scalars_pandas_df = scalars_dfs

    bf_result = scalars_df.pivot(
        values=values, index=index, columns=columns
    ).to_pandas()
    pd_result = scalars_pandas_df.pivot(values=values, index=index, columns=columns)

    # Pandas produces NaN, where bq dataframes produces pd.NA
    bf_result = bf_result.fillna(float("nan"))
    pd_result = pd_result.fillna(float("nan"))
    pd.testing.assert_frame_equal(bf_result, pd_result, check_dtype=False)


@pytest.mark.parametrize(
    ("values", "index", "columns"),
    [
        (["goals", "assists"], ["team_name", "season"], ["position"]),
        (["goals", "assists"], ["season"], ["team_name", "position"]),
    ],
)
def test_df_pivot_hockey(hockey_df, hockey_pandas_df, values, index, columns):
    bf_result = (
        hockey_df.reset_index()
        .pivot(values=values, index=index, columns=columns)
        .to_pandas()
    )
    pd_result = hockey_pandas_df.reset_index().pivot(
        values=values, index=index, columns=columns
    )

    # Pandas produces NaN, where bq dataframes produces pd.NA
    pd.testing.assert_frame_equal(bf_result, pd_result, check_dtype=False)


@pytest.mark.parametrize(
    ("values", "index", "columns", "aggfunc"),
    [
        (("culmen_length_mm", "body_mass_g"), "species", "sex", "std"),
        (["body_mass_g", "culmen_length_mm"], ("species", "island"), "sex", "sum"),
        ("body_mass_g", "sex", ["island", "species"], "mean"),
        ("culmen_depth_mm", "island", "species", "max"),
    ],
)
def test_df_pivot_table(
    penguins_df_default_index,
    penguins_pandas_df_default_index,
    values,
    index,
    columns,
    aggfunc,
):
    bf_result = penguins_df_default_index.pivot_table(
        values=values, index=index, columns=columns, aggfunc=aggfunc
    ).to_pandas()
    pd_result = penguins_pandas_df_default_index.pivot_table(
        values=values, index=index, columns=columns, aggfunc=aggfunc
    )
    pd.testing.assert_frame_equal(
        bf_result, pd_result, check_dtype=False, check_column_type=False
    )


def test_ipython_key_completions_with_drop(scalars_dfs):
    scalars_df, scalars_pandas_df = scalars_dfs
    col_names = "string_col"
    bf_dataframe = scalars_df.drop(columns=col_names)
    pd_dataframe = scalars_pandas_df.drop(columns=col_names)
    expected = pd_dataframe.columns.tolist()

    results = bf_dataframe._ipython_key_completions_()

    assert col_names not in results
    assert results == expected
    # _ipython_key_completions_ is called with square brackets
    # so only column names are relevant with tab completion
    assert "to_gbq" not in results
    assert "merge" not in results
    assert "drop" not in results


def test_ipython_key_completions_with_rename(scalars_dfs):
    scalars_df, scalars_pandas_df = scalars_dfs
    col_name_dict = {"string_col": "a_renamed_column"}
    bf_dataframe = scalars_df.rename(columns=col_name_dict)
    pd_dataframe = scalars_pandas_df.rename(columns=col_name_dict)
    expected = pd_dataframe.columns.tolist()

    results = bf_dataframe._ipython_key_completions_()

    assert "string_col" not in results
    assert "a_renamed_column" in results
    assert results == expected
    # _ipython_key_completions_ is called with square brackets
    # so only column names are relevant with tab completion
    assert "to_gbq" not in results
    assert "merge" not in results
    assert "drop" not in results


def test__dir__with_drop(scalars_dfs):
    scalars_df, scalars_pandas_df = scalars_dfs
    col_names = "string_col"
    bf_dataframe = scalars_df.drop(columns=col_names)
    pd_dataframe = scalars_pandas_df.drop(columns=col_names)
    expected = pd_dataframe.columns.tolist()

    results = dir(bf_dataframe)

    assert col_names not in results
    assert frozenset(expected) <= frozenset(results)
    # __dir__ is called with a '.' and displays all methods, columns names, etc.
    assert "to_gbq" in results
    assert "merge" in results
    assert "drop" in results


def test__dir__with_rename(scalars_dfs):
    scalars_df, scalars_pandas_df = scalars_dfs
    col_name_dict = {"string_col": "a_renamed_column"}
    bf_dataframe = scalars_df.rename(columns=col_name_dict)
    pd_dataframe = scalars_pandas_df.rename(columns=col_name_dict)
    expected = pd_dataframe.columns.tolist()

    results = dir(bf_dataframe)

    assert "string_col" not in results
    assert "a_renamed_column" in results
    assert frozenset(expected) <= frozenset(results)
    # __dir__ is called with a '.' and displays all methods, columns names, etc.
    assert "to_gbq" in results
    assert "merge" in results
    assert "drop" in results


def test_loc_select_columns_w_repeats(scalars_df_index, scalars_pandas_df_index):
    bf_result = scalars_df_index[["int64_col", "int64_col", "int64_too"]].to_pandas()
    pd_result = scalars_pandas_df_index[["int64_col", "int64_col", "int64_too"]]
    pd.testing.assert_frame_equal(
        bf_result,
        pd_result,
    )


@pytest.mark.parametrize(
    ("start", "stop", "step"),
    [
        (0, 0, None),
        (None, None, None),
        (1, None, None),
        (None, 4, None),
        (None, None, 2),
        (None, 50000000000, 1),
        (5, 4, None),
        (3, None, 2),
        (1, 7, 2),
        (1, 7, 50000000000),
    ],
)
def test_iloc_slice(scalars_df_index, scalars_pandas_df_index, start, stop, step):
    bf_result = scalars_df_index.iloc[start:stop:step].to_pandas()
    pd_result = scalars_pandas_df_index.iloc[start:stop:step]
    pd.testing.assert_frame_equal(
        bf_result,
        pd_result,
    )


@pytest.mark.parametrize(
    ("start", "stop", "step"),
    [
        (0, 0, None),
    ],
)
def test_iloc_slice_after_cache(
    scalars_df_index, scalars_pandas_df_index, start, stop, step
):
    scalars_df_index.cache()
    bf_result = scalars_df_index.iloc[start:stop:step].to_pandas()
    pd_result = scalars_pandas_df_index.iloc[start:stop:step]
    pd.testing.assert_frame_equal(
        bf_result,
        pd_result,
    )


def test_iloc_slice_zero_step(scalars_df_index):
    with pytest.raises(ValueError):
        scalars_df_index.iloc[0:0:0]


@pytest.mark.parametrize(
    ("ordered"),
    [
        (True),
        (False),
    ],
)
def test_iloc_slice_nested(scalars_df_index, scalars_pandas_df_index, ordered):
    bf_result = scalars_df_index.iloc[1:].iloc[1:].to_pandas(ordered=ordered)
    pd_result = scalars_pandas_df_index.iloc[1:].iloc[1:]

    assert_pandas_df_equal(bf_result, pd_result, ignore_order=not ordered)


@pytest.mark.parametrize(
    "index",
    [0, 5, -2, (2,)],
)
def test_iloc_single_integer(scalars_df_index, scalars_pandas_df_index, index):
    bf_result = scalars_df_index.iloc[index]
    pd_result = scalars_pandas_df_index.iloc[index]

    pd.testing.assert_series_equal(
        bf_result,
        pd_result,
    )


@pytest.mark.parametrize(
    "index",
    [(2, 5), (5, 0), (0, 0)],
)
def test_iloc_tuple(scalars_df_index, scalars_pandas_df_index, index):
    bf_result = scalars_df_index.iloc[index]
    pd_result = scalars_pandas_df_index.iloc[index]

    assert bf_result == pd_result


@pytest.mark.parametrize(
    "index",
    [(slice(None), [1, 2, 3]), (slice(1, 7, 2), [2, 5, 3])],
)
def test_iloc_tuple_multi_columns(scalars_df_index, scalars_pandas_df_index, index):
    bf_result = scalars_df_index.iloc[index].to_pandas()
    pd_result = scalars_pandas_df_index.iloc[index]

    pd.testing.assert_frame_equal(bf_result, pd_result)


def test_iloc_tuple_multi_columns_single_row(scalars_df_index, scalars_pandas_df_index):
    index = (2, [2, 1, 3, -4])
    bf_result = scalars_df_index.iloc[index]
    pd_result = scalars_pandas_df_index.iloc[index]
    pd.testing.assert_series_equal(bf_result, pd_result)


@pytest.mark.parametrize(
    ("index", "error"),
    [
        ((1, 1, 1), pd.errors.IndexingError),
        (("asd", "asd", "asd"), pd.errors.IndexingError),
        (("asd"), TypeError),
    ],
)
def test_iloc_tuple_errors(scalars_df_index, scalars_pandas_df_index, index, error):
    with pytest.raises(error):
        scalars_df_index.iloc[index]
    with pytest.raises(error):
        scalars_pandas_df_index.iloc[index]


@pytest.mark.parametrize(
    "index",
    [(2, 5), (5, 0), (0, 0)],
)
def test_iat(scalars_df_index, scalars_pandas_df_index, index):
    bf_result = scalars_df_index.iat[index]
    pd_result = scalars_pandas_df_index.iat[index]

    assert bf_result == pd_result


@pytest.mark.parametrize(
    ("index", "error"),
    [
        (0, TypeError),
        ("asd", ValueError),
        ((1, 2, 3), TypeError),
        (("asd", "asd"), ValueError),
    ],
)
def test_iat_errors(scalars_df_index, scalars_pandas_df_index, index, error):
    with pytest.raises(error):
        scalars_pandas_df_index.iat[index]
    with pytest.raises(error):
        scalars_df_index.iat[index]


def test_iloc_single_integer_out_of_bound_error(scalars_df_index):
    with pytest.raises(IndexError, match="single positional indexer is out-of-bounds"):
        scalars_df_index.iloc[99]


def test_loc_bool_series(scalars_df_index, scalars_pandas_df_index):
    bf_result = scalars_df_index.loc[scalars_df_index.bool_col].to_pandas()
    pd_result = scalars_pandas_df_index.loc[scalars_pandas_df_index.bool_col]

    pd.testing.assert_frame_equal(
        bf_result,
        pd_result,
    )


def test_loc_list_select_rows_and_columns(scalars_df_index, scalars_pandas_df_index):
    idx_list = [0, 3, 5]
    bf_result = scalars_df_index.loc[idx_list, ["bool_col", "int64_col"]].to_pandas()
    pd_result = scalars_pandas_df_index.loc[idx_list, ["bool_col", "int64_col"]]

    pd.testing.assert_frame_equal(
        bf_result,
        pd_result,
    )


def test_loc_select_column(scalars_df_index, scalars_pandas_df_index):
    bf_result = scalars_df_index.loc[:, "int64_col"].to_pandas()
    pd_result = scalars_pandas_df_index.loc[:, "int64_col"]
    pd.testing.assert_series_equal(
        bf_result,
        pd_result,
    )


def test_loc_select_with_column_condition(scalars_df_index, scalars_pandas_df_index):
    bf_result = scalars_df_index.loc[:, scalars_df_index.dtypes == "Int64"].to_pandas()
    pd_result = scalars_pandas_df_index.loc[
        :, scalars_pandas_df_index.dtypes == "Int64"
    ]
    pd.testing.assert_frame_equal(
        bf_result,
        pd_result,
    )


def test_loc_select_with_column_condition_bf_series(
    scalars_df_index, scalars_pandas_df_index
):
    # (b/347072677) GEOGRAPH type doesn't support DISTINCT op
    columns = [
        item for item in scalars_pandas_df_index.columns if item != "geography_col"
    ]
    scalars_df_index = scalars_df_index[columns]
    scalars_pandas_df_index = scalars_pandas_df_index[columns]

    size_half = len(scalars_pandas_df_index) / 2
    bf_result = scalars_df_index.loc[
        :, scalars_df_index.nunique() > size_half
    ].to_pandas()
    pd_result = scalars_pandas_df_index.loc[
        :, scalars_pandas_df_index.nunique() > size_half
    ]
    pd.testing.assert_frame_equal(
        bf_result,
        pd_result,
    )


def test_loc_single_index_with_duplicate(scalars_df_index, scalars_pandas_df_index):
    scalars_df_index = scalars_df_index.set_index("string_col", drop=False)
    scalars_pandas_df_index = scalars_pandas_df_index.set_index(
        "string_col", drop=False
    )
    index = "Hello, World!"
    bf_result = scalars_df_index.loc[index]
    pd_result = scalars_pandas_df_index.loc[index]
    pd.testing.assert_frame_equal(
        bf_result.to_pandas(),
        pd_result,
    )


def test_loc_single_index_no_duplicate(scalars_df_index, scalars_pandas_df_index):
    scalars_df_index = scalars_df_index.set_index("int64_too", drop=False)
    scalars_pandas_df_index = scalars_pandas_df_index.set_index("int64_too", drop=False)
    index = -2345
    bf_result = scalars_df_index.loc[index]
    pd_result = scalars_pandas_df_index.loc[index]
    pd.testing.assert_series_equal(
        bf_result,
        pd_result,
    )


def test_at_with_duplicate(scalars_df_index, scalars_pandas_df_index):
    scalars_df_index = scalars_df_index.set_index("string_col", drop=False)
    scalars_pandas_df_index = scalars_pandas_df_index.set_index(
        "string_col", drop=False
    )
    index = "Hello, World!"
    bf_result = scalars_df_index.at[index, "int64_too"]
    pd_result = scalars_pandas_df_index.at[index, "int64_too"]
    pd.testing.assert_series_equal(
        bf_result.to_pandas(),
        pd_result,
    )


def test_at_no_duplicate(scalars_df_index, scalars_pandas_df_index):
    scalars_df_index = scalars_df_index.set_index("int64_too", drop=False)
    scalars_pandas_df_index = scalars_pandas_df_index.set_index("int64_too", drop=False)
    index = -2345
    bf_result = scalars_df_index.at[index, "string_col"]
    pd_result = scalars_pandas_df_index.at[index, "string_col"]
    assert bf_result == pd_result


def test_loc_setitem_bool_series_scalar_new_col(scalars_dfs):
    scalars_df, scalars_pandas_df = scalars_dfs
    bf_df = scalars_df.copy()
    pd_df = scalars_pandas_df.copy()
    bf_df.loc[bf_df["int64_too"] == 0, "new_col"] = 99
    pd_df.loc[pd_df["int64_too"] == 0, "new_col"] = 99

    # pandas uses float64 instead
    pd_df["new_col"] = pd_df["new_col"].astype("Float64")

    pd.testing.assert_frame_equal(
        bf_df.to_pandas(),
        pd_df,
    )


@pytest.mark.parametrize(
    ("col", "value"),
    [
        ("string_col", "hello"),
        ("int64_col", 3),
        ("float64_col", 3.5),
    ],
)
def test_loc_setitem_bool_series_scalar_existing_col(scalars_dfs, col, value):
    if pd.__version__.startswith("1."):
        pytest.skip("this loc overload not supported in pandas 1.x.")

    scalars_df, scalars_pandas_df = scalars_dfs
    bf_df = scalars_df.copy()
    pd_df = scalars_pandas_df.copy()
    bf_df.loc[bf_df["int64_too"] == 1, col] = value
    pd_df.loc[pd_df["int64_too"] == 1, col] = value

    pd.testing.assert_frame_equal(
        bf_df.to_pandas(),
        pd_df,
    )


def test_loc_setitem_bool_series_scalar_error(scalars_dfs):
    if pd.__version__.startswith("1."):
        pytest.skip("this loc overload not supported in pandas 1.x.")

    scalars_df, scalars_pandas_df = scalars_dfs
    bf_df = scalars_df.copy()
    pd_df = scalars_pandas_df.copy()

    with pytest.raises(Exception):
        bf_df.loc[bf_df["int64_too"] == 1, "string_col"] = 99
    with pytest.raises(Exception):
        pd_df.loc[pd_df["int64_too"] == 1, "string_col"] = 99


@pytest.mark.parametrize(
    ("col", "op"),
    [
        # Int aggregates
        pytest.param("int64_col", lambda x: x.sum(), id="int-sum"),
        pytest.param("int64_col", lambda x: x.min(), id="int-min"),
        pytest.param("int64_col", lambda x: x.max(), id="int-max"),
        pytest.param("int64_col", lambda x: x.count(), id="int-count"),
        pytest.param("int64_col", lambda x: x.nunique(), id="int-nunique"),
        # Float aggregates
        pytest.param("float64_col", lambda x: x.count(), id="float-count"),
        pytest.param("float64_col", lambda x: x.nunique(), id="float-nunique"),
        # Bool aggregates
        pytest.param("bool_col", lambda x: x.sum(), id="bool-sum"),
        pytest.param("bool_col", lambda x: x.count(), id="bool-count"),
        pytest.param("bool_col", lambda x: x.nunique(), id="bool-nunique"),
        # String aggregates
        pytest.param("string_col", lambda x: x.count(), id="string-count"),
        pytest.param("string_col", lambda x: x.nunique(), id="string-nunique"),
    ],
)
def test_dataframe_aggregate_int(scalars_df_index, scalars_pandas_df_index, col, op):
    bf_result = op(scalars_df_index[[col]]).to_pandas()
    pd_result = op(scalars_pandas_df_index[[col]])

    # Check dtype separately
    assert bf_result.dtype == "Int64"
    # Is otherwise "object" dtype
    pd_result.index = pd_result.index.astype("string[pyarrow]")
    # Pandas may produce narrower numeric types
    assert_series_equal(pd_result, bf_result, check_dtype=False, check_index_type=False)


@pytest.mark.parametrize(
    ("col", "op"),
    [
        pytest.param("bool_col", lambda x: x.min(), id="bool-min"),
        pytest.param("bool_col", lambda x: x.max(), id="bool-max"),
    ],
)
def test_dataframe_aggregate_bool(scalars_df_index, scalars_pandas_df_index, col, op):
    bf_result = op(scalars_df_index[[col]]).to_pandas()
    pd_result = op(scalars_pandas_df_index[[col]])

    # Check dtype separately
    assert bf_result.dtype == "boolean"

    # Pandas may produce narrower numeric types
    # Pandas has object index type
    pd_result.index = pd_result.index.astype("string[pyarrow]")
    assert_series_equal(pd_result, bf_result, check_dtype=False, check_index_type=False)


@pytest.mark.parametrize(
    ("op", "bf_dtype"),
    [
        (lambda x: x.sum(numeric_only=True), "Float64"),
        (lambda x: x.mean(numeric_only=True), "Float64"),
        (lambda x: x.min(numeric_only=True), "Float64"),
        (lambda x: x.max(numeric_only=True), "Float64"),
        (lambda x: x.std(numeric_only=True), "Float64"),
        (lambda x: x.var(numeric_only=True), "Float64"),
        (lambda x: x.count(numeric_only=False), "Int64"),
        (lambda x: x.nunique(), "Int64"),
    ],
    ids=["sum", "mean", "min", "max", "std", "var", "count", "nunique"],
)
def test_dataframe_aggregates(scalars_dfs_maybe_ordered, op, bf_dtype):
    scalars_df_index, scalars_pandas_df_index = scalars_dfs_maybe_ordered
    col_names = ["int64_too", "float64_col", "string_col", "int64_col", "bool_col"]
    bf_series = op(scalars_df_index[col_names])
    bf_result = bf_series
    pd_result = op(scalars_pandas_df_index[col_names])

    # Check dtype separately
    assert bf_result.dtype == bf_dtype

    # Pandas may produce narrower numeric types, but bigframes always produces Float64
    # Pandas has object index type
    pd_result.index = pd_result.index.astype("string[pyarrow]")
    assert_series_equivalent(
        pd_result,
        bf_result,
        check_dtype=False,
        check_index_type=False,
    )


@pytest.mark.parametrize(
    ("op"),
    [
        (lambda x: x.sum(axis=1, numeric_only=True)),
        (lambda x: x.mean(axis=1, numeric_only=True)),
        (lambda x: x.min(axis=1, numeric_only=True)),
        (lambda x: x.max(axis=1, numeric_only=True)),
        (lambda x: x.std(axis=1, numeric_only=True)),
        (lambda x: x.var(axis=1, numeric_only=True)),
    ],
    ids=["sum", "mean", "min", "max", "std", "var"],
)
def test_dataframe_aggregates_axis_1(scalars_df_index, scalars_pandas_df_index, op):
    col_names = ["int64_too", "int64_col", "float64_col", "bool_col", "string_col"]
    bf_result = op(scalars_df_index[col_names]).to_pandas()
    pd_result = op(scalars_pandas_df_index[col_names])

    # Pandas may produce narrower numeric types, but bigframes always produces Float64
    pd_result = pd_result.astype("Float64")
    # Pandas has object index type
    pd.testing.assert_series_equal(pd_result, bf_result, check_index_type=False)


def test_dataframe_aggregates_median(scalars_df_index, scalars_pandas_df_index):
    col_names = ["int64_too", "float64_col", "int64_col", "bool_col"]
    bf_result = scalars_df_index[col_names].median(numeric_only=True).to_pandas()
    pd_result = scalars_pandas_df_index[col_names].agg(["min", "max"])

    # Pandas may produce narrower numeric types, but bigframes always produces Float64
    pd_result = pd_result.astype("Float64")

    # Median is an approximation, but double-check that median is plausible.
    for col in col_names:
        assert (pd_result.loc["min", col] <= bf_result[col]) and (
            bf_result[col] <= pd_result.loc["max", col]
        )


def test_dataframe_aggregates_quantile_mono(scalars_df_index, scalars_pandas_df_index):
    q = 0.45
    col_names = ["int64_too", "int64_col", "float64_col"]
    bf_result = scalars_df_index[col_names].quantile(q=q).to_pandas()
    pd_result = scalars_pandas_df_index[col_names].quantile(q=q)

    # Pandas may produce narrower numeric types, but bigframes always produces Float64
    pd_result = pd_result.astype("Float64")

    pd.testing.assert_series_equal(bf_result, pd_result, check_index_type=False)


def test_dataframe_aggregates_quantile_multi(scalars_df_index, scalars_pandas_df_index):
    q = [0, 0.33, 0.67, 1.0]
    col_names = ["int64_too", "int64_col", "float64_col"]
    bf_result = scalars_df_index[col_names].quantile(q=q).to_pandas()
    pd_result = scalars_pandas_df_index[col_names].quantile(q=q)

    # Pandas may produce narrower numeric types, but bigframes always produces Float64
    pd_result = pd_result.astype("Float64")
    pd_result.index = pd_result.index.astype("Float64")

    pd.testing.assert_frame_equal(bf_result, pd_result)


@pytest.mark.parametrize(
    ("op"),
    [
        (lambda x: x.all(bool_only=True)),
        (lambda x: x.any(bool_only=True)),
        (lambda x: x.all(axis=1, bool_only=True)),
        (lambda x: x.any(axis=1, bool_only=True)),
    ],
    ids=["all_axis0", "any_axis0", "all_axis1", "any_axis1"],
)
def test_dataframe_bool_aggregates(scalars_df_index, scalars_pandas_df_index, op):
    # Pandas will drop nullable 'boolean' dtype so we convert first to bool, then cast back later
    scalars_df_index = scalars_df_index.assign(
        bool_col=scalars_df_index.bool_col.fillna(False)
    )
    scalars_pandas_df_index = scalars_pandas_df_index.assign(
        bool_col=scalars_pandas_df_index.bool_col.fillna(False).astype("bool")
    )
    bf_series = op(scalars_df_index)
    pd_series = op(scalars_pandas_df_index).astype("boolean")
    bf_result = bf_series.to_pandas()

    pd_series.index = pd_series.index.astype(bf_result.index.dtype)
    pd.testing.assert_series_equal(pd_series, bf_result, check_index_type=False)


def test_dataframe_prod(scalars_df_index, scalars_pandas_df_index):
    col_names = ["int64_too", "float64_col"]
    bf_series = scalars_df_index[col_names].prod()
    pd_series = scalars_pandas_df_index[col_names].prod()
    bf_result = bf_series.to_pandas()

    # Pandas may produce narrower numeric types, but bigframes always produces Float64
    pd_series = pd_series.astype("Float64")
    # Pandas has object index type
    pd.testing.assert_series_equal(pd_series, bf_result, check_index_type=False)


def test_df_skew_too_few_values(scalars_dfs):
    columns = ["float64_col", "int64_col"]
    scalars_df, scalars_pandas_df = scalars_dfs
    bf_result = scalars_df[columns].head(2).skew().to_pandas()
    pd_result = scalars_pandas_df[columns].head(2).skew()

    # Pandas may produce narrower numeric types, but bigframes always produces Float64
    pd_result = pd_result.astype("Float64")

    pd.testing.assert_series_equal(pd_result, bf_result, check_index_type=False)


@pytest.mark.parametrize(
    ("ordered"),
    [
        (True),
        (False),
    ],
)
def test_df_skew(scalars_dfs, ordered):
    columns = ["float64_col", "int64_col"]
    scalars_df, scalars_pandas_df = scalars_dfs
    bf_result = scalars_df[columns].skew().to_pandas(ordered=ordered)
    pd_result = scalars_pandas_df[columns].skew()

    # Pandas may produce narrower numeric types, but bigframes always produces Float64
    pd_result = pd_result.astype("Float64")

    assert_series_equal(
        pd_result, bf_result, check_index_type=False, ignore_order=not ordered
    )


def test_df_kurt_too_few_values(scalars_dfs):
    columns = ["float64_col", "int64_col"]
    scalars_df, scalars_pandas_df = scalars_dfs
    bf_result = scalars_df[columns].head(2).kurt().to_pandas()
    pd_result = scalars_pandas_df[columns].head(2).kurt()

    # Pandas may produce narrower numeric types, but bigframes always produces Float64
    pd_result = pd_result.astype("Float64")

    pd.testing.assert_series_equal(pd_result, bf_result, check_index_type=False)


def test_df_kurt(scalars_dfs):
    columns = ["float64_col", "int64_col"]
    scalars_df, scalars_pandas_df = scalars_dfs
    bf_result = scalars_df[columns].kurt().to_pandas()
    pd_result = scalars_pandas_df[columns].kurt()

    # Pandas may produce narrower numeric types, but bigframes always produces Float64
    pd_result = pd_result.astype("Float64")

    pd.testing.assert_series_equal(pd_result, bf_result, check_index_type=False)


@pytest.mark.parametrize(
    ("frac", "n", "random_state"),
    [
        (None, 4, None),
        (0.5, None, None),
        (None, 4, 10),
        (0.5, None, 10),
        (None, None, None),
    ],
    ids=[
        "n_wo_random_state",
        "frac_wo_random_state",
        "n_w_random_state",
        "frac_w_random_state",
        "n_default",
    ],
)
def test_sample(scalars_dfs, frac, n, random_state):
    scalars_df, _ = scalars_dfs
    df = scalars_df.sample(frac=frac, n=n, random_state=random_state)
    bf_result = df.to_pandas()

    n = 1 if n is None else n
    expected_sample_size = round(frac * scalars_df.shape[0]) if frac is not None else n
    assert bf_result.shape[0] == expected_sample_size
    assert bf_result.shape[1] == scalars_df.shape[1]


def test_sample_determinism(penguins_df_default_index):
    df = penguins_df_default_index.sample(n=100, random_state=12345).head(15)
    bf_result = df.to_pandas()
    bf_result2 = df.to_pandas()

    pandas.testing.assert_frame_equal(bf_result, bf_result2)


def test_sample_raises_value_error(scalars_dfs):
    scalars_df, _ = scalars_dfs
    with pytest.raises(
        ValueError, match="Only one of 'n' or 'frac' parameter can be specified."
    ):
        scalars_df.sample(frac=0.5, n=4)


def test_sample_args_sort(scalars_dfs):
    scalars_df, _ = scalars_dfs
    index = [4, 3, 2, 5, 1, 0]
    scalars_df = scalars_df.iloc[index]

    kwargs = {"frac": 1.0, "random_state": 333}

    df = scalars_df.sample(**kwargs).to_pandas()
    assert df.index.values != index
    assert df.index.values != sorted(index)

    df = scalars_df.sample(sort="random", **kwargs).to_pandas()
    assert df.index.values != index
    assert df.index.values != sorted(index)

    df = scalars_df.sample(sort=True, **kwargs).to_pandas()
    assert df.index.values == sorted(index)

    df = scalars_df.sample(sort=False, **kwargs).to_pandas()
    assert df.index.values == index


@pytest.mark.parametrize(
    ("axis",),
    [
        (None,),
        (0,),
        (1,),
    ],
)
def test_df_add_prefix(scalars_df_index, scalars_pandas_df_index, axis):
    if pd.__version__.startswith("1."):
        pytest.skip("add_prefix axis parameter not supported in pandas 1.x.")
    bf_result = scalars_df_index.add_prefix("prefix_", axis).to_pandas()

    pd_result = scalars_pandas_df_index.add_prefix("prefix_", axis)

    pd.testing.assert_frame_equal(
        bf_result,
        pd_result,
        check_index_type=False,
    )


@pytest.mark.parametrize(
    ("axis",),
    [
        (0,),
        (1,),
    ],
)
def test_df_add_suffix(scalars_df_index, scalars_pandas_df_index, axis):
    if pd.__version__.startswith("1."):
        pytest.skip("add_prefix axis parameter not supported in pandas 1.x.")
    bf_result = scalars_df_index.add_suffix("_suffix", axis).to_pandas()

    pd_result = scalars_pandas_df_index.add_suffix("_suffix", axis)

    pd.testing.assert_frame_equal(
        bf_result,
        pd_result,
        check_index_type=False,
    )


def test_df_astype_error_error(session):
    input = pd.DataFrame(["hello", "world", "3.11", "4000"])
    with pytest.raises(ValueError):
        session.read_pandas(input).astype("Float64", errors="bad_value")


def test_df_columns_filter_items(scalars_df_index, scalars_pandas_df_index):
    if pd.__version__.startswith("2.0") or pd.__version__.startswith("1."):
        pytest.skip("pandas filter items behavior different pre-2.1")
    bf_result = scalars_df_index.filter(items=["string_col", "int64_col"]).to_pandas()

    pd_result = scalars_pandas_df_index.filter(items=["string_col", "int64_col"])
    # Ignore column ordering as pandas order differently depending on version
    pd.testing.assert_frame_equal(
        bf_result.sort_index(axis=1),
        pd_result.sort_index(axis=1),
    )


def test_df_columns_filter_like(scalars_df_index, scalars_pandas_df_index):
    bf_result = scalars_df_index.filter(like="64_col").to_pandas()

    pd_result = scalars_pandas_df_index.filter(like="64_col")

    pd.testing.assert_frame_equal(
        bf_result,
        pd_result,
    )


def test_df_columns_filter_regex(scalars_df_index, scalars_pandas_df_index):
    bf_result = scalars_df_index.filter(regex="^[^_]+$").to_pandas()

    pd_result = scalars_pandas_df_index.filter(regex="^[^_]+$")

    pd.testing.assert_frame_equal(
        bf_result,
        pd_result,
    )


def test_df_rows_filter_items(scalars_df_index, scalars_pandas_df_index):
    if pd.__version__.startswith("2.0") or pd.__version__.startswith("1."):
        pytest.skip("pandas filter items behavior different pre-2.1")
    bf_result = scalars_df_index.filter(items=[5, 1, 3], axis=0).to_pandas()

    pd_result = scalars_pandas_df_index.filter(items=[5, 1, 3], axis=0)

    # Pandas uses int64 instead of Int64 (nullable) dtype.
    pd_result.index = pd_result.index.astype(pd.Int64Dtype())
    # Ignore ordering as pandas order differently depending on version
    assert_pandas_df_equal(
        bf_result,
        pd_result,
        ignore_order=True,
        check_names=False,
    )


def test_df_rows_filter_like(scalars_df_index, scalars_pandas_df_index):
    scalars_df_index = scalars_df_index.copy().set_index("string_col")
    scalars_pandas_df_index = scalars_pandas_df_index.copy().set_index("string_col")

    bf_result = scalars_df_index.filter(like="ello", axis=0).to_pandas()

    pd_result = scalars_pandas_df_index.filter(like="ello", axis=0)

    pd.testing.assert_frame_equal(
        bf_result,
        pd_result,
    )


def test_df_rows_filter_regex(scalars_df_index, scalars_pandas_df_index):
    scalars_df_index = scalars_df_index.copy().set_index("string_col")
    scalars_pandas_df_index = scalars_pandas_df_index.copy().set_index("string_col")

    bf_result = scalars_df_index.filter(regex="^[GH].*", axis=0).to_pandas()

    pd_result = scalars_pandas_df_index.filter(regex="^[GH].*", axis=0)

    pd.testing.assert_frame_equal(
        bf_result,
        pd_result,
    )


def test_df_reindex_rows_list(scalars_dfs_maybe_ordered):
    scalars_df_index, scalars_pandas_df_index = scalars_dfs_maybe_ordered
    bf_result = scalars_df_index.reindex(index=[5, 1, 3, 99, 1])

    pd_result = scalars_pandas_df_index.reindex(index=[5, 1, 3, 99, 1])

    # Pandas uses int64 instead of Int64 (nullable) dtype.
    pd_result.index = pd_result.index.astype(pd.Int64Dtype())
    assert_dfs_equivalent(
        pd_result,
        bf_result,
    )


def test_df_reindex_rows_index(scalars_df_index, scalars_pandas_df_index):
    bf_result = scalars_df_index.reindex(
        index=pd.Index([5, 1, 3, 99, 1], name="newname")
    ).to_pandas()

    pd_result = scalars_pandas_df_index.reindex(
        index=pd.Index([5, 1, 3, 99, 1], name="newname")
    )

    # Pandas uses int64 instead of Int64 (nullable) dtype.
    pd_result.index = pd_result.index.astype(pd.Int64Dtype())
    pd.testing.assert_frame_equal(
        bf_result,
        pd_result,
    )


def test_df_reindex_nonunique(scalars_df_index):
    with pytest.raises(ValueError):
        # int64_too is non-unique
        scalars_df_index.set_index("int64_too").reindex(
            index=[5, 1, 3, 99, 1], validate=True
        )


def test_df_reindex_columns(scalars_df_index, scalars_pandas_df_index):
    bf_result = scalars_df_index.reindex(
        columns=["not_a_col", "int64_col", "int64_too"]
    ).to_pandas()

    pd_result = scalars_pandas_df_index.reindex(
        columns=["not_a_col", "int64_col", "int64_too"]
    )

    # Pandas uses float64 as default for newly created empty column, bf uses Float64
    pd_result.not_a_col = pd_result.not_a_col.astype(pandas.Float64Dtype())
    pd.testing.assert_frame_equal(
        bf_result,
        pd_result,
    )


def test_df_reindex_columns_with_same_order(scalars_df_index, scalars_pandas_df_index):
    # First, make sure the two dataframes have the same columns in order.
    columns = ["int64_col", "int64_too"]
    bf = scalars_df_index[columns]
    pd_df = scalars_pandas_df_index[columns]

    bf_result = bf.reindex(columns=columns).to_pandas()
    pd_result = pd_df.reindex(columns=columns)

    pd.testing.assert_frame_equal(
        bf_result,
        pd_result,
    )


def test_df_equals_identical(scalars_df_index, scalars_pandas_df_index):
    unsupported = [
        "geography_col",
    ]
    scalars_df_index = scalars_df_index.drop(columns=unsupported)
    scalars_pandas_df_index = scalars_pandas_df_index.drop(columns=unsupported)

    bf_result = scalars_df_index.equals(scalars_df_index)
    pd_result = scalars_pandas_df_index.equals(scalars_pandas_df_index)

    assert pd_result == bf_result


def test_df_equals_series(scalars_df_index, scalars_pandas_df_index):
    bf_result = scalars_df_index[["int64_col"]].equals(scalars_df_index["int64_col"])
    pd_result = scalars_pandas_df_index[["int64_col"]].equals(
        scalars_pandas_df_index["int64_col"]
    )

    assert pd_result == bf_result


def test_df_equals_different_dtype(scalars_df_index, scalars_pandas_df_index):
    columns = ["int64_col", "int64_too"]
    scalars_df_index = scalars_df_index[columns]
    scalars_pandas_df_index = scalars_pandas_df_index[columns]

    bf_modified = scalars_df_index.copy()
    bf_modified = bf_modified.astype("Float64")

    pd_modified = scalars_pandas_df_index.copy()
    pd_modified = pd_modified.astype("Float64")

    bf_result = scalars_df_index.equals(bf_modified)
    pd_result = scalars_pandas_df_index.equals(pd_modified)

    assert pd_result == bf_result


def test_df_equals_different_values(scalars_df_index, scalars_pandas_df_index):
    columns = ["int64_col", "int64_too"]
    scalars_df_index = scalars_df_index[columns]
    scalars_pandas_df_index = scalars_pandas_df_index[columns]

    bf_modified = scalars_df_index.copy()
    bf_modified["int64_col"] = bf_modified.int64_col + 1

    pd_modified = scalars_pandas_df_index.copy()
    pd_modified["int64_col"] = pd_modified.int64_col + 1

    bf_result = scalars_df_index.equals(bf_modified)
    pd_result = scalars_pandas_df_index.equals(pd_modified)

    assert pd_result == bf_result


def test_df_equals_extra_column(scalars_df_index, scalars_pandas_df_index):
    columns = ["int64_col", "int64_too"]
    more_columns = ["int64_col", "int64_too", "float64_col"]

    bf_result = scalars_df_index[columns].equals(scalars_df_index[more_columns])
    pd_result = scalars_pandas_df_index[columns].equals(
        scalars_pandas_df_index[more_columns]
    )

    assert pd_result == bf_result


def test_df_reindex_like(scalars_df_index, scalars_pandas_df_index):
    reindex_target_bf = scalars_df_index.reindex(
        columns=["not_a_col", "int64_col", "int64_too"], index=[5, 1, 3, 99, 1]
    )
    bf_result = scalars_df_index.reindex_like(reindex_target_bf).to_pandas()

    reindex_target_pd = scalars_pandas_df_index.reindex(
        columns=["not_a_col", "int64_col", "int64_too"], index=[5, 1, 3, 99, 1]
    )
    pd_result = scalars_pandas_df_index.reindex_like(reindex_target_pd)

    # Pandas uses float64 as default for newly created empty column, bf uses Float64
    # Pandas uses int64 instead of Int64 (nullable) dtype.
    pd_result.index = pd_result.index.astype(pd.Int64Dtype())
    # Pandas uses float64 as default for newly created empty column, bf uses Float64
    pd_result.not_a_col = pd_result.not_a_col.astype(pandas.Float64Dtype())
    pd.testing.assert_frame_equal(
        bf_result,
        pd_result,
    )


def test_df_values(scalars_df_index, scalars_pandas_df_index):
    bf_result = scalars_df_index.values

    pd_result = scalars_pandas_df_index.values
    # Numpy isn't equipped to compare non-numeric objects, so convert back to dataframe
    pd.testing.assert_frame_equal(
        pd.DataFrame(bf_result), pd.DataFrame(pd_result), check_dtype=False
    )


def test_df_to_numpy(scalars_df_index, scalars_pandas_df_index):
    bf_result = scalars_df_index.to_numpy()

    pd_result = scalars_pandas_df_index.to_numpy()
    # Numpy isn't equipped to compare non-numeric objects, so convert back to dataframe
    pd.testing.assert_frame_equal(
        pd.DataFrame(bf_result), pd.DataFrame(pd_result), check_dtype=False
    )


def test_df___array__(scalars_df_index, scalars_pandas_df_index):
    bf_result = scalars_df_index.__array__()

    pd_result = scalars_pandas_df_index.__array__()
    # Numpy isn't equipped to compare non-numeric objects, so convert back to dataframe
    pd.testing.assert_frame_equal(
        pd.DataFrame(bf_result), pd.DataFrame(pd_result), check_dtype=False
    )


@pytest.mark.parametrize(
    ("key",),
    [
        ("hello",),
        (2,),
        ("int64_col",),
        (None,),
    ],
)
def test_df_contains(scalars_df_index, scalars_pandas_df_index, key):
    bf_result = key in scalars_df_index
    pd_result = key in scalars_pandas_df_index

    assert bf_result == pd_result


def test_df_getattr_attribute_error_when_pandas_has(scalars_df_index):
    # swapaxes is implemented in pandas but not in bigframes
    with pytest.raises(AttributeError):
        scalars_df_index.swapaxes()


def test_df_getattr_attribute_error(scalars_df_index):
    with pytest.raises(AttributeError):
        scalars_df_index.not_a_method()


def test_df_getattr_axes():
    df = dataframe.DataFrame(
        [[1, 1, 1], [1, 1, 1]], columns=["index", "columns", "my_column"]
    )
    assert isinstance(df.index, bigframes.core.indexes.Index)
    assert isinstance(df.columns, pandas.Index)
    assert isinstance(df.my_column, series.Series)


def test_df_setattr_index():
    pd_df = pandas.DataFrame(
        [[1, 1, 1], [1, 1, 1]], columns=["index", "columns", "my_column"]
    )
    bf_df = dataframe.DataFrame(pd_df)

    pd_df.index = pandas.Index([4, 5])
    bf_df.index = [4, 5]

    assert_pandas_df_equal(
        pd_df, bf_df.to_pandas(), check_index_type=False, check_dtype=False
    )


def test_df_setattr_columns():
    pd_df = pandas.DataFrame(
        [[1, 1, 1], [1, 1, 1]], columns=["index", "columns", "my_column"]
    )
    bf_df = dataframe.DataFrame(pd_df)

    pd_df.columns = typing.cast(pandas.Index, pandas.Index([4, 5, 6]))

    bf_df.columns = pandas.Index([4, 5, 6])

    assert_pandas_df_equal(
        pd_df, bf_df.to_pandas(), check_index_type=False, check_dtype=False
    )


def test_df_setattr_modify_column():
    pd_df = pandas.DataFrame(
        [[1, 1, 1], [1, 1, 1]], columns=["index", "columns", "my_column"]
    )
    bf_df = dataframe.DataFrame(pd_df)
    pd_df.my_column = [4, 5]
    bf_df.my_column = [4, 5]

    assert_pandas_df_equal(
        pd_df, bf_df.to_pandas(), check_index_type=False, check_dtype=False
    )


def test_loc_list_string_index(scalars_df_index, scalars_pandas_df_index):
    index_list = scalars_pandas_df_index.string_col.iloc[[0, 1, 1, 5]].values

    scalars_df_index = scalars_df_index.set_index("string_col")
    scalars_pandas_df_index = scalars_pandas_df_index.set_index("string_col")

    bf_result = scalars_df_index.loc[index_list].to_pandas()
    pd_result = scalars_pandas_df_index.loc[index_list]

    pd.testing.assert_frame_equal(
        bf_result,
        pd_result,
    )


def test_loc_list_integer_index(scalars_df_index, scalars_pandas_df_index):
    index_list = [3, 2, 1, 3, 2, 1]

    bf_result = scalars_df_index.loc[index_list]
    pd_result = scalars_pandas_df_index.loc[index_list]

    pd.testing.assert_frame_equal(
        bf_result.to_pandas(),
        pd_result,
    )


def test_loc_list_multiindex(scalars_dfs_maybe_ordered):
    scalars_df_index, scalars_pandas_df_index = scalars_dfs_maybe_ordered
    scalars_df_multiindex = scalars_df_index.set_index(["string_col", "int64_col"])
    scalars_pandas_df_multiindex = scalars_pandas_df_index.set_index(
        ["string_col", "int64_col"]
    )
    index_list = [("Hello, World!", -234892), ("Hello, World!", 123456789)]

    bf_result = scalars_df_multiindex.loc[index_list]
    pd_result = scalars_pandas_df_multiindex.loc[index_list]

    assert_dfs_equivalent(
        pd_result,
        bf_result,
    )


@pytest.mark.parametrize(
    "index_list",
    [
        [0, 1, 2, 3, 4, 4],
        [0, 0, 0, 5, 4, 7, -2, -5, 3],
        [-1, -2, -3, -4, -5, -5],
    ],
)
def test_iloc_list(scalars_df_index, scalars_pandas_df_index, index_list):
    bf_result = scalars_df_index.iloc[index_list]
    pd_result = scalars_pandas_df_index.iloc[index_list]

    pd.testing.assert_frame_equal(
        bf_result.to_pandas(),
        pd_result,
    )


@pytest.mark.parametrize(
    "index_list",
    [
        [0, 1, 2, 3, 4, 4],
        [0, 0, 0, 5, 4, 7, -2, -5, 3],
        [-1, -2, -3, -4, -5, -5],
    ],
)
def test_iloc_list_partial_ordering(
    scalars_df_partial_ordering, scalars_pandas_df_index, index_list
):
    bf_result = scalars_df_partial_ordering.iloc[index_list]
    pd_result = scalars_pandas_df_index.iloc[index_list]

    pd.testing.assert_frame_equal(
        bf_result.to_pandas(),
        pd_result,
    )


def test_iloc_list_multiindex(scalars_dfs):
    scalars_df, scalars_pandas_df = scalars_dfs
    scalars_df = scalars_df.copy()
    scalars_pandas_df = scalars_pandas_df.copy()
    scalars_df = scalars_df.set_index(["bytes_col", "numeric_col"])
    scalars_pandas_df = scalars_pandas_df.set_index(["bytes_col", "numeric_col"])

    index_list = [0, 0, 0, 5, 4, 7]

    bf_result = scalars_df.iloc[index_list]
    pd_result = scalars_pandas_df.iloc[index_list]

    pd.testing.assert_frame_equal(
        bf_result.to_pandas(),
        pd_result,
    )


def test_iloc_empty_list(scalars_df_index, scalars_pandas_df_index):

    index_list: List[int] = []

    bf_result = scalars_df_index.iloc[index_list]
    pd_result = scalars_pandas_df_index.iloc[index_list]

    bf_result = bf_result.to_pandas()
    assert bf_result.shape == pd_result.shape  # types are known to be different


def test_rename_axis(scalars_df_index, scalars_pandas_df_index):
    bf_result = scalars_df_index.rename_axis("newindexname")
    pd_result = scalars_pandas_df_index.rename_axis("newindexname")

    pd.testing.assert_frame_equal(
        bf_result.to_pandas(),
        pd_result,
    )


def test_rename_axis_nonstring(scalars_df_index, scalars_pandas_df_index):
    bf_result = scalars_df_index.rename_axis((4,))
    pd_result = scalars_pandas_df_index.rename_axis((4,))

    pd.testing.assert_frame_equal(
        bf_result.to_pandas(),
        pd_result,
    )


def test_loc_bf_series_string_index(scalars_df_index, scalars_pandas_df_index):
    pd_string_series = scalars_pandas_df_index.string_col.iloc[[0, 5, 1, 1, 5]]
    bf_string_series = scalars_df_index.string_col.iloc[[0, 5, 1, 1, 5]]

    scalars_df_index = scalars_df_index.set_index("string_col")
    scalars_pandas_df_index = scalars_pandas_df_index.set_index("string_col")

    bf_result = scalars_df_index.loc[bf_string_series]
    pd_result = scalars_pandas_df_index.loc[pd_string_series]

    pd.testing.assert_frame_equal(
        bf_result.to_pandas(),
        pd_result,
    )


def test_loc_bf_series_multiindex(scalars_df_index, scalars_pandas_df_index):
    pd_string_series = scalars_pandas_df_index.string_col.iloc[[0, 5, 1, 1, 5]]
    bf_string_series = scalars_df_index.string_col.iloc[[0, 5, 1, 1, 5]]

    scalars_df_multiindex = scalars_df_index.set_index(["string_col", "int64_col"])
    scalars_pandas_df_multiindex = scalars_pandas_df_index.set_index(
        ["string_col", "int64_col"]
    )

    bf_result = scalars_df_multiindex.loc[bf_string_series]
    pd_result = scalars_pandas_df_multiindex.loc[pd_string_series]

    pd.testing.assert_frame_equal(
        bf_result.to_pandas(),
        pd_result,
    )


def test_loc_bf_index_integer_index(scalars_df_index, scalars_pandas_df_index):
    pd_index = scalars_pandas_df_index.iloc[[0, 5, 1, 1, 5]].index
    bf_index = scalars_df_index.iloc[[0, 5, 1, 1, 5]].index

    bf_result = scalars_df_index.loc[bf_index]
    pd_result = scalars_pandas_df_index.loc[pd_index]

    pd.testing.assert_frame_equal(
        bf_result.to_pandas(),
        pd_result,
    )


def test_loc_bf_index_integer_index_renamed_col(
    scalars_df_index, scalars_pandas_df_index
):
    scalars_df_index = scalars_df_index.rename(columns={"int64_col": "rename"})
    scalars_pandas_df_index = scalars_pandas_df_index.rename(
        columns={"int64_col": "rename"}
    )

    pd_index = scalars_pandas_df_index.iloc[[0, 5, 1, 1, 5]].index
    bf_index = scalars_df_index.iloc[[0, 5, 1, 1, 5]].index

    bf_result = scalars_df_index.loc[bf_index]
    pd_result = scalars_pandas_df_index.loc[pd_index]

    pd.testing.assert_frame_equal(
        bf_result.to_pandas(),
        pd_result,
    )


@pytest.mark.parametrize(
    ("subset"),
    [
        None,
        "bool_col",
        ["bool_col", "int64_too"],
    ],
)
@pytest.mark.parametrize(
    ("keep",),
    [
        ("first",),
        ("last",),
        (False,),
    ],
)
def test_df_drop_duplicates(scalars_df_index, scalars_pandas_df_index, keep, subset):
    columns = ["bool_col", "int64_too", "int64_col"]
    bf_df = scalars_df_index[columns].drop_duplicates(subset, keep=keep).to_pandas()
    pd_df = scalars_pandas_df_index[columns].drop_duplicates(subset, keep=keep)
    pd.testing.assert_frame_equal(
        pd_df,
        bf_df,
    )


@pytest.mark.parametrize(
    ("keep",),
    [
        ("first",),
        ("last",),
        (False,),
    ],
)
def test_df_drop_duplicates_w_json(json_df, keep):
    bf_df = json_df.drop_duplicates(keep=keep).to_pandas()

    # drop_duplicates relies on pa.compute.dictionary_encode, which is incompatible
    # with Arrow string extension types. Temporary conversion to standard Pandas
    # strings is required.
    json_pandas_df = json_df.to_pandas()
    json_pandas_df["json_col"] = json_pandas_df["json_col"].astype(
        pd.StringDtype(storage="pyarrow")
    )

    pd_df = json_pandas_df.drop_duplicates(keep=keep)
    pd_df["json_col"] = pd_df["json_col"].astype(dtypes.JSON_DTYPE)
    pd.testing.assert_frame_equal(
        pd_df,
        bf_df,
    )


@pytest.mark.parametrize(
    ("subset"),
    [
        None,
        ["bool_col"],
    ],
)
@pytest.mark.parametrize(
    ("keep",),
    [
        ("first",),
        ("last",),
        (False,),
    ],
)
def test_df_duplicated(scalars_df_index, scalars_pandas_df_index, keep, subset):
    columns = ["bool_col", "int64_too", "int64_col"]
    bf_series = scalars_df_index[columns].duplicated(subset, keep=keep).to_pandas()
    pd_series = scalars_pandas_df_index[columns].duplicated(subset, keep=keep)
    pd.testing.assert_series_equal(pd_series, bf_series, check_dtype=False)


def test_df_from_dict_columns_orient():
    data = {"a": [1, 2], "b": [3.3, 2.4]}
    bf_result = dataframe.DataFrame.from_dict(data, orient="columns").to_pandas()
    pd_result = pd.DataFrame.from_dict(data, orient="columns")
    assert_pandas_df_equal(
        pd_result, bf_result, check_dtype=False, check_index_type=False
    )


def test_df_from_dict_index_orient():
    data = {"a": [1, 2], "b": [3.3, 2.4]}
    bf_result = dataframe.DataFrame.from_dict(
        data, orient="index", columns=["col1", "col2"]
    ).to_pandas()
    pd_result = pd.DataFrame.from_dict(data, orient="index", columns=["col1", "col2"])
    assert_pandas_df_equal(
        pd_result, bf_result, check_dtype=False, check_index_type=False
    )


def test_df_from_dict_tight_orient():
    data = {
        "index": [("i1", "i2"), ("i3", "i4")],
        "columns": ["col1", "col2"],
        "data": [[1, 2.6], [3, 4.5]],
        "index_names": ["in1", "in2"],
        "column_names": ["column_axis"],
    }

    bf_result = dataframe.DataFrame.from_dict(data, orient="tight").to_pandas()
    pd_result = pd.DataFrame.from_dict(data, orient="tight")
    assert_pandas_df_equal(
        pd_result, bf_result, check_dtype=False, check_index_type=False
    )


def test_df_from_records():
    records = ((1, "a"), (2.5, "b"), (3.3, "c"), (4.9, "d"))

    bf_result = dataframe.DataFrame.from_records(
        records, columns=["c1", "c2"]
    ).to_pandas()
    pd_result = pd.DataFrame.from_records(records, columns=["c1", "c2"])
    assert_pandas_df_equal(
        pd_result, bf_result, check_dtype=False, check_index_type=False
    )


def test_df_to_dict(scalars_df_index, scalars_pandas_df_index):
    unsupported = ["numeric_col"]  # formatted differently
    bf_result = scalars_df_index.drop(columns=unsupported).to_dict()
    pd_result = scalars_pandas_df_index.drop(columns=unsupported).to_dict()

    assert bf_result == pd_result


def test_df_to_excel(scalars_df_index, scalars_pandas_df_index):
    unsupported = ["timestamp_col"]
    with tempfile.TemporaryFile() as bf_result_file, tempfile.TemporaryFile() as pd_result_file:
        scalars_df_index.drop(columns=unsupported).to_excel(bf_result_file)
        scalars_pandas_df_index.drop(columns=unsupported).to_excel(pd_result_file)
        bf_result = bf_result_file.read()
        pd_result = bf_result_file.read()

    assert bf_result == pd_result


def test_df_to_latex(scalars_df_index, scalars_pandas_df_index):
    unsupported = ["numeric_col"]  # formatted differently
    bf_result = scalars_df_index.drop(columns=unsupported).to_latex()
    pd_result = scalars_pandas_df_index.drop(columns=unsupported).to_latex()

    assert bf_result == pd_result


def test_df_to_json_local_str(scalars_df_index, scalars_pandas_df_index):
    bf_result = scalars_df_index.to_json()
    # default_handler for arrow types that have no default conversion
    pd_result = scalars_pandas_df_index.to_json(default_handler=str)

    assert bf_result == pd_result


def test_df_to_json_local_file(scalars_df_index, scalars_pandas_df_index):
    # TODO: supply a reason why this isn't compatible with pandas 1.x
    pytest.importorskip("pandas", minversion="2.0.0")
    # duration not fully supported at pandas level
    scalars_df_index = scalars_df_index.drop(columns="duration_col")
    scalars_pandas_df_index = scalars_pandas_df_index.drop(columns="duration_col")
    with tempfile.TemporaryFile() as bf_result_file, tempfile.TemporaryFile() as pd_result_file:
        scalars_df_index.to_json(bf_result_file, orient="table")
        # default_handler for arrow types that have no default conversion
        scalars_pandas_df_index.to_json(
            pd_result_file, orient="table", default_handler=str
        )

        bf_result = bf_result_file.read()
        pd_result = pd_result_file.read()

    assert bf_result == pd_result


def test_df_to_csv_local_str(scalars_df_index, scalars_pandas_df_index):
    bf_result = scalars_df_index.to_csv()
    # default_handler for arrow types that have no default conversion
    pd_result = scalars_pandas_df_index.to_csv()

    assert bf_result == pd_result


def test_df_to_csv_local_file(scalars_df_index, scalars_pandas_df_index):
    with tempfile.TemporaryFile() as bf_result_file, tempfile.TemporaryFile() as pd_result_file:
        scalars_df_index.to_csv(bf_result_file)
        scalars_pandas_df_index.to_csv(pd_result_file)

        bf_result = bf_result_file.read()
        pd_result = pd_result_file.read()

    assert bf_result == pd_result


def test_df_to_parquet_local_bytes(scalars_df_index, scalars_pandas_df_index):
    # GEOGRAPHY not supported in parquet export.
    unsupported = ["geography_col"]

    bf_result = scalars_df_index.drop(columns=unsupported).to_parquet()
    # default_handler for arrow types that have no default conversion
    pd_result = scalars_pandas_df_index.drop(columns=unsupported).to_parquet()

    assert bf_result == pd_result


def test_df_to_parquet_local_file(scalars_df_index, scalars_pandas_df_index):
    # GEOGRAPHY not supported in parquet export.
    unsupported = ["geography_col"]
    with tempfile.TemporaryFile() as bf_result_file, tempfile.TemporaryFile() as pd_result_file:
        scalars_df_index.drop(columns=unsupported).to_parquet(bf_result_file)
        scalars_pandas_df_index.drop(columns=unsupported).to_parquet(pd_result_file)

        bf_result = bf_result_file.read()
        pd_result = pd_result_file.read()

    assert bf_result == pd_result


def test_df_to_records(scalars_df_index, scalars_pandas_df_index):
    unsupported = ["numeric_col"]
    bf_result = scalars_df_index.drop(columns=unsupported).to_records()
    pd_result = scalars_pandas_df_index.drop(columns=unsupported).to_records()

    for bfi, pdi in zip(bf_result, pd_result):
        for bfj, pdj in zip(bfi, pdi):
            assert pd.isna(bfj) and pd.isna(pdj) or bfj == pdj


def test_df_to_string(scalars_df_index, scalars_pandas_df_index):
    unsupported = ["numeric_col"]  # formatted differently

    bf_result = scalars_df_index.drop(columns=unsupported).to_string()
    pd_result = scalars_pandas_df_index.drop(columns=unsupported).to_string()

    assert bf_result == pd_result


def test_df_to_html(scalars_df_index, scalars_pandas_df_index):
    unsupported = ["numeric_col"]  # formatted differently

    bf_result = scalars_df_index.drop(columns=unsupported).to_html()
    pd_result = scalars_pandas_df_index.drop(columns=unsupported).to_html()

    assert bf_result == pd_result


def test_df_to_markdown(scalars_df_index, scalars_pandas_df_index):
    # Nulls have bug from tabulate https://github.com/astanin/python-tabulate/issues/231
    bf_result = scalars_df_index.dropna().to_markdown()
    pd_result = scalars_pandas_df_index.dropna().to_markdown()

    assert bf_result == pd_result


def test_df_to_pickle(scalars_df_index, scalars_pandas_df_index):
    with tempfile.TemporaryFile() as bf_result_file, tempfile.TemporaryFile() as pd_result_file:
        scalars_df_index.to_pickle(bf_result_file)
        scalars_pandas_df_index.to_pickle(pd_result_file)
        bf_result = bf_result_file.read()
        pd_result = pd_result_file.read()

    assert bf_result == pd_result


def test_df_to_orc(scalars_df_index, scalars_pandas_df_index):
    unsupported = [
        "numeric_col",
        "bytes_col",
        "date_col",
        "datetime_col",
        "time_col",
        "timestamp_col",
        "geography_col",
        "duration_col",
    ]

    bf_result_file = tempfile.TemporaryFile()
    pd_result_file = tempfile.TemporaryFile()
    scalars_df_index.drop(columns=unsupported).to_orc(bf_result_file)
    scalars_pandas_df_index.drop(columns=unsupported).reset_index().to_orc(
        pd_result_file
    )
    bf_result = bf_result_file.read()
    pd_result = bf_result_file.read()

    assert bf_result == pd_result


@pytest.mark.parametrize(
    ("expr",),
    [
        ("new_col = int64_col + int64_too",),
        ("new_col = (rowindex > 3) | bool_col",),
        ("int64_too = bool_col\nnew_col2 = rowindex",),
    ],
)
def test_df_eval(scalars_dfs, expr):
    # TODO: supply a reason why this isn't compatible with pandas 1.x
    pytest.importorskip("pandas", minversion="2.0.0")
    scalars_df, scalars_pandas_df = scalars_dfs

    bf_result = scalars_df.eval(expr).to_pandas()
    pd_result = scalars_pandas_df.eval(expr)

    pd.testing.assert_frame_equal(bf_result, pd_result)


@pytest.mark.parametrize(
    ("expr",),
    [
        ("int64_col > int64_too",),
        ("bool_col",),
        ("((int64_col - int64_too) % @local_var) == 0",),
    ],
)
def test_df_query(scalars_dfs, expr):
    # TODO: supply a reason why this isn't compatible with pandas 1.x
    pytest.importorskip("pandas", minversion="2.0.0")
    # local_var is referenced in expressions
    local_var = 3  # NOQA
    scalars_df, scalars_pandas_df = scalars_dfs

    bf_result = scalars_df.query(expr).to_pandas()
    pd_result = scalars_pandas_df.query(expr)

    pd.testing.assert_frame_equal(bf_result, pd_result)


@pytest.mark.parametrize(
    ("subset", "normalize", "ascending", "dropna"),
    [
        (None, False, False, False),
        (None, True, True, True),
        ("bool_col", True, False, True),
    ],
)
def test_df_value_counts(scalars_dfs, subset, normalize, ascending, dropna):
    if pd.__version__.startswith("1."):
        pytest.skip("pandas 1.x produces different column labels.")
    scalars_df, scalars_pandas_df = scalars_dfs

    bf_result = (
        scalars_df[["string_col", "bool_col"]]
        .value_counts(subset, normalize=normalize, ascending=ascending, dropna=dropna)
        .to_pandas()
    )
    pd_result = scalars_pandas_df[["string_col", "bool_col"]].value_counts(
        subset, normalize=normalize, ascending=ascending, dropna=dropna
    )

    pd.testing.assert_series_equal(
        bf_result, pd_result, check_dtype=False, check_index_type=False
    )


@pytest.mark.parametrize(
    ("na_option", "method", "ascending", "numeric_only"),
    [
        ("keep", "average", True, True),
        ("top", "min", False, False),
        ("bottom", "max", False, False),
        ("top", "first", False, False),
        ("bottom", "dense", False, False),
    ],
)
def test_df_rank_with_nulls(
    scalars_df_index,
    scalars_pandas_df_index,
    na_option,
    method,
    ascending,
    numeric_only,
):
    unsupported_columns = ["geography_col"]
    bf_result = (
        scalars_df_index.drop(columns=unsupported_columns)
        .rank(
            na_option=na_option,
            method=method,
            ascending=ascending,
            numeric_only=numeric_only,
        )
        .to_pandas()
    )
    pd_result = (
        scalars_pandas_df_index.drop(columns=unsupported_columns)
        .rank(
            na_option=na_option,
            method=method,
            ascending=ascending,
            numeric_only=numeric_only,
        )
        .astype(pd.Float64Dtype())
    )

    pd.testing.assert_frame_equal(
        bf_result,
        pd_result,
    )


def test_df_bool_interpretation_error(scalars_df_index):
    with pytest.raises(ValueError):
        True if scalars_df_index else False


def test_query_job_setters(scalars_df_default_index: dataframe.DataFrame):
    # if allow_large_results=False, might not create query job
    with bigframes.option_context("compute.allow_large_results", True):
        job_ids = set()
        repr(scalars_df_default_index)
        assert scalars_df_default_index.query_job is not None
        job_ids.add(scalars_df_default_index.query_job.job_id)
        scalars_df_default_index.to_pandas(allow_large_results=True)
        job_ids.add(scalars_df_default_index.query_job.job_id)

        assert len(job_ids) == 2


def test_df_cached(scalars_df_index):
    df = scalars_df_index.set_index(["int64_too", "int64_col"]).sort_values(
        "string_col"
    )
    df = df[df["rowindex_2"] % 2 == 0]

    df_cached_copy = df.cache()
    pandas.testing.assert_frame_equal(df.to_pandas(), df_cached_copy.to_pandas())


def test_assign_after_binop_row_joins():
    pd_df = pd.DataFrame(
        {
            "idx1": [1, 1, 1, 1, 2, 2, 2, 2],
            "idx2": [10, 10, 20, 20, 10, 10, 20, 20],
            "metric1": [10, 14, 2, 13, 6, 2, 9, 5],
            "metric2": [25, -3, 8, 2, -1, 0, 0, -4],
        },
        dtype=pd.Int64Dtype(),
    ).set_index(["idx1", "idx2"])
    bf_df = dataframe.DataFrame(pd_df)

    # Expect implicit joiner to be used, preserving input cardinality rather than getting relational join
    bf_df["metric_diff"] = bf_df.metric1 - bf_df.metric2
    pd_df["metric_diff"] = pd_df.metric1 - pd_df.metric2

    assert_pandas_df_equal(bf_df.to_pandas(), pd_df)


def test_df_cache_with_implicit_join(scalars_df_index):
    """expectation is that cache will be used, but no explicit join will be performed"""
    df = scalars_df_index[["int64_col", "int64_too"]].sort_index().reset_index() + 3
    df.cache()
    bf_result = df + (df * 2)
    sql = bf_result.sql

    # Very crude asserts, want sql to not use join and not use base table, only reference cached table
    assert "JOIN" not in sql
    assert "bigframes_testing" not in sql


def test_df_dot_inline(session):
    df1 = pd.DataFrame([[1, 2, 3], [2, 5, 7]])
    df2 = pd.DataFrame([[2, 4, 8], [1, 5, 10], [3, 6, 9]])

    bf1 = session.read_pandas(df1)
    bf2 = session.read_pandas(df2)
    bf_result = bf1.dot(bf2).to_pandas()
    pd_result = df1.dot(df2)

    # Patch pandas dtypes for testing parity
    # Pandas uses int64 instead of Int64 (nullable) dtype.
    for name in pd_result.columns:
        pd_result[name] = pd_result[name].astype(pd.Int64Dtype())
    pd_result.index = pd_result.index.astype(pd.Int64Dtype())

    pd.testing.assert_frame_equal(
        bf_result,
        pd_result,
    )


def test_df_dot(
    matrix_2by3_df, matrix_2by3_pandas_df, matrix_3by4_df, matrix_3by4_pandas_df
):
    bf_result = matrix_2by3_df.dot(matrix_3by4_df).to_pandas()
    pd_result = matrix_2by3_pandas_df.dot(matrix_3by4_pandas_df)

    # Patch pandas dtypes for testing parity
    # Pandas result is object instead of Int64 (nullable) dtype.
    for name in pd_result.columns:
        pd_result[name] = pd_result[name].astype(pd.Int64Dtype())

    pd.testing.assert_frame_equal(
        bf_result,
        pd_result,
    )


def test_df_dot_operator(
    matrix_2by3_df, matrix_2by3_pandas_df, matrix_3by4_df, matrix_3by4_pandas_df
):
    bf_result = (matrix_2by3_df @ matrix_3by4_df).to_pandas()
    pd_result = matrix_2by3_pandas_df @ matrix_3by4_pandas_df

    # Patch pandas dtypes for testing parity
    # Pandas result is object instead of Int64 (nullable) dtype.
    for name in pd_result.columns:
        pd_result[name] = pd_result[name].astype(pd.Int64Dtype())

    pd.testing.assert_frame_equal(
        bf_result,
        pd_result,
    )


def test_df_dot_series_inline():
    left = [[1, 2, 3], [2, 5, 7]]
    right = [2, 1, 3]

    bf1 = dataframe.DataFrame(left)
    bf2 = series.Series(right)
    bf_result = bf1.dot(bf2).to_pandas()

    df1 = pd.DataFrame(left)
    df2 = pd.Series(right)
    pd_result = df1.dot(df2)

    # Patch pandas dtypes for testing parity
    # Pandas result is int64 instead of Int64 (nullable) dtype.
    pd_result = pd_result.astype(pd.Int64Dtype())
    pd_result.index = pd_result.index.astype(pd.Int64Dtype())

    pd.testing.assert_series_equal(
        bf_result,
        pd_result,
    )


def test_df_dot_series(
    matrix_2by3_df, matrix_2by3_pandas_df, matrix_3by4_df, matrix_3by4_pandas_df
):
    bf_result = matrix_2by3_df.dot(matrix_3by4_df["x"]).to_pandas()
    pd_result = matrix_2by3_pandas_df.dot(matrix_3by4_pandas_df["x"])

    # Patch pandas dtypes for testing parity
    # Pandas result is object instead of Int64 (nullable) dtype.
    pd_result = pd_result.astype(pd.Int64Dtype())

    pd.testing.assert_series_equal(
        bf_result,
        pd_result,
    )


def test_df_dot_operator_series(
    matrix_2by3_df, matrix_2by3_pandas_df, matrix_3by4_df, matrix_3by4_pandas_df
):
    bf_result = (matrix_2by3_df @ matrix_3by4_df["x"]).to_pandas()
    pd_result = matrix_2by3_pandas_df @ matrix_3by4_pandas_df["x"]

    # Patch pandas dtypes for testing parity
    # Pandas result is object instead of Int64 (nullable) dtype.
    pd_result = pd_result.astype(pd.Int64Dtype())

    pd.testing.assert_series_equal(
        bf_result,
        pd_result,
    )


# TODO(tswast): We may be able to re-enable this test after we break large
# queries up in https://github.com/googleapis/python-bigquery-dataframes/pull/427
@pytest.mark.skipif(
    sys.version_info >= (3, 12),
    # See: https://github.com/python/cpython/issues/112282
    reason="setrecursionlimit has no effect on the Python C stack since Python 3.12.",
)
def test_recursion_limit(scalars_df_index):
    scalars_df_index = scalars_df_index[["int64_too", "int64_col", "float64_col"]]
    for i in range(400):
        scalars_df_index = scalars_df_index + 4
    scalars_df_index.to_pandas()


@pytest.mark.skipif(
    reason="b/366477265: Skip until query complexity error can be reliably triggered."
)
def test_query_complexity_error(scalars_df_index):
    # This test requires automatic caching/query decomposition to be turned off
    bf_df = scalars_df_index
    for _ in range(8):
        bf_df = bf_df.merge(bf_df, on="int64_col").head(30)
        bf_df = bf_df[bf_df.columns[:20]]

    with pytest.raises(
        bigframes.exceptions.QueryComplexityError, match=r"Try using DataFrame\.cache"
    ):
        bf_df.to_pandas()


def test_query_complexity_repeated_joins(
    scalars_df_index, scalars_pandas_df_index, with_multiquery_execution
):
    pd_df = scalars_pandas_df_index
    bf_df = scalars_df_index
    for _ in range(8):
        # recursively join, resuling in 2^8 - 1 = 255 joins
        pd_df = pd_df.merge(pd_df, on="int64_col").head(30)
        pd_df = pd_df[pd_df.columns[:20]]
        bf_df = bf_df.merge(bf_df, on="int64_col").head(30)
        bf_df = bf_df[bf_df.columns[:20]]

    bf_result = bf_df.to_pandas()
    pd_result = pd_df
    assert_pandas_df_equal(bf_result, pd_result, check_index_type=False)


def test_query_complexity_repeated_subtrees(
    scalars_df_index, scalars_pandas_df_index, with_multiquery_execution
):
    # Recursively union the data, if fully inlined has 10^5 identical root tables.
    pd_df = scalars_pandas_df_index
    bf_df = scalars_df_index
    for _ in range(5):
        pd_df = pd.concat(10 * [pd_df]).head(5)
        bf_df = bpd.concat(10 * [bf_df]).head(5)
    bf_result = bf_df.to_pandas()
    pd_result = pd_df
    assert_pandas_df_equal(bf_result, pd_result)


@pytest.mark.skipif(
    sys.version_info >= (3, 12),
    # See: https://github.com/python/cpython/issues/112282
    reason="setrecursionlimit has no effect on the Python C stack since Python 3.12.",
)
def test_query_complexity_repeated_analytic(scalars_df_index, scalars_pandas_df_index):
    bf_df = scalars_df_index[["int64_col", "int64_too"]]
    pd_df = scalars_pandas_df_index[["int64_col", "int64_too"]]
    # Uses LAG analytic operator, each in a new SELECT
    for _ in range(50):
        bf_df = bf_df.diff()
        pd_df = pd_df.diff()
    bf_result = bf_df.to_pandas()
    pd_result = pd_df
    assert_pandas_df_equal(bf_result, pd_result)


def test_to_gbq_and_create_dataset(session, scalars_df_index, dataset_id_not_created):
    dataset_id = dataset_id_not_created
    destination_table = f"{dataset_id}.scalars_df"

    result_table = scalars_df_index.to_gbq(destination_table)
    assert (
        result_table == destination_table
        if destination_table
        else result_table is not None
    )

    loaded_scalars_df_index = session.read_gbq(result_table)
    assert not loaded_scalars_df_index.empty


def test_read_gbq_to_pandas_no_exec(unordered_session: bigframes.Session):
    metrics = unordered_session._metrics
    execs_pre = metrics.execution_count
    df = unordered_session.read_gbq("bigquery-public-data.ml_datasets.penguins")
    df.to_pandas()
    execs_post = metrics.execution_count
    assert df.shape == (344, 7)
    assert execs_pre == execs_post


def test_to_gbq_table_labels(scalars_df_index):
    destination_table = "bigframes-dev.bigframes_tests_sys.table_labels"
    result_table = scalars_df_index.to_gbq(
        destination_table, labels={"test": "labels"}, if_exists="replace"
    )
    client = scalars_df_index._session.bqclient
    table = client.get_table(result_table)
    assert table.labels
    assert table.labels["test"] == "labels"


@pytest.mark.parametrize(
    ("col_names", "ignore_index"),
    [
        pytest.param(["A"], False, id="one_array_false"),
        pytest.param(["A"], True, id="one_array_true"),
        pytest.param(["B"], False, id="one_float_false"),
        pytest.param(["B"], True, id="one_float_true"),
        pytest.param(["A", "C"], False, id="two_arrays_false"),
        pytest.param(["A", "C"], True, id="two_arrays_true"),
    ],
)
def test_dataframe_explode(col_names, ignore_index, session):
    data = {
        "A": [[0, 1, 2], [], [3, 4]],
        "B": 3,
        "C": [["a", "b", "c"], np.nan, ["d", "e"]],
    }

    metrics = session._metrics
    df = bpd.DataFrame(data, session=session)
    pd_df = df.to_pandas()
    pd_result = pd_df.explode(col_names, ignore_index=ignore_index)
    bf_result = df.explode(col_names, ignore_index=ignore_index)

    # Check that to_pandas() results in at most a single query execution
    execs_pre = metrics.execution_count
    bf_materialized = bf_result.to_pandas()
    execs_post = metrics.execution_count

    pd.testing.assert_frame_equal(
        bf_materialized,
        pd_result,
        check_index_type=False,
        check_dtype=False,
    )
    # we test this property on this method in particular as compilation
    # is non-deterministic and won't use the query cache as implemented
    assert execs_post - execs_pre <= 1


@pytest.mark.parametrize(
    ("ignore_index", "ordered"),
    [
        pytest.param(True, True, id="include_index_ordered"),
        pytest.param(True, False, id="include_index_unordered"),
        pytest.param(False, True, id="ignore_index_ordered"),
    ],
)
def test_dataframe_explode_reserve_order(ignore_index, ordered):
    data = {
        "a": [np.random.randint(0, 10, 10) for _ in range(10)],
        "b": [np.random.randint(0, 10, 10) for _ in range(10)],
    }
    df = bpd.DataFrame(data)
    pd_df = pd.DataFrame(data)

    res = df.explode(["a", "b"], ignore_index=ignore_index).to_pandas(ordered=ordered)
    pd_res = pd_df.explode(["a", "b"], ignore_index=ignore_index).astype(
        pd.Int64Dtype()
    )
    pd.testing.assert_frame_equal(
        res if ordered else res.sort_index(),
        pd_res,
        check_index_type=False,
    )


@pytest.mark.parametrize(
    ("col_names"),
    [
        pytest.param([], id="empty", marks=pytest.mark.xfail(raises=ValueError)),
        pytest.param(
            ["A", "A"], id="duplicate", marks=pytest.mark.xfail(raises=ValueError)
        ),
        pytest.param("unknown", id="unknown", marks=pytest.mark.xfail(raises=KeyError)),
    ],
)
def test_dataframe_explode_xfail(col_names):
    df = bpd.DataFrame({"A": [[0, 1, 2], [], [3, 4]]})
    df.explode(col_names)


@pytest.mark.parametrize(
    ("on", "rule", "origin"),
    [
        pytest.param("datetime_col", "100D", "start"),
        pytest.param("datetime_col", "30W", "start"),
        pytest.param("datetime_col", "5M", "epoch"),
        pytest.param("datetime_col", "3Q", "start_day"),
        pytest.param("datetime_col", "3YE", "start"),
        pytest.param(
            "int64_col", "100D", "start", marks=pytest.mark.xfail(raises=TypeError)
        ),
        pytest.param(
            "datetime_col", "100D", "end", marks=pytest.mark.xfail(raises=ValueError)
        ),
    ],
)
def test__resample_with_column(
    scalars_df_index, scalars_pandas_df_index, on, rule, origin
):
    # TODO: supply a reason why this isn't compatible with pandas 1.x
    pytest.importorskip("pandas", minversion="2.0.0")
    bf_result = (
        scalars_df_index._resample(rule=rule, on=on, origin=origin)[
            ["int64_col", "int64_too"]
        ]
        .max()
        .to_pandas()
    )
    pd_result = scalars_pandas_df_index.resample(rule=rule, on=on, origin=origin)[
        ["int64_col", "int64_too"]
    ].max()
    pd.testing.assert_frame_equal(
        bf_result, pd_result, check_dtype=False, check_index_type=False
    )


@pytest.mark.parametrize(
    ("append", "level", "col", "rule"),
    [
        pytest.param(False, None, "timestamp_col", "100d"),
        pytest.param(True, 1, "timestamp_col", "1200h"),
        pytest.param(False, None, "datetime_col", "100d"),
    ],
)
def test__resample_with_index(
    scalars_df_index, scalars_pandas_df_index, append, level, col, rule
):
    # TODO: supply a reason why this isn't compatible with pandas 1.x
    pytest.importorskip("pandas", minversion="2.0.0")
    scalars_df_index = scalars_df_index.set_index(col, append=append)
    scalars_pandas_df_index = scalars_pandas_df_index.set_index(col, append=append)
    bf_result = (
        scalars_df_index[["int64_col", "int64_too"]]
        ._resample(rule=rule, level=level)
        .min()
        .to_pandas()
    )
    pd_result = (
        scalars_pandas_df_index[["int64_col", "int64_too"]]
        .resample(rule=rule, level=level)
        .min()
    )
    assert_pandas_df_equal(bf_result, pd_result)


@pytest.mark.parametrize(
    ("rule", "origin", "data"),
    [
        (
            "5h",
            "epoch",
            {
                "timestamp_col": pd.date_range(
                    start="2021-01-01 13:00:00", periods=30, freq="1h"
                ),
                "int64_col": range(30),
                "int64_too": range(10, 40),
            },
        ),
        (
            "75min",
            "start_day",
            {
                "timestamp_col": pd.date_range(
                    start="2021-01-01 13:00:00", periods=30, freq="10min"
                ),
                "int64_col": range(30),
                "int64_too": range(10, 40),
            },
        ),
        (
            "7s",
            "epoch",
            {
                "timestamp_col": pd.date_range(
                    start="2021-01-01 13:00:00", periods=30, freq="1s"
                ),
                "int64_col": range(30),
                "int64_too": range(10, 40),
            },
        ),
    ],
)
def test__resample_start_time(rule, origin, data):
    # TODO: supply a reason why this isn't compatible with pandas 1.x
    pytest.importorskip("pandas", minversion="2.0.0")
    col = "timestamp_col"
    scalars_df_index = bpd.DataFrame(data).set_index(col)
    scalars_pandas_df_index = pd.DataFrame(data).set_index(col)
    scalars_pandas_df_index.index.name = None

    bf_result = scalars_df_index._resample(rule=rule, origin=origin).min().to_pandas()

    pd_result = scalars_pandas_df_index.resample(rule=rule, origin=origin).min()

    pd.testing.assert_frame_equal(
        bf_result, pd_result, check_dtype=False, check_index_type=False
    )


@pytest.mark.parametrize(
    "dtype",
    [
        pytest.param("string[pyarrow]", id="type-string"),
        pytest.param(pd.StringDtype(storage="pyarrow"), id="type-literal"),
        pytest.param(
            {"bool_col": "string[pyarrow]", "int64_col": pd.Float64Dtype()},
            id="multiple-types",
        ),
    ],
)
def test_df_astype(scalars_dfs, dtype):
    bf_df, pd_df = scalars_dfs
    target_cols = ["bool_col", "int64_col"]
    bf_df = bf_df[target_cols]
    pd_df = pd_df[target_cols]

    bf_result = bf_df.astype(dtype).to_pandas()
    pd_result = pd_df.astype(dtype)

    pd.testing.assert_frame_equal(bf_result, pd_result, check_index_type=False)


def test_df_astype_python_types(scalars_dfs):
    bf_df, pd_df = scalars_dfs
    target_cols = ["bool_col", "int64_col"]
    bf_df = bf_df[target_cols]
    pd_df = pd_df[target_cols]

    bf_result = bf_df.astype({"bool_col": str, "int64_col": float}).to_pandas()
    pd_result = pd_df.astype(
        {"bool_col": "string[pyarrow]", "int64_col": pd.Float64Dtype()}
    )

    pd.testing.assert_frame_equal(bf_result, pd_result, check_index_type=False)


def test_astype_invalid_type_fail(scalars_dfs):
    bf_df, _ = scalars_dfs

    with pytest.raises(TypeError, match=r".*Share your usecase with.*"):
        bf_df.astype(123)


def test_agg_with_dict_lists(scalars_dfs):
    bf_df, pd_df = scalars_dfs
    agg_funcs = {
        "int64_too": ["min", "max"],
        "int64_col": ["min", "count"],
    }

    bf_result = bf_df.agg(agg_funcs).to_pandas()
    pd_result = pd_df.agg(agg_funcs)

    pd.testing.assert_frame_equal(
        bf_result, pd_result, check_dtype=False, check_index_type=False
    )


def test_agg_with_dict_list_and_str(scalars_dfs):
    bf_df, pd_df = scalars_dfs
    agg_funcs = {
        "int64_too": ["min", "max"],
        "int64_col": "sum",
    }

    bf_result = bf_df.agg(agg_funcs).to_pandas()
    pd_result = pd_df.agg(agg_funcs)

    pd.testing.assert_frame_equal(
        bf_result, pd_result, check_dtype=False, check_index_type=False
    )


def test_agg_with_dict_strs(scalars_dfs):
    bf_df, pd_df = scalars_dfs
    agg_funcs = {
        "int64_too": "min",
        "int64_col": "sum",
        "float64_col": "max",
    }

    bf_result = bf_df.agg(agg_funcs).to_pandas()
    pd_result = pd_df.agg(agg_funcs)
    pd_result.index = pd_result.index.astype("string[pyarrow]")

    pd.testing.assert_series_equal(
        bf_result, pd_result, check_dtype=False, check_index_type=False
    )


def test_agg_with_dict_containing_non_existing_col_raise_key_error(scalars_dfs):
    bf_df, _ = scalars_dfs
    agg_funcs = {
        "int64_too": ["min", "max"],
        "nonexisting_col": ["count"],
    }

    with pytest.raises(KeyError):
        bf_df.agg(agg_funcs)