code: import numpy as np
  import pytest
  
  import pandas as pd
  from pandas.core.arrays.integer import (
      Int8Dtype,
      Int16Dtype,
      Int32Dtype,
      Int64Dtype,
      UInt8Dtype,
      UInt16Dtype,
      UInt32Dtype,
      UInt64Dtype,
  )
  
  
  @pytest.fixture(
      params=[
          Int8Dtype,
          Int16Dtype,
          Int32Dtype,
          Int64Dtype,
          UInt8Dtype,
          UInt16Dtype,
          UInt32Dtype,
          UInt64Dtype,
      ]
  )
  def dtype(request):
      """Parametrized fixture returning integer 'dtype'"""
      return request.param()
  
  
  @pytest.fixture
  def data(dtype):
      """
      Fixture returning 'data' array with valid and missing values according to
      parametrized integer 'dtype'.
  
      Used to test dtype conversion with and without missing values.
      """
      return pd.array(
          list(range(8)) + [np.nan] + list(range(10, 98)) + [np.nan] + [99, 100],
          dtype=dtype,
      )
  
  
  @pytest.fixture
  def data_missing(dtype):
      """
      Fixture returning array with exactly one NaN and one valid integer,
      according to parametrized integer 'dtype'.
  
      Used to test dtype conversion with and without missing values.
      """
      return pd.array([np.nan, 1], dtype=dtype)
  
  
  @pytest.fixture(params=["data", "data_missing"])
  def all_data(request, data, data_missing):
      """Parametrized fixture returning 'data' or 'data_missing' integer arrays.
  
      Used to test dtype conversion with and without missing values.
      """
      if request.param == "data":
          return data
      elif request.param == "data_missing":
          return data_missing
   from __future__ import annotations
  
  from datetime import datetime
  import re
  
  import numpy as np
  import pytest
  
  from pandas.compat import np_version_under1p20
  
  import pandas as pd
  from pandas import (
      DataFrame,
      Index,
      Series,
      Timestamp,
      date_range,
  )
  import pandas._testing as tm
  
  
  @pytest.fixture
  def mix_ab() -> dict[str, list[int | str]]:
      return {"a": list(range(4)), "b": list("ab..")}
  
  
  @pytest.fixture
  def mix_abc() -> dict[str, list[float | str]]:
      return {"a": list(range(4)), "b": list("ab.."), "c": ["a", "b", np.nan, "d"]}
  
  
  class TestDataFrameReplace:
      def test_replace_inplace(self, datetime_frame, float_string_frame):
          datetime_frame["A"][:5] = np.nan
          datetime_frame["A"][-5:] = np.nan
  
          tsframe = datetime_frame.copy()
          return_value = tsframe.replace(np.nan, 0, inplace=True)
          assert return_value is None
          tm.assert_frame_equal(tsframe, datetime_frame.fillna(0))
  
          # mixed type
          mf = float_string_frame
          mf.iloc[5:20, mf.columns.get_loc("foo")] = np.nan
          mf.iloc[-10:, mf.columns.get_loc("A")] = np.nan
  
          result = float_string_frame.replace(np.nan, 0)
          expected = float_string_frame.fillna(value=0)
          tm.assert_frame_equal(result, expected)
  
          tsframe = datetime_frame.copy()
          return_value = tsframe.replace([np.nan], [0], inplace=True)
          assert return_value is None
          tm.assert_frame_equal(tsframe, datetime_frame.fillna(0))
  
      @pytest.mark.parametrize(
          "to_replace,values,expected",
          [
              # lists of regexes and values
              # list of [re1, re2, ..., reN] -> [v1, v2, ..., vN]
              (
                  [r"\s*\.\s*", r"e|f|g"],
                  [np.nan, "crap"],
                  {
                      "a": ["a", "b", np.nan, np.nan],
                      "b": ["crap"] * 3 + ["h"],
                      "c": ["h", "crap", "l", "o"],
                  },
              ),
              # list of [re1, re2, ..., reN] -> [re1, re2, .., reN]
              (
                  [r"\s*(\.)\s*", r"(e|f|g)"],
                  [r"\1\1", r"\1_crap"],
                  {
                      "a": ["a", "b", "..", ".."],
                      "b": ["e_crap", "f_crap", "g_crap", "h"],
                      "c": ["h", "e_crap", "l", "o"],
                  },
              ),
              # list of [re1, re2, ..., reN] -> [(re1 or v1), (re2 or v2), ..., (reN
              # or vN)]
              (
                  [r"\s*(\.)\s*", r"e"],
                  [r"\1\1", r"crap"],
                  {
                      "a": ["a", "b", "..", ".."],
                      "b": ["crap", "f", "g", "h"],
                      "c": ["h", "crap", "l", "o"],
                  },
              ),
          ],
      )
      @pytest.mark.parametrize("inplace", [True, False])
      @pytest.mark.parametrize("use_value_regex_args", [True, False])
      def test_regex_replace_list_obj(
          self, to_replace, values, expected, inplace, use_value_regex_args
      ):
          df = DataFrame({"a": list("ab.."), "b": list("efgh"), "c": list("helo")})
  
          if use_value_regex_args:
              result = df.replace(value=values, regex=to_replace, inplace=inplace)
          else:
              result = df.replace(to_replace, values, regex=True, inplace=inplace)
  
          if inplace:
              assert result is None
              result = df
  
          expected = DataFrame(expected)
          tm.assert_frame_equal(result, expected)
  
      def test_regex_replace_list_mixed(self, mix_ab):
          # mixed frame to make sure this doesn't break things
          dfmix = DataFrame(mix_ab)
  
          # lists of regexes and values
          # list of [re1, re2, ..., reN] -> [v1, v2, ..., vN]
          to_replace_res = [r"\s*\.\s*", r"a"]
          values = [np.nan, "crap"]
          mix2 = {"a": list(range(4)), "b": list("ab.."), "c": list("halo")}
          dfmix2 = DataFrame(mix2)
          res = dfmix2.replace(to_replace_res, values, regex=True)
          expec = DataFrame(
              {
                  "a": mix2["a"],
                  "b": ["crap", "b", np.nan, np.nan],
                  "c": ["h", "crap", "l", "o"],
              }
          )
          tm.assert_frame_equal(res, expec)
  
          # list of [re1, re2, ..., reN] -> [re1, re2, .., reN]
          to_replace_res = [r"\s*(\.)\s*", r"(a|b)"]
          values = [r"\1\1", r"\1_crap"]
          res = dfmix.replace(to_replace_res, values, regex=True)
          expec = DataFrame({"a": mix_ab["a"], "b": ["a_crap", "b_crap", "..", ".."]})
          tm.assert_frame_equal(res, expec)
  
          # list of [re1, re2, ..., reN] -> [(re1 or v1), (re2 or v2), ..., (reN
          # or vN)]
          to_replace_res = [r"\s*(\.)\s*", r"a", r"(b)"]
          values = [r"\1\1", r"crap", r"\1_crap"]
          res = dfmix.replace(to_replace_res, values, regex=True)
          expec = DataFrame({"a": mix_ab["a"], "b": ["crap", "b_crap", "..", ".."]})
          tm.assert_frame_equal(res, expec)
  
          to_replace_res = [r"\s*(\.)\s*", r"a", r"(b)"]
          values = [r"\1\1", r"crap", r"\1_crap"]
          res = dfmix.replace(regex=to_replace_res, value=values)
          expec = DataFrame({"a": mix_ab["a"], "b": ["crap", "b_crap", "..", ".."]})
          tm.assert_frame_equal(res, expec)
  
      def test_regex_replace_list_mixed_inplace(self, mix_ab):
          dfmix = DataFrame(mix_ab)
          # the same inplace
          # lists of regexes and values
          # list of [re1, re2, ..., reN] -> [v1, v2, ..., vN]
          to_replace_res = [r"\s*\.\s*", r"a"]
          values = [np.nan, "crap"]
          res = dfmix.copy()
          return_value = res.replace(to_replace_res, values, inplace=True, regex=True)
          assert return_value is None
          expec = DataFrame({"a": mix_ab["a"], "b": ["crap", "b", np.nan, np.nan]})
          tm.assert_frame_equal(res, expec)
  
          # list of [re1, re2, ..., reN] -> [re1, re2, .., reN]
          to_replace_res = [r"\s*(\.)\s*", r"(a|b)"]
          values = [r"\1\1", r"\1_crap"]
          res = dfmix.copy()
          return_value = res.replace(to_replace_res, values, inplace=True, regex=True)
          assert return_value is None
          expec = DataFrame({"a": mix_ab["a"], "b": ["a_crap", "b_crap", "..", ".."]})
          tm.assert_frame_equal(res, expec)
  
          # list of [re1, re2, ..., reN] -> [(re1 or v1), (re2 or v2), ..., (reN
          # or vN)]
          to_replace_res = [r"\s*(\.)\s*", r"a", r"(b)"]
          values = [r"\1\1", r"crap", r"\1_crap"]
          res = dfmix.copy()
          return_value = res.replace(to_replace_res, values, inplace=True, regex=True)
          assert return_value is None
          expec = DataFrame({"a": mix_ab["a"], "b": ["crap", "b_crap", "..", ".."]})
          tm.assert_frame_equal(res, expec)
  
          to_replace_res = [r"\s*(\.)\s*", r"a", r"(b)"]
          values = [r"\1\1", r"crap", r"\1_crap"]
          res = dfmix.copy()
          return_value = res.replace(regex=to_replace_res, value=values, inplace=True)
          assert return_value is None
          expec = DataFrame({"a": mix_ab["a"], "b": ["crap", "b_crap", "..", ".."]})
          tm.assert_frame_equal(res, expec)
  
      def test_regex_replace_dict_mixed(self, mix_abc):
          dfmix = DataFrame(mix_abc)
  
          # dicts
          # single dict {re1: v1}, search the whole frame
          # need test for this...
  
          # list of dicts {re1: v1, re2: v2, ..., re3: v3}, search the whole
          # frame
          res = dfmix.replace({"b": r"\s*\.\s*"}, {"b": np.nan}, regex=True)
          res2 = dfmix.copy()
          return_value = res2.replace(
              {"b": r"\s*\.\s*"}, {"b": np.nan}, inplace=True, regex=True
          )
          assert return_value is None
          expec = DataFrame(
              {"a": mix_abc["a"], "b": ["a", "b", np.nan, np.nan], "c": mix_abc["c"]}
          )
          tm.assert_frame_equal(res, expec)
          tm.assert_frame_equal(res2, expec)
  
          # list of dicts {re1: re11, re2: re12, ..., reN: re1N}, search the
          # whole frame
          res = dfmix.replace({"b": r"\s*(\.)\s*"}, {"b": r"\1ty"}, regex=True)
          res2 = dfmix.copy()
          return_value = res2.replace(
              {"b": r"\s*(\.)\s*"}, {"b": r"\1ty"}, inplace=True, regex=True
          )
          assert return_value is None
          expec = DataFrame(
              {"a": mix_abc["a"], "b": ["a", "b", ".ty", ".ty"], "c": mix_abc["c"]}
          )
          tm.assert_frame_equal(res, expec)
          tm.assert_frame_equal(res2, expec)
  
          res = dfmix.replace(regex={"b": r"\s*(\.)\s*"}, value={"b": r"\1ty"})
          res2 = dfmix.copy()
          return_value = res2.replace(
              regex={"b": r"\s*(\.)\s*"}, value={"b": r"\1ty"}, inplace=True
          )
          assert return_value is None
          expec = DataFrame(
              {"a": mix_abc["a"], "b": ["a", "b", ".ty", ".ty"], "c": mix_abc["c"]}
          )
          tm.assert_frame_equal(res, expec)
          tm.assert_frame_equal(res2, expec)
  
          # scalar -> dict
          # to_replace regex, {value: value}
          expec = DataFrame(
              {"a": mix_abc["a"], "b": [np.nan, "b", ".", "."], "c": mix_abc["c"]}
          )
          res = dfmix.replace("a", {"b": np.nan}, regex=True)
          res2 = dfmix.copy()
          return_value = res2.replace("a", {"b": np.nan}, regex=True, inplace=True)
          assert return_value is None
          tm.assert_frame_equal(res, expec)
          tm.assert_frame_equal(res2, expec)
  
          res = dfmix.replace("a", {"b": np.nan}, regex=True)
          res2 = dfmix.copy()
          return_value = res2.replace(regex="a", value={"b": np.nan}, inplace=True)
          assert return_value is None
          expec = DataFrame(
              {"a": mix_abc["a"], "b": [np.nan, "b", ".", "."], "c": mix_abc["c"]}
          )
          tm.assert_frame_equal(res, expec)
          tm.assert_frame_equal(res2, expec)
  
      def test_regex_replace_dict_nested(self, mix_abc):
          # nested dicts will not work until this is implemented for Series
          dfmix = DataFrame(mix_abc)
          res = dfmix.replace({"b": {r"\s*\.\s*": np.nan}}, regex=True)
          res2 = dfmix.copy()
          res4 = dfmix.copy()
          return_value = res2.replace(
              {"b": {r"\s*\.\s*": np.nan}}, inplace=True, regex=True
          )
          assert return_value is None
          res3 = dfmix.replace(regex={"b": {r"\s*\.\s*": np.nan}})
          return_value = res4.replace(regex={"b": {r"\s*\.\s*": np.nan}}, inplace=True)
          assert return_value is None
          expec = DataFrame(
              {"a": mix_abc["a"], "b": ["a", "b", np.nan, np.nan], "c": mix_abc["c"]}
          )
          tm.assert_frame_equal(res, expec)
          tm.assert_frame_equal(res2, expec)
          tm.assert_frame_equal(res3, expec)
          tm.assert_frame_equal(res4, expec)
  
      def test_regex_replace_dict_nested_non_first_character(self, any_string_dtype):
          # GH 25259
          dtype = any_string_dtype
          df = DataFrame({"first": ["abc", "bca", "cab"]}, dtype=dtype)
          expected = DataFrame({"first": [".bc", "bc.", "c.b"]}, dtype=dtype)
          result = df.replace({"a": "."}, regex=True)
          tm.assert_frame_equal(result, expected)
  
      def test_regex_replace_dict_nested_gh4115(self):
          df = DataFrame({"Type": ["Q", "T", "Q", "Q", "T"], "tmp": 2})
          expected = DataFrame({"Type": [0, 1, 0, 0, 1], "tmp": 2})
          result = df.replace({"Type": {"Q": 0, "T": 1}})
          tm.assert_frame_equal(result, expected)
  
      def test_regex_replace_list_to_scalar(self, mix_abc):
          df = DataFrame(mix_abc)
          expec = DataFrame(
              {
                  "a": mix_abc["a"],
                  "b": np.array([np.nan] * 4),
                  "c": [np.nan, np.nan, np.nan, "d"],
              }
          )
          res = df.replace([r"\s*\.\s*", "a|b"], np.nan, regex=True)
          res2 = df.copy()
          res3 = df.copy()
          return_value = res2.replace(
              [r"\s*\.\s*", "a|b"], np.nan, regex=True, inplace=True
          )
          assert return_value is None
          return_value = res3.replace(
              regex=[r"\s*\.\s*", "a|b"], value=np.nan, inplace=True
          )
          assert return_value is None
          tm.assert_frame_equal(res, expec)
          tm.assert_frame_equal(res2, expec)
          tm.assert_frame_equal(res3, expec)
  
      def test_regex_replace_str_to_numeric(self, mix_abc):
          # what happens when you try to replace a numeric value with a regex?
          df = DataFrame(mix_abc)
          res = df.replace(r"\s*\.\s*", 0, regex=True)
          res2 = df.copy()
          return_value = res2.replace(r"\s*\.\s*", 0, inplace=True, regex=True)
          assert return_value is None
          res3 = df.copy()
          return_value = res3.replace(regex=r"\s*\.\s*", value=0, inplace=True)
          assert return_value is None
          expec = DataFrame({"a": mix_abc["a"], "b": ["a", "b", 0, 0], "c": mix_abc["c"]})
          tm.assert_frame_equal(res, expec)
          tm.assert_frame_equal(res2, expec)
          tm.assert_frame_equal(res3, expec)
  
      def test_regex_replace_regex_list_to_numeric(self, mix_abc):
          df = DataFrame(mix_abc)
          res = df.replace([r"\s*\.\s*", "b"], 0, regex=True)
          res2 = df.copy()
          return_value = res2.replace([r"\s*\.\s*", "b"], 0, regex=True, inplace=True)
          assert return_value is None
          res3 = df.copy()
          return_value = res3.replace(regex=[r"\s*\.\s*", "b"], value=0, inplace=True)
          assert return_value is None
          expec = DataFrame(
              {"a": mix_abc["a"], "b": ["a", 0, 0, 0], "c": ["a", 0, np.nan, "d"]}
          )
          tm.assert_frame_equal(res, expec)
          tm.assert_frame_equal(res2, expec)
          tm.assert_frame_equal(res3, expec)
  
      def test_regex_replace_series_of_regexes(self, mix_abc):
          df = DataFrame(mix_abc)
          s1 = Series({"b": r"\s*\.\s*"})
          s2 = Series({"b": np.nan})
          res = df.replace(s1, s2, regex=True)
          res2 = df.copy()
          return_value = res2.replace(s1, s2, inplace=True, regex=True)
          assert return_value is None
          res3 = df.copy()
          return_value = res3.replace(regex=s1, value=s2, inplace=True)
          assert return_value is None
          expec = DataFrame(
              {"a": mix_abc["a"], "b": ["a", "b", np.nan, np.nan], "c": mix_abc["c"]}
          )
          tm.assert_frame_equal(res, expec)
          tm.assert_frame_equal(res2, expec)
          tm.assert_frame_equal(res3, expec)
  
      def test_regex_replace_numeric_to_object_conversion(self, mix_abc):
          df = DataFrame(mix_abc)
          expec = DataFrame({"a": ["a", 1, 2, 3], "b": mix_abc["b"], "c": mix_abc["c"]})
          res = df.replace(0, "a")
          tm.assert_frame_equal(res, expec)
          assert res.a.dtype == np.object_
  
      @pytest.mark.parametrize(
          "to_replace", [{"": np.nan, ",": ""}, {",": "", "": np.nan}]
      )
      def test_joint_simple_replace_and_regex_replace(self, to_replace):
          # GH-39338
          df = DataFrame(
              {
                  "col1": ["1,000", "a", "3"],
                  "col2": ["a", "", "b"],
                  "col3": ["a", "b", "c"],
              }
          )
          result = df.replace(regex=to_replace)
          expected = DataFrame(
              {
                  "col1": ["1000", "a", "3"],
                  "col2": ["a", np.nan, "b"],
                  "col3": ["a", "b", "c"],
              }
          )
          tm.assert_frame_equal(result, expected)
  
      @pytest.mark.parametrize("metachar", ["[]", "()", r"\d", r"\w", r"\s"])
      def test_replace_regex_metachar(self, metachar):
          df = DataFrame({"a": [metachar, "else"]})
          result = df.replace({"a": {metachar: "paren"}})
          expected = DataFrame({"a": ["paren", "else"]})
          tm.assert_frame_equal(result, expected)
  
      @pytest.mark.parametrize(
          "data,to_replace,expected",
          [
              (["xax", "xbx"], {"a": "c", "b": "d"}, ["xcx", "xdx"]),
              (["d", "", ""], {r"^\s*$": pd.NA}, ["d", pd.NA, pd.NA]),
          ],
      )
      def test_regex_replace_string_types(
          self, data, to_replace, expected, frame_or_series, any_string_dtype
      ):
          # GH-41333, GH-35977
          dtype = any_string_dtype
          obj = frame_or_series(data, dtype=dtype)
          result = obj.replace(to_replace, regex=True)
          expected = frame_or_series(expected, dtype=dtype)
  
          tm.assert_equal(result, expected)
  
      def test_replace(self, datetime_frame):
          datetime_frame["A"][:5] = np.nan
          datetime_frame["A"][-5:] = np.nan
  
          zero_filled = datetime_frame.replace(np.nan, -1e8)
          tm.assert_frame_equal(zero_filled, datetime_frame.fillna(-1e8))
          tm.assert_frame_equal(zero_filled.replace(-1e8, np.nan), datetime_frame)
  
          datetime_frame["A"][:5] = np.nan
          datetime_frame["A"][-5:] = np.nan
          datetime_frame["B"][:5] = -1e8
  
          # empty
          df = DataFrame(index=["a", "b"])
          tm.assert_frame_equal(df, df.replace(5, 7))
  
          # GH 11698
          # test for mixed data types.
          df = DataFrame(
              [("-", pd.to_datetime("20150101")), ("a", pd.to_datetime("20150102"))]
          )
          df1 = df.replace("-", np.nan)
          expected_df = DataFrame(
              [(np.nan, pd.to_datetime("20150101")), ("a", pd.to_datetime("20150102"))]
          )
          tm.assert_frame_equal(df1, expected_df)
  
      def test_replace_list(self):
          obj = {"a": list("ab.."), "b": list("efgh"), "c": list("helo")}
          dfobj = DataFrame(obj)
  
          # lists of regexes and values
          # list of [v1, v2, ..., vN] -> [v1, v2, ..., vN]
          to_replace_res = [r".", r"e"]
          values = [np.nan, "crap"]
          res = dfobj.replace(to_replace_res, values)
          expec = DataFrame(
              {
                  "a": ["a", "b", np.nan, np.nan],
                  "b": ["crap", "f", "g", "h"],
                  "c": ["h", "crap", "l", "o"],
              }
          )
          tm.assert_frame_equal(res, expec)
  
          # list of [v1, v2, ..., vN] -> [v1, v2, .., vN]
          to_replace_res = [r".", r"f"]
          values = [r"..", r"crap"]
          res = dfobj.replace(to_replace_res, values)
          expec = DataFrame(
              {
                  "a": ["a", "b", "..", ".."],
                  "b": ["e", "crap", "g", "h"],
                  "c": ["h", "e", "l", "o"],
              }
          )
          tm.assert_frame_equal(res, expec)
  
      def test_replace_with_empty_list(self, frame_or_series):
          # GH 21977
          ser = Series([["a", "b"], [], np.nan, [1]])
          obj = DataFrame({"col": ser})
          obj = tm.get_obj(obj, frame_or_series)
          expected = obj
          result = obj.replace([], np.nan)
          tm.assert_equal(result, expected)
  
          # GH 19266
          msg = (
              "NumPy boolean array indexing assignment cannot assign {size} "
              "input values to the 1 output values where the mask is true"
          )
          with pytest.raises(ValueError, match=msg.format(size=0)):
              obj.replace({np.nan: []})
          with pytest.raises(ValueError, match=msg.format(size=2)):
              obj.replace({np.nan: ["dummy", "alt"]})
  
      def test_replace_series_dict(self):
          # from GH 3064
          df = DataFrame({"zero": {"a": 0.0, "b": 1}, "one": {"a": 2.0, "b": 0}})
          result = df.replace(0, {"zero": 0.5, "one": 1.0})
          expected = DataFrame({"zero": {"a": 0.5, "b": 1}, "one": {"a": 2.0, "b": 1.0}})
          tm.assert_frame_equal(result, expected)
  
          result = df.replace(0, df.mean())
          tm.assert_frame_equal(result, expected)
  
          # series to series/dict
          df = DataFrame({"zero": {"a": 0.0, "b": 1}, "one": {"a": 2.0, "b": 0}})
          s = Series({"zero": 0.0, "one": 2.0})
          result = df.replace(s, {"zero": 0.5, "one": 1.0})
          expected = DataFrame({"zero": {"a": 0.5, "b": 1}, "one": {"a": 1.0, "b": 0.0}})
          tm.assert_frame_equal(result, expected)
  
          result = df.replace(s, df.mean())
          tm.assert_frame_equal(result, expected)
  
      def test_replace_convert(self):
          # gh 3907
          df = DataFrame([["foo", "bar", "bah"], ["bar", "foo", "bah"]])
          m = {"foo": 1, "bar": 2, "bah": 3}
          rep = df.replace(m)
          expec = Series([np.int64] * 3)
          res = rep.dtypes
          tm.assert_series_equal(expec, res)
  
      def test_replace_mixed(self, float_string_frame):
          mf = float_string_frame
          mf.iloc[5:20, mf.columns.get_loc("foo")] = np.nan
          mf.iloc[-10:, mf.columns.get_loc("A")] = np.nan
  
          result = float_string_frame.replace(np.nan, -18)
          expected = float_string_frame.fillna(value=-18)
          tm.assert_frame_equal(result, expected)
          tm.assert_frame_equal(result.replace(-18, np.nan), float_string_frame)
  
          result = float_string_frame.replace(np.nan, -1e8)
          expected = float_string_frame.fillna(value=-1e8)
          tm.assert_frame_equal(result, expected)
          tm.assert_frame_equal(result.replace(-1e8, np.nan), float_string_frame)
  
      def test_replace_mixed_int_block_upcasting(self):
  
          # int block upcasting
          df = DataFrame(
              {
                  "A": Series([1.0, 2.0], dtype="float64"),
                  "B": Series([0, 1], dtype="int64"),
              }
          )
          expected = DataFrame(
              {
                  "A": Series([1.0, 2.0], dtype="float64"),
                  "B": Series([0.5, 1], dtype="float64"),
              }
          )
          result = df.replace(0, 0.5)
          tm.assert_frame_equal(result, expected)
  
          return_value = df.replace(0, 0.5, inplace=True)
          assert return_value is None
          tm.assert_frame_equal(df, expected)
  
      def test_replace_mixed_int_block_splitting(self):
  
          # int block splitting
          df = DataFrame(
              {
                  "A": Series([1.0, 2.0], dtype="float64"),
                  "B": Series([0, 1], dtype="int64"),
                  "C": Series([1, 2], dtype="int64"),
              }
          )
          expected = DataFrame(
              {
                  "A": Series([1.0, 2.0], dtype="float64"),
                  "B": Series([0.5, 1], dtype="float64"),
                  "C": Series([1, 2], dtype="int64"),
              }
          )
          result = df.replace(0, 0.5)
          tm.assert_frame_equal(result, expected)
  
      def test_replace_mixed2(self):
  
          # to object block upcasting
          df = DataFrame(
              {
                  "A": Series([1.0, 2.0], dtype="float64"),
                  "B": Series([0, 1], dtype="int64"),
              }
          )
          expected = DataFrame(
              {
                  "A": Series([1, "foo"], dtype="object"),
                  "B": Series([0, 1], dtype="int64"),
              }
          )
          result = df.replace(2, "foo")
          tm.assert_frame_equal(result, expected)
  
          expected = DataFrame(
              {
                  "A": Series(["foo", "bar"], dtype="object"),
                  "B": Series([0, "foo"], dtype="object"),
              }
          )
          result = df.replace([1, 2], ["foo", "bar"])
          tm.assert_frame_equal(result, expected)
  
      def test_replace_mixed3(self):
          # test case from
          df = DataFrame(
              {"A": Series([3, 0], dtype="int64"), "B": Series([0, 3], dtype="int64")}
          )
          result = df.replace(3, df.mean().to_dict())
          expected = df.copy().astype("float64")
          m = df.mean()
          expected.iloc[0, 0] = m[0]
          expected.iloc[1, 1] = m[1]
          tm.assert_frame_equal(result, expected)
  
      def test_replace_nullable_int_with_string_doesnt_cast(self):
          # GH#25438 don't cast df['a'] to float64
          df = DataFrame({"a": [1, 2, 3, np.nan], "b": ["some", "strings", "here", "he"]})
          df["a"] = df["a"].astype("Int64")
  
          res = df.replace("", np.nan)
          tm.assert_series_equal(res["a"], df["a"])
  
      @pytest.mark.parametrize("dtype", ["boolean", "Int64", "Float64"])
      def test_replace_with_nullable_column(self, dtype):
          # GH-44499
          nullable_ser = Series([1, 0, 1], dtype=dtype)
          df = DataFrame({"A": ["A", "B", "x"], "B": nullable_ser})
          result = df.replace("x", "X")
          expected = DataFrame({"A": ["A", "B", "X"], "B": nullable_ser})
          tm.assert_frame_equal(result, expected)
  
      def test_replace_simple_nested_dict(self):
          df = DataFrame({"col": range(1, 5)})
          expected = DataFrame({"col": ["a", 2, 3, "b"]})
  
          result = df.replace({"col": {1: "a", 4: "b"}})
          tm.assert_frame_equal(expected, result)
  
          # in this case, should be the same as the not nested version
          result = df.replace({1: "a", 4: "b"})
          tm.assert_frame_equal(expected, result)
  
      def test_replace_simple_nested_dict_with_nonexistent_value(self):
          df = DataFrame({"col": range(1, 5)})
          expected = DataFrame({"col": ["a", 2, 3, "b"]})
  
          result = df.replace({-1: "-", 1: "a", 4: "b"})
          tm.assert_frame_equal(expected, result)
  
          result = df.replace({"col": {-1: "-", 1: "a", 4: "b"}})
          tm.assert_frame_equal(expected, result)
  
      def test_replace_NA_with_None(self):
          # gh-45601
          df = DataFrame({"value": [42, None]}).astype({"value": "Int64"})
          result = df.replace({pd.NA: None})
          expected = DataFrame({"value": [42, None]}, dtype=object)
          tm.assert_frame_equal(result, expected)
  
      def test_replace_NAT_with_None(self):
          # gh-45836
          df = DataFrame([pd.NaT, pd.NaT])
          result = df.replace({pd.NaT: None, np.NaN: None})
          expected = DataFrame([None, None])
          tm.assert_frame_equal(result, expected)
  
      def test_replace_with_None_keeps_categorical(self):
          # gh-46634
          cat_series = Series(["b", "b", "b", "d"], dtype="category")
          df = DataFrame(
              {
                  "id": Series([5, 4, 3, 2], dtype="float64"),
                  "col": cat_series,
              }
          )
          result = df.replace({3: None})
  
          expected = DataFrame(
              {
                  "id": Series([5.0, 4.0, None, 2.0], dtype="object"),
                  "col": cat_series,
              }
          )
          tm.assert_frame_equal(result, expected)
  
      def test_replace_value_is_none(self, datetime_frame):
          orig_value = datetime_frame.iloc[0, 0]
          orig2 = datetime_frame.iloc[1, 0]
  
          datetime_frame.iloc[0, 0] = np.nan
          datetime_frame.iloc[1, 0] = 1
  
          result = datetime_frame.replace(to_replace={np.nan: 0})
          expected = datetime_frame.T.replace(to_replace={np.nan: 0}).T
          tm.assert_frame_equal(result, expected)
  
          result = datetime_frame.replace(to_replace={np.nan: 0, 1: -1e8})
          tsframe = datetime_frame.copy()
          tsframe.iloc[0, 0] = 0
          tsframe.iloc[1, 0] = -1e8
          expected = tsframe
          tm.assert_frame_equal(expected, result)
          datetime_frame.iloc[0, 0] = orig_value
          datetime_frame.iloc[1, 0] = orig2
  
      def test_replace_for_new_dtypes(self, datetime_frame):
  
          # dtypes
          tsframe = datetime_frame.copy().astype(np.float32)
          tsframe["A"][:5] = np.nan
          tsframe["A"][-5:] = np.nan
  
          zero_filled = tsframe.replace(np.nan, -1e8)
          tm.assert_frame_equal(zero_filled, tsframe.fillna(-1e8))
          tm.assert_frame_equal(zero_filled.replace(-1e8, np.nan), tsframe)
  
          tsframe["A"][:5] = np.nan
          tsframe["A"][-5:] = np.nan
          tsframe["B"][:5] = -1e8
  
          b = tsframe["B"]
          b[b == -1e8] = np.nan
          tsframe["B"] = b
          result = tsframe.fillna(method="bfill")
          tm.assert_frame_equal(result, tsframe.fillna(method="bfill"))
  
      @pytest.mark.parametrize(
          "frame, to_replace, value, expected",
          [
              (DataFrame({"ints": [1, 2, 3]}), 1, 0, DataFrame({"ints": [0, 2, 3]})),
              (
                  DataFrame({"ints": [1, 2, 3]}, dtype=np.int32),
                  1,
                  0,
                  DataFrame({"ints": [0, 2, 3]}, dtype=np.int32),
              ),
              (
                  DataFrame({"ints": [1, 2, 3]}, dtype=np.int16),
                  1,
                  0,
                  DataFrame({"ints": [0, 2, 3]}, dtype=np.int16),
              ),
              (
                  DataFrame({"bools": [True, False, True]}),
                  False,
                  True,
                  DataFrame({"bools": [True, True, True]}),
              ),
              (
                  DataFrame({"complex": [1j, 2j, 3j]}),
                  1j,
                  0,
                  DataFrame({"complex": [0j, 2j, 3j]}),
              ),
              (
                  DataFrame(
                      {
                          "datetime64": Index(
                              [
                                  datetime(2018, 5, 28),
                                  datetime(2018, 7, 28),
                                  datetime(2018, 5, 28),
                              ]
                          )
                      }
                  ),
                  datetime(2018, 5, 28),
                  datetime(2018, 7, 28),
                  DataFrame({"datetime64": Index([datetime(2018, 7, 28)] * 3)}),
              ),
              # GH 20380
              (
                  DataFrame({"dt": [datetime(3017, 12, 20)], "str": ["foo"]}),
                  "foo",
                  "bar",
                  DataFrame({"dt": [datetime(3017, 12, 20)], "str": ["bar"]}),
              ),
              # GH 36782
              (
                  DataFrame({"dt": [datetime(2920, 10, 1)]}),
                  datetime(2920, 10, 1),
                  datetime(2020, 10, 1),
                  DataFrame({"dt": [datetime(2020, 10, 1)]}),
              ),
              (
                  DataFrame(
                      {
                          "A": date_range("20130101", periods=3, tz="US/Eastern"),
                          "B": [0, np.nan, 2],
                      }
                  ),
                  Timestamp("20130102", tz="US/Eastern"),
                  Timestamp("20130104", tz="US/Eastern"),
                  DataFrame(
                      {
                          "A": [
                              Timestamp("20130101", tz="US/Eastern"),
                              Timestamp("20130104", tz="US/Eastern"),
                              Timestamp("20130103", tz="US/Eastern"),
                          ],
                          "B": [0, np.nan, 2],
                      }
                  ),
              ),
              # GH 35376
              (
                  DataFrame([[1, 1.0], [2, 2.0]]),
                  1.0,
                  5,
                  DataFrame([[5, 5.0], [2, 2.0]]),
              ),
              (
                  DataFrame([[1, 1.0], [2, 2.0]]),
                  1,
                  5,
                  DataFrame([[5, 5.0], [2, 2.0]]),
              ),
              (
                  DataFrame([[1, 1.0], [2, 2.0]]),
                  1.0,
                  5.0,
                  DataFrame([[5, 5.0], [2, 2.0]]),
              ),
              (
                  DataFrame([[1, 1.0], [2, 2.0]]),
                  1,
                  5.0,
                  DataFrame([[5, 5.0], [2, 2.0]]),
              ),
          ],
      )
      def test_replace_dtypes(self, frame, to_replace, value, expected):
          result = getattr(frame, "replace")(to_replace, value)
          tm.assert_frame_equal(result, expected)
  
      def test_replace_input_formats_listlike(self):
          # both dicts
          to_rep = {"A": np.nan, "B": 0, "C": ""}
          values = {"A": 0, "B": -1, "C": "missing"}
          df = DataFrame(
              {"A": [np.nan, 0, np.inf], "B": [0, 2, 5], "C": ["", "asdf", "fd"]}
          )
          filled = df.replace(to_rep, values)
          expected = {k: v.replace(to_rep[k], values[k]) for k, v in df.items()}
          tm.assert_frame_equal(filled, DataFrame(expected))
  
          result = df.replace([0, 2, 5], [5, 2, 0])
          expected = DataFrame(
              {"A": [np.nan, 5, np.inf], "B": [5, 2, 0], "C": ["", "asdf", "fd"]}
          )
          tm.assert_frame_equal(result, expected)
  
          # scalar to dict
          values = {"A": 0, "B": -1, "C": "missing"}
          df = DataFrame(
              {"A": [np.nan, 0, np.nan], "B": [0, 2, 5], "C": ["", "asdf", "fd"]}
          )
          filled = df.replace(np.nan, values)
          expected = {k: v.replace(np.nan, values[k]) for k, v in df.items()}
          tm.assert_frame_equal(filled, DataFrame(expected))
  
          # list to list
          to_rep = [np.nan, 0, ""]
          values = [-2, -1, "missing"]
          result = df.replace(to_rep, values)
          expected = df.copy()
          for i in range(len(to_rep)):
              return_value = expected.replace(to_rep[i], values[i], inplace=True)
              assert return_value is None
          tm.assert_frame_equal(result, expected)
  
          msg = r"Replacement lists must match in length\. Expecting 3 got 2"
          with pytest.raises(ValueError, match=msg):
              df.replace(to_rep, values[1:])
  
      def test_replace_input_formats_scalar(self):
          df = DataFrame(
              {"A": [np.nan, 0, np.inf], "B": [0, 2, 5], "C": ["", "asdf", "fd"]}
          )
  
          # dict to scalar
          to_rep = {"A": np.nan, "B": 0, "C": ""}
          filled = df.replace(to_rep, 0)
          expected = {k: v.replace(to_rep[k], 0) for k, v in df.items()}
          tm.assert_frame_equal(filled, DataFrame(expected))
  
          msg = "value argument must be scalar, dict, or Series"
          with pytest.raises(TypeError, match=msg):
              df.replace(to_rep, [np.nan, 0, ""])
  
          # list to scalar
          to_rep = [np.nan, 0, ""]
          result = df.replace(to_rep, -1)
          expected = df.copy()
          for i in range(len(to_rep)):
              return_value = expected.replace(to_rep[i], -1, inplace=True)
              assert return_value is None
          tm.assert_frame_equal(result, expected)
  
      def test_replace_limit(self):
          # TODO
          pass
  
      def test_replace_dict_no_regex(self):
          answer = Series(
              {
                  0: "Strongly Agree",
                  1: "Agree",
                  2: "Neutral",
                  3: "Disagree",
                  4: "Strongly Disagree",
              }
          )
          weights = {
              "Agree": 4,
              "Disagree": 2,
              "Neutral": 3,
              "Strongly Agree": 5,
              "Strongly Disagree": 1,
          }
          expected = Series({0: 5, 1: 4, 2: 3, 3: 2, 4: 1})
          result = answer.replace(weights)
          tm.assert_series_equal(result, expected)
  
      def test_replace_series_no_regex(self):
          answer = Series(
              {
                  0: "Strongly Agree",
                  1: "Agree",
                  2: "Neutral",
                  3: "Disagree",
                  4: "Strongly Disagree",
              }
          )
          weights = Series(
              {
                  "Agree": 4,
                  "Disagree": 2,
                  "Neutral": 3,
                  "Strongly Agree": 5,
                  "Strongly Disagree": 1,
              }
          )
          expected = Series({0: 5, 1: 4, 2: 3, 3: 2, 4: 1})
          result = answer.replace(weights)
          tm.assert_series_equal(result, expected)
  
      def test_replace_dict_tuple_list_ordering_remains_the_same(self):
          df = DataFrame({"A": [np.nan, 1]})
          res1 = df.replace(to_replace={np.nan: 0, 1: -1e8})
          res2 = df.replace(to_replace=(1, np.nan), value=[-1e8, 0])
          res3 = df.replace(to_replace=[1, np.nan], value=[-1e8, 0])
  
          expected = DataFrame({"A": [0, -1e8]})
          tm.assert_frame_equal(res1, res2)
          tm.assert_frame_equal(res2, res3)
          tm.assert_frame_equal(res3, expected)
  
      def test_replace_doesnt_replace_without_regex(self):
          df = DataFrame(
              {
                  "fol": [1, 2, 2, 3],
                  "T_opp": ["0", "vr", "0", "0"],
                  "T_Dir": ["0", "0", "0", "bt"],
                  "T_Enh": ["vo", "0", "0", "0"],
              }
          )
          res = df.replace({r"\D": 1})
          tm.assert_frame_equal(df, res)
  
      def test_replace_bool_with_string(self):
          df = DataFrame({"a": [True, False], "b": list("ab")})
          result = df.replace(True, "a")
          expected = DataFrame({"a": ["a", False], "b": df.b})
          tm.assert_frame_equal(result, expected)
  
      def test_replace_pure_bool_with_string_no_op(self):
          df = DataFrame(np.random.rand(2, 2) > 0.5)
          result = df.replace("asdf", "fdsa")
          tm.assert_frame_equal(df, result)
  
      def test_replace_bool_with_bool(self):
          df = DataFrame(np.random.rand(2, 2) > 0.5)
          result = df.replace(False, True)
          expected = DataFrame(np.ones((2, 2), dtype=bool))
          tm.assert_frame_equal(result, expected)
  
      def test_replace_with_dict_with_bool_keys(self):
          df = DataFrame({0: [True, False], 1: [False, True]})
          result = df.replace({"asdf": "asdb", True: "yes"})
          expected = DataFrame({0: ["yes", False], 1: [False, "yes"]})
          tm.assert_frame_equal(result, expected)
  
      def test_replace_dict_strings_vs_ints(self):
          # GH#34789
          df = DataFrame({"Y0": [1, 2], "Y1": [3, 4]})
          result = df.replace({"replace_string": "test"})
  
          tm.assert_frame_equal(result, df)
  
          result = df["Y0"].replace({"replace_string": "test"})
          tm.assert_series_equal(result, df["Y0"])
  
      def test_replace_truthy(self):
          df = DataFrame({"a": [True, True]})
          r = df.replace([np.inf, -np.inf], np.nan)
          e = df
          tm.assert_frame_equal(r, e)
  
      def test_nested_dict_overlapping_keys_replace_int(self):
          # GH 27660 keep behaviour consistent for simple dictionary and
          # nested dictionary replacement
          df = DataFrame({"a": list(range(1, 5))})
  
          result = df.replace({"a": dict(zip(range(1, 5), range(2, 6)))})
          expected = df.replace(dict(zip(range(1, 5), range(2, 6))))
          tm.assert_frame_equal(result, expected)
  
      def test_nested_dict_overlapping_keys_replace_str(self):
          # GH 27660
          a = np.arange(1, 5)
          astr = a.astype(str)
          bstr = np.arange(2, 6).astype(str)
          df = DataFrame({"a": astr})
          result = df.replace(dict(zip(astr, bstr)))
          expected = df.replace({"a": dict(zip(astr, bstr))})
          tm.assert_frame_equal(result, expected)
  
      def test_replace_swapping_bug(self):
          df = DataFrame({"a": [True, False, True]})
          res = df.replace({"a": {True: "Y", False: "N"}})
          expect = DataFrame({"a": ["Y", "N", "Y"]})
          tm.assert_frame_equal(res, expect)
  
          df = DataFrame({"a": [0, 1, 0]})
          res = df.replace({"a": {0: "Y", 1: "N"}})
          expect = DataFrame({"a": ["Y", "N", "Y"]})
          tm.assert_frame_equal(res, expect)
  
      def test_replace_period(self):
          d = {
              "fname": {
                  "out_augmented_AUG_2011.json": pd.Period(year=2011, month=8, freq="M"),
                  "out_augmented_JAN_2011.json": pd.Period(year=2011, month=1, freq="M"),
                  "out_augmented_MAY_2012.json": pd.Period(year=2012, month=5, freq="M"),
                  "out_augmented_SUBSIDY_WEEK.json": pd.Period(
                      year=2011, month=4, freq="M"
                  ),
                  "out_augmented_AUG_2012.json": pd.Period(year=2012, month=8, freq="M"),
                  "out_augmented_MAY_2011.json": pd.Period(year=2011, month=5, freq="M"),
                  "out_augmented_SEP_2013.json": pd.Period(year=2013, month=9, freq="M"),
              }
          }
  
          df = DataFrame(
              [
                  "out_augmented_AUG_2012.json",
                  "out_augmented_SEP_2013.json",
                  "out_augmented_SUBSIDY_WEEK.json",
                  "out_augmented_MAY_2012.json",
                  "out_augmented_MAY_2011.json",
                  "out_augmented_AUG_2011.json",
                  "out_augmented_JAN_2011.json",
              ],
              columns=["fname"],
          )
          assert set(df.fname.values) == set(d["fname"].keys())
  
          expected = DataFrame({"fname": [d["fname"][k] for k in df.fname.values]})
          assert expected.dtypes[0] == "Period[M]"
          result = df.replace(d)
          tm.assert_frame_equal(result, expected)
  
      def test_replace_datetime(self):
          d = {
              "fname": {
                  "out_augmented_AUG_2011.json": Timestamp("2011-08"),
                  "out_augmented_JAN_2011.json": Timestamp("2011-01"),
                  "out_augmented_MAY_2012.json": Timestamp("2012-05"),
                  "out_augmented_SUBSIDY_WEEK.json": Timestamp("2011-04"),
                  "out_augmented_AUG_2012.json": Timestamp("2012-08"),
                  "out_augmented_MAY_2011.json": Timestamp("2011-05"),
                  "out_augmented_SEP_2013.json": Timestamp("2013-09"),
              }
          }
  
          df = DataFrame(
              [
                  "out_augmented_AUG_2012.json",
                  "out_augmented_SEP_2013.json",
                  "out_augmented_SUBSIDY_WEEK.json",
                  "out_augmented_MAY_2012.json",
                  "out_augmented_MAY_2011.json",
                  "out_augmented_AUG_2011.json",
                  "out_augmented_JAN_2011.json",
              ],
              columns=["fname"],
          )
          assert set(df.fname.values) == set(d["fname"].keys())
          expected = DataFrame({"fname": [d["fname"][k] for k in df.fname.values]})
          result = df.replace(d)
          tm.assert_frame_equal(result, expected)
  
      def test_replace_datetimetz(self):
  
          # GH 11326
          # behaving poorly when presented with a datetime64[ns, tz]
          df = DataFrame(
              {
                  "A": date_range("20130101", periods=3, tz="US/Eastern"),
                  "B": [0, np.nan, 2],
              }
          )
          result = df.replace(np.nan, 1)
          expected = DataFrame(
              {
                  "A": date_range("20130101", periods=3, tz="US/Eastern"),
                  "B": Series([0, 1, 2], dtype="float64"),
              }
          )
          tm.assert_frame_equal(result, expected)
  
          result = df.fillna(1)
          tm.assert_frame_equal(result, expected)
  
          result = df.replace(0, np.nan)
          expected = DataFrame(
              {
                  "A": date_range("20130101", periods=3, tz="US/Eastern"),
                  "B": [np.nan, np.nan, 2],
              }
          )
          tm.assert_frame_equal(result, expected)
  
          result = df.replace(
              Timestamp("20130102", tz="US/Eastern"),
              Timestamp("20130104", tz="US/Eastern"),
          )
          expected = DataFrame(
              {
                  "A": [
                      Timestamp("20130101", tz="US/Eastern"),
                      Timestamp("20130104", tz="US/Eastern"),
                      Timestamp("20130103", tz="US/Eastern"),
                  ],
                  "B": [0, np.nan, 2],
              }
          )
          tm.assert_frame_equal(result, expected)
  
          result = df.copy()
          result.iloc[1, 0] = np.nan
          result = result.replace({"A": pd.NaT}, Timestamp("20130104", tz="US/Eastern"))
          tm.assert_frame_equal(result, expected)
  
          # coerce to object
          result = df.copy()
          result.iloc[1, 0] = np.nan
          with tm.assert_produces_warning(FutureWarning, match="mismatched timezone"):
              result = result.replace(
                  {"A": pd.NaT}, Timestamp("20130104", tz="US/Pacific")
              )
          expected = DataFrame(
              {
                  "A": [
                      Timestamp("20130101", tz="US/Eastern"),
                      Timestamp("20130104", tz="US/Pacific"),
                      # once deprecation is enforced
                      # Timestamp("20130104", tz="US/Pacific").tz_convert("US/Eastern"),
                      Timestamp("20130103", tz="US/Eastern"),
                  ],
                  "B": [0, np.nan, 2],
              }
          )
          tm.assert_frame_equal(result, expected)
  
          result = df.copy()
          result.iloc[1, 0] = np.nan
          result = result.replace({"A": np.nan}, Timestamp("20130104"))
          expected = DataFrame(
              {
                  "A": [
                      Timestamp("20130101", tz="US/Eastern"),
                      Timestamp("20130104"),
                      Timestamp("20130103", tz="US/Eastern"),
                  ],
                  "B": [0, np.nan, 2],
              }
          )
          tm.assert_frame_equal(result, expected)
  
      def test_replace_with_empty_dictlike(self, mix_abc):
          # GH 15289
          df = DataFrame(mix_abc)
          tm.assert_frame_equal(df, df.replace({}))
          tm.assert_frame_equal(df, df.replace(Series([], dtype=object)))
  
          tm.assert_frame_equal(df, df.replace({"b": {}}))
          tm.assert_frame_equal(df, df.replace(Series({"b": {}})))
  
      @pytest.mark.parametrize(
          "to_replace, method, expected",
          [
              (0, "bfill", {"A": [1, 1, 2], "B": [5, np.nan, 7], "C": ["a", "b", "c"]}),
              (
                  np.nan,
                  "bfill",
                  {"A": [0, 1, 2], "B": [5.0, 7.0, 7.0], "C": ["a", "b", "c"]},
              ),
              ("d", "ffill", {"A": [0, 1, 2], "B": [5, np.nan, 7], "C": ["a", "b", "c"]}),
              (
                  [0, 2],
                  "bfill",
                  {"A": [1, 1, 2], "B": [5, np.nan, 7], "C": ["a", "b", "c"]},
              ),
              (
                  [1, 2],
                  "pad",
                  {"A": [0, 0, 0], "B": [5, np.nan, 7], "C": ["a", "b", "c"]},
              ),
              (
                  (1, 2),
                  "bfill",
                  {"A": [0, 2, 2], "B": [5, np.nan, 7], "C": ["a", "b", "c"]},
              ),
              (
                  ["b", "c"],
                  "ffill",
                  {"A": [0, 1, 2], "B": [5, np.nan, 7], "C": ["a", "a", "a"]},
              ),
          ],
      )
      def test_replace_method(self, to_replace, method, expected):
          # GH 19632
          df = DataFrame({"A": [0, 1, 2], "B": [5, np.nan, 7], "C": ["a", "b", "c"]})
  
          result = df.replace(to_replace=to_replace, value=None, method=method)
          expected = DataFrame(expected)
          tm.assert_frame_equal(result, expected)
  
      @pytest.mark.parametrize(
          "replace_dict, final_data",
          [({"a": 1, "b": 1}, [[3, 3], [2, 2]]), ({"a": 1, "b": 2}, [[3, 1], [2, 3]])],
      )
      def test_categorical_replace_with_dict(self, replace_dict, final_data):
          # GH 26988
          df = DataFrame([[1, 1], [2, 2]], columns=["a", "b"], dtype="category")
  
          final_data = np.array(final_data)
  
          a = pd.Categorical(final_data[:, 0], categories=[3, 2])
  
          ex_cat = [3, 2] if replace_dict["b"] == 1 else [1, 3]
          b = pd.Categorical(final_data[:, 1], categories=ex_cat)
  
          expected = DataFrame({"a": a, "b": b})
          result = df.replace(replace_dict, 3)
          tm.assert_frame_equal(result, expected)
          msg = (
              r"Attributes of DataFrame.iloc\[:, 0\] \(column name=\"a\"\) are "
              "different"
          )
          with pytest.raises(AssertionError, match=msg):
              # ensure non-inplace call does not affect original
              tm.assert_frame_equal(df, expected)
          return_value = df.replace(replace_dict, 3, inplace=True)
          assert return_value is None
          tm.assert_frame_equal(df, expected)
  
      @pytest.mark.parametrize(
          "df, to_replace, exp",
          [
              (
                  {"col1": [1, 2, 3], "col2": [4, 5, 6]},
                  {4: 5, 5: 6, 6: 7},
                  {"col1": [1, 2, 3], "col2": [5, 6, 7]},
              ),
              (
                  {"col1": [1, 2, 3], "col2": ["4", "5", "6"]},
                  {"4": "5", "5": "6", "6": "7"},
                  {"col1": [1, 2, 3], "col2": ["5", "6", "7"]},
              ),
          ],
      )
      def test_replace_commutative(self, df, to_replace, exp):
          # GH 16051
          # DataFrame.replace() overwrites when values are non-numeric
          # also added to data frame whilst issue was for series
  
          df = DataFrame(df)
  
          expected = DataFrame(exp)
          result = df.replace(to_replace)
          tm.assert_frame_equal(result, expected)
  
      @pytest.mark.parametrize(
          "replacer",
          [
              Timestamp("20170827"),
              np.int8(1),
              np.int16(1),
              np.float32(1),
              np.float64(1),
          ],
      )
      def test_replace_replacer_dtype(self, request, replacer):
          # GH26632
          if np.isscalar(replacer) and replacer.dtype.itemsize < 8:
              request.node.add_marker(
                  pytest.mark.xfail(
                      np_version_under1p20, reason="np.putmask doesn't coerce dtype"
                  )
              )
          df = DataFrame(["a"])
          result = df.replace({"a": replacer, "b": replacer})
          expected = DataFrame([replacer])
          tm.assert_frame_equal(result, expected)
  
      def test_replace_after_convert_dtypes(self):
          # GH31517
          df = DataFrame({"grp": [1, 2, 3, 4, 5]}, dtype="Int64")
          result = df.replace(1, 10)
          expected = DataFrame({"grp": [10, 2, 3, 4, 5]}, dtype="Int64")
          tm.assert_frame_equal(result, expected)
  
      def test_replace_invalid_to_replace(self):
          # GH 18634
          # API: replace() should raise an exception if invalid argument is given
          df = DataFrame({"one": ["a", "b ", "c"], "two": ["d ", "e ", "f "]})
          msg = (
              r"Expecting 'to_replace' to be either a scalar, array-like, "
              r"dict or None, got invalid type.*"
          )
          with pytest.raises(TypeError, match=msg):
              df.replace(lambda x: x.strip())
  
      @pytest.mark.parametrize("dtype", ["float", "float64", "int64", "Int64", "boolean"])
      @pytest.mark.parametrize("value", [np.nan, pd.NA])
      def test_replace_no_replacement_dtypes(self, dtype, value):
          # https://github.com/pandas-dev/pandas/issues/32988
          df = DataFrame(np.eye(2), dtype=dtype)
          result = df.replace(to_replace=[None, -np.inf, np.inf], value=value)
          tm.assert_frame_equal(result, df)
  
      @pytest.mark.parametrize("replacement", [np.nan, 5])
      def test_replace_with_duplicate_columns(self, replacement):
          # GH 24798
          result = DataFrame({"A": [1, 2, 3], "A1": [4, 5, 6], "B": [7, 8, 9]})
          result.columns = list("AAB")
  
          expected = DataFrame(
              {"A": [1, 2, 3], "A1": [4, 5, 6], "B": [replacement, 8, 9]}
          )
          expected.columns = list("AAB")
  
          result["B"] = result["B"].replace(7, replacement)
  
          tm.assert_frame_equal(result, expected)
  
      @pytest.mark.parametrize("value", [pd.Period("2020-01"), pd.Interval(0, 5)])
      def test_replace_ea_ignore_float(self, frame_or_series, value):
          # GH#34871
          obj = DataFrame({"Per": [value] * 3})
          obj = tm.get_obj(obj, frame_or_series)
  
          expected = obj.copy()
          result = obj.replace(1.0, 0.0)
          tm.assert_equal(expected, result)
  
      def test_replace_value_category_type(self):
          """
          Test for #23305: to ensure category dtypes are maintained
          after replace with direct values
          """
  
          # create input data
          input_dict = {
              "col1": [1, 2, 3, 4],
              "col2": ["a", "b", "c", "d"],
              "col3": [1.5, 2.5, 3.5, 4.5],
              "col4": ["cat1", "cat2", "cat3", "cat4"],
              "col5": ["obj1", "obj2", "obj3", "obj4"],
          }
          # explicitly cast columns as category and order them
          input_df = DataFrame(data=input_dict).astype(
              {"col2": "category", "col4": "category"}
          )
          input_df["col2"] = input_df["col2"].cat.reorder_categories(
              ["a", "b", "c", "d"], ordered=True
          )
          input_df["col4"] = input_df["col4"].cat.reorder_categories(
              ["cat1", "cat2", "cat3", "cat4"], ordered=True
          )
  
          # create expected dataframe
          expected_dict = {
              "col1": [1, 2, 3, 4],
              "col2": ["a", "b", "c", "z"],
              "col3": [1.5, 2.5, 3.5, 4.5],
              "col4": ["cat1", "catX", "cat3", "cat4"],
              "col5": ["obj9", "obj2", "obj3", "obj4"],
          }
          # explicitly cast columns as category and order them
          expected = DataFrame(data=expected_dict).astype(
              {"col2": "category", "col4": "category"}
          )
          expected["col2"] = expected["col2"].cat.reorder_categories(
              ["a", "b", "c", "z"], ordered=True
          )
          expected["col4"] = expected["col4"].cat.reorder_categories(
              ["cat1", "catX", "cat3", "cat4"], ordered=True
          )
  
          # replace values in input dataframe
          input_df = input_df.replace("d", "z")
          input_df = input_df.replace("obj1", "obj9")
          result = input_df.replace("cat2", "catX")
  
          tm.assert_frame_equal(result, expected)
  
      def test_replace_dict_category_type(self):
          """
          Test to ensure category dtypes are maintained
          after replace with dict values
          """
          # GH#35268, GH#44940
  
          # create input dataframe
          input_dict = {"col1": ["a"], "col2": ["obj1"], "col3": ["cat1"]}
          # explicitly cast columns as category
          input_df = DataFrame(data=input_dict).astype(
              {"col1": "category", "col2": "category", "col3": "category"}
          )
  
          # create expected dataframe
          expected_dict = {"col1": ["z"], "col2": ["obj9"], "col3": ["catX"]}
          # explicitly cast columns as category
          expected = DataFrame(data=expected_dict).astype(
              {"col1": "category", "col2": "category", "col3": "category"}
          )
  
          # replace values in input dataframe using a dict
          result = input_df.replace({"a": "z", "obj1": "obj9", "cat1": "catX"})
  
          tm.assert_frame_equal(result, expected)
  
      def test_replace_with_compiled_regex(self):
          # https://github.com/pandas-dev/pandas/issues/35680
          df = DataFrame(["a", "b", "c"])
          regex = re.compile("^a$")
          result = df.replace({regex: "z"}, regex=True)
          expected = DataFrame(["z", "b", "c"])
          tm.assert_frame_equal(result, expected)
  
      def test_replace_intervals(self):
          # https://github.com/pandas-dev/pandas/issues/35931
          df = DataFrame({"a": [pd.Interval(0, 1), pd.Interval(0, 1)]})
          result = df.replace({"a": {pd.Interval(0, 1): "x"}})
          expected = DataFrame({"a": ["x", "x"]})
          tm.assert_frame_equal(result, expected)
  
      def test_replace_unicode(self):
          # GH: 16784
          columns_values_map = {"positive": {"正面": 1, "中立": 1, "负面": 0}}
          df1 = DataFrame({"positive": np.ones(3)})
          result = df1.replace(columns_values_map)
          expected = DataFrame({"positive": np.ones(3)})
          tm.assert_frame_equal(result, expected)
  
      def test_replace_bytes(self, frame_or_series):
          # GH#38900
          obj = frame_or_series(["o"]).astype("|S")
          expected = obj.copy()
          obj = obj.replace({None: np.nan})
          tm.assert_equal(obj, expected)
  
      @pytest.mark.parametrize(
          "data, to_replace, value, expected",
          [
              ([1], [1.0], [0], [0]),
              ([1], [1], [0], [0]),
              ([1.0], [1.0], [0], [0.0]),
              ([1.0], [1], [0], [0.0]),
          ],
      )
      @pytest.mark.parametrize("box", [list, tuple, np.array])
      def test_replace_list_with_mixed_type(
          self, data, to_replace, value, expected, box, frame_or_series
      ):
          # GH#40371
          obj = frame_or_series(data)
          expected = frame_or_series(expected)
          result = obj.replace(box(to_replace), value)
          tm.assert_equal(result, expected)
  
  
  class TestDataFrameReplaceRegex:
      @pytest.mark.parametrize(
          "data",
          [
              {"a": list("ab.."), "b": list("efgh")},
              {"a": list("ab.."), "b": list(range(4))},
          ],
      )
      @pytest.mark.parametrize(
          "to_replace,value", [(r"\s*\.\s*", np.nan), (r"\s*(\.)\s*", r"\1\1\1")]
      )
      @pytest.mark.parametrize("compile_regex", [True, False])
      @pytest.mark.parametrize("regex_kwarg", [True, False])
      @pytest.mark.parametrize("inplace", [True, False])
      def test_regex_replace_scalar(
          self, data, to_replace, value, compile_regex, regex_kwarg, inplace
      ):
          df = DataFrame(data)
          expected = df.copy()
  
          if compile_regex:
              to_replace = re.compile(to_replace)
  
          if regex_kwarg:
              regex = to_replace
              to_replace = None
          else:
              regex = True
  
          result = df.replace(to_replace, value, inplace=inplace, regex=regex)
  
          if inplace:
              assert result is None
              result = df
  
          if value is np.nan:
              expected_replace_val = np.nan
          else:
              expected_replace_val = "..."
  
          expected.loc[expected["a"] == ".", "a"] = expected_replace_val
          tm.assert_frame_equal(result, expected)
  
      @pytest.mark.parametrize("regex", [False, True])
      def test_replace_regex_dtype_frame(self, regex):
          # GH-48644
          df1 = DataFrame({"A": ["0"], "B": ["0"]})
          expected_df1 = DataFrame({"A": [1], "B": [1]})
          result_df1 = df1.replace(to_replace="0", value=1, regex=regex)
          tm.assert_frame_equal(result_df1, expected_df1)
  
          df2 = DataFrame({"A": ["0"], "B": ["1"]})
          expected_df2 = DataFrame({"A": [1], "B": ["1"]})
          result_df2 = df2.replace(to_replace="0", value=1, regex=regex)
          tm.assert_frame_equal(result_df2, expected_df2)
  
      def test_replace_with_value_also_being_replaced(self):
          # GH46306
          df = DataFrame({"A": [0, 1, 2], "B": [1, 0, 2]})
          result = df.replace({0: 1, 1: np.nan})
          expected = DataFrame({"A": [1, np.nan, 2], "B": [np.nan, 1, 2]})
          tm.assert_frame_equal(result, expected)
  
apis: pandas.array
apis: numpy.ones pandas.Series pandas.Period pandas._testing.assert_frame_equal pandas.Categorical pandas._testing.assert_series_equal numpy.isscalar numpy.float64 pandas._testing.assert_produces_warning pandas._testing.assert_equal pandas.to_datetime numpy.random.rand pandas.Timestamp pandas._testing.get_obj numpy.int8 numpy.eye pandas.date_range numpy.float32 numpy.arange numpy.int16 pandas.DataFrame numpy.array pandas.Interval