from __future__ import annotations from datetime import datetime import re import numpy as np import pytest 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.loc[datetime_frame.index[:5], "A"] = np.nan datetime_frame.loc[datetime_frame.index[-5:], "A"] = 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.loc[datetime_frame.index[:5], "A"] = np.nan datetime_frame.loc[datetime_frame.index[-5:], "A"] = 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.loc[datetime_frame.index[:5], "A"] = np.nan datetime_frame.loc[datetime_frame.index[-5:], "A"] = np.nan datetime_frame.loc[datetime_frame.index[:5], "B"] = -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.loc[tsframe.index[:5], "A"] = np.nan tsframe.loc[tsframe.index[-5:], "A"] = 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.loc[tsframe.index[:5], "A"] = np.nan tsframe.loc[tsframe.index[-5:], "A"] = np.nan tsframe.loc[tsframe.index[:5], "B"] = -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 = 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 rep, value in zip(to_rep, values): return_value = expected.replace(rep, value, 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 rep in to_rep: return_value = expected.replace(rep, -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) # pre-2.0 this would coerce to object with mismatched tzs result = df.copy() result.iloc[1, 0] = np.nan result = result.replace({"A": pd.NaT}, Timestamp("20130104", tz="US/Pacific")) expected = DataFrame( { "A": [ Timestamp("20130101", tz="US/Eastern"), 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 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) @pytest.mark.parametrize("val", [2, np.nan, 2.0]) def test_replace_value_none_dtype_numeric(self, val): # GH#48231 df = DataFrame({"a": [1, val]}) result = df.replace(val, None) expected = DataFrame({"a": [1, None]}, dtype=object) tm.assert_frame_equal(result, expected) df = DataFrame({"a": [1, val]}) result = df.replace({val: None}) tm.assert_frame_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) def test_replace_categorical_no_replacement(self): # GH#46672 df = DataFrame( { "a": ["one", "two", None, "three"], "b": ["one", None, "two", "three"], }, dtype="category", ) expected = df.copy() result = df.replace(to_replace=[".", "def"], value=["_", None]) tm.assert_frame_equal(result, expected)