code: # Licensed to Modin Development Team under one or more contributor license agreements.
  # See the NOTICE file distributed with this work for additional information regarding
  # copyright ownership.  The Modin Development Team licenses this file to you under the
  # Apache License, Version 2.0 (the "License"); you may not use this file except in
  # compliance with the License.  You may obtain a copy of the License at
  #
  #     http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software distributed under
  # the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF
  # ANY KIND, either express or implied. See the License for the specific language
  # governing permissions and limitations under the License.
  
  import pytest
  import numpy as np
  import pandas
  from pandas.testing import assert_index_equal
  import matplotlib
  import modin.pandas as pd
  from modin.utils import get_current_backend
  
  from modin.pandas.test.utils import (
      random_state,
      RAND_LOW,
      RAND_HIGH,
      df_equals,
      df_is_empty,
      arg_keys,
      name_contains,
      test_data,
      test_data_values,
      test_data_keys,
      test_data_with_duplicates_values,
      test_data_with_duplicates_keys,
      numeric_dfs,
      test_func_keys,
      test_func_values,
      indices_keys,
      indices_values,
      axis_keys,
      axis_values,
      bool_arg_keys,
      bool_arg_values,
      int_arg_keys,
      int_arg_values,
      eval_general,
      create_test_dfs,
  )
  from modin.config import NPartitions
  
  NPartitions.put(4)
  
  # Force matplotlib to not use any Xwindows backend.
  matplotlib.use("Agg")
  
  
  def eval_insert(modin_df, pandas_df, **kwargs):
      if "col" in kwargs and "column" not in kwargs:
          kwargs["column"] = kwargs.pop("col")
      _kwargs = {"loc": 0, "column": "New column"}
      _kwargs.update(kwargs)
  
      eval_general(
          modin_df,
          pandas_df,
          operation=lambda df, **kwargs: df.insert(**kwargs),
          **_kwargs,
      )
  
  
  def test_indexing():
      modin_df = pd.DataFrame(
          dict(a=[1, 2, 3], b=[4, 5, 6], c=[7, 8, 9]), index=["a", "b", "c"]
      )
      pandas_df = pandas.DataFrame(
          dict(a=[1, 2, 3], b=[4, 5, 6], c=[7, 8, 9]), index=["a", "b", "c"]
      )
  
      modin_result = modin_df
      pandas_result = pandas_df
      df_equals(modin_result, pandas_result)
  
      modin_result = modin_df["b"]
      pandas_result = pandas_df["b"]
      df_equals(modin_result, pandas_result)
  
      modin_result = modin_df[["b"]]
      pandas_result = pandas_df[["b"]]
      df_equals(modin_result, pandas_result)
  
      modin_result = modin_df[["b", "a"]]
      pandas_result = pandas_df[["b", "a"]]
      df_equals(modin_result, pandas_result)
  
      modin_result = modin_df.loc["b"]
      pandas_result = pandas_df.loc["b"]
      df_equals(modin_result, pandas_result)
  
      modin_result = modin_df.loc[["b"]]
      pandas_result = pandas_df.loc[["b"]]
      df_equals(modin_result, pandas_result)
  
      modin_result = modin_df.loc[["b", "a"]]
      pandas_result = pandas_df.loc[["b", "a"]]
      df_equals(modin_result, pandas_result)
  
      modin_result = modin_df.loc[["b", "a"], ["a", "c"]]
      pandas_result = pandas_df.loc[["b", "a"], ["a", "c"]]
      df_equals(modin_result, pandas_result)
  
      modin_result = modin_df.loc[:, ["a", "c"]]
      pandas_result = pandas_df.loc[:, ["a", "c"]]
      df_equals(modin_result, pandas_result)
  
      modin_result = modin_df.loc[:, ["c"]]
      pandas_result = pandas_df.loc[:, ["c"]]
      df_equals(modin_result, pandas_result)
  
      modin_result = modin_df.loc[[]]
      pandas_result = pandas_df.loc[[]]
      df_equals(modin_result, pandas_result)
  
  
  def test_empty_df():
      df = pd.DataFrame(index=["a", "b"])
      df_is_empty(df)
      assert_index_equal(df.index, pd.Index(["a", "b"]))
      assert len(df.columns) == 0
  
      df = pd.DataFrame(columns=["a", "b"])
      df_is_empty(df)
      assert len(df.index) == 0
      assert_index_equal(df.columns, pd.Index(["a", "b"]))
  
      df = pd.DataFrame()
      df_is_empty(df)
      assert len(df.index) == 0
      assert len(df.columns) == 0
  
      df = pd.DataFrame(index=["a", "b"])
      df_is_empty(df)
      assert_index_equal(df.index, pd.Index(["a", "b"]))
      assert len(df.columns) == 0
  
      df = pd.DataFrame(columns=["a", "b"])
      df_is_empty(df)
      assert len(df.index) == 0
      assert_index_equal(df.columns, pd.Index(["a", "b"]))
  
      df = pd.DataFrame()
      df_is_empty(df)
      assert len(df.index) == 0
      assert len(df.columns) == 0
  
      df = pd.DataFrame()
      pd_df = pandas.DataFrame()
      df["a"] = [1, 2, 3, 4, 5]
      pd_df["a"] = [1, 2, 3, 4, 5]
      df_equals(df, pd_df)
  
      df = pd.DataFrame()
      pd_df = pandas.DataFrame()
      df["a"] = list("ABCDEF")
      pd_df["a"] = list("ABCDEF")
      df_equals(df, pd_df)
  
      df = pd.DataFrame()
      pd_df = pandas.DataFrame()
      df["a"] = pd.Series([1, 2, 3, 4, 5])
      pd_df["a"] = pandas.Series([1, 2, 3, 4, 5])
      df_equals(df, pd_df)
  
  
  @pytest.mark.parametrize("data", test_data_values, ids=test_data_keys)
  def test_abs(request, data):
      modin_df = pd.DataFrame(data)
      pandas_df = pandas.DataFrame(data)
  
      try:
          pandas_result = pandas_df.abs()
      except Exception as e:
          with pytest.raises(type(e)):
              modin_df.abs()
      else:
          modin_result = modin_df.abs()
          df_equals(modin_result, pandas_result)
  
  
  @pytest.mark.parametrize("data", test_data_values, ids=test_data_keys)
  def test_add_prefix(data):
      modin_df = pd.DataFrame(data)
      pandas_df = pandas.DataFrame(data)
  
      test_prefix = "TEST"
      new_modin_df = modin_df.add_prefix(test_prefix)
      new_pandas_df = pandas_df.add_prefix(test_prefix)
      df_equals(new_modin_df.columns, new_pandas_df.columns)
  
  
  @pytest.mark.parametrize("data", test_data_values, ids=test_data_keys)
  @pytest.mark.parametrize("testfunc", test_func_values, ids=test_func_keys)
  def test_applymap(request, data, testfunc):
      modin_df = pd.DataFrame(data)
      pandas_df = pandas.DataFrame(data)
  
      with pytest.raises(ValueError):
          x = 2
          modin_df.applymap(x)
  
      try:
          pandas_result = pandas_df.applymap(testfunc)
      except Exception as e:
          with pytest.raises(type(e)):
              modin_df.applymap(testfunc)
      else:
          modin_result = modin_df.applymap(testfunc)
          df_equals(modin_result, pandas_result)
  
  
  @pytest.mark.parametrize("data", test_data_values, ids=test_data_keys)
  @pytest.mark.parametrize("testfunc", test_func_values, ids=test_func_keys)
  def test_applymap_numeric(request, data, testfunc):
      modin_df = pd.DataFrame(data)
      pandas_df = pandas.DataFrame(data)
  
      if name_contains(request.node.name, numeric_dfs):
          try:
              pandas_result = pandas_df.applymap(testfunc)
          except Exception as e:
              with pytest.raises(type(e)):
                  modin_df.applymap(testfunc)
          else:
              modin_result = modin_df.applymap(testfunc)
              df_equals(modin_result, pandas_result)
  
  
  @pytest.mark.parametrize("data", test_data_values, ids=test_data_keys)
  def test_add_suffix(data):
      modin_df = pd.DataFrame(data)
      pandas_df = pandas.DataFrame(data)
  
      test_suffix = "TEST"
      new_modin_df = modin_df.add_suffix(test_suffix)
      new_pandas_df = pandas_df.add_suffix(test_suffix)
  
      df_equals(new_modin_df.columns, new_pandas_df.columns)
  
  
  @pytest.mark.parametrize("data", test_data_values, ids=test_data_keys)
  def test_at(data):
      modin_df = pd.DataFrame(data)
      pandas_df = pandas.DataFrame(data)
  
      key1 = modin_df.columns[0]
      # Scaler
      df_equals(modin_df.at[0, key1], pandas_df.at[0, key1])
  
      # Series
      df_equals(modin_df.loc[0].at[key1], pandas_df.loc[0].at[key1])
  
      # Write Item
      modin_df_copy = modin_df.copy()
      pandas_df_copy = pandas_df.copy()
      modin_df_copy.at[1, key1] = modin_df.at[0, key1]
      pandas_df_copy.at[1, key1] = pandas_df.at[0, key1]
      df_equals(modin_df_copy, pandas_df_copy)
  
  
  @pytest.mark.parametrize("data", test_data_values, ids=test_data_keys)
  def test_axes(data):
      modin_df = pd.DataFrame(data)
      pandas_df = pandas.DataFrame(data)
  
      for modin_axis, pd_axis in zip(modin_df.axes, pandas_df.axes):
          assert np.array_equal(modin_axis, pd_axis)
  
  
  @pytest.mark.parametrize("data", test_data_values, ids=test_data_keys)
  def test_copy(data):
      modin_df = pd.DataFrame(data)
      pandas_df = pandas.DataFrame(data)  # noqa F841
  
      # pandas_df is unused but there so there won't be confusing list comprehension
      # stuff in the pytest.mark.parametrize
      new_modin_df = modin_df.copy()
  
      assert new_modin_df is not modin_df
      if get_current_backend() != "BaseOnPython":
          assert np.array_equal(
              new_modin_df._query_compiler._modin_frame._partitions,
              modin_df._query_compiler._modin_frame._partitions,
          )
      assert new_modin_df is not modin_df
      df_equals(new_modin_df, modin_df)
  
      # Shallow copy tests
      modin_df = pd.DataFrame(data)
      modin_df_cp = modin_df.copy(False)
  
      modin_df[modin_df.columns[0]] = 0
      df_equals(modin_df, modin_df_cp)
  
  
  @pytest.mark.parametrize("data", test_data_values, ids=test_data_keys)
  def test_dtypes(data):
      modin_df = pd.DataFrame(data)
      pandas_df = pandas.DataFrame(data)
  
      df_equals(modin_df.dtypes, pandas_df.dtypes)
  
  
  @pytest.mark.parametrize("data", test_data_values, ids=test_data_keys)
  @pytest.mark.parametrize("key", indices_values, ids=indices_keys)
  def test_get(data, key):
      modin_df = pd.DataFrame(data)
      pandas_df = pandas.DataFrame(data)
  
      df_equals(modin_df.get(key), pandas_df.get(key))
      df_equals(
          modin_df.get(key, default="default"), pandas_df.get(key, default="default")
      )
  
  
  @pytest.mark.parametrize("data", test_data_values, ids=test_data_keys)
  @pytest.mark.parametrize(
      "dummy_na", bool_arg_values, ids=arg_keys("dummy_na", bool_arg_keys)
  )
  @pytest.mark.parametrize(
      "drop_first", bool_arg_values, ids=arg_keys("drop_first", bool_arg_keys)
  )
  def test_get_dummies(request, data, dummy_na, drop_first):
      modin_df = pd.DataFrame(data)
      pandas_df = pandas.DataFrame(data)
  
      try:
          pandas_result = pandas.get_dummies(
              pandas_df, dummy_na=dummy_na, drop_first=drop_first
          )
      except Exception as e:
          with pytest.raises(type(e)):
              pd.get_dummies(modin_df, dummy_na=dummy_na, drop_first=drop_first)
      else:
          modin_result = pd.get_dummies(
              modin_df, dummy_na=dummy_na, drop_first=drop_first
          )
          df_equals(modin_result, pandas_result)
  
  
  @pytest.mark.parametrize("data", test_data_values, ids=test_data_keys)
  def test_isna(data):
      pandas_df = pandas.DataFrame(data)
      modin_df = pd.DataFrame(data)
  
      pandas_result = pandas_df.isna()
      modin_result = modin_df.isna()
  
      df_equals(modin_result, pandas_result)
  
  
  @pytest.mark.parametrize("data", test_data_values, ids=test_data_keys)
  def test_isnull(data):
      pandas_df = pandas.DataFrame(data)
      modin_df = pd.DataFrame(data)
  
      pandas_result = pandas_df.isnull()
      modin_result = modin_df.isnull()
  
      df_equals(modin_result, pandas_result)
  
  
  @pytest.mark.parametrize("data", test_data_values, ids=test_data_keys)
  def test_append(data):
      modin_df = pd.DataFrame(data)
      pandas_df = pandas.DataFrame(data)
  
      data_to_append = {"append_a": 2, "append_b": 1000}
  
      ignore_idx_values = [True, False]
  
      for ignore in ignore_idx_values:
          try:
              pandas_result = pandas_df.append(data_to_append, ignore_index=ignore)
          except Exception as e:
              with pytest.raises(type(e)):
                  modin_df.append(data_to_append, ignore_index=ignore)
          else:
              modin_result = modin_df.append(data_to_append, ignore_index=ignore)
              df_equals(modin_result, pandas_result)
  
      try:
          pandas_result = pandas_df.append(pandas_df.iloc[-1])
      except Exception as e:
          with pytest.raises(type(e)):
              modin_df.append(modin_df.iloc[-1])
      else:
          modin_result = modin_df.append(modin_df.iloc[-1])
          df_equals(modin_result, pandas_result)
  
      try:
          pandas_result = pandas_df.append(list(pandas_df.iloc[-1]))
      except Exception as e:
          with pytest.raises(type(e)):
              modin_df.append(list(modin_df.iloc[-1]))
      else:
          modin_result = modin_df.append(list(modin_df.iloc[-1]))
          # Pandas has bug where sort=False is ignored
          # (https://github.com/pandas-dev/pandas/issues/35092), but Modin
          # now does the right thing, so for now manually sort to workaround
          # this. Once the Pandas bug is fixed and Modin upgrades to that
          # Pandas release, this sort will cause the test to fail, and the
          # next three lines should be deleted.
          if get_current_backend() != "BaseOnPython":
              assert list(modin_result.columns) == list(modin_df.columns) + [0]
              modin_result = modin_result[[0] + sorted(modin_df.columns)]
          df_equals(modin_result, pandas_result)
  
      verify_integrity_values = [True, False]
  
      for verify_integrity in verify_integrity_values:
          try:
              pandas_result = pandas_df.append(
                  [pandas_df, pandas_df], verify_integrity=verify_integrity
              )
          except Exception as e:
              with pytest.raises(type(e)):
                  modin_df.append([modin_df, modin_df], verify_integrity=verify_integrity)
          else:
              modin_result = modin_df.append(
                  [modin_df, modin_df], verify_integrity=verify_integrity
              )
              df_equals(modin_result, pandas_result)
  
          try:
              pandas_result = pandas_df.append(
                  pandas_df, verify_integrity=verify_integrity
              )
          except Exception as e:
              with pytest.raises(type(e)):
                  modin_df.append(modin_df, verify_integrity=verify_integrity)
          else:
              modin_result = modin_df.append(modin_df, verify_integrity=verify_integrity)
              df_equals(modin_result, pandas_result)
  
  
  def test_astype():
      td = pandas.DataFrame(test_data["int_data"])[["col1", "index", "col3", "col4"]]
      modin_df = pd.DataFrame(td.values, index=td.index, columns=td.columns)
      expected_df = pandas.DataFrame(td.values, index=td.index, columns=td.columns)
  
      modin_df_casted = modin_df.astype(np.int32)
      expected_df_casted = expected_df.astype(np.int32)
      df_equals(modin_df_casted, expected_df_casted)
  
      modin_df_casted = modin_df.astype(np.float64)
      expected_df_casted = expected_df.astype(np.float64)
      df_equals(modin_df_casted, expected_df_casted)
  
      modin_df_casted = modin_df.astype(str)
      expected_df_casted = expected_df.astype(str)
      df_equals(modin_df_casted, expected_df_casted)
  
      modin_df_casted = modin_df.astype("category")
      expected_df_casted = expected_df.astype("category")
      df_equals(modin_df_casted, expected_df_casted)
  
      dtype_dict = {"col1": np.int32, "index": np.int64, "col3": str}
      modin_df_casted = modin_df.astype(dtype_dict)
      expected_df_casted = expected_df.astype(dtype_dict)
      df_equals(modin_df_casted, expected_df_casted)
  
      # Ignore lint because this is testing bad input
      bad_dtype_dict = {"index": np.int32, "index": np.int64, "index": str}  # noqa F601
      modin_df_casted = modin_df.astype(bad_dtype_dict)
      expected_df_casted = expected_df.astype(bad_dtype_dict)
      df_equals(modin_df_casted, expected_df_casted)
  
      modin_df = pd.DataFrame(index=["row1"], columns=["col1"])
      modin_df["col1"]["row1"] = 11
      modin_df_casted = modin_df.astype(int)
      expected_df = pandas.DataFrame(index=["row1"], columns=["col1"])
      expected_df["col1"]["row1"] = 11
      expected_df_casted = expected_df.astype(int)
      df_equals(modin_df_casted, expected_df_casted)
  
      with pytest.raises(KeyError):
          modin_df.astype({"not_exists": np.uint8})
  
  
  def test_astype_category():
      modin_df = pd.DataFrame(
          {"col1": ["A", "A", "B", "B", "A"], "col2": [1, 2, 3, 4, 5]}
      )
      pandas_df = pandas.DataFrame(
          {"col1": ["A", "A", "B", "B", "A"], "col2": [1, 2, 3, 4, 5]}
      )
  
      modin_result = modin_df.astype({"col1": "category"})
      pandas_result = pandas_df.astype({"col1": "category"})
      df_equals(modin_result, pandas_result)
      assert modin_result.dtypes.equals(pandas_result.dtypes)
  
      modin_result = modin_df.astype("category")
      pandas_result = pandas_df.astype("category")
      df_equals(modin_result, pandas_result)
      assert modin_result.dtypes.equals(pandas_result.dtypes)
  
  
  def test_astype_category_large():
      series_length = 10_000
      modin_df = pd.DataFrame(
          {
              "col1": ["str{0}".format(i) for i in range(0, series_length)],
              "col2": [i for i in range(0, series_length)],
          }
      )
      pandas_df = pandas.DataFrame(
          {
              "col1": ["str{0}".format(i) for i in range(0, series_length)],
              "col2": [i for i in range(0, series_length)],
          }
      )
  
      modin_result = modin_df.astype({"col1": "category"})
      pandas_result = pandas_df.astype({"col1": "category"})
      df_equals(modin_result, pandas_result)
      assert modin_result.dtypes.equals(pandas_result.dtypes)
  
      modin_result = modin_df.astype("category")
      pandas_result = pandas_df.astype("category")
      df_equals(modin_result, pandas_result)
      assert modin_result.dtypes.equals(pandas_result.dtypes)
  
  
  @pytest.mark.parametrize("data", test_data_values, ids=test_data_keys)
  @pytest.mark.parametrize("axis", axis_values, ids=axis_keys)
  def test_clip(request, data, axis):
      modin_df = pd.DataFrame(data)
      pandas_df = pandas.DataFrame(data)
  
      if name_contains(request.node.name, numeric_dfs):
          ind_len = (
              len(modin_df.index)
              if not pandas.DataFrame()._get_axis_number(axis)
              else len(modin_df.columns)
          )
          # set bounds
          lower, upper = np.sort(random_state.random_integers(RAND_LOW, RAND_HIGH, 2))
          lower_list = random_state.random_integers(RAND_LOW, RAND_HIGH, ind_len)
          upper_list = random_state.random_integers(RAND_LOW, RAND_HIGH, ind_len)
  
          # test only upper scalar bound
          modin_result = modin_df.clip(None, upper, axis=axis)
          pandas_result = pandas_df.clip(None, upper, axis=axis)
          df_equals(modin_result, pandas_result)
  
          # test lower and upper scalar bound
          modin_result = modin_df.clip(lower, upper, axis=axis)
          pandas_result = pandas_df.clip(lower, upper, axis=axis)
          df_equals(modin_result, pandas_result)
  
          # test lower and upper list bound on each column
          modin_result = modin_df.clip(lower_list, upper_list, axis=axis)
          pandas_result = pandas_df.clip(lower_list, upper_list, axis=axis)
          df_equals(modin_result, pandas_result)
  
          # test only upper list bound on each column
          modin_result = modin_df.clip(np.nan, upper_list, axis=axis)
          pandas_result = pandas_df.clip(np.nan, upper_list, axis=axis)
          df_equals(modin_result, pandas_result)
  
          with pytest.raises(ValueError):
              modin_df.clip(lower=[1, 2, 3], axis=None)
  
  
  def test_drop():
      frame_data = {"A": [1, 2, 3, 4], "B": [0, 1, 2, 3]}
      simple = pandas.DataFrame(frame_data)
      modin_simple = pd.DataFrame(frame_data)
      df_equals(modin_simple.drop("A", axis=1), simple[["B"]])
      df_equals(modin_simple.drop(["A", "B"], axis="columns"), simple[[]])
      df_equals(modin_simple.drop([0, 1, 3], axis=0), simple.loc[[2], :])
      df_equals(modin_simple.drop([0, 3], axis="index"), simple.loc[[1, 2], :])
  
      pytest.raises(ValueError, modin_simple.drop, 5)
      pytest.raises(ValueError, modin_simple.drop, "C", 1)
      pytest.raises(ValueError, modin_simple.drop, [1, 5])
      pytest.raises(ValueError, modin_simple.drop, ["A", "C"], 1)
  
      # errors = 'ignore'
      df_equals(modin_simple.drop(5, errors="ignore"), simple)
      df_equals(modin_simple.drop([0, 5], errors="ignore"), simple.loc[[1, 2, 3], :])
      df_equals(modin_simple.drop("C", axis=1, errors="ignore"), simple)
      df_equals(modin_simple.drop(["A", "C"], axis=1, errors="ignore"), simple[["B"]])
  
      # non-unique
      nu_df = pandas.DataFrame(
          zip(range(3), range(-3, 1), list("abc")), columns=["a", "a", "b"]
      )
      modin_nu_df = pd.DataFrame(nu_df)
      df_equals(modin_nu_df.drop("a", axis=1), nu_df[["b"]])
      df_equals(modin_nu_df.drop("b", axis="columns"), nu_df["a"])
      df_equals(modin_nu_df.drop([]), nu_df)
  
      nu_df = nu_df.set_index(pandas.Index(["X", "Y", "X"]))
      nu_df.columns = list("abc")
      modin_nu_df = pd.DataFrame(nu_df)
      df_equals(modin_nu_df.drop("X", axis="rows"), nu_df.loc[["Y"], :])
      df_equals(modin_nu_df.drop(["X", "Y"], axis=0), nu_df.loc[[], :])
  
      # inplace cache issue
      frame_data = random_state.randn(10, 3)
      df = pandas.DataFrame(frame_data, columns=list("abc"))
      modin_df = pd.DataFrame(frame_data, columns=list("abc"))
      expected = df[~(df.b > 0)]
      modin_df.drop(labels=df[df.b > 0].index, inplace=True)
      df_equals(modin_df, expected)
  
      midx = pd.MultiIndex(
          levels=[["lama", "cow", "falcon"], ["speed", "weight", "length"]],
          codes=[[0, 0, 0, 1, 1, 1, 2, 2, 2], [0, 1, 2, 0, 1, 2, 0, 1, 2]],
      )
      df = pd.DataFrame(
          index=midx,
          columns=["big", "small"],
          data=[
              [45, 30],
              [200, 100],
              [1.5, 1],
              [30, 20],
              [250, 150],
              [1.5, 0.8],
              [320, 250],
              [1, 0.8],
              [0.3, 0.2],
          ],
      )
      with pytest.warns(UserWarning):
          df.drop(index="length", level=1)
  
  
  def test_drop_api_equivalence():
      # equivalence of the labels/axis and index/columns API's
      frame_data = [[1, 2, 3], [3, 4, 5], [5, 6, 7]]
  
      modin_df = pd.DataFrame(frame_data, index=["a", "b", "c"], columns=["d", "e", "f"])
  
      modin_df1 = modin_df.drop("a")
      modin_df2 = modin_df.drop(index="a")
      df_equals(modin_df1, modin_df2)
  
      modin_df1 = modin_df.drop("d", 1)
      modin_df2 = modin_df.drop(columns="d")
      df_equals(modin_df1, modin_df2)
  
      modin_df1 = modin_df.drop(labels="e", axis=1)
      modin_df2 = modin_df.drop(columns="e")
      df_equals(modin_df1, modin_df2)
  
      modin_df1 = modin_df.drop(["a"], axis=0)
      modin_df2 = modin_df.drop(index=["a"])
      df_equals(modin_df1, modin_df2)
  
      modin_df1 = modin_df.drop(["a"], axis=0).drop(["d"], axis=1)
      modin_df2 = modin_df.drop(index=["a"], columns=["d"])
      df_equals(modin_df1, modin_df2)
  
      with pytest.raises(ValueError):
          modin_df.drop(labels="a", index="b")
  
      with pytest.raises(ValueError):
          modin_df.drop(labels="a", columns="b")
  
      with pytest.raises(ValueError):
          modin_df.drop(axis=1)
  
  
  @pytest.mark.parametrize("data", test_data_values, ids=test_data_keys)
  def test_drop_transpose(data):
      modin_df = pd.DataFrame(data)
      pandas_df = pandas.DataFrame(data)
      modin_result = modin_df.T.drop(columns=[0, 1, 2])
      pandas_result = pandas_df.T.drop(columns=[0, 1, 2])
      df_equals(modin_result, pandas_result)
  
      modin_result = modin_df.T.drop(index=["col3", "col1"])
      pandas_result = pandas_df.T.drop(index=["col3", "col1"])
      df_equals(modin_result, pandas_result)
  
      modin_result = modin_df.T.drop(columns=[0, 1, 2], index=["col3", "col1"])
      pandas_result = pandas_df.T.drop(columns=[0, 1, 2], index=["col3", "col1"])
      df_equals(modin_result, pandas_result)
  
  
  def test_droplevel():
      df = (
          pd.DataFrame([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]])
          .set_index([0, 1])
          .rename_axis(["a", "b"])
      )
      df.columns = pd.MultiIndex.from_tuples(
          [("c", "e"), ("d", "f")], names=["level_1", "level_2"]
      )
      df.droplevel("a")
      df.droplevel("level_2", axis=1)
  
  
  @pytest.mark.parametrize(
      "data", test_data_with_duplicates_values, ids=test_data_with_duplicates_keys
  )
  @pytest.mark.parametrize(
      "keep", ["last", "first", False], ids=["last", "first", "False"]
  )
  @pytest.mark.parametrize(
      "subset",
      [None, "col1", "name", ("col1", "col3"), ["col1", "col3", "col7"]],
      ids=["None", "string", "name", "tuple", "list"],
  )
  def test_drop_duplicates(data, keep, subset):
      modin_df = pd.DataFrame(data)
      pandas_df = pandas.DataFrame(data)
  
      try:
          pandas_df.drop_duplicates(keep=keep, inplace=False, subset=subset)
      except Exception as e:
          with pytest.raises(type(e)):
              modin_df.drop_duplicates(keep=keep, inplace=False, subset=subset)
      else:
          df_equals(
              pandas_df.drop_duplicates(keep=keep, inplace=False, subset=subset),
              modin_df.drop_duplicates(keep=keep, inplace=False, subset=subset),
          )
  
      try:
          pandas_results = pandas_df.drop_duplicates(
              keep=keep, inplace=True, subset=subset
          )
      except Exception as e:
          with pytest.raises(type(e)):
              modin_df.drop_duplicates(keep=keep, inplace=True, subset=subset)
      else:
          modin_results = modin_df.drop_duplicates(keep=keep, inplace=True, subset=subset)
          df_equals(modin_results, pandas_results)
  
  
  def test_drop_duplicates_with_missing_index_values():
      data = {
          "columns": ["value", "time", "id"],
          "index": [
              4,
              5,
              6,
              7,
              8,
              9,
              10,
              11,
              12,
              13,
              14,
              15,
              20,
              21,
              22,
              23,
              24,
              25,
              26,
              27,
              32,
              33,
              34,
              35,
              36,
              37,
              38,
              39,
              40,
              41,
          ],
          "data": [
              ["3", 1279213398000.0, 88.0],
              ["3", 1279204682000.0, 88.0],
              ["0", 1245772835000.0, 448.0],
              ["0", 1270564258000.0, 32.0],
              ["0", 1267106669000.0, 118.0],
              ["7", 1300621123000.0, 5.0],
              ["0", 1251130752000.0, 957.0],
              ["0", 1311683506000.0, 62.0],
              ["9", 1283692698000.0, 89.0],
              ["9", 1270234253000.0, 64.0],
              ["0", 1285088818000.0, 50.0],
              ["0", 1218212725000.0, 695.0],
              ["2", 1383933968000.0, 348.0],
              ["0", 1368227625000.0, 257.0],
              ["1", 1454514093000.0, 446.0],
              ["1", 1428497427000.0, 134.0],
              ["1", 1459184936000.0, 568.0],
              ["1", 1502293302000.0, 599.0],
              ["1", 1491833358000.0, 829.0],
              ["1", 1485431534000.0, 806.0],
              ["8", 1351800505000.0, 101.0],
              ["0", 1357247721000.0, 916.0],
              ["0", 1335804423000.0, 370.0],
              ["24", 1327547726000.0, 720.0],
              ["0", 1332334140000.0, 415.0],
              ["0", 1309543100000.0, 30.0],
              ["18", 1309541141000.0, 30.0],
              ["0", 1298979435000.0, 48.0],
              ["14", 1276098160000.0, 59.0],
              ["0", 1233936302000.0, 109.0],
          ],
      }
  
      pandas_df = pandas.DataFrame(
          data["data"], index=data["index"], columns=data["columns"]
      )
      modin_df = pd.DataFrame(data["data"], index=data["index"], columns=data["columns"])
      modin_result = modin_df.sort_values(["id", "time"]).drop_duplicates(["id"])
      pandas_result = pandas_df.sort_values(["id", "time"]).drop_duplicates(["id"])
      df_equals(modin_result, pandas_result)
  
  
  def test_drop_duplicates_after_sort():
      data = [
          {"value": 1, "time": 2},
          {"value": 1, "time": 1},
          {"value": 2, "time": 1},
          {"value": 2, "time": 2},
      ]
      modin_df = pd.DataFrame(data)
      pandas_df = pandas.DataFrame(data)
  
      modin_result = modin_df.sort_values(["value", "time"]).drop_duplicates(["value"])
      pandas_result = pandas_df.sort_values(["value", "time"]).drop_duplicates(["value"])
      df_equals(modin_result, pandas_result)
  
  
  @pytest.mark.parametrize("data", test_data_values, ids=test_data_keys)
  @pytest.mark.parametrize("axis", axis_values, ids=axis_keys)
  @pytest.mark.parametrize("how", ["any", "all"], ids=["any", "all"])
  def test_dropna(data, axis, how):
      modin_df = pd.DataFrame(data)
      pandas_df = pandas.DataFrame(data)
  
      with pytest.raises(ValueError):
          modin_df.dropna(axis=axis, how="invalid")
  
      with pytest.raises(TypeError):
          modin_df.dropna(axis=axis, how=None, thresh=None)
  
      with pytest.raises(KeyError):
          modin_df.dropna(axis=axis, subset=["NotExists"], how=how)
  
      modin_result = modin_df.dropna(axis=axis, how=how)
      pandas_result = pandas_df.dropna(axis=axis, how=how)
      df_equals(modin_result, pandas_result)
  
  
  @pytest.mark.parametrize("data", test_data_values, ids=test_data_keys)
  def test_dropna_inplace(data):
      modin_df = pd.DataFrame(data)
      pandas_df = pandas.DataFrame(data)
      pandas_result = pandas_df.dropna()
      modin_df.dropna(inplace=True)
      df_equals(modin_df, pandas_result)
  
      modin_df = pd.DataFrame(data)
      pandas_df = pandas.DataFrame(data)
      pandas_df.dropna(thresh=2, inplace=True)
      modin_df.dropna(thresh=2, inplace=True)
      df_equals(modin_df, pandas_df)
  
      modin_df = pd.DataFrame(data)
      pandas_df = pandas.DataFrame(data)
      pandas_df.dropna(axis=1, how="any", inplace=True)
      modin_df.dropna(axis=1, how="any", inplace=True)
      df_equals(modin_df, pandas_df)
  
  
  @pytest.mark.parametrize("data", test_data_values, ids=test_data_keys)
  def test_dropna_multiple_axes(data):
      modin_df = pd.DataFrame(data)
  
      with pytest.raises(TypeError):
          modin_df.dropna(how="all", axis=[0, 1])
      with pytest.raises(TypeError):
          modin_df.dropna(how="all", axis=(0, 1))
  
  
  @pytest.mark.parametrize("data", test_data_values, ids=test_data_keys)
  def test_dropna_subset(request, data):
      modin_df = pd.DataFrame(data)
      pandas_df = pandas.DataFrame(data)
  
      if "empty_data" not in request.node.name:
          column_subset = modin_df.columns[0:2]
          df_equals(
              modin_df.dropna(how="all", subset=column_subset),
              pandas_df.dropna(how="all", subset=column_subset),
          )
          df_equals(
              modin_df.dropna(how="any", subset=column_subset),
              pandas_df.dropna(how="any", subset=column_subset),
          )
  
          row_subset = modin_df.index[0:2]
          df_equals(
              modin_df.dropna(how="all", axis=1, subset=row_subset),
              pandas_df.dropna(how="all", axis=1, subset=row_subset),
          )
          df_equals(
              modin_df.dropna(how="any", axis=1, subset=row_subset),
              pandas_df.dropna(how="any", axis=1, subset=row_subset),
          )
  
  
  @pytest.mark.parametrize("data", test_data_values, ids=test_data_keys)
  @pytest.mark.parametrize("axis,subset", [(0, list("EF")), (1, [4, 5])])
  def test_dropna_subset_error(data, axis, subset):
      eval_general(*create_test_dfs(data), lambda df: df.dropna(axis=axis, subset=subset))
  
  
  @pytest.mark.parametrize("data", test_data_values, ids=test_data_keys)
  @pytest.mark.parametrize("astype", ["category", "int32", "float"])
  def test_insert_dtypes(data, astype):
      modin_df, pandas_df = pd.DataFrame(data), pandas.DataFrame(data)
  
      # categories with NaN works incorrect for now
      if astype == "category" and pandas_df.iloc[:, 0].isnull().any():
          return
  
      eval_insert(
          modin_df,
          pandas_df,
          col="TypeSaver",
          value=lambda df: df.iloc[:, 0].astype(astype),
      )
  
  
  @pytest.mark.parametrize("data", test_data_values, ids=test_data_keys)
  @pytest.mark.parametrize("loc", int_arg_values, ids=arg_keys("loc", int_arg_keys))
  def test_insert_loc(data, loc):
      modin_df, pandas_df = pd.DataFrame(data), pandas.DataFrame(data)
      value = modin_df.iloc[:, 0]
  
      eval_insert(modin_df, pandas_df, loc=loc, value=value)
  
  
  @pytest.mark.parametrize("data", test_data_values, ids=test_data_keys)
  def test_insert(data):
      modin_df, pandas_df = pd.DataFrame(data), pandas.DataFrame(data)
  
      eval_insert(
          modin_df, pandas_df, col="Duplicate", value=lambda df: df[df.columns[0]]
      )
      eval_insert(modin_df, pandas_df, col="Scalar", value=100)
      eval_insert(
          pd.DataFrame(columns=list("ab")),
          pandas.DataFrame(columns=list("ab")),
          col=lambda df: df.columns[0],
          value=lambda df: df[df.columns[0]],
      )
      eval_insert(
          pd.DataFrame(index=modin_df.index),
          pandas.DataFrame(index=pandas_df.index),
          col=lambda df: df.columns[0],
          value=lambda df: df[df.columns[0]],
      )
      eval_insert(
          modin_df,
          pandas_df,
          col="DataFrame insert",
          value=lambda df: df[[df.columns[0]]],
      )
      eval_insert(
          modin_df,
          pandas_df,
          col="Different indices",
          value=lambda df: df[[df.columns[0]]].set_index(df.index[::-1]),
      )
  
      # Bad inserts
      eval_insert(modin_df, pandas_df, col="Bad Column", value=lambda df: df)
      eval_insert(
          modin_df,
          pandas_df,
          col="Too Short",
          value=lambda df: list(df[df.columns[0]])[:-1],
      )
      eval_insert(
          modin_df,
          pandas_df,
          col=lambda df: df.columns[0],
          value=lambda df: df[df.columns[0]],
      )
      eval_insert(
          modin_df,
          pandas_df,
          loc=lambda df: len(df.columns) + 100,
          col="Bad Loc",
          value=100,
      )
  
  
  @pytest.mark.parametrize("data", test_data_values, ids=test_data_keys)
  def test_ndim(data):
      modin_df = pd.DataFrame(data)
      pandas_df = pandas.DataFrame(data)
  
      assert modin_df.ndim == pandas_df.ndim
  
  
  @pytest.mark.parametrize("data", test_data_values, ids=test_data_keys)
  def test_notna(data):
      modin_df = pd.DataFrame(data)
      pandas_df = pandas.DataFrame(data)
  
      df_equals(modin_df.notna(), pandas_df.notna())
  
  
  @pytest.mark.parametrize("data", test_data_values, ids=test_data_keys)
  def test_notnull(data):
      modin_df = pd.DataFrame(data)
      pandas_df = pandas.DataFrame(data)
  
      df_equals(modin_df.notnull(), pandas_df.notnull())
  
  
  @pytest.mark.parametrize("data", test_data_values, ids=test_data_keys)
  def test_round(data):
      modin_df = pd.DataFrame(data)
      pandas_df = pandas.DataFrame(data)
  
      df_equals(modin_df.round(), pandas_df.round())
      df_equals(modin_df.round(1), pandas_df.round(1))
  
  
  @pytest.mark.parametrize("data", test_data_values, ids=test_data_keys)
  @pytest.mark.parametrize("axis", axis_values, ids=axis_keys)
  def test_set_axis(data, axis):
      modin_df = pd.DataFrame(data)
      pandas_df = pandas.DataFrame(data)
  
      x = pandas.DataFrame()._get_axis_number(axis)
      index = modin_df.columns if x else modin_df.index
      labels = ["{0}_{1}".format(index[i], i) for i in range(modin_df.shape[x])]
  
      modin_result = modin_df.set_axis(labels, axis=axis, inplace=False)
      pandas_result = pandas_df.set_axis(labels, axis=axis, inplace=False)
      df_equals(modin_result, pandas_result)
  
      modin_df_copy = modin_df.copy()
      modin_df.set_axis(labels, axis=axis, inplace=True)
  
      # Check that the copy and original are different
      try:
          df_equals(modin_df, modin_df_copy)
      except AssertionError:
          assert True
      else:
          assert False
  
      pandas_df.set_axis(labels, axis=axis, inplace=True)
      df_equals(modin_df, pandas_df)
  
  
  @pytest.mark.parametrize("data", test_data_values, ids=test_data_keys)
  @pytest.mark.parametrize("drop", bool_arg_values, ids=arg_keys("drop", bool_arg_keys))
  @pytest.mark.parametrize(
      "append", bool_arg_values, ids=arg_keys("append", bool_arg_keys)
  )
  def test_set_index(request, data, drop, append):
      modin_df = pd.DataFrame(data)
      pandas_df = pandas.DataFrame(data)
  
      if "empty_data" not in request.node.name:
          key = modin_df.columns[0]
          modin_result = modin_df.set_index(key, drop=drop, append=append, inplace=False)
          pandas_result = pandas_df.set_index(
              key, drop=drop, append=append, inplace=False
          )
          df_equals(modin_result, pandas_result)
  
          modin_df_copy = modin_df.copy()
          modin_df.set_index(key, drop=drop, append=append, inplace=True)
  
          # Check that the copy and original are different
          try:
              df_equals(modin_df, modin_df_copy)
          except AssertionError:
              assert True
          else:
              assert False
  
          pandas_df.set_index(key, drop=drop, append=append, inplace=True)
          df_equals(modin_df, pandas_df)
  
  
  @pytest.mark.parametrize("data", test_data_values, ids=test_data_keys)
  def test_shape(data):
      modin_df = pd.DataFrame(data)
      pandas_df = pandas.DataFrame(data)
  
      assert modin_df.shape == pandas_df.shape
  
  
  @pytest.mark.parametrize("data", test_data_values, ids=test_data_keys)
  def test_size(data):
      modin_df = pd.DataFrame(data)
      pandas_df = pandas.DataFrame(data)
  
      assert modin_df.size == pandas_df.size
  
  
  def test_squeeze():
      frame_data = {
          "col1": [0, 1, 2, 3],
          "col2": [4, 5, 6, 7],
          "col3": [8, 9, 10, 11],
          "col4": [12, 13, 14, 15],
          "col5": [0, 0, 0, 0],
      }
      frame_data_2 = {"col1": [0, 1, 2, 3]}
      frame_data_3 = {
          "col1": [0],
          "col2": [4],
          "col3": [8],
          "col4": [12],
          "col5": [0],
      }
      frame_data_4 = {"col1": [2]}
      frame_data_5 = {"col1": ["string"]}
      # Different data for different cases
      pandas_df = pandas.DataFrame(frame_data).squeeze()
      modin_df = pd.DataFrame(frame_data).squeeze()
      df_equals(modin_df, pandas_df)
  
      pandas_df_2 = pandas.DataFrame(frame_data_2).squeeze()
      modin_df_2 = pd.DataFrame(frame_data_2).squeeze()
      df_equals(modin_df_2, pandas_df_2)
  
      pandas_df_3 = pandas.DataFrame(frame_data_3).squeeze()
      modin_df_3 = pd.DataFrame(frame_data_3).squeeze()
      df_equals(modin_df_3, pandas_df_3)
  
      pandas_df_4 = pandas.DataFrame(frame_data_4).squeeze()
      modin_df_4 = pd.DataFrame(frame_data_4).squeeze()
      df_equals(modin_df_4, pandas_df_4)
  
      pandas_df_5 = pandas.DataFrame(frame_data_5).squeeze()
      modin_df_5 = pd.DataFrame(frame_data_5).squeeze()
      df_equals(modin_df_5, pandas_df_5)
  
      data = [
          [
              pd.Timestamp("2019-01-02"),
              pd.Timestamp("2019-01-03"),
              pd.Timestamp("2019-01-04"),
              pd.Timestamp("2019-01-05"),
          ],
          [1, 1, 1, 2],
      ]
      df = pd.DataFrame(data, index=["date", "value"]).T
      pf = pandas.DataFrame(data, index=["date", "value"]).T
      df.set_index("date", inplace=True)
      pf.set_index("date", inplace=True)
      df_equals(df.iloc[0], pf.iloc[0])
  
  
  @pytest.mark.parametrize("data", test_data_values, ids=test_data_keys)
  def test_transpose(data):
      modin_df = pd.DataFrame(data)
      pandas_df = pandas.DataFrame(data)
  
      df_equals(modin_df.T, pandas_df.T)
      df_equals(modin_df.transpose(), pandas_df.transpose())
  
      # Test for map across full axis for select indices
      df_equals(modin_df.T.dropna(), pandas_df.T.dropna())
      # Test for map across full axis
      df_equals(modin_df.T.nunique(), pandas_df.T.nunique())
      # Test for map across blocks
      df_equals(modin_df.T.notna(), pandas_df.T.notna())
  
  
  @pytest.mark.parametrize(
      "data, other_data",
      [
          ({"A": [1, 2, 3], "B": [400, 500, 600]}, {"B": [4, 5, 6], "C": [7, 8, 9]}),
          (
              {"A": ["a", "b", "c"], "B": ["x", "y", "z"]},
              {"B": ["d", "e", "f", "g", "h", "i"]},
          ),
          ({"A": [1, 2, 3], "B": [400, 500, 600]}, {"B": [4, np.nan, 6]}),
      ],
  )
  def test_update(data, other_data):
      modin_df, pandas_df = pd.DataFrame(data), pandas.DataFrame(data)
      other_modin_df, other_pandas_df = (
          pd.DataFrame(other_data),
          pandas.DataFrame(other_data),
      )
      modin_df.update(other_modin_df)
      pandas_df.update(other_pandas_df)
      df_equals(modin_df, pandas_df)
  
      with pytest.raises(ValueError):
          modin_df.update(other_modin_df, errors="raise")
  
  
  @pytest.mark.parametrize("data", test_data_values, ids=test_data_keys)
  def test___neg__(request, data):
      modin_df = pd.DataFrame(data)
      pandas_df = pandas.DataFrame(data)
  
      try:
          pandas_result = pandas_df.__neg__()
      except Exception as e:
          with pytest.raises(type(e)):
              modin_df.__neg__()
      else:
          modin_result = modin_df.__neg__()
          df_equals(modin_result, pandas_result)
  
  
  @pytest.mark.parametrize("data", test_data_values, ids=test_data_keys)
  def test___invert__(data):
      modin_df = pd.DataFrame(data)
      pandas_df = pandas.DataFrame(data)
      try:
          pandas_result = ~pandas_df
      except Exception as e:
          with pytest.raises(type(e)):
              repr(~modin_df)
      else:
          modin_result = ~modin_df
          df_equals(modin_result, pandas_result)
  
  
  def test___hash__():
      data = test_data_values[0]
      with pytest.warns(UserWarning):
          try:
              pd.DataFrame(data).__hash__()
          except TypeError:
              pass
  
  
  @pytest.mark.parametrize("data", test_data_values, ids=test_data_keys)
  def test___delitem__(request, data):
      modin_df = pd.DataFrame(data)
      pandas_df = pandas.DataFrame(data)
  
      if "empty_data" not in request.node.name:
          key = pandas_df.columns[0]
  
          modin_df = modin_df.copy()
          pandas_df = pandas_df.copy()
          modin_df.__delitem__(key)
          pandas_df.__delitem__(key)
          df_equals(modin_df, pandas_df)
  
          # Issue 2027
          last_label = pandas_df.iloc[:, -1].name
          modin_df.__delitem__(last_label)
          pandas_df.__delitem__(last_label)
          df_equals(modin_df, pandas_df)
  
  
  @pytest.mark.parametrize("data", test_data_values, ids=test_data_keys)
  def test___nonzero__(data):
      modin_df = pd.DataFrame(data)
      pandas_df = pandas.DataFrame(data)  # noqa F841
  
      with pytest.raises(ValueError):
          # Always raises ValueError
          modin_df.__nonzero__()
  
  
  @pytest.mark.parametrize("data", test_data_values, ids=test_data_keys)
  def test___abs__(request, data):
      modin_df = pd.DataFrame(data)
      pandas_df = pandas.DataFrame(data)
  
      try:
          pandas_result = abs(pandas_df)
      except Exception as e:
          with pytest.raises(type(e)):
              abs(modin_df)
      else:
          modin_result = abs(modin_df)
          df_equals(modin_result, pandas_result)
  
  
  def test___round__():
      data = test_data_values[0]
      with pytest.warns(UserWarning):
          pd.DataFrame(data).__round__()
  
apis: pandas.Series pandas.DataFrame numpy.array_equal matplotlib.use pandas.Index pandas.get_dummies