from copy import deepcopy import numpy as np import pytest from pandas.errors import PerformanceWarning import pandas as pd from pandas import ( DataFrame, Index, MultiIndex, Series, concat, ) import pandas._testing as tm class TestIndexConcat: def test_concat_ignore_index(self, sort): frame1 = DataFrame( {"test1": ["a", "b", "c"], "test2": [1, 2, 3], "test3": [4.5, 3.2, 1.2]} ) frame2 = DataFrame({"test3": [5.2, 2.2, 4.3]}) frame1.index = Index(["x", "y", "z"]) frame2.index = Index(["x", "y", "q"]) v1 = concat([frame1, frame2], axis=1, ignore_index=True, sort=sort) nan = np.nan expected = DataFrame( [ [nan, nan, nan, 4.3], ["a", 1, 4.5, 5.2], ["b", 2, 3.2, 2.2], ["c", 3, 1.2, nan], ], index=Index(["q", "x", "y", "z"]), ) if not sort: expected = expected.loc[["x", "y", "z", "q"]] tm.assert_frame_equal(v1, expected) @pytest.mark.parametrize( "name_in1,name_in2,name_in3,name_out", [ ("idx", "idx", "idx", "idx"), ("idx", "idx", None, None), ("idx", None, None, None), ("idx1", "idx2", None, None), ("idx1", "idx1", "idx2", None), ("idx1", "idx2", "idx3", None), (None, None, None, None), ], ) def test_concat_same_index_names(self, name_in1, name_in2, name_in3, name_out): # GH13475 indices = [ Index(["a", "b", "c"], name=name_in1), Index(["b", "c", "d"], name=name_in2), Index(["c", "d", "e"], name=name_in3), ] frames = [ DataFrame({c: [0, 1, 2]}, index=i) for i, c in zip(indices, ["x", "y", "z"]) ] result = concat(frames, axis=1) exp_ind = Index(["a", "b", "c", "d", "e"], name=name_out) expected = DataFrame( { "x": [0, 1, 2, np.nan, np.nan], "y": [np.nan, 0, 1, 2, np.nan], "z": [np.nan, np.nan, 0, 1, 2], }, index=exp_ind, ) tm.assert_frame_equal(result, expected) def test_concat_rename_index(self): a = DataFrame( np.random.rand(3, 3), columns=list("ABC"), index=Index(list("abc"), name="index_a"), ) b = DataFrame( np.random.rand(3, 3), columns=list("ABC"), index=Index(list("abc"), name="index_b"), ) result = concat([a, b], keys=["key0", "key1"], names=["lvl0", "lvl1"]) exp = concat([a, b], keys=["key0", "key1"], names=["lvl0"]) names = list(exp.index.names) names[1] = "lvl1" exp.index.set_names(names, inplace=True) tm.assert_frame_equal(result, exp) assert result.index.names == exp.index.names def test_concat_copy_index_series(self, axis, using_copy_on_write): # GH 29879 ser = Series([1, 2]) comb = concat([ser, ser], axis=axis, copy=True) if not using_copy_on_write or axis in [0, "index"]: assert comb.index is not ser.index else: assert comb.index is ser.index def test_concat_copy_index_frame(self, axis, using_copy_on_write): # GH 29879 df = DataFrame([[1, 2], [3, 4]], columns=["a", "b"]) comb = concat([df, df], axis=axis, copy=True) if not using_copy_on_write: assert comb.index is not df.index assert comb.columns is not df.columns elif axis in [0, "index"]: assert comb.index is not df.index assert comb.columns is df.columns elif axis in [1, "columns"]: assert comb.index is df.index assert comb.columns is not df.columns def test_default_index(self): # is_series and ignore_index s1 = Series([1, 2, 3], name="x") s2 = Series([4, 5, 6], name="y") res = concat([s1, s2], axis=1, ignore_index=True) assert isinstance(res.columns, pd.RangeIndex) exp = DataFrame([[1, 4], [2, 5], [3, 6]]) # use check_index_type=True to check the result have # RangeIndex (default index) tm.assert_frame_equal(res, exp, check_index_type=True, check_column_type=True) # is_series and all inputs have no names s1 = Series([1, 2, 3]) s2 = Series([4, 5, 6]) res = concat([s1, s2], axis=1, ignore_index=False) assert isinstance(res.columns, pd.RangeIndex) exp = DataFrame([[1, 4], [2, 5], [3, 6]]) exp.columns = pd.RangeIndex(2) tm.assert_frame_equal(res, exp, check_index_type=True, check_column_type=True) # is_dataframe and ignore_index df1 = DataFrame({"A": [1, 2], "B": [5, 6]}) df2 = DataFrame({"A": [3, 4], "B": [7, 8]}) res = concat([df1, df2], axis=0, ignore_index=True) exp = DataFrame([[1, 5], [2, 6], [3, 7], [4, 8]], columns=["A", "B"]) tm.assert_frame_equal(res, exp, check_index_type=True, check_column_type=True) res = concat([df1, df2], axis=1, ignore_index=True) exp = DataFrame([[1, 5, 3, 7], [2, 6, 4, 8]]) tm.assert_frame_equal(res, exp, check_index_type=True, check_column_type=True) def test_dups_index(self): # GH 4771 # single dtypes df = DataFrame( np.random.randint(0, 10, size=40).reshape(10, 4), columns=["A", "A", "C", "C"], ) result = concat([df, df], axis=1) tm.assert_frame_equal(result.iloc[:, :4], df) tm.assert_frame_equal(result.iloc[:, 4:], df) result = concat([df, df], axis=0) tm.assert_frame_equal(result.iloc[:10], df) tm.assert_frame_equal(result.iloc[10:], df) # multi dtypes df = concat( [ DataFrame(np.random.randn(10, 4), columns=["A", "A", "B", "B"]), DataFrame( np.random.randint(0, 10, size=20).reshape(10, 2), columns=["A", "C"] ), ], axis=1, ) result = concat([df, df], axis=1) tm.assert_frame_equal(result.iloc[:, :6], df) tm.assert_frame_equal(result.iloc[:, 6:], df) result = concat([df, df], axis=0) tm.assert_frame_equal(result.iloc[:10], df) tm.assert_frame_equal(result.iloc[10:], df) # append result = df.iloc[0:8, :]._append(df.iloc[8:]) tm.assert_frame_equal(result, df) result = df.iloc[0:8, :]._append(df.iloc[8:9])._append(df.iloc[9:10]) tm.assert_frame_equal(result, df) expected = concat([df, df], axis=0) result = df._append(df) tm.assert_frame_equal(result, expected) class TestMultiIndexConcat: def test_concat_multiindex_with_keys(self, multiindex_dataframe_random_data): frame = multiindex_dataframe_random_data index = frame.index result = concat([frame, frame], keys=[0, 1], names=["iteration"]) assert result.index.names == ("iteration",) + index.names tm.assert_frame_equal(result.loc[0], frame) tm.assert_frame_equal(result.loc[1], frame) assert result.index.nlevels == 3 def test_concat_multiindex_with_none_in_index_names(self): # GH 15787 index = MultiIndex.from_product([[1], range(5)], names=["level1", None]) df = DataFrame({"col": range(5)}, index=index, dtype=np.int32) result = concat([df, df], keys=[1, 2], names=["level2"]) index = MultiIndex.from_product( [[1, 2], [1], range(5)], names=["level2", "level1", None] ) expected = DataFrame({"col": list(range(5)) * 2}, index=index, dtype=np.int32) tm.assert_frame_equal(result, expected) result = concat([df, df[:2]], keys=[1, 2], names=["level2"]) level2 = [1] * 5 + [2] * 2 level1 = [1] * 7 no_name = list(range(5)) + list(range(2)) tuples = list(zip(level2, level1, no_name)) index = MultiIndex.from_tuples(tuples, names=["level2", "level1", None]) expected = DataFrame({"col": no_name}, index=index, dtype=np.int32) tm.assert_frame_equal(result, expected) def test_concat_multiindex_rangeindex(self): # GH13542 # when multi-index levels are RangeIndex objects # there is a bug in concat with objects of len 1 df = DataFrame(np.random.randn(9, 2)) df.index = MultiIndex( levels=[pd.RangeIndex(3), pd.RangeIndex(3)], codes=[np.repeat(np.arange(3), 3), np.tile(np.arange(3), 3)], ) res = concat([df.iloc[[2, 3, 4], :], df.iloc[[5], :]]) exp = df.iloc[[2, 3, 4, 5], :] tm.assert_frame_equal(res, exp) def test_concat_multiindex_dfs_with_deepcopy(self): # GH 9967 example_multiindex1 = MultiIndex.from_product([["a"], ["b"]]) example_dataframe1 = DataFrame([0], index=example_multiindex1) example_multiindex2 = MultiIndex.from_product([["a"], ["c"]]) example_dataframe2 = DataFrame([1], index=example_multiindex2) example_dict = {"s1": example_dataframe1, "s2": example_dataframe2} expected_index = MultiIndex( levels=[["s1", "s2"], ["a"], ["b", "c"]], codes=[[0, 1], [0, 0], [0, 1]], names=["testname", None, None], ) expected = DataFrame([[0], [1]], index=expected_index) result_copy = concat(deepcopy(example_dict), names=["testname"]) tm.assert_frame_equal(result_copy, expected) result_no_copy = concat(example_dict, names=["testname"]) tm.assert_frame_equal(result_no_copy, expected) @pytest.mark.parametrize( "mi1_list", [ [["a"], range(2)], [["b"], np.arange(2.0, 4.0)], [["c"], ["A", "B"]], [["d"], pd.date_range(start="2017", end="2018", periods=2)], ], ) @pytest.mark.parametrize( "mi2_list", [ [["a"], range(2)], [["b"], np.arange(2.0, 4.0)], [["c"], ["A", "B"]], [["d"], pd.date_range(start="2017", end="2018", periods=2)], ], ) def test_concat_with_various_multiindex_dtypes( self, mi1_list: list, mi2_list: list ): # GitHub #23478 mi1 = MultiIndex.from_product(mi1_list) mi2 = MultiIndex.from_product(mi2_list) df1 = DataFrame(np.zeros((1, len(mi1))), columns=mi1) df2 = DataFrame(np.zeros((1, len(mi2))), columns=mi2) if mi1_list[0] == mi2_list[0]: expected_mi = MultiIndex( levels=[mi1_list[0], list(mi1_list[1])], codes=[[0, 0, 0, 0], [0, 1, 0, 1]], ) else: expected_mi = MultiIndex( levels=[ mi1_list[0] + mi2_list[0], list(mi1_list[1]) + list(mi2_list[1]), ], codes=[[0, 0, 1, 1], [0, 1, 2, 3]], ) expected_df = DataFrame(np.zeros((1, len(expected_mi))), columns=expected_mi) with tm.assert_produces_warning(None): result_df = concat((df1, df2), axis=1) tm.assert_frame_equal(expected_df, result_df) def test_concat_multiindex_(self): # GitHub #44786 df = DataFrame({"col": ["a", "b", "c"]}, index=["1", "2", "2"]) df = concat([df], keys=["X"]) iterables = [["X"], ["1", "2", "2"]] result_index = df.index expected_index = MultiIndex.from_product(iterables) tm.assert_index_equal(result_index, expected_index) result_df = df expected_df = DataFrame( {"col": ["a", "b", "c"]}, index=MultiIndex.from_product(iterables) ) tm.assert_frame_equal(result_df, expected_df) def test_concat_with_key_not_unique(self): # GitHub #46519 df1 = DataFrame({"name": [1]}) df2 = DataFrame({"name": [2]}) df3 = DataFrame({"name": [3]}) df_a = concat([df1, df2, df3], keys=["x", "y", "x"]) # the warning is caused by indexing unsorted multi-index with tm.assert_produces_warning( PerformanceWarning, match="indexing past lexsort depth" ): out_a = df_a.loc[("x", 0), :] df_b = DataFrame( {"name": [1, 2, 3]}, index=Index([("x", 0), ("y", 0), ("x", 0)]) ) with tm.assert_produces_warning( PerformanceWarning, match="indexing past lexsort depth" ): out_b = df_b.loc[("x", 0)] tm.assert_frame_equal(out_a, out_b) df1 = DataFrame({"name": ["a", "a", "b"]}) df2 = DataFrame({"name": ["a", "b"]}) df3 = DataFrame({"name": ["c", "d"]}) df_a = concat([df1, df2, df3], keys=["x", "y", "x"]) with tm.assert_produces_warning( PerformanceWarning, match="indexing past lexsort depth" ): out_a = df_a.loc[("x", 0), :] df_b = DataFrame( { "a": ["x", "x", "x", "y", "y", "x", "x"], "b": [0, 1, 2, 0, 1, 0, 1], "name": list("aababcd"), } ).set_index(["a", "b"]) df_b.index.names = [None, None] with tm.assert_produces_warning( PerformanceWarning, match="indexing past lexsort depth" ): out_b = df_b.loc[("x", 0), :] tm.assert_frame_equal(out_a, out_b) def test_concat_with_duplicated_levels(self): # keyword levels should be unique df1 = DataFrame({"A": [1]}, index=["x"]) df2 = DataFrame({"A": [1]}, index=["y"]) msg = r"Level values not unique: \['x', 'y', 'y'\]" with pytest.raises(ValueError, match=msg): concat([df1, df2], keys=["x", "y"], levels=[["x", "y", "y"]]) @pytest.mark.parametrize("levels", [[["x", "y"]], [["x", "y", "y"]]]) def test_concat_with_levels_with_none_keys(self, levels): df1 = DataFrame({"A": [1]}, index=["x"]) df2 = DataFrame({"A": [1]}, index=["y"]) msg = "levels supported only when keys is not None" with pytest.raises(ValueError, match=msg): concat([df1, df2], levels=levels) def test_concat_range_index_result(self): # GH#47501 df1 = DataFrame({"a": [1, 2]}) df2 = DataFrame({"b": [1, 2]}) result = concat([df1, df2], sort=True, axis=1) expected = DataFrame({"a": [1, 2], "b": [1, 2]}) tm.assert_frame_equal(result, expected) expected_index = pd.RangeIndex(0, 2) tm.assert_index_equal(result.index, expected_index, exact=True) def test_concat_index_keep_dtype(self): # GH#47329 df1 = DataFrame([[0, 1, 1]], columns=Index([1, 2, 3], dtype="object")) df2 = DataFrame([[0, 1]], columns=Index([1, 2], dtype="object")) result = concat([df1, df2], ignore_index=True, join="outer", sort=True) expected = DataFrame( [[0, 1, 1.0], [0, 1, np.nan]], columns=Index([1, 2, 3], dtype="object") ) tm.assert_frame_equal(result, expected) def test_concat_index_keep_dtype_ea_numeric(self, any_numeric_ea_dtype): # GH#47329 df1 = DataFrame( [[0, 1, 1]], columns=Index([1, 2, 3], dtype=any_numeric_ea_dtype) ) df2 = DataFrame([[0, 1]], columns=Index([1, 2], dtype=any_numeric_ea_dtype)) result = concat([df1, df2], ignore_index=True, join="outer", sort=True) expected = DataFrame( [[0, 1, 1.0], [0, 1, np.nan]], columns=Index([1, 2, 3], dtype=any_numeric_ea_dtype), ) tm.assert_frame_equal(result, expected) @pytest.mark.parametrize("dtype", ["Int8", "Int16", "Int32"]) def test_concat_index_find_common(self, dtype): # GH#47329 df1 = DataFrame([[0, 1, 1]], columns=Index([1, 2, 3], dtype=dtype)) df2 = DataFrame([[0, 1]], columns=Index([1, 2], dtype="Int32")) result = concat([df1, df2], ignore_index=True, join="outer", sort=True) expected = DataFrame( [[0, 1, 1.0], [0, 1, np.nan]], columns=Index([1, 2, 3], dtype="Int32") ) tm.assert_frame_equal(result, expected) def test_concat_axis_1_sort_false_rangeindex(self): # GH 46675 s1 = Series(["a", "b", "c"]) s2 = Series(["a", "b"]) s3 = Series(["a", "b", "c", "d"]) s4 = Series([], dtype=object) result = concat( [s1, s2, s3, s4], sort=False, join="outer", ignore_index=False, axis=1 ) expected = DataFrame( [ ["a"] * 3 + [np.nan], ["b"] * 3 + [np.nan], ["c", np.nan] * 2, [np.nan] * 2 + ["d"] + [np.nan], ], dtype=object, ) tm.assert_frame_equal( result, expected, check_index_type=True, check_column_type=True )