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"""\nTests the usecols functionality during parsing\nfor all of the parsers defined in parsers.py\n"""\nfrom io import StringIO\n\nimport pytest\n\nfrom pandas import DataFrame\nimport pandas._testing as tm\n\npytestmark = pytest.mark.filterwarnings(\n "ignore:Passing a BlockManager to DataFrame:DeprecationWarning"\n)\n\n\ndef test_usecols_with_unicode_strings(all_parsers):\n # see gh-13219\n data = """AAA,BBB,CCC,DDD\n0.056674973,8,True,a\n2.613230982,2,False,b\n3.568935038,7,False,a"""\n parser = all_parsers\n\n exp_data = {\n "AAA": {\n 0: 0.056674972999999997,\n 1: 2.6132309819999997,\n 2: 3.5689350380000002,\n },\n "BBB": {0: 8, 1: 2, 2: 7},\n }\n expected = DataFrame(exp_data)\n\n result = parser.read_csv(StringIO(data), usecols=["AAA", "BBB"])\n tm.assert_frame_equal(result, expected)\n\n\ndef test_usecols_with_single_byte_unicode_strings(all_parsers):\n # see gh-13219\n data = """A,B,C,D\n0.056674973,8,True,a\n2.613230982,2,False,b\n3.568935038,7,False,a"""\n parser = all_parsers\n\n exp_data = {\n "A": {\n 0: 0.056674972999999997,\n 1: 2.6132309819999997,\n 2: 3.5689350380000002,\n },\n "B": {0: 8, 1: 2, 2: 7},\n }\n expected = DataFrame(exp_data)\n\n result = parser.read_csv(StringIO(data), usecols=["A", "B"])\n tm.assert_frame_equal(result, expected)\n\n\n@pytest.mark.parametrize("usecols", [["AAA", b"BBB"], [b"AAA", "BBB"]])\ndef test_usecols_with_mixed_encoding_strings(all_parsers, usecols):\n data = """AAA,BBB,CCC,DDD\n0.056674973,8,True,a\n2.613230982,2,False,b\n3.568935038,7,False,a"""\n parser = all_parsers\n _msg_validate_usecols_arg = (\n "'usecols' must either be list-like "\n "of all strings, all unicode, all "\n "integers or a callable."\n )\n with pytest.raises(ValueError, match=_msg_validate_usecols_arg):\n parser.read_csv(StringIO(data), usecols=usecols)\n\n\n@pytest.mark.parametrize("usecols", [["あああ", "いい"], ["あああ", "いい"]])\ndef test_usecols_with_multi_byte_characters(all_parsers, usecols):\n data = """あああ,いい,ううう,ええええ\n0.056674973,8,True,a\n2.613230982,2,False,b\n3.568935038,7,False,a"""\n parser = all_parsers\n\n exp_data = {\n "あああ": {\n 0: 0.056674972999999997,\n 1: 2.6132309819999997,\n 2: 3.5689350380000002,\n },\n "いい": {0: 8, 1: 2, 2: 7},\n }\n expected = DataFrame(exp_data)\n\n result = parser.read_csv(StringIO(data), usecols=usecols)\n tm.assert_frame_equal(result, expected)\n | .venv\Lib\site-packages\pandas\tests\io\parser\usecols\test_strings.py | test_strings.py | Python | 2,588 | 0.95 | 0.052083 | 0.025316 | awesome-app | 907 | 2024-05-23T17:04:17.232111 | GPL-3.0 | true | ec6c6230f4894e9e6fd933722c046fff |
"""\nTests the usecols functionality during parsing\nfor all of the parsers defined in parsers.py\n"""\nfrom io import StringIO\n\nimport numpy as np\nimport pytest\n\nfrom pandas.errors import ParserError\n\nfrom pandas import (\n DataFrame,\n Index,\n array,\n)\nimport pandas._testing as tm\n\npytestmark = pytest.mark.filterwarnings(\n "ignore:Passing a BlockManager to DataFrame:DeprecationWarning"\n)\n\n_msg_validate_usecols_arg = (\n "'usecols' must either be list-like "\n "of all strings, all unicode, all "\n "integers or a callable."\n)\n_msg_validate_usecols_names = (\n "Usecols do not match columns, columns expected but not found: {0}"\n)\n_msg_pyarrow_requires_names = (\n "The pyarrow engine does not allow 'usecols' to be integer column "\n "positions. Pass a list of string column names instead."\n)\n\nxfail_pyarrow = pytest.mark.usefixtures("pyarrow_xfail")\nskip_pyarrow = pytest.mark.usefixtures("pyarrow_skip")\n\npytestmark = pytest.mark.filterwarnings(\n "ignore:Passing a BlockManager to DataFrame is deprecated:DeprecationWarning"\n)\n\n\ndef test_raise_on_mixed_dtype_usecols(all_parsers):\n # See gh-12678\n data = """a,b,c\n 1000,2000,3000\n 4000,5000,6000\n """\n usecols = [0, "b", 2]\n parser = all_parsers\n\n with pytest.raises(ValueError, match=_msg_validate_usecols_arg):\n parser.read_csv(StringIO(data), usecols=usecols)\n\n\n@pytest.mark.parametrize("usecols", [(1, 2), ("b", "c")])\ndef test_usecols(all_parsers, usecols, request):\n data = """\\na,b,c\n1,2,3\n4,5,6\n7,8,9\n10,11,12"""\n parser = all_parsers\n if parser.engine == "pyarrow" and isinstance(usecols[0], int):\n with pytest.raises(ValueError, match=_msg_pyarrow_requires_names):\n parser.read_csv(StringIO(data), usecols=usecols)\n return\n\n result = parser.read_csv(StringIO(data), usecols=usecols)\n\n expected = DataFrame([[2, 3], [5, 6], [8, 9], [11, 12]], columns=["b", "c"])\n tm.assert_frame_equal(result, expected)\n\n\ndef test_usecols_with_names(all_parsers):\n data = """\\na,b,c\n1,2,3\n4,5,6\n7,8,9\n10,11,12"""\n parser = all_parsers\n names = ["foo", "bar"]\n\n if parser.engine == "pyarrow":\n with pytest.raises(ValueError, match=_msg_pyarrow_requires_names):\n parser.read_csv(StringIO(data), names=names, usecols=[1, 2], header=0)\n return\n\n result = parser.read_csv(StringIO(data), names=names, usecols=[1, 2], header=0)\n\n expected = DataFrame([[2, 3], [5, 6], [8, 9], [11, 12]], columns=names)\n tm.assert_frame_equal(result, expected)\n\n\n@pytest.mark.parametrize(\n "names,usecols", [(["b", "c"], [1, 2]), (["a", "b", "c"], ["b", "c"])]\n)\ndef test_usecols_relative_to_names(all_parsers, names, usecols):\n data = """\\n1,2,3\n4,5,6\n7,8,9\n10,11,12"""\n parser = all_parsers\n if parser.engine == "pyarrow" and not isinstance(usecols[0], int):\n # ArrowKeyError: Column 'fb' in include_columns does not exist\n pytest.skip(reason="https://github.com/apache/arrow/issues/38676")\n\n result = parser.read_csv(StringIO(data), names=names, header=None, usecols=usecols)\n\n expected = DataFrame([[2, 3], [5, 6], [8, 9], [11, 12]], columns=["b", "c"])\n tm.assert_frame_equal(result, expected)\n\n\ndef test_usecols_relative_to_names2(all_parsers):\n # see gh-5766\n data = """\\n1,2,3\n4,5,6\n7,8,9\n10,11,12"""\n parser = all_parsers\n\n result = parser.read_csv(\n StringIO(data), names=["a", "b"], header=None, usecols=[0, 1]\n )\n\n expected = DataFrame([[1, 2], [4, 5], [7, 8], [10, 11]], columns=["a", "b"])\n tm.assert_frame_equal(result, expected)\n\n\n# regex mismatch: "Length mismatch: Expected axis has 1 elements"\n@xfail_pyarrow\ndef test_usecols_name_length_conflict(all_parsers):\n data = """\\n1,2,3\n4,5,6\n7,8,9\n10,11,12"""\n parser = all_parsers\n msg = "Number of passed names did not match number of header fields in the file"\n with pytest.raises(ValueError, match=msg):\n parser.read_csv(StringIO(data), names=["a", "b"], header=None, usecols=[1])\n\n\ndef test_usecols_single_string(all_parsers):\n # see gh-20558\n parser = all_parsers\n data = """foo, bar, baz\n1000, 2000, 3000\n4000, 5000, 6000"""\n\n with pytest.raises(ValueError, match=_msg_validate_usecols_arg):\n parser.read_csv(StringIO(data), usecols="foo")\n\n\n@skip_pyarrow # CSV parse error in one case, AttributeError in another\n@pytest.mark.parametrize(\n "data", ["a,b,c,d\n1,2,3,4\n5,6,7,8", "a,b,c,d\n1,2,3,4,\n5,6,7,8,"]\n)\ndef test_usecols_index_col_false(all_parsers, data):\n # see gh-9082\n parser = all_parsers\n usecols = ["a", "c", "d"]\n expected = DataFrame({"a": [1, 5], "c": [3, 7], "d": [4, 8]})\n\n result = parser.read_csv(StringIO(data), usecols=usecols, index_col=False)\n tm.assert_frame_equal(result, expected)\n\n\n@pytest.mark.parametrize("index_col", ["b", 0])\n@pytest.mark.parametrize("usecols", [["b", "c"], [1, 2]])\ndef test_usecols_index_col_conflict(all_parsers, usecols, index_col, request):\n # see gh-4201: test that index_col as integer reflects usecols\n parser = all_parsers\n data = "a,b,c,d\nA,a,1,one\nB,b,2,two"\n\n if parser.engine == "pyarrow" and isinstance(usecols[0], int):\n with pytest.raises(ValueError, match=_msg_pyarrow_requires_names):\n parser.read_csv(StringIO(data), usecols=usecols, index_col=index_col)\n return\n\n expected = DataFrame({"c": [1, 2]}, index=Index(["a", "b"], name="b"))\n\n result = parser.read_csv(StringIO(data), usecols=usecols, index_col=index_col)\n tm.assert_frame_equal(result, expected)\n\n\ndef test_usecols_index_col_conflict2(all_parsers):\n # see gh-4201: test that index_col as integer reflects usecols\n parser = all_parsers\n data = "a,b,c,d\nA,a,1,one\nB,b,2,two"\n\n expected = DataFrame({"b": ["a", "b"], "c": [1, 2], "d": ("one", "two")})\n expected = expected.set_index(["b", "c"])\n\n result = parser.read_csv(\n StringIO(data), usecols=["b", "c", "d"], index_col=["b", "c"]\n )\n tm.assert_frame_equal(result, expected)\n\n\n@skip_pyarrow # CSV parse error: Expected 3 columns, got 4\ndef test_usecols_implicit_index_col(all_parsers):\n # see gh-2654\n parser = all_parsers\n data = "a,b,c\n4,apple,bat,5.7\n8,orange,cow,10"\n\n result = parser.read_csv(StringIO(data), usecols=["a", "b"])\n expected = DataFrame({"a": ["apple", "orange"], "b": ["bat", "cow"]}, index=[4, 8])\n tm.assert_frame_equal(result, expected)\n\n\ndef test_usecols_index_col_middle(all_parsers):\n # GH#9098\n parser = all_parsers\n data = """a,b,c,d\n1,2,3,4\n"""\n result = parser.read_csv(StringIO(data), usecols=["b", "c", "d"], index_col="c")\n expected = DataFrame({"b": [2], "d": [4]}, index=Index([3], name="c"))\n tm.assert_frame_equal(result, expected)\n\n\ndef test_usecols_index_col_end(all_parsers):\n # GH#9098\n parser = all_parsers\n data = """a,b,c,d\n1,2,3,4\n"""\n result = parser.read_csv(StringIO(data), usecols=["b", "c", "d"], index_col="d")\n expected = DataFrame({"b": [2], "c": [3]}, index=Index([4], name="d"))\n tm.assert_frame_equal(result, expected)\n\n\ndef test_usecols_regex_sep(all_parsers):\n # see gh-2733\n parser = all_parsers\n data = "a b c\n4 apple bat 5.7\n8 orange cow 10"\n\n if parser.engine == "pyarrow":\n msg = "the 'pyarrow' engine does not support regex separators"\n with pytest.raises(ValueError, match=msg):\n parser.read_csv(StringIO(data), sep=r"\s+", usecols=("a", "b"))\n return\n\n result = parser.read_csv(StringIO(data), sep=r"\s+", usecols=("a", "b"))\n\n expected = DataFrame({"a": ["apple", "orange"], "b": ["bat", "cow"]}, index=[4, 8])\n tm.assert_frame_equal(result, expected)\n\n\ndef test_usecols_with_whitespace(all_parsers):\n parser = all_parsers\n data = "a b c\n4 apple bat 5.7\n8 orange cow 10"\n\n depr_msg = "The 'delim_whitespace' keyword in pd.read_csv is deprecated"\n\n if parser.engine == "pyarrow":\n msg = "The 'delim_whitespace' option is not supported with the 'pyarrow' engine"\n with pytest.raises(ValueError, match=msg):\n with tm.assert_produces_warning(\n FutureWarning, match=depr_msg, check_stacklevel=False\n ):\n parser.read_csv(\n StringIO(data), delim_whitespace=True, usecols=("a", "b")\n )\n return\n\n with tm.assert_produces_warning(\n FutureWarning, match=depr_msg, check_stacklevel=False\n ):\n result = parser.read_csv(\n StringIO(data), delim_whitespace=True, usecols=("a", "b")\n )\n expected = DataFrame({"a": ["apple", "orange"], "b": ["bat", "cow"]}, index=[4, 8])\n tm.assert_frame_equal(result, expected)\n\n\n@pytest.mark.parametrize(\n "usecols,expected",\n [\n # Column selection by index.\n ([0, 1], DataFrame(data=[[1000, 2000], [4000, 5000]], columns=["2", "0"])),\n # Column selection by name.\n (\n ["0", "1"],\n DataFrame(data=[[2000, 3000], [5000, 6000]], columns=["0", "1"]),\n ),\n ],\n)\ndef test_usecols_with_integer_like_header(all_parsers, usecols, expected, request):\n parser = all_parsers\n data = """2,0,1\n1000,2000,3000\n4000,5000,6000"""\n\n if parser.engine == "pyarrow" and isinstance(usecols[0], int):\n with pytest.raises(ValueError, match=_msg_pyarrow_requires_names):\n parser.read_csv(StringIO(data), usecols=usecols)\n return\n\n result = parser.read_csv(StringIO(data), usecols=usecols)\n tm.assert_frame_equal(result, expected)\n\n\n@xfail_pyarrow # mismatched shape\ndef test_empty_usecols(all_parsers):\n data = "a,b,c\n1,2,3\n4,5,6"\n expected = DataFrame(columns=Index([]))\n parser = all_parsers\n\n result = parser.read_csv(StringIO(data), usecols=set())\n tm.assert_frame_equal(result, expected)\n\n\ndef test_np_array_usecols(all_parsers):\n # see gh-12546\n parser = all_parsers\n data = "a,b,c\n1,2,3"\n usecols = np.array(["a", "b"])\n\n expected = DataFrame([[1, 2]], columns=usecols)\n result = parser.read_csv(StringIO(data), usecols=usecols)\n tm.assert_frame_equal(result, expected)\n\n\n@pytest.mark.parametrize(\n "usecols,expected",\n [\n (\n lambda x: x.upper() in ["AAA", "BBB", "DDD"],\n DataFrame(\n {\n "AaA": {\n 0: 0.056674972999999997,\n 1: 2.6132309819999997,\n 2: 3.5689350380000002,\n },\n "bBb": {0: 8, 1: 2, 2: 7},\n "ddd": {0: "a", 1: "b", 2: "a"},\n }\n ),\n ),\n (lambda x: False, DataFrame(columns=Index([]))),\n ],\n)\ndef test_callable_usecols(all_parsers, usecols, expected):\n # see gh-14154\n data = """AaA,bBb,CCC,ddd\n0.056674973,8,True,a\n2.613230982,2,False,b\n3.568935038,7,False,a"""\n parser = all_parsers\n\n if parser.engine == "pyarrow":\n msg = "The pyarrow engine does not allow 'usecols' to be a callable"\n with pytest.raises(ValueError, match=msg):\n parser.read_csv(StringIO(data), usecols=usecols)\n return\n\n result = parser.read_csv(StringIO(data), usecols=usecols)\n tm.assert_frame_equal(result, expected)\n\n\n# ArrowKeyError: Column 'fa' in include_columns does not exist in CSV file\n@skip_pyarrow\n@pytest.mark.parametrize("usecols", [["a", "c"], lambda x: x in ["a", "c"]])\ndef test_incomplete_first_row(all_parsers, usecols):\n # see gh-6710\n data = "1,2\n1,2,3"\n parser = all_parsers\n names = ["a", "b", "c"]\n expected = DataFrame({"a": [1, 1], "c": [np.nan, 3]})\n\n result = parser.read_csv(StringIO(data), names=names, usecols=usecols)\n tm.assert_frame_equal(result, expected)\n\n\n@skip_pyarrow # CSV parse error: Expected 3 columns, got 4\n@pytest.mark.parametrize(\n "data,usecols,kwargs,expected",\n [\n # see gh-8985\n (\n "19,29,39\n" * 2 + "10,20,30,40",\n [0, 1, 2],\n {"header": None},\n DataFrame([[19, 29, 39], [19, 29, 39], [10, 20, 30]]),\n ),\n # see gh-9549\n (\n ("A,B,C\n1,2,3\n3,4,5\n1,2,4,5,1,6\n1,2,3,,,1,\n1,2,3\n5,6,7"),\n ["A", "B", "C"],\n {},\n DataFrame(\n {\n "A": [1, 3, 1, 1, 1, 5],\n "B": [2, 4, 2, 2, 2, 6],\n "C": [3, 5, 4, 3, 3, 7],\n }\n ),\n ),\n ],\n)\ndef test_uneven_length_cols(all_parsers, data, usecols, kwargs, expected):\n # see gh-8985\n parser = all_parsers\n result = parser.read_csv(StringIO(data), usecols=usecols, **kwargs)\n tm.assert_frame_equal(result, expected)\n\n\n@pytest.mark.parametrize(\n "usecols,kwargs,expected,msg",\n [\n (\n ["a", "b", "c", "d"],\n {},\n DataFrame({"a": [1, 5], "b": [2, 6], "c": [3, 7], "d": [4, 8]}),\n None,\n ),\n (\n ["a", "b", "c", "f"],\n {},\n None,\n _msg_validate_usecols_names.format(r"\['f'\]"),\n ),\n (["a", "b", "f"], {}, None, _msg_validate_usecols_names.format(r"\['f'\]")),\n (\n ["a", "b", "f", "g"],\n {},\n None,\n _msg_validate_usecols_names.format(r"\[('f', 'g'|'g', 'f')\]"),\n ),\n # see gh-14671\n (\n None,\n {"header": 0, "names": ["A", "B", "C", "D"]},\n DataFrame({"A": [1, 5], "B": [2, 6], "C": [3, 7], "D": [4, 8]}),\n None,\n ),\n (\n ["A", "B", "C", "f"],\n {"header": 0, "names": ["A", "B", "C", "D"]},\n None,\n _msg_validate_usecols_names.format(r"\['f'\]"),\n ),\n (\n ["A", "B", "f"],\n {"names": ["A", "B", "C", "D"]},\n None,\n _msg_validate_usecols_names.format(r"\['f'\]"),\n ),\n ],\n)\ndef test_raises_on_usecols_names_mismatch(\n all_parsers, usecols, kwargs, expected, msg, request\n):\n data = "a,b,c,d\n1,2,3,4\n5,6,7,8"\n kwargs.update(usecols=usecols)\n parser = all_parsers\n\n if parser.engine == "pyarrow" and not (\n usecols is not None and expected is not None\n ):\n # everything but the first case\n # ArrowKeyError: Column 'f' in include_columns does not exist in CSV file\n pytest.skip(reason="https://github.com/apache/arrow/issues/38676")\n\n if expected is None:\n with pytest.raises(ValueError, match=msg):\n parser.read_csv(StringIO(data), **kwargs)\n else:\n result = parser.read_csv(StringIO(data), **kwargs)\n tm.assert_frame_equal(result, expected)\n\n\n@pytest.mark.parametrize("usecols", [["A", "C"], [0, 2]])\ndef test_usecols_subset_names_mismatch_orig_columns(all_parsers, usecols, request):\n data = "a,b,c,d\n1,2,3,4\n5,6,7,8"\n names = ["A", "B", "C", "D"]\n parser = all_parsers\n\n if parser.engine == "pyarrow":\n if isinstance(usecols[0], int):\n with pytest.raises(ValueError, match=_msg_pyarrow_requires_names):\n parser.read_csv(StringIO(data), header=0, names=names, usecols=usecols)\n return\n # "pyarrow.lib.ArrowKeyError: Column 'A' in include_columns does not exist"\n pytest.skip(reason="https://github.com/apache/arrow/issues/38676")\n\n result = parser.read_csv(StringIO(data), header=0, names=names, usecols=usecols)\n expected = DataFrame({"A": [1, 5], "C": [3, 7]})\n tm.assert_frame_equal(result, expected)\n\n\n@pytest.mark.parametrize("names", [None, ["a", "b"]])\ndef test_usecols_indices_out_of_bounds(all_parsers, names):\n # GH#25623 & GH 41130; enforced in 2.0\n parser = all_parsers\n data = """\na,b\n1,2\n """\n\n err = ParserError\n msg = "Defining usecols with out-of-bounds"\n if parser.engine == "pyarrow":\n err = ValueError\n msg = _msg_pyarrow_requires_names\n\n with pytest.raises(err, match=msg):\n parser.read_csv(StringIO(data), usecols=[0, 2], names=names, header=0)\n\n\ndef test_usecols_additional_columns(all_parsers):\n # GH#46997\n parser = all_parsers\n usecols = lambda header: header.strip() in ["a", "b", "c"]\n\n if parser.engine == "pyarrow":\n msg = "The pyarrow engine does not allow 'usecols' to be a callable"\n with pytest.raises(ValueError, match=msg):\n parser.read_csv(StringIO("a,b\nx,y,z"), index_col=False, usecols=usecols)\n return\n result = parser.read_csv(StringIO("a,b\nx,y,z"), index_col=False, usecols=usecols)\n expected = DataFrame({"a": ["x"], "b": "y"})\n tm.assert_frame_equal(result, expected)\n\n\ndef test_usecols_additional_columns_integer_columns(all_parsers):\n # GH#46997\n parser = all_parsers\n usecols = lambda header: header.strip() in ["0", "1"]\n if parser.engine == "pyarrow":\n msg = "The pyarrow engine does not allow 'usecols' to be a callable"\n with pytest.raises(ValueError, match=msg):\n parser.read_csv(StringIO("0,1\nx,y,z"), index_col=False, usecols=usecols)\n return\n result = parser.read_csv(StringIO("0,1\nx,y,z"), index_col=False, usecols=usecols)\n expected = DataFrame({"0": ["x"], "1": "y"})\n tm.assert_frame_equal(result, expected)\n\n\ndef test_usecols_dtype(all_parsers):\n parser = all_parsers\n data = """\ncol1,col2,col3\na,1,x\nb,2,y\n"""\n result = parser.read_csv(\n StringIO(data),\n usecols=["col1", "col2"],\n dtype={"col1": "string", "col2": "uint8", "col3": "string"},\n )\n expected = DataFrame(\n {"col1": array(["a", "b"]), "col2": np.array([1, 2], dtype="uint8")}\n )\n tm.assert_frame_equal(result, expected)\n | .venv\Lib\site-packages\pandas\tests\io\parser\usecols\test_usecols_basic.py | test_usecols_basic.py | Python | 17,646 | 0.95 | 0.076377 | 0.060345 | vue-tools | 592 | 2024-05-15T19:14:47.153316 | GPL-3.0 | true | 3414431c4abd1ed4fb61b2b1660a024c |
\n\n | .venv\Lib\site-packages\pandas\tests\io\parser\usecols\__pycache__\test_parse_dates.cpython-313.pyc | test_parse_dates.cpython-313.pyc | Other | 6,901 | 0.95 | 0.027027 | 0.018349 | node-utils | 368 | 2024-11-26T13:50:26.930625 | BSD-3-Clause | true | ad6edb84dcf8c20477f6a2593d4fcfe2 |
\n\n | .venv\Lib\site-packages\pandas\tests\io\parser\usecols\__pycache__\test_strings.cpython-313.pyc | test_strings.cpython-313.pyc | Other | 3,289 | 0.95 | 0.018182 | 0 | node-utils | 21 | 2025-04-14T06:10:07.079740 | MIT | true | a6486da6915c9f14dfc98622559f7a00 |
\n\n | .venv\Lib\site-packages\pandas\tests\io\parser\usecols\__pycache__\test_usecols_basic.cpython-313.pyc | test_usecols_basic.cpython-313.pyc | Other | 23,220 | 0.95 | 0.003125 | 0 | node-utils | 541 | 2024-10-25T23:50:34.591514 | BSD-3-Clause | true | b4da403f18c1b7c39b673397590c3e02 |
\n\n | .venv\Lib\site-packages\pandas\tests\io\parser\usecols\__pycache__\__init__.cpython-313.pyc | __init__.cpython-313.pyc | Other | 205 | 0.7 | 0 | 0 | vue-tools | 14 | 2025-01-06T04:31:39.585758 | GPL-3.0 | true | 9a19713c2b07a41fb2c102d02dc1c9c5 |
\n\n | .venv\Lib\site-packages\pandas\tests\io\parser\__pycache__\conftest.cpython-313.pyc | conftest.cpython-313.pyc | Other | 11,290 | 0.95 | 0.104348 | 0 | react-lib | 397 | 2025-01-18T10:55:08.477019 | BSD-3-Clause | true | 6e76836e45245d2584e319255dd30c06 |
\n\n | .venv\Lib\site-packages\pandas\tests\io\parser\__pycache__\test_comment.cpython-313.pyc | test_comment.cpython-313.pyc | Other | 9,935 | 0.8 | 0.006211 | 0.0375 | python-kit | 38 | 2023-11-19T18:23:37.941954 | MIT | true | 3f96b044d61ebacf86e9db31698a98c5 |
\n\n | .venv\Lib\site-packages\pandas\tests\io\parser\__pycache__\test_compression.cpython-313.pyc | test_compression.cpython-313.pyc | Other | 10,787 | 0.95 | 0.00625 | 0.012903 | vue-tools | 330 | 2023-08-22T05:14:47.657492 | MIT | true | d60aa8a33e81dfc8063b0048745d6b75 |
\n\n | .venv\Lib\site-packages\pandas\tests\io\parser\__pycache__\test_concatenate_chunks.cpython-313.pyc | test_concatenate_chunks.cpython-313.pyc | Other | 2,205 | 0.95 | 0 | 0.055556 | awesome-app | 353 | 2024-08-05T22:39:17.398643 | GPL-3.0 | true | 3b77db8f81ec0e20c05001b8c1526ea7 |
\n\n | .venv\Lib\site-packages\pandas\tests\io\parser\__pycache__\test_converters.cpython-313.pyc | test_converters.cpython-313.pyc | Other | 10,879 | 0.95 | 0.00641 | 0 | react-lib | 647 | 2024-12-08T11:45:56.290668 | Apache-2.0 | true | a19133d0fac49f90601a64d0cfce0e35 |
\n\n | .venv\Lib\site-packages\pandas\tests\io\parser\__pycache__\test_c_parser_only.cpython-313.pyc | test_c_parser_only.cpython-313.pyc | Other | 26,353 | 0.8 | 0.007937 | 0.010753 | react-lib | 271 | 2024-04-07T01:26:54.605213 | GPL-3.0 | true | 80a9614c995c321f33bbd5c0bf09e305 |
\n\n | .venv\Lib\site-packages\pandas\tests\io\parser\__pycache__\test_dialect.cpython-313.pyc | test_dialect.cpython-313.pyc | Other | 7,089 | 0.8 | 0.02381 | 0 | python-kit | 171 | 2024-04-06T11:26:57.656837 | GPL-3.0 | true | 47ba82e4a314dc89f53bcf1d16d160ff |
\n\n | .venv\Lib\site-packages\pandas\tests\io\parser\__pycache__\test_encoding.cpython-313.pyc | test_encoding.cpython-313.pyc | Other | 15,528 | 0.95 | 0.004386 | 0 | awesome-app | 290 | 2024-01-12T17:32:37.877152 | Apache-2.0 | true | 7253e5bc891f25377126c845b500deef |
\n\n | .venv\Lib\site-packages\pandas\tests\io\parser\__pycache__\test_header.cpython-313.pyc | test_header.cpython-313.pyc | Other | 24,818 | 0.8 | 0.002907 | 0 | node-utils | 410 | 2024-04-18T20:43:11.735088 | Apache-2.0 | true | fde49a210f0f4aebe426520091d3bf3d |
\n\n | .venv\Lib\site-packages\pandas\tests\io\parser\__pycache__\test_index_col.cpython-313.pyc | test_index_col.cpython-313.pyc | Other | 15,123 | 0.8 | 0.009756 | 0.00495 | python-kit | 377 | 2024-01-11T15:27:23.349929 | BSD-3-Clause | true | 3af20c4bc43d96e4bb9282335541f0be |
\n\n | .venv\Lib\site-packages\pandas\tests\io\parser\__pycache__\test_mangle_dupes.cpython-313.pyc | test_mangle_dupes.cpython-313.pyc | Other | 6,767 | 0.8 | 0.010526 | 0 | python-kit | 829 | 2025-07-09T06:26:38.196589 | BSD-3-Clause | true | 54688ec1d8f17d1452016bca14529a2a |
\n\n | .venv\Lib\site-packages\pandas\tests\io\parser\__pycache__\test_multi_thread.cpython-313.pyc | test_multi_thread.cpython-313.pyc | Other | 6,435 | 0.95 | 0.054545 | 0.019608 | python-kit | 747 | 2024-03-23T06:14:38.209164 | MIT | true | 0f4bf2026578b6afc2c581a87a1d3a8d |
\n\n | .venv\Lib\site-packages\pandas\tests\io\parser\__pycache__\test_na_values.cpython-313.pyc | test_na_values.cpython-313.pyc | Other | 28,670 | 0.95 | 0.009732 | 0.00495 | python-kit | 537 | 2023-11-30T15:07:41.953871 | BSD-3-Clause | true | 244191e894b4d66541a9c41fe2e85776 |
\n\n | .venv\Lib\site-packages\pandas\tests\io\parser\__pycache__\test_network.cpython-313.pyc | test_network.cpython-313.pyc | Other | 15,782 | 0.95 | 0.007092 | 0.007246 | react-lib | 870 | 2024-02-15T00:00:59.375880 | GPL-3.0 | true | b04cd6fc310fd3fc13e5d80369c045c6 |
\n\n | .venv\Lib\site-packages\pandas\tests\io\parser\__pycache__\test_parse_dates.cpython-313.pyc | test_parse_dates.cpython-313.pyc | Other | 69,550 | 0.75 | 0.004608 | 0.014085 | python-kit | 116 | 2024-06-12T22:08:40.497000 | GPL-3.0 | true | 313d58c555a2588a55a6bb185378951c |
\n\n | .venv\Lib\site-packages\pandas\tests\io\parser\__pycache__\test_python_parser_only.cpython-313.pyc | test_python_parser_only.cpython-313.pyc | Other | 22,885 | 0.95 | 0.003096 | 0.015674 | react-lib | 406 | 2025-01-09T09:39:56.913174 | BSD-3-Clause | true | 4cbe854a08ad0e3eb1ebbb1a940391ea |
\n\n | .venv\Lib\site-packages\pandas\tests\io\parser\__pycache__\test_quoting.cpython-313.pyc | test_quoting.cpython-313.pyc | Other | 8,224 | 0.8 | 0.021505 | 0 | awesome-app | 655 | 2023-09-02T05:07:50.512479 | BSD-3-Clause | true | 0060a36ec6131c92fc5bd9bcb8bafc0b |
\n\n | .venv\Lib\site-packages\pandas\tests\io\parser\__pycache__\test_read_fwf.cpython-313.pyc | test_read_fwf.cpython-313.pyc | Other | 35,204 | 0.95 | 0.001996 | 0.00211 | python-kit | 803 | 2023-09-02T15:40:10.105528 | MIT | true | 51a3d10546a0cf82ed0645de26046412 |
\n\n | .venv\Lib\site-packages\pandas\tests\io\parser\__pycache__\test_skiprows.cpython-313.pyc | test_skiprows.cpython-313.pyc | Other | 12,706 | 0.8 | 0.004425 | 0.045662 | awesome-app | 216 | 2025-01-20T02:58:45.178852 | GPL-3.0 | true | 78584ce2653b4eeba0c5a2960cb98418 |
\n\n | .venv\Lib\site-packages\pandas\tests\io\parser\__pycache__\test_textreader.cpython-313.pyc | test_textreader.cpython-313.pyc | Other | 17,538 | 0.95 | 0.004566 | 0.004673 | python-kit | 69 | 2024-01-14T05:41:07.498336 | BSD-3-Clause | true | accff1ba9c80e4b6c6e4199383c615c0 |
\n\n | .venv\Lib\site-packages\pandas\tests\io\parser\__pycache__\test_unsupported.cpython-313.pyc | test_unsupported.cpython-313.pyc | Other | 13,104 | 0.95 | 0.009709 | 0 | react-lib | 429 | 2023-11-14T22:02:22.696119 | GPL-3.0 | true | 2444e9897ea78b7a9f9cd2630ea1d63d |
\n\n | .venv\Lib\site-packages\pandas\tests\io\parser\__pycache__\test_upcast.cpython-313.pyc | test_upcast.cpython-313.pyc | Other | 4,860 | 0.95 | 0 | 0 | node-utils | 109 | 2024-08-13T07:02:36.470700 | MIT | true | 45be5c31aee22fec7b0f1ad649e96492 |
\n\n | .venv\Lib\site-packages\pandas\tests\io\parser\__pycache__\__init__.cpython-313.pyc | __init__.cpython-313.pyc | Other | 197 | 0.7 | 0 | 0 | node-utils | 87 | 2025-04-17T01:14:06.801950 | Apache-2.0 | true | 7b1c7d23e61404379e8db41b93e26808 |
from collections.abc import Generator\nfrom contextlib import contextmanager\nimport pathlib\nimport tempfile\n\nimport pytest\n\nfrom pandas.io.pytables import HDFStore\n\ntables = pytest.importorskip("tables")\n# set these parameters so we don't have file sharing\ntables.parameters.MAX_NUMEXPR_THREADS = 1\ntables.parameters.MAX_BLOSC_THREADS = 1\ntables.parameters.MAX_THREADS = 1\n\n\ndef safe_close(store):\n try:\n if store is not None:\n store.close()\n except OSError:\n pass\n\n\n# contextmanager to ensure the file cleanup\n@contextmanager\ndef ensure_clean_store(\n path, mode="a", complevel=None, complib=None, fletcher32=False\n) -> Generator[HDFStore, None, None]:\n with tempfile.TemporaryDirectory() as tmpdirname:\n tmp_path = pathlib.Path(tmpdirname, path)\n with HDFStore(\n tmp_path,\n mode=mode,\n complevel=complevel,\n complib=complib,\n fletcher32=fletcher32,\n ) as store:\n yield store\n\n\ndef _maybe_remove(store, key):\n """\n For tests using tables, try removing the table to be sure there is\n no content from previous tests using the same table name.\n """\n try:\n store.remove(key)\n except (ValueError, KeyError):\n pass\n | .venv\Lib\site-packages\pandas\tests\io\pytables\common.py | common.py | Python | 1,264 | 0.95 | 0.14 | 0.04878 | awesome-app | 557 | 2024-07-28T03:26:07.396883 | Apache-2.0 | true | a4254a7f5a9eac8de858e609f63404f7 |
import uuid\n\nimport pytest\n\n\n@pytest.fixture\ndef setup_path():\n """Fixture for setup path"""\n return f"tmp.__{uuid.uuid4()}__.h5"\n | .venv\Lib\site-packages\pandas\tests\io\pytables\conftest.py | conftest.py | Python | 136 | 0.85 | 0.222222 | 0 | awesome-app | 146 | 2024-08-22T07:27:55.075109 | MIT | true | f34023ea7919545e883ac3035d777f17 |
import datetime\nfrom datetime import timedelta\nimport re\n\nimport numpy as np\nimport pytest\n\nfrom pandas._libs.tslibs import Timestamp\nimport pandas.util._test_decorators as td\n\nimport pandas as pd\nfrom pandas import (\n DataFrame,\n Index,\n Series,\n _testing as tm,\n concat,\n date_range,\n read_hdf,\n)\nfrom pandas.tests.io.pytables.common import (\n _maybe_remove,\n ensure_clean_store,\n)\n\npytestmark = [pytest.mark.single_cpu]\n\ntables = pytest.importorskip("tables")\n\n\n@pytest.mark.filterwarnings("ignore::tables.NaturalNameWarning")\ndef test_append(setup_path):\n with ensure_clean_store(setup_path) as store:\n # this is allowed by almost always don't want to do it\n # tables.NaturalNameWarning):\n df = DataFrame(\n np.random.default_rng(2).standard_normal((20, 4)),\n columns=Index(list("ABCD")),\n index=date_range("2000-01-01", periods=20, freq="B"),\n )\n _maybe_remove(store, "df1")\n store.append("df1", df[:10])\n store.append("df1", df[10:])\n tm.assert_frame_equal(store["df1"], df)\n\n _maybe_remove(store, "df2")\n store.put("df2", df[:10], format="table")\n store.append("df2", df[10:])\n tm.assert_frame_equal(store["df2"], df)\n\n _maybe_remove(store, "df3")\n store.append("/df3", df[:10])\n store.append("/df3", df[10:])\n tm.assert_frame_equal(store["df3"], df)\n\n # this is allowed by almost always don't want to do it\n # tables.NaturalNameWarning\n _maybe_remove(store, "/df3 foo")\n store.append("/df3 foo", df[:10])\n store.append("/df3 foo", df[10:])\n tm.assert_frame_equal(store["df3 foo"], df)\n\n # dtype issues - mizxed type in a single object column\n df = DataFrame(data=[[1, 2], [0, 1], [1, 2], [0, 0]])\n df["mixed_column"] = "testing"\n df.loc[2, "mixed_column"] = np.nan\n _maybe_remove(store, "df")\n store.append("df", df)\n tm.assert_frame_equal(store["df"], df)\n\n # uints - test storage of uints\n uint_data = DataFrame(\n {\n "u08": Series(\n np.random.default_rng(2).integers(0, high=255, size=5),\n dtype=np.uint8,\n ),\n "u16": Series(\n np.random.default_rng(2).integers(0, high=65535, size=5),\n dtype=np.uint16,\n ),\n "u32": Series(\n np.random.default_rng(2).integers(0, high=2**30, size=5),\n dtype=np.uint32,\n ),\n "u64": Series(\n [2**58, 2**59, 2**60, 2**61, 2**62],\n dtype=np.uint64,\n ),\n },\n index=np.arange(5),\n )\n _maybe_remove(store, "uints")\n store.append("uints", uint_data)\n tm.assert_frame_equal(store["uints"], uint_data, check_index_type=True)\n\n # uints - test storage of uints in indexable columns\n _maybe_remove(store, "uints")\n # 64-bit indices not yet supported\n store.append("uints", uint_data, data_columns=["u08", "u16", "u32"])\n tm.assert_frame_equal(store["uints"], uint_data, check_index_type=True)\n\n\ndef test_append_series(setup_path):\n with ensure_clean_store(setup_path) as store:\n # basic\n ss = Series(range(20), dtype=np.float64, index=[f"i_{i}" for i in range(20)])\n ts = Series(\n np.arange(10, dtype=np.float64), index=date_range("2020-01-01", periods=10)\n )\n ns = Series(np.arange(100))\n\n store.append("ss", ss)\n result = store["ss"]\n tm.assert_series_equal(result, ss)\n assert result.name is None\n\n store.append("ts", ts)\n result = store["ts"]\n tm.assert_series_equal(result, ts)\n assert result.name is None\n\n ns.name = "foo"\n store.append("ns", ns)\n result = store["ns"]\n tm.assert_series_equal(result, ns)\n assert result.name == ns.name\n\n # select on the values\n expected = ns[ns > 60]\n result = store.select("ns", "foo>60")\n tm.assert_series_equal(result, expected)\n\n # select on the index and values\n expected = ns[(ns > 70) & (ns.index < 90)]\n result = store.select("ns", "foo>70 and index<90")\n tm.assert_series_equal(result, expected, check_index_type=True)\n\n # multi-index\n mi = DataFrame(np.random.default_rng(2).standard_normal((5, 1)), columns=["A"])\n mi["B"] = np.arange(len(mi))\n mi["C"] = "foo"\n mi.loc[3:5, "C"] = "bar"\n mi.set_index(["C", "B"], inplace=True)\n s = mi.stack(future_stack=True)\n s.index = s.index.droplevel(2)\n store.append("mi", s)\n tm.assert_series_equal(store["mi"], s, check_index_type=True)\n\n\ndef test_append_some_nans(setup_path):\n with ensure_clean_store(setup_path) as store:\n df = DataFrame(\n {\n "A": Series(np.random.default_rng(2).standard_normal(20)).astype(\n "int32"\n ),\n "A1": np.random.default_rng(2).standard_normal(20),\n "A2": np.random.default_rng(2).standard_normal(20),\n "B": "foo",\n "C": "bar",\n "D": Timestamp("2001-01-01").as_unit("ns"),\n "E": Timestamp("2001-01-02").as_unit("ns"),\n },\n index=np.arange(20),\n )\n # some nans\n _maybe_remove(store, "df1")\n df.loc[0:15, ["A1", "B", "D", "E"]] = np.nan\n store.append("df1", df[:10])\n store.append("df1", df[10:])\n tm.assert_frame_equal(store["df1"], df, check_index_type=True)\n\n # first column\n df1 = df.copy()\n df1["A1"] = np.nan\n _maybe_remove(store, "df1")\n store.append("df1", df1[:10])\n store.append("df1", df1[10:])\n tm.assert_frame_equal(store["df1"], df1, check_index_type=True)\n\n # 2nd column\n df2 = df.copy()\n df2["A2"] = np.nan\n _maybe_remove(store, "df2")\n store.append("df2", df2[:10])\n store.append("df2", df2[10:])\n tm.assert_frame_equal(store["df2"], df2, check_index_type=True)\n\n # datetimes\n df3 = df.copy()\n df3["E"] = np.nan\n _maybe_remove(store, "df3")\n store.append("df3", df3[:10])\n store.append("df3", df3[10:])\n tm.assert_frame_equal(store["df3"], df3, check_index_type=True)\n\n\ndef test_append_all_nans(setup_path, using_infer_string):\n with ensure_clean_store(setup_path) as store:\n df = DataFrame(\n {\n "A1": np.random.default_rng(2).standard_normal(20),\n "A2": np.random.default_rng(2).standard_normal(20),\n },\n index=np.arange(20),\n )\n df.loc[0:15, :] = np.nan\n\n # nan some entire rows (dropna=True)\n _maybe_remove(store, "df")\n store.append("df", df[:10], dropna=True)\n store.append("df", df[10:], dropna=True)\n tm.assert_frame_equal(store["df"], df[-4:], check_index_type=True)\n\n # nan some entire rows (dropna=False)\n _maybe_remove(store, "df2")\n store.append("df2", df[:10], dropna=False)\n store.append("df2", df[10:], dropna=False)\n tm.assert_frame_equal(store["df2"], df, check_index_type=True)\n\n # tests the option io.hdf.dropna_table\n with pd.option_context("io.hdf.dropna_table", False):\n _maybe_remove(store, "df3")\n store.append("df3", df[:10])\n store.append("df3", df[10:])\n tm.assert_frame_equal(store["df3"], df)\n\n with pd.option_context("io.hdf.dropna_table", True):\n _maybe_remove(store, "df4")\n store.append("df4", df[:10])\n store.append("df4", df[10:])\n tm.assert_frame_equal(store["df4"], df[-4:])\n\n # nan some entire rows (string are still written!)\n df = DataFrame(\n {\n "A1": np.random.default_rng(2).standard_normal(20),\n "A2": np.random.default_rng(2).standard_normal(20),\n "B": "foo",\n "C": "bar",\n },\n index=np.arange(20),\n )\n\n df.loc[0:15, :] = np.nan\n\n _maybe_remove(store, "df")\n store.append("df", df[:10], dropna=True)\n store.append("df", df[10:], dropna=True)\n result = store["df"]\n expected = df\n if using_infer_string:\n # TODO: Test is incorrect when not using_infer_string.\n # Should take the last 4 rows uncondiationally.\n expected = expected[-4:]\n tm.assert_frame_equal(result, expected, check_index_type=True)\n\n _maybe_remove(store, "df2")\n store.append("df2", df[:10], dropna=False)\n store.append("df2", df[10:], dropna=False)\n tm.assert_frame_equal(store["df2"], df, check_index_type=True)\n\n # nan some entire rows (but since we have dates they are still\n # written!)\n df = DataFrame(\n {\n "A1": np.random.default_rng(2).standard_normal(20),\n "A2": np.random.default_rng(2).standard_normal(20),\n "B": "foo",\n "C": "bar",\n "D": Timestamp("2001-01-01").as_unit("ns"),\n "E": Timestamp("2001-01-02").as_unit("ns"),\n },\n index=np.arange(20),\n )\n\n df.loc[0:15, :] = np.nan\n\n _maybe_remove(store, "df")\n store.append("df", df[:10], dropna=True)\n store.append("df", df[10:], dropna=True)\n tm.assert_frame_equal(store["df"], df, check_index_type=True)\n\n _maybe_remove(store, "df2")\n store.append("df2", df[:10], dropna=False)\n store.append("df2", df[10:], dropna=False)\n tm.assert_frame_equal(store["df2"], df, check_index_type=True)\n\n\ndef test_append_frame_column_oriented(setup_path):\n with ensure_clean_store(setup_path) as store:\n # column oriented\n df = DataFrame(\n np.random.default_rng(2).standard_normal((10, 4)),\n columns=Index(list("ABCD")),\n index=date_range("2000-01-01", periods=10, freq="B"),\n )\n df.index = df.index._with_freq(None) # freq doesn't round-trip\n\n _maybe_remove(store, "df1")\n store.append("df1", df.iloc[:, :2], axes=["columns"])\n store.append("df1", df.iloc[:, 2:])\n tm.assert_frame_equal(store["df1"], df)\n\n result = store.select("df1", "columns=A")\n expected = df.reindex(columns=["A"])\n tm.assert_frame_equal(expected, result)\n\n # selection on the non-indexable\n result = store.select("df1", ("columns=A", "index=df.index[0:4]"))\n expected = df.reindex(columns=["A"], index=df.index[0:4])\n tm.assert_frame_equal(expected, result)\n\n # this isn't supported\n msg = re.escape(\n "passing a filterable condition to a non-table indexer "\n "[Filter: Not Initialized]"\n )\n with pytest.raises(TypeError, match=msg):\n store.select("df1", "columns=A and index>df.index[4]")\n\n\ndef test_append_with_different_block_ordering(setup_path):\n # GH 4096; using same frames, but different block orderings\n with ensure_clean_store(setup_path) as store:\n for i in range(10):\n df = DataFrame(\n np.random.default_rng(2).standard_normal((10, 2)), columns=list("AB")\n )\n df["index"] = range(10)\n df["index"] += i * 10\n df["int64"] = Series([1] * len(df), dtype="int64")\n df["int16"] = Series([1] * len(df), dtype="int16")\n\n if i % 2 == 0:\n del df["int64"]\n df["int64"] = Series([1] * len(df), dtype="int64")\n if i % 3 == 0:\n a = df.pop("A")\n df["A"] = a\n\n df.set_index("index", inplace=True)\n\n store.append("df", df)\n\n # test a different ordering but with more fields (like invalid\n # combinations)\n with ensure_clean_store(setup_path) as store:\n df = DataFrame(\n np.random.default_rng(2).standard_normal((10, 2)),\n columns=list("AB"),\n dtype="float64",\n )\n df["int64"] = Series([1] * len(df), dtype="int64")\n df["int16"] = Series([1] * len(df), dtype="int16")\n store.append("df", df)\n\n # store additional fields in different blocks\n df["int16_2"] = Series([1] * len(df), dtype="int16")\n msg = re.escape(\n "cannot match existing table structure for [int16] on appending data"\n )\n with pytest.raises(ValueError, match=msg):\n store.append("df", df)\n\n # store multiple additional fields in different blocks\n df["float_3"] = Series([1.0] * len(df), dtype="float64")\n msg = re.escape(\n "cannot match existing table structure for [A,B] on appending data"\n )\n with pytest.raises(ValueError, match=msg):\n store.append("df", df)\n\n\ndef test_append_with_strings(setup_path):\n with ensure_clean_store(setup_path) as store:\n\n def check_col(key, name, size):\n assert (\n getattr(store.get_storer(key).table.description, name).itemsize == size\n )\n\n # avoid truncation on elements\n df = DataFrame([[123, "asdqwerty"], [345, "dggnhebbsdfbdfb"]])\n store.append("df_big", df)\n tm.assert_frame_equal(store.select("df_big"), df)\n check_col("df_big", "values_block_1", 15)\n\n # appending smaller string ok\n df2 = DataFrame([[124, "asdqy"], [346, "dggnhefbdfb"]])\n store.append("df_big", df2)\n expected = concat([df, df2])\n tm.assert_frame_equal(store.select("df_big"), expected)\n check_col("df_big", "values_block_1", 15)\n\n # avoid truncation on elements\n df = DataFrame([[123, "asdqwerty"], [345, "dggnhebbsdfbdfb"]])\n store.append("df_big2", df, min_itemsize={"values": 50})\n tm.assert_frame_equal(store.select("df_big2"), df)\n check_col("df_big2", "values_block_1", 50)\n\n # bigger string on next append\n store.append("df_new", df)\n df_new = DataFrame([[124, "abcdefqhij"], [346, "abcdefghijklmnopqrtsuvwxyz"]])\n msg = (\n r"Trying to store a string with len \[26\] in "\n r"\[values_block_1\] column but\n"\n r"this column has a limit of \[15\]!\n"\n "Consider using min_itemsize to preset the sizes on these "\n "columns"\n )\n with pytest.raises(ValueError, match=msg):\n store.append("df_new", df_new)\n\n # min_itemsize on Series index (GH 11412)\n df = DataFrame(\n {\n "A": [0.0, 1.0, 2.0, 3.0, 4.0],\n "B": [0.0, 1.0, 0.0, 1.0, 0.0],\n "C": Index(["foo1", "foo2", "foo3", "foo4", "foo5"]),\n "D": date_range("20130101", periods=5),\n }\n ).set_index("C")\n store.append("ss", df["B"], min_itemsize={"index": 4})\n tm.assert_series_equal(store.select("ss"), df["B"])\n\n # same as above, with data_columns=True\n store.append("ss2", df["B"], data_columns=True, min_itemsize={"index": 4})\n tm.assert_series_equal(store.select("ss2"), df["B"])\n\n # min_itemsize in index without appending (GH 10381)\n store.put("ss3", df, format="table", min_itemsize={"index": 6})\n # just make sure there is a longer string:\n df2 = df.copy().reset_index().assign(C="longer").set_index("C")\n store.append("ss3", df2)\n tm.assert_frame_equal(store.select("ss3"), concat([df, df2]))\n\n # same as above, with a Series\n store.put("ss4", df["B"], format="table", min_itemsize={"index": 6})\n store.append("ss4", df2["B"])\n tm.assert_series_equal(store.select("ss4"), concat([df["B"], df2["B"]]))\n\n # with nans\n _maybe_remove(store, "df")\n df = DataFrame(\n np.random.default_rng(2).standard_normal((10, 4)),\n columns=Index(list("ABCD")),\n index=date_range("2000-01-01", periods=10, freq="B"),\n )\n df["string"] = "foo"\n df.loc[df.index[1:4], "string"] = np.nan\n df["string2"] = "bar"\n df.loc[df.index[4:8], "string2"] = np.nan\n df["string3"] = "bah"\n df.loc[df.index[1:], "string3"] = np.nan\n store.append("df", df)\n result = store.select("df")\n tm.assert_frame_equal(result, df)\n\n with ensure_clean_store(setup_path) as store:\n df = DataFrame({"A": "foo", "B": "bar"}, index=range(10))\n\n # a min_itemsize that creates a data_column\n _maybe_remove(store, "df")\n store.append("df", df, min_itemsize={"A": 200})\n check_col("df", "A", 200)\n assert store.get_storer("df").data_columns == ["A"]\n\n # a min_itemsize that creates a data_column2\n _maybe_remove(store, "df")\n store.append("df", df, data_columns=["B"], min_itemsize={"A": 200})\n check_col("df", "A", 200)\n assert store.get_storer("df").data_columns == ["B", "A"]\n\n # a min_itemsize that creates a data_column2\n _maybe_remove(store, "df")\n store.append("df", df, data_columns=["B"], min_itemsize={"values": 200})\n check_col("df", "B", 200)\n check_col("df", "values_block_0", 200)\n assert store.get_storer("df").data_columns == ["B"]\n\n # infer the .typ on subsequent appends\n _maybe_remove(store, "df")\n store.append("df", df[:5], min_itemsize=200)\n store.append("df", df[5:], min_itemsize=200)\n tm.assert_frame_equal(store["df"], df)\n\n # invalid min_itemsize keys\n df = DataFrame(["foo", "foo", "foo", "barh", "barh", "barh"], columns=["A"])\n _maybe_remove(store, "df")\n msg = re.escape(\n "min_itemsize has the key [foo] which is not an axis or data_column"\n )\n with pytest.raises(ValueError, match=msg):\n store.append("df", df, min_itemsize={"foo": 20, "foobar": 20})\n\n\ndef test_append_with_empty_string(setup_path):\n with ensure_clean_store(setup_path) as store:\n # with all empty strings (GH 12242)\n df = DataFrame({"x": ["a", "b", "c", "d", "e", "f", ""]})\n store.append("df", df[:-1], min_itemsize={"x": 1})\n store.append("df", df[-1:], min_itemsize={"x": 1})\n tm.assert_frame_equal(store.select("df"), df)\n\n\ndef test_append_with_data_columns(setup_path):\n with ensure_clean_store(setup_path) as store:\n df = DataFrame(\n np.random.default_rng(2).standard_normal((10, 4)),\n columns=Index(list("ABCD")),\n index=date_range("2000-01-01", periods=10, freq="B"),\n )\n df.iloc[0, df.columns.get_loc("B")] = 1.0\n _maybe_remove(store, "df")\n store.append("df", df[:2], data_columns=["B"])\n store.append("df", df[2:])\n tm.assert_frame_equal(store["df"], df)\n\n # check that we have indices created\n assert store._handle.root.df.table.cols.index.is_indexed is True\n assert store._handle.root.df.table.cols.B.is_indexed is True\n\n # data column searching\n result = store.select("df", "B>0")\n expected = df[df.B > 0]\n tm.assert_frame_equal(result, expected)\n\n # data column searching (with an indexable and a data_columns)\n result = store.select("df", "B>0 and index>df.index[3]")\n df_new = df.reindex(index=df.index[4:])\n expected = df_new[df_new.B > 0]\n tm.assert_frame_equal(result, expected)\n\n # data column selection with a string data_column\n df_new = df.copy()\n df_new["string"] = "foo"\n df_new.loc[df_new.index[1:4], "string"] = np.nan\n df_new.loc[df_new.index[5:6], "string"] = "bar"\n _maybe_remove(store, "df")\n store.append("df", df_new, data_columns=["string"])\n result = store.select("df", "string='foo'")\n expected = df_new[df_new.string == "foo"]\n tm.assert_frame_equal(result, expected)\n\n # using min_itemsize and a data column\n def check_col(key, name, size):\n assert (\n getattr(store.get_storer(key).table.description, name).itemsize == size\n )\n\n with ensure_clean_store(setup_path) as store:\n _maybe_remove(store, "df")\n store.append("df", df_new, data_columns=["string"], min_itemsize={"string": 30})\n check_col("df", "string", 30)\n _maybe_remove(store, "df")\n store.append("df", df_new, data_columns=["string"], min_itemsize=30)\n check_col("df", "string", 30)\n _maybe_remove(store, "df")\n store.append("df", df_new, data_columns=["string"], min_itemsize={"values": 30})\n check_col("df", "string", 30)\n\n with ensure_clean_store(setup_path) as store:\n df_new["string2"] = "foobarbah"\n df_new["string_block1"] = "foobarbah1"\n df_new["string_block2"] = "foobarbah2"\n _maybe_remove(store, "df")\n store.append(\n "df",\n df_new,\n data_columns=["string", "string2"],\n min_itemsize={"string": 30, "string2": 40, "values": 50},\n )\n check_col("df", "string", 30)\n check_col("df", "string2", 40)\n check_col("df", "values_block_1", 50)\n\n with ensure_clean_store(setup_path) as store:\n # multiple data columns\n df_new = df.copy()\n df_new.iloc[0, df_new.columns.get_loc("A")] = 1.0\n df_new.iloc[0, df_new.columns.get_loc("B")] = -1.0\n df_new["string"] = "foo"\n\n sl = df_new.columns.get_loc("string")\n df_new.iloc[1:4, sl] = np.nan\n df_new.iloc[5:6, sl] = "bar"\n\n df_new["string2"] = "foo"\n sl = df_new.columns.get_loc("string2")\n df_new.iloc[2:5, sl] = np.nan\n df_new.iloc[7:8, sl] = "bar"\n _maybe_remove(store, "df")\n store.append("df", df_new, data_columns=["A", "B", "string", "string2"])\n result = store.select("df", "string='foo' and string2='foo' and A>0 and B<0")\n expected = df_new[\n (df_new.string == "foo")\n & (df_new.string2 == "foo")\n & (df_new.A > 0)\n & (df_new.B < 0)\n ]\n tm.assert_frame_equal(result, expected, check_freq=False)\n # FIXME: 2020-05-07 freq check randomly fails in the CI\n\n # yield an empty frame\n result = store.select("df", "string='foo' and string2='cool'")\n expected = df_new[(df_new.string == "foo") & (df_new.string2 == "cool")]\n tm.assert_frame_equal(result, expected)\n\n with ensure_clean_store(setup_path) as store:\n # doc example\n df_dc = df.copy()\n df_dc["string"] = "foo"\n df_dc.loc[df_dc.index[4:6], "string"] = np.nan\n df_dc.loc[df_dc.index[7:9], "string"] = "bar"\n df_dc["string2"] = "cool"\n df_dc["datetime"] = Timestamp("20010102").as_unit("ns")\n df_dc.loc[df_dc.index[3:5], ["A", "B", "datetime"]] = np.nan\n\n _maybe_remove(store, "df_dc")\n store.append(\n "df_dc", df_dc, data_columns=["B", "C", "string", "string2", "datetime"]\n )\n result = store.select("df_dc", "B>0")\n\n expected = df_dc[df_dc.B > 0]\n tm.assert_frame_equal(result, expected)\n\n result = store.select("df_dc", ["B > 0", "C > 0", "string == foo"])\n expected = df_dc[(df_dc.B > 0) & (df_dc.C > 0) & (df_dc.string == "foo")]\n tm.assert_frame_equal(result, expected, check_freq=False)\n # FIXME: 2020-12-07 intermittent build failures here with freq of\n # None instead of BDay(4)\n\n with ensure_clean_store(setup_path) as store:\n # doc example part 2\n\n index = date_range("1/1/2000", periods=8)\n df_dc = DataFrame(\n np.random.default_rng(2).standard_normal((8, 3)),\n index=index,\n columns=["A", "B", "C"],\n )\n df_dc["string"] = "foo"\n df_dc.loc[df_dc.index[4:6], "string"] = np.nan\n df_dc.loc[df_dc.index[7:9], "string"] = "bar"\n df_dc[["B", "C"]] = df_dc[["B", "C"]].abs()\n df_dc["string2"] = "cool"\n\n # on-disk operations\n store.append("df_dc", df_dc, data_columns=["B", "C", "string", "string2"])\n\n result = store.select("df_dc", "B>0")\n expected = df_dc[df_dc.B > 0]\n tm.assert_frame_equal(result, expected)\n\n result = store.select("df_dc", ["B > 0", "C > 0", 'string == "foo"'])\n expected = df_dc[(df_dc.B > 0) & (df_dc.C > 0) & (df_dc.string == "foo")]\n tm.assert_frame_equal(result, expected)\n\n\ndef test_append_hierarchical(tmp_path, setup_path, multiindex_dataframe_random_data):\n df = multiindex_dataframe_random_data\n df.columns.name = None\n\n with ensure_clean_store(setup_path) as store:\n store.append("mi", df)\n result = store.select("mi")\n tm.assert_frame_equal(result, df)\n\n # GH 3748\n result = store.select("mi", columns=["A", "B"])\n expected = df.reindex(columns=["A", "B"])\n tm.assert_frame_equal(result, expected)\n\n path = tmp_path / "test.hdf"\n df.to_hdf(path, key="df", format="table")\n result = read_hdf(path, "df", columns=["A", "B"])\n expected = df.reindex(columns=["A", "B"])\n tm.assert_frame_equal(result, expected)\n\n\ndef test_append_misc(setup_path):\n with ensure_clean_store(setup_path) as store:\n df = DataFrame(\n 1.1 * np.arange(120).reshape((30, 4)),\n columns=Index(list("ABCD")),\n index=Index([f"i-{i}" for i in range(30)]),\n )\n store.append("df", df, chunksize=1)\n result = store.select("df")\n tm.assert_frame_equal(result, df)\n\n store.append("df1", df, expectedrows=10)\n result = store.select("df1")\n tm.assert_frame_equal(result, df)\n\n\n@pytest.mark.parametrize("chunksize", [10, 200, 1000])\ndef test_append_misc_chunksize(setup_path, chunksize):\n # more chunksize in append tests\n df = DataFrame(\n 1.1 * np.arange(120).reshape((30, 4)),\n columns=Index(list("ABCD")),\n index=Index([f"i-{i}" for i in range(30)]),\n )\n df["string"] = "foo"\n df["float322"] = 1.0\n df["float322"] = df["float322"].astype("float32")\n df["bool"] = df["float322"] > 0\n df["time1"] = Timestamp("20130101").as_unit("ns")\n df["time2"] = Timestamp("20130102").as_unit("ns")\n with ensure_clean_store(setup_path, mode="w") as store:\n store.append("obj", df, chunksize=chunksize)\n result = store.select("obj")\n tm.assert_frame_equal(result, df)\n\n\ndef test_append_misc_empty_frame(setup_path):\n # empty frame, GH4273\n with ensure_clean_store(setup_path) as store:\n # 0 len\n df_empty = DataFrame(columns=list("ABC"))\n store.append("df", df_empty)\n with pytest.raises(KeyError, match="'No object named df in the file'"):\n store.select("df")\n\n # repeated append of 0/non-zero frames\n df = DataFrame(np.random.default_rng(2).random((10, 3)), columns=list("ABC"))\n store.append("df", df)\n tm.assert_frame_equal(store.select("df"), df)\n store.append("df", df_empty)\n tm.assert_frame_equal(store.select("df"), df)\n\n # store\n df = DataFrame(columns=list("ABC"))\n store.put("df2", df)\n tm.assert_frame_equal(store.select("df2"), df)\n\n\n# TODO(ArrayManager) currently we rely on falling back to BlockManager, but\n# the conversion from AM->BM converts the invalid object dtype column into\n# a datetime64 column no longer raising an error\n@td.skip_array_manager_not_yet_implemented\ndef test_append_raise(setup_path, using_infer_string):\n with ensure_clean_store(setup_path) as store:\n # test append with invalid input to get good error messages\n\n # list in column\n df = DataFrame(\n 1.1 * np.arange(120).reshape((30, 4)),\n columns=Index(list("ABCD")),\n index=Index([f"i-{i}" for i in range(30)]),\n )\n df["invalid"] = [["a"]] * len(df)\n assert df.dtypes["invalid"] == np.object_\n msg = re.escape(\n """Cannot serialize the column [invalid]\nbecause its data contents are not [string] but [mixed] object dtype"""\n )\n with pytest.raises(TypeError, match=msg):\n store.append("df", df)\n\n # multiple invalid columns\n df["invalid2"] = [["a"]] * len(df)\n df["invalid3"] = [["a"]] * len(df)\n with pytest.raises(TypeError, match=msg):\n store.append("df", df)\n\n # datetime with embedded nans as object\n df = DataFrame(\n 1.1 * np.arange(120).reshape((30, 4)),\n columns=Index(list("ABCD")),\n index=Index([f"i-{i}" for i in range(30)]),\n )\n s = Series(datetime.datetime(2001, 1, 2), index=df.index)\n s = s.astype(object)\n s[0:5] = np.nan\n df["invalid"] = s\n assert df.dtypes["invalid"] == np.object_\n msg = "too many timezones in this block, create separate data columns"\n with pytest.raises(TypeError, match=msg):\n store.append("df", df)\n\n # directly ndarray\n msg = "value must be None, Series, or DataFrame"\n with pytest.raises(TypeError, match=msg):\n store.append("df", np.arange(10))\n\n # series directly\n msg = re.escape(\n "cannot properly create the storer for: "\n "[group->df,value-><class 'pandas.core.series.Series'>]"\n )\n with pytest.raises(TypeError, match=msg):\n store.append("df", Series(np.arange(10)))\n\n # appending an incompatible table\n df = DataFrame(\n 1.1 * np.arange(120).reshape((30, 4)),\n columns=Index(list("ABCD")),\n index=Index([f"i-{i}" for i in range(30)]),\n )\n store.append("df", df)\n\n df["foo"] = "foo"\n msg = re.escape(\n "invalid combination of [non_index_axes] on appending data "\n "[(1, ['A', 'B', 'C', 'D', 'foo'])] vs current table "\n "[(1, ['A', 'B', 'C', 'D'])]"\n )\n with pytest.raises(ValueError, match=msg):\n store.append("df", df)\n\n # incompatible type (GH 41897)\n _maybe_remove(store, "df")\n df["foo"] = Timestamp("20130101")\n store.append("df", df)\n df["foo"] = "bar"\n msg = re.escape(\n "Cannot serialize the column [foo] "\n "because its data contents are not [string] "\n "but [datetime64[s]] object dtype"\n )\n with pytest.raises(ValueError, match=msg):\n store.append("df", df)\n\n\ndef test_append_with_timedelta(setup_path):\n # GH 3577\n # append timedelta\n\n ts = Timestamp("20130101").as_unit("ns")\n df = DataFrame(\n {\n "A": ts,\n "B": [ts + timedelta(days=i, seconds=10) for i in range(10)],\n }\n )\n df["C"] = df["A"] - df["B"]\n df.loc[3:5, "C"] = np.nan\n\n with ensure_clean_store(setup_path) as store:\n # table\n _maybe_remove(store, "df")\n store.append("df", df, data_columns=True)\n result = store.select("df")\n tm.assert_frame_equal(result, df)\n\n result = store.select("df", where="C<100000")\n tm.assert_frame_equal(result, df)\n\n result = store.select("df", where="C<pd.Timedelta('-3D')")\n tm.assert_frame_equal(result, df.iloc[3:])\n\n result = store.select("df", "C<'-3D'")\n tm.assert_frame_equal(result, df.iloc[3:])\n\n # a bit hacky here as we don't really deal with the NaT properly\n\n result = store.select("df", "C<'-500000s'")\n result = result.dropna(subset=["C"])\n tm.assert_frame_equal(result, df.iloc[6:])\n\n result = store.select("df", "C<'-3.5D'")\n result = result.iloc[1:]\n tm.assert_frame_equal(result, df.iloc[4:])\n\n # fixed\n _maybe_remove(store, "df2")\n store.put("df2", df)\n result = store.select("df2")\n tm.assert_frame_equal(result, df)\n\n\ndef test_append_to_multiple(setup_path):\n df1 = DataFrame(\n np.random.default_rng(2).standard_normal((10, 4)),\n columns=Index(list("ABCD")),\n index=date_range("2000-01-01", periods=10, freq="B"),\n )\n df2 = df1.copy().rename(columns="{}_2".format)\n df2["foo"] = "bar"\n df = concat([df1, df2], axis=1)\n\n with ensure_clean_store(setup_path) as store:\n # exceptions\n msg = "append_to_multiple requires a selector that is in passed dict"\n with pytest.raises(ValueError, match=msg):\n store.append_to_multiple(\n {"df1": ["A", "B"], "df2": None}, df, selector="df3"\n )\n\n with pytest.raises(ValueError, match=msg):\n store.append_to_multiple({"df1": None, "df2": None}, df, selector="df3")\n\n msg = (\n "append_to_multiple must have a dictionary specified as the way to "\n "split the value"\n )\n with pytest.raises(ValueError, match=msg):\n store.append_to_multiple("df1", df, "df1")\n\n # regular operation\n store.append_to_multiple({"df1": ["A", "B"], "df2": None}, df, selector="df1")\n result = store.select_as_multiple(\n ["df1", "df2"], where=["A>0", "B>0"], selector="df1"\n )\n expected = df[(df.A > 0) & (df.B > 0)]\n tm.assert_frame_equal(result, expected)\n\n\ndef test_append_to_multiple_dropna(setup_path):\n df1 = DataFrame(\n np.random.default_rng(2).standard_normal((10, 4)),\n columns=Index(list("ABCD")),\n index=date_range("2000-01-01", periods=10, freq="B"),\n )\n df2 = DataFrame(\n np.random.default_rng(2).standard_normal((10, 4)),\n columns=Index(list("ABCD")),\n index=date_range("2000-01-01", periods=10, freq="B"),\n ).rename(columns="{}_2".format)\n df1.iloc[1, df1.columns.get_indexer(["A", "B"])] = np.nan\n df = concat([df1, df2], axis=1)\n\n with ensure_clean_store(setup_path) as store:\n # dropna=True should guarantee rows are synchronized\n store.append_to_multiple(\n {"df1": ["A", "B"], "df2": None}, df, selector="df1", dropna=True\n )\n result = store.select_as_multiple(["df1", "df2"])\n expected = df.dropna()\n tm.assert_frame_equal(result, expected, check_index_type=True)\n tm.assert_index_equal(store.select("df1").index, store.select("df2").index)\n\n\ndef test_append_to_multiple_dropna_false(setup_path):\n df1 = DataFrame(\n np.random.default_rng(2).standard_normal((10, 4)),\n columns=Index(list("ABCD")),\n index=date_range("2000-01-01", periods=10, freq="B"),\n )\n df2 = df1.copy().rename(columns="{}_2".format)\n df1.iloc[1, df1.columns.get_indexer(["A", "B"])] = np.nan\n df = concat([df1, df2], axis=1)\n\n with ensure_clean_store(setup_path) as store, pd.option_context(\n "io.hdf.dropna_table", True\n ):\n # dropna=False shouldn't synchronize row indexes\n store.append_to_multiple(\n {"df1a": ["A", "B"], "df2a": None}, df, selector="df1a", dropna=False\n )\n\n msg = "all tables must have exactly the same nrows!"\n with pytest.raises(ValueError, match=msg):\n store.select_as_multiple(["df1a", "df2a"])\n\n assert not store.select("df1a").index.equals(store.select("df2a").index)\n\n\ndef test_append_to_multiple_min_itemsize(setup_path):\n # GH 11238\n df = DataFrame(\n {\n "IX": np.arange(1, 21),\n "Num": np.arange(1, 21),\n "BigNum": np.arange(1, 21) * 88,\n "Str": ["a" for _ in range(20)],\n "LongStr": ["abcde" for _ in range(20)],\n }\n )\n expected = df.iloc[[0]]\n\n with ensure_clean_store(setup_path) as store:\n store.append_to_multiple(\n {\n "index": ["IX"],\n "nums": ["Num", "BigNum"],\n "strs": ["Str", "LongStr"],\n },\n df.iloc[[0]],\n "index",\n min_itemsize={"Str": 10, "LongStr": 100, "Num": 2},\n )\n result = store.select_as_multiple(["index", "nums", "strs"])\n tm.assert_frame_equal(result, expected, check_index_type=True)\n\n\ndef test_append_string_nan_rep(setup_path):\n # GH 16300\n df = DataFrame({"A": "a", "B": "foo"}, index=np.arange(10))\n df_nan = df.copy()\n df_nan.loc[0:4, :] = np.nan\n msg = "NaN representation is too large for existing column size"\n\n with ensure_clean_store(setup_path) as store:\n # string column too small\n store.append("sa", df["A"])\n with pytest.raises(ValueError, match=msg):\n store.append("sa", df_nan["A"])\n\n # nan_rep too big\n store.append("sb", df["B"], nan_rep="bars")\n with pytest.raises(ValueError, match=msg):\n store.append("sb", df_nan["B"])\n\n # smaller modified nan_rep\n store.append("sc", df["A"], nan_rep="n")\n store.append("sc", df_nan["A"])\n result = store["sc"]\n expected = concat([df["A"], df_nan["A"]])\n tm.assert_series_equal(result, expected)\n | .venv\Lib\site-packages\pandas\tests\io\pytables\test_append.py | test_append.py | Python | 37,420 | 0.95 | 0.039409 | 0.106729 | react-lib | 495 | 2024-05-18T17:43:22.321780 | Apache-2.0 | true | b9fa8cefba3b681a5c68570f06375abc |
import numpy as np\nimport pytest\n\nfrom pandas import (\n Categorical,\n DataFrame,\n Series,\n _testing as tm,\n concat,\n read_hdf,\n)\nfrom pandas.tests.io.pytables.common import (\n _maybe_remove,\n ensure_clean_store,\n)\n\npytestmark = [pytest.mark.single_cpu]\n\n\ndef test_categorical(setup_path):\n with ensure_clean_store(setup_path) as store:\n # Basic\n _maybe_remove(store, "s")\n s = Series(\n Categorical(\n ["a", "b", "b", "a", "a", "c"],\n categories=["a", "b", "c", "d"],\n ordered=False,\n )\n )\n store.append("s", s, format="table")\n result = store.select("s")\n tm.assert_series_equal(s, result)\n\n _maybe_remove(store, "s_ordered")\n s = Series(\n Categorical(\n ["a", "b", "b", "a", "a", "c"],\n categories=["a", "b", "c", "d"],\n ordered=True,\n )\n )\n store.append("s_ordered", s, format="table")\n result = store.select("s_ordered")\n tm.assert_series_equal(s, result)\n\n _maybe_remove(store, "df")\n df = DataFrame({"s": s, "vals": [1, 2, 3, 4, 5, 6]})\n store.append("df", df, format="table")\n result = store.select("df")\n tm.assert_frame_equal(result, df)\n\n # Dtypes\n _maybe_remove(store, "si")\n s = Series([1, 1, 2, 2, 3, 4, 5]).astype("category")\n store.append("si", s)\n result = store.select("si")\n tm.assert_series_equal(result, s)\n\n _maybe_remove(store, "si2")\n s = Series([1, 1, np.nan, 2, 3, 4, 5]).astype("category")\n store.append("si2", s)\n result = store.select("si2")\n tm.assert_series_equal(result, s)\n\n # Multiple\n _maybe_remove(store, "df2")\n df2 = df.copy()\n df2["s2"] = Series(list("abcdefg")).astype("category")\n store.append("df2", df2)\n result = store.select("df2")\n tm.assert_frame_equal(result, df2)\n\n # Make sure the metadata is OK\n info = store.info()\n assert "/df2 " in info\n # df2._mgr.blocks[0] and df2._mgr.blocks[2] are Categorical\n assert "/df2/meta/values_block_0/meta" in info\n assert "/df2/meta/values_block_2/meta" in info\n\n # unordered\n _maybe_remove(store, "s2")\n s = Series(\n Categorical(\n ["a", "b", "b", "a", "a", "c"],\n categories=["a", "b", "c", "d"],\n ordered=False,\n )\n )\n store.append("s2", s, format="table")\n result = store.select("s2")\n tm.assert_series_equal(result, s)\n\n # Query\n _maybe_remove(store, "df3")\n store.append("df3", df, data_columns=["s"])\n expected = df[df.s.isin(["b", "c"])]\n result = store.select("df3", where=['s in ["b","c"]'])\n tm.assert_frame_equal(result, expected)\n\n expected = df[df.s.isin(["b", "c"])]\n result = store.select("df3", where=['s = ["b","c"]'])\n tm.assert_frame_equal(result, expected)\n\n expected = df[df.s.isin(["d"])]\n result = store.select("df3", where=['s in ["d"]'])\n tm.assert_frame_equal(result, expected)\n\n expected = df[df.s.isin(["f"])]\n result = store.select("df3", where=['s in ["f"]'])\n tm.assert_frame_equal(result, expected)\n\n # Appending with same categories is ok\n store.append("df3", df)\n\n df = concat([df, df])\n expected = df[df.s.isin(["b", "c"])]\n result = store.select("df3", where=['s in ["b","c"]'])\n tm.assert_frame_equal(result, expected)\n\n # Appending must have the same categories\n df3 = df.copy()\n df3["s"] = df3["s"].cat.remove_unused_categories()\n\n msg = "cannot append a categorical with different categories to the existing"\n with pytest.raises(ValueError, match=msg):\n store.append("df3", df3)\n\n # Remove, and make sure meta data is removed (its a recursive\n # removal so should be).\n result = store.select("df3/meta/s/meta")\n assert result is not None\n store.remove("df3")\n\n with pytest.raises(\n KeyError, match="'No object named df3/meta/s/meta in the file'"\n ):\n store.select("df3/meta/s/meta")\n\n\ndef test_categorical_conversion(tmp_path, setup_path):\n # GH13322\n # Check that read_hdf with categorical columns doesn't return rows if\n # where criteria isn't met.\n obsids = ["ESP_012345_6789", "ESP_987654_3210"]\n imgids = ["APF00006np", "APF0001imm"]\n data = [4.3, 9.8]\n\n # Test without categories\n df = DataFrame({"obsids": obsids, "imgids": imgids, "data": data})\n\n # We are expecting an empty DataFrame matching types of df\n expected = df.iloc[[], :]\n path = tmp_path / setup_path\n df.to_hdf(path, key="df", format="table", data_columns=True)\n result = read_hdf(path, "df", where="obsids=B")\n tm.assert_frame_equal(result, expected)\n\n # Test with categories\n df.obsids = df.obsids.astype("category")\n df.imgids = df.imgids.astype("category")\n\n # We are expecting an empty DataFrame matching types of df\n expected = df.iloc[[], :]\n path = tmp_path / setup_path\n df.to_hdf(path, key="df", format="table", data_columns=True)\n result = read_hdf(path, "df", where="obsids=B")\n tm.assert_frame_equal(result, expected)\n\n\ndef test_categorical_nan_only_columns(tmp_path, setup_path):\n # GH18413\n # Check that read_hdf with categorical columns with NaN-only values can\n # be read back.\n df = DataFrame(\n {\n "a": ["a", "b", "c", np.nan],\n "b": [np.nan, np.nan, np.nan, np.nan],\n "c": [1, 2, 3, 4],\n "d": Series([None] * 4, dtype=object),\n }\n )\n df["a"] = df.a.astype("category")\n df["b"] = df.b.astype("category")\n df["d"] = df.b.astype("category")\n expected = df\n path = tmp_path / setup_path\n df.to_hdf(path, key="df", format="table", data_columns=True)\n result = read_hdf(path, "df")\n tm.assert_frame_equal(result, expected)\n\n\n@pytest.mark.parametrize(\n "where, df, expected",\n [\n ('col=="q"', DataFrame({"col": ["a", "b", "s"]}), DataFrame({"col": []})),\n ('col=="a"', DataFrame({"col": ["a", "b", "s"]}), DataFrame({"col": ["a"]})),\n ],\n)\ndef test_convert_value(\n tmp_path, setup_path, where: str, df: DataFrame, expected: DataFrame\n):\n # GH39420\n # Check that read_hdf with categorical columns can filter by where condition.\n df.col = df.col.astype("category")\n max_widths = {"col": 1}\n categorical_values = sorted(df.col.unique())\n expected.col = expected.col.astype("category")\n expected.col = expected.col.cat.set_categories(categorical_values)\n\n path = tmp_path / setup_path\n df.to_hdf(path, key="df", format="table", min_itemsize=max_widths)\n result = read_hdf(path, where=where)\n tm.assert_frame_equal(result, expected)\n | .venv\Lib\site-packages\pandas\tests\io\pytables\test_categorical.py | test_categorical.py | Python | 6,996 | 0.95 | 0.023364 | 0.126374 | python-kit | 839 | 2024-04-10T14:04:05.348454 | GPL-3.0 | true | ee6bad80d711e9776a08c7603abf4f9e |
import pytest\n\nimport pandas as pd\nimport pandas._testing as tm\n\ntables = pytest.importorskip("tables")\n\n\n@pytest.fixture\ndef pytables_hdf5_file(tmp_path):\n """\n Use PyTables to create a simple HDF5 file.\n """\n table_schema = {\n "c0": tables.Time64Col(pos=0),\n "c1": tables.StringCol(5, pos=1),\n "c2": tables.Int64Col(pos=2),\n }\n\n t0 = 1_561_105_000.0\n\n testsamples = [\n {"c0": t0, "c1": "aaaaa", "c2": 1},\n {"c0": t0 + 1, "c1": "bbbbb", "c2": 2},\n {"c0": t0 + 2, "c1": "ccccc", "c2": 10**5},\n {"c0": t0 + 3, "c1": "ddddd", "c2": 4_294_967_295},\n ]\n\n objname = "pandas_test_timeseries"\n\n path = tmp_path / "written_with_pytables.h5"\n with tables.open_file(path, mode="w") as f:\n t = f.create_table("/", name=objname, description=table_schema)\n for sample in testsamples:\n for key, value in sample.items():\n t.row[key] = value\n t.row.append()\n\n yield path, objname, pd.DataFrame(testsamples)\n\n\nclass TestReadPyTablesHDF5:\n """\n A group of tests which covers reading HDF5 files written by plain PyTables\n (not written by pandas).\n\n Was introduced for regression-testing issue 11188.\n """\n\n def test_read_complete(self, pytables_hdf5_file):\n path, objname, df = pytables_hdf5_file\n result = pd.read_hdf(path, key=objname)\n expected = df\n tm.assert_frame_equal(result, expected, check_index_type=True)\n\n def test_read_with_start(self, pytables_hdf5_file):\n path, objname, df = pytables_hdf5_file\n # This is a regression test for pandas-dev/pandas/issues/11188\n result = pd.read_hdf(path, key=objname, start=1)\n expected = df[1:].reset_index(drop=True)\n tm.assert_frame_equal(result, expected, check_index_type=True)\n\n def test_read_with_stop(self, pytables_hdf5_file):\n path, objname, df = pytables_hdf5_file\n # This is a regression test for pandas-dev/pandas/issues/11188\n result = pd.read_hdf(path, key=objname, stop=1)\n expected = df[:1].reset_index(drop=True)\n tm.assert_frame_equal(result, expected, check_index_type=True)\n\n def test_read_with_startstop(self, pytables_hdf5_file):\n path, objname, df = pytables_hdf5_file\n # This is a regression test for pandas-dev/pandas/issues/11188\n result = pd.read_hdf(path, key=objname, start=1, stop=2)\n expected = df[1:2].reset_index(drop=True)\n tm.assert_frame_equal(result, expected, check_index_type=True)\n | .venv\Lib\site-packages\pandas\tests\io\pytables\test_compat.py | test_compat.py | Python | 2,547 | 0.95 | 0.16 | 0.050847 | node-utils | 175 | 2024-02-22T09:50:27.779562 | MIT | true | 4808ef496be6a780d30ae09db277cde9 |
import numpy as np\nimport pytest\n\nimport pandas as pd\nfrom pandas import (\n DataFrame,\n Series,\n)\nimport pandas._testing as tm\nfrom pandas.tests.io.pytables.common import ensure_clean_store\n\nfrom pandas.io.pytables import read_hdf\n\n\ndef test_complex_fixed(tmp_path, setup_path):\n df = DataFrame(\n np.random.default_rng(2).random((4, 5)).astype(np.complex64),\n index=list("abcd"),\n columns=list("ABCDE"),\n )\n\n path = tmp_path / setup_path\n df.to_hdf(path, key="df")\n reread = read_hdf(path, "df")\n tm.assert_frame_equal(df, reread)\n\n df = DataFrame(\n np.random.default_rng(2).random((4, 5)).astype(np.complex128),\n index=list("abcd"),\n columns=list("ABCDE"),\n )\n path = tmp_path / setup_path\n df.to_hdf(path, key="df")\n reread = read_hdf(path, "df")\n tm.assert_frame_equal(df, reread)\n\n\ndef test_complex_table(tmp_path, setup_path):\n df = DataFrame(\n np.random.default_rng(2).random((4, 5)).astype(np.complex64),\n index=list("abcd"),\n columns=list("ABCDE"),\n )\n\n path = tmp_path / setup_path\n df.to_hdf(path, key="df", format="table")\n reread = read_hdf(path, key="df")\n tm.assert_frame_equal(df, reread)\n\n df = DataFrame(\n np.random.default_rng(2).random((4, 5)).astype(np.complex128),\n index=list("abcd"),\n columns=list("ABCDE"),\n )\n\n path = tmp_path / setup_path\n df.to_hdf(path, key="df", format="table", mode="w")\n reread = read_hdf(path, "df")\n tm.assert_frame_equal(df, reread)\n\n\ndef test_complex_mixed_fixed(tmp_path, setup_path):\n complex64 = np.array(\n [1.0 + 1.0j, 1.0 + 1.0j, 1.0 + 1.0j, 1.0 + 1.0j], dtype=np.complex64\n )\n complex128 = np.array(\n [1.0 + 1.0j, 1.0 + 1.0j, 1.0 + 1.0j, 1.0 + 1.0j], dtype=np.complex128\n )\n df = DataFrame(\n {\n "A": [1, 2, 3, 4],\n "B": ["a", "b", "c", "d"],\n "C": complex64,\n "D": complex128,\n "E": [1.0, 2.0, 3.0, 4.0],\n },\n index=list("abcd"),\n )\n path = tmp_path / setup_path\n df.to_hdf(path, key="df")\n reread = read_hdf(path, "df")\n tm.assert_frame_equal(df, reread)\n\n\ndef test_complex_mixed_table(tmp_path, setup_path):\n complex64 = np.array(\n [1.0 + 1.0j, 1.0 + 1.0j, 1.0 + 1.0j, 1.0 + 1.0j], dtype=np.complex64\n )\n complex128 = np.array(\n [1.0 + 1.0j, 1.0 + 1.0j, 1.0 + 1.0j, 1.0 + 1.0j], dtype=np.complex128\n )\n df = DataFrame(\n {\n "A": [1, 2, 3, 4],\n "B": ["a", "b", "c", "d"],\n "C": complex64,\n "D": complex128,\n "E": [1.0, 2.0, 3.0, 4.0],\n },\n index=list("abcd"),\n )\n\n with ensure_clean_store(setup_path) as store:\n store.append("df", df, data_columns=["A", "B"])\n result = store.select("df", where="A>2")\n tm.assert_frame_equal(df.loc[df.A > 2], result)\n\n path = tmp_path / setup_path\n df.to_hdf(path, key="df", format="table")\n reread = read_hdf(path, "df")\n tm.assert_frame_equal(df, reread)\n\n\ndef test_complex_across_dimensions_fixed(tmp_path, setup_path):\n complex128 = np.array([1.0 + 1.0j, 1.0 + 1.0j, 1.0 + 1.0j, 1.0 + 1.0j])\n s = Series(complex128, index=list("abcd"))\n df = DataFrame({"A": s, "B": s})\n\n objs = [s, df]\n comps = [tm.assert_series_equal, tm.assert_frame_equal]\n for obj, comp in zip(objs, comps):\n path = tmp_path / setup_path\n obj.to_hdf(path, key="obj", format="fixed")\n reread = read_hdf(path, "obj")\n comp(obj, reread)\n\n\ndef test_complex_across_dimensions(tmp_path, setup_path):\n complex128 = np.array([1.0 + 1.0j, 1.0 + 1.0j, 1.0 + 1.0j, 1.0 + 1.0j])\n s = Series(complex128, index=list("abcd"))\n df = DataFrame({"A": s, "B": s})\n\n path = tmp_path / setup_path\n df.to_hdf(path, key="obj", format="table")\n reread = read_hdf(path, "obj")\n tm.assert_frame_equal(df, reread)\n\n\ndef test_complex_indexing_error(setup_path):\n complex128 = np.array(\n [1.0 + 1.0j, 1.0 + 1.0j, 1.0 + 1.0j, 1.0 + 1.0j], dtype=np.complex128\n )\n df = DataFrame(\n {"A": [1, 2, 3, 4], "B": ["a", "b", "c", "d"], "C": complex128},\n index=list("abcd"),\n )\n\n msg = (\n "Columns containing complex values can be stored "\n "but cannot be indexed when using table format. "\n "Either use fixed format, set index=False, "\n "or do not include the columns containing complex "\n "values to data_columns when initializing the table."\n )\n\n with ensure_clean_store(setup_path) as store:\n with pytest.raises(TypeError, match=msg):\n store.append("df", df, data_columns=["C"])\n\n\ndef test_complex_series_error(tmp_path, setup_path):\n complex128 = np.array([1.0 + 1.0j, 1.0 + 1.0j, 1.0 + 1.0j, 1.0 + 1.0j])\n s = Series(complex128, index=list("abcd"))\n\n msg = (\n "Columns containing complex values can be stored "\n "but cannot be indexed when using table format. "\n "Either use fixed format, set index=False, "\n "or do not include the columns containing complex "\n "values to data_columns when initializing the table."\n )\n\n path = tmp_path / setup_path\n with pytest.raises(TypeError, match=msg):\n s.to_hdf(path, key="obj", format="t")\n\n path = tmp_path / setup_path\n s.to_hdf(path, key="obj", format="t", index=False)\n reread = read_hdf(path, "obj")\n tm.assert_series_equal(s, reread)\n\n\ndef test_complex_append(setup_path):\n df = DataFrame(\n {\n "a": np.random.default_rng(2).standard_normal(100).astype(np.complex128),\n "b": np.random.default_rng(2).standard_normal(100),\n }\n )\n\n with ensure_clean_store(setup_path) as store:\n store.append("df", df, data_columns=["b"])\n store.append("df", df)\n result = store.select("df")\n tm.assert_frame_equal(pd.concat([df, df], axis=0), result)\n | .venv\Lib\site-packages\pandas\tests\io\pytables\test_complex.py | test_complex.py | Python | 5,948 | 0.85 | 0.051282 | 0 | react-lib | 185 | 2024-09-21T10:56:45.918429 | GPL-3.0 | true | 4152bd2145df0371c4b0f13b9432fcb5 |
import datetime\nfrom io import BytesIO\nimport re\n\nimport numpy as np\nimport pytest\n\nfrom pandas import (\n CategoricalIndex,\n DataFrame,\n HDFStore,\n Index,\n MultiIndex,\n _testing as tm,\n date_range,\n read_hdf,\n)\nfrom pandas.tests.io.pytables.common import ensure_clean_store\n\nfrom pandas.io.pytables import (\n Term,\n _maybe_adjust_name,\n)\n\npytestmark = [pytest.mark.single_cpu]\n\n\ndef test_pass_spec_to_storer(setup_path):\n df = DataFrame(\n 1.1 * np.arange(120).reshape((30, 4)),\n columns=Index(list("ABCD"), dtype=object),\n index=Index([f"i-{i}" for i in range(30)], dtype=object),\n )\n\n with ensure_clean_store(setup_path) as store:\n store.put("df", df)\n msg = (\n "cannot pass a column specification when reading a Fixed format "\n "store. this store must be selected in its entirety"\n )\n with pytest.raises(TypeError, match=msg):\n store.select("df", columns=["A"])\n msg = (\n "cannot pass a where specification when reading from a Fixed "\n "format store. this store must be selected in its entirety"\n )\n with pytest.raises(TypeError, match=msg):\n store.select("df", where=[("columns=A")])\n\n\ndef test_table_index_incompatible_dtypes(setup_path):\n df1 = DataFrame({"a": [1, 2, 3]})\n df2 = DataFrame({"a": [4, 5, 6]}, index=date_range("1/1/2000", periods=3))\n\n with ensure_clean_store(setup_path) as store:\n store.put("frame", df1, format="table")\n msg = re.escape("incompatible kind in col [integer - datetime64[ns]]")\n with pytest.raises(TypeError, match=msg):\n store.put("frame", df2, format="table", append=True)\n\n\ndef test_unimplemented_dtypes_table_columns(setup_path):\n with ensure_clean_store(setup_path) as store:\n dtypes = [("date", datetime.date(2001, 1, 2))]\n\n # currently not supported dtypes ####\n for n, f in dtypes:\n df = DataFrame(\n 1.1 * np.arange(120).reshape((30, 4)),\n columns=Index(list("ABCD"), dtype=object),\n index=Index([f"i-{i}" for i in range(30)], dtype=object),\n )\n df[n] = f\n msg = re.escape(f"[{n}] is not implemented as a table column")\n with pytest.raises(TypeError, match=msg):\n store.append(f"df1_{n}", df)\n\n # frame\n df = DataFrame(\n 1.1 * np.arange(120).reshape((30, 4)),\n columns=Index(list("ABCD"), dtype=object),\n index=Index([f"i-{i}" for i in range(30)], dtype=object),\n )\n df["obj1"] = "foo"\n df["obj2"] = "bar"\n df["datetime1"] = datetime.date(2001, 1, 2)\n df = df._consolidate()\n\n with ensure_clean_store(setup_path) as store:\n # this fails because we have a date in the object block......\n msg = "|".join(\n [\n re.escape(\n "Cannot serialize the column [datetime1]\nbecause its data "\n "contents are not [string] but [date] object dtype"\n ),\n re.escape("[date] is not implemented as a table column"),\n ]\n )\n with pytest.raises(TypeError, match=msg):\n store.append("df_unimplemented", df)\n\n\ndef test_invalid_terms(tmp_path, setup_path):\n with ensure_clean_store(setup_path) as store:\n df = DataFrame(\n np.random.default_rng(2).standard_normal((10, 4)),\n columns=Index(list("ABCD"), dtype=object),\n index=date_range("2000-01-01", periods=10, freq="B"),\n )\n df["string"] = "foo"\n df.loc[df.index[0:4], "string"] = "bar"\n\n store.put("df", df, format="table")\n\n # some invalid terms\n msg = re.escape("__init__() missing 1 required positional argument: 'where'")\n with pytest.raises(TypeError, match=msg):\n Term()\n\n # more invalid\n msg = re.escape(\n "cannot process expression [df.index[3]], "\n "[2000-01-06 00:00:00] is not a valid condition"\n )\n with pytest.raises(ValueError, match=msg):\n store.select("df", "df.index[3]")\n\n msg = "invalid syntax"\n with pytest.raises(SyntaxError, match=msg):\n store.select("df", "index>")\n\n # from the docs\n path = tmp_path / setup_path\n dfq = DataFrame(\n np.random.default_rng(2).standard_normal((10, 4)),\n columns=list("ABCD"),\n index=date_range("20130101", periods=10),\n )\n dfq.to_hdf(path, key="dfq", format="table", data_columns=True)\n\n # check ok\n read_hdf(path, "dfq", where="index>Timestamp('20130104') & columns=['A', 'B']")\n read_hdf(path, "dfq", where="A>0 or C>0")\n\n # catch the invalid reference\n path = tmp_path / setup_path\n dfq = DataFrame(\n np.random.default_rng(2).standard_normal((10, 4)),\n columns=list("ABCD"),\n index=date_range("20130101", periods=10),\n )\n dfq.to_hdf(path, key="dfq", format="table")\n\n msg = (\n r"The passed where expression: A>0 or C>0\n\s*"\n r"contains an invalid variable reference\n\s*"\n r"all of the variable references must be a reference to\n\s*"\n r"an axis \(e.g. 'index' or 'columns'\), or a data_column\n\s*"\n r"The currently defined references are: index,columns\n"\n )\n with pytest.raises(ValueError, match=msg):\n read_hdf(path, "dfq", where="A>0 or C>0")\n\n\ndef test_append_with_diff_col_name_types_raises_value_error(setup_path):\n df = DataFrame(np.random.default_rng(2).standard_normal((10, 1)))\n df2 = DataFrame({"a": np.random.default_rng(2).standard_normal(10)})\n df3 = DataFrame({(1, 2): np.random.default_rng(2).standard_normal(10)})\n df4 = DataFrame({("1", 2): np.random.default_rng(2).standard_normal(10)})\n df5 = DataFrame({("1", 2, object): np.random.default_rng(2).standard_normal(10)})\n\n with ensure_clean_store(setup_path) as store:\n name = "df_diff_valerror"\n store.append(name, df)\n\n for d in (df2, df3, df4, df5):\n msg = re.escape(\n "cannot match existing table structure for [0] on appending data"\n )\n with pytest.raises(ValueError, match=msg):\n store.append(name, d)\n\n\ndef test_invalid_complib(setup_path):\n df = DataFrame(\n np.random.default_rng(2).random((4, 5)),\n index=list("abcd"),\n columns=list("ABCDE"),\n )\n with tm.ensure_clean(setup_path) as path:\n msg = r"complib only supports \[.*\] compression."\n with pytest.raises(ValueError, match=msg):\n df.to_hdf(path, key="df", complib="foolib")\n\n\n@pytest.mark.parametrize(\n "idx",\n [\n date_range("2019", freq="D", periods=3, tz="UTC"),\n CategoricalIndex(list("abc")),\n ],\n)\ndef test_to_hdf_multiindex_extension_dtype(idx, tmp_path, setup_path):\n # GH 7775\n mi = MultiIndex.from_arrays([idx, idx])\n df = DataFrame(0, index=mi, columns=["a"])\n path = tmp_path / setup_path\n with pytest.raises(NotImplementedError, match="Saving a MultiIndex"):\n df.to_hdf(path, key="df")\n\n\ndef test_unsuppored_hdf_file_error(datapath):\n # GH 9539\n data_path = datapath("io", "data", "legacy_hdf/incompatible_dataset.h5")\n message = (\n r"Dataset\(s\) incompatible with Pandas data types, "\n "not table, or no datasets found in HDF5 file."\n )\n\n with pytest.raises(ValueError, match=message):\n read_hdf(data_path)\n\n\ndef test_read_hdf_errors(setup_path, tmp_path):\n df = DataFrame(\n np.random.default_rng(2).random((4, 5)),\n index=list("abcd"),\n columns=list("ABCDE"),\n )\n\n path = tmp_path / setup_path\n msg = r"File [\S]* does not exist"\n with pytest.raises(OSError, match=msg):\n read_hdf(path, "key")\n\n df.to_hdf(path, key="df")\n store = HDFStore(path, mode="r")\n store.close()\n\n msg = "The HDFStore must be open for reading."\n with pytest.raises(OSError, match=msg):\n read_hdf(store, "df")\n\n\ndef test_read_hdf_generic_buffer_errors():\n msg = "Support for generic buffers has not been implemented."\n with pytest.raises(NotImplementedError, match=msg):\n read_hdf(BytesIO(b""), "df")\n\n\n@pytest.mark.parametrize("bad_version", [(1, 2), (1,), [], "12", "123"])\ndef test_maybe_adjust_name_bad_version_raises(bad_version):\n msg = "Version is incorrect, expected sequence of 3 integers"\n with pytest.raises(ValueError, match=msg):\n _maybe_adjust_name("values_block_0", version=bad_version)\n | .venv\Lib\site-packages\pandas\tests\io\pytables\test_errors.py | test_errors.py | Python | 8,549 | 0.95 | 0.078125 | 0.047393 | node-utils | 34 | 2024-12-14T16:14:46.275403 | GPL-3.0 | true | ad71a6ca598b38a979371f018117d308 |
import os\n\nimport numpy as np\nimport pytest\n\nfrom pandas.compat import (\n PY311,\n is_ci_environment,\n is_platform_linux,\n is_platform_little_endian,\n)\nfrom pandas.errors import (\n ClosedFileError,\n PossibleDataLossError,\n)\n\nfrom pandas import (\n DataFrame,\n HDFStore,\n Index,\n Series,\n _testing as tm,\n date_range,\n read_hdf,\n)\nfrom pandas.tests.io.pytables.common import (\n _maybe_remove,\n ensure_clean_store,\n tables,\n)\n\nfrom pandas.io import pytables\nfrom pandas.io.pytables import Term\n\npytestmark = [pytest.mark.single_cpu]\n\n\n@pytest.mark.parametrize("mode", ["r", "r+", "a", "w"])\ndef test_mode(setup_path, tmp_path, mode, using_infer_string):\n df = DataFrame(\n np.random.default_rng(2).standard_normal((10, 4)),\n columns=Index(list("ABCD"), dtype=object),\n index=date_range("2000-01-01", periods=10, freq="B"),\n )\n msg = r"[\S]* does not exist"\n path = tmp_path / setup_path\n\n # constructor\n if mode in ["r", "r+"]:\n with pytest.raises(OSError, match=msg):\n HDFStore(path, mode=mode)\n\n else:\n with HDFStore(path, mode=mode) as store:\n assert store._handle.mode == mode\n\n path = tmp_path / setup_path\n\n # context\n if mode in ["r", "r+"]:\n with pytest.raises(OSError, match=msg):\n with HDFStore(path, mode=mode) as store:\n pass\n else:\n with HDFStore(path, mode=mode) as store:\n assert store._handle.mode == mode\n\n path = tmp_path / setup_path\n\n # conv write\n if mode in ["r", "r+"]:\n with pytest.raises(OSError, match=msg):\n df.to_hdf(path, key="df", mode=mode)\n df.to_hdf(path, key="df", mode="w")\n else:\n df.to_hdf(path, key="df", mode=mode)\n\n # conv read\n if mode in ["w"]:\n msg = (\n "mode w is not allowed while performing a read. "\n r"Allowed modes are r, r\+ and a."\n )\n with pytest.raises(ValueError, match=msg):\n read_hdf(path, "df", mode=mode)\n else:\n result = read_hdf(path, "df", mode=mode)\n if using_infer_string:\n df.columns = df.columns.astype("str")\n tm.assert_frame_equal(result, df)\n\n\ndef test_default_mode(tmp_path, setup_path, using_infer_string):\n # read_hdf uses default mode\n df = DataFrame(\n np.random.default_rng(2).standard_normal((10, 4)),\n columns=Index(list("ABCD"), dtype=object),\n index=date_range("2000-01-01", periods=10, freq="B"),\n )\n path = tmp_path / setup_path\n df.to_hdf(path, key="df", mode="w")\n result = read_hdf(path, "df")\n expected = df.copy()\n if using_infer_string:\n expected.columns = expected.columns.astype("str")\n tm.assert_frame_equal(result, expected)\n\n\ndef test_reopen_handle(tmp_path, setup_path):\n path = tmp_path / setup_path\n\n store = HDFStore(path, mode="a")\n store["a"] = Series(\n np.arange(10, dtype=np.float64), index=date_range("2020-01-01", periods=10)\n )\n\n msg = (\n r"Re-opening the file \[[\S]*\] with mode \[a\] will delete the "\n "current file!"\n )\n # invalid mode change\n with pytest.raises(PossibleDataLossError, match=msg):\n store.open("w")\n\n store.close()\n assert not store.is_open\n\n # truncation ok here\n store.open("w")\n assert store.is_open\n assert len(store) == 0\n store.close()\n assert not store.is_open\n\n store = HDFStore(path, mode="a")\n store["a"] = Series(\n np.arange(10, dtype=np.float64), index=date_range("2020-01-01", periods=10)\n )\n\n # reopen as read\n store.open("r")\n assert store.is_open\n assert len(store) == 1\n assert store._mode == "r"\n store.close()\n assert not store.is_open\n\n # reopen as append\n store.open("a")\n assert store.is_open\n assert len(store) == 1\n assert store._mode == "a"\n store.close()\n assert not store.is_open\n\n # reopen as append (again)\n store.open("a")\n assert store.is_open\n assert len(store) == 1\n assert store._mode == "a"\n store.close()\n assert not store.is_open\n\n\ndef test_open_args(setup_path, using_infer_string):\n with tm.ensure_clean(setup_path) as path:\n df = DataFrame(\n 1.1 * np.arange(120).reshape((30, 4)),\n columns=Index(list("ABCD"), dtype=object),\n index=Index([f"i-{i}" for i in range(30)], dtype=object),\n )\n\n # create an in memory store\n store = HDFStore(\n path, mode="a", driver="H5FD_CORE", driver_core_backing_store=0\n )\n store["df"] = df\n store.append("df2", df)\n\n expected = df.copy()\n if using_infer_string:\n expected.index = expected.index.astype("str")\n expected.columns = expected.columns.astype("str")\n\n tm.assert_frame_equal(store["df"], expected)\n tm.assert_frame_equal(store["df2"], expected)\n\n store.close()\n\n # the file should not have actually been written\n assert not os.path.exists(path)\n\n\ndef test_flush(setup_path):\n with ensure_clean_store(setup_path) as store:\n store["a"] = Series(range(5))\n store.flush()\n store.flush(fsync=True)\n\n\ndef test_complibs_default_settings(tmp_path, setup_path, using_infer_string):\n # GH15943\n df = DataFrame(\n 1.1 * np.arange(120).reshape((30, 4)),\n columns=Index(list("ABCD"), dtype=object),\n index=Index([f"i-{i}" for i in range(30)], dtype=object),\n )\n\n # Set complevel and check if complib is automatically set to\n # default value\n tmpfile = tmp_path / setup_path\n df.to_hdf(tmpfile, key="df", complevel=9)\n result = read_hdf(tmpfile, "df")\n expected = df.copy()\n if using_infer_string:\n expected.index = expected.index.astype("str")\n expected.columns = expected.columns.astype("str")\n tm.assert_frame_equal(result, expected)\n\n with tables.open_file(tmpfile, mode="r") as h5file:\n for node in h5file.walk_nodes(where="/df", classname="Leaf"):\n assert node.filters.complevel == 9\n assert node.filters.complib == "zlib"\n\n # Set complib and check to see if compression is disabled\n tmpfile = tmp_path / setup_path\n df.to_hdf(tmpfile, key="df", complib="zlib")\n result = read_hdf(tmpfile, "df")\n expected = df.copy()\n if using_infer_string:\n expected.index = expected.index.astype("str")\n expected.columns = expected.columns.astype("str")\n tm.assert_frame_equal(result, expected)\n\n with tables.open_file(tmpfile, mode="r") as h5file:\n for node in h5file.walk_nodes(where="/df", classname="Leaf"):\n assert node.filters.complevel == 0\n assert node.filters.complib is None\n\n # Check if not setting complib or complevel results in no compression\n tmpfile = tmp_path / setup_path\n df.to_hdf(tmpfile, key="df")\n result = read_hdf(tmpfile, "df")\n expected = df.copy()\n if using_infer_string:\n expected.index = expected.index.astype("str")\n expected.columns = expected.columns.astype("str")\n tm.assert_frame_equal(result, expected)\n\n with tables.open_file(tmpfile, mode="r") as h5file:\n for node in h5file.walk_nodes(where="/df", classname="Leaf"):\n assert node.filters.complevel == 0\n assert node.filters.complib is None\n\n\ndef test_complibs_default_settings_override(tmp_path, setup_path):\n # Check if file-defaults can be overridden on a per table basis\n df = DataFrame(\n 1.1 * np.arange(120).reshape((30, 4)),\n columns=Index(list("ABCD"), dtype=object),\n index=Index([f"i-{i}" for i in range(30)], dtype=object),\n )\n tmpfile = tmp_path / setup_path\n store = HDFStore(tmpfile)\n store.append("dfc", df, complevel=9, complib="blosc")\n store.append("df", df)\n store.close()\n\n with tables.open_file(tmpfile, mode="r") as h5file:\n for node in h5file.walk_nodes(where="/df", classname="Leaf"):\n assert node.filters.complevel == 0\n assert node.filters.complib is None\n for node in h5file.walk_nodes(where="/dfc", classname="Leaf"):\n assert node.filters.complevel == 9\n assert node.filters.complib == "blosc"\n\n\n@pytest.mark.parametrize("lvl", range(10))\n@pytest.mark.parametrize("lib", tables.filters.all_complibs)\n@pytest.mark.filterwarnings("ignore:object name is not a valid")\n@pytest.mark.skipif(\n not PY311 and is_ci_environment() and is_platform_linux(),\n reason="Segfaulting in a CI environment"\n # with xfail, would sometimes raise UnicodeDecodeError\n # invalid state byte\n)\ndef test_complibs(tmp_path, lvl, lib, request):\n # GH14478\n if PY311 and is_platform_linux() and lib == "blosc2" and lvl != 0:\n request.applymarker(\n pytest.mark.xfail(reason=f"Fails for {lib} on Linux and PY > 3.11")\n )\n df = DataFrame(\n np.ones((30, 4)), columns=list("ABCD"), index=np.arange(30).astype(np.str_)\n )\n\n # Remove lzo if its not available on this platform\n if not tables.which_lib_version("lzo"):\n pytest.skip("lzo not available")\n # Remove bzip2 if its not available on this platform\n if not tables.which_lib_version("bzip2"):\n pytest.skip("bzip2 not available")\n\n tmpfile = tmp_path / f"{lvl}_{lib}.h5"\n gname = f"{lvl}_{lib}"\n\n # Write and read file to see if data is consistent\n df.to_hdf(tmpfile, key=gname, complib=lib, complevel=lvl)\n result = read_hdf(tmpfile, gname)\n tm.assert_frame_equal(result, df)\n\n # Open file and check metadata for correct amount of compression\n with tables.open_file(tmpfile, mode="r") as h5table:\n for node in h5table.walk_nodes(where="/" + gname, classname="Leaf"):\n assert node.filters.complevel == lvl\n if lvl == 0:\n assert node.filters.complib is None\n else:\n assert node.filters.complib == lib\n\n\n@pytest.mark.skipif(\n not is_platform_little_endian(), reason="reason platform is not little endian"\n)\ndef test_encoding(setup_path):\n with ensure_clean_store(setup_path) as store:\n df = DataFrame({"A": "foo", "B": "bar"}, index=range(5))\n df.loc[2, "A"] = np.nan\n df.loc[3, "B"] = np.nan\n _maybe_remove(store, "df")\n store.append("df", df, encoding="ascii")\n tm.assert_frame_equal(store["df"], df)\n\n expected = df.reindex(columns=["A"])\n result = store.select("df", Term("columns=A", encoding="ascii"))\n tm.assert_frame_equal(result, expected)\n\n\n@pytest.mark.parametrize(\n "val",\n [\n [b"E\xc9, 17", b"", b"a", b"b", b"c"],\n [b"E\xc9, 17", b"a", b"b", b"c"],\n [b"EE, 17", b"", b"a", b"b", b"c"],\n [b"E\xc9, 17", b"\xf8\xfc", b"a", b"b", b"c"],\n [b"", b"a", b"b", b"c"],\n [b"\xf8\xfc", b"a", b"b", b"c"],\n [b"A\xf8\xfc", b"", b"a", b"b", b"c"],\n [np.nan, b"", b"b", b"c"],\n [b"A\xf8\xfc", np.nan, b"", b"b", b"c"],\n ],\n)\n@pytest.mark.parametrize("dtype", ["category", None])\ndef test_latin_encoding(tmp_path, setup_path, dtype, val):\n enc = "latin-1"\n nan_rep = ""\n key = "data"\n\n val = [x.decode(enc) if isinstance(x, bytes) else x for x in val]\n ser = Series(val, dtype=dtype)\n\n store = tmp_path / setup_path\n ser.to_hdf(store, key=key, format="table", encoding=enc, nan_rep=nan_rep)\n retr = read_hdf(store, key)\n\n # TODO:(3.0): once Categorical replace deprecation is enforced,\n # we may be able to re-simplify the construction of s_nan\n if dtype == "category":\n if nan_rep in ser.cat.categories:\n s_nan = ser.cat.remove_categories([nan_rep])\n else:\n s_nan = ser\n else:\n s_nan = ser.replace(nan_rep, np.nan)\n\n tm.assert_series_equal(s_nan, retr)\n\n\ndef test_multiple_open_close(tmp_path, setup_path):\n # gh-4409: open & close multiple times\n\n path = tmp_path / setup_path\n\n df = DataFrame(\n 1.1 * np.arange(120).reshape((30, 4)),\n columns=Index(list("ABCD"), dtype=object),\n index=Index([f"i-{i}" for i in range(30)], dtype=object),\n )\n df.to_hdf(path, key="df", mode="w", format="table")\n\n # single\n store = HDFStore(path)\n assert "CLOSED" not in store.info()\n assert store.is_open\n\n store.close()\n assert "CLOSED" in store.info()\n assert not store.is_open\n\n path = tmp_path / setup_path\n\n if pytables._table_file_open_policy_is_strict:\n # multiples\n store1 = HDFStore(path)\n msg = (\n r"The file [\S]* is already opened\. Please close it before "\n r"reopening in write mode\."\n )\n with pytest.raises(ValueError, match=msg):\n HDFStore(path)\n\n store1.close()\n else:\n # multiples\n store1 = HDFStore(path)\n store2 = HDFStore(path)\n\n assert "CLOSED" not in store1.info()\n assert "CLOSED" not in store2.info()\n assert store1.is_open\n assert store2.is_open\n\n store1.close()\n assert "CLOSED" in store1.info()\n assert not store1.is_open\n assert "CLOSED" not in store2.info()\n assert store2.is_open\n\n store2.close()\n assert "CLOSED" in store1.info()\n assert "CLOSED" in store2.info()\n assert not store1.is_open\n assert not store2.is_open\n\n # nested close\n store = HDFStore(path, mode="w")\n store.append("df", df)\n\n store2 = HDFStore(path)\n store2.append("df2", df)\n store2.close()\n assert "CLOSED" in store2.info()\n assert not store2.is_open\n\n store.close()\n assert "CLOSED" in store.info()\n assert not store.is_open\n\n # double closing\n store = HDFStore(path, mode="w")\n store.append("df", df)\n\n store2 = HDFStore(path)\n store.close()\n assert "CLOSED" in store.info()\n assert not store.is_open\n\n store2.close()\n assert "CLOSED" in store2.info()\n assert not store2.is_open\n\n # ops on a closed store\n path = tmp_path / setup_path\n\n df = DataFrame(\n 1.1 * np.arange(120).reshape((30, 4)),\n columns=Index(list("ABCD"), dtype=object),\n index=Index([f"i-{i}" for i in range(30)], dtype=object),\n )\n df.to_hdf(path, key="df", mode="w", format="table")\n\n store = HDFStore(path)\n store.close()\n\n msg = r"[\S]* file is not open!"\n with pytest.raises(ClosedFileError, match=msg):\n store.keys()\n\n with pytest.raises(ClosedFileError, match=msg):\n "df" in store\n\n with pytest.raises(ClosedFileError, match=msg):\n len(store)\n\n with pytest.raises(ClosedFileError, match=msg):\n store["df"]\n\n with pytest.raises(ClosedFileError, match=msg):\n store.select("df")\n\n with pytest.raises(ClosedFileError, match=msg):\n store.get("df")\n\n with pytest.raises(ClosedFileError, match=msg):\n store.append("df2", df)\n\n with pytest.raises(ClosedFileError, match=msg):\n store.put("df3", df)\n\n with pytest.raises(ClosedFileError, match=msg):\n store.get_storer("df2")\n\n with pytest.raises(ClosedFileError, match=msg):\n store.remove("df2")\n\n with pytest.raises(ClosedFileError, match=msg):\n store.select("df")\n\n msg = "'HDFStore' object has no attribute 'df'"\n with pytest.raises(AttributeError, match=msg):\n store.df\n\n\ndef test_fspath():\n with tm.ensure_clean("foo.h5") as path:\n with HDFStore(path) as store:\n assert os.fspath(store) == str(path)\n | .venv\Lib\site-packages\pandas\tests\io\pytables\test_file_handling.py | test_file_handling.py | Python | 15,572 | 0.95 | 0.10058 | 0.08076 | vue-tools | 156 | 2024-01-05T17:46:03.534781 | BSD-3-Clause | true | 88a43abb17ecf698df2ceb52071549f5 |
import numpy as np\nimport pytest\n\nfrom pandas import (\n DataFrame,\n HDFStore,\n Index,\n Series,\n date_range,\n)\nfrom pandas.tests.io.pytables.common import (\n ensure_clean_store,\n tables,\n)\n\npytestmark = [pytest.mark.single_cpu]\n\n\ndef test_keys(setup_path):\n with ensure_clean_store(setup_path) as store:\n store["a"] = Series(\n np.arange(10, dtype=np.float64), index=date_range("2020-01-01", periods=10)\n )\n store["b"] = Series(\n range(10), dtype="float64", index=[f"i_{i}" for i in range(10)]\n )\n store["c"] = DataFrame(\n 1.1 * np.arange(120).reshape((30, 4)),\n columns=Index(list("ABCD"), dtype=object),\n index=Index([f"i-{i}" for i in range(30)], dtype=object),\n )\n\n assert len(store) == 3\n expected = {"/a", "/b", "/c"}\n assert set(store.keys()) == expected\n assert set(store) == expected\n\n\ndef test_non_pandas_keys(tmp_path, setup_path):\n class Table1(tables.IsDescription):\n value1 = tables.Float32Col()\n\n class Table2(tables.IsDescription):\n value2 = tables.Float32Col()\n\n class Table3(tables.IsDescription):\n value3 = tables.Float32Col()\n\n path = tmp_path / setup_path\n with tables.open_file(path, mode="w") as h5file:\n group = h5file.create_group("/", "group")\n h5file.create_table(group, "table1", Table1, "Table 1")\n h5file.create_table(group, "table2", Table2, "Table 2")\n h5file.create_table(group, "table3", Table3, "Table 3")\n with HDFStore(path) as store:\n assert len(store.keys(include="native")) == 3\n expected = {"/group/table1", "/group/table2", "/group/table3"}\n assert set(store.keys(include="native")) == expected\n assert set(store.keys(include="pandas")) == set()\n for name in expected:\n df = store.get(name)\n assert len(df.columns) == 1\n\n\ndef test_keys_illegal_include_keyword_value(setup_path):\n with ensure_clean_store(setup_path) as store:\n with pytest.raises(\n ValueError,\n match="`include` should be either 'pandas' or 'native' but is 'illegal'",\n ):\n store.keys(include="illegal")\n\n\ndef test_keys_ignore_hdf_softlink(setup_path):\n # GH 20523\n # Puts a softlink into HDF file and rereads\n\n with ensure_clean_store(setup_path) as store:\n df = DataFrame({"A": range(5), "B": range(5)})\n store.put("df", df)\n\n assert store.keys() == ["/df"]\n\n store._handle.create_soft_link(store._handle.root, "symlink", "df")\n\n # Should ignore the softlink\n assert store.keys() == ["/df"]\n | .venv\Lib\site-packages\pandas\tests\io\pytables\test_keys.py | test_keys.py | Python | 2,673 | 0.95 | 0.114943 | 0.043478 | vue-tools | 80 | 2024-02-09T04:38:53.852147 | MIT | true | dd31650655e4cd14afc7a3cf4d6efdd9 |
import re\n\nimport numpy as np\nimport pytest\n\nfrom pandas._libs.tslibs import Timestamp\n\nimport pandas as pd\nfrom pandas import (\n DataFrame,\n HDFStore,\n Index,\n MultiIndex,\n Series,\n _testing as tm,\n concat,\n date_range,\n)\nfrom pandas.tests.io.pytables.common import (\n _maybe_remove,\n ensure_clean_store,\n)\nfrom pandas.util import _test_decorators as td\n\npytestmark = [pytest.mark.single_cpu]\n\n\ndef test_format_type(tmp_path, setup_path):\n df = DataFrame({"A": [1, 2]})\n with HDFStore(tmp_path / setup_path) as store:\n store.put("a", df, format="fixed")\n store.put("b", df, format="table")\n\n assert store.get_storer("a").format_type == "fixed"\n assert store.get_storer("b").format_type == "table"\n\n\ndef test_format_kwarg_in_constructor(tmp_path, setup_path):\n # GH 13291\n\n msg = "format is not a defined argument for HDFStore"\n\n with pytest.raises(ValueError, match=msg):\n HDFStore(tmp_path / setup_path, format="table")\n\n\ndef test_api_default_format(tmp_path, setup_path):\n # default_format option\n with ensure_clean_store(setup_path) as store:\n df = DataFrame(\n 1.1 * np.arange(120).reshape((30, 4)),\n columns=Index(list("ABCD")),\n index=Index([f"i-{i}" for i in range(30)]),\n )\n\n with pd.option_context("io.hdf.default_format", "fixed"):\n _maybe_remove(store, "df")\n store.put("df", df)\n assert not store.get_storer("df").is_table\n\n msg = "Can only append to Tables"\n with pytest.raises(ValueError, match=msg):\n store.append("df2", df)\n\n with pd.option_context("io.hdf.default_format", "table"):\n _maybe_remove(store, "df")\n store.put("df", df)\n assert store.get_storer("df").is_table\n\n _maybe_remove(store, "df2")\n store.append("df2", df)\n assert store.get_storer("df").is_table\n\n path = tmp_path / setup_path\n df = DataFrame(\n 1.1 * np.arange(120).reshape((30, 4)),\n columns=Index(list("ABCD")),\n index=Index([f"i-{i}" for i in range(30)]),\n )\n\n with pd.option_context("io.hdf.default_format", "fixed"):\n df.to_hdf(path, key="df")\n with HDFStore(path) as store:\n assert not store.get_storer("df").is_table\n with pytest.raises(ValueError, match=msg):\n df.to_hdf(path, key="df2", append=True)\n\n with pd.option_context("io.hdf.default_format", "table"):\n df.to_hdf(path, key="df3")\n with HDFStore(path) as store:\n assert store.get_storer("df3").is_table\n df.to_hdf(path, key="df4", append=True)\n with HDFStore(path) as store:\n assert store.get_storer("df4").is_table\n\n\ndef test_put(setup_path):\n with ensure_clean_store(setup_path) as store:\n ts = Series(\n np.arange(10, dtype=np.float64), index=date_range("2020-01-01", periods=10)\n )\n df = DataFrame(\n np.random.default_rng(2).standard_normal((20, 4)),\n columns=Index(list("ABCD")),\n index=date_range("2000-01-01", periods=20, freq="B"),\n )\n store["a"] = ts\n store["b"] = df[:10]\n store["foo/bar/bah"] = df[:10]\n store["foo"] = df[:10]\n store["/foo"] = df[:10]\n store.put("c", df[:10], format="table")\n\n # not OK, not a table\n msg = "Can only append to Tables"\n with pytest.raises(ValueError, match=msg):\n store.put("b", df[10:], append=True)\n\n # node does not currently exist, test _is_table_type returns False\n # in this case\n _maybe_remove(store, "f")\n with pytest.raises(ValueError, match=msg):\n store.put("f", df[10:], append=True)\n\n # can't put to a table (use append instead)\n with pytest.raises(ValueError, match=msg):\n store.put("c", df[10:], append=True)\n\n # overwrite table\n store.put("c", df[:10], format="table", append=False)\n tm.assert_frame_equal(df[:10], store["c"])\n\n\ndef test_put_string_index(setup_path):\n with ensure_clean_store(setup_path) as store:\n index = Index([f"I am a very long string index: {i}" for i in range(20)])\n s = Series(np.arange(20), index=index)\n df = DataFrame({"A": s, "B": s})\n\n store["a"] = s\n tm.assert_series_equal(store["a"], s)\n\n store["b"] = df\n tm.assert_frame_equal(store["b"], df)\n\n # mixed length\n index = Index(\n ["abcdefghijklmnopqrstuvwxyz1234567890"]\n + [f"I am a very long string index: {i}" for i in range(20)]\n )\n s = Series(np.arange(21), index=index)\n df = DataFrame({"A": s, "B": s})\n store["a"] = s\n tm.assert_series_equal(store["a"], s)\n\n store["b"] = df\n tm.assert_frame_equal(store["b"], df)\n\n\ndef test_put_compression(setup_path):\n with ensure_clean_store(setup_path) as store:\n df = DataFrame(\n np.random.default_rng(2).standard_normal((10, 4)),\n columns=Index(list("ABCD")),\n index=date_range("2000-01-01", periods=10, freq="B"),\n )\n\n store.put("c", df, format="table", complib="zlib")\n tm.assert_frame_equal(store["c"], df)\n\n # can't compress if format='fixed'\n msg = "Compression not supported on Fixed format stores"\n with pytest.raises(ValueError, match=msg):\n store.put("b", df, format="fixed", complib="zlib")\n\n\n@td.skip_if_windows\ndef test_put_compression_blosc(setup_path):\n df = DataFrame(\n np.random.default_rng(2).standard_normal((10, 4)),\n columns=Index(list("ABCD")),\n index=date_range("2000-01-01", periods=10, freq="B"),\n )\n\n with ensure_clean_store(setup_path) as store:\n # can't compress if format='fixed'\n msg = "Compression not supported on Fixed format stores"\n with pytest.raises(ValueError, match=msg):\n store.put("b", df, format="fixed", complib="blosc")\n\n store.put("c", df, format="table", complib="blosc")\n tm.assert_frame_equal(store["c"], df)\n\n\ndef test_put_datetime_ser(setup_path):\n # https://github.com/pandas-dev/pandas/pull/60663\n ser = Series(3 * [Timestamp("20010102").as_unit("ns")])\n with ensure_clean_store(setup_path) as store:\n store.put("ser", ser)\n expected = ser.copy()\n result = store.get("ser")\n tm.assert_series_equal(result, expected)\n\n\ndef test_put_mixed_type(setup_path, using_infer_string):\n df = DataFrame(\n np.random.default_rng(2).standard_normal((10, 4)),\n columns=Index(list("ABCD")),\n index=date_range("2000-01-01", periods=10, freq="B"),\n )\n df["obj1"] = "foo"\n df["obj2"] = "bar"\n df["bool1"] = df["A"] > 0\n df["bool2"] = df["B"] > 0\n df["bool3"] = True\n df["int1"] = 1\n df["int2"] = 2\n df["timestamp1"] = Timestamp("20010102").as_unit("ns")\n df["timestamp2"] = Timestamp("20010103").as_unit("ns")\n df["datetime1"] = Timestamp("20010102").as_unit("ns")\n df["datetime2"] = Timestamp("20010103").as_unit("ns")\n df.loc[df.index[3:6], ["obj1"]] = np.nan\n df = df._consolidate()\n\n with ensure_clean_store(setup_path) as store:\n _maybe_remove(store, "df")\n\n warning = None if using_infer_string else pd.errors.PerformanceWarning\n with tm.assert_produces_warning(warning):\n store.put("df", df)\n\n expected = store.get("df")\n tm.assert_frame_equal(expected, df)\n\n\ndef test_put_str_frame(setup_path, string_dtype_arguments):\n # https://github.com/pandas-dev/pandas/pull/60663\n dtype = pd.StringDtype(*string_dtype_arguments)\n df = DataFrame({"a": pd.array(["x", pd.NA, "y"], dtype=dtype)})\n with ensure_clean_store(setup_path) as store:\n _maybe_remove(store, "df")\n\n store.put("df", df)\n expected_dtype = "str" if dtype.na_value is np.nan else "string"\n expected = df.astype(expected_dtype)\n result = store.get("df")\n tm.assert_frame_equal(result, expected)\n\n\ndef test_put_str_series(setup_path, string_dtype_arguments):\n # https://github.com/pandas-dev/pandas/pull/60663\n dtype = pd.StringDtype(*string_dtype_arguments)\n ser = Series(["x", pd.NA, "y"], dtype=dtype)\n with ensure_clean_store(setup_path) as store:\n _maybe_remove(store, "df")\n\n store.put("ser", ser)\n expected_dtype = "str" if dtype.na_value is np.nan else "string"\n expected = ser.astype(expected_dtype)\n result = store.get("ser")\n tm.assert_series_equal(result, expected)\n\n\n@pytest.mark.parametrize("format", ["table", "fixed"])\n@pytest.mark.parametrize(\n "index",\n [\n Index([str(i) for i in range(10)]),\n Index(np.arange(10, dtype=float)),\n Index(np.arange(10)),\n date_range("2020-01-01", periods=10),\n pd.period_range("2020-01-01", periods=10),\n ],\n)\ndef test_store_index_types(setup_path, format, index):\n # GH5386\n # test storing various index types\n\n with ensure_clean_store(setup_path) as store:\n df = DataFrame(\n np.random.default_rng(2).standard_normal((10, 2)),\n columns=list("AB"),\n index=index,\n )\n _maybe_remove(store, "df")\n store.put("df", df, format=format)\n tm.assert_frame_equal(df, store["df"])\n\n\ndef test_column_multiindex(setup_path, using_infer_string):\n # GH 4710\n # recreate multi-indexes properly\n\n index = MultiIndex.from_tuples(\n [("A", "a"), ("A", "b"), ("B", "a"), ("B", "b")], names=["first", "second"]\n )\n df = DataFrame(np.arange(12).reshape(3, 4), columns=index)\n expected = df.set_axis(df.index.to_numpy())\n\n with ensure_clean_store(setup_path) as store:\n if using_infer_string:\n # TODO(infer_string) make this work for string dtype\n msg = "Saving a MultiIndex with an extension dtype is not supported."\n with pytest.raises(NotImplementedError, match=msg):\n store.put("df", df)\n return\n store.put("df", df)\n tm.assert_frame_equal(\n store["df"], expected, check_index_type=True, check_column_type=True\n )\n\n store.put("df1", df, format="table")\n tm.assert_frame_equal(\n store["df1"], expected, check_index_type=True, check_column_type=True\n )\n\n msg = re.escape("cannot use a multi-index on axis [1] with data_columns ['A']")\n with pytest.raises(ValueError, match=msg):\n store.put("df2", df, format="table", data_columns=["A"])\n msg = re.escape("cannot use a multi-index on axis [1] with data_columns True")\n with pytest.raises(ValueError, match=msg):\n store.put("df3", df, format="table", data_columns=True)\n\n # appending multi-column on existing table (see GH 6167)\n with ensure_clean_store(setup_path) as store:\n store.append("df2", df)\n store.append("df2", df)\n\n tm.assert_frame_equal(store["df2"], concat((df, df)))\n\n # non_index_axes name\n df = DataFrame(np.arange(12).reshape(3, 4), columns=Index(list("ABCD"), name="foo"))\n expected = df.set_axis(df.index.to_numpy())\n\n with ensure_clean_store(setup_path) as store:\n store.put("df1", df, format="table")\n tm.assert_frame_equal(\n store["df1"], expected, check_index_type=True, check_column_type=True\n )\n\n\ndef test_store_multiindex(setup_path):\n # validate multi-index names\n # GH 5527\n with ensure_clean_store(setup_path) as store:\n\n def make_index(names=None):\n dti = date_range("2013-12-01", "2013-12-02")\n mi = MultiIndex.from_product([dti, range(2), range(3)], names=names)\n return mi\n\n # no names\n _maybe_remove(store, "df")\n df = DataFrame(np.zeros((12, 2)), columns=["a", "b"], index=make_index())\n store.append("df", df)\n tm.assert_frame_equal(store.select("df"), df)\n\n # partial names\n _maybe_remove(store, "df")\n df = DataFrame(\n np.zeros((12, 2)),\n columns=["a", "b"],\n index=make_index(["date", None, None]),\n )\n store.append("df", df)\n tm.assert_frame_equal(store.select("df"), df)\n\n # series\n _maybe_remove(store, "ser")\n ser = Series(np.zeros(12), index=make_index(["date", None, None]))\n store.append("ser", ser)\n xp = Series(np.zeros(12), index=make_index(["date", "level_1", "level_2"]))\n tm.assert_series_equal(store.select("ser"), xp)\n\n # dup with column\n _maybe_remove(store, "df")\n df = DataFrame(\n np.zeros((12, 2)),\n columns=["a", "b"],\n index=make_index(["date", "a", "t"]),\n )\n msg = "duplicate names/columns in the multi-index when storing as a table"\n with pytest.raises(ValueError, match=msg):\n store.append("df", df)\n\n # dup within level\n _maybe_remove(store, "df")\n df = DataFrame(\n np.zeros((12, 2)),\n columns=["a", "b"],\n index=make_index(["date", "date", "date"]),\n )\n with pytest.raises(ValueError, match=msg):\n store.append("df", df)\n\n # fully names\n _maybe_remove(store, "df")\n df = DataFrame(\n np.zeros((12, 2)),\n columns=["a", "b"],\n index=make_index(["date", "s", "t"]),\n )\n store.append("df", df)\n tm.assert_frame_equal(store.select("df"), df)\n\n\n@pytest.mark.parametrize("format", ["fixed", "table"])\ndef test_store_periodindex(tmp_path, setup_path, format):\n # GH 7796\n # test of PeriodIndex in HDFStore\n df = DataFrame(\n np.random.default_rng(2).standard_normal((5, 1)),\n index=pd.period_range("20220101", freq="M", periods=5),\n )\n\n path = tmp_path / setup_path\n df.to_hdf(path, key="df", mode="w", format=format)\n expected = pd.read_hdf(path, "df")\n tm.assert_frame_equal(df, expected)\n | .venv\Lib\site-packages\pandas\tests\io\pytables\test_put.py | test_put.py | Python | 14,053 | 0.95 | 0.069212 | 0.087977 | node-utils | 568 | 2024-07-27T23:18:25.984007 | GPL-3.0 | true | 7b3114481efd3fb8b20f1c9257fd8df4 |
import pytest\n\nimport pandas.util._test_decorators as td\n\nimport pandas as pd\nimport pandas._testing as tm\n\n\n@td.skip_if_installed("tables")\ndef test_pytables_raises():\n df = pd.DataFrame({"A": [1, 2]})\n with pytest.raises(ImportError, match="tables"):\n with tm.ensure_clean("foo.h5") as path:\n df.to_hdf(path, key="df")\n | .venv\Lib\site-packages\pandas\tests\io\pytables\test_pytables_missing.py | test_pytables_missing.py | Python | 345 | 0.85 | 0.071429 | 0 | vue-tools | 324 | 2024-01-19T23:45:39.213284 | BSD-3-Clause | true | 4490b6b658f88c33496b714143c07fc4 |
from contextlib import closing\nfrom pathlib import Path\nimport re\n\nimport numpy as np\nimport pytest\n\nfrom pandas._libs.tslibs import Timestamp\nfrom pandas.compat import is_platform_windows\n\nimport pandas as pd\nfrom pandas import (\n DataFrame,\n HDFStore,\n Index,\n Series,\n _testing as tm,\n date_range,\n read_hdf,\n)\nfrom pandas.tests.io.pytables.common import (\n _maybe_remove,\n ensure_clean_store,\n)\nfrom pandas.util import _test_decorators as td\n\nfrom pandas.io.pytables import TableIterator\n\npytestmark = [pytest.mark.single_cpu]\n\n\ndef test_read_missing_key_close_store(tmp_path, setup_path):\n # GH 25766\n path = tmp_path / setup_path\n df = DataFrame({"a": range(2), "b": range(2)})\n df.to_hdf(path, key="k1")\n\n with pytest.raises(KeyError, match="'No object named k2 in the file'"):\n read_hdf(path, "k2")\n\n # smoke test to test that file is properly closed after\n # read with KeyError before another write\n df.to_hdf(path, key="k2")\n\n\ndef test_read_index_error_close_store(tmp_path, setup_path):\n # GH 25766\n path = tmp_path / setup_path\n df = DataFrame({"A": [], "B": []}, index=[])\n df.to_hdf(path, key="k1")\n\n with pytest.raises(IndexError, match=r"list index out of range"):\n read_hdf(path, "k1", stop=0)\n\n # smoke test to test that file is properly closed after\n # read with IndexError before another write\n df.to_hdf(path, key="k1")\n\n\ndef test_read_missing_key_opened_store(tmp_path, setup_path):\n # GH 28699\n path = tmp_path / setup_path\n df = DataFrame({"a": range(2), "b": range(2)})\n df.to_hdf(path, key="k1")\n\n with HDFStore(path, "r") as store:\n with pytest.raises(KeyError, match="'No object named k2 in the file'"):\n read_hdf(store, "k2")\n\n # Test that the file is still open after a KeyError and that we can\n # still read from it.\n read_hdf(store, "k1")\n\n\ndef test_read_column(setup_path):\n df = DataFrame(\n np.random.default_rng(2).standard_normal((10, 4)),\n columns=Index(list("ABCD")),\n index=date_range("2000-01-01", periods=10, freq="B"),\n )\n\n with ensure_clean_store(setup_path) as store:\n _maybe_remove(store, "df")\n\n # GH 17912\n # HDFStore.select_column should raise a KeyError\n # exception if the key is not a valid store\n with pytest.raises(KeyError, match="No object named df in the file"):\n store.select_column("df", "index")\n\n store.append("df", df)\n # error\n with pytest.raises(\n KeyError, match=re.escape("'column [foo] not found in the table'")\n ):\n store.select_column("df", "foo")\n\n msg = re.escape("select_column() got an unexpected keyword argument 'where'")\n with pytest.raises(TypeError, match=msg):\n store.select_column("df", "index", where=["index>5"])\n\n # valid\n result = store.select_column("df", "index")\n tm.assert_almost_equal(result.values, Series(df.index).values)\n assert isinstance(result, Series)\n\n # not a data indexable column\n msg = re.escape(\n "column [values_block_0] can not be extracted individually; "\n "it is not data indexable"\n )\n with pytest.raises(ValueError, match=msg):\n store.select_column("df", "values_block_0")\n\n # a data column\n df2 = df.copy()\n df2["string"] = "foo"\n store.append("df2", df2, data_columns=["string"])\n result = store.select_column("df2", "string")\n tm.assert_almost_equal(result.values, df2["string"].values)\n\n # a data column with NaNs, result excludes the NaNs\n df3 = df.copy()\n df3["string"] = "foo"\n df3.loc[df3.index[4:6], "string"] = np.nan\n store.append("df3", df3, data_columns=["string"])\n result = store.select_column("df3", "string")\n tm.assert_almost_equal(result.values, df3["string"].values)\n\n # start/stop\n result = store.select_column("df3", "string", start=2)\n tm.assert_almost_equal(result.values, df3["string"].values[2:])\n\n result = store.select_column("df3", "string", start=-2)\n tm.assert_almost_equal(result.values, df3["string"].values[-2:])\n\n result = store.select_column("df3", "string", stop=2)\n tm.assert_almost_equal(result.values, df3["string"].values[:2])\n\n result = store.select_column("df3", "string", stop=-2)\n tm.assert_almost_equal(result.values, df3["string"].values[:-2])\n\n result = store.select_column("df3", "string", start=2, stop=-2)\n tm.assert_almost_equal(result.values, df3["string"].values[2:-2])\n\n result = store.select_column("df3", "string", start=-2, stop=2)\n tm.assert_almost_equal(result.values, df3["string"].values[-2:2])\n\n # GH 10392 - make sure column name is preserved\n df4 = DataFrame({"A": np.random.default_rng(2).standard_normal(10), "B": "foo"})\n store.append("df4", df4, data_columns=True)\n expected = df4["B"]\n result = store.select_column("df4", "B")\n tm.assert_series_equal(result, expected)\n\n\ndef test_pytables_native_read(datapath):\n with ensure_clean_store(\n datapath("io", "data", "legacy_hdf/pytables_native.h5"), mode="r"\n ) as store:\n d2 = store["detector/readout"]\n assert isinstance(d2, DataFrame)\n\n\n@pytest.mark.skipif(is_platform_windows(), reason="native2 read fails oddly on windows")\ndef test_pytables_native2_read(datapath):\n with ensure_clean_store(\n datapath("io", "data", "legacy_hdf", "pytables_native2.h5"), mode="r"\n ) as store:\n str(store)\n d1 = store["detector"]\n assert isinstance(d1, DataFrame)\n\n\ndef test_legacy_table_fixed_format_read_py2(datapath):\n # GH 24510\n # legacy table with fixed format written in Python 2\n with ensure_clean_store(\n datapath("io", "data", "legacy_hdf", "legacy_table_fixed_py2.h5"), mode="r"\n ) as store:\n result = store.select("df")\n expected = DataFrame(\n [[1, 2, 3, "D"]],\n columns=["A", "B", "C", "D"],\n index=Index(["ABC"], name="INDEX_NAME"),\n )\n tm.assert_frame_equal(expected, result)\n\n\ndef test_legacy_table_fixed_format_read_datetime_py2(datapath):\n # GH 31750\n # legacy table with fixed format and datetime64 column written in Python 2\n expected = DataFrame(\n [[Timestamp("2020-02-06T18:00")]],\n columns=["A"],\n index=Index(["date"]),\n dtype="M8[ns]",\n )\n with ensure_clean_store(\n datapath("io", "data", "legacy_hdf", "legacy_table_fixed_datetime_py2.h5"),\n mode="r",\n ) as store:\n result = store.select("df")\n tm.assert_frame_equal(expected, result)\n\n\ndef test_legacy_table_read_py2(datapath):\n # issue: 24925\n # legacy table written in Python 2\n with ensure_clean_store(\n datapath("io", "data", "legacy_hdf", "legacy_table_py2.h5"), mode="r"\n ) as store:\n result = store.select("table")\n\n expected = DataFrame({"a": ["a", "b"], "b": [2, 3]})\n tm.assert_frame_equal(expected, result)\n\n\ndef test_read_hdf_open_store(tmp_path, setup_path, using_infer_string):\n # GH10330\n # No check for non-string path_or-buf, and no test of open store\n df = DataFrame(\n np.random.default_rng(2).random((4, 5)),\n index=list("abcd"),\n columns=list("ABCDE"),\n )\n df.index.name = "letters"\n df = df.set_index(keys="E", append=True)\n\n path = tmp_path / setup_path\n if using_infer_string:\n # TODO(infer_string) make this work for string dtype\n msg = "Saving a MultiIndex with an extension dtype is not supported."\n with pytest.raises(NotImplementedError, match=msg):\n df.to_hdf(path, key="df", mode="w")\n return\n df.to_hdf(path, key="df", mode="w")\n direct = read_hdf(path, "df")\n with HDFStore(path, mode="r") as store:\n indirect = read_hdf(store, "df")\n tm.assert_frame_equal(direct, indirect)\n assert store.is_open\n\n\ndef test_read_hdf_index_not_view(tmp_path, setup_path):\n # GH 37441\n # Ensure that the index of the DataFrame is not a view\n # into the original recarray that pytables reads in\n df = DataFrame(\n np.random.default_rng(2).random((4, 5)),\n index=[0, 1, 2, 3],\n columns=list("ABCDE"),\n )\n\n path = tmp_path / setup_path\n df.to_hdf(path, key="df", mode="w", format="table")\n\n df2 = read_hdf(path, "df")\n assert df2.index._data.base is None\n tm.assert_frame_equal(df, df2)\n\n\ndef test_read_hdf_iterator(tmp_path, setup_path):\n df = DataFrame(\n np.random.default_rng(2).random((4, 5)),\n index=list("abcd"),\n columns=list("ABCDE"),\n )\n df.index.name = "letters"\n df = df.set_index(keys="E", append=True)\n\n path = tmp_path / setup_path\n df.to_hdf(path, key="df", mode="w", format="t")\n direct = read_hdf(path, "df")\n iterator = read_hdf(path, "df", iterator=True)\n with closing(iterator.store):\n assert isinstance(iterator, TableIterator)\n indirect = next(iterator.__iter__())\n tm.assert_frame_equal(direct, indirect)\n\n\ndef test_read_nokey(tmp_path, setup_path):\n # GH10443\n df = DataFrame(\n np.random.default_rng(2).random((4, 5)),\n index=list("abcd"),\n columns=list("ABCDE"),\n )\n\n # Categorical dtype not supported for "fixed" format. So no need\n # to test with that dtype in the dataframe here.\n path = tmp_path / setup_path\n df.to_hdf(path, key="df", mode="a")\n reread = read_hdf(path)\n tm.assert_frame_equal(df, reread)\n df.to_hdf(path, key="df2", mode="a")\n\n msg = "key must be provided when HDF5 file contains multiple datasets."\n with pytest.raises(ValueError, match=msg):\n read_hdf(path)\n\n\ndef test_read_nokey_table(tmp_path, setup_path):\n # GH13231\n df = DataFrame({"i": range(5), "c": Series(list("abacd"), dtype="category")})\n\n path = tmp_path / setup_path\n df.to_hdf(path, key="df", mode="a", format="table")\n reread = read_hdf(path)\n tm.assert_frame_equal(df, reread)\n df.to_hdf(path, key="df2", mode="a", format="table")\n\n msg = "key must be provided when HDF5 file contains multiple datasets."\n with pytest.raises(ValueError, match=msg):\n read_hdf(path)\n\n\ndef test_read_nokey_empty(tmp_path, setup_path):\n path = tmp_path / setup_path\n store = HDFStore(path)\n store.close()\n msg = re.escape(\n "Dataset(s) incompatible with Pandas data types, not table, or no "\n "datasets found in HDF5 file."\n )\n with pytest.raises(ValueError, match=msg):\n read_hdf(path)\n\n\ndef test_read_from_pathlib_path(tmp_path, setup_path):\n # GH11773\n expected = DataFrame(\n np.random.default_rng(2).random((4, 5)),\n index=list("abcd"),\n columns=list("ABCDE"),\n )\n filename = tmp_path / setup_path\n path_obj = Path(filename)\n\n expected.to_hdf(path_obj, key="df", mode="a")\n actual = read_hdf(path_obj, key="df")\n\n tm.assert_frame_equal(expected, actual)\n\n\n@td.skip_if_no("py.path")\ndef test_read_from_py_localpath(tmp_path, setup_path):\n # GH11773\n from py.path import local as LocalPath\n\n expected = DataFrame(\n np.random.default_rng(2).random((4, 5)),\n index=list("abcd"),\n columns=list("ABCDE"),\n )\n filename = tmp_path / setup_path\n path_obj = LocalPath(filename)\n\n expected.to_hdf(path_obj, key="df", mode="a")\n actual = read_hdf(path_obj, key="df")\n\n tm.assert_frame_equal(expected, actual)\n\n\n@pytest.mark.parametrize("format", ["fixed", "table"])\ndef test_read_hdf_series_mode_r(tmp_path, format, setup_path):\n # GH 16583\n # Tests that reading a Series saved to an HDF file\n # still works if a mode='r' argument is supplied\n series = Series(range(10), dtype=np.float64)\n path = tmp_path / setup_path\n series.to_hdf(path, key="data", format=format)\n result = read_hdf(path, key="data", mode="r")\n tm.assert_series_equal(result, series)\n\n\n@pytest.mark.filterwarnings(r"ignore:Period with BDay freq is deprecated:FutureWarning")\n@pytest.mark.filterwarnings(r"ignore:PeriodDtype\[B\] is deprecated:FutureWarning")\ndef test_read_py2_hdf_file_in_py3(datapath):\n # GH 16781\n\n # tests reading a PeriodIndex DataFrame written in Python2 in Python3\n\n # the file was generated in Python 2.7 like so:\n #\n # df = DataFrame([1.,2,3], index=pd.PeriodIndex(\n # ['2015-01-01', '2015-01-02', '2015-01-05'], freq='B'))\n # df.to_hdf('periodindex_0.20.1_x86_64_darwin_2.7.13.h5', 'p')\n\n expected = DataFrame(\n [1.0, 2, 3],\n index=pd.PeriodIndex(["2015-01-01", "2015-01-02", "2015-01-05"], freq="B"),\n )\n\n with ensure_clean_store(\n datapath(\n "io", "data", "legacy_hdf", "periodindex_0.20.1_x86_64_darwin_2.7.13.h5"\n ),\n mode="r",\n ) as store:\n result = store["p"]\n tm.assert_frame_equal(result, expected)\n\n\ndef test_read_infer_string(tmp_path, setup_path):\n # GH#54431\n df = DataFrame({"a": ["a", "b", None]})\n path = tmp_path / setup_path\n df.to_hdf(path, key="data", format="table")\n with pd.option_context("future.infer_string", True):\n result = read_hdf(path, key="data", mode="r")\n expected = DataFrame(\n {"a": ["a", "b", None]},\n dtype=pd.StringDtype(na_value=np.nan),\n columns=Index(["a"], dtype=pd.StringDtype(na_value=np.nan)),\n )\n tm.assert_frame_equal(result, expected)\n | .venv\Lib\site-packages\pandas\tests\io\pytables\test_read.py | test_read.py | Python | 13,520 | 0.95 | 0.06235 | 0.144144 | python-kit | 267 | 2025-04-01T09:59:09.837483 | MIT | true | b799cdd74ac9900e2b42c4da58857f3f |
import pytest\n\nfrom pandas import (\n DataFrame,\n DatetimeIndex,\n Series,\n _testing as tm,\n date_range,\n errors,\n read_hdf,\n)\nfrom pandas.tests.io.pytables.common import (\n _maybe_remove,\n ensure_clean_store,\n)\n\npytestmark = pytest.mark.single_cpu\n\n\ndef test_retain_index_attributes(setup_path, unit):\n # GH 3499, losing frequency info on index recreation\n dti = date_range("2000-1-1", periods=3, freq="h", unit=unit)\n df = DataFrame({"A": Series(range(3), index=dti)})\n\n with ensure_clean_store(setup_path) as store:\n _maybe_remove(store, "data")\n store.put("data", df, format="table")\n\n result = store.get("data")\n tm.assert_frame_equal(df, result)\n\n for attr in ["freq", "tz", "name"]:\n for idx in ["index", "columns"]:\n assert getattr(getattr(df, idx), attr, None) == getattr(\n getattr(result, idx), attr, None\n )\n\n dti2 = date_range("2002-1-1", periods=3, freq="D", unit=unit)\n # try to append a table with a different frequency\n with tm.assert_produces_warning(errors.AttributeConflictWarning):\n df2 = DataFrame({"A": Series(range(3), index=dti2)})\n store.append("data", df2)\n\n assert store.get_storer("data").info["index"]["freq"] is None\n\n # this is ok\n _maybe_remove(store, "df2")\n dti3 = DatetimeIndex(\n ["2001-01-01", "2001-01-02", "2002-01-01"], dtype=f"M8[{unit}]"\n )\n df2 = DataFrame(\n {\n "A": Series(\n range(3),\n index=dti3,\n )\n }\n )\n store.append("df2", df2)\n dti4 = date_range("2002-1-1", periods=3, freq="D", unit=unit)\n df3 = DataFrame({"A": Series(range(3), index=dti4)})\n store.append("df2", df3)\n\n\ndef test_retain_index_attributes2(tmp_path, setup_path):\n path = tmp_path / setup_path\n\n with tm.assert_produces_warning(errors.AttributeConflictWarning):\n df = DataFrame(\n {"A": Series(range(3), index=date_range("2000-1-1", periods=3, freq="h"))}\n )\n df.to_hdf(path, key="data", mode="w", append=True)\n df2 = DataFrame(\n {"A": Series(range(3), index=date_range("2002-1-1", periods=3, freq="D"))}\n )\n\n df2.to_hdf(path, key="data", append=True)\n\n idx = date_range("2000-1-1", periods=3, freq="h")\n idx.name = "foo"\n df = DataFrame({"A": Series(range(3), index=idx)})\n df.to_hdf(path, key="data", mode="w", append=True)\n\n assert read_hdf(path, key="data").index.name == "foo"\n\n with tm.assert_produces_warning(errors.AttributeConflictWarning):\n idx2 = date_range("2001-1-1", periods=3, freq="h")\n idx2.name = "bar"\n df2 = DataFrame({"A": Series(range(3), index=idx2)})\n df2.to_hdf(path, key="data", append=True)\n\n assert read_hdf(path, "data").index.name is None\n | .venv\Lib\site-packages\pandas\tests\io\pytables\test_retain_attributes.py | test_retain_attributes.py | Python | 2,970 | 0.95 | 0.054348 | 0.040541 | node-utils | 321 | 2024-07-16T10:40:11.123922 | MIT | true | a03f721fab44b383f741d3874cf8ae67 |
import datetime\nimport re\n\nimport numpy as np\nimport pytest\n\nfrom pandas._libs.tslibs import Timestamp\nfrom pandas.compat import is_platform_windows\n\nimport pandas as pd\nfrom pandas import (\n DataFrame,\n DatetimeIndex,\n Index,\n Series,\n _testing as tm,\n bdate_range,\n date_range,\n read_hdf,\n)\nfrom pandas.tests.io.pytables.common import (\n _maybe_remove,\n ensure_clean_store,\n)\nfrom pandas.util import _test_decorators as td\n\npytestmark = [pytest.mark.single_cpu]\n\n\ndef test_conv_read_write():\n with tm.ensure_clean() as path:\n\n def roundtrip(key, obj, **kwargs):\n obj.to_hdf(path, key=key, **kwargs)\n return read_hdf(path, key)\n\n o = Series(\n np.arange(10, dtype=np.float64), index=date_range("2020-01-01", periods=10)\n )\n tm.assert_series_equal(o, roundtrip("series", o))\n\n o = Series(range(10), dtype="float64", index=[f"i_{i}" for i in range(10)])\n tm.assert_series_equal(o, roundtrip("string_series", o))\n\n o = DataFrame(\n 1.1 * np.arange(120).reshape((30, 4)),\n columns=Index(list("ABCD")),\n index=Index([f"i-{i}" for i in range(30)]),\n )\n tm.assert_frame_equal(o, roundtrip("frame", o))\n\n # table\n df = DataFrame({"A": range(5), "B": range(5)})\n df.to_hdf(path, key="table", append=True)\n result = read_hdf(path, "table", where=["index>2"])\n tm.assert_frame_equal(df[df.index > 2], result)\n\n\ndef test_long_strings(setup_path):\n # GH6166\n data = ["a" * 50] * 10\n df = DataFrame({"a": data}, index=data)\n\n with ensure_clean_store(setup_path) as store:\n store.append("df", df, data_columns=["a"])\n\n result = store.select("df")\n tm.assert_frame_equal(df, result)\n\n\ndef test_api(tmp_path, setup_path):\n # GH4584\n # API issue when to_hdf doesn't accept append AND format args\n path = tmp_path / setup_path\n\n df = DataFrame(range(20))\n df.iloc[:10].to_hdf(path, key="df", append=True, format="table")\n df.iloc[10:].to_hdf(path, key="df", append=True, format="table")\n tm.assert_frame_equal(read_hdf(path, "df"), df)\n\n # append to False\n df.iloc[:10].to_hdf(path, key="df", append=False, format="table")\n df.iloc[10:].to_hdf(path, key="df", append=True, format="table")\n tm.assert_frame_equal(read_hdf(path, "df"), df)\n\n\ndef test_api_append(tmp_path, setup_path):\n path = tmp_path / setup_path\n\n df = DataFrame(range(20))\n df.iloc[:10].to_hdf(path, key="df", append=True)\n df.iloc[10:].to_hdf(path, key="df", append=True, format="table")\n tm.assert_frame_equal(read_hdf(path, "df"), df)\n\n # append to False\n df.iloc[:10].to_hdf(path, key="df", append=False, format="table")\n df.iloc[10:].to_hdf(path, key="df", append=True)\n tm.assert_frame_equal(read_hdf(path, "df"), df)\n\n\ndef test_api_2(tmp_path, setup_path):\n path = tmp_path / setup_path\n\n df = DataFrame(range(20))\n df.to_hdf(path, key="df", append=False, format="fixed")\n tm.assert_frame_equal(read_hdf(path, "df"), df)\n\n df.to_hdf(path, key="df", append=False, format="f")\n tm.assert_frame_equal(read_hdf(path, "df"), df)\n\n df.to_hdf(path, key="df", append=False)\n tm.assert_frame_equal(read_hdf(path, "df"), df)\n\n df.to_hdf(path, key="df")\n tm.assert_frame_equal(read_hdf(path, "df"), df)\n\n with ensure_clean_store(setup_path) as store:\n df = DataFrame(range(20))\n\n _maybe_remove(store, "df")\n store.append("df", df.iloc[:10], append=True, format="table")\n store.append("df", df.iloc[10:], append=True, format="table")\n tm.assert_frame_equal(store.select("df"), df)\n\n # append to False\n _maybe_remove(store, "df")\n store.append("df", df.iloc[:10], append=False, format="table")\n store.append("df", df.iloc[10:], append=True, format="table")\n tm.assert_frame_equal(store.select("df"), df)\n\n # formats\n _maybe_remove(store, "df")\n store.append("df", df.iloc[:10], append=False, format="table")\n store.append("df", df.iloc[10:], append=True, format="table")\n tm.assert_frame_equal(store.select("df"), df)\n\n _maybe_remove(store, "df")\n store.append("df", df.iloc[:10], append=False, format="table")\n store.append("df", df.iloc[10:], append=True, format=None)\n tm.assert_frame_equal(store.select("df"), df)\n\n\ndef test_api_invalid(tmp_path, setup_path):\n path = tmp_path / setup_path\n # Invalid.\n df = DataFrame(\n 1.1 * np.arange(120).reshape((30, 4)),\n columns=Index(list("ABCD")),\n index=Index([f"i-{i}" for i in range(30)]),\n )\n\n msg = "Can only append to Tables"\n\n with pytest.raises(ValueError, match=msg):\n df.to_hdf(path, key="df", append=True, format="f")\n\n with pytest.raises(ValueError, match=msg):\n df.to_hdf(path, key="df", append=True, format="fixed")\n\n msg = r"invalid HDFStore format specified \[foo\]"\n\n with pytest.raises(TypeError, match=msg):\n df.to_hdf(path, key="df", append=True, format="foo")\n\n with pytest.raises(TypeError, match=msg):\n df.to_hdf(path, key="df", append=False, format="foo")\n\n # File path doesn't exist\n path = ""\n msg = f"File {path} does not exist"\n\n with pytest.raises(FileNotFoundError, match=msg):\n read_hdf(path, "df")\n\n\ndef test_get(setup_path):\n with ensure_clean_store(setup_path) as store:\n store["a"] = Series(\n np.arange(10, dtype=np.float64), index=date_range("2020-01-01", periods=10)\n )\n left = store.get("a")\n right = store["a"]\n tm.assert_series_equal(left, right)\n\n left = store.get("/a")\n right = store["/a"]\n tm.assert_series_equal(left, right)\n\n with pytest.raises(KeyError, match="'No object named b in the file'"):\n store.get("b")\n\n\ndef test_put_integer(setup_path):\n # non-date, non-string index\n df = DataFrame(np.random.default_rng(2).standard_normal((50, 100)))\n _check_roundtrip(df, tm.assert_frame_equal, setup_path)\n\n\ndef test_table_values_dtypes_roundtrip(setup_path, using_infer_string):\n with ensure_clean_store(setup_path) as store:\n df1 = DataFrame({"a": [1, 2, 3]}, dtype="f8")\n store.append("df_f8", df1)\n tm.assert_series_equal(df1.dtypes, store["df_f8"].dtypes)\n\n df2 = DataFrame({"a": [1, 2, 3]}, dtype="i8")\n store.append("df_i8", df2)\n tm.assert_series_equal(df2.dtypes, store["df_i8"].dtypes)\n\n # incompatible dtype\n msg = re.escape(\n "Cannot serialize the column [a] "\n "because its data contents are not [float] "\n "but [integer] object dtype"\n )\n with pytest.raises(ValueError, match=msg):\n store.append("df_i8", df1)\n\n # check creation/storage/retrieval of float32 (a bit hacky to\n # actually create them thought)\n df1 = DataFrame(np.array([[1], [2], [3]], dtype="f4"), columns=["A"])\n store.append("df_f4", df1)\n tm.assert_series_equal(df1.dtypes, store["df_f4"].dtypes)\n assert df1.dtypes.iloc[0] == "float32"\n\n # check with mixed dtypes\n df1 = DataFrame(\n {\n c: Series(np.random.default_rng(2).integers(5), dtype=c)\n for c in ["float32", "float64", "int32", "int64", "int16", "int8"]\n }\n )\n df1["string"] = "foo"\n df1["float322"] = 1.0\n df1["float322"] = df1["float322"].astype("float32")\n df1["bool"] = df1["float32"] > 0\n df1["time1"] = Timestamp("20130101")\n df1["time2"] = Timestamp("20130102")\n\n store.append("df_mixed_dtypes1", df1)\n result = store.select("df_mixed_dtypes1").dtypes.value_counts()\n result.index = [str(i) for i in result.index]\n str_dtype = "str" if using_infer_string else "object"\n expected = Series(\n {\n "float32": 2,\n "float64": 1,\n "int32": 1,\n "bool": 1,\n "int16": 1,\n "int8": 1,\n "int64": 1,\n str_dtype: 1,\n "datetime64[ns]": 2,\n },\n name="count",\n )\n result = result.sort_index()\n expected = expected.sort_index()\n tm.assert_series_equal(result, expected)\n\n\n@pytest.mark.filterwarnings("ignore::pandas.errors.PerformanceWarning")\ndef test_series(setup_path):\n s = Series(range(10), dtype="float64", index=[f"i_{i}" for i in range(10)])\n _check_roundtrip(s, tm.assert_series_equal, path=setup_path)\n\n ts = Series(\n np.arange(10, dtype=np.float64), index=date_range("2020-01-01", periods=10)\n )\n _check_roundtrip(ts, tm.assert_series_equal, path=setup_path)\n\n ts2 = Series(ts.index, Index(ts.index))\n _check_roundtrip(ts2, tm.assert_series_equal, path=setup_path)\n\n ts3 = Series(ts.values, Index(np.asarray(ts.index)))\n _check_roundtrip(\n ts3, tm.assert_series_equal, path=setup_path, check_index_type=False\n )\n\n\ndef test_float_index(setup_path):\n # GH #454\n index = np.random.default_rng(2).standard_normal(10)\n s = Series(np.random.default_rng(2).standard_normal(10), index=index)\n _check_roundtrip(s, tm.assert_series_equal, path=setup_path)\n\n\ndef test_tuple_index(setup_path):\n # GH #492\n col = np.arange(10)\n idx = [(0.0, 1.0), (2.0, 3.0), (4.0, 5.0)]\n data = np.random.default_rng(2).standard_normal(30).reshape((3, 10))\n DF = DataFrame(data, index=idx, columns=col)\n\n with tm.assert_produces_warning(pd.errors.PerformanceWarning):\n _check_roundtrip(DF, tm.assert_frame_equal, path=setup_path)\n\n\n@pytest.mark.filterwarnings("ignore::pandas.errors.PerformanceWarning")\ndef test_index_types(setup_path):\n values = np.random.default_rng(2).standard_normal(2)\n\n func = lambda lhs, rhs: tm.assert_series_equal(lhs, rhs, check_index_type=True)\n\n ser = Series(values, [0, "y"])\n _check_roundtrip(ser, func, path=setup_path)\n\n ser = Series(values, [datetime.datetime.today(), 0])\n _check_roundtrip(ser, func, path=setup_path)\n\n ser = Series(values, ["y", 0])\n _check_roundtrip(ser, func, path=setup_path)\n\n ser = Series(values, [datetime.date.today(), "a"])\n _check_roundtrip(ser, func, path=setup_path)\n\n ser = Series(values, [0, "y"])\n _check_roundtrip(ser, func, path=setup_path)\n\n ser = Series(values, [datetime.datetime.today(), 0])\n _check_roundtrip(ser, func, path=setup_path)\n\n ser = Series(values, ["y", 0])\n _check_roundtrip(ser, func, path=setup_path)\n\n ser = Series(values, [datetime.date.today(), "a"])\n _check_roundtrip(ser, func, path=setup_path)\n\n ser = Series(values, [1.23, "b"])\n _check_roundtrip(ser, func, path=setup_path)\n\n ser = Series(values, [1, 1.53])\n _check_roundtrip(ser, func, path=setup_path)\n\n ser = Series(values, [1, 5])\n _check_roundtrip(ser, func, path=setup_path)\n\n dti = DatetimeIndex(["2012-01-01", "2012-01-02"], dtype="M8[ns]")\n ser = Series(values, index=dti)\n _check_roundtrip(ser, func, path=setup_path)\n\n ser.index = ser.index.as_unit("s")\n _check_roundtrip(ser, func, path=setup_path)\n\n\ndef test_timeseries_preepoch(setup_path, request):\n dr = bdate_range("1/1/1940", "1/1/1960")\n ts = Series(np.random.default_rng(2).standard_normal(len(dr)), index=dr)\n try:\n _check_roundtrip(ts, tm.assert_series_equal, path=setup_path)\n except OverflowError:\n if is_platform_windows():\n request.applymarker(\n pytest.mark.xfail("known failure on some windows platforms")\n )\n raise\n\n\n@pytest.mark.parametrize(\n "compression", [False, pytest.param(True, marks=td.skip_if_windows)]\n)\ndef test_frame(compression, setup_path):\n df = DataFrame(\n 1.1 * np.arange(120).reshape((30, 4)),\n columns=Index(list("ABCD")),\n index=Index([f"i-{i}" for i in range(30)]),\n )\n\n # put in some random NAs\n df.iloc[0, 0] = np.nan\n df.iloc[5, 3] = np.nan\n\n _check_roundtrip_table(\n df, tm.assert_frame_equal, path=setup_path, compression=compression\n )\n _check_roundtrip(\n df, tm.assert_frame_equal, path=setup_path, compression=compression\n )\n\n tdf = DataFrame(\n np.random.default_rng(2).standard_normal((10, 4)),\n columns=Index(list("ABCD")),\n index=date_range("2000-01-01", periods=10, freq="B"),\n )\n _check_roundtrip(\n tdf, tm.assert_frame_equal, path=setup_path, compression=compression\n )\n\n with ensure_clean_store(setup_path) as store:\n # not consolidated\n df["foo"] = np.random.default_rng(2).standard_normal(len(df))\n store["df"] = df\n recons = store["df"]\n assert recons._mgr.is_consolidated()\n\n # empty\n df2 = df[:0]\n # Prevent df2 from having index with inferred_type as string\n df2.index = Index([])\n _check_roundtrip(df2[:0], tm.assert_frame_equal, path=setup_path)\n\n\ndef test_empty_series_frame(setup_path):\n s0 = Series(dtype=object)\n s1 = Series(name="myseries", dtype=object)\n df0 = DataFrame()\n df1 = DataFrame(index=["a", "b", "c"])\n df2 = DataFrame(columns=["d", "e", "f"])\n\n _check_roundtrip(s0, tm.assert_series_equal, path=setup_path)\n _check_roundtrip(s1, tm.assert_series_equal, path=setup_path)\n _check_roundtrip(df0, tm.assert_frame_equal, path=setup_path)\n _check_roundtrip(df1, tm.assert_frame_equal, path=setup_path)\n _check_roundtrip(df2, tm.assert_frame_equal, path=setup_path)\n\n\n@pytest.mark.parametrize("dtype", [np.int64, np.float64, object, "m8[ns]", "M8[ns]"])\ndef test_empty_series(dtype, setup_path):\n s = Series(dtype=dtype)\n _check_roundtrip(s, tm.assert_series_equal, path=setup_path)\n\n\ndef test_can_serialize_dates(setup_path):\n rng = [x.date() for x in bdate_range("1/1/2000", "1/30/2000")]\n frame = DataFrame(\n np.random.default_rng(2).standard_normal((len(rng), 4)), index=rng\n )\n\n _check_roundtrip(frame, tm.assert_frame_equal, path=setup_path)\n\n\ndef test_store_hierarchical(\n setup_path, using_infer_string, multiindex_dataframe_random_data\n):\n frame = multiindex_dataframe_random_data\n\n if using_infer_string:\n # TODO(infer_string) make this work for string dtype\n msg = "Saving a MultiIndex with an extension dtype is not supported."\n with pytest.raises(NotImplementedError, match=msg):\n _check_roundtrip(frame, tm.assert_frame_equal, path=setup_path)\n return\n _check_roundtrip(frame, tm.assert_frame_equal, path=setup_path)\n _check_roundtrip(frame.T, tm.assert_frame_equal, path=setup_path)\n _check_roundtrip(frame["A"], tm.assert_series_equal, path=setup_path)\n\n # check that the names are stored\n with ensure_clean_store(setup_path) as store:\n store["frame"] = frame\n recons = store["frame"]\n tm.assert_frame_equal(recons, frame)\n\n\n@pytest.mark.parametrize(\n "compression", [False, pytest.param(True, marks=td.skip_if_windows)]\n)\ndef test_store_mixed(compression, setup_path):\n def _make_one():\n df = DataFrame(\n 1.1 * np.arange(120).reshape((30, 4)),\n columns=Index(list("ABCD")),\n index=Index([f"i-{i}" for i in range(30)]),\n )\n df["obj1"] = "foo"\n df["obj2"] = "bar"\n df["bool1"] = df["A"] > 0\n df["bool2"] = df["B"] > 0\n df["int1"] = 1\n df["int2"] = 2\n return df._consolidate()\n\n df1 = _make_one()\n df2 = _make_one()\n\n _check_roundtrip(df1, tm.assert_frame_equal, path=setup_path)\n _check_roundtrip(df2, tm.assert_frame_equal, path=setup_path)\n\n with ensure_clean_store(setup_path) as store:\n store["obj"] = df1\n tm.assert_frame_equal(store["obj"], df1)\n store["obj"] = df2\n tm.assert_frame_equal(store["obj"], df2)\n\n # check that can store Series of all of these types\n _check_roundtrip(\n df1["obj1"],\n tm.assert_series_equal,\n path=setup_path,\n compression=compression,\n )\n _check_roundtrip(\n df1["bool1"],\n tm.assert_series_equal,\n path=setup_path,\n compression=compression,\n )\n _check_roundtrip(\n df1["int1"],\n tm.assert_series_equal,\n path=setup_path,\n compression=compression,\n )\n\n\ndef _check_roundtrip(obj, comparator, path, compression=False, **kwargs):\n options = {}\n if compression:\n options["complib"] = "blosc"\n\n with ensure_clean_store(path, "w", **options) as store:\n store["obj"] = obj\n retrieved = store["obj"]\n comparator(retrieved, obj, **kwargs)\n\n\ndef _check_roundtrip_table(obj, comparator, path, compression=False):\n options = {}\n if compression:\n options["complib"] = "blosc"\n\n with ensure_clean_store(path, "w", **options) as store:\n store.put("obj", obj, format="table")\n retrieved = store["obj"]\n\n comparator(retrieved, obj)\n\n\ndef test_unicode_index(setup_path):\n unicode_values = ["\u03c3", "\u03c3\u03c3"]\n\n s = Series(\n np.random.default_rng(2).standard_normal(len(unicode_values)),\n unicode_values,\n )\n _check_roundtrip(s, tm.assert_series_equal, path=setup_path)\n\n\ndef test_unicode_longer_encoded(setup_path):\n # GH 11234\n char = "\u0394"\n df = DataFrame({"A": [char]})\n with ensure_clean_store(setup_path) as store:\n store.put("df", df, format="table", encoding="utf-8")\n result = store.get("df")\n tm.assert_frame_equal(result, df)\n\n df = DataFrame({"A": ["a", char], "B": ["b", "b"]})\n with ensure_clean_store(setup_path) as store:\n store.put("df", df, format="table", encoding="utf-8")\n result = store.get("df")\n tm.assert_frame_equal(result, df)\n\n\ndef test_store_datetime_mixed(setup_path):\n df = DataFrame({"a": [1, 2, 3], "b": [1.0, 2.0, 3.0], "c": ["a", "b", "c"]})\n ts = Series(\n np.arange(10, dtype=np.float64), index=date_range("2020-01-01", periods=10)\n )\n df["d"] = ts.index[:3]\n _check_roundtrip(df, tm.assert_frame_equal, path=setup_path)\n\n\ndef test_round_trip_equals(tmp_path, setup_path):\n # GH 9330\n df = DataFrame({"B": [1, 2], "A": ["x", "y"]})\n\n path = tmp_path / setup_path\n df.to_hdf(path, key="df", format="table")\n other = read_hdf(path, "df")\n tm.assert_frame_equal(df, other)\n assert df.equals(other)\n assert other.equals(df)\n\n\ndef test_infer_string_columns(tmp_path, setup_path):\n # GH#\n pytest.importorskip("pyarrow")\n path = tmp_path / setup_path\n with pd.option_context("future.infer_string", True):\n df = DataFrame(1, columns=list("ABCD"), index=list(range(10))).set_index(\n ["A", "B"]\n )\n expected = df.copy()\n df.to_hdf(path, key="df", format="table")\n\n result = read_hdf(path, "df")\n tm.assert_frame_equal(result, expected)\n | .venv\Lib\site-packages\pandas\tests\io\pytables\test_round_trip.py | test_round_trip.py | Python | 18,936 | 0.95 | 0.076661 | 0.059211 | awesome-app | 810 | 2023-11-19T05:57:34.644307 | Apache-2.0 | true | 77916d9b8cde9a4e047c3e41b6130f16 |
import numpy as np\nimport pytest\n\nfrom pandas._libs.tslibs import Timestamp\n\nimport pandas as pd\nfrom pandas import (\n DataFrame,\n HDFStore,\n Index,\n MultiIndex,\n Series,\n _testing as tm,\n bdate_range,\n concat,\n date_range,\n isna,\n read_hdf,\n)\nfrom pandas.tests.io.pytables.common import (\n _maybe_remove,\n ensure_clean_store,\n)\n\nfrom pandas.io.pytables import Term\n\npytestmark = [pytest.mark.single_cpu]\n\n\ndef test_select_columns_in_where(setup_path):\n # GH 6169\n # recreate multi-indexes when columns is passed\n # in the `where` argument\n index = MultiIndex(\n levels=[["foo", "bar", "baz", "qux"], ["one", "two", "three"]],\n codes=[[0, 0, 0, 1, 1, 2, 2, 3, 3, 3], [0, 1, 2, 0, 1, 1, 2, 0, 1, 2]],\n names=["foo_name", "bar_name"],\n )\n\n # With a DataFrame\n df = DataFrame(\n np.random.default_rng(2).standard_normal((10, 3)),\n index=index,\n columns=["A", "B", "C"],\n )\n\n with ensure_clean_store(setup_path) as store:\n store.put("df", df, format="table")\n expected = df[["A"]]\n\n tm.assert_frame_equal(store.select("df", columns=["A"]), expected)\n\n tm.assert_frame_equal(store.select("df", where="columns=['A']"), expected)\n\n # With a Series\n s = Series(np.random.default_rng(2).standard_normal(10), index=index, name="A")\n with ensure_clean_store(setup_path) as store:\n store.put("s", s, format="table")\n tm.assert_series_equal(store.select("s", where="columns=['A']"), s)\n\n\ndef test_select_with_dups(setup_path):\n # single dtypes\n df = DataFrame(\n np.random.default_rng(2).standard_normal((10, 4)), columns=["A", "A", "B", "B"]\n )\n df.index = date_range("20130101 9:30", periods=10, freq="min")\n\n with ensure_clean_store(setup_path) as store:\n store.append("df", df)\n\n result = store.select("df")\n expected = df\n tm.assert_frame_equal(result, expected, by_blocks=True)\n\n result = store.select("df", columns=df.columns)\n expected = df\n tm.assert_frame_equal(result, expected, by_blocks=True)\n\n result = store.select("df", columns=["A"])\n expected = df.loc[:, ["A"]]\n tm.assert_frame_equal(result, expected)\n\n # dups across dtypes\n df = concat(\n [\n DataFrame(\n np.random.default_rng(2).standard_normal((10, 4)),\n columns=["A", "A", "B", "B"],\n ),\n DataFrame(\n np.random.default_rng(2).integers(0, 10, size=20).reshape(10, 2),\n columns=["A", "C"],\n ),\n ],\n axis=1,\n )\n df.index = date_range("20130101 9:30", periods=10, freq="min")\n\n with ensure_clean_store(setup_path) as store:\n store.append("df", df)\n\n result = store.select("df")\n expected = df\n tm.assert_frame_equal(result, expected, by_blocks=True)\n\n result = store.select("df", columns=df.columns)\n expected = df\n tm.assert_frame_equal(result, expected, by_blocks=True)\n\n expected = df.loc[:, ["A"]]\n result = store.select("df", columns=["A"])\n tm.assert_frame_equal(result, expected, by_blocks=True)\n\n expected = df.loc[:, ["B", "A"]]\n result = store.select("df", columns=["B", "A"])\n tm.assert_frame_equal(result, expected, by_blocks=True)\n\n # duplicates on both index and columns\n with ensure_clean_store(setup_path) as store:\n store.append("df", df)\n store.append("df", df)\n\n expected = df.loc[:, ["B", "A"]]\n expected = concat([expected, expected])\n result = store.select("df", columns=["B", "A"])\n tm.assert_frame_equal(result, expected, by_blocks=True)\n\n\ndef test_select(setup_path):\n with ensure_clean_store(setup_path) as store:\n # select with columns=\n df = DataFrame(\n np.random.default_rng(2).standard_normal((10, 4)),\n columns=Index(list("ABCD")),\n index=date_range("2000-01-01", periods=10, freq="B"),\n )\n _maybe_remove(store, "df")\n store.append("df", df)\n result = store.select("df", columns=["A", "B"])\n expected = df.reindex(columns=["A", "B"])\n tm.assert_frame_equal(expected, result)\n\n # equivalently\n result = store.select("df", [("columns=['A', 'B']")])\n expected = df.reindex(columns=["A", "B"])\n tm.assert_frame_equal(expected, result)\n\n # with a data column\n _maybe_remove(store, "df")\n store.append("df", df, data_columns=["A"])\n result = store.select("df", ["A > 0"], columns=["A", "B"])\n expected = df[df.A > 0].reindex(columns=["A", "B"])\n tm.assert_frame_equal(expected, result)\n\n # all a data columns\n _maybe_remove(store, "df")\n store.append("df", df, data_columns=True)\n result = store.select("df", ["A > 0"], columns=["A", "B"])\n expected = df[df.A > 0].reindex(columns=["A", "B"])\n tm.assert_frame_equal(expected, result)\n\n # with a data column, but different columns\n _maybe_remove(store, "df")\n store.append("df", df, data_columns=["A"])\n result = store.select("df", ["A > 0"], columns=["C", "D"])\n expected = df[df.A > 0].reindex(columns=["C", "D"])\n tm.assert_frame_equal(expected, result)\n\n\ndef test_select_dtypes(setup_path):\n with ensure_clean_store(setup_path) as store:\n # with a Timestamp data column (GH #2637)\n df = DataFrame(\n {\n "ts": bdate_range("2012-01-01", periods=300),\n "A": np.random.default_rng(2).standard_normal(300),\n }\n )\n _maybe_remove(store, "df")\n store.append("df", df, data_columns=["ts", "A"])\n\n result = store.select("df", "ts>=Timestamp('2012-02-01')")\n expected = df[df.ts >= Timestamp("2012-02-01")]\n tm.assert_frame_equal(expected, result)\n\n # bool columns (GH #2849)\n df = DataFrame(\n np.random.default_rng(2).standard_normal((5, 2)), columns=["A", "B"]\n )\n df["object"] = "foo"\n df.loc[4:5, "object"] = "bar"\n df["boolv"] = df["A"] > 0\n _maybe_remove(store, "df")\n store.append("df", df, data_columns=True)\n\n expected = df[df.boolv == True].reindex(columns=["A", "boolv"]) # noqa: E712\n for v in [True, "true", 1]:\n result = store.select("df", f"boolv == {v}", columns=["A", "boolv"])\n tm.assert_frame_equal(expected, result)\n\n expected = df[df.boolv == False].reindex(columns=["A", "boolv"]) # noqa: E712\n for v in [False, "false", 0]:\n result = store.select("df", f"boolv == {v}", columns=["A", "boolv"])\n tm.assert_frame_equal(expected, result)\n\n # integer index\n df = DataFrame(\n {\n "A": np.random.default_rng(2).random(20),\n "B": np.random.default_rng(2).random(20),\n }\n )\n _maybe_remove(store, "df_int")\n store.append("df_int", df)\n result = store.select("df_int", "index<10 and columns=['A']")\n expected = df.reindex(index=list(df.index)[0:10], columns=["A"])\n tm.assert_frame_equal(expected, result)\n\n # float index\n df = DataFrame(\n {\n "A": np.random.default_rng(2).random(20),\n "B": np.random.default_rng(2).random(20),\n "index": np.arange(20, dtype="f8"),\n }\n )\n _maybe_remove(store, "df_float")\n store.append("df_float", df)\n result = store.select("df_float", "index<10.0 and columns=['A']")\n expected = df.reindex(index=list(df.index)[0:10], columns=["A"])\n tm.assert_frame_equal(expected, result)\n\n with ensure_clean_store(setup_path) as store:\n # floats w/o NaN\n df = DataFrame({"cols": range(11), "values": range(11)}, dtype="float64")\n df["cols"] = (df["cols"] + 10).apply(str)\n\n store.append("df1", df, data_columns=True)\n result = store.select("df1", where="values>2.0")\n expected = df[df["values"] > 2.0]\n tm.assert_frame_equal(expected, result)\n\n # floats with NaN\n df.iloc[0] = np.nan\n expected = df[df["values"] > 2.0]\n\n store.append("df2", df, data_columns=True, index=False)\n result = store.select("df2", where="values>2.0")\n tm.assert_frame_equal(expected, result)\n\n # https://github.com/PyTables/PyTables/issues/282\n # bug in selection when 0th row has a np.nan and an index\n # store.append('df3',df,data_columns=True)\n # result = store.select(\n # 'df3', where='values>2.0')\n # tm.assert_frame_equal(expected, result)\n\n # not in first position float with NaN ok too\n df = DataFrame({"cols": range(11), "values": range(11)}, dtype="float64")\n df["cols"] = (df["cols"] + 10).apply(str)\n\n df.iloc[1] = np.nan\n expected = df[df["values"] > 2.0]\n\n store.append("df4", df, data_columns=True)\n result = store.select("df4", where="values>2.0")\n tm.assert_frame_equal(expected, result)\n\n # test selection with comparison against numpy scalar\n # GH 11283\n with ensure_clean_store(setup_path) as store:\n df = DataFrame(\n 1.1 * np.arange(120).reshape((30, 4)),\n columns=Index(list("ABCD")),\n index=Index([f"i-{i}" for i in range(30)]),\n )\n\n expected = df[df["A"] > 0]\n\n store.append("df", df, data_columns=True)\n np_zero = np.float64(0) # noqa: F841\n result = store.select("df", where=["A>np_zero"])\n tm.assert_frame_equal(expected, result)\n\n\ndef test_select_with_many_inputs(setup_path):\n with ensure_clean_store(setup_path) as store:\n df = DataFrame(\n {\n "ts": bdate_range("2012-01-01", periods=300),\n "A": np.random.default_rng(2).standard_normal(300),\n "B": range(300),\n "users": ["a"] * 50\n + ["b"] * 50\n + ["c"] * 100\n + [f"a{i:03d}" for i in range(100)],\n }\n )\n _maybe_remove(store, "df")\n store.append("df", df, data_columns=["ts", "A", "B", "users"])\n\n # regular select\n result = store.select("df", "ts>=Timestamp('2012-02-01')")\n expected = df[df.ts >= Timestamp("2012-02-01")]\n tm.assert_frame_equal(expected, result)\n\n # small selector\n result = store.select("df", "ts>=Timestamp('2012-02-01') & users=['a','b','c']")\n expected = df[\n (df.ts >= Timestamp("2012-02-01")) & df.users.isin(["a", "b", "c"])\n ]\n tm.assert_frame_equal(expected, result)\n\n # big selector along the columns\n selector = ["a", "b", "c"] + [f"a{i:03d}" for i in range(60)]\n result = store.select("df", "ts>=Timestamp('2012-02-01') and users=selector")\n expected = df[(df.ts >= Timestamp("2012-02-01")) & df.users.isin(selector)]\n tm.assert_frame_equal(expected, result)\n\n selector = range(100, 200)\n result = store.select("df", "B=selector")\n expected = df[df.B.isin(selector)]\n tm.assert_frame_equal(expected, result)\n assert len(result) == 100\n\n # big selector along the index\n selector = Index(df.ts[0:100].values)\n result = store.select("df", "ts=selector")\n expected = df[df.ts.isin(selector.values)]\n tm.assert_frame_equal(expected, result)\n assert len(result) == 100\n\n\ndef test_select_iterator(tmp_path, setup_path):\n # single table\n with ensure_clean_store(setup_path) as store:\n df = DataFrame(\n np.random.default_rng(2).standard_normal((10, 4)),\n columns=Index(list("ABCD")),\n index=date_range("2000-01-01", periods=10, freq="B"),\n )\n _maybe_remove(store, "df")\n store.append("df", df)\n\n expected = store.select("df")\n\n results = list(store.select("df", iterator=True))\n result = concat(results)\n tm.assert_frame_equal(expected, result)\n\n results = list(store.select("df", chunksize=2))\n assert len(results) == 5\n result = concat(results)\n tm.assert_frame_equal(expected, result)\n\n results = list(store.select("df", chunksize=2))\n result = concat(results)\n tm.assert_frame_equal(result, expected)\n\n path = tmp_path / setup_path\n\n df = DataFrame(\n np.random.default_rng(2).standard_normal((10, 4)),\n columns=Index(list("ABCD")),\n index=date_range("2000-01-01", periods=10, freq="B"),\n )\n df.to_hdf(path, key="df_non_table")\n\n msg = "can only use an iterator or chunksize on a table"\n with pytest.raises(TypeError, match=msg):\n read_hdf(path, "df_non_table", chunksize=2)\n\n with pytest.raises(TypeError, match=msg):\n read_hdf(path, "df_non_table", iterator=True)\n\n path = tmp_path / setup_path\n\n df = DataFrame(\n np.random.default_rng(2).standard_normal((10, 4)),\n columns=Index(list("ABCD")),\n index=date_range("2000-01-01", periods=10, freq="B"),\n )\n df.to_hdf(path, key="df", format="table")\n\n results = list(read_hdf(path, "df", chunksize=2))\n result = concat(results)\n\n assert len(results) == 5\n tm.assert_frame_equal(result, df)\n tm.assert_frame_equal(result, read_hdf(path, "df"))\n\n # multiple\n\n with ensure_clean_store(setup_path) as store:\n df1 = DataFrame(\n np.random.default_rng(2).standard_normal((10, 4)),\n columns=Index(list("ABCD")),\n index=date_range("2000-01-01", periods=10, freq="B"),\n )\n store.append("df1", df1, data_columns=True)\n df2 = df1.copy().rename(columns="{}_2".format)\n df2["foo"] = "bar"\n store.append("df2", df2)\n\n df = concat([df1, df2], axis=1)\n\n # full selection\n expected = store.select_as_multiple(["df1", "df2"], selector="df1")\n results = list(\n store.select_as_multiple(["df1", "df2"], selector="df1", chunksize=2)\n )\n result = concat(results)\n tm.assert_frame_equal(expected, result)\n\n\ndef test_select_iterator_complete_8014(setup_path):\n # GH 8014\n # using iterator and where clause\n chunksize = 1e4\n\n # no iterator\n with ensure_clean_store(setup_path) as store:\n expected = DataFrame(\n np.random.default_rng(2).standard_normal((100064, 4)),\n columns=Index(list("ABCD")),\n index=date_range("2000-01-01", periods=100064, freq="s"),\n )\n _maybe_remove(store, "df")\n store.append("df", expected)\n\n beg_dt = expected.index[0]\n end_dt = expected.index[-1]\n\n # select w/o iteration and no where clause works\n result = store.select("df")\n tm.assert_frame_equal(expected, result)\n\n # select w/o iterator and where clause, single term, begin\n # of range, works\n where = f"index >= '{beg_dt}'"\n result = store.select("df", where=where)\n tm.assert_frame_equal(expected, result)\n\n # select w/o iterator and where clause, single term, end\n # of range, works\n where = f"index <= '{end_dt}'"\n result = store.select("df", where=where)\n tm.assert_frame_equal(expected, result)\n\n # select w/o iterator and where clause, inclusive range,\n # works\n where = f"index >= '{beg_dt}' & index <= '{end_dt}'"\n result = store.select("df", where=where)\n tm.assert_frame_equal(expected, result)\n\n # with iterator, full range\n with ensure_clean_store(setup_path) as store:\n expected = DataFrame(\n np.random.default_rng(2).standard_normal((100064, 4)),\n columns=Index(list("ABCD")),\n index=date_range("2000-01-01", periods=100064, freq="s"),\n )\n _maybe_remove(store, "df")\n store.append("df", expected)\n\n beg_dt = expected.index[0]\n end_dt = expected.index[-1]\n\n # select w/iterator and no where clause works\n results = list(store.select("df", chunksize=chunksize))\n result = concat(results)\n tm.assert_frame_equal(expected, result)\n\n # select w/iterator and where clause, single term, begin of range\n where = f"index >= '{beg_dt}'"\n results = list(store.select("df", where=where, chunksize=chunksize))\n result = concat(results)\n tm.assert_frame_equal(expected, result)\n\n # select w/iterator and where clause, single term, end of range\n where = f"index <= '{end_dt}'"\n results = list(store.select("df", where=where, chunksize=chunksize))\n result = concat(results)\n tm.assert_frame_equal(expected, result)\n\n # select w/iterator and where clause, inclusive range\n where = f"index >= '{beg_dt}' & index <= '{end_dt}'"\n results = list(store.select("df", where=where, chunksize=chunksize))\n result = concat(results)\n tm.assert_frame_equal(expected, result)\n\n\ndef test_select_iterator_non_complete_8014(setup_path):\n # GH 8014\n # using iterator and where clause\n chunksize = 1e4\n\n # with iterator, non complete range\n with ensure_clean_store(setup_path) as store:\n expected = DataFrame(\n np.random.default_rng(2).standard_normal((100064, 4)),\n columns=Index(list("ABCD")),\n index=date_range("2000-01-01", periods=100064, freq="s"),\n )\n _maybe_remove(store, "df")\n store.append("df", expected)\n\n beg_dt = expected.index[1]\n end_dt = expected.index[-2]\n\n # select w/iterator and where clause, single term, begin of range\n where = f"index >= '{beg_dt}'"\n results = list(store.select("df", where=where, chunksize=chunksize))\n result = concat(results)\n rexpected = expected[expected.index >= beg_dt]\n tm.assert_frame_equal(rexpected, result)\n\n # select w/iterator and where clause, single term, end of range\n where = f"index <= '{end_dt}'"\n results = list(store.select("df", where=where, chunksize=chunksize))\n result = concat(results)\n rexpected = expected[expected.index <= end_dt]\n tm.assert_frame_equal(rexpected, result)\n\n # select w/iterator and where clause, inclusive range\n where = f"index >= '{beg_dt}' & index <= '{end_dt}'"\n results = list(store.select("df", where=where, chunksize=chunksize))\n result = concat(results)\n rexpected = expected[(expected.index >= beg_dt) & (expected.index <= end_dt)]\n tm.assert_frame_equal(rexpected, result)\n\n # with iterator, empty where\n with ensure_clean_store(setup_path) as store:\n expected = DataFrame(\n np.random.default_rng(2).standard_normal((100064, 4)),\n columns=Index(list("ABCD")),\n index=date_range("2000-01-01", periods=100064, freq="s"),\n )\n _maybe_remove(store, "df")\n store.append("df", expected)\n\n end_dt = expected.index[-1]\n\n # select w/iterator and where clause, single term, begin of range\n where = f"index > '{end_dt}'"\n results = list(store.select("df", where=where, chunksize=chunksize))\n assert 0 == len(results)\n\n\ndef test_select_iterator_many_empty_frames(setup_path):\n # GH 8014\n # using iterator and where clause can return many empty\n # frames.\n chunksize = 10_000\n\n # with iterator, range limited to the first chunk\n with ensure_clean_store(setup_path) as store:\n expected = DataFrame(\n np.random.default_rng(2).standard_normal((100064, 4)),\n columns=Index(list("ABCD")),\n index=date_range("2000-01-01", periods=100064, freq="s"),\n )\n _maybe_remove(store, "df")\n store.append("df", expected)\n\n beg_dt = expected.index[0]\n end_dt = expected.index[chunksize - 1]\n\n # select w/iterator and where clause, single term, begin of range\n where = f"index >= '{beg_dt}'"\n results = list(store.select("df", where=where, chunksize=chunksize))\n result = concat(results)\n rexpected = expected[expected.index >= beg_dt]\n tm.assert_frame_equal(rexpected, result)\n\n # select w/iterator and where clause, single term, end of range\n where = f"index <= '{end_dt}'"\n results = list(store.select("df", where=where, chunksize=chunksize))\n\n assert len(results) == 1\n result = concat(results)\n rexpected = expected[expected.index <= end_dt]\n tm.assert_frame_equal(rexpected, result)\n\n # select w/iterator and where clause, inclusive range\n where = f"index >= '{beg_dt}' & index <= '{end_dt}'"\n results = list(store.select("df", where=where, chunksize=chunksize))\n\n # should be 1, is 10\n assert len(results) == 1\n result = concat(results)\n rexpected = expected[(expected.index >= beg_dt) & (expected.index <= end_dt)]\n tm.assert_frame_equal(rexpected, result)\n\n # select w/iterator and where clause which selects\n # *nothing*.\n #\n # To be consistent with Python idiom I suggest this should\n # return [] e.g. `for e in []: print True` never prints\n # True.\n\n where = f"index <= '{beg_dt}' & index >= '{end_dt}'"\n results = list(store.select("df", where=where, chunksize=chunksize))\n\n # should be []\n assert len(results) == 0\n\n\ndef test_frame_select(setup_path):\n df = DataFrame(\n np.random.default_rng(2).standard_normal((10, 4)),\n columns=Index(list("ABCD")),\n index=date_range("2000-01-01", periods=10, freq="B"),\n )\n\n with ensure_clean_store(setup_path) as store:\n store.put("frame", df, format="table")\n date = df.index[len(df) // 2]\n\n crit1 = Term("index>=date")\n assert crit1.env.scope["date"] == date\n\n crit2 = "columns=['A', 'D']"\n crit3 = "columns=A"\n\n result = store.select("frame", [crit1, crit2])\n expected = df.loc[date:, ["A", "D"]]\n tm.assert_frame_equal(result, expected)\n\n result = store.select("frame", [crit3])\n expected = df.loc[:, ["A"]]\n tm.assert_frame_equal(result, expected)\n\n # invalid terms\n df = DataFrame(\n np.random.default_rng(2).standard_normal((10, 4)),\n columns=Index(list("ABCD")),\n index=date_range("2000-01-01", periods=10, freq="B"),\n )\n store.append("df_time", df)\n msg = "day is out of range for month: 0"\n with pytest.raises(ValueError, match=msg):\n store.select("df_time", "index>0")\n\n # can't select if not written as table\n # store['frame'] = df\n # with pytest.raises(ValueError):\n # store.select('frame', [crit1, crit2])\n\n\ndef test_frame_select_complex(setup_path):\n # select via complex criteria\n\n df = DataFrame(\n np.random.default_rng(2).standard_normal((10, 4)),\n columns=Index(list("ABCD")),\n index=date_range("2000-01-01", periods=10, freq="B"),\n )\n df["string"] = "foo"\n df.loc[df.index[0:4], "string"] = "bar"\n\n with ensure_clean_store(setup_path) as store:\n store.put("df", df, format="table", data_columns=["string"])\n\n # empty\n result = store.select("df", 'index>df.index[3] & string="bar"')\n expected = df.loc[(df.index > df.index[3]) & (df.string == "bar")]\n tm.assert_frame_equal(result, expected)\n\n result = store.select("df", 'index>df.index[3] & string="foo"')\n expected = df.loc[(df.index > df.index[3]) & (df.string == "foo")]\n tm.assert_frame_equal(result, expected)\n\n # or\n result = store.select("df", 'index>df.index[3] | string="bar"')\n expected = df.loc[(df.index > df.index[3]) | (df.string == "bar")]\n tm.assert_frame_equal(result, expected)\n\n result = store.select(\n "df", '(index>df.index[3] & index<=df.index[6]) | string="bar"'\n )\n expected = df.loc[\n ((df.index > df.index[3]) & (df.index <= df.index[6]))\n | (df.string == "bar")\n ]\n tm.assert_frame_equal(result, expected)\n\n # invert\n result = store.select("df", 'string!="bar"')\n expected = df.loc[df.string != "bar"]\n tm.assert_frame_equal(result, expected)\n\n # invert not implemented in numexpr :(\n msg = "cannot use an invert condition when passing to numexpr"\n with pytest.raises(NotImplementedError, match=msg):\n store.select("df", '~(string="bar")')\n\n # invert ok for filters\n result = store.select("df", "~(columns=['A','B'])")\n expected = df.loc[:, df.columns.difference(["A", "B"])]\n tm.assert_frame_equal(result, expected)\n\n # in\n result = store.select("df", "index>df.index[3] & columns in ['A','B']")\n expected = df.loc[df.index > df.index[3]].reindex(columns=["A", "B"])\n tm.assert_frame_equal(result, expected)\n\n\ndef test_frame_select_complex2(tmp_path):\n pp = tmp_path / "params.hdf"\n hh = tmp_path / "hist.hdf"\n\n # use non-trivial selection criteria\n params = DataFrame({"A": [1, 1, 2, 2, 3]})\n params.to_hdf(pp, key="df", mode="w", format="table", data_columns=["A"])\n\n selection = read_hdf(pp, "df", where="A=[2,3]")\n hist = DataFrame(\n np.random.default_rng(2).standard_normal((25, 1)),\n columns=["data"],\n index=MultiIndex.from_tuples(\n [(i, j) for i in range(5) for j in range(5)], names=["l1", "l2"]\n ),\n )\n\n hist.to_hdf(hh, key="df", mode="w", format="table")\n\n expected = read_hdf(hh, "df", where="l1=[2, 3, 4]")\n\n # scope with list like\n l0 = selection.index.tolist() # noqa: F841\n with HDFStore(hh) as store:\n result = store.select("df", where="l1=l0")\n tm.assert_frame_equal(result, expected)\n\n result = read_hdf(hh, "df", where="l1=l0")\n tm.assert_frame_equal(result, expected)\n\n # index\n index = selection.index # noqa: F841\n result = read_hdf(hh, "df", where="l1=index")\n tm.assert_frame_equal(result, expected)\n\n result = read_hdf(hh, "df", where="l1=selection.index")\n tm.assert_frame_equal(result, expected)\n\n result = read_hdf(hh, "df", where="l1=selection.index.tolist()")\n tm.assert_frame_equal(result, expected)\n\n result = read_hdf(hh, "df", where="l1=list(selection.index)")\n tm.assert_frame_equal(result, expected)\n\n # scope with index\n with HDFStore(hh) as store:\n result = store.select("df", where="l1=index")\n tm.assert_frame_equal(result, expected)\n\n result = store.select("df", where="l1=selection.index")\n tm.assert_frame_equal(result, expected)\n\n result = store.select("df", where="l1=selection.index.tolist()")\n tm.assert_frame_equal(result, expected)\n\n result = store.select("df", where="l1=list(selection.index)")\n tm.assert_frame_equal(result, expected)\n\n\ndef test_invalid_filtering(setup_path):\n # can't use more than one filter (atm)\n\n df = DataFrame(\n np.random.default_rng(2).standard_normal((10, 4)),\n columns=Index(list("ABCD")),\n index=date_range("2000-01-01", periods=10, freq="B"),\n )\n\n with ensure_clean_store(setup_path) as store:\n store.put("df", df, format="table")\n\n msg = "unable to collapse Joint Filters"\n # not implemented\n with pytest.raises(NotImplementedError, match=msg):\n store.select("df", "columns=['A'] | columns=['B']")\n\n # in theory we could deal with this\n with pytest.raises(NotImplementedError, match=msg):\n store.select("df", "columns=['A','B'] & columns=['C']")\n\n\ndef test_string_select(setup_path):\n # GH 2973\n with ensure_clean_store(setup_path) as store:\n df = DataFrame(\n np.random.default_rng(2).standard_normal((10, 4)),\n columns=Index(list("ABCD")),\n index=date_range("2000-01-01", periods=10, freq="B"),\n )\n\n # test string ==/!=\n df["x"] = "none"\n df.loc[df.index[2:7], "x"] = ""\n\n store.append("df", df, data_columns=["x"])\n\n result = store.select("df", "x=none")\n expected = df[df.x == "none"]\n tm.assert_frame_equal(result, expected)\n\n result = store.select("df", "x!=none")\n expected = df[df.x != "none"]\n tm.assert_frame_equal(result, expected)\n\n df2 = df.copy()\n df2.loc[df2.x == "", "x"] = np.nan\n\n store.append("df2", df2, data_columns=["x"])\n result = store.select("df2", "x!=none")\n expected = df2[isna(df2.x)]\n tm.assert_frame_equal(result, expected)\n\n # int ==/!=\n df["int"] = 1\n df.loc[df.index[2:7], "int"] = 2\n\n store.append("df3", df, data_columns=["int"])\n\n result = store.select("df3", "int=2")\n expected = df[df.int == 2]\n tm.assert_frame_equal(result, expected)\n\n result = store.select("df3", "int!=2")\n expected = df[df.int != 2]\n tm.assert_frame_equal(result, expected)\n\n\ndef test_select_as_multiple(setup_path):\n df1 = DataFrame(\n np.random.default_rng(2).standard_normal((10, 4)),\n columns=Index(list("ABCD")),\n index=date_range("2000-01-01", periods=10, freq="B"),\n )\n df2 = df1.copy().rename(columns="{}_2".format)\n df2["foo"] = "bar"\n\n with ensure_clean_store(setup_path) as store:\n msg = "keys must be a list/tuple"\n # no tables stored\n with pytest.raises(TypeError, match=msg):\n store.select_as_multiple(None, where=["A>0", "B>0"], selector="df1")\n\n store.append("df1", df1, data_columns=["A", "B"])\n store.append("df2", df2)\n\n # exceptions\n with pytest.raises(TypeError, match=msg):\n store.select_as_multiple(None, where=["A>0", "B>0"], selector="df1")\n\n with pytest.raises(TypeError, match=msg):\n store.select_as_multiple([None], where=["A>0", "B>0"], selector="df1")\n\n msg = "'No object named df3 in the file'"\n with pytest.raises(KeyError, match=msg):\n store.select_as_multiple(\n ["df1", "df3"], where=["A>0", "B>0"], selector="df1"\n )\n\n with pytest.raises(KeyError, match=msg):\n store.select_as_multiple(["df3"], where=["A>0", "B>0"], selector="df1")\n\n with pytest.raises(KeyError, match="'No object named df4 in the file'"):\n store.select_as_multiple(\n ["df1", "df2"], where=["A>0", "B>0"], selector="df4"\n )\n\n # default select\n result = store.select("df1", ["A>0", "B>0"])\n expected = store.select_as_multiple(\n ["df1"], where=["A>0", "B>0"], selector="df1"\n )\n tm.assert_frame_equal(result, expected)\n expected = store.select_as_multiple("df1", where=["A>0", "B>0"], selector="df1")\n tm.assert_frame_equal(result, expected)\n\n # multiple\n result = store.select_as_multiple(\n ["df1", "df2"], where=["A>0", "B>0"], selector="df1"\n )\n expected = concat([df1, df2], axis=1)\n expected = expected[(expected.A > 0) & (expected.B > 0)]\n tm.assert_frame_equal(result, expected, check_freq=False)\n # FIXME: 2021-01-20 this is failing with freq None vs 4B on some builds\n\n # multiple (diff selector)\n result = store.select_as_multiple(\n ["df1", "df2"], where="index>df2.index[4]", selector="df2"\n )\n expected = concat([df1, df2], axis=1)\n expected = expected[5:]\n tm.assert_frame_equal(result, expected)\n\n # test exception for diff rows\n df3 = df1.copy().head(2)\n store.append("df3", df3)\n msg = "all tables must have exactly the same nrows!"\n with pytest.raises(ValueError, match=msg):\n store.select_as_multiple(\n ["df1", "df3"], where=["A>0", "B>0"], selector="df1"\n )\n\n\ndef test_nan_selection_bug_4858(setup_path):\n with ensure_clean_store(setup_path) as store:\n df = DataFrame({"cols": range(6), "values": range(6)}, dtype="float64")\n df["cols"] = (df["cols"] + 10).apply(str)\n df.iloc[0] = np.nan\n\n expected = DataFrame(\n {"cols": ["13.0", "14.0", "15.0"], "values": [3.0, 4.0, 5.0]},\n index=[3, 4, 5],\n )\n\n # write w/o the index on that particular column\n store.append("df", df, data_columns=True, index=["cols"])\n result = store.select("df", where="values>2.0")\n tm.assert_frame_equal(result, expected)\n\n\ndef test_query_with_nested_special_character(setup_path):\n df = DataFrame(\n {\n "a": ["a", "a", "c", "b", "test & test", "c", "b", "e"],\n "b": [1, 2, 3, 4, 5, 6, 7, 8],\n }\n )\n expected = df[df.a == "test & test"]\n with ensure_clean_store(setup_path) as store:\n store.append("test", df, format="table", data_columns=True)\n result = store.select("test", 'a = "test & test"')\n tm.assert_frame_equal(expected, result)\n\n\ndef test_query_long_float_literal(setup_path):\n # GH 14241\n df = DataFrame({"A": [1000000000.0009, 1000000000.0011, 1000000000.0015]})\n\n with ensure_clean_store(setup_path) as store:\n store.append("test", df, format="table", data_columns=True)\n\n cutoff = 1000000000.0006\n result = store.select("test", f"A < {cutoff:.4f}")\n assert result.empty\n\n cutoff = 1000000000.0010\n result = store.select("test", f"A > {cutoff:.4f}")\n expected = df.loc[[1, 2], :]\n tm.assert_frame_equal(expected, result)\n\n exact = 1000000000.0011\n result = store.select("test", f"A == {exact:.4f}")\n expected = df.loc[[1], :]\n tm.assert_frame_equal(expected, result)\n\n\ndef test_query_compare_column_type(setup_path):\n # GH 15492\n df = DataFrame(\n {\n "date": ["2014-01-01", "2014-01-02"],\n "real_date": date_range("2014-01-01", periods=2),\n "float": [1.1, 1.2],\n "int": [1, 2],\n },\n columns=["date", "real_date", "float", "int"],\n )\n\n with ensure_clean_store(setup_path) as store:\n store.append("test", df, format="table", data_columns=True)\n\n ts = Timestamp("2014-01-01") # noqa: F841\n result = store.select("test", where="real_date > ts")\n expected = df.loc[[1], :]\n tm.assert_frame_equal(expected, result)\n\n for op in ["<", ">", "=="]:\n # non strings to string column always fail\n for v in [2.1, True, Timestamp("2014-01-01"), pd.Timedelta(1, "s")]:\n query = f"date {op} v"\n msg = f"Cannot compare {v} of type {type(v)} to string column"\n with pytest.raises(TypeError, match=msg):\n store.select("test", where=query)\n\n # strings to other columns must be convertible to type\n v = "a"\n for col in ["int", "float", "real_date"]:\n query = f"{col} {op} v"\n if col == "real_date":\n msg = 'Given date string "a" not likely a datetime'\n else:\n msg = "could not convert string to"\n with pytest.raises(ValueError, match=msg):\n store.select("test", where=query)\n\n for v, col in zip(\n ["1", "1.1", "2014-01-01"], ["int", "float", "real_date"]\n ):\n query = f"{col} {op} v"\n result = store.select("test", where=query)\n\n if op == "==":\n expected = df.loc[[0], :]\n elif op == ">":\n expected = df.loc[[1], :]\n else:\n expected = df.loc[[], :]\n tm.assert_frame_equal(expected, result)\n\n\n@pytest.mark.parametrize("where", ["", (), (None,), [], [None]])\ndef test_select_empty_where(tmp_path, where):\n # GH26610\n\n df = DataFrame([1, 2, 3])\n path = tmp_path / "empty_where.h5"\n with HDFStore(path) as store:\n store.put("df", df, "t")\n result = read_hdf(store, "df", where=where)\n tm.assert_frame_equal(result, df)\n\n\ndef test_select_large_integer(tmp_path):\n path = tmp_path / "large_int.h5"\n\n df = DataFrame(\n zip(\n ["a", "b", "c", "d"],\n [-9223372036854775801, -9223372036854775802, -9223372036854775803, 123],\n ),\n columns=["x", "y"],\n )\n with HDFStore(path) as s:\n s.append("data", df, data_columns=True, index=False)\n result = s.select("data", where="y==-9223372036854775801").get("y").get(0)\n expected = df["y"][0]\n\n assert expected == result\n | .venv\Lib\site-packages\pandas\tests\io\pytables\test_select.py | test_select.py | Python | 36,832 | 0.95 | 0.037285 | 0.128571 | awesome-app | 373 | 2025-04-07T02:42:48.197554 | Apache-2.0 | true | 196aa5f93f7936a1056bb8d4a2fafd07 |
import contextlib\nimport datetime as dt\nimport hashlib\nimport tempfile\nimport time\n\nimport numpy as np\nimport pytest\n\nfrom pandas._config import using_string_dtype\n\nfrom pandas.compat import HAS_PYARROW\n\nimport pandas as pd\nfrom pandas import (\n DataFrame,\n DatetimeIndex,\n Index,\n MultiIndex,\n Series,\n Timestamp,\n concat,\n date_range,\n period_range,\n timedelta_range,\n)\nimport pandas._testing as tm\nfrom pandas.tests.io.pytables.common import (\n _maybe_remove,\n ensure_clean_store,\n)\n\nfrom pandas.io.pytables import (\n HDFStore,\n read_hdf,\n)\n\npytestmark = [pytest.mark.single_cpu]\n\ntables = pytest.importorskip("tables")\n\n\ndef test_context(setup_path):\n with tm.ensure_clean(setup_path) as path:\n try:\n with HDFStore(path) as tbl:\n raise ValueError("blah")\n except ValueError:\n pass\n with tm.ensure_clean(setup_path) as path:\n with HDFStore(path) as tbl:\n tbl["a"] = DataFrame(\n 1.1 * np.arange(120).reshape((30, 4)),\n columns=Index(list("ABCD"), dtype=object),\n index=Index([f"i-{i}" for i in range(30)], dtype=object),\n )\n assert len(tbl) == 1\n assert type(tbl["a"]) == DataFrame\n\n\ndef test_no_track_times(tmp_path, setup_path):\n # GH 32682\n # enables to set track_times (see `pytables` `create_table` documentation)\n\n def checksum(filename, hash_factory=hashlib.md5, chunk_num_blocks=128):\n h = hash_factory()\n with open(filename, "rb") as f:\n for chunk in iter(lambda: f.read(chunk_num_blocks * h.block_size), b""):\n h.update(chunk)\n return h.digest()\n\n def create_h5_and_return_checksum(tmp_path, track_times):\n path = tmp_path / setup_path\n df = DataFrame({"a": [1]})\n\n with HDFStore(path, mode="w") as hdf:\n hdf.put(\n "table",\n df,\n format="table",\n data_columns=True,\n index=None,\n track_times=track_times,\n )\n\n return checksum(path)\n\n checksum_0_tt_false = create_h5_and_return_checksum(tmp_path, track_times=False)\n checksum_0_tt_true = create_h5_and_return_checksum(tmp_path, track_times=True)\n\n # sleep is necessary to create h5 with different creation time\n time.sleep(1)\n\n checksum_1_tt_false = create_h5_and_return_checksum(tmp_path, track_times=False)\n checksum_1_tt_true = create_h5_and_return_checksum(tmp_path, track_times=True)\n\n # checksums are the same if track_time = False\n assert checksum_0_tt_false == checksum_1_tt_false\n\n # checksums are NOT same if track_time = True\n assert checksum_0_tt_true != checksum_1_tt_true\n\n\ndef test_iter_empty(setup_path):\n with ensure_clean_store(setup_path) as store:\n # GH 12221\n assert list(store) == []\n\n\ndef test_repr(setup_path, using_infer_string):\n with ensure_clean_store(setup_path) as store:\n repr(store)\n store.info()\n store["a"] = Series(\n np.arange(10, dtype=np.float64), index=date_range("2020-01-01", periods=10)\n )\n store["b"] = Series(\n range(10), dtype="float64", index=[f"i_{i}" for i in range(10)]\n )\n store["c"] = DataFrame(\n 1.1 * np.arange(120).reshape((30, 4)),\n columns=Index(list("ABCD"), dtype=object),\n index=Index([f"i-{i}" for i in range(30)], dtype=object),\n )\n\n df = DataFrame(\n 1.1 * np.arange(120).reshape((30, 4)),\n columns=Index(list("ABCD"), dtype=object),\n index=Index([f"i-{i}" for i in range(30)], dtype=object),\n )\n df["obj1"] = "foo"\n df["obj2"] = "bar"\n df["bool1"] = df["A"] > 0\n df["bool2"] = df["B"] > 0\n df["bool3"] = True\n df["int1"] = 1\n df["int2"] = 2\n df["timestamp1"] = Timestamp("20010102")\n df["timestamp2"] = Timestamp("20010103")\n df["datetime1"] = dt.datetime(2001, 1, 2, 0, 0)\n df["datetime2"] = dt.datetime(2001, 1, 3, 0, 0)\n df.loc[df.index[3:6], ["obj1"]] = np.nan\n df = df._consolidate()\n\n warning = None if using_infer_string else pd.errors.PerformanceWarning\n msg = "cannot\nmap directly to c-types .* dtype='object'"\n with tm.assert_produces_warning(warning, match=msg):\n store["df"] = df\n\n # make a random group in hdf space\n store._handle.create_group(store._handle.root, "bah")\n\n assert store.filename in repr(store)\n assert store.filename in str(store)\n store.info()\n\n # storers\n with ensure_clean_store(setup_path) as store:\n df = DataFrame(\n 1.1 * np.arange(120).reshape((30, 4)),\n columns=Index(list("ABCD"), dtype=object),\n index=Index([f"i-{i}" for i in range(30)], dtype=object),\n )\n store.append("df", df)\n\n s = store.get_storer("df")\n repr(s)\n str(s)\n\n\ndef test_contains(setup_path):\n with ensure_clean_store(setup_path) as store:\n store["a"] = Series(\n np.arange(10, dtype=np.float64), index=date_range("2020-01-01", periods=10)\n )\n store["b"] = DataFrame(\n 1.1 * np.arange(120).reshape((30, 4)),\n columns=Index(list("ABCD"), dtype=object),\n index=Index([f"i-{i}" for i in range(30)], dtype=object),\n )\n store["foo/bar"] = DataFrame(\n 1.1 * np.arange(120).reshape((30, 4)),\n columns=Index(list("ABCD"), dtype=object),\n index=Index([f"i-{i}" for i in range(30)], dtype=object),\n )\n assert "a" in store\n assert "b" in store\n assert "c" not in store\n assert "foo/bar" in store\n assert "/foo/bar" in store\n assert "/foo/b" not in store\n assert "bar" not in store\n\n # gh-2694: tables.NaturalNameWarning\n with tm.assert_produces_warning(\n tables.NaturalNameWarning, check_stacklevel=False\n ):\n store["node())"] = DataFrame(\n 1.1 * np.arange(120).reshape((30, 4)),\n columns=Index(list("ABCD"), dtype=object),\n index=Index([f"i-{i}" for i in range(30)], dtype=object),\n )\n assert "node())" in store\n\n\ndef test_versioning(setup_path):\n with ensure_clean_store(setup_path) as store:\n store["a"] = Series(\n np.arange(10, dtype=np.float64), index=date_range("2020-01-01", periods=10)\n )\n store["b"] = DataFrame(\n 1.1 * np.arange(120).reshape((30, 4)),\n columns=Index(list("ABCD"), dtype=object),\n index=Index([f"i-{i}" for i in range(30)], dtype=object),\n )\n df = DataFrame(\n np.random.default_rng(2).standard_normal((20, 4)),\n columns=Index(list("ABCD"), dtype=object),\n index=date_range("2000-01-01", periods=20, freq="B"),\n )\n _maybe_remove(store, "df1")\n store.append("df1", df[:10])\n store.append("df1", df[10:])\n assert store.root.a._v_attrs.pandas_version == "0.15.2"\n assert store.root.b._v_attrs.pandas_version == "0.15.2"\n assert store.root.df1._v_attrs.pandas_version == "0.15.2"\n\n # write a file and wipe its versioning\n _maybe_remove(store, "df2")\n store.append("df2", df)\n\n # this is an error because its table_type is appendable, but no\n # version info\n store.get_node("df2")._v_attrs.pandas_version = None\n\n msg = "'NoneType' object has no attribute 'startswith'"\n\n with pytest.raises(Exception, match=msg):\n store.select("df2")\n\n\n@pytest.mark.parametrize(\n "where, expected",\n [\n (\n "/",\n {\n "": ({"first_group", "second_group"}, set()),\n "/first_group": (set(), {"df1", "df2"}),\n "/second_group": ({"third_group"}, {"df3", "s1"}),\n "/second_group/third_group": (set(), {"df4"}),\n },\n ),\n (\n "/second_group",\n {\n "/second_group": ({"third_group"}, {"df3", "s1"}),\n "/second_group/third_group": (set(), {"df4"}),\n },\n ),\n ],\n)\ndef test_walk(where, expected):\n # GH10143\n objs = {\n "df1": DataFrame([1, 2, 3]),\n "df2": DataFrame([4, 5, 6]),\n "df3": DataFrame([6, 7, 8]),\n "df4": DataFrame([9, 10, 11]),\n "s1": Series([10, 9, 8]),\n # Next 3 items aren't pandas objects and should be ignored\n "a1": np.array([[1, 2, 3], [4, 5, 6]]),\n "tb1": np.array([(1, 2, 3), (4, 5, 6)], dtype="i,i,i"),\n "tb2": np.array([(7, 8, 9), (10, 11, 12)], dtype="i,i,i"),\n }\n\n with ensure_clean_store("walk_groups.hdf", mode="w") as store:\n store.put("/first_group/df1", objs["df1"])\n store.put("/first_group/df2", objs["df2"])\n store.put("/second_group/df3", objs["df3"])\n store.put("/second_group/s1", objs["s1"])\n store.put("/second_group/third_group/df4", objs["df4"])\n # Create non-pandas objects\n store._handle.create_array("/first_group", "a1", objs["a1"])\n store._handle.create_table("/first_group", "tb1", obj=objs["tb1"])\n store._handle.create_table("/second_group", "tb2", obj=objs["tb2"])\n\n assert len(list(store.walk(where=where))) == len(expected)\n for path, groups, leaves in store.walk(where=where):\n assert path in expected\n expected_groups, expected_frames = expected[path]\n assert expected_groups == set(groups)\n assert expected_frames == set(leaves)\n for leaf in leaves:\n frame_path = "/".join([path, leaf])\n obj = store.get(frame_path)\n if "df" in leaf:\n tm.assert_frame_equal(obj, objs[leaf])\n else:\n tm.assert_series_equal(obj, objs[leaf])\n\n\ndef test_getattr(setup_path):\n with ensure_clean_store(setup_path) as store:\n s = Series(\n np.arange(10, dtype=np.float64), index=date_range("2020-01-01", periods=10)\n )\n store["a"] = s\n\n # test attribute access\n result = store.a\n tm.assert_series_equal(result, s)\n result = getattr(store, "a")\n tm.assert_series_equal(result, s)\n\n df = DataFrame(\n np.random.default_rng(2).standard_normal((10, 4)),\n columns=Index(list("ABCD")),\n index=date_range("2000-01-01", periods=10, freq="B"),\n )\n store["df"] = df\n result = store.df\n tm.assert_frame_equal(result, df)\n\n # errors\n for x in ["d", "mode", "path", "handle", "complib"]:\n msg = f"'HDFStore' object has no attribute '{x}'"\n with pytest.raises(AttributeError, match=msg):\n getattr(store, x)\n\n # not stores\n for x in ["mode", "path", "handle", "complib"]:\n getattr(store, f"_{x}")\n\n\ndef test_store_dropna(tmp_path, setup_path):\n df_with_missing = DataFrame(\n {"col1": [0.0, np.nan, 2.0], "col2": [1.0, np.nan, np.nan]},\n index=list("abc"),\n )\n df_without_missing = DataFrame(\n {"col1": [0.0, 2.0], "col2": [1.0, np.nan]}, index=list("ac")\n )\n\n # # Test to make sure defaults are to not drop.\n # # Corresponding to Issue 9382\n path = tmp_path / setup_path\n df_with_missing.to_hdf(path, key="df", format="table")\n reloaded = read_hdf(path, "df")\n tm.assert_frame_equal(df_with_missing, reloaded)\n\n path = tmp_path / setup_path\n df_with_missing.to_hdf(path, key="df", format="table", dropna=False)\n reloaded = read_hdf(path, "df")\n tm.assert_frame_equal(df_with_missing, reloaded)\n\n path = tmp_path / setup_path\n df_with_missing.to_hdf(path, key="df", format="table", dropna=True)\n reloaded = read_hdf(path, "df")\n tm.assert_frame_equal(df_without_missing, reloaded)\n\n\ndef test_keyword_deprecation(tmp_path, setup_path):\n # GH 54229\n path = tmp_path / setup_path\n\n msg = (\n "Starting with pandas version 3.0 all arguments of to_hdf except for the "\n "argument 'path_or_buf' will be keyword-only."\n )\n df = DataFrame([{"A": 1, "B": 2, "C": 3}, {"A": 1, "B": 2, "C": 3}])\n\n with tm.assert_produces_warning(FutureWarning, match=msg):\n df.to_hdf(path, "key")\n\n\ndef test_to_hdf_with_min_itemsize(tmp_path, setup_path):\n path = tmp_path / setup_path\n\n # min_itemsize in index with to_hdf (GH 10381)\n df = DataFrame(\n {\n "A": [0.0, 1.0, 2.0, 3.0, 4.0],\n "B": [0.0, 1.0, 0.0, 1.0, 0.0],\n "C": Index(["foo1", "foo2", "foo3", "foo4", "foo5"]),\n "D": date_range("20130101", periods=5),\n }\n ).set_index("C")\n df.to_hdf(path, key="ss3", format="table", min_itemsize={"index": 6})\n # just make sure there is a longer string:\n df2 = df.copy().reset_index().assign(C="longer").set_index("C")\n df2.to_hdf(path, key="ss3", append=True, format="table")\n tm.assert_frame_equal(read_hdf(path, "ss3"), concat([df, df2]))\n\n # same as above, with a Series\n df["B"].to_hdf(path, key="ss4", format="table", min_itemsize={"index": 6})\n df2["B"].to_hdf(path, key="ss4", append=True, format="table")\n tm.assert_series_equal(read_hdf(path, "ss4"), concat([df["B"], df2["B"]]))\n\n\n@pytest.mark.xfail(\n using_string_dtype() and HAS_PYARROW,\n reason="TODO(infer_string): can't encode '\ud800': surrogates not allowed",\n)\n@pytest.mark.parametrize("format", ["fixed", "table"])\ndef test_to_hdf_errors(tmp_path, format, setup_path):\n data = ["\ud800foo"]\n ser = Series(data, index=Index(data))\n path = tmp_path / setup_path\n # GH 20835\n ser.to_hdf(path, key="table", format=format, errors="surrogatepass")\n\n result = read_hdf(path, "table", errors="surrogatepass")\n tm.assert_series_equal(result, ser)\n\n\ndef test_create_table_index(setup_path):\n with ensure_clean_store(setup_path) as store:\n\n def col(t, column):\n return getattr(store.get_storer(t).table.cols, column)\n\n # data columns\n df = DataFrame(\n np.random.default_rng(2).standard_normal((10, 4)),\n columns=Index(list("ABCD")),\n index=date_range("2000-01-01", periods=10, freq="B"),\n )\n df["string"] = "foo"\n df["string2"] = "bar"\n store.append("f", df, data_columns=["string", "string2"])\n assert col("f", "index").is_indexed is True\n assert col("f", "string").is_indexed is True\n assert col("f", "string2").is_indexed is True\n\n # specify index=columns\n store.append("f2", df, index=["string"], data_columns=["string", "string2"])\n assert col("f2", "index").is_indexed is False\n assert col("f2", "string").is_indexed is True\n assert col("f2", "string2").is_indexed is False\n\n # try to index a non-table\n _maybe_remove(store, "f2")\n store.put("f2", df)\n msg = "cannot create table index on a Fixed format store"\n with pytest.raises(TypeError, match=msg):\n store.create_table_index("f2")\n\n\ndef test_create_table_index_data_columns_argument(setup_path):\n # GH 28156\n\n with ensure_clean_store(setup_path) as store:\n\n def col(t, column):\n return getattr(store.get_storer(t).table.cols, column)\n\n # data columns\n df = DataFrame(\n np.random.default_rng(2).standard_normal((10, 4)),\n columns=Index(list("ABCD")),\n index=date_range("2000-01-01", periods=10, freq="B"),\n )\n df["string"] = "foo"\n df["string2"] = "bar"\n store.append("f", df, data_columns=["string"])\n assert col("f", "index").is_indexed is True\n assert col("f", "string").is_indexed is True\n\n msg = "'Cols' object has no attribute 'string2'"\n with pytest.raises(AttributeError, match=msg):\n col("f", "string2").is_indexed\n\n # try to index a col which isn't a data_column\n msg = (\n "column string2 is not a data_column.\n"\n "In order to read column string2 you must reload the dataframe \n"\n "into HDFStore and include string2 with the data_columns argument."\n )\n with pytest.raises(AttributeError, match=msg):\n store.create_table_index("f", columns=["string2"])\n\n\ndef test_mi_data_columns(setup_path):\n # GH 14435\n idx = MultiIndex.from_arrays(\n [date_range("2000-01-01", periods=5), range(5)], names=["date", "id"]\n )\n df = DataFrame({"a": [1.1, 1.2, 1.3, 1.4, 1.5]}, index=idx)\n\n with ensure_clean_store(setup_path) as store:\n store.append("df", df, data_columns=True)\n\n actual = store.select("df", where="id == 1")\n expected = df.iloc[[1], :]\n tm.assert_frame_equal(actual, expected)\n\n\ndef test_table_mixed_dtypes(setup_path):\n # frame\n df = DataFrame(\n 1.1 * np.arange(120).reshape((30, 4)),\n columns=Index(list("ABCD")),\n index=Index([f"i-{i}" for i in range(30)]),\n )\n df["obj1"] = "foo"\n df["obj2"] = "bar"\n df["bool1"] = df["A"] > 0\n df["bool2"] = df["B"] > 0\n df["bool3"] = True\n df["int1"] = 1\n df["int2"] = 2\n df["timestamp1"] = Timestamp("20010102").as_unit("ns")\n df["timestamp2"] = Timestamp("20010103").as_unit("ns")\n df["datetime1"] = Timestamp("20010102").as_unit("ns")\n df["datetime2"] = Timestamp("20010103").as_unit("ns")\n df.loc[df.index[3:6], ["obj1"]] = np.nan\n df = df._consolidate()\n\n with ensure_clean_store(setup_path) as store:\n store.append("df1_mixed", df)\n tm.assert_frame_equal(store.select("df1_mixed"), df)\n\n\ndef test_calendar_roundtrip_issue(setup_path):\n # 8591\n # doc example from tseries holiday section\n weekmask_egypt = "Sun Mon Tue Wed Thu"\n holidays = [\n "2012-05-01",\n dt.datetime(2013, 5, 1),\n np.datetime64("2014-05-01"),\n ]\n bday_egypt = pd.offsets.CustomBusinessDay(\n holidays=holidays, weekmask=weekmask_egypt\n )\n mydt = dt.datetime(2013, 4, 30)\n dts = date_range(mydt, periods=5, freq=bday_egypt)\n\n s = Series(dts.weekday, dts).map(Series("Mon Tue Wed Thu Fri Sat Sun".split()))\n\n with ensure_clean_store(setup_path) as store:\n store.put("fixed", s)\n result = store.select("fixed")\n tm.assert_series_equal(result, s)\n\n store.append("table", s)\n result = store.select("table")\n tm.assert_series_equal(result, s)\n\n\ndef test_remove(setup_path):\n with ensure_clean_store(setup_path) as store:\n ts = Series(\n np.arange(10, dtype=np.float64), index=date_range("2020-01-01", periods=10)\n )\n df = DataFrame(\n 1.1 * np.arange(120).reshape((30, 4)),\n columns=Index(list("ABCD")),\n index=Index([f"i-{i}" for i in range(30)]),\n )\n store["a"] = ts\n store["b"] = df\n _maybe_remove(store, "a")\n assert len(store) == 1\n tm.assert_frame_equal(df, store["b"])\n\n _maybe_remove(store, "b")\n assert len(store) == 0\n\n # nonexistence\n with pytest.raises(\n KeyError, match="'No object named a_nonexistent_store in the file'"\n ):\n store.remove("a_nonexistent_store")\n\n # pathing\n store["a"] = ts\n store["b/foo"] = df\n _maybe_remove(store, "foo")\n _maybe_remove(store, "b/foo")\n assert len(store) == 1\n\n store["a"] = ts\n store["b/foo"] = df\n _maybe_remove(store, "b")\n assert len(store) == 1\n\n # __delitem__\n store["a"] = ts\n store["b"] = df\n del store["a"]\n del store["b"]\n assert len(store) == 0\n\n\ndef test_same_name_scoping(setup_path):\n with ensure_clean_store(setup_path) as store:\n df = DataFrame(\n np.random.default_rng(2).standard_normal((20, 2)),\n index=date_range("20130101", periods=20),\n )\n store.put("df", df, format="table")\n expected = df[df.index > Timestamp("20130105")]\n\n result = store.select("df", "index>datetime.datetime(2013,1,5)")\n tm.assert_frame_equal(result, expected)\n\n # changes what 'datetime' points to in the namespace where\n # 'select' does the lookup\n\n # technically an error, but allow it\n result = store.select("df", "index>datetime.datetime(2013,1,5)")\n tm.assert_frame_equal(result, expected)\n\n result = store.select("df", "index>datetime(2013,1,5)")\n tm.assert_frame_equal(result, expected)\n\n\ndef test_store_index_name(setup_path):\n df = DataFrame(\n 1.1 * np.arange(120).reshape((30, 4)),\n columns=Index(list("ABCD")),\n index=Index([f"i-{i}" for i in range(30)]),\n )\n df.index.name = "foo"\n\n with ensure_clean_store(setup_path) as store:\n store["frame"] = df\n recons = store["frame"]\n tm.assert_frame_equal(recons, df)\n\n\n@pytest.mark.parametrize("tz", [None, "US/Pacific"])\n@pytest.mark.parametrize("table_format", ["table", "fixed"])\ndef test_store_index_name_numpy_str(tmp_path, table_format, setup_path, unit, tz):\n # GH #13492\n idx = DatetimeIndex(\n [dt.date(2000, 1, 1), dt.date(2000, 1, 2)],\n name="cols\u05d2",\n ).tz_localize(tz)\n idx1 = (\n DatetimeIndex(\n [dt.date(2010, 1, 1), dt.date(2010, 1, 2)],\n name="rows\u05d0",\n )\n .as_unit(unit)\n .tz_localize(tz)\n )\n df = DataFrame(np.arange(4).reshape(2, 2), columns=idx, index=idx1)\n\n # This used to fail, returning numpy strings instead of python strings.\n path = tmp_path / setup_path\n df.to_hdf(path, key="df", format=table_format)\n df2 = read_hdf(path, "df")\n\n tm.assert_frame_equal(df, df2, check_names=True)\n\n assert isinstance(df2.index.name, str)\n assert isinstance(df2.columns.name, str)\n\n\ndef test_store_series_name(setup_path):\n df = DataFrame(\n 1.1 * np.arange(120).reshape((30, 4)),\n columns=Index(list("ABCD")),\n index=Index([f"i-{i}" for i in range(30)]),\n )\n series = df["A"]\n\n with ensure_clean_store(setup_path) as store:\n store["series"] = series\n recons = store["series"]\n tm.assert_series_equal(recons, series)\n\n\ndef test_overwrite_node(setup_path):\n with ensure_clean_store(setup_path) as store:\n store["a"] = DataFrame(\n np.random.default_rng(2).standard_normal((10, 4)),\n columns=Index(list("ABCD")),\n index=date_range("2000-01-01", periods=10, freq="B"),\n )\n ts = Series(\n np.arange(10, dtype=np.float64), index=date_range("2020-01-01", periods=10)\n )\n store["a"] = ts\n\n tm.assert_series_equal(store["a"], ts)\n\n\ndef test_coordinates(setup_path):\n df = DataFrame(\n np.random.default_rng(2).standard_normal((10, 4)),\n columns=Index(list("ABCD")),\n index=date_range("2000-01-01", periods=10, freq="B"),\n )\n\n with ensure_clean_store(setup_path) as store:\n _maybe_remove(store, "df")\n store.append("df", df)\n\n # all\n c = store.select_as_coordinates("df")\n assert (c.values == np.arange(len(df.index))).all()\n\n # get coordinates back & test vs frame\n _maybe_remove(store, "df")\n\n df = DataFrame({"A": range(5), "B": range(5)})\n store.append("df", df)\n c = store.select_as_coordinates("df", ["index<3"])\n assert (c.values == np.arange(3)).all()\n result = store.select("df", where=c)\n expected = df.loc[0:2, :]\n tm.assert_frame_equal(result, expected)\n\n c = store.select_as_coordinates("df", ["index>=3", "index<=4"])\n assert (c.values == np.arange(2) + 3).all()\n result = store.select("df", where=c)\n expected = df.loc[3:4, :]\n tm.assert_frame_equal(result, expected)\n assert isinstance(c, Index)\n\n # multiple tables\n _maybe_remove(store, "df1")\n _maybe_remove(store, "df2")\n df1 = DataFrame(\n np.random.default_rng(2).standard_normal((10, 4)),\n columns=Index(list("ABCD")),\n index=date_range("2000-01-01", periods=10, freq="B"),\n )\n df2 = df1.copy().rename(columns="{}_2".format)\n store.append("df1", df1, data_columns=["A", "B"])\n store.append("df2", df2)\n\n c = store.select_as_coordinates("df1", ["A>0", "B>0"])\n df1_result = store.select("df1", c)\n df2_result = store.select("df2", c)\n result = concat([df1_result, df2_result], axis=1)\n\n expected = concat([df1, df2], axis=1)\n expected = expected[(expected.A > 0) & (expected.B > 0)]\n tm.assert_frame_equal(result, expected, check_freq=False)\n # FIXME: 2021-01-18 on some (mostly windows) builds we get freq=None\n # but expect freq="18B"\n\n # pass array/mask as the coordinates\n with ensure_clean_store(setup_path) as store:\n df = DataFrame(\n np.random.default_rng(2).standard_normal((1000, 2)),\n index=date_range("20000101", periods=1000),\n )\n store.append("df", df)\n c = store.select_column("df", "index")\n where = c[DatetimeIndex(c).month == 5].index\n expected = df.iloc[where]\n\n # locations\n result = store.select("df", where=where)\n tm.assert_frame_equal(result, expected)\n\n # boolean\n result = store.select("df", where=where)\n tm.assert_frame_equal(result, expected)\n\n # invalid\n msg = (\n "where must be passed as a string, PyTablesExpr, "\n "or list-like of PyTablesExpr"\n )\n with pytest.raises(TypeError, match=msg):\n store.select("df", where=np.arange(len(df), dtype="float64"))\n\n with pytest.raises(TypeError, match=msg):\n store.select("df", where=np.arange(len(df) + 1))\n\n with pytest.raises(TypeError, match=msg):\n store.select("df", where=np.arange(len(df)), start=5)\n\n with pytest.raises(TypeError, match=msg):\n store.select("df", where=np.arange(len(df)), start=5, stop=10)\n\n # selection with filter\n selection = date_range("20000101", periods=500)\n result = store.select("df", where="index in selection")\n expected = df[df.index.isin(selection)]\n tm.assert_frame_equal(result, expected)\n\n # list\n df = DataFrame(np.random.default_rng(2).standard_normal((10, 2)))\n store.append("df2", df)\n result = store.select("df2", where=[0, 3, 5])\n expected = df.iloc[[0, 3, 5]]\n tm.assert_frame_equal(result, expected)\n\n # boolean\n where = [True] * 10\n where[-2] = False\n result = store.select("df2", where=where)\n expected = df.loc[where]\n tm.assert_frame_equal(result, expected)\n\n # start/stop\n result = store.select("df2", start=5, stop=10)\n expected = df[5:10]\n tm.assert_frame_equal(result, expected)\n\n\ndef test_start_stop_table(setup_path):\n with ensure_clean_store(setup_path) as store:\n # table\n df = DataFrame(\n {\n "A": np.random.default_rng(2).random(20),\n "B": np.random.default_rng(2).random(20),\n }\n )\n store.append("df", df)\n\n result = store.select("df", "columns=['A']", start=0, stop=5)\n expected = df.loc[0:4, ["A"]]\n tm.assert_frame_equal(result, expected)\n\n # out of range\n result = store.select("df", "columns=['A']", start=30, stop=40)\n assert len(result) == 0\n expected = df.loc[30:40, ["A"]]\n tm.assert_frame_equal(result, expected)\n\n\ndef test_start_stop_multiple(setup_path):\n # GH 16209\n with ensure_clean_store(setup_path) as store:\n df = DataFrame({"foo": [1, 2], "bar": [1, 2]})\n\n store.append_to_multiple(\n {"selector": ["foo"], "data": None}, df, selector="selector"\n )\n result = store.select_as_multiple(\n ["selector", "data"], selector="selector", start=0, stop=1\n )\n expected = df.loc[[0], ["foo", "bar"]]\n tm.assert_frame_equal(result, expected)\n\n\ndef test_start_stop_fixed(setup_path):\n with ensure_clean_store(setup_path) as store:\n # fixed, GH 8287\n df = DataFrame(\n {\n "A": np.random.default_rng(2).random(20),\n "B": np.random.default_rng(2).random(20),\n },\n index=date_range("20130101", periods=20),\n )\n store.put("df", df)\n\n result = store.select("df", start=0, stop=5)\n expected = df.iloc[0:5, :]\n tm.assert_frame_equal(result, expected)\n\n result = store.select("df", start=5, stop=10)\n expected = df.iloc[5:10, :]\n tm.assert_frame_equal(result, expected)\n\n # out of range\n result = store.select("df", start=30, stop=40)\n expected = df.iloc[30:40, :]\n tm.assert_frame_equal(result, expected)\n\n # series\n s = df.A\n store.put("s", s)\n result = store.select("s", start=0, stop=5)\n expected = s.iloc[0:5]\n tm.assert_series_equal(result, expected)\n\n result = store.select("s", start=5, stop=10)\n expected = s.iloc[5:10]\n tm.assert_series_equal(result, expected)\n\n # sparse; not implemented\n df = DataFrame(\n 1.1 * np.arange(120).reshape((30, 4)),\n columns=Index(list("ABCD")),\n index=Index([f"i-{i}" for i in range(30)]),\n )\n df.iloc[3:5, 1:3] = np.nan\n df.iloc[8:10, -2] = np.nan\n\n\ndef test_select_filter_corner(setup_path):\n df = DataFrame(np.random.default_rng(2).standard_normal((50, 100)))\n df.index = [f"{c:3d}" for c in df.index]\n df.columns = [f"{c:3d}" for c in df.columns]\n\n with ensure_clean_store(setup_path) as store:\n store.put("frame", df, format="table")\n\n crit = "columns=df.columns[:75]"\n result = store.select("frame", [crit])\n tm.assert_frame_equal(result, df.loc[:, df.columns[:75]])\n\n crit = "columns=df.columns[:75:2]"\n result = store.select("frame", [crit])\n tm.assert_frame_equal(result, df.loc[:, df.columns[:75:2]])\n\n\ndef test_path_pathlib():\n df = DataFrame(\n 1.1 * np.arange(120).reshape((30, 4)),\n columns=Index(list("ABCD")),\n index=Index([f"i-{i}" for i in range(30)]),\n )\n\n result = tm.round_trip_pathlib(\n lambda p: df.to_hdf(p, key="df"), lambda p: read_hdf(p, "df")\n )\n tm.assert_frame_equal(df, result)\n\n\n@pytest.mark.parametrize("start, stop", [(0, 2), (1, 2), (None, None)])\ndef test_contiguous_mixed_data_table(start, stop, setup_path):\n # GH 17021\n df = DataFrame(\n {\n "a": Series([20111010, 20111011, 20111012]),\n "b": Series(["ab", "cd", "ab"]),\n }\n )\n\n with ensure_clean_store(setup_path) as store:\n store.append("test_dataset", df)\n\n result = store.select("test_dataset", start=start, stop=stop)\n tm.assert_frame_equal(df[start:stop], result)\n\n\ndef test_path_pathlib_hdfstore():\n df = DataFrame(\n 1.1 * np.arange(120).reshape((30, 4)),\n columns=Index(list("ABCD")),\n index=Index([f"i-{i}" for i in range(30)]),\n )\n\n def writer(path):\n with HDFStore(path) as store:\n df.to_hdf(store, key="df")\n\n def reader(path):\n with HDFStore(path) as store:\n return read_hdf(store, "df")\n\n result = tm.round_trip_pathlib(writer, reader)\n tm.assert_frame_equal(df, result)\n\n\ndef test_pickle_path_localpath():\n df = DataFrame(\n 1.1 * np.arange(120).reshape((30, 4)),\n columns=Index(list("ABCD")),\n index=Index([f"i-{i}" for i in range(30)]),\n )\n result = tm.round_trip_pathlib(\n lambda p: df.to_hdf(p, key="df"), lambda p: read_hdf(p, "df")\n )\n tm.assert_frame_equal(df, result)\n\n\ndef test_path_localpath_hdfstore():\n df = DataFrame(\n 1.1 * np.arange(120).reshape((30, 4)),\n columns=Index(list("ABCD")),\n index=Index([f"i-{i}" for i in range(30)]),\n )\n\n def writer(path):\n with HDFStore(path) as store:\n df.to_hdf(store, key="df")\n\n def reader(path):\n with HDFStore(path) as store:\n return read_hdf(store, "df")\n\n result = tm.round_trip_localpath(writer, reader)\n tm.assert_frame_equal(df, result)\n\n\n@pytest.mark.parametrize("propindexes", [True, False])\ndef test_copy(propindexes):\n df = DataFrame(\n 1.1 * np.arange(120).reshape((30, 4)),\n columns=Index(list("ABCD")),\n index=Index([f"i-{i}" for i in range(30)]),\n )\n\n with tm.ensure_clean() as path:\n with HDFStore(path) as st:\n st.append("df", df, data_columns=["A"])\n with tempfile.NamedTemporaryFile() as new_f:\n with HDFStore(path) as store:\n with contextlib.closing(\n store.copy(new_f.name, keys=None, propindexes=propindexes)\n ) as tstore:\n # check keys\n keys = store.keys()\n assert set(keys) == set(tstore.keys())\n # check indices & nrows\n for k in tstore.keys():\n if tstore.get_storer(k).is_table:\n new_t = tstore.get_storer(k)\n orig_t = store.get_storer(k)\n\n assert orig_t.nrows == new_t.nrows\n\n # check propindixes\n if propindexes:\n for a in orig_t.axes:\n if a.is_indexed:\n assert new_t[a.name].is_indexed\n\n\ndef test_duplicate_column_name(tmp_path, setup_path):\n df = DataFrame(columns=["a", "a"], data=[[0, 0]])\n\n path = tmp_path / setup_path\n msg = "Columns index has to be unique for fixed format"\n with pytest.raises(ValueError, match=msg):\n df.to_hdf(path, key="df", format="fixed")\n\n df.to_hdf(path, key="df", format="table")\n other = read_hdf(path, "df")\n\n tm.assert_frame_equal(df, other)\n assert df.equals(other)\n assert other.equals(df)\n\n\ndef test_preserve_timedeltaindex_type(setup_path):\n # GH9635\n df = DataFrame(np.random.default_rng(2).normal(size=(10, 5)))\n df.index = timedelta_range(start="0s", periods=10, freq="1s", name="example")\n\n with ensure_clean_store(setup_path) as store:\n store["df"] = df\n tm.assert_frame_equal(store["df"], df)\n\n\ndef test_columns_multiindex_modified(tmp_path, setup_path):\n # BUG: 7212\n\n df = DataFrame(\n np.random.default_rng(2).random((4, 5)),\n index=list("abcd"),\n columns=list("ABCDE"),\n )\n df.index.name = "letters"\n df = df.set_index(keys="E", append=True)\n\n data_columns = df.index.names + df.columns.tolist()\n path = tmp_path / setup_path\n df.to_hdf(\n path,\n key="df",\n mode="a",\n append=True,\n data_columns=data_columns,\n index=False,\n )\n cols2load = list("BCD")\n cols2load_original = list(cols2load)\n # GH#10055 make sure read_hdf call does not alter cols2load inplace\n read_hdf(path, "df", columns=cols2load)\n assert cols2load_original == cols2load\n\n\n@pytest.mark.filterwarnings(r"ignore:PeriodDtype\[B\] is deprecated:FutureWarning")\n@pytest.mark.parametrize(\n "columns",\n [\n Index([0, 1], dtype=np.int64),\n Index([0.0, 1.0], dtype=np.float64),\n date_range("2020-01-01", periods=2),\n timedelta_range("1 day", periods=2),\n period_range("2020-01-01", periods=2, freq="D"),\n ],\n)\ndef test_to_hdf_with_object_column_names_should_fail(tmp_path, setup_path, columns):\n # GH9057\n df = DataFrame(np.random.default_rng(2).standard_normal((10, 2)), columns=columns)\n path = tmp_path / setup_path\n msg = "cannot have non-object label DataIndexableCol"\n with pytest.raises(ValueError, match=msg):\n df.to_hdf(path, key="df", format="table", data_columns=True)\n\n\n@pytest.mark.parametrize("dtype", [None, "category"])\ndef test_to_hdf_with_object_column_names_should_run(tmp_path, setup_path, dtype):\n # GH9057\n df = DataFrame(\n np.random.default_rng(2).standard_normal((10, 2)),\n columns=Index(["a", "b"], dtype=dtype),\n )\n path = tmp_path / setup_path\n df.to_hdf(path, key="df", format="table", data_columns=True)\n result = read_hdf(path, "df", where=f"index = [{df.index[0]}]")\n assert len(result)\n\n\ndef test_hdfstore_strides(setup_path):\n # GH22073\n df = DataFrame({"a": [1, 2, 3, 4], "b": [5, 6, 7, 8]})\n with ensure_clean_store(setup_path) as store:\n store.put("df", df)\n assert df["a"].values.strides == store["df"]["a"].values.strides\n\n\ndef test_store_bool_index(tmp_path, setup_path):\n # GH#48667\n df = DataFrame([[1]], columns=[True], index=Index([False], dtype="bool"))\n expected = df.copy()\n\n # # Test to make sure defaults are to not drop.\n # # Corresponding to Issue 9382\n path = tmp_path / setup_path\n df.to_hdf(path, key="a")\n result = read_hdf(path, "a")\n tm.assert_frame_equal(expected, result)\n | .venv\Lib\site-packages\pandas\tests\io\pytables\test_store.py | test_store.py | Python | 37,387 | 0.95 | 0.078831 | 0.082073 | react-lib | 364 | 2024-12-25T05:49:48.792561 | GPL-3.0 | true | 72fd98e9cd6d446f3917646295eb64d0 |
import numpy as np\nimport pytest\n\nfrom pandas import (\n DataFrame,\n Series,\n)\nimport pandas._testing as tm\n\nfrom pandas.io.pytables import (\n HDFStore,\n read_hdf,\n)\n\npytest.importorskip("tables")\n\n\nclass TestHDFStoreSubclass:\n # GH 33748\n def test_supported_for_subclass_dataframe(self, tmp_path):\n data = {"a": [1, 2], "b": [3, 4]}\n sdf = tm.SubclassedDataFrame(data, dtype=np.intp)\n\n expected = DataFrame(data, dtype=np.intp)\n\n path = tmp_path / "temp.h5"\n sdf.to_hdf(path, key="df")\n result = read_hdf(path, "df")\n tm.assert_frame_equal(result, expected)\n\n path = tmp_path / "temp.h5"\n with HDFStore(path) as store:\n store.put("df", sdf)\n result = read_hdf(path, "df")\n tm.assert_frame_equal(result, expected)\n\n def test_supported_for_subclass_series(self, tmp_path):\n data = [1, 2, 3]\n sser = tm.SubclassedSeries(data, dtype=np.intp)\n\n expected = Series(data, dtype=np.intp)\n\n path = tmp_path / "temp.h5"\n sser.to_hdf(path, key="ser")\n result = read_hdf(path, "ser")\n tm.assert_series_equal(result, expected)\n\n path = tmp_path / "temp.h5"\n with HDFStore(path) as store:\n store.put("ser", sser)\n result = read_hdf(path, "ser")\n tm.assert_series_equal(result, expected)\n | .venv\Lib\site-packages\pandas\tests\io\pytables\test_subclass.py | test_subclass.py | Python | 1,369 | 0.95 | 0.057692 | 0.025 | node-utils | 187 | 2024-09-13T15:46:13.012028 | GPL-3.0 | true | 96b9f8090156fb620178d1fe40477cae |
from datetime import (\n date,\n timedelta,\n)\n\nimport numpy as np\nimport pytest\n\nfrom pandas._libs.tslibs.timezones import maybe_get_tz\nimport pandas.util._test_decorators as td\n\nimport pandas as pd\nfrom pandas import (\n DataFrame,\n DatetimeIndex,\n Series,\n Timestamp,\n date_range,\n)\nimport pandas._testing as tm\nfrom pandas.tests.io.pytables.common import (\n _maybe_remove,\n ensure_clean_store,\n)\n\n\ndef _compare_with_tz(a, b):\n tm.assert_frame_equal(a, b)\n\n # compare the zones on each element\n for c in a.columns:\n for i in a.index:\n a_e = a.loc[i, c]\n b_e = b.loc[i, c]\n if not (a_e == b_e and a_e.tz == b_e.tz):\n raise AssertionError(f"invalid tz comparison [{a_e}] [{b_e}]")\n\n\n# use maybe_get_tz instead of dateutil.tz.gettz to handle the windows\n# filename issues.\ngettz_dateutil = lambda x: maybe_get_tz("dateutil/" + x)\ngettz_pytz = lambda x: x\n\n\n@pytest.mark.parametrize("gettz", [gettz_dateutil, gettz_pytz])\ndef test_append_with_timezones(setup_path, gettz):\n # as columns\n\n # Single-tzinfo, no DST transition\n df_est = DataFrame(\n {\n "A": [\n Timestamp("20130102 2:00:00", tz=gettz("US/Eastern")).as_unit("ns")\n + timedelta(hours=1) * i\n for i in range(5)\n ]\n }\n )\n\n # frame with all columns having same tzinfo, but different sides\n # of DST transition\n df_crosses_dst = DataFrame(\n {\n "A": Timestamp("20130102", tz=gettz("US/Eastern")).as_unit("ns"),\n "B": Timestamp("20130603", tz=gettz("US/Eastern")).as_unit("ns"),\n },\n index=range(5),\n )\n\n df_mixed_tz = DataFrame(\n {\n "A": Timestamp("20130102", tz=gettz("US/Eastern")).as_unit("ns"),\n "B": Timestamp("20130102", tz=gettz("EET")).as_unit("ns"),\n },\n index=range(5),\n )\n\n df_different_tz = DataFrame(\n {\n "A": Timestamp("20130102", tz=gettz("US/Eastern")).as_unit("ns"),\n "B": Timestamp("20130102", tz=gettz("CET")).as_unit("ns"),\n },\n index=range(5),\n )\n\n with ensure_clean_store(setup_path) as store:\n _maybe_remove(store, "df_tz")\n store.append("df_tz", df_est, data_columns=["A"])\n result = store["df_tz"]\n _compare_with_tz(result, df_est)\n tm.assert_frame_equal(result, df_est)\n\n # select with tz aware\n expected = df_est[df_est.A >= df_est.A[3]]\n result = store.select("df_tz", where="A>=df_est.A[3]")\n _compare_with_tz(result, expected)\n\n # ensure we include dates in DST and STD time here.\n _maybe_remove(store, "df_tz")\n store.append("df_tz", df_crosses_dst)\n result = store["df_tz"]\n _compare_with_tz(result, df_crosses_dst)\n tm.assert_frame_equal(result, df_crosses_dst)\n\n msg = (\n r"invalid info for \[values_block_1\] for \[tz\], "\n r"existing_value \[(dateutil/.*)?(US/Eastern|America/New_York)\] "\n r"conflicts with new value \[(dateutil/.*)?EET\]"\n )\n with pytest.raises(ValueError, match=msg):\n store.append("df_tz", df_mixed_tz)\n\n # this is ok\n _maybe_remove(store, "df_tz")\n store.append("df_tz", df_mixed_tz, data_columns=["A", "B"])\n result = store["df_tz"]\n _compare_with_tz(result, df_mixed_tz)\n tm.assert_frame_equal(result, df_mixed_tz)\n\n # can't append with diff timezone\n msg = (\n r"invalid info for \[B\] for \[tz\], "\n r"existing_value \[(dateutil/.*)?EET\] "\n r"conflicts with new value \[(dateutil/.*)?CET\]"\n )\n with pytest.raises(ValueError, match=msg):\n store.append("df_tz", df_different_tz)\n\n\n@pytest.mark.parametrize("gettz", [gettz_dateutil, gettz_pytz])\ndef test_append_with_timezones_as_index(setup_path, gettz):\n # GH#4098 example\n\n dti = date_range("2000-1-1", periods=3, freq="h", tz=gettz("US/Eastern"))\n dti = dti._with_freq(None) # freq doesn't round-trip\n\n df = DataFrame({"A": Series(range(3), index=dti)})\n\n with ensure_clean_store(setup_path) as store:\n _maybe_remove(store, "df")\n store.put("df", df)\n result = store.select("df")\n tm.assert_frame_equal(result, df)\n\n _maybe_remove(store, "df")\n store.append("df", df)\n result = store.select("df")\n tm.assert_frame_equal(result, df)\n\n\ndef test_roundtrip_tz_aware_index(setup_path, unit):\n # GH 17618\n ts = Timestamp("2000-01-01 01:00:00", tz="US/Eastern")\n dti = DatetimeIndex([ts]).as_unit(unit)\n df = DataFrame(data=[0], index=dti)\n\n with ensure_clean_store(setup_path) as store:\n store.put("frame", df, format="fixed")\n recons = store["frame"]\n tm.assert_frame_equal(recons, df)\n\n value = recons.index[0]._value\n denom = {"ns": 1, "us": 1000, "ms": 10**6, "s": 10**9}[unit]\n assert value == 946706400000000000 // denom\n\n\ndef test_store_index_name_with_tz(setup_path):\n # GH 13884\n df = DataFrame({"A": [1, 2]})\n df.index = DatetimeIndex([1234567890123456787, 1234567890123456788])\n df.index = df.index.tz_localize("UTC")\n df.index.name = "foo"\n\n with ensure_clean_store(setup_path) as store:\n store.put("frame", df, format="table")\n recons = store["frame"]\n tm.assert_frame_equal(recons, df)\n\n\ndef test_tseries_select_index_column(setup_path):\n # GH7777\n # selecting a UTC datetimeindex column did\n # not preserve UTC tzinfo set before storing\n\n # check that no tz still works\n rng = date_range("1/1/2000", "1/30/2000")\n frame = DataFrame(\n np.random.default_rng(2).standard_normal((len(rng), 4)), index=rng\n )\n\n with ensure_clean_store(setup_path) as store:\n store.append("frame", frame)\n result = store.select_column("frame", "index")\n assert rng.tz == DatetimeIndex(result.values).tz\n\n # check utc\n rng = date_range("1/1/2000", "1/30/2000", tz="UTC")\n frame = DataFrame(\n np.random.default_rng(2).standard_normal((len(rng), 4)), index=rng\n )\n\n with ensure_clean_store(setup_path) as store:\n store.append("frame", frame)\n result = store.select_column("frame", "index")\n assert rng.tz == result.dt.tz\n\n # double check non-utc\n rng = date_range("1/1/2000", "1/30/2000", tz="US/Eastern")\n frame = DataFrame(\n np.random.default_rng(2).standard_normal((len(rng), 4)), index=rng\n )\n\n with ensure_clean_store(setup_path) as store:\n store.append("frame", frame)\n result = store.select_column("frame", "index")\n assert rng.tz == result.dt.tz\n\n\ndef test_timezones_fixed_format_frame_non_empty(setup_path):\n with ensure_clean_store(setup_path) as store:\n # index\n rng = date_range("1/1/2000", "1/30/2000", tz="US/Eastern")\n rng = rng._with_freq(None) # freq doesn't round-trip\n df = DataFrame(\n np.random.default_rng(2).standard_normal((len(rng), 4)), index=rng\n )\n store["df"] = df\n result = store["df"]\n tm.assert_frame_equal(result, df)\n\n # as data\n # GH11411\n _maybe_remove(store, "df")\n df = DataFrame(\n {\n "A": rng,\n "B": rng.tz_convert("UTC").tz_localize(None),\n "C": rng.tz_convert("CET"),\n "D": range(len(rng)),\n },\n index=rng,\n )\n store["df"] = df\n result = store["df"]\n tm.assert_frame_equal(result, df)\n\n\ndef test_timezones_fixed_format_empty(setup_path, tz_aware_fixture, frame_or_series):\n # GH 20594\n\n dtype = pd.DatetimeTZDtype(tz=tz_aware_fixture)\n\n obj = Series(dtype=dtype, name="A")\n if frame_or_series is DataFrame:\n obj = obj.to_frame()\n\n with ensure_clean_store(setup_path) as store:\n store["obj"] = obj\n result = store["obj"]\n tm.assert_equal(result, obj)\n\n\ndef test_timezones_fixed_format_series_nonempty(setup_path, tz_aware_fixture):\n # GH 20594\n\n dtype = pd.DatetimeTZDtype(tz=tz_aware_fixture)\n\n with ensure_clean_store(setup_path) as store:\n s = Series([0], dtype=dtype)\n store["s"] = s\n result = store["s"]\n tm.assert_series_equal(result, s)\n\n\ndef test_fixed_offset_tz(setup_path):\n rng = date_range("1/1/2000 00:00:00-07:00", "1/30/2000 00:00:00-07:00")\n frame = DataFrame(\n np.random.default_rng(2).standard_normal((len(rng), 4)), index=rng\n )\n\n with ensure_clean_store(setup_path) as store:\n store["frame"] = frame\n recons = store["frame"]\n tm.assert_index_equal(recons.index, rng)\n assert rng.tz == recons.index.tz\n\n\n@td.skip_if_windows\ndef test_store_timezone(setup_path):\n # GH2852\n # issue storing datetime.date with a timezone as it resets when read\n # back in a new timezone\n\n # original method\n with ensure_clean_store(setup_path) as store:\n today = date(2013, 9, 10)\n df = DataFrame([1, 2, 3], index=[today, today, today])\n store["obj1"] = df\n result = store["obj1"]\n tm.assert_frame_equal(result, df)\n\n # with tz setting\n with ensure_clean_store(setup_path) as store:\n with tm.set_timezone("EST5EDT"):\n today = date(2013, 9, 10)\n df = DataFrame([1, 2, 3], index=[today, today, today])\n store["obj1"] = df\n\n with tm.set_timezone("CST6CDT"):\n result = store["obj1"]\n\n tm.assert_frame_equal(result, df)\n\n\ndef test_legacy_datetimetz_object(datapath):\n # legacy from < 0.17.0\n # 8260\n expected = DataFrame(\n {\n "A": Timestamp("20130102", tz="US/Eastern").as_unit("ns"),\n "B": Timestamp("20130603", tz="CET").as_unit("ns"),\n },\n index=range(5),\n )\n with ensure_clean_store(\n datapath("io", "data", "legacy_hdf", "datetimetz_object.h5"), mode="r"\n ) as store:\n result = store["df"]\n tm.assert_frame_equal(result, expected)\n\n\ndef test_dst_transitions(setup_path):\n # make sure we are not failing on transitions\n with ensure_clean_store(setup_path) as store:\n times = date_range(\n "2013-10-26 23:00",\n "2013-10-27 01:00",\n tz="Europe/London",\n freq="h",\n ambiguous="infer",\n )\n times = times._with_freq(None) # freq doesn't round-trip\n\n for i in [times, times + pd.Timedelta("10min")]:\n _maybe_remove(store, "df")\n df = DataFrame({"A": range(len(i)), "B": i}, index=i)\n store.append("df", df)\n result = store.select("df")\n tm.assert_frame_equal(result, df)\n\n\ndef test_read_with_where_tz_aware_index(tmp_path, setup_path):\n # GH 11926\n periods = 10\n dts = date_range("20151201", periods=periods, freq="D", tz="UTC")\n mi = pd.MultiIndex.from_arrays([dts, range(periods)], names=["DATE", "NO"])\n expected = DataFrame({"MYCOL": 0}, index=mi)\n\n key = "mykey"\n path = tmp_path / setup_path\n with pd.HDFStore(path) as store:\n store.append(key, expected, format="table", append=True)\n result = pd.read_hdf(path, key, where="DATE > 20151130")\n tm.assert_frame_equal(result, expected)\n\n\ndef test_py2_created_with_datetimez(datapath):\n # The test HDF5 file was created in Python 2, but could not be read in\n # Python 3.\n #\n # GH26443\n index = DatetimeIndex(["2019-01-01T18:00"], dtype="M8[ns, America/New_York]")\n expected = DataFrame({"data": 123}, index=index)\n with ensure_clean_store(\n datapath("io", "data", "legacy_hdf", "gh26443.h5"), mode="r"\n ) as store:\n result = store["key"]\n tm.assert_frame_equal(result, expected)\n | .venv\Lib\site-packages\pandas\tests\io\pytables\test_timezones.py | test_timezones.py | Python | 11,804 | 0.95 | 0.066138 | 0.124183 | vue-tools | 658 | 2024-05-13T13:38:20.176431 | MIT | true | c5b9b622157545362b4dc61ff006c46e |
import datetime\n\nimport numpy as np\nimport pytest\n\nfrom pandas import (\n DataFrame,\n DatetimeIndex,\n Series,\n _testing as tm,\n date_range,\n period_range,\n)\nfrom pandas.tests.io.pytables.common import ensure_clean_store\n\npytestmark = pytest.mark.single_cpu\n\n\n@pytest.mark.parametrize("unit", ["us", "ns"])\ndef test_store_datetime_fractional_secs(setup_path, unit):\n dt = datetime.datetime(2012, 1, 2, 3, 4, 5, 123456)\n dti = DatetimeIndex([dt], dtype=f"M8[{unit}]")\n series = Series([0], index=dti)\n with ensure_clean_store(setup_path) as store:\n store["a"] = series\n assert store["a"].index[0] == dt\n\n\n@pytest.mark.filterwarnings(r"ignore:PeriodDtype\[B\] is deprecated:FutureWarning")\ndef test_tseries_indices_series(setup_path):\n with ensure_clean_store(setup_path) as store:\n idx = date_range("2020-01-01", periods=10)\n ser = Series(np.random.default_rng(2).standard_normal(len(idx)), idx)\n store["a"] = ser\n result = store["a"]\n\n tm.assert_series_equal(result, ser)\n assert result.index.freq == ser.index.freq\n tm.assert_class_equal(result.index, ser.index, obj="series index")\n\n idx = period_range("2020-01-01", periods=10, freq="D")\n ser = Series(np.random.default_rng(2).standard_normal(len(idx)), idx)\n store["a"] = ser\n result = store["a"]\n\n tm.assert_series_equal(result, ser)\n assert result.index.freq == ser.index.freq\n tm.assert_class_equal(result.index, ser.index, obj="series index")\n\n\n@pytest.mark.filterwarnings(r"ignore:PeriodDtype\[B\] is deprecated:FutureWarning")\ndef test_tseries_indices_frame(setup_path):\n with ensure_clean_store(setup_path) as store:\n idx = date_range("2020-01-01", periods=10)\n df = DataFrame(\n np.random.default_rng(2).standard_normal((len(idx), 3)), index=idx\n )\n store["a"] = df\n result = store["a"]\n\n tm.assert_frame_equal(result, df)\n assert result.index.freq == df.index.freq\n tm.assert_class_equal(result.index, df.index, obj="dataframe index")\n\n idx = period_range("2020-01-01", periods=10, freq="D")\n df = DataFrame(np.random.default_rng(2).standard_normal((len(idx), 3)), idx)\n store["a"] = df\n result = store["a"]\n\n tm.assert_frame_equal(result, df)\n assert result.index.freq == df.index.freq\n tm.assert_class_equal(result.index, df.index, obj="dataframe index")\n | .venv\Lib\site-packages\pandas\tests\io\pytables\test_time_series.py | test_time_series.py | Python | 2,481 | 0.85 | 0.041667 | 0 | vue-tools | 967 | 2025-01-07T02:15:19.744781 | GPL-3.0 | true | dcee0f982953a38db3c799907801c68a |
\n\n | .venv\Lib\site-packages\pandas\tests\io\pytables\__pycache__\common.cpython-313.pyc | common.cpython-313.pyc | Other | 2,071 | 0.95 | 0.023256 | 0 | node-utils | 813 | 2024-01-10T06:34:02.873436 | BSD-3-Clause | true | acebd63842d31ddfdfc0a8f6b20018de |
\n\n | .venv\Lib\site-packages\pandas\tests\io\pytables\__pycache__\conftest.cpython-313.pyc | conftest.cpython-313.pyc | Other | 515 | 0.8 | 0.25 | 0 | python-kit | 956 | 2024-06-05T06:14:02.871772 | BSD-3-Clause | true | 1ab1e55a36ddc1877c7e8f42410d42f1 |
\n\n | .venv\Lib\site-packages\pandas\tests\io\pytables\__pycache__\test_append.cpython-313.pyc | test_append.cpython-313.pyc | Other | 53,365 | 0.95 | 0.007278 | 0.013255 | awesome-app | 519 | 2025-03-07T09:34:19.901124 | BSD-3-Clause | true | 33a3de56e285582f82873689f0a03f3a |
\n\n | .venv\Lib\site-packages\pandas\tests\io\pytables\__pycache__\test_categorical.cpython-313.pyc | test_categorical.cpython-313.pyc | Other | 9,001 | 0.8 | 0 | 0 | vue-tools | 616 | 2024-08-19T04:10:57.356791 | Apache-2.0 | true | 6340910e40ad739e7c70d9f58af27bd7 |
\n\n | .venv\Lib\site-packages\pandas\tests\io\pytables\__pycache__\test_compat.cpython-313.pyc | test_compat.cpython-313.pyc | Other | 3,808 | 0.95 | 0.034483 | 0.074074 | vue-tools | 445 | 2024-05-29T13:41:21.287932 | MIT | true | f9b6ee6c8ce3d0ccb4667a4836c0ff36 |
\n\n | .venv\Lib\site-packages\pandas\tests\io\pytables\__pycache__\test_complex.cpython-313.pyc | test_complex.cpython-313.pyc | Other | 8,827 | 0.8 | 0 | 0.017544 | react-lib | 174 | 2025-03-12T17:39:45.959255 | GPL-3.0 | true | 4a73221362b6a4728e2321e8dc8958cb |
\n\n | .venv\Lib\site-packages\pandas\tests\io\pytables\__pycache__\test_errors.cpython-313.pyc | test_errors.cpython-313.pyc | Other | 14,666 | 0.95 | 0.015873 | 0 | react-lib | 983 | 2023-10-10T05:51:18.606378 | MIT | true | 9c56fcc39c0d1795231798e3408b2335 |
\n\n | .venv\Lib\site-packages\pandas\tests\io\pytables\__pycache__\test_file_handling.cpython-313.pyc | test_file_handling.cpython-313.pyc | Other | 25,128 | 0.8 | 0.006231 | 0.00639 | react-lib | 605 | 2023-12-20T10:32:14.816026 | GPL-3.0 | true | 9076085996993eeeb8f5b9b57450e927 |
\n\n | .venv\Lib\site-packages\pandas\tests\io\pytables\__pycache__\test_keys.cpython-313.pyc | test_keys.cpython-313.pyc | Other | 5,599 | 0.8 | 0 | 0.025 | awesome-app | 140 | 2024-12-07T01:18:38.646385 | MIT | true | 882e4821566ac2d32c00bd93ecd380c9 |
\n\n | .venv\Lib\site-packages\pandas\tests\io\pytables\__pycache__\test_put.cpython-313.pyc | test_put.cpython-313.pyc | Other | 23,303 | 0.8 | 0.002841 | 0.008671 | awesome-app | 656 | 2025-05-05T15:34:26.767389 | MIT | true | 662f7f225b9238b929080bb173f4e5e9 |
\n\n | .venv\Lib\site-packages\pandas\tests\io\pytables\__pycache__\test_pytables_missing.cpython-313.pyc | test_pytables_missing.cpython-313.pyc | Other | 1,125 | 0.8 | 0 | 0 | python-kit | 131 | 2025-02-28T08:01:07.241235 | MIT | true | 22ea563337234d44fdb9cbdf5e5040a6 |
\n\n | .venv\Lib\site-packages\pandas\tests\io\pytables\__pycache__\test_read.cpython-313.pyc | test_read.cpython-313.pyc | Other | 19,972 | 0.8 | 0 | 0 | node-utils | 855 | 2023-10-03T22:10:21.217462 | GPL-3.0 | true | ed2bea2cc1ffeb07a5bccd4c3e87a9c4 |
\n\n | .venv\Lib\site-packages\pandas\tests\io\pytables\__pycache__\test_retain_attributes.cpython-313.pyc | test_retain_attributes.cpython-313.pyc | Other | 4,348 | 0.8 | 0 | 0.027778 | node-utils | 836 | 2024-05-27T14:44:26.771011 | MIT | true | c87f5fda65beff48b6acc9992cfed624 |
\n\n | .venv\Lib\site-packages\pandas\tests\io\pytables\__pycache__\test_round_trip.cpython-313.pyc | test_round_trip.cpython-313.pyc | Other | 30,343 | 0.95 | 0 | 0.013263 | vue-tools | 799 | 2025-06-26T15:00:31.327004 | GPL-3.0 | true | 76b4b04dd63090eb1c2eeddc61929679 |
\n\n | .venv\Lib\site-packages\pandas\tests\io\pytables\__pycache__\test_select.cpython-313.pyc | test_select.cpython-313.pyc | Other | 47,232 | 0.8 | 0.001575 | 0.004754 | vue-tools | 933 | 2024-04-03T02:57:29.781367 | MIT | true | 40cf936f6bbaa235efab47f3e7b50ac3 |
\n\n | .venv\Lib\site-packages\pandas\tests\io\pytables\__pycache__\test_store.cpython-313.pyc | test_store.cpython-313.pyc | Other | 60,290 | 0.75 | 0.002717 | 0.005666 | node-utils | 2 | 2024-05-26T00:20:47.988098 | MIT | true | dfd040c37e2cebe4836beac5740168b2 |
\n\n | .venv\Lib\site-packages\pandas\tests\io\pytables\__pycache__\test_subclass.cpython-313.pyc | test_subclass.cpython-313.pyc | Other | 2,694 | 0.95 | 0 | 0 | awesome-app | 370 | 2025-05-06T22:20:34.192437 | MIT | true | 4bcce097bee7ed99ae1184ab968342cc |
\n\n | .venv\Lib\site-packages\pandas\tests\io\pytables\__pycache__\test_timezones.cpython-313.pyc | test_timezones.cpython-313.pyc | Other | 16,968 | 0.8 | 0.015936 | 0 | react-lib | 649 | 2024-02-04T08:54:29.126666 | Apache-2.0 | true | 4af929af7e6c1753c496da4b9911de97 |
\n\n | .venv\Lib\site-packages\pandas\tests\io\pytables\__pycache__\test_time_series.cpython-313.pyc | test_time_series.cpython-313.pyc | Other | 4,633 | 0.8 | 0 | 0.052632 | node-utils | 144 | 2024-10-17T00:28:37.992604 | Apache-2.0 | true | fb9275c76a7f531b6e05c30ff08bdc48 |
\n\n | .venv\Lib\site-packages\pandas\tests\io\pytables\__pycache__\__init__.cpython-313.pyc | __init__.cpython-313.pyc | Other | 199 | 0.7 | 0 | 0 | python-kit | 834 | 2025-04-16T12:49:06.095877 | Apache-2.0 | true | d0731ad7c73f435e27e33026ab288251 |
from hypothesis import (\n assume,\n example,\n given,\n strategies as st,\n)\nimport numpy as np\nimport pytest\n\nfrom pandas._libs.byteswap import (\n read_double_with_byteswap,\n read_float_with_byteswap,\n read_uint16_with_byteswap,\n read_uint32_with_byteswap,\n read_uint64_with_byteswap,\n)\n\nimport pandas._testing as tm\n\n\n@given(read_offset=st.integers(0, 11), number=st.integers(min_value=0))\n@example(number=2**16, read_offset=0)\n@example(number=2**32, read_offset=0)\n@example(number=2**64, read_offset=0)\n@pytest.mark.parametrize("int_type", [np.uint16, np.uint32, np.uint64])\n@pytest.mark.parametrize("should_byteswap", [True, False])\ndef test_int_byteswap(read_offset, number, int_type, should_byteswap):\n assume(number < 2 ** (8 * int_type(0).itemsize))\n _test(number, int_type, read_offset, should_byteswap)\n\n\n@pytest.mark.filterwarnings("ignore:overflow encountered:RuntimeWarning")\n@given(read_offset=st.integers(0, 11), number=st.floats())\n@pytest.mark.parametrize("float_type", [np.float32, np.float64])\n@pytest.mark.parametrize("should_byteswap", [True, False])\ndef test_float_byteswap(read_offset, number, float_type, should_byteswap):\n _test(number, float_type, read_offset, should_byteswap)\n\n\ndef _test(number, number_type, read_offset, should_byteswap):\n number = number_type(number)\n data = np.random.default_rng(2).integers(0, 256, size=20, dtype="uint8")\n data[read_offset : read_offset + number.itemsize] = number[None].view("uint8")\n swap_func = {\n np.float32: read_float_with_byteswap,\n np.float64: read_double_with_byteswap,\n np.uint16: read_uint16_with_byteswap,\n np.uint32: read_uint32_with_byteswap,\n np.uint64: read_uint64_with_byteswap,\n }[type(number)]\n output_number = number_type(swap_func(bytes(data), read_offset, should_byteswap))\n if should_byteswap:\n tm.assert_equal(output_number, number.byteswap())\n else:\n tm.assert_equal(output_number, number)\n | .venv\Lib\site-packages\pandas\tests\io\sas\test_byteswap.py | test_byteswap.py | Python | 1,987 | 0.85 | 0.072727 | 0 | python-kit | 923 | 2024-12-30T00:25:54.116880 | Apache-2.0 | true | c5501b8847a25a904e15393b23cd2cb0 |
from io import StringIO\n\nimport pytest\n\nfrom pandas import read_sas\nimport pandas._testing as tm\n\n\nclass TestSas:\n def test_sas_buffer_format(self):\n # see gh-14947\n b = StringIO("")\n\n msg = (\n "If this is a buffer object rather than a string "\n "name, you must specify a format string"\n )\n with pytest.raises(ValueError, match=msg):\n read_sas(b)\n\n def test_sas_read_no_format_or_extension(self):\n # see gh-24548\n msg = "unable to infer format of SAS file.+"\n with tm.ensure_clean("test_file_no_extension") as path:\n with pytest.raises(ValueError, match=msg):\n read_sas(path)\n\n\ndef test_sas_archive(datapath):\n fname_uncompressed = datapath("io", "sas", "data", "airline.sas7bdat")\n df_uncompressed = read_sas(fname_uncompressed)\n fname_compressed = datapath("io", "sas", "data", "airline.sas7bdat.gz")\n df_compressed = read_sas(fname_compressed, format="sas7bdat")\n tm.assert_frame_equal(df_uncompressed, df_compressed)\n | .venv\Lib\site-packages\pandas\tests\io\sas\test_sas.py | test_sas.py | Python | 1,057 | 0.95 | 0.117647 | 0.076923 | node-utils | 100 | 2025-05-28T23:09:39.094292 | Apache-2.0 | true | f1e09c2e6796b963f5445b627b54456b |
import contextlib\nfrom datetime import datetime\nimport io\nimport os\nfrom pathlib import Path\n\nimport numpy as np\nimport pytest\n\nfrom pandas.compat import IS64\nfrom pandas.errors import EmptyDataError\nimport pandas.util._test_decorators as td\n\nimport pandas as pd\nimport pandas._testing as tm\n\nfrom pandas.io.sas.sas7bdat import SAS7BDATReader\n\n\n@pytest.fixture\ndef dirpath(datapath):\n return datapath("io", "sas", "data")\n\n\n@pytest.fixture(params=[(1, range(1, 16)), (2, [16])])\ndef data_test_ix(request, dirpath):\n i, test_ix = request.param\n fname = os.path.join(dirpath, f"test_sas7bdat_{i}.csv")\n df = pd.read_csv(fname)\n epoch = datetime(1960, 1, 1)\n t1 = pd.to_timedelta(df["Column4"], unit="d")\n df["Column4"] = (epoch + t1).astype("M8[s]")\n t2 = pd.to_timedelta(df["Column12"], unit="d")\n df["Column12"] = (epoch + t2).astype("M8[s]")\n for k in range(df.shape[1]):\n col = df.iloc[:, k]\n if col.dtype == np.int64:\n df.isetitem(k, df.iloc[:, k].astype(np.float64))\n return df, test_ix\n\n\n# https://github.com/cython/cython/issues/1720\nclass TestSAS7BDAT:\n @pytest.mark.slow\n def test_from_file(self, dirpath, data_test_ix):\n expected, test_ix = data_test_ix\n for k in test_ix:\n fname = os.path.join(dirpath, f"test{k}.sas7bdat")\n df = pd.read_sas(fname, encoding="utf-8")\n tm.assert_frame_equal(df, expected)\n\n @pytest.mark.slow\n def test_from_buffer(self, dirpath, data_test_ix):\n expected, test_ix = data_test_ix\n for k in test_ix:\n fname = os.path.join(dirpath, f"test{k}.sas7bdat")\n with open(fname, "rb") as f:\n byts = f.read()\n buf = io.BytesIO(byts)\n with pd.read_sas(\n buf, format="sas7bdat", iterator=True, encoding="utf-8"\n ) as rdr:\n df = rdr.read()\n tm.assert_frame_equal(df, expected)\n\n @pytest.mark.slow\n def test_from_iterator(self, dirpath, data_test_ix):\n expected, test_ix = data_test_ix\n for k in test_ix:\n fname = os.path.join(dirpath, f"test{k}.sas7bdat")\n with pd.read_sas(fname, iterator=True, encoding="utf-8") as rdr:\n df = rdr.read(2)\n tm.assert_frame_equal(df, expected.iloc[0:2, :])\n df = rdr.read(3)\n tm.assert_frame_equal(df, expected.iloc[2:5, :])\n\n @pytest.mark.slow\n def test_path_pathlib(self, dirpath, data_test_ix):\n expected, test_ix = data_test_ix\n for k in test_ix:\n fname = Path(os.path.join(dirpath, f"test{k}.sas7bdat"))\n df = pd.read_sas(fname, encoding="utf-8")\n tm.assert_frame_equal(df, expected)\n\n @td.skip_if_no("py.path")\n @pytest.mark.slow\n def test_path_localpath(self, dirpath, data_test_ix):\n from py.path import local as LocalPath\n\n expected, test_ix = data_test_ix\n for k in test_ix:\n fname = LocalPath(os.path.join(dirpath, f"test{k}.sas7bdat"))\n df = pd.read_sas(fname, encoding="utf-8")\n tm.assert_frame_equal(df, expected)\n\n @pytest.mark.slow\n @pytest.mark.parametrize("chunksize", (3, 5, 10, 11))\n @pytest.mark.parametrize("k", range(1, 17))\n def test_iterator_loop(self, dirpath, k, chunksize):\n # github #13654\n fname = os.path.join(dirpath, f"test{k}.sas7bdat")\n with pd.read_sas(fname, chunksize=chunksize, encoding="utf-8") as rdr:\n y = 0\n for x in rdr:\n y += x.shape[0]\n assert y == rdr.row_count\n\n def test_iterator_read_too_much(self, dirpath):\n # github #14734\n fname = os.path.join(dirpath, "test1.sas7bdat")\n with pd.read_sas(\n fname, format="sas7bdat", iterator=True, encoding="utf-8"\n ) as rdr:\n d1 = rdr.read(rdr.row_count + 20)\n\n with pd.read_sas(fname, iterator=True, encoding="utf-8") as rdr:\n d2 = rdr.read(rdr.row_count + 20)\n tm.assert_frame_equal(d1, d2)\n\n\ndef test_encoding_options(datapath):\n fname = datapath("io", "sas", "data", "test1.sas7bdat")\n df1 = pd.read_sas(fname)\n df2 = pd.read_sas(fname, encoding="utf-8")\n for col in df1.columns:\n try:\n df1[col] = df1[col].str.decode("utf-8")\n except AttributeError:\n pass\n tm.assert_frame_equal(df1, df2)\n\n with contextlib.closing(SAS7BDATReader(fname, convert_header_text=False)) as rdr:\n df3 = rdr.read()\n for x, y in zip(df1.columns, df3.columns):\n assert x == y.decode()\n\n\ndef test_encoding_infer(datapath):\n fname = datapath("io", "sas", "data", "test1.sas7bdat")\n\n with pd.read_sas(fname, encoding="infer", iterator=True) as df1_reader:\n # check: is encoding inferred correctly from file\n assert df1_reader.inferred_encoding == "cp1252"\n df1 = df1_reader.read()\n\n with pd.read_sas(fname, encoding="cp1252", iterator=True) as df2_reader:\n df2 = df2_reader.read()\n\n # check: reader reads correct information\n tm.assert_frame_equal(df1, df2)\n\n\ndef test_productsales(datapath):\n fname = datapath("io", "sas", "data", "productsales.sas7bdat")\n df = pd.read_sas(fname, encoding="utf-8")\n fname = datapath("io", "sas", "data", "productsales.csv")\n df0 = pd.read_csv(fname, parse_dates=["MONTH"])\n vn = ["ACTUAL", "PREDICT", "QUARTER", "YEAR"]\n df0[vn] = df0[vn].astype(np.float64)\n\n df0["MONTH"] = df0["MONTH"].astype("M8[s]")\n tm.assert_frame_equal(df, df0)\n\n\ndef test_12659(datapath):\n fname = datapath("io", "sas", "data", "test_12659.sas7bdat")\n df = pd.read_sas(fname)\n fname = datapath("io", "sas", "data", "test_12659.csv")\n df0 = pd.read_csv(fname)\n df0 = df0.astype(np.float64)\n tm.assert_frame_equal(df, df0)\n\n\ndef test_airline(datapath):\n fname = datapath("io", "sas", "data", "airline.sas7bdat")\n df = pd.read_sas(fname)\n fname = datapath("io", "sas", "data", "airline.csv")\n df0 = pd.read_csv(fname)\n df0 = df0.astype(np.float64)\n tm.assert_frame_equal(df, df0)\n\n\ndef test_date_time(datapath):\n # Support of different SAS date/datetime formats (PR #15871)\n fname = datapath("io", "sas", "data", "datetime.sas7bdat")\n df = pd.read_sas(fname)\n fname = datapath("io", "sas", "data", "datetime.csv")\n df0 = pd.read_csv(\n fname, parse_dates=["Date1", "Date2", "DateTime", "DateTimeHi", "Taiw"]\n )\n # GH 19732: Timestamps imported from sas will incur floating point errors\n # See GH#56014 for discussion of the correct "expected" results\n # We are really just testing that we are "close". This only seems to be\n # an issue near the implementation bounds.\n\n df[df.columns[3]] = df.iloc[:, 3].dt.round("us")\n df0["Date1"] = df0["Date1"].astype("M8[s]")\n df0["Date2"] = df0["Date2"].astype("M8[s]")\n df0["DateTime"] = df0["DateTime"].astype("M8[ms]")\n df0["Taiw"] = df0["Taiw"].astype("M8[s]")\n\n res = df0["DateTimeHi"].astype("M8[us]").dt.round("ms")\n df0["DateTimeHi"] = res.astype("M8[ms]")\n\n if not IS64:\n # No good reason for this, just what we get on the CI\n df0.loc[0, "DateTimeHi"] += np.timedelta64(1, "ms")\n df0.loc[[2, 3], "DateTimeHi"] -= np.timedelta64(1, "ms")\n tm.assert_frame_equal(df, df0)\n\n\n@pytest.mark.parametrize("column", ["WGT", "CYL"])\ndef test_compact_numerical_values(datapath, column):\n # Regression test for #21616\n fname = datapath("io", "sas", "data", "cars.sas7bdat")\n df = pd.read_sas(fname, encoding="latin-1")\n # The two columns CYL and WGT in cars.sas7bdat have column\n # width < 8 and only contain integral values.\n # Test that pandas doesn't corrupt the numbers by adding\n # decimals.\n result = df[column]\n expected = df[column].round()\n tm.assert_series_equal(result, expected, check_exact=True)\n\n\ndef test_many_columns(datapath):\n # Test for looking for column information in more places (PR #22628)\n fname = datapath("io", "sas", "data", "many_columns.sas7bdat")\n\n df = pd.read_sas(fname, encoding="latin-1")\n\n fname = datapath("io", "sas", "data", "many_columns.csv")\n df0 = pd.read_csv(fname, encoding="latin-1")\n tm.assert_frame_equal(df, df0)\n\n\ndef test_inconsistent_number_of_rows(datapath):\n # Regression test for issue #16615. (PR #22628)\n fname = datapath("io", "sas", "data", "load_log.sas7bdat")\n df = pd.read_sas(fname, encoding="latin-1")\n assert len(df) == 2097\n\n\ndef test_zero_variables(datapath):\n # Check if the SAS file has zero variables (PR #18184)\n fname = datapath("io", "sas", "data", "zero_variables.sas7bdat")\n with pytest.raises(EmptyDataError, match="No columns to parse from file"):\n pd.read_sas(fname)\n\n\n@pytest.mark.parametrize("encoding", [None, "utf8"])\ndef test_zero_rows(datapath, encoding):\n # GH 18198\n fname = datapath("io", "sas", "data", "zero_rows.sas7bdat")\n result = pd.read_sas(fname, encoding=encoding)\n str_value = b"a" if encoding is None else "a"\n expected = pd.DataFrame([{"char_field": str_value, "num_field": 1.0}]).iloc[:0]\n tm.assert_frame_equal(result, expected)\n\n\ndef test_corrupt_read(datapath):\n # We don't really care about the exact failure, the important thing is\n # that the resource should be cleaned up afterwards (BUG #35566)\n fname = datapath("io", "sas", "data", "corrupt.sas7bdat")\n msg = "'SAS7BDATReader' object has no attribute 'row_count'"\n with pytest.raises(AttributeError, match=msg):\n pd.read_sas(fname)\n\n\ndef test_max_sas_date(datapath):\n # GH 20927\n # NB. max datetime in SAS dataset is 31DEC9999:23:59:59.999\n # but this is read as 29DEC9999:23:59:59.998993 by a buggy\n # sas7bdat module\n # See also GH#56014 for discussion of the correct "expected" results.\n fname = datapath("io", "sas", "data", "max_sas_date.sas7bdat")\n df = pd.read_sas(fname, encoding="iso-8859-1")\n\n expected = pd.DataFrame(\n {\n "text": ["max", "normal"],\n "dt_as_float": [253717747199.999, 1880323199.999],\n "dt_as_dt": np.array(\n [\n datetime(9999, 12, 29, 23, 59, 59, 999000),\n datetime(2019, 8, 1, 23, 59, 59, 999000),\n ],\n dtype="M8[ms]",\n ),\n "date_as_float": [2936547.0, 21762.0],\n "date_as_date": np.array(\n [\n datetime(9999, 12, 29),\n datetime(2019, 8, 1),\n ],\n dtype="M8[s]",\n ),\n },\n columns=["text", "dt_as_float", "dt_as_dt", "date_as_float", "date_as_date"],\n )\n\n if not IS64:\n # No good reason for this, just what we get on the CI\n expected.loc[:, "dt_as_dt"] -= np.timedelta64(1, "ms")\n\n tm.assert_frame_equal(df, expected)\n\n\ndef test_max_sas_date_iterator(datapath):\n # GH 20927\n # when called as an iterator, only those chunks with a date > pd.Timestamp.max\n # are returned as datetime.datetime, if this happens that whole chunk is returned\n # as datetime.datetime\n col_order = ["text", "dt_as_float", "dt_as_dt", "date_as_float", "date_as_date"]\n fname = datapath("io", "sas", "data", "max_sas_date.sas7bdat")\n results = []\n for df in pd.read_sas(fname, encoding="iso-8859-1", chunksize=1):\n # GH 19732: Timestamps imported from sas will incur floating point errors\n df.reset_index(inplace=True, drop=True)\n results.append(df)\n expected = [\n pd.DataFrame(\n {\n "text": ["max"],\n "dt_as_float": [253717747199.999],\n "dt_as_dt": np.array(\n [datetime(9999, 12, 29, 23, 59, 59, 999000)], dtype="M8[ms]"\n ),\n "date_as_float": [2936547.0],\n "date_as_date": np.array([datetime(9999, 12, 29)], dtype="M8[s]"),\n },\n columns=col_order,\n ),\n pd.DataFrame(\n {\n "text": ["normal"],\n "dt_as_float": [1880323199.999],\n "dt_as_dt": np.array(["2019-08-01 23:59:59.999"], dtype="M8[ms]"),\n "date_as_float": [21762.0],\n "date_as_date": np.array(["2019-08-01"], dtype="M8[s]"),\n },\n columns=col_order,\n ),\n ]\n if not IS64:\n # No good reason for this, just what we get on the CI\n expected[0].loc[0, "dt_as_dt"] -= np.timedelta64(1, "ms")\n expected[1].loc[0, "dt_as_dt"] -= np.timedelta64(1, "ms")\n\n tm.assert_frame_equal(results[0], expected[0])\n tm.assert_frame_equal(results[1], expected[1])\n\n\ndef test_null_date(datapath):\n fname = datapath("io", "sas", "data", "dates_null.sas7bdat")\n df = pd.read_sas(fname, encoding="utf-8")\n\n expected = pd.DataFrame(\n {\n "datecol": np.array(\n [\n datetime(9999, 12, 29),\n np.datetime64("NaT"),\n ],\n dtype="M8[s]",\n ),\n "datetimecol": np.array(\n [\n datetime(9999, 12, 29, 23, 59, 59, 999000),\n np.datetime64("NaT"),\n ],\n dtype="M8[ms]",\n ),\n },\n )\n if not IS64:\n # No good reason for this, just what we get on the CI\n expected.loc[0, "datetimecol"] -= np.timedelta64(1, "ms")\n tm.assert_frame_equal(df, expected)\n\n\ndef test_meta2_page(datapath):\n # GH 35545\n fname = datapath("io", "sas", "data", "test_meta2_page.sas7bdat")\n df = pd.read_sas(fname)\n assert len(df) == 1000\n\n\n@pytest.mark.parametrize(\n "test_file, override_offset, override_value, expected_msg",\n [\n ("test2.sas7bdat", 0x10000 + 55229, 0x80 | 0x0F, "Out of bounds"),\n ("test2.sas7bdat", 0x10000 + 55229, 0x10, "unknown control byte"),\n ("test3.sas7bdat", 118170, 184, "Out of bounds"),\n ],\n)\ndef test_rle_rdc_exceptions(\n datapath, test_file, override_offset, override_value, expected_msg\n):\n """Errors in RLE/RDC decompression should propagate."""\n with open(datapath("io", "sas", "data", test_file), "rb") as fd:\n data = bytearray(fd.read())\n data[override_offset] = override_value\n with pytest.raises(Exception, match=expected_msg):\n pd.read_sas(io.BytesIO(data), format="sas7bdat")\n\n\ndef test_0x40_control_byte(datapath):\n # GH 31243\n fname = datapath("io", "sas", "data", "0x40controlbyte.sas7bdat")\n df = pd.read_sas(fname, encoding="ascii")\n fname = datapath("io", "sas", "data", "0x40controlbyte.csv")\n df0 = pd.read_csv(fname, dtype="str")\n tm.assert_frame_equal(df, df0)\n\n\ndef test_0x00_control_byte(datapath):\n # GH 47099\n fname = datapath("io", "sas", "data", "0x00controlbyte.sas7bdat.bz2")\n df = next(pd.read_sas(fname, chunksize=11_000))\n assert df.shape == (11_000, 20)\n | .venv\Lib\site-packages\pandas\tests\io\sas\test_sas7bdat.py | test_sas7bdat.py | Python | 14,942 | 0.95 | 0.137767 | 0.108571 | react-lib | 21 | 2023-10-21T22:32:16.502479 | Apache-2.0 | true | d08eb0285b7bd7f3219c5a54d7e5b7aa |
import numpy as np\nimport pytest\n\nimport pandas as pd\nimport pandas._testing as tm\n\nfrom pandas.io.sas.sasreader import read_sas\n\n# CSV versions of test xpt files were obtained using the R foreign library\n\n# Numbers in a SAS xport file are always float64, so need to convert\n# before making comparisons.\n\n\ndef numeric_as_float(data):\n for v in data.columns:\n if data[v].dtype is np.dtype("int64"):\n data[v] = data[v].astype(np.float64)\n\n\nclass TestXport:\n @pytest.fixture\n def file01(self, datapath):\n return datapath("io", "sas", "data", "DEMO_G.xpt")\n\n @pytest.fixture\n def file02(self, datapath):\n return datapath("io", "sas", "data", "SSHSV1_A.xpt")\n\n @pytest.fixture\n def file03(self, datapath):\n return datapath("io", "sas", "data", "DRXFCD_G.xpt")\n\n @pytest.fixture\n def file04(self, datapath):\n return datapath("io", "sas", "data", "paxraw_d_short.xpt")\n\n @pytest.fixture\n def file05(self, datapath):\n return datapath("io", "sas", "data", "DEMO_PUF.cpt")\n\n @pytest.mark.slow\n def test1_basic(self, file01):\n # Tests with DEMO_G.xpt (all numeric file)\n\n # Compare to this\n data_csv = pd.read_csv(file01.replace(".xpt", ".csv"))\n numeric_as_float(data_csv)\n\n # Read full file\n data = read_sas(file01, format="xport")\n tm.assert_frame_equal(data, data_csv)\n num_rows = data.shape[0]\n\n # Test reading beyond end of file\n with read_sas(file01, format="xport", iterator=True) as reader:\n data = reader.read(num_rows + 100)\n assert data.shape[0] == num_rows\n\n # Test incremental read with `read` method.\n with read_sas(file01, format="xport", iterator=True) as reader:\n data = reader.read(10)\n tm.assert_frame_equal(data, data_csv.iloc[0:10, :])\n\n # Test incremental read with `get_chunk` method.\n with read_sas(file01, format="xport", chunksize=10) as reader:\n data = reader.get_chunk()\n tm.assert_frame_equal(data, data_csv.iloc[0:10, :])\n\n # Test read in loop\n m = 0\n with read_sas(file01, format="xport", chunksize=100) as reader:\n for x in reader:\n m += x.shape[0]\n assert m == num_rows\n\n # Read full file with `read_sas` method\n data = read_sas(file01)\n tm.assert_frame_equal(data, data_csv)\n\n def test1_index(self, file01):\n # Tests with DEMO_G.xpt using index (all numeric file)\n\n # Compare to this\n data_csv = pd.read_csv(file01.replace(".xpt", ".csv"))\n data_csv = data_csv.set_index("SEQN")\n numeric_as_float(data_csv)\n\n # Read full file\n data = read_sas(file01, index="SEQN", format="xport")\n tm.assert_frame_equal(data, data_csv, check_index_type=False)\n\n # Test incremental read with `read` method.\n with read_sas(file01, index="SEQN", format="xport", iterator=True) as reader:\n data = reader.read(10)\n tm.assert_frame_equal(data, data_csv.iloc[0:10, :], check_index_type=False)\n\n # Test incremental read with `get_chunk` method.\n with read_sas(file01, index="SEQN", format="xport", chunksize=10) as reader:\n data = reader.get_chunk()\n tm.assert_frame_equal(data, data_csv.iloc[0:10, :], check_index_type=False)\n\n def test1_incremental(self, file01):\n # Test with DEMO_G.xpt, reading full file incrementally\n\n data_csv = pd.read_csv(file01.replace(".xpt", ".csv"))\n data_csv = data_csv.set_index("SEQN")\n numeric_as_float(data_csv)\n\n with read_sas(file01, index="SEQN", chunksize=1000) as reader:\n all_data = list(reader)\n data = pd.concat(all_data, axis=0)\n\n tm.assert_frame_equal(data, data_csv, check_index_type=False)\n\n def test2(self, file02):\n # Test with SSHSV1_A.xpt\n\n # Compare to this\n data_csv = pd.read_csv(file02.replace(".xpt", ".csv"))\n numeric_as_float(data_csv)\n\n data = read_sas(file02)\n tm.assert_frame_equal(data, data_csv)\n\n def test2_binary(self, file02):\n # Test with SSHSV1_A.xpt, read as a binary file\n\n # Compare to this\n data_csv = pd.read_csv(file02.replace(".xpt", ".csv"))\n numeric_as_float(data_csv)\n\n with open(file02, "rb") as fd:\n # GH#35693 ensure that if we pass an open file, we\n # dont incorrectly close it in read_sas\n data = read_sas(fd, format="xport")\n\n tm.assert_frame_equal(data, data_csv)\n\n def test_multiple_types(self, file03):\n # Test with DRXFCD_G.xpt (contains text and numeric variables)\n\n # Compare to this\n data_csv = pd.read_csv(file03.replace(".xpt", ".csv"))\n\n data = read_sas(file03, encoding="utf-8")\n tm.assert_frame_equal(data, data_csv)\n\n def test_truncated_float_support(self, file04):\n # Test with paxraw_d_short.xpt, a shortened version of:\n # http://wwwn.cdc.gov/Nchs/Nhanes/2005-2006/PAXRAW_D.ZIP\n # This file has truncated floats (5 bytes in this case).\n\n # GH 11713\n\n data_csv = pd.read_csv(file04.replace(".xpt", ".csv"))\n\n data = read_sas(file04, format="xport")\n tm.assert_frame_equal(data.astype("int64"), data_csv)\n\n def test_cport_header_found_raises(self, file05):\n # Test with DEMO_PUF.cpt, the beginning of puf2019_1_fall.xpt\n # from https://www.cms.gov/files/zip/puf2019.zip\n # (despite the extension, it's a cpt file)\n msg = "Header record indicates a CPORT file, which is not readable."\n with pytest.raises(ValueError, match=msg):\n read_sas(file05, format="xport")\n | .venv\Lib\site-packages\pandas\tests\io\sas\test_xport.py | test_xport.py | Python | 5,728 | 0.95 | 0.113772 | 0.260163 | python-kit | 429 | 2024-09-05T06:12:11.816360 | GPL-3.0 | true | 1541133eb599f96bf93d87ebed8044b6 |
\n\n | .venv\Lib\site-packages\pandas\tests\io\sas\__pycache__\test_byteswap.cpython-313.pyc | test_byteswap.cpython-313.pyc | Other | 3,437 | 0.8 | 0 | 0 | awesome-app | 218 | 2023-11-09T07:41:40.773160 | BSD-3-Clause | true | a2e05a1bfe1bda9cc9e5fba13770f104 |
\n\n | .venv\Lib\site-packages\pandas\tests\io\sas\__pycache__\test_sas.cpython-313.pyc | test_sas.cpython-313.pyc | Other | 2,174 | 0.7 | 0 | 0 | python-kit | 835 | 2025-01-28T17:30:32.075187 | MIT | true | 7ee685ccc702bd3d506c6df4729beaf3 |
\n\n | .venv\Lib\site-packages\pandas\tests\io\sas\__pycache__\test_sas7bdat.cpython-313.pyc | test_sas7bdat.cpython-313.pyc | Other | 22,479 | 0.8 | 0 | 0.008734 | vue-tools | 852 | 2025-04-12T00:53:18.301924 | GPL-3.0 | true | 420b6c7c2e4f440e82c27df8fc8b084c |
\n\n | .venv\Lib\site-packages\pandas\tests\io\sas\__pycache__\test_xport.cpython-313.pyc | test_xport.cpython-313.pyc | Other | 8,094 | 0.8 | 0 | 0 | node-utils | 603 | 2024-03-23T19:51:55.676098 | BSD-3-Clause | true | e13ecd8c971266e6767a92ba589798d6 |
\n\n | .venv\Lib\site-packages\pandas\tests\io\sas\__pycache__\__init__.cpython-313.pyc | __init__.cpython-313.pyc | Other | 194 | 0.7 | 0 | 0 | python-kit | 532 | 2024-06-21T01:27:02.974570 | MIT | true | d15e505983578f90b2b13ef37fe664b4 |
from pathlib import Path\n\nimport pytest\n\n\n@pytest.fixture\ndef xml_data_path():\n return Path(__file__).parent.parent / "data" / "xml"\n\n\n@pytest.fixture\ndef xml_books(xml_data_path, datapath):\n return datapath(xml_data_path / "books.xml")\n\n\n@pytest.fixture\ndef xml_doc_ch_utf(xml_data_path, datapath):\n return datapath(xml_data_path / "doc_ch_utf.xml")\n\n\n@pytest.fixture\ndef xml_baby_names(xml_data_path, datapath):\n return datapath(xml_data_path / "baby_names.xml")\n\n\n@pytest.fixture\ndef kml_cta_rail_lines(xml_data_path, datapath):\n return datapath(xml_data_path / "cta_rail_lines.kml")\n\n\n@pytest.fixture\ndef xsl_flatten_doc(xml_data_path, datapath):\n return datapath(xml_data_path / "flatten_doc.xsl")\n\n\n@pytest.fixture\ndef xsl_row_field_output(xml_data_path, datapath):\n return datapath(xml_data_path / "row_field_output.xsl")\n | .venv\Lib\site-packages\pandas\tests\io\xml\conftest.py | conftest.py | Python | 850 | 0.85 | 0.184211 | 0 | node-utils | 916 | 2025-01-08T21:22:02.481637 | Apache-2.0 | true | d2b003ed162b0cb943a97351194e0ed8 |
from __future__ import annotations\n\nfrom io import (\n BytesIO,\n StringIO,\n)\nimport os\n\nimport numpy as np\nimport pytest\n\nimport pandas.util._test_decorators as td\n\nfrom pandas import (\n NA,\n DataFrame,\n Index,\n)\nimport pandas._testing as tm\n\nfrom pandas.io.common import get_handle\nfrom pandas.io.xml import read_xml\n\n# CHECKLIST\n\n# [x] - ValueError: "Values for parser can only be lxml or etree."\n\n# etree\n# [x] - ImportError: "lxml not found, please install or use the etree parser."\n# [X] - TypeError: "...is not a valid type for attr_cols"\n# [X] - TypeError: "...is not a valid type for elem_cols"\n# [X] - LookupError: "unknown encoding"\n# [X] - KeyError: "...is not included in namespaces"\n# [X] - KeyError: "no valid column"\n# [X] - ValueError: "To use stylesheet, you need lxml installed..."\n# [] - OSError: (NEED PERMISSOIN ISSUE, DISK FULL, ETC.)\n# [X] - FileNotFoundError: "No such file or directory"\n# [X] - PermissionError: "Forbidden"\n\n# lxml\n# [X] - TypeError: "...is not a valid type for attr_cols"\n# [X] - TypeError: "...is not a valid type for elem_cols"\n# [X] - LookupError: "unknown encoding"\n# [] - OSError: (NEED PERMISSOIN ISSUE, DISK FULL, ETC.)\n# [X] - FileNotFoundError: "No such file or directory"\n# [X] - KeyError: "...is not included in namespaces"\n# [X] - KeyError: "no valid column"\n# [X] - ValueError: "stylesheet is not a url, file, or xml string."\n# [] - LookupError: (NEED WRONG ENCODING FOR FILE OUTPUT)\n# [] - URLError: (USUALLY DUE TO NETWORKING)\n# [] - HTTPError: (NEED AN ONLINE STYLESHEET)\n# [X] - OSError: "failed to load external entity"\n# [X] - XMLSyntaxError: "Opening and ending tag mismatch"\n# [X] - XSLTApplyError: "Cannot resolve URI"\n# [X] - XSLTParseError: "failed to compile"\n# [X] - PermissionError: "Forbidden"\n\n\n@pytest.fixture\ndef geom_df():\n return DataFrame(\n {\n "shape": ["square", "circle", "triangle"],\n "degrees": [360, 360, 180],\n "sides": [4, np.nan, 3],\n }\n )\n\n\n@pytest.fixture\ndef planet_df():\n return DataFrame(\n {\n "planet": [\n "Mercury",\n "Venus",\n "Earth",\n "Mars",\n "Jupiter",\n "Saturn",\n "Uranus",\n "Neptune",\n ],\n "type": [\n "terrestrial",\n "terrestrial",\n "terrestrial",\n "terrestrial",\n "gas giant",\n "gas giant",\n "ice giant",\n "ice giant",\n ],\n "location": [\n "inner",\n "inner",\n "inner",\n "inner",\n "outer",\n "outer",\n "outer",\n "outer",\n ],\n "mass": [\n 0.330114,\n 4.86747,\n 5.97237,\n 0.641712,\n 1898.187,\n 568.3174,\n 86.8127,\n 102.4126,\n ],\n }\n )\n\n\n@pytest.fixture\ndef from_file_expected():\n return """\\n<?xml version='1.0' encoding='utf-8'?>\n<data>\n <row>\n <index>0</index>\n <category>cooking</category>\n <title>Everyday Italian</title>\n <author>Giada De Laurentiis</author>\n <year>2005</year>\n <price>30.0</price>\n </row>\n <row>\n <index>1</index>\n <category>children</category>\n <title>Harry Potter</title>\n <author>J K. Rowling</author>\n <year>2005</year>\n <price>29.99</price>\n </row>\n <row>\n <index>2</index>\n <category>web</category>\n <title>Learning XML</title>\n <author>Erik T. Ray</author>\n <year>2003</year>\n <price>39.95</price>\n </row>\n</data>"""\n\n\ndef equalize_decl(doc):\n # etree and lxml differ on quotes and case in xml declaration\n if doc is not None:\n doc = doc.replace(\n '<?xml version="1.0" encoding="utf-8"?',\n "<?xml version='1.0' encoding='utf-8'?",\n )\n return doc\n\n\n@pytest.fixture(params=["rb", "r"])\ndef mode(request):\n return request.param\n\n\n@pytest.fixture(params=[pytest.param("lxml", marks=td.skip_if_no("lxml")), "etree"])\ndef parser(request):\n return request.param\n\n\n# FILE OUTPUT\n\n\ndef test_file_output_str_read(xml_books, parser, from_file_expected):\n df_file = read_xml(xml_books, parser=parser)\n\n with tm.ensure_clean("test.xml") as path:\n df_file.to_xml(path, parser=parser)\n with open(path, "rb") as f:\n output = f.read().decode("utf-8").strip()\n\n output = equalize_decl(output)\n\n assert output == from_file_expected\n\n\ndef test_file_output_bytes_read(xml_books, parser, from_file_expected):\n df_file = read_xml(xml_books, parser=parser)\n\n with tm.ensure_clean("test.xml") as path:\n df_file.to_xml(path, parser=parser)\n with open(path, "rb") as f:\n output = f.read().decode("utf-8").strip()\n\n output = equalize_decl(output)\n\n assert output == from_file_expected\n\n\ndef test_str_output(xml_books, parser, from_file_expected):\n df_file = read_xml(xml_books, parser=parser)\n\n output = df_file.to_xml(parser=parser)\n output = equalize_decl(output)\n\n assert output == from_file_expected\n\n\ndef test_wrong_file_path(parser, geom_df):\n path = "/my/fake/path/output.xml"\n\n with pytest.raises(\n OSError,\n match=(r"Cannot save file into a non-existent directory: .*path"),\n ):\n geom_df.to_xml(path, parser=parser)\n\n\n# INDEX\n\n\ndef test_index_false(xml_books, parser):\n expected = """\\n<?xml version='1.0' encoding='utf-8'?>\n<data>\n <row>\n <category>cooking</category>\n <title>Everyday Italian</title>\n <author>Giada De Laurentiis</author>\n <year>2005</year>\n <price>30.0</price>\n </row>\n <row>\n <category>children</category>\n <title>Harry Potter</title>\n <author>J K. Rowling</author>\n <year>2005</year>\n <price>29.99</price>\n </row>\n <row>\n <category>web</category>\n <title>Learning XML</title>\n <author>Erik T. Ray</author>\n <year>2003</year>\n <price>39.95</price>\n </row>\n</data>"""\n\n df_file = read_xml(xml_books, parser=parser)\n\n with tm.ensure_clean("test.xml") as path:\n df_file.to_xml(path, index=False, parser=parser)\n with open(path, "rb") as f:\n output = f.read().decode("utf-8").strip()\n\n output = equalize_decl(output)\n\n assert output == expected\n\n\ndef test_index_false_rename_row_root(xml_books, parser):\n expected = """\\n<?xml version='1.0' encoding='utf-8'?>\n<books>\n <book>\n <category>cooking</category>\n <title>Everyday Italian</title>\n <author>Giada De Laurentiis</author>\n <year>2005</year>\n <price>30.0</price>\n </book>\n <book>\n <category>children</category>\n <title>Harry Potter</title>\n <author>J K. Rowling</author>\n <year>2005</year>\n <price>29.99</price>\n </book>\n <book>\n <category>web</category>\n <title>Learning XML</title>\n <author>Erik T. Ray</author>\n <year>2003</year>\n <price>39.95</price>\n </book>\n</books>"""\n\n df_file = read_xml(xml_books, parser=parser)\n\n with tm.ensure_clean("test.xml") as path:\n df_file.to_xml(\n path, index=False, root_name="books", row_name="book", parser=parser\n )\n with open(path, "rb") as f:\n output = f.read().decode("utf-8").strip()\n\n output = equalize_decl(output)\n\n assert output == expected\n\n\n@pytest.mark.parametrize(\n "offset_index", [list(range(10, 13)), [str(i) for i in range(10, 13)]]\n)\ndef test_index_false_with_offset_input_index(parser, offset_index, geom_df):\n """\n Tests that the output does not contain the `<index>` field when the index of the\n input Dataframe has an offset.\n\n This is a regression test for issue #42458.\n """\n\n expected = """\\n<?xml version='1.0' encoding='utf-8'?>\n<data>\n <row>\n <shape>square</shape>\n <degrees>360</degrees>\n <sides>4.0</sides>\n </row>\n <row>\n <shape>circle</shape>\n <degrees>360</degrees>\n <sides/>\n </row>\n <row>\n <shape>triangle</shape>\n <degrees>180</degrees>\n <sides>3.0</sides>\n </row>\n</data>"""\n\n offset_geom_df = geom_df.copy()\n offset_geom_df.index = Index(offset_index)\n output = offset_geom_df.to_xml(index=False, parser=parser)\n output = equalize_decl(output)\n\n assert output == expected\n\n\n# NA_REP\n\nna_expected = """\\n<?xml version='1.0' encoding='utf-8'?>\n<data>\n <row>\n <index>0</index>\n <shape>square</shape>\n <degrees>360</degrees>\n <sides>4.0</sides>\n </row>\n <row>\n <index>1</index>\n <shape>circle</shape>\n <degrees>360</degrees>\n <sides/>\n </row>\n <row>\n <index>2</index>\n <shape>triangle</shape>\n <degrees>180</degrees>\n <sides>3.0</sides>\n </row>\n</data>"""\n\n\ndef test_na_elem_output(parser, geom_df):\n output = geom_df.to_xml(parser=parser)\n output = equalize_decl(output)\n\n assert output == na_expected\n\n\ndef test_na_empty_str_elem_option(parser, geom_df):\n output = geom_df.to_xml(na_rep="", parser=parser)\n output = equalize_decl(output)\n\n assert output == na_expected\n\n\ndef test_na_empty_elem_option(parser, geom_df):\n expected = """\\n<?xml version='1.0' encoding='utf-8'?>\n<data>\n <row>\n <index>0</index>\n <shape>square</shape>\n <degrees>360</degrees>\n <sides>4.0</sides>\n </row>\n <row>\n <index>1</index>\n <shape>circle</shape>\n <degrees>360</degrees>\n <sides>0.0</sides>\n </row>\n <row>\n <index>2</index>\n <shape>triangle</shape>\n <degrees>180</degrees>\n <sides>3.0</sides>\n </row>\n</data>"""\n\n output = geom_df.to_xml(na_rep="0.0", parser=parser)\n output = equalize_decl(output)\n\n assert output == expected\n\n\n# ATTR_COLS\n\n\ndef test_attrs_cols_nan_output(parser, geom_df):\n expected = """\\n<?xml version='1.0' encoding='utf-8'?>\n<data>\n <row index="0" shape="square" degrees="360" sides="4.0"/>\n <row index="1" shape="circle" degrees="360"/>\n <row index="2" shape="triangle" degrees="180" sides="3.0"/>\n</data>"""\n\n output = geom_df.to_xml(attr_cols=["shape", "degrees", "sides"], parser=parser)\n output = equalize_decl(output)\n\n assert output == expected\n\n\ndef test_attrs_cols_prefix(parser, geom_df):\n expected = """\\n<?xml version='1.0' encoding='utf-8'?>\n<doc:data xmlns:doc="http://example.xom">\n <doc:row doc:index="0" doc:shape="square" \\ndoc:degrees="360" doc:sides="4.0"/>\n <doc:row doc:index="1" doc:shape="circle" \\ndoc:degrees="360"/>\n <doc:row doc:index="2" doc:shape="triangle" \\ndoc:degrees="180" doc:sides="3.0"/>\n</doc:data>"""\n\n output = geom_df.to_xml(\n attr_cols=["index", "shape", "degrees", "sides"],\n namespaces={"doc": "http://example.xom"},\n prefix="doc",\n parser=parser,\n )\n output = equalize_decl(output)\n\n assert output == expected\n\n\ndef test_attrs_unknown_column(parser, geom_df):\n with pytest.raises(KeyError, match=("no valid column")):\n geom_df.to_xml(attr_cols=["shape", "degree", "sides"], parser=parser)\n\n\ndef test_attrs_wrong_type(parser, geom_df):\n with pytest.raises(TypeError, match=("is not a valid type for attr_cols")):\n geom_df.to_xml(attr_cols='"shape", "degree", "sides"', parser=parser)\n\n\n# ELEM_COLS\n\n\ndef test_elems_cols_nan_output(parser, geom_df):\n elems_cols_expected = """\\n<?xml version='1.0' encoding='utf-8'?>\n<data>\n <row>\n <degrees>360</degrees>\n <sides>4.0</sides>\n <shape>square</shape>\n </row>\n <row>\n <degrees>360</degrees>\n <sides/>\n <shape>circle</shape>\n </row>\n <row>\n <degrees>180</degrees>\n <sides>3.0</sides>\n <shape>triangle</shape>\n </row>\n</data>"""\n\n output = geom_df.to_xml(\n index=False, elem_cols=["degrees", "sides", "shape"], parser=parser\n )\n output = equalize_decl(output)\n\n assert output == elems_cols_expected\n\n\ndef test_elems_unknown_column(parser, geom_df):\n with pytest.raises(KeyError, match=("no valid column")):\n geom_df.to_xml(elem_cols=["shape", "degree", "sides"], parser=parser)\n\n\ndef test_elems_wrong_type(parser, geom_df):\n with pytest.raises(TypeError, match=("is not a valid type for elem_cols")):\n geom_df.to_xml(elem_cols='"shape", "degree", "sides"', parser=parser)\n\n\ndef test_elems_and_attrs_cols(parser, geom_df):\n elems_cols_expected = """\\n<?xml version='1.0' encoding='utf-8'?>\n<data>\n <row shape="square">\n <degrees>360</degrees>\n <sides>4.0</sides>\n </row>\n <row shape="circle">\n <degrees>360</degrees>\n <sides/>\n </row>\n <row shape="triangle">\n <degrees>180</degrees>\n <sides>3.0</sides>\n </row>\n</data>"""\n\n output = geom_df.to_xml(\n index=False,\n elem_cols=["degrees", "sides"],\n attr_cols=["shape"],\n parser=parser,\n )\n output = equalize_decl(output)\n\n assert output == elems_cols_expected\n\n\n# HIERARCHICAL COLUMNS\n\n\ndef test_hierarchical_columns(parser, planet_df):\n expected = """\\n<?xml version='1.0' encoding='utf-8'?>\n<data>\n <row>\n <location>inner</location>\n <type>terrestrial</type>\n <count_mass>4</count_mass>\n <sum_mass>11.81</sum_mass>\n <mean_mass>2.95</mean_mass>\n </row>\n <row>\n <location>outer</location>\n <type>gas giant</type>\n <count_mass>2</count_mass>\n <sum_mass>2466.5</sum_mass>\n <mean_mass>1233.25</mean_mass>\n </row>\n <row>\n <location>outer</location>\n <type>ice giant</type>\n <count_mass>2</count_mass>\n <sum_mass>189.23</sum_mass>\n <mean_mass>94.61</mean_mass>\n </row>\n <row>\n <location>All</location>\n <type/>\n <count_mass>8</count_mass>\n <sum_mass>2667.54</sum_mass>\n <mean_mass>333.44</mean_mass>\n </row>\n</data>"""\n\n pvt = planet_df.pivot_table(\n index=["location", "type"],\n values="mass",\n aggfunc=["count", "sum", "mean"],\n margins=True,\n ).round(2)\n\n output = pvt.to_xml(parser=parser)\n output = equalize_decl(output)\n\n assert output == expected\n\n\ndef test_hierarchical_attrs_columns(parser, planet_df):\n expected = """\\n<?xml version='1.0' encoding='utf-8'?>\n<data>\n <row location="inner" type="terrestrial" count_mass="4" \\nsum_mass="11.81" mean_mass="2.95"/>\n <row location="outer" type="gas giant" count_mass="2" \\nsum_mass="2466.5" mean_mass="1233.25"/>\n <row location="outer" type="ice giant" count_mass="2" \\nsum_mass="189.23" mean_mass="94.61"/>\n <row location="All" type="" count_mass="8" \\nsum_mass="2667.54" mean_mass="333.44"/>\n</data>"""\n\n pvt = planet_df.pivot_table(\n index=["location", "type"],\n values="mass",\n aggfunc=["count", "sum", "mean"],\n margins=True,\n ).round(2)\n\n output = pvt.to_xml(attr_cols=list(pvt.reset_index().columns.values), parser=parser)\n output = equalize_decl(output)\n\n assert output == expected\n\n\n# MULTIINDEX\n\n\ndef test_multi_index(parser, planet_df):\n expected = """\\n<?xml version='1.0' encoding='utf-8'?>\n<data>\n <row>\n <location>inner</location>\n <type>terrestrial</type>\n <count>4</count>\n <sum>11.81</sum>\n <mean>2.95</mean>\n </row>\n <row>\n <location>outer</location>\n <type>gas giant</type>\n <count>2</count>\n <sum>2466.5</sum>\n <mean>1233.25</mean>\n </row>\n <row>\n <location>outer</location>\n <type>ice giant</type>\n <count>2</count>\n <sum>189.23</sum>\n <mean>94.61</mean>\n </row>\n</data>"""\n\n agg = (\n planet_df.groupby(["location", "type"])["mass"]\n .agg(["count", "sum", "mean"])\n .round(2)\n )\n\n output = agg.to_xml(parser=parser)\n output = equalize_decl(output)\n\n assert output == expected\n\n\ndef test_multi_index_attrs_cols(parser, planet_df):\n expected = """\\n<?xml version='1.0' encoding='utf-8'?>\n<data>\n <row location="inner" type="terrestrial" count="4" \\nsum="11.81" mean="2.95"/>\n <row location="outer" type="gas giant" count="2" \\nsum="2466.5" mean="1233.25"/>\n <row location="outer" type="ice giant" count="2" \\nsum="189.23" mean="94.61"/>\n</data>"""\n\n agg = (\n planet_df.groupby(["location", "type"])["mass"]\n .agg(["count", "sum", "mean"])\n .round(2)\n )\n output = agg.to_xml(attr_cols=list(agg.reset_index().columns.values), parser=parser)\n output = equalize_decl(output)\n\n assert output == expected\n\n\n# NAMESPACE\n\n\ndef test_default_namespace(parser, geom_df):\n expected = """\\n<?xml version='1.0' encoding='utf-8'?>\n<data xmlns="http://example.com">\n <row>\n <index>0</index>\n <shape>square</shape>\n <degrees>360</degrees>\n <sides>4.0</sides>\n </row>\n <row>\n <index>1</index>\n <shape>circle</shape>\n <degrees>360</degrees>\n <sides/>\n </row>\n <row>\n <index>2</index>\n <shape>triangle</shape>\n <degrees>180</degrees>\n <sides>3.0</sides>\n </row>\n</data>"""\n\n output = geom_df.to_xml(namespaces={"": "http://example.com"}, parser=parser)\n output = equalize_decl(output)\n\n assert output == expected\n\n\ndef test_unused_namespaces(parser, geom_df):\n expected = """\\n<?xml version='1.0' encoding='utf-8'?>\n<data xmlns:oth="http://other.org" xmlns:ex="http://example.com">\n <row>\n <index>0</index>\n <shape>square</shape>\n <degrees>360</degrees>\n <sides>4.0</sides>\n </row>\n <row>\n <index>1</index>\n <shape>circle</shape>\n <degrees>360</degrees>\n <sides/>\n </row>\n <row>\n <index>2</index>\n <shape>triangle</shape>\n <degrees>180</degrees>\n <sides>3.0</sides>\n </row>\n</data>"""\n\n output = geom_df.to_xml(\n namespaces={"oth": "http://other.org", "ex": "http://example.com"},\n parser=parser,\n )\n output = equalize_decl(output)\n\n assert output == expected\n\n\n# PREFIX\n\n\ndef test_namespace_prefix(parser, geom_df):\n expected = """\\n<?xml version='1.0' encoding='utf-8'?>\n<doc:data xmlns:doc="http://example.com">\n <doc:row>\n <doc:index>0</doc:index>\n <doc:shape>square</doc:shape>\n <doc:degrees>360</doc:degrees>\n <doc:sides>4.0</doc:sides>\n </doc:row>\n <doc:row>\n <doc:index>1</doc:index>\n <doc:shape>circle</doc:shape>\n <doc:degrees>360</doc:degrees>\n <doc:sides/>\n </doc:row>\n <doc:row>\n <doc:index>2</doc:index>\n <doc:shape>triangle</doc:shape>\n <doc:degrees>180</doc:degrees>\n <doc:sides>3.0</doc:sides>\n </doc:row>\n</doc:data>"""\n\n output = geom_df.to_xml(\n namespaces={"doc": "http://example.com"}, prefix="doc", parser=parser\n )\n output = equalize_decl(output)\n\n assert output == expected\n\n\ndef test_missing_prefix_in_nmsp(parser, geom_df):\n with pytest.raises(KeyError, match=("doc is not included in namespaces")):\n geom_df.to_xml(\n namespaces={"": "http://example.com"}, prefix="doc", parser=parser\n )\n\n\ndef test_namespace_prefix_and_default(parser, geom_df):\n expected = """\\n<?xml version='1.0' encoding='utf-8'?>\n<doc:data xmlns:doc="http://other.org" xmlns="http://example.com">\n <doc:row>\n <doc:index>0</doc:index>\n <doc:shape>square</doc:shape>\n <doc:degrees>360</doc:degrees>\n <doc:sides>4.0</doc:sides>\n </doc:row>\n <doc:row>\n <doc:index>1</doc:index>\n <doc:shape>circle</doc:shape>\n <doc:degrees>360</doc:degrees>\n <doc:sides/>\n </doc:row>\n <doc:row>\n <doc:index>2</doc:index>\n <doc:shape>triangle</doc:shape>\n <doc:degrees>180</doc:degrees>\n <doc:sides>3.0</doc:sides>\n </doc:row>\n</doc:data>"""\n\n output = geom_df.to_xml(\n namespaces={"": "http://example.com", "doc": "http://other.org"},\n prefix="doc",\n parser=parser,\n )\n output = equalize_decl(output)\n\n assert output == expected\n\n\n# ENCODING\n\nencoding_expected = """\\n<?xml version='1.0' encoding='ISO-8859-1'?>\n<data>\n <row>\n <index>0</index>\n <rank>1</rank>\n <malename>José</malename>\n <femalename>Sofía</femalename>\n </row>\n <row>\n <index>1</index>\n <rank>2</rank>\n <malename>Luis</malename>\n <femalename>Valentina</femalename>\n </row>\n <row>\n <index>2</index>\n <rank>3</rank>\n <malename>Carlos</malename>\n <femalename>Isabella</femalename>\n </row>\n <row>\n <index>3</index>\n <rank>4</rank>\n <malename>Juan</malename>\n <femalename>Camila</femalename>\n </row>\n <row>\n <index>4</index>\n <rank>5</rank>\n <malename>Jorge</malename>\n <femalename>Valeria</femalename>\n </row>\n</data>"""\n\n\ndef test_encoding_option_str(xml_baby_names, parser):\n df_file = read_xml(xml_baby_names, parser=parser, encoding="ISO-8859-1").head(5)\n\n output = df_file.to_xml(encoding="ISO-8859-1", parser=parser)\n\n if output is not None:\n # etree and lxml differ on quotes and case in xml declaration\n output = output.replace(\n '<?xml version="1.0" encoding="ISO-8859-1"?',\n "<?xml version='1.0' encoding='ISO-8859-1'?",\n )\n\n assert output == encoding_expected\n\n\ndef test_correct_encoding_file(xml_baby_names):\n pytest.importorskip("lxml")\n df_file = read_xml(xml_baby_names, encoding="ISO-8859-1", parser="lxml")\n\n with tm.ensure_clean("test.xml") as path:\n df_file.to_xml(path, index=False, encoding="ISO-8859-1", parser="lxml")\n\n\n@pytest.mark.parametrize("encoding", ["UTF-8", "UTF-16", "ISO-8859-1"])\ndef test_wrong_encoding_option_lxml(xml_baby_names, parser, encoding):\n pytest.importorskip("lxml")\n df_file = read_xml(xml_baby_names, encoding="ISO-8859-1", parser="lxml")\n\n with tm.ensure_clean("test.xml") as path:\n df_file.to_xml(path, index=False, encoding=encoding, parser=parser)\n\n\ndef test_misspelled_encoding(parser, geom_df):\n with pytest.raises(LookupError, match=("unknown encoding")):\n geom_df.to_xml(encoding="uft-8", parser=parser)\n\n\n# PRETTY PRINT\n\n\ndef test_xml_declaration_pretty_print(geom_df):\n pytest.importorskip("lxml")\n expected = """\\n<data>\n <row>\n <index>0</index>\n <shape>square</shape>\n <degrees>360</degrees>\n <sides>4.0</sides>\n </row>\n <row>\n <index>1</index>\n <shape>circle</shape>\n <degrees>360</degrees>\n <sides/>\n </row>\n <row>\n <index>2</index>\n <shape>triangle</shape>\n <degrees>180</degrees>\n <sides>3.0</sides>\n </row>\n</data>"""\n\n output = geom_df.to_xml(xml_declaration=False)\n\n assert output == expected\n\n\ndef test_no_pretty_print_with_decl(parser, geom_df):\n expected = (\n "<?xml version='1.0' encoding='utf-8'?>\n"\n "<data><row><index>0</index><shape>square</shape>"\n "<degrees>360</degrees><sides>4.0</sides></row><row>"\n "<index>1</index><shape>circle</shape><degrees>360"\n "</degrees><sides/></row><row><index>2</index><shape>"\n "triangle</shape><degrees>180</degrees><sides>3.0</sides>"\n "</row></data>"\n )\n\n output = geom_df.to_xml(pretty_print=False, parser=parser)\n output = equalize_decl(output)\n\n # etree adds space for closed tags\n if output is not None:\n output = output.replace(" />", "/>")\n\n assert output == expected\n\n\ndef test_no_pretty_print_no_decl(parser, geom_df):\n expected = (\n "<data><row><index>0</index><shape>square</shape>"\n "<degrees>360</degrees><sides>4.0</sides></row><row>"\n "<index>1</index><shape>circle</shape><degrees>360"\n "</degrees><sides/></row><row><index>2</index><shape>"\n "triangle</shape><degrees>180</degrees><sides>3.0</sides>"\n "</row></data>"\n )\n\n output = geom_df.to_xml(xml_declaration=False, pretty_print=False, parser=parser)\n\n # etree adds space for closed tags\n if output is not None:\n output = output.replace(" />", "/>")\n\n assert output == expected\n\n\n# PARSER\n\n\n@td.skip_if_installed("lxml")\ndef test_default_parser_no_lxml(geom_df):\n with pytest.raises(\n ImportError, match=("lxml not found, please install or use the etree parser.")\n ):\n geom_df.to_xml()\n\n\ndef test_unknown_parser(geom_df):\n with pytest.raises(\n ValueError, match=("Values for parser can only be lxml or etree.")\n ):\n geom_df.to_xml(parser="bs4")\n\n\n# STYLESHEET\n\nxsl_expected = """\\n<?xml version="1.0" encoding="utf-8"?>\n<data>\n <row>\n <field field="index">0</field>\n <field field="shape">square</field>\n <field field="degrees">360</field>\n <field field="sides">4.0</field>\n </row>\n <row>\n <field field="index">1</field>\n <field field="shape">circle</field>\n <field field="degrees">360</field>\n <field field="sides"/>\n </row>\n <row>\n <field field="index">2</field>\n <field field="shape">triangle</field>\n <field field="degrees">180</field>\n <field field="sides">3.0</field>\n </row>\n</data>"""\n\n\ndef test_stylesheet_file_like(xsl_row_field_output, mode, geom_df):\n pytest.importorskip("lxml")\n with open(\n xsl_row_field_output, mode, encoding="utf-8" if mode == "r" else None\n ) as f:\n assert geom_df.to_xml(stylesheet=f) == xsl_expected\n\n\ndef test_stylesheet_io(xsl_row_field_output, mode, geom_df):\n # note: By default the bodies of untyped functions are not checked,\n # consider using --check-untyped-defs\n pytest.importorskip("lxml")\n xsl_obj: BytesIO | StringIO # type: ignore[annotation-unchecked]\n\n with open(\n xsl_row_field_output, mode, encoding="utf-8" if mode == "r" else None\n ) as f:\n if mode == "rb":\n xsl_obj = BytesIO(f.read())\n else:\n xsl_obj = StringIO(f.read())\n\n output = geom_df.to_xml(stylesheet=xsl_obj)\n\n assert output == xsl_expected\n\n\ndef test_stylesheet_buffered_reader(xsl_row_field_output, mode, geom_df):\n pytest.importorskip("lxml")\n with open(\n xsl_row_field_output, mode, encoding="utf-8" if mode == "r" else None\n ) as f:\n xsl_obj = f.read()\n\n output = geom_df.to_xml(stylesheet=xsl_obj)\n\n assert output == xsl_expected\n\n\ndef test_stylesheet_wrong_path(geom_df):\n lxml_etree = pytest.importorskip("lxml.etree")\n\n xsl = os.path.join("data", "xml", "row_field_output.xslt")\n\n with pytest.raises(\n lxml_etree.XMLSyntaxError,\n match=("Start tag expected, '<' not found"),\n ):\n geom_df.to_xml(stylesheet=xsl)\n\n\n@pytest.mark.parametrize("val", ["", b""])\ndef test_empty_string_stylesheet(val, geom_df):\n lxml_etree = pytest.importorskip("lxml.etree")\n\n msg = "|".join(\n [\n "Document is empty",\n "Start tag expected, '<' not found",\n # Seen on Mac with lxml 4.9.1\n r"None \(line 0\)",\n ]\n )\n\n with pytest.raises(lxml_etree.XMLSyntaxError, match=msg):\n geom_df.to_xml(stylesheet=val)\n\n\ndef test_incorrect_xsl_syntax(geom_df):\n lxml_etree = pytest.importorskip("lxml.etree")\n\n xsl = """\\n<xsl:stylesheet version="1.0" xmlns:xsl="http://www.w3.org/1999/XSL/Transform">\n <xsl:output method="xml" encoding="utf-8" indent="yes" >\n <xsl:strip-space elements="*"/>\n\n <xsl:template match="@*|node()">\n <xsl:copy>\n <xsl:apply-templates select="@*|node()"/>\n </xsl:copy>\n </xsl:template>\n\n <xsl:template match="row/*">\n <field>\n <xsl:attribute name="field">\n <xsl:value-of select="name()"/>\n </xsl:attribute>\n <xsl:value-of select="text()"/>\n </field>\n </xsl:template>\n</xsl:stylesheet>"""\n\n with pytest.raises(\n lxml_etree.XMLSyntaxError, match=("Opening and ending tag mismatch")\n ):\n geom_df.to_xml(stylesheet=xsl)\n\n\ndef test_incorrect_xsl_eval(geom_df):\n lxml_etree = pytest.importorskip("lxml.etree")\n\n xsl = """\\n<xsl:stylesheet version="1.0" xmlns:xsl="http://www.w3.org/1999/XSL/Transform">\n <xsl:output method="xml" encoding="utf-8" indent="yes" />\n <xsl:strip-space elements="*"/>\n\n <xsl:template match="@*|node(*)">\n <xsl:copy>\n <xsl:apply-templates select="@*|node()"/>\n </xsl:copy>\n </xsl:template>\n\n <xsl:template match="row/*">\n <field>\n <xsl:attribute name="field">\n <xsl:value-of select="name()"/>\n </xsl:attribute>\n <xsl:value-of select="text()"/>\n </field>\n </xsl:template>\n</xsl:stylesheet>"""\n\n with pytest.raises(lxml_etree.XSLTParseError, match=("failed to compile")):\n geom_df.to_xml(stylesheet=xsl)\n\n\ndef test_incorrect_xsl_apply(geom_df):\n lxml_etree = pytest.importorskip("lxml.etree")\n\n xsl = """\\n<xsl:stylesheet version="1.0" xmlns:xsl="http://www.w3.org/1999/XSL/Transform">\n <xsl:output method="xml" encoding="utf-8" indent="yes" />\n <xsl:strip-space elements="*"/>\n\n <xsl:template match="@*|node()">\n <xsl:copy>\n <xsl:copy-of select="document('non_existent.xml')/*"/>\n </xsl:copy>\n </xsl:template>\n</xsl:stylesheet>"""\n\n with pytest.raises(lxml_etree.XSLTApplyError, match=("Cannot resolve URI")):\n with tm.ensure_clean("test.xml") as path:\n geom_df.to_xml(path, stylesheet=xsl)\n\n\ndef test_stylesheet_with_etree(geom_df):\n xsl = """\\n<xsl:stylesheet version="1.0" xmlns:xsl="http://www.w3.org/1999/XSL/Transform">\n <xsl:output method="xml" encoding="utf-8" indent="yes" />\n <xsl:strip-space elements="*"/>\n\n <xsl:template match="@*|node(*)">\n <xsl:copy>\n <xsl:apply-templates select="@*|node()"/>\n </xsl:copy>\n </xsl:template>"""\n\n with pytest.raises(\n ValueError, match=("To use stylesheet, you need lxml installed")\n ):\n geom_df.to_xml(parser="etree", stylesheet=xsl)\n\n\ndef test_style_to_csv(geom_df):\n pytest.importorskip("lxml")\n xsl = """\\n<xsl:stylesheet version="1.0" xmlns:xsl="http://www.w3.org/1999/XSL/Transform">\n <xsl:output method="text" indent="yes" />\n <xsl:strip-space elements="*"/>\n\n <xsl:param name="delim">,</xsl:param>\n <xsl:template match="/data">\n <xsl:text>,shape,degrees,sides
</xsl:text>\n <xsl:apply-templates select="row"/>\n </xsl:template>\n\n <xsl:template match="row">\n <xsl:value-of select="concat(index, $delim, shape, $delim,\n degrees, $delim, sides)"/>\n <xsl:text>
</xsl:text>\n </xsl:template>\n</xsl:stylesheet>"""\n\n out_csv = geom_df.to_csv(lineterminator="\n")\n\n if out_csv is not None:\n out_csv = out_csv.strip()\n out_xml = geom_df.to_xml(stylesheet=xsl)\n\n assert out_csv == out_xml\n\n\ndef test_style_to_string(geom_df):\n pytest.importorskip("lxml")\n xsl = """\\n<xsl:stylesheet version="1.0" xmlns:xsl="http://www.w3.org/1999/XSL/Transform">\n <xsl:output method="text" indent="yes" />\n <xsl:strip-space elements="*"/>\n\n <xsl:param name="delim"><xsl:text> </xsl:text></xsl:param>\n <xsl:template match="/data">\n <xsl:text> shape degrees sides
</xsl:text>\n <xsl:apply-templates select="row"/>\n </xsl:template>\n\n <xsl:template match="row">\n <xsl:value-of select="concat(index, ' ',\n substring($delim, 1, string-length('triangle')\n - string-length(shape) + 1),\n shape,\n substring($delim, 1, string-length(name(degrees))\n - string-length(degrees) + 2),\n degrees,\n substring($delim, 1, string-length(name(sides))\n - string-length(sides) + 2),\n sides)"/>\n <xsl:text>
</xsl:text>\n </xsl:template>\n</xsl:stylesheet>"""\n\n out_str = geom_df.to_string()\n out_xml = geom_df.to_xml(na_rep="NaN", stylesheet=xsl)\n\n assert out_xml == out_str\n\n\ndef test_style_to_json(geom_df):\n pytest.importorskip("lxml")\n xsl = """\\n<xsl:stylesheet version="1.0" xmlns:xsl="http://www.w3.org/1999/XSL/Transform">\n <xsl:output method="text" indent="yes" />\n <xsl:strip-space elements="*"/>\n\n <xsl:param name="quot">"</xsl:param>\n\n <xsl:template match="/data">\n <xsl:text>{"shape":{</xsl:text>\n <xsl:apply-templates select="descendant::row/shape"/>\n <xsl:text>},"degrees":{</xsl:text>\n <xsl:apply-templates select="descendant::row/degrees"/>\n <xsl:text>},"sides":{</xsl:text>\n <xsl:apply-templates select="descendant::row/sides"/>\n <xsl:text>}}</xsl:text>\n </xsl:template>\n\n <xsl:template match="shape|degrees|sides">\n <xsl:variable name="val">\n <xsl:if test = ".=''">\n <xsl:value-of select="'null'"/>\n </xsl:if>\n <xsl:if test = "number(text()) = text()">\n <xsl:value-of select="text()"/>\n </xsl:if>\n <xsl:if test = "number(text()) != text()">\n <xsl:value-of select="concat($quot, text(), $quot)"/>\n </xsl:if>\n </xsl:variable>\n <xsl:value-of select="concat($quot, preceding-sibling::index,\n $quot,':', $val)"/>\n <xsl:if test="preceding-sibling::index != //row[last()]/index">\n <xsl:text>,</xsl:text>\n </xsl:if>\n </xsl:template>\n</xsl:stylesheet>"""\n\n out_json = geom_df.to_json()\n out_xml = geom_df.to_xml(stylesheet=xsl)\n\n assert out_json == out_xml\n\n\n# COMPRESSION\n\n\ngeom_xml = """\\n<?xml version='1.0' encoding='utf-8'?>\n<data>\n <row>\n <index>0</index>\n <shape>square</shape>\n <degrees>360</degrees>\n <sides>4.0</sides>\n </row>\n <row>\n <index>1</index>\n <shape>circle</shape>\n <degrees>360</degrees>\n <sides/>\n </row>\n <row>\n <index>2</index>\n <shape>triangle</shape>\n <degrees>180</degrees>\n <sides>3.0</sides>\n </row>\n</data>"""\n\n\ndef test_compression_output(parser, compression_only, geom_df):\n with tm.ensure_clean() as path:\n geom_df.to_xml(path, parser=parser, compression=compression_only)\n\n with get_handle(\n path,\n "r",\n compression=compression_only,\n ) as handle_obj:\n output = handle_obj.handle.read()\n\n output = equalize_decl(output)\n\n assert geom_xml == output.strip()\n\n\ndef test_filename_and_suffix_comp(\n parser, compression_only, geom_df, compression_to_extension\n):\n compfile = "xml." + compression_to_extension[compression_only]\n with tm.ensure_clean(filename=compfile) as path:\n geom_df.to_xml(path, parser=parser, compression=compression_only)\n\n with get_handle(\n path,\n "r",\n compression=compression_only,\n ) as handle_obj:\n output = handle_obj.handle.read()\n\n output = equalize_decl(output)\n\n assert geom_xml == output.strip()\n\n\ndef test_ea_dtypes(any_numeric_ea_dtype, parser):\n # GH#43903\n expected = """<?xml version='1.0' encoding='utf-8'?>\n<data>\n <row>\n <index>0</index>\n <a/>\n </row>\n</data>"""\n df = DataFrame({"a": [NA]}).astype(any_numeric_ea_dtype)\n result = df.to_xml(parser=parser)\n assert equalize_decl(result).strip() == expected\n\n\ndef test_unsuported_compression(parser, geom_df):\n with pytest.raises(ValueError, match="Unrecognized compression type"):\n with tm.ensure_clean() as path:\n geom_df.to_xml(path, parser=parser, compression="7z")\n\n\n# STORAGE OPTIONS\n\n\n@pytest.mark.single_cpu\ndef test_s3_permission_output(parser, s3_public_bucket, geom_df):\n s3fs = pytest.importorskip("s3fs")\n pytest.importorskip("lxml")\n\n with tm.external_error_raised((PermissionError, FileNotFoundError)):\n fs = s3fs.S3FileSystem(anon=True)\n fs.ls(s3_public_bucket.name)\n\n geom_df.to_xml(\n f"s3://{s3_public_bucket.name}/geom.xml", compression="zip", parser=parser\n )\n | .venv\Lib\site-packages\pandas\tests\io\xml\test_to_xml.py | test_to_xml.py | Python | 35,612 | 0.95 | 0.064 | 0.048007 | node-utils | 434 | 2024-01-15T04:26:30.068031 | Apache-2.0 | true | c7a77ea52ff7d2b51ae6914dab831ef1 |
from __future__ import annotations\n\nfrom io import (\n BytesIO,\n StringIO,\n)\nfrom lzma import LZMAError\nimport os\nfrom tarfile import ReadError\nfrom urllib.error import HTTPError\nfrom xml.etree.ElementTree import ParseError\nfrom zipfile import BadZipFile\n\nimport numpy as np\nimport pytest\n\nfrom pandas.compat._optional import import_optional_dependency\nfrom pandas.errors import (\n EmptyDataError,\n ParserError,\n)\nimport pandas.util._test_decorators as td\n\nimport pandas as pd\nfrom pandas import (\n NA,\n DataFrame,\n Series,\n)\nimport pandas._testing as tm\n\nfrom pandas.io.common import get_handle\nfrom pandas.io.xml import read_xml\n\n# CHECK LIST\n\n# [x] - ValueError: "Values for parser can only be lxml or etree."\n\n# etree\n# [X] - ImportError: "lxml not found, please install or use the etree parser."\n# [X] - TypeError: "expected str, bytes or os.PathLike object, not NoneType"\n# [X] - ValueError: "Either element or attributes can be parsed not both."\n# [X] - ValueError: "xpath does not return any nodes..."\n# [X] - SyntaxError: "You have used an incorrect or unsupported XPath"\n# [X] - ValueError: "names does not match length of child elements in xpath."\n# [X] - TypeError: "...is not a valid type for names"\n# [X] - ValueError: "To use stylesheet, you need lxml installed..."\n# [] - URLError: (GENERAL ERROR WITH HTTPError AS SUBCLASS)\n# [X] - HTTPError: "HTTP Error 404: Not Found"\n# [] - OSError: (GENERAL ERROR WITH FileNotFoundError AS SUBCLASS)\n# [X] - FileNotFoundError: "No such file or directory"\n# [] - ParseError (FAILSAFE CATCH ALL FOR VERY COMPLEX XML)\n# [X] - UnicodeDecodeError: "'utf-8' codec can't decode byte 0xe9..."\n# [X] - UnicodeError: "UTF-16 stream does not start with BOM"\n# [X] - BadZipFile: "File is not a zip file"\n# [X] - OSError: "Invalid data stream"\n# [X] - LZMAError: "Input format not supported by decoder"\n# [X] - ValueError: "Unrecognized compression type"\n# [X] - PermissionError: "Forbidden"\n\n# lxml\n# [X] - ValueError: "Either element or attributes can be parsed not both."\n# [X] - AttributeError: "__enter__"\n# [X] - XSLTApplyError: "Cannot resolve URI"\n# [X] - XSLTParseError: "document is not a stylesheet"\n# [X] - ValueError: "xpath does not return any nodes."\n# [X] - XPathEvalError: "Invalid expression"\n# [] - XPathSyntaxError: (OLD VERSION IN lxml FOR XPATH ERRORS)\n# [X] - TypeError: "empty namespace prefix is not supported in XPath"\n# [X] - ValueError: "names does not match length of child elements in xpath."\n# [X] - TypeError: "...is not a valid type for names"\n# [X] - LookupError: "unknown encoding"\n# [] - URLError: (USUALLY DUE TO NETWORKING)\n# [X - HTTPError: "HTTP Error 404: Not Found"\n# [X] - OSError: "failed to load external entity"\n# [X] - XMLSyntaxError: "Start tag expected, '<' not found"\n# [] - ParserError: (FAILSAFE CATCH ALL FOR VERY COMPLEX XML\n# [X] - ValueError: "Values for parser can only be lxml or etree."\n# [X] - UnicodeDecodeError: "'utf-8' codec can't decode byte 0xe9..."\n# [X] - UnicodeError: "UTF-16 stream does not start with BOM"\n# [X] - BadZipFile: "File is not a zip file"\n# [X] - OSError: "Invalid data stream"\n# [X] - LZMAError: "Input format not supported by decoder"\n# [X] - ValueError: "Unrecognized compression type"\n# [X] - PermissionError: "Forbidden"\n\ngeom_df = DataFrame(\n {\n "shape": ["square", "circle", "triangle"],\n "degrees": [360, 360, 180],\n "sides": [4, np.nan, 3],\n }\n)\n\nxml_default_nmsp = """\\n<?xml version='1.0' encoding='utf-8'?>\n<data xmlns="http://example.com">\n <row>\n <shape>square</shape>\n <degrees>360</degrees>\n <sides>4</sides>\n </row>\n <row>\n <shape>circle</shape>\n <degrees>360</degrees>\n <sides/>\n </row>\n <row>\n <shape>triangle</shape>\n <degrees>180</degrees>\n <sides>3</sides>\n </row>\n</data>"""\n\nxml_prefix_nmsp = """\\n<?xml version='1.0' encoding='utf-8'?>\n<doc:data xmlns:doc="http://example.com">\n <doc:row>\n <doc:shape>square</doc:shape>\n <doc:degrees>360</doc:degrees>\n <doc:sides>4.0</doc:sides>\n </doc:row>\n <doc:row>\n <doc:shape>circle</doc:shape>\n <doc:degrees>360</doc:degrees>\n <doc:sides/>\n </doc:row>\n <doc:row>\n <doc:shape>triangle</doc:shape>\n <doc:degrees>180</doc:degrees>\n <doc:sides>3.0</doc:sides>\n </doc:row>\n</doc:data>"""\n\n\ndf_kml = DataFrame(\n {\n "id": {\n 0: "ID_00001",\n 1: "ID_00002",\n 2: "ID_00003",\n 3: "ID_00004",\n 4: "ID_00005",\n },\n "name": {\n 0: "Blue Line (Forest Park)",\n 1: "Red, Purple Line",\n 2: "Red, Purple Line",\n 3: "Red, Purple Line",\n 4: "Red, Purple Line",\n },\n "styleUrl": {\n 0: "#LineStyle01",\n 1: "#LineStyle01",\n 2: "#LineStyle01",\n 3: "#LineStyle01",\n 4: "#LineStyle01",\n },\n "extrude": {0: 0, 1: 0, 2: 0, 3: 0, 4: 0},\n "altitudeMode": {\n 0: "clampedToGround",\n 1: "clampedToGround",\n 2: "clampedToGround",\n 3: "clampedToGround",\n 4: "clampedToGround",\n },\n "coordinates": {\n 0: (\n "-87.77678526964958,41.8708863930319,0 "\n "-87.77826234150609,41.87097820122218,0 "\n "-87.78251583439344,41.87130129991005,0 "\n "-87.78418294588424,41.87145055520308,0 "\n "-87.7872369165933,41.8717239119163,0 "\n "-87.79160214925886,41.87210797280065,0"\n ),\n 1: (\n "-87.65758750947528,41.96427269188822,0 "\n "-87.65802133507393,41.96581929055245,0 "\n "-87.65819033925305,41.96621846093642,0 "\n "-87.6583189819129,41.96650362897086,0 "\n "-87.65835858701473,41.96669002089185,0 "\n "-87.65838428411853,41.96688150295095,0 "\n "-87.65842208882658,41.96745896091846,0 "\n "-87.65846556843937,41.9683761425439,0 "\n "-87.65849296214573,41.96913893870342,0"\n ),\n 2: (\n "-87.65492939166126,41.95377494531437,0 "\n "-87.65557043199591,41.95376544118533,0 "\n "-87.65606302030132,41.95376391658746,0 "\n "-87.65623502146268,41.95377379126367,0 "\n "-87.65634748981634,41.95380103566435,0 "\n "-87.65646537904269,41.95387703994676,0 "\n "-87.65656532461145,41.95396622645799,0 "\n "-87.65664760856414,41.95404201996044,0 "\n "-87.65671750555913,41.95416647054043,0 "\n "-87.65673983607117,41.95429949810849,0 "\n "-87.65673866475777,41.95441024240925,0 "\n "-87.6567690255541,41.95490657227902,0 "\n "-87.65683672482363,41.95692259283837,0 "\n "-87.6568900886376,41.95861070983142,0 "\n "-87.65699865558875,41.96181418669004,0 "\n "-87.65756347177603,41.96397045777844,0 "\n "-87.65758750947528,41.96427269188822,0"\n ),\n 3: (\n "-87.65362593118043,41.94742799535678,0 "\n "-87.65363554415794,41.94819886386848,0 "\n "-87.6536456393239,41.95059994675451,0 "\n "-87.65365831235026,41.95108288489359,0 "\n "-87.6536604873874,41.9519954657554,0 "\n "-87.65362592053201,41.95245597302328,0 "\n "-87.65367158496069,41.95311153649393,0 "\n "-87.65368468595476,41.9533202828916,0 "\n "-87.65369271253692,41.95343095587119,0 "\n "-87.65373335834569,41.95351536301472,0 "\n "-87.65378605844126,41.95358212680591,0 "\n "-87.65385067928185,41.95364452823767,0 "\n "-87.6539390793817,41.95370263886964,0 "\n "-87.6540786298351,41.95373403675265,0 "\n "-87.65430648647626,41.9537535411832,0 "\n "-87.65492939166126,41.95377494531437,0"\n ),\n 4: (\n "-87.65345391792157,41.94217681262115,0 "\n "-87.65342448305786,41.94237224420864,0 "\n "-87.65339745703922,41.94268217746244,0 "\n "-87.65337753982941,41.94288140770284,0 "\n "-87.65336256753105,41.94317369618263,0 "\n "-87.65338799707138,41.94357253961736,0 "\n "-87.65340240886648,41.94389158188269,0 "\n "-87.65341837392448,41.94406444407721,0 "\n "-87.65342275247338,41.94421065714904,0 "\n "-87.65347469646018,41.94434829382345,0 "\n "-87.65351486483024,41.94447699917548,0 "\n "-87.65353483605053,41.9453896864472,0 "\n "-87.65361975532807,41.94689193720703,0 "\n "-87.65362593118043,41.94742799535678,0"\n ),\n },\n }\n)\n\n\ndef test_literal_xml_deprecation():\n # GH 53809\n pytest.importorskip("lxml")\n msg = (\n "Passing literal xml to 'read_xml' is deprecated and "\n "will be removed in a future version. To read from a "\n "literal string, wrap it in a 'StringIO' object."\n )\n\n with tm.assert_produces_warning(FutureWarning, match=msg):\n read_xml(xml_default_nmsp)\n\n\n@pytest.fixture(params=["rb", "r"])\ndef mode(request):\n return request.param\n\n\n@pytest.fixture(params=[pytest.param("lxml", marks=td.skip_if_no("lxml")), "etree"])\ndef parser(request):\n return request.param\n\n\ndef read_xml_iterparse(data, **kwargs):\n with tm.ensure_clean() as path:\n with open(path, "w", encoding="utf-8") as f:\n f.write(data)\n return read_xml(path, **kwargs)\n\n\ndef read_xml_iterparse_comp(comp_path, compression_only, **kwargs):\n with get_handle(comp_path, "r", compression=compression_only) as handles:\n with tm.ensure_clean() as path:\n with open(path, "w", encoding="utf-8") as f:\n f.write(handles.handle.read())\n return read_xml(path, **kwargs)\n\n\n# FILE / URL\n\n\ndef test_parser_consistency_file(xml_books):\n pytest.importorskip("lxml")\n df_file_lxml = read_xml(xml_books, parser="lxml")\n df_file_etree = read_xml(xml_books, parser="etree")\n\n df_iter_lxml = read_xml(\n xml_books,\n parser="lxml",\n iterparse={"book": ["category", "title", "year", "author", "price"]},\n )\n df_iter_etree = read_xml(\n xml_books,\n parser="etree",\n iterparse={"book": ["category", "title", "year", "author", "price"]},\n )\n\n tm.assert_frame_equal(df_file_lxml, df_file_etree)\n tm.assert_frame_equal(df_file_lxml, df_iter_lxml)\n tm.assert_frame_equal(df_iter_lxml, df_iter_etree)\n\n\n@pytest.mark.network\n@pytest.mark.single_cpu\ndef test_parser_consistency_url(parser, httpserver):\n httpserver.serve_content(content=xml_default_nmsp)\n\n df_xpath = read_xml(StringIO(xml_default_nmsp), parser=parser)\n df_iter = read_xml(\n BytesIO(xml_default_nmsp.encode()),\n parser=parser,\n iterparse={"row": ["shape", "degrees", "sides"]},\n )\n\n tm.assert_frame_equal(df_xpath, df_iter)\n\n\ndef test_file_like(xml_books, parser, mode):\n with open(xml_books, mode, encoding="utf-8" if mode == "r" else None) as f:\n df_file = read_xml(f, parser=parser)\n\n df_expected = DataFrame(\n {\n "category": ["cooking", "children", "web"],\n "title": ["Everyday Italian", "Harry Potter", "Learning XML"],\n "author": ["Giada De Laurentiis", "J K. Rowling", "Erik T. Ray"],\n "year": [2005, 2005, 2003],\n "price": [30.00, 29.99, 39.95],\n }\n )\n\n tm.assert_frame_equal(df_file, df_expected)\n\n\ndef test_file_io(xml_books, parser, mode):\n with open(xml_books, mode, encoding="utf-8" if mode == "r" else None) as f:\n xml_obj = f.read()\n\n df_io = read_xml(\n (BytesIO(xml_obj) if isinstance(xml_obj, bytes) else StringIO(xml_obj)),\n parser=parser,\n )\n\n df_expected = DataFrame(\n {\n "category": ["cooking", "children", "web"],\n "title": ["Everyday Italian", "Harry Potter", "Learning XML"],\n "author": ["Giada De Laurentiis", "J K. Rowling", "Erik T. Ray"],\n "year": [2005, 2005, 2003],\n "price": [30.00, 29.99, 39.95],\n }\n )\n\n tm.assert_frame_equal(df_io, df_expected)\n\n\ndef test_file_buffered_reader_string(xml_books, parser, mode):\n with open(xml_books, mode, encoding="utf-8" if mode == "r" else None) as f:\n xml_obj = f.read()\n\n if mode == "rb":\n xml_obj = StringIO(xml_obj.decode())\n elif mode == "r":\n xml_obj = StringIO(xml_obj)\n\n df_str = read_xml(xml_obj, parser=parser)\n\n df_expected = DataFrame(\n {\n "category": ["cooking", "children", "web"],\n "title": ["Everyday Italian", "Harry Potter", "Learning XML"],\n "author": ["Giada De Laurentiis", "J K. Rowling", "Erik T. Ray"],\n "year": [2005, 2005, 2003],\n "price": [30.00, 29.99, 39.95],\n }\n )\n\n tm.assert_frame_equal(df_str, df_expected)\n\n\ndef test_file_buffered_reader_no_xml_declaration(xml_books, parser, mode):\n with open(xml_books, mode, encoding="utf-8" if mode == "r" else None) as f:\n next(f)\n xml_obj = f.read()\n\n if mode == "rb":\n xml_obj = StringIO(xml_obj.decode())\n elif mode == "r":\n xml_obj = StringIO(xml_obj)\n\n df_str = read_xml(xml_obj, parser=parser)\n\n df_expected = DataFrame(\n {\n "category": ["cooking", "children", "web"],\n "title": ["Everyday Italian", "Harry Potter", "Learning XML"],\n "author": ["Giada De Laurentiis", "J K. Rowling", "Erik T. Ray"],\n "year": [2005, 2005, 2003],\n "price": [30.00, 29.99, 39.95],\n }\n )\n\n tm.assert_frame_equal(df_str, df_expected)\n\n\ndef test_string_charset(parser):\n txt = "<中文標籤><row><c1>1</c1><c2>2</c2></row></中文標籤>"\n\n df_str = read_xml(StringIO(txt), parser=parser)\n\n df_expected = DataFrame({"c1": 1, "c2": 2}, index=[0])\n\n tm.assert_frame_equal(df_str, df_expected)\n\n\ndef test_file_charset(xml_doc_ch_utf, parser):\n df_file = read_xml(xml_doc_ch_utf, parser=parser)\n\n df_expected = DataFrame(\n {\n "問": [\n "問 若箇是邪而言破邪 何者是正而道(Sorry, this is Big5 only)申正",\n "問 既破有得申無得 亦應但破性執申假名以不",\n "問 既破性申假 亦應但破有申無 若有無兩洗 亦應性假雙破耶",\n ],\n "答": [\n "".join(\n [\n "答 邪既無量 正亦多途 大略為言不出二種 謂",\n "有得與無得 有得是邪須破 無得是正須申\n\t\t故",\n ]\n ),\n None,\n "答 不例 有無皆是性 所以須雙破 既分性假異 故有破不破",\n ],\n "a": [\n None,\n "答 性執是有得 假名是無得 今破有得申無得 即是破性執申假名也",\n None,\n ],\n }\n )\n\n tm.assert_frame_equal(df_file, df_expected)\n\n\ndef test_file_handle_close(xml_books, parser):\n with open(xml_books, "rb") as f:\n read_xml(BytesIO(f.read()), parser=parser)\n\n assert not f.closed\n\n\n@pytest.mark.parametrize("val", ["", b""])\ndef test_empty_string_lxml(val):\n lxml_etree = pytest.importorskip("lxml.etree")\n\n msg = "|".join(\n [\n "Document is empty",\n # Seen on Mac with lxml 4.91\n r"None \(line 0\)",\n ]\n )\n with pytest.raises(lxml_etree.XMLSyntaxError, match=msg):\n if isinstance(val, str):\n read_xml(StringIO(val), parser="lxml")\n else:\n read_xml(BytesIO(val), parser="lxml")\n\n\n@pytest.mark.parametrize("val", ["", b""])\ndef test_empty_string_etree(val):\n with pytest.raises(ParseError, match="no element found"):\n if isinstance(val, str):\n read_xml(StringIO(val), parser="etree")\n else:\n read_xml(BytesIO(val), parser="etree")\n\n\ndef test_wrong_file_path(parser):\n msg = (\n "Passing literal xml to 'read_xml' is deprecated and "\n "will be removed in a future version. To read from a "\n "literal string, wrap it in a 'StringIO' object."\n )\n filename = os.path.join("data", "html", "books.xml")\n\n with pytest.raises(\n FutureWarning,\n match=msg,\n ):\n read_xml(filename, parser=parser)\n\n\n@pytest.mark.network\n@pytest.mark.single_cpu\ndef test_url(httpserver, xml_file):\n pytest.importorskip("lxml")\n with open(xml_file, encoding="utf-8") as f:\n httpserver.serve_content(content=f.read())\n df_url = read_xml(httpserver.url, xpath=".//book[count(*)=4]")\n\n df_expected = DataFrame(\n {\n "category": ["cooking", "children", "web"],\n "title": ["Everyday Italian", "Harry Potter", "Learning XML"],\n "author": ["Giada De Laurentiis", "J K. Rowling", "Erik T. Ray"],\n "year": [2005, 2005, 2003],\n "price": [30.00, 29.99, 39.95],\n }\n )\n\n tm.assert_frame_equal(df_url, df_expected)\n\n\n@pytest.mark.network\n@pytest.mark.single_cpu\ndef test_wrong_url(parser, httpserver):\n httpserver.serve_content("NOT FOUND", code=404)\n with pytest.raises(HTTPError, match=("HTTP Error 404: NOT FOUND")):\n read_xml(httpserver.url, xpath=".//book[count(*)=4]", parser=parser)\n\n\n# CONTENT\n\n\ndef test_whitespace(parser):\n xml = """\n <data>\n <row sides=" 4 ">\n <shape>\n square\n </shape>\n <degrees>	360	</degrees>\n </row>\n <row sides=" 0 ">\n <shape>\n circle\n </shape>\n <degrees>	360	</degrees>\n </row>\n <row sides=" 3 ">\n <shape>\n triangle\n </shape>\n <degrees>	180	</degrees>\n </row>\n </data>"""\n\n df_xpath = read_xml(StringIO(xml), parser=parser, dtype="string")\n\n df_iter = read_xml_iterparse(\n xml,\n parser=parser,\n iterparse={"row": ["sides", "shape", "degrees"]},\n dtype="string",\n )\n\n df_expected = DataFrame(\n {\n "sides": [" 4 ", " 0 ", " 3 "],\n "shape": [\n "\n square\n ",\n "\n circle\n ",\n "\n triangle\n ",\n ],\n "degrees": ["\t360\t", "\t360\t", "\t180\t"],\n },\n dtype="string",\n )\n\n tm.assert_frame_equal(df_xpath, df_expected)\n tm.assert_frame_equal(df_iter, df_expected)\n\n\n# XPATH\n\n\ndef test_empty_xpath_lxml(xml_books):\n pytest.importorskip("lxml")\n with pytest.raises(ValueError, match=("xpath does not return any nodes")):\n read_xml(xml_books, xpath=".//python", parser="lxml")\n\n\ndef test_bad_xpath_etree(xml_books):\n with pytest.raises(\n SyntaxError, match=("You have used an incorrect or unsupported XPath")\n ):\n read_xml(xml_books, xpath=".//[book]", parser="etree")\n\n\ndef test_bad_xpath_lxml(xml_books):\n lxml_etree = pytest.importorskip("lxml.etree")\n\n with pytest.raises(lxml_etree.XPathEvalError, match=("Invalid expression")):\n read_xml(xml_books, xpath=".//[book]", parser="lxml")\n\n\n# NAMESPACE\n\n\ndef test_default_namespace(parser):\n df_nmsp = read_xml(\n StringIO(xml_default_nmsp),\n xpath=".//ns:row",\n namespaces={"ns": "http://example.com"},\n parser=parser,\n )\n\n df_iter = read_xml_iterparse(\n xml_default_nmsp,\n parser=parser,\n iterparse={"row": ["shape", "degrees", "sides"]},\n )\n\n df_expected = DataFrame(\n {\n "shape": ["square", "circle", "triangle"],\n "degrees": [360, 360, 180],\n "sides": [4.0, float("nan"), 3.0],\n }\n )\n\n tm.assert_frame_equal(df_nmsp, df_expected)\n tm.assert_frame_equal(df_iter, df_expected)\n\n\ndef test_prefix_namespace(parser):\n df_nmsp = read_xml(\n StringIO(xml_prefix_nmsp),\n xpath=".//doc:row",\n namespaces={"doc": "http://example.com"},\n parser=parser,\n )\n df_iter = read_xml_iterparse(\n xml_prefix_nmsp, parser=parser, iterparse={"row": ["shape", "degrees", "sides"]}\n )\n\n df_expected = DataFrame(\n {\n "shape": ["square", "circle", "triangle"],\n "degrees": [360, 360, 180],\n "sides": [4.0, float("nan"), 3.0],\n }\n )\n\n tm.assert_frame_equal(df_nmsp, df_expected)\n tm.assert_frame_equal(df_iter, df_expected)\n\n\ndef test_consistency_default_namespace():\n pytest.importorskip("lxml")\n df_lxml = read_xml(\n StringIO(xml_default_nmsp),\n xpath=".//ns:row",\n namespaces={"ns": "http://example.com"},\n parser="lxml",\n )\n\n df_etree = read_xml(\n StringIO(xml_default_nmsp),\n xpath=".//doc:row",\n namespaces={"doc": "http://example.com"},\n parser="etree",\n )\n\n tm.assert_frame_equal(df_lxml, df_etree)\n\n\ndef test_consistency_prefix_namespace():\n pytest.importorskip("lxml")\n df_lxml = read_xml(\n StringIO(xml_prefix_nmsp),\n xpath=".//doc:row",\n namespaces={"doc": "http://example.com"},\n parser="lxml",\n )\n\n df_etree = read_xml(\n StringIO(xml_prefix_nmsp),\n xpath=".//doc:row",\n namespaces={"doc": "http://example.com"},\n parser="etree",\n )\n\n tm.assert_frame_equal(df_lxml, df_etree)\n\n\n# PREFIX\n\n\ndef test_missing_prefix_with_default_namespace(xml_books, parser):\n with pytest.raises(ValueError, match=("xpath does not return any nodes")):\n read_xml(xml_books, xpath=".//Placemark", parser=parser)\n\n\ndef test_missing_prefix_definition_etree(kml_cta_rail_lines):\n with pytest.raises(SyntaxError, match=("you used an undeclared namespace prefix")):\n read_xml(kml_cta_rail_lines, xpath=".//kml:Placemark", parser="etree")\n\n\ndef test_missing_prefix_definition_lxml(kml_cta_rail_lines):\n lxml_etree = pytest.importorskip("lxml.etree")\n\n with pytest.raises(lxml_etree.XPathEvalError, match=("Undefined namespace prefix")):\n read_xml(kml_cta_rail_lines, xpath=".//kml:Placemark", parser="lxml")\n\n\n@pytest.mark.parametrize("key", ["", None])\ndef test_none_namespace_prefix(key):\n pytest.importorskip("lxml")\n with pytest.raises(\n TypeError, match=("empty namespace prefix is not supported in XPath")\n ):\n read_xml(\n StringIO(xml_default_nmsp),\n xpath=".//kml:Placemark",\n namespaces={key: "http://www.opengis.net/kml/2.2"},\n parser="lxml",\n )\n\n\n# ELEMS AND ATTRS\n\n\ndef test_file_elems_and_attrs(xml_books, parser):\n df_file = read_xml(xml_books, parser=parser)\n df_iter = read_xml(\n xml_books,\n parser=parser,\n iterparse={"book": ["category", "title", "author", "year", "price"]},\n )\n df_expected = DataFrame(\n {\n "category": ["cooking", "children", "web"],\n "title": ["Everyday Italian", "Harry Potter", "Learning XML"],\n "author": ["Giada De Laurentiis", "J K. Rowling", "Erik T. Ray"],\n "year": [2005, 2005, 2003],\n "price": [30.00, 29.99, 39.95],\n }\n )\n\n tm.assert_frame_equal(df_file, df_expected)\n tm.assert_frame_equal(df_iter, df_expected)\n\n\ndef test_file_only_attrs(xml_books, parser):\n df_file = read_xml(xml_books, attrs_only=True, parser=parser)\n df_iter = read_xml(xml_books, parser=parser, iterparse={"book": ["category"]})\n df_expected = DataFrame({"category": ["cooking", "children", "web"]})\n\n tm.assert_frame_equal(df_file, df_expected)\n tm.assert_frame_equal(df_iter, df_expected)\n\n\ndef test_file_only_elems(xml_books, parser):\n df_file = read_xml(xml_books, elems_only=True, parser=parser)\n df_iter = read_xml(\n xml_books,\n parser=parser,\n iterparse={"book": ["title", "author", "year", "price"]},\n )\n df_expected = DataFrame(\n {\n "title": ["Everyday Italian", "Harry Potter", "Learning XML"],\n "author": ["Giada De Laurentiis", "J K. Rowling", "Erik T. Ray"],\n "year": [2005, 2005, 2003],\n "price": [30.00, 29.99, 39.95],\n }\n )\n\n tm.assert_frame_equal(df_file, df_expected)\n tm.assert_frame_equal(df_iter, df_expected)\n\n\ndef test_elem_and_attrs_only(kml_cta_rail_lines, parser):\n with pytest.raises(\n ValueError,\n match=("Either element or attributes can be parsed not both"),\n ):\n read_xml(kml_cta_rail_lines, elems_only=True, attrs_only=True, parser=parser)\n\n\ndef test_empty_attrs_only(parser):\n xml = """\n <data>\n <row>\n <shape sides="4">square</shape>\n <degrees>360</degrees>\n </row>\n <row>\n <shape sides="0">circle</shape>\n <degrees>360</degrees>\n </row>\n <row>\n <shape sides="3">triangle</shape>\n <degrees>180</degrees>\n </row>\n </data>"""\n\n with pytest.raises(\n ValueError,\n match=("xpath does not return any nodes or attributes"),\n ):\n read_xml(StringIO(xml), xpath="./row", attrs_only=True, parser=parser)\n\n\ndef test_empty_elems_only(parser):\n xml = """\n <data>\n <row sides="4" shape="square" degrees="360"/>\n <row sides="0" shape="circle" degrees="360"/>\n <row sides="3" shape="triangle" degrees="180"/>\n </data>"""\n\n with pytest.raises(\n ValueError,\n match=("xpath does not return any nodes or attributes"),\n ):\n read_xml(StringIO(xml), xpath="./row", elems_only=True, parser=parser)\n\n\ndef test_attribute_centric_xml():\n pytest.importorskip("lxml")\n xml = """\\n<?xml version="1.0" encoding="UTF-8"?>\n<TrainSchedule>\n <Stations>\n <station Name="Manhattan" coords="31,460,195,498"/>\n <station Name="Laraway Road" coords="63,409,194,455"/>\n <station Name="179th St (Orland Park)" coords="0,364,110,395"/>\n <station Name="153rd St (Orland Park)" coords="7,333,113,362"/>\n <station Name="143rd St (Orland Park)" coords="17,297,115,330"/>\n <station Name="Palos Park" coords="128,281,239,303"/>\n <station Name="Palos Heights" coords="148,257,283,279"/>\n <station Name="Worth" coords="170,230,248,255"/>\n <station Name="Chicago Ridge" coords="70,187,208,214"/>\n <station Name="Oak Lawn" coords="166,159,266,185"/>\n <station Name="Ashburn" coords="197,133,336,157"/>\n <station Name="Wrightwood" coords="219,106,340,133"/>\n <station Name="Chicago Union Sta" coords="220,0,360,43"/>\n </Stations>\n</TrainSchedule>"""\n\n df_lxml = read_xml(StringIO(xml), xpath=".//station")\n df_etree = read_xml(StringIO(xml), xpath=".//station", parser="etree")\n\n df_iter_lx = read_xml_iterparse(xml, iterparse={"station": ["Name", "coords"]})\n df_iter_et = read_xml_iterparse(\n xml, parser="etree", iterparse={"station": ["Name", "coords"]}\n )\n\n tm.assert_frame_equal(df_lxml, df_etree)\n tm.assert_frame_equal(df_iter_lx, df_iter_et)\n\n\n# NAMES\n\n\ndef test_names_option_output(xml_books, parser):\n df_file = read_xml(\n xml_books, names=["Col1", "Col2", "Col3", "Col4", "Col5"], parser=parser\n )\n df_iter = read_xml(\n xml_books,\n parser=parser,\n names=["Col1", "Col2", "Col3", "Col4", "Col5"],\n iterparse={"book": ["category", "title", "author", "year", "price"]},\n )\n\n df_expected = DataFrame(\n {\n "Col1": ["cooking", "children", "web"],\n "Col2": ["Everyday Italian", "Harry Potter", "Learning XML"],\n "Col3": ["Giada De Laurentiis", "J K. Rowling", "Erik T. Ray"],\n "Col4": [2005, 2005, 2003],\n "Col5": [30.00, 29.99, 39.95],\n }\n )\n\n tm.assert_frame_equal(df_file, df_expected)\n tm.assert_frame_equal(df_iter, df_expected)\n\n\ndef test_repeat_names(parser):\n xml = """\\n<shapes>\n <shape type="2D">\n <name>circle</name>\n <type>curved</type>\n </shape>\n <shape type="3D">\n <name>sphere</name>\n <type>curved</type>\n </shape>\n</shapes>"""\n df_xpath = read_xml(\n StringIO(xml),\n xpath=".//shape",\n parser=parser,\n names=["type_dim", "shape", "type_edge"],\n )\n\n df_iter = read_xml_iterparse(\n xml,\n parser=parser,\n iterparse={"shape": ["type", "name", "type"]},\n names=["type_dim", "shape", "type_edge"],\n )\n\n df_expected = DataFrame(\n {\n "type_dim": ["2D", "3D"],\n "shape": ["circle", "sphere"],\n "type_edge": ["curved", "curved"],\n }\n )\n\n tm.assert_frame_equal(df_xpath, df_expected)\n tm.assert_frame_equal(df_iter, df_expected)\n\n\ndef test_repeat_values_new_names(parser):\n xml = """\\n<shapes>\n <shape>\n <name>rectangle</name>\n <family>rectangle</family>\n </shape>\n <shape>\n <name>square</name>\n <family>rectangle</family>\n </shape>\n <shape>\n <name>ellipse</name>\n <family>ellipse</family>\n </shape>\n <shape>\n <name>circle</name>\n <family>ellipse</family>\n </shape>\n</shapes>"""\n df_xpath = read_xml(\n StringIO(xml), xpath=".//shape", parser=parser, names=["name", "group"]\n )\n\n df_iter = read_xml_iterparse(\n xml,\n parser=parser,\n iterparse={"shape": ["name", "family"]},\n names=["name", "group"],\n )\n\n df_expected = DataFrame(\n {\n "name": ["rectangle", "square", "ellipse", "circle"],\n "group": ["rectangle", "rectangle", "ellipse", "ellipse"],\n }\n )\n\n tm.assert_frame_equal(df_xpath, df_expected)\n tm.assert_frame_equal(df_iter, df_expected)\n\n\ndef test_repeat_elements(parser):\n xml = """\\n<shapes>\n <shape>\n <value item="name">circle</value>\n <value item="family">ellipse</value>\n <value item="degrees">360</value>\n <value item="sides">0</value>\n </shape>\n <shape>\n <value item="name">triangle</value>\n <value item="family">polygon</value>\n <value item="degrees">180</value>\n <value item="sides">3</value>\n </shape>\n <shape>\n <value item="name">square</value>\n <value item="family">polygon</value>\n <value item="degrees">360</value>\n <value item="sides">4</value>\n </shape>\n</shapes>"""\n df_xpath = read_xml(\n StringIO(xml),\n xpath=".//shape",\n parser=parser,\n names=["name", "family", "degrees", "sides"],\n )\n\n df_iter = read_xml_iterparse(\n xml,\n parser=parser,\n iterparse={"shape": ["value", "value", "value", "value"]},\n names=["name", "family", "degrees", "sides"],\n )\n\n df_expected = DataFrame(\n {\n "name": ["circle", "triangle", "square"],\n "family": ["ellipse", "polygon", "polygon"],\n "degrees": [360, 180, 360],\n "sides": [0, 3, 4],\n }\n )\n\n tm.assert_frame_equal(df_xpath, df_expected)\n tm.assert_frame_equal(df_iter, df_expected)\n\n\ndef test_names_option_wrong_length(xml_books, parser):\n with pytest.raises(ValueError, match=("names does not match length")):\n read_xml(xml_books, names=["Col1", "Col2", "Col3"], parser=parser)\n\n\ndef test_names_option_wrong_type(xml_books, parser):\n with pytest.raises(TypeError, match=("is not a valid type for names")):\n read_xml(xml_books, names="Col1, Col2, Col3", parser=parser)\n\n\n# ENCODING\n\n\ndef test_wrong_encoding(xml_baby_names, parser):\n with pytest.raises(UnicodeDecodeError, match=("'utf-8' codec can't decode")):\n read_xml(xml_baby_names, parser=parser)\n\n\ndef test_utf16_encoding(xml_baby_names, parser):\n with pytest.raises(\n UnicodeError,\n match=(\n "UTF-16 stream does not start with BOM|"\n "'utf-16(-le)?' codec can't decode byte"\n ),\n ):\n read_xml(xml_baby_names, encoding="UTF-16", parser=parser)\n\n\ndef test_unknown_encoding(xml_baby_names, parser):\n with pytest.raises(LookupError, match=("unknown encoding: UFT-8")):\n read_xml(xml_baby_names, encoding="UFT-8", parser=parser)\n\n\ndef test_ascii_encoding(xml_baby_names, parser):\n with pytest.raises(UnicodeDecodeError, match=("'ascii' codec can't decode byte")):\n read_xml(xml_baby_names, encoding="ascii", parser=parser)\n\n\ndef test_parser_consistency_with_encoding(xml_baby_names):\n pytest.importorskip("lxml")\n df_xpath_lxml = read_xml(xml_baby_names, parser="lxml", encoding="ISO-8859-1")\n df_xpath_etree = read_xml(xml_baby_names, parser="etree", encoding="iso-8859-1")\n\n df_iter_lxml = read_xml(\n xml_baby_names,\n parser="lxml",\n encoding="ISO-8859-1",\n iterparse={"row": ["rank", "malename", "femalename"]},\n )\n df_iter_etree = read_xml(\n xml_baby_names,\n parser="etree",\n encoding="ISO-8859-1",\n iterparse={"row": ["rank", "malename", "femalename"]},\n )\n\n tm.assert_frame_equal(df_xpath_lxml, df_xpath_etree)\n tm.assert_frame_equal(df_xpath_etree, df_iter_etree)\n tm.assert_frame_equal(df_iter_lxml, df_iter_etree)\n\n\ndef test_wrong_encoding_for_lxml():\n pytest.importorskip("lxml")\n # GH#45133\n data = """<data>\n <row>\n <a>c</a>\n </row>\n</data>\n"""\n with pytest.raises(TypeError, match="encoding None"):\n read_xml(StringIO(data), parser="lxml", encoding=None)\n\n\ndef test_none_encoding_etree():\n # GH#45133\n data = """<data>\n <row>\n <a>c</a>\n </row>\n</data>\n"""\n result = read_xml(StringIO(data), parser="etree", encoding=None)\n expected = DataFrame({"a": ["c"]})\n tm.assert_frame_equal(result, expected)\n\n\n# PARSER\n\n\n@td.skip_if_installed("lxml")\ndef test_default_parser_no_lxml(xml_books):\n with pytest.raises(\n ImportError, match=("lxml not found, please install or use the etree parser.")\n ):\n read_xml(xml_books)\n\n\ndef test_wrong_parser(xml_books):\n with pytest.raises(\n ValueError, match=("Values for parser can only be lxml or etree.")\n ):\n read_xml(xml_books, parser="bs4")\n\n\n# STYLESHEET\n\n\ndef test_stylesheet_file(kml_cta_rail_lines, xsl_flatten_doc):\n pytest.importorskip("lxml")\n df_style = read_xml(\n kml_cta_rail_lines,\n xpath=".//k:Placemark",\n namespaces={"k": "http://www.opengis.net/kml/2.2"},\n stylesheet=xsl_flatten_doc,\n )\n\n df_iter = read_xml(\n kml_cta_rail_lines,\n iterparse={\n "Placemark": [\n "id",\n "name",\n "styleUrl",\n "extrude",\n "altitudeMode",\n "coordinates",\n ]\n },\n )\n\n tm.assert_frame_equal(df_kml, df_style)\n tm.assert_frame_equal(df_kml, df_iter)\n\n\ndef test_stylesheet_file_like(kml_cta_rail_lines, xsl_flatten_doc, mode):\n pytest.importorskip("lxml")\n with open(xsl_flatten_doc, mode, encoding="utf-8" if mode == "r" else None) as f:\n df_style = read_xml(\n kml_cta_rail_lines,\n xpath=".//k:Placemark",\n namespaces={"k": "http://www.opengis.net/kml/2.2"},\n stylesheet=f,\n )\n\n tm.assert_frame_equal(df_kml, df_style)\n\n\ndef test_stylesheet_io(kml_cta_rail_lines, xsl_flatten_doc, mode):\n # note: By default the bodies of untyped functions are not checked,\n # consider using --check-untyped-defs\n pytest.importorskip("lxml")\n xsl_obj: BytesIO | StringIO # type: ignore[annotation-unchecked]\n\n with open(xsl_flatten_doc, mode, encoding="utf-8" if mode == "r" else None) as f:\n if mode == "rb":\n xsl_obj = BytesIO(f.read())\n else:\n xsl_obj = StringIO(f.read())\n\n df_style = read_xml(\n kml_cta_rail_lines,\n xpath=".//k:Placemark",\n namespaces={"k": "http://www.opengis.net/kml/2.2"},\n stylesheet=xsl_obj,\n )\n\n tm.assert_frame_equal(df_kml, df_style)\n\n\ndef test_stylesheet_buffered_reader(kml_cta_rail_lines, xsl_flatten_doc, mode):\n pytest.importorskip("lxml")\n with open(xsl_flatten_doc, mode, encoding="utf-8" if mode == "r" else None) as f:\n xsl_obj = f.read()\n\n df_style = read_xml(\n kml_cta_rail_lines,\n xpath=".//k:Placemark",\n namespaces={"k": "http://www.opengis.net/kml/2.2"},\n stylesheet=xsl_obj,\n )\n\n tm.assert_frame_equal(df_kml, df_style)\n\n\ndef test_style_charset():\n pytest.importorskip("lxml")\n xml = "<中文標籤><row><c1>1</c1><c2>2</c2></row></中文標籤>"\n\n xsl = """\\n<xsl:stylesheet version="1.0" xmlns:xsl="http://www.w3.org/1999/XSL/Transform">\n <xsl:output omit-xml-declaration="yes" indent="yes"/>\n <xsl:strip-space elements="*"/>\n\n <xsl:template match="node()|@*">\n <xsl:copy>\n <xsl:apply-templates select="node()|@*"/>\n </xsl:copy>\n </xsl:template>\n\n <xsl:template match="中文標籤">\n <根>\n <xsl:apply-templates />\n </根>\n </xsl:template>\n\n</xsl:stylesheet>"""\n\n df_orig = read_xml(StringIO(xml))\n df_style = read_xml(StringIO(xml), stylesheet=xsl)\n\n tm.assert_frame_equal(df_orig, df_style)\n\n\ndef test_not_stylesheet(kml_cta_rail_lines, xml_books):\n lxml_etree = pytest.importorskip("lxml.etree")\n\n with pytest.raises(\n lxml_etree.XSLTParseError, match=("document is not a stylesheet")\n ):\n read_xml(kml_cta_rail_lines, stylesheet=xml_books)\n\n\ndef test_incorrect_xsl_syntax(kml_cta_rail_lines):\n lxml_etree = pytest.importorskip("lxml.etree")\n\n xsl = """\\n<xsl:stylesheet version="1.0" xmlns:xsl="http://www.w3.org/1999/XSL/Transform"\n xmlns:k="http://www.opengis.net/kml/2.2"/>\n <xsl:output method="xml" omit-xml-declaration="yes"\n cdata-section-elements="k:description" indent="yes"/>\n <xsl:strip-space elements="*"/>\n\n <xsl:template match="node()|@*">\n <xsl:copy>\n <xsl:apply-templates select="node()|@*"/>\n </xsl:copy>\n </xsl:template>\n\n <xsl:template match="k:MultiGeometry|k:LineString">\n <xsl:apply-templates select='*'/>\n </xsl:template>\n\n <xsl:template match="k:description|k:Snippet|k:Style"/>\n</xsl:stylesheet>"""\n\n with pytest.raises(\n lxml_etree.XMLSyntaxError, match=("Extra content at the end of the document")\n ):\n read_xml(kml_cta_rail_lines, stylesheet=xsl)\n\n\ndef test_incorrect_xsl_eval(kml_cta_rail_lines):\n lxml_etree = pytest.importorskip("lxml.etree")\n\n xsl = """\\n<xsl:stylesheet version="1.0" xmlns:xsl="http://www.w3.org/1999/XSL/Transform"\n xmlns:k="http://www.opengis.net/kml/2.2">\n <xsl:output method="xml" omit-xml-declaration="yes"\n cdata-section-elements="k:description" indent="yes"/>\n <xsl:strip-space elements="*"/>\n\n <xsl:template match="node(*)|@*">\n <xsl:copy>\n <xsl:apply-templates select="node()|@*"/>\n </xsl:copy>\n </xsl:template>\n\n <xsl:template match="k:MultiGeometry|k:LineString">\n <xsl:apply-templates select='*'/>\n </xsl:template>\n\n <xsl:template match="k:description|k:Snippet|k:Style"/>\n</xsl:stylesheet>"""\n\n with pytest.raises(lxml_etree.XSLTParseError, match=("failed to compile")):\n read_xml(kml_cta_rail_lines, stylesheet=xsl)\n\n\ndef test_incorrect_xsl_apply(kml_cta_rail_lines):\n lxml_etree = pytest.importorskip("lxml.etree")\n\n xsl = """\\n<xsl:stylesheet version="1.0" xmlns:xsl="http://www.w3.org/1999/XSL/Transform">\n <xsl:output method="xml" encoding="utf-8" indent="yes" />\n <xsl:strip-space elements="*"/>\n\n <xsl:template match="@*|node()">\n <xsl:copy>\n <xsl:copy-of select="document('non_existent.xml')/*"/>\n </xsl:copy>\n </xsl:template>\n</xsl:stylesheet>"""\n\n with pytest.raises(lxml_etree.XSLTApplyError, match=("Cannot resolve URI")):\n read_xml(kml_cta_rail_lines, stylesheet=xsl)\n\n\ndef test_wrong_stylesheet(kml_cta_rail_lines, xml_data_path):\n xml_etree = pytest.importorskip("lxml.etree")\n\n xsl = xml_data_path / "flatten.xsl"\n\n with pytest.raises(\n xml_etree.XMLSyntaxError,\n match=("Start tag expected, '<' not found"),\n ):\n read_xml(kml_cta_rail_lines, stylesheet=xsl)\n\n\ndef test_stylesheet_file_close(kml_cta_rail_lines, xsl_flatten_doc, mode):\n # note: By default the bodies of untyped functions are not checked,\n # consider using --check-untyped-defs\n pytest.importorskip("lxml")\n xsl_obj: BytesIO | StringIO # type: ignore[annotation-unchecked]\n\n with open(xsl_flatten_doc, mode, encoding="utf-8" if mode == "r" else None) as f:\n if mode == "rb":\n xsl_obj = BytesIO(f.read())\n else:\n xsl_obj = StringIO(f.read())\n\n read_xml(kml_cta_rail_lines, stylesheet=xsl_obj)\n\n assert not f.closed\n\n\ndef test_stylesheet_with_etree(kml_cta_rail_lines, xsl_flatten_doc):\n pytest.importorskip("lxml")\n with pytest.raises(\n ValueError, match=("To use stylesheet, you need lxml installed")\n ):\n read_xml(kml_cta_rail_lines, parser="etree", stylesheet=xsl_flatten_doc)\n\n\n@pytest.mark.parametrize("val", ["", b""])\ndef test_empty_stylesheet(val):\n pytest.importorskip("lxml")\n msg = (\n "Passing literal xml to 'read_xml' is deprecated and "\n "will be removed in a future version. To read from a "\n "literal string, wrap it in a 'StringIO' object."\n )\n kml = os.path.join("data", "xml", "cta_rail_lines.kml")\n\n with pytest.raises(FutureWarning, match=msg):\n read_xml(kml, stylesheet=val)\n\n\n# ITERPARSE\ndef test_file_like_iterparse(xml_books, parser, mode):\n with open(xml_books, mode, encoding="utf-8" if mode == "r" else None) as f:\n if mode == "r" and parser == "lxml":\n with pytest.raises(\n TypeError, match=("reading file objects must return bytes objects")\n ):\n read_xml(\n f,\n parser=parser,\n iterparse={\n "book": ["category", "title", "year", "author", "price"]\n },\n )\n return None\n else:\n df_filelike = read_xml(\n f,\n parser=parser,\n iterparse={"book": ["category", "title", "year", "author", "price"]},\n )\n\n df_expected = DataFrame(\n {\n "category": ["cooking", "children", "web"],\n "title": ["Everyday Italian", "Harry Potter", "Learning XML"],\n "author": ["Giada De Laurentiis", "J K. Rowling", "Erik T. Ray"],\n "year": [2005, 2005, 2003],\n "price": [30.00, 29.99, 39.95],\n }\n )\n\n tm.assert_frame_equal(df_filelike, df_expected)\n\n\ndef test_file_io_iterparse(xml_books, parser, mode):\n funcIO = StringIO if mode == "r" else BytesIO\n with open(\n xml_books,\n mode,\n encoding="utf-8" if mode == "r" else None,\n ) as f:\n with funcIO(f.read()) as b:\n if mode == "r" and parser == "lxml":\n with pytest.raises(\n TypeError, match=("reading file objects must return bytes objects")\n ):\n read_xml(\n b,\n parser=parser,\n iterparse={\n "book": ["category", "title", "year", "author", "price"]\n },\n )\n return None\n else:\n df_fileio = read_xml(\n b,\n parser=parser,\n iterparse={\n "book": ["category", "title", "year", "author", "price"]\n },\n )\n\n df_expected = DataFrame(\n {\n "category": ["cooking", "children", "web"],\n "title": ["Everyday Italian", "Harry Potter", "Learning XML"],\n "author": ["Giada De Laurentiis", "J K. Rowling", "Erik T. Ray"],\n "year": [2005, 2005, 2003],\n "price": [30.00, 29.99, 39.95],\n }\n )\n\n tm.assert_frame_equal(df_fileio, df_expected)\n\n\n@pytest.mark.network\n@pytest.mark.single_cpu\ndef test_url_path_error(parser, httpserver, xml_file):\n with open(xml_file, encoding="utf-8") as f:\n httpserver.serve_content(content=f.read())\n with pytest.raises(\n ParserError, match=("iterparse is designed for large XML files")\n ):\n read_xml(\n httpserver.url,\n parser=parser,\n iterparse={"row": ["shape", "degrees", "sides", "date"]},\n )\n\n\ndef test_compression_error(parser, compression_only):\n with tm.ensure_clean(filename="geom_xml.zip") as path:\n geom_df.to_xml(path, parser=parser, compression=compression_only)\n\n with pytest.raises(\n ParserError, match=("iterparse is designed for large XML files")\n ):\n read_xml(\n path,\n parser=parser,\n iterparse={"row": ["shape", "degrees", "sides", "date"]},\n compression=compression_only,\n )\n\n\ndef test_wrong_dict_type(xml_books, parser):\n with pytest.raises(TypeError, match="list is not a valid type for iterparse"):\n read_xml(\n xml_books,\n parser=parser,\n iterparse=["category", "title", "year", "author", "price"],\n )\n\n\ndef test_wrong_dict_value(xml_books, parser):\n with pytest.raises(\n TypeError, match="<class 'str'> is not a valid type for value in iterparse"\n ):\n read_xml(xml_books, parser=parser, iterparse={"book": "category"})\n\n\ndef test_bad_xml(parser):\n bad_xml = """\\n<?xml version='1.0' encoding='utf-8'?>\n <row>\n <shape>square</shape>\n <degrees>00360</degrees>\n <sides>4.0</sides>\n <date>2020-01-01</date>\n </row>\n <row>\n <shape>circle</shape>\n <degrees>00360</degrees>\n <sides/>\n <date>2021-01-01</date>\n </row>\n <row>\n <shape>triangle</shape>\n <degrees>00180</degrees>\n <sides>3.0</sides>\n <date>2022-01-01</date>\n </row>\n"""\n with tm.ensure_clean(filename="bad.xml") as path:\n with open(path, "w", encoding="utf-8") as f:\n f.write(bad_xml)\n\n with pytest.raises(\n SyntaxError,\n match=(\n "Extra content at the end of the document|"\n "junk after document element"\n ),\n ):\n read_xml(\n path,\n parser=parser,\n parse_dates=["date"],\n iterparse={"row": ["shape", "degrees", "sides", "date"]},\n )\n\n\ndef test_comment(parser):\n xml = """\\n<!-- comment before root -->\n<shapes>\n <!-- comment within root -->\n <shape>\n <name>circle</name>\n <type>2D</type>\n </shape>\n <shape>\n <name>sphere</name>\n <type>3D</type>\n <!-- comment within child -->\n </shape>\n <!-- comment within root -->\n</shapes>\n<!-- comment after root -->"""\n\n df_xpath = read_xml(StringIO(xml), xpath=".//shape", parser=parser)\n\n df_iter = read_xml_iterparse(\n xml, parser=parser, iterparse={"shape": ["name", "type"]}\n )\n\n df_expected = DataFrame(\n {\n "name": ["circle", "sphere"],\n "type": ["2D", "3D"],\n }\n )\n\n tm.assert_frame_equal(df_xpath, df_expected)\n tm.assert_frame_equal(df_iter, df_expected)\n\n\ndef test_dtd(parser):\n xml = """\\n<?xml version="1.0" encoding="UTF-8"?>\n<!DOCTYPE non-profits [\n <!ELEMENT shapes (shape*) >\n <!ELEMENT shape ( name, type )>\n <!ELEMENT name (#PCDATA)>\n]>\n<shapes>\n <shape>\n <name>circle</name>\n <type>2D</type>\n </shape>\n <shape>\n <name>sphere</name>\n <type>3D</type>\n </shape>\n</shapes>"""\n\n df_xpath = read_xml(StringIO(xml), xpath=".//shape", parser=parser)\n\n df_iter = read_xml_iterparse(\n xml, parser=parser, iterparse={"shape": ["name", "type"]}\n )\n\n df_expected = DataFrame(\n {\n "name": ["circle", "sphere"],\n "type": ["2D", "3D"],\n }\n )\n\n tm.assert_frame_equal(df_xpath, df_expected)\n tm.assert_frame_equal(df_iter, df_expected)\n\n\ndef test_processing_instruction(parser):\n xml = """\\n<?xml version="1.0" encoding="UTF-8"?>\n<?xml-stylesheet type="text/xsl" href="style.xsl"?>\n<?display table-view?>\n<?sort alpha-ascending?>\n<?textinfo whitespace is allowed ?>\n<?elementnames <shape>, <name>, <type> ?>\n<shapes>\n <shape>\n <name>circle</name>\n <type>2D</type>\n </shape>\n <shape>\n <name>sphere</name>\n <type>3D</type>\n </shape>\n</shapes>"""\n\n df_xpath = read_xml(StringIO(xml), xpath=".//shape", parser=parser)\n\n df_iter = read_xml_iterparse(\n xml, parser=parser, iterparse={"shape": ["name", "type"]}\n )\n\n df_expected = DataFrame(\n {\n "name": ["circle", "sphere"],\n "type": ["2D", "3D"],\n }\n )\n\n tm.assert_frame_equal(df_xpath, df_expected)\n tm.assert_frame_equal(df_iter, df_expected)\n\n\ndef test_no_result(xml_books, parser):\n with pytest.raises(\n ParserError, match="No result from selected items in iterparse."\n ):\n read_xml(\n xml_books,\n parser=parser,\n iterparse={"node": ["attr1", "elem1", "elem2", "elem3"]},\n )\n\n\ndef test_empty_data(xml_books, parser):\n with pytest.raises(EmptyDataError, match="No columns to parse from file"):\n read_xml(\n xml_books,\n parser=parser,\n iterparse={"book": ["attr1", "elem1", "elem2", "elem3"]},\n )\n\n\ndef test_online_stylesheet():\n pytest.importorskip("lxml")\n xml = """\\n<?xml version="1.0" encoding="UTF-8"?>\n<catalog>\n <cd>\n <title>Empire Burlesque</title>\n <artist>Bob Dylan</artist>\n <country>USA</country>\n <company>Columbia</company>\n <price>10.90</price>\n <year>1985</year>\n </cd>\n <cd>\n <title>Hide your heart</title>\n <artist>Bonnie Tyler</artist>\n <country>UK</country>\n <company>CBS Records</company>\n <price>9.90</price>\n <year>1988</year>\n </cd>\n <cd>\n <title>Greatest Hits</title>\n <artist>Dolly Parton</artist>\n <country>USA</country>\n <company>RCA</company>\n <price>9.90</price>\n <year>1982</year>\n </cd>\n <cd>\n <title>Still got the blues</title>\n <artist>Gary Moore</artist>\n <country>UK</country>\n <company>Virgin records</company>\n <price>10.20</price>\n <year>1990</year>\n </cd>\n <cd>\n <title>Eros</title>\n <artist>Eros Ramazzotti</artist>\n <country>EU</country>\n <company>BMG</company>\n <price>9.90</price>\n <year>1997</year>\n </cd>\n <cd>\n <title>One night only</title>\n <artist>Bee Gees</artist>\n <country>UK</country>\n <company>Polydor</company>\n <price>10.90</price>\n <year>1998</year>\n </cd>\n <cd>\n <title>Sylvias Mother</title>\n <artist>Dr.Hook</artist>\n <country>UK</country>\n <company>CBS</company>\n <price>8.10</price>\n <year>1973</year>\n </cd>\n <cd>\n <title>Maggie May</title>\n <artist>Rod Stewart</artist>\n <country>UK</country>\n <company>Pickwick</company>\n <price>8.50</price>\n <year>1990</year>\n </cd>\n <cd>\n <title>Romanza</title>\n <artist>Andrea Bocelli</artist>\n <country>EU</country>\n <company>Polydor</company>\n <price>10.80</price>\n <year>1996</year>\n </cd>\n <cd>\n <title>When a man loves a woman</title>\n <artist>Percy Sledge</artist>\n <country>USA</country>\n <company>Atlantic</company>\n <price>8.70</price>\n <year>1987</year>\n </cd>\n <cd>\n <title>Black angel</title>\n <artist>Savage Rose</artist>\n <country>EU</country>\n <company>Mega</company>\n <price>10.90</price>\n <year>1995</year>\n </cd>\n <cd>\n <title>1999 Grammy Nominees</title>\n <artist>Many</artist>\n <country>USA</country>\n <company>Grammy</company>\n <price>10.20</price>\n <year>1999</year>\n </cd>\n <cd>\n <title>For the good times</title>\n <artist>Kenny Rogers</artist>\n <country>UK</country>\n <company>Mucik Master</company>\n <price>8.70</price>\n <year>1995</year>\n </cd>\n <cd>\n <title>Big Willie style</title>\n <artist>Will Smith</artist>\n <country>USA</country>\n <company>Columbia</company>\n <price>9.90</price>\n <year>1997</year>\n </cd>\n <cd>\n <title>Tupelo Honey</title>\n <artist>Van Morrison</artist>\n <country>UK</country>\n <company>Polydor</company>\n <price>8.20</price>\n <year>1971</year>\n </cd>\n <cd>\n <title>Soulsville</title>\n <artist>Jorn Hoel</artist>\n <country>Norway</country>\n <company>WEA</company>\n <price>7.90</price>\n <year>1996</year>\n </cd>\n <cd>\n <title>The very best of</title>\n <artist>Cat Stevens</artist>\n <country>UK</country>\n <company>Island</company>\n <price>8.90</price>\n <year>1990</year>\n </cd>\n <cd>\n <title>Stop</title>\n <artist>Sam Brown</artist>\n <country>UK</country>\n <company>A and M</company>\n <price>8.90</price>\n <year>1988</year>\n </cd>\n <cd>\n <title>Bridge of Spies</title>\n <artist>T`Pau</artist>\n <country>UK</country>\n <company>Siren</company>\n <price>7.90</price>\n <year>1987</year>\n </cd>\n <cd>\n <title>Private Dancer</title>\n <artist>Tina Turner</artist>\n <country>UK</country>\n <company>Capitol</company>\n <price>8.90</price>\n <year>1983</year>\n </cd>\n <cd>\n <title>Midt om natten</title>\n <artist>Kim Larsen</artist>\n <country>EU</country>\n <company>Medley</company>\n <price>7.80</price>\n <year>1983</year>\n </cd>\n <cd>\n <title>Pavarotti Gala Concert</title>\n <artist>Luciano Pavarotti</artist>\n <country>UK</country>\n <company>DECCA</company>\n <price>9.90</price>\n <year>1991</year>\n </cd>\n <cd>\n <title>The dock of the bay</title>\n <artist>Otis Redding</artist>\n <country>USA</country>\n <COMPANY>Stax Records</COMPANY>\n <PRICE>7.90</PRICE>\n <YEAR>1968</YEAR>\n </cd>\n <cd>\n <title>Picture book</title>\n <artist>Simply Red</artist>\n <country>EU</country>\n <company>Elektra</company>\n <price>7.20</price>\n <year>1985</year>\n </cd>\n <cd>\n <title>Red</title>\n <artist>The Communards</artist>\n <country>UK</country>\n <company>London</company>\n <price>7.80</price>\n <year>1987</year>\n </cd>\n <cd>\n <title>Unchain my heart</title>\n <artist>Joe Cocker</artist>\n <country>USA</country>\n <company>EMI</company>\n <price>8.20</price>\n <year>1987</year>\n </cd>\n</catalog>\n"""\n xsl = """\\n<?xml version="1.0" encoding="UTF-8"?>\n<xsl:stylesheet version="1.0" xmlns:xsl="http://www.w3.org/1999/XSL/Transform">\n<xsl:template match="/">\n<html>\n<body>\n <h2>My CD Collection</h2>\n <table border="1">\n <tr bgcolor="#9acd32">\n <th style="text-align:left">Title</th>\n <th style="text-align:left">Artist</th>\n </tr>\n <xsl:for-each select="catalog/cd">\n <tr>\n <td><xsl:value-of select="title"/></td>\n <td><xsl:value-of select="artist"/></td>\n </tr>\n </xsl:for-each>\n </table>\n</body>\n</html>\n</xsl:template>\n</xsl:stylesheet>\n"""\n\n df_xsl = read_xml(\n StringIO(xml),\n xpath=".//tr[td and position() <= 6]",\n names=["title", "artist"],\n stylesheet=xsl,\n )\n\n df_expected = DataFrame(\n {\n "title": {\n 0: "Empire Burlesque",\n 1: "Hide your heart",\n 2: "Greatest Hits",\n 3: "Still got the blues",\n 4: "Eros",\n },\n "artist": {\n 0: "Bob Dylan",\n 1: "Bonnie Tyler",\n 2: "Dolly Parton",\n 3: "Gary Moore",\n 4: "Eros Ramazzotti",\n },\n }\n )\n\n tm.assert_frame_equal(df_expected, df_xsl)\n\n\n# COMPRESSION\n\n\ndef test_compression_read(parser, compression_only):\n with tm.ensure_clean() as comp_path:\n geom_df.to_xml(\n comp_path, index=False, parser=parser, compression=compression_only\n )\n\n df_xpath = read_xml(comp_path, parser=parser, compression=compression_only)\n\n df_iter = read_xml_iterparse_comp(\n comp_path,\n compression_only,\n parser=parser,\n iterparse={"row": ["shape", "degrees", "sides"]},\n compression=compression_only,\n )\n\n tm.assert_frame_equal(df_xpath, geom_df)\n tm.assert_frame_equal(df_iter, geom_df)\n\n\ndef test_wrong_compression(parser, compression, compression_only):\n actual_compression = compression\n attempted_compression = compression_only\n\n if actual_compression == attempted_compression:\n pytest.skip(f"{actual_compression} == {attempted_compression}")\n\n errors = {\n "bz2": (OSError, "Invalid data stream"),\n "gzip": (OSError, "Not a gzipped file"),\n "zip": (BadZipFile, "File is not a zip file"),\n "tar": (ReadError, "file could not be opened successfully"),\n }\n zstd = import_optional_dependency("zstandard", errors="ignore")\n if zstd is not None:\n errors["zstd"] = (zstd.ZstdError, "Unknown frame descriptor")\n lzma = import_optional_dependency("lzma", errors="ignore")\n if lzma is not None:\n errors["xz"] = (LZMAError, "Input format not supported by decoder")\n error_cls, error_str = errors[attempted_compression]\n\n with tm.ensure_clean() as path:\n geom_df.to_xml(path, parser=parser, compression=actual_compression)\n\n with pytest.raises(error_cls, match=error_str):\n read_xml(path, parser=parser, compression=attempted_compression)\n\n\ndef test_unsuported_compression(parser):\n with pytest.raises(ValueError, match="Unrecognized compression type"):\n with tm.ensure_clean() as path:\n read_xml(path, parser=parser, compression="7z")\n\n\n# STORAGE OPTIONS\n\n\n@pytest.mark.network\n@pytest.mark.single_cpu\ndef test_s3_parser_consistency(s3_public_bucket_with_data, s3so):\n pytest.importorskip("s3fs")\n pytest.importorskip("lxml")\n s3 = f"s3://{s3_public_bucket_with_data.name}/books.xml"\n\n df_lxml = read_xml(s3, parser="lxml", storage_options=s3so)\n\n df_etree = read_xml(s3, parser="etree", storage_options=s3so)\n\n tm.assert_frame_equal(df_lxml, df_etree)\n\n\ndef test_read_xml_nullable_dtypes(\n parser, string_storage, dtype_backend, using_infer_string\n):\n # GH#50500\n data = """<?xml version='1.0' encoding='utf-8'?>\n<data xmlns="http://example.com">\n<row>\n <a>x</a>\n <b>1</b>\n <c>4.0</c>\n <d>x</d>\n <e>2</e>\n <f>4.0</f>\n <g></g>\n <h>True</h>\n <i>False</i>\n</row>\n<row>\n <a>y</a>\n <b>2</b>\n <c>5.0</c>\n <d></d>\n <e></e>\n <f></f>\n <g></g>\n <h>False</h>\n <i></i>\n</row>\n</data>"""\n\n with pd.option_context("mode.string_storage", string_storage):\n result = read_xml(StringIO(data), parser=parser, dtype_backend=dtype_backend)\n\n if dtype_backend == "pyarrow":\n pa = pytest.importorskip("pyarrow")\n string_dtype = pd.ArrowDtype(pa.string())\n else:\n string_dtype = pd.StringDtype(string_storage)\n\n expected = DataFrame(\n {\n "a": Series(["x", "y"], dtype=string_dtype),\n "b": Series([1, 2], dtype="Int64"),\n "c": Series([4.0, 5.0], dtype="Float64"),\n "d": Series(["x", None], dtype=string_dtype),\n "e": Series([2, NA], dtype="Int64"),\n "f": Series([4.0, NA], dtype="Float64"),\n "g": Series([NA, NA], dtype="Int64"),\n "h": Series([True, False], dtype="boolean"),\n "i": Series([False, NA], dtype="boolean"),\n }\n )\n\n if dtype_backend == "pyarrow":\n pa = pytest.importorskip("pyarrow")\n from pandas.arrays import ArrowExtensionArray\n\n expected = DataFrame(\n {\n col: ArrowExtensionArray(pa.array(expected[col], from_pandas=True))\n for col in expected.columns\n }\n )\n expected["g"] = ArrowExtensionArray(pa.array([None, None]))\n\n # the storage of the str columns' Index is also affected by the\n # string_storage setting -> ignore that for checking the result\n tm.assert_frame_equal(result, expected, check_column_type=False)\n\n\ndef test_invalid_dtype_backend():\n msg = (\n "dtype_backend numpy is invalid, only 'numpy_nullable' and "\n "'pyarrow' are allowed."\n )\n with pytest.raises(ValueError, match=msg):\n read_xml("test", dtype_backend="numpy")\n | .venv\Lib\site-packages\pandas\tests\io\xml\test_xml.py | test_xml.py | Python | 60,641 | 0.75 | 0.060125 | 0.041475 | vue-tools | 724 | 2024-01-15T01:21:06.224375 | GPL-3.0 | true | 51febf98d64563c9d477455a6d7d4853 |
from __future__ import annotations\n\nfrom io import StringIO\n\nimport pytest\n\nfrom pandas.errors import ParserWarning\nimport pandas.util._test_decorators as td\n\nfrom pandas import (\n DataFrame,\n DatetimeIndex,\n Series,\n to_datetime,\n)\nimport pandas._testing as tm\n\nfrom pandas.io.xml import read_xml\n\n\n@pytest.fixture(params=[pytest.param("lxml", marks=td.skip_if_no("lxml")), "etree"])\ndef parser(request):\n return request.param\n\n\n@pytest.fixture(\n params=[None, {"book": ["category", "title", "author", "year", "price"]}]\n)\ndef iterparse(request):\n return request.param\n\n\ndef read_xml_iterparse(data, **kwargs):\n with tm.ensure_clean() as path:\n with open(path, "w", encoding="utf-8") as f:\n f.write(data)\n return read_xml(path, **kwargs)\n\n\nxml_types = """\\n<?xml version='1.0' encoding='utf-8'?>\n<data>\n <row>\n <shape>square</shape>\n <degrees>00360</degrees>\n <sides>4.0</sides>\n </row>\n <row>\n <shape>circle</shape>\n <degrees>00360</degrees>\n <sides/>\n </row>\n <row>\n <shape>triangle</shape>\n <degrees>00180</degrees>\n <sides>3.0</sides>\n </row>\n</data>"""\n\nxml_dates = """<?xml version='1.0' encoding='utf-8'?>\n<data>\n <row>\n <shape>square</shape>\n <degrees>00360</degrees>\n <sides>4.0</sides>\n <date>2020-01-01</date>\n </row>\n <row>\n <shape>circle</shape>\n <degrees>00360</degrees>\n <sides/>\n <date>2021-01-01</date>\n </row>\n <row>\n <shape>triangle</shape>\n <degrees>00180</degrees>\n <sides>3.0</sides>\n <date>2022-01-01</date>\n </row>\n</data>"""\n\n\n# DTYPE\n\n\ndef test_dtype_single_str(parser):\n df_result = read_xml(StringIO(xml_types), dtype={"degrees": "str"}, parser=parser)\n df_iter = read_xml_iterparse(\n xml_types,\n parser=parser,\n dtype={"degrees": "str"},\n iterparse={"row": ["shape", "degrees", "sides"]},\n )\n\n df_expected = DataFrame(\n {\n "shape": ["square", "circle", "triangle"],\n "degrees": ["00360", "00360", "00180"],\n "sides": [4.0, float("nan"), 3.0],\n }\n )\n\n tm.assert_frame_equal(df_result, df_expected)\n tm.assert_frame_equal(df_iter, df_expected)\n\n\ndef test_dtypes_all_str(parser):\n df_result = read_xml(StringIO(xml_dates), dtype="string", parser=parser)\n df_iter = read_xml_iterparse(\n xml_dates,\n parser=parser,\n dtype="string",\n iterparse={"row": ["shape", "degrees", "sides", "date"]},\n )\n\n df_expected = DataFrame(\n {\n "shape": ["square", "circle", "triangle"],\n "degrees": ["00360", "00360", "00180"],\n "sides": ["4.0", None, "3.0"],\n "date": ["2020-01-01", "2021-01-01", "2022-01-01"],\n },\n dtype="string",\n )\n\n tm.assert_frame_equal(df_result, df_expected)\n tm.assert_frame_equal(df_iter, df_expected)\n\n\ndef test_dtypes_with_names(parser):\n df_result = read_xml(\n StringIO(xml_dates),\n names=["Col1", "Col2", "Col3", "Col4"],\n dtype={"Col2": "string", "Col3": "Int64", "Col4": "datetime64[ns]"},\n parser=parser,\n )\n df_iter = read_xml_iterparse(\n xml_dates,\n parser=parser,\n names=["Col1", "Col2", "Col3", "Col4"],\n dtype={"Col2": "string", "Col3": "Int64", "Col4": "datetime64[ns]"},\n iterparse={"row": ["shape", "degrees", "sides", "date"]},\n )\n\n df_expected = DataFrame(\n {\n "Col1": ["square", "circle", "triangle"],\n "Col2": Series(["00360", "00360", "00180"]).astype("string"),\n "Col3": Series([4.0, float("nan"), 3.0]).astype("Int64"),\n "Col4": DatetimeIndex(\n ["2020-01-01", "2021-01-01", "2022-01-01"], dtype="M8[ns]"\n ),\n }\n )\n\n tm.assert_frame_equal(df_result, df_expected)\n tm.assert_frame_equal(df_iter, df_expected)\n\n\ndef test_dtype_nullable_int(parser):\n df_result = read_xml(StringIO(xml_types), dtype={"sides": "Int64"}, parser=parser)\n df_iter = read_xml_iterparse(\n xml_types,\n parser=parser,\n dtype={"sides": "Int64"},\n iterparse={"row": ["shape", "degrees", "sides"]},\n )\n\n df_expected = DataFrame(\n {\n "shape": ["square", "circle", "triangle"],\n "degrees": [360, 360, 180],\n "sides": Series([4.0, float("nan"), 3.0]).astype("Int64"),\n }\n )\n\n tm.assert_frame_equal(df_result, df_expected)\n tm.assert_frame_equal(df_iter, df_expected)\n\n\ndef test_dtype_float(parser):\n df_result = read_xml(StringIO(xml_types), dtype={"degrees": "float"}, parser=parser)\n df_iter = read_xml_iterparse(\n xml_types,\n parser=parser,\n dtype={"degrees": "float"},\n iterparse={"row": ["shape", "degrees", "sides"]},\n )\n\n df_expected = DataFrame(\n {\n "shape": ["square", "circle", "triangle"],\n "degrees": Series([360, 360, 180]).astype("float"),\n "sides": [4.0, float("nan"), 3.0],\n }\n )\n\n tm.assert_frame_equal(df_result, df_expected)\n tm.assert_frame_equal(df_iter, df_expected)\n\n\ndef test_wrong_dtype(xml_books, parser, iterparse):\n with pytest.raises(\n ValueError, match=('Unable to parse string "Everyday Italian" at position 0')\n ):\n read_xml(\n xml_books, dtype={"title": "Int64"}, parser=parser, iterparse=iterparse\n )\n\n\ndef test_both_dtype_converters(parser):\n df_expected = DataFrame(\n {\n "shape": ["square", "circle", "triangle"],\n "degrees": ["00360", "00360", "00180"],\n "sides": [4.0, float("nan"), 3.0],\n }\n )\n\n with tm.assert_produces_warning(ParserWarning, match="Both a converter and dtype"):\n df_result = read_xml(\n StringIO(xml_types),\n dtype={"degrees": "str"},\n converters={"degrees": str},\n parser=parser,\n )\n df_iter = read_xml_iterparse(\n xml_types,\n dtype={"degrees": "str"},\n converters={"degrees": str},\n parser=parser,\n iterparse={"row": ["shape", "degrees", "sides"]},\n )\n\n tm.assert_frame_equal(df_result, df_expected)\n tm.assert_frame_equal(df_iter, df_expected)\n\n\n# CONVERTERS\n\n\ndef test_converters_str(parser):\n df_result = read_xml(\n StringIO(xml_types), converters={"degrees": str}, parser=parser\n )\n df_iter = read_xml_iterparse(\n xml_types,\n parser=parser,\n converters={"degrees": str},\n iterparse={"row": ["shape", "degrees", "sides"]},\n )\n\n df_expected = DataFrame(\n {\n "shape": ["square", "circle", "triangle"],\n "degrees": ["00360", "00360", "00180"],\n "sides": [4.0, float("nan"), 3.0],\n }\n )\n\n tm.assert_frame_equal(df_result, df_expected)\n tm.assert_frame_equal(df_iter, df_expected)\n\n\ndef test_converters_date(parser):\n convert_to_datetime = lambda x: to_datetime(x)\n df_result = read_xml(\n StringIO(xml_dates), converters={"date": convert_to_datetime}, parser=parser\n )\n df_iter = read_xml_iterparse(\n xml_dates,\n parser=parser,\n converters={"date": convert_to_datetime},\n iterparse={"row": ["shape", "degrees", "sides", "date"]},\n )\n\n df_expected = DataFrame(\n {\n "shape": ["square", "circle", "triangle"],\n "degrees": [360, 360, 180],\n "sides": [4.0, float("nan"), 3.0],\n "date": to_datetime(["2020-01-01", "2021-01-01", "2022-01-01"]),\n }\n )\n\n tm.assert_frame_equal(df_result, df_expected)\n tm.assert_frame_equal(df_iter, df_expected)\n\n\ndef test_wrong_converters_type(xml_books, parser, iterparse):\n with pytest.raises(TypeError, match=("Type converters must be a dict or subclass")):\n read_xml(\n xml_books, converters={"year", str}, parser=parser, iterparse=iterparse\n )\n\n\ndef test_callable_func_converters(xml_books, parser, iterparse):\n with pytest.raises(TypeError, match=("'float' object is not callable")):\n read_xml(\n xml_books, converters={"year": float()}, parser=parser, iterparse=iterparse\n )\n\n\ndef test_callable_str_converters(xml_books, parser, iterparse):\n with pytest.raises(TypeError, match=("'str' object is not callable")):\n read_xml(\n xml_books, converters={"year": "float"}, parser=parser, iterparse=iterparse\n )\n\n\n# PARSE DATES\n\n\ndef test_parse_dates_column_name(parser):\n df_result = read_xml(StringIO(xml_dates), parse_dates=["date"], parser=parser)\n df_iter = read_xml_iterparse(\n xml_dates,\n parser=parser,\n parse_dates=["date"],\n iterparse={"row": ["shape", "degrees", "sides", "date"]},\n )\n\n df_expected = DataFrame(\n {\n "shape": ["square", "circle", "triangle"],\n "degrees": [360, 360, 180],\n "sides": [4.0, float("nan"), 3.0],\n "date": to_datetime(["2020-01-01", "2021-01-01", "2022-01-01"]),\n }\n )\n\n tm.assert_frame_equal(df_result, df_expected)\n tm.assert_frame_equal(df_iter, df_expected)\n\n\ndef test_parse_dates_column_index(parser):\n df_result = read_xml(StringIO(xml_dates), parse_dates=[3], parser=parser)\n df_iter = read_xml_iterparse(\n xml_dates,\n parser=parser,\n parse_dates=[3],\n iterparse={"row": ["shape", "degrees", "sides", "date"]},\n )\n\n df_expected = DataFrame(\n {\n "shape": ["square", "circle", "triangle"],\n "degrees": [360, 360, 180],\n "sides": [4.0, float("nan"), 3.0],\n "date": to_datetime(["2020-01-01", "2021-01-01", "2022-01-01"]),\n }\n )\n\n tm.assert_frame_equal(df_result, df_expected)\n tm.assert_frame_equal(df_iter, df_expected)\n\n\ndef test_parse_dates_true(parser):\n df_result = read_xml(StringIO(xml_dates), parse_dates=True, parser=parser)\n\n df_iter = read_xml_iterparse(\n xml_dates,\n parser=parser,\n parse_dates=True,\n iterparse={"row": ["shape", "degrees", "sides", "date"]},\n )\n\n df_expected = DataFrame(\n {\n "shape": ["square", "circle", "triangle"],\n "degrees": [360, 360, 180],\n "sides": [4.0, float("nan"), 3.0],\n "date": ["2020-01-01", "2021-01-01", "2022-01-01"],\n }\n )\n\n tm.assert_frame_equal(df_result, df_expected)\n tm.assert_frame_equal(df_iter, df_expected)\n\n\ndef test_parse_dates_dictionary(parser):\n xml = """<?xml version='1.0' encoding='utf-8'?>\n<data>\n <row>\n <shape>square</shape>\n <degrees>360</degrees>\n <sides>4.0</sides>\n <year>2020</year>\n <month>12</month>\n <day>31</day>\n </row>\n <row>\n <shape>circle</shape>\n <degrees>360</degrees>\n <sides/>\n <year>2021</year>\n <month>12</month>\n <day>31</day>\n </row>\n <row>\n <shape>triangle</shape>\n <degrees>180</degrees>\n <sides>3.0</sides>\n <year>2022</year>\n <month>12</month>\n <day>31</day>\n </row>\n</data>"""\n\n df_result = read_xml(\n StringIO(xml), parse_dates={"date_end": ["year", "month", "day"]}, parser=parser\n )\n df_iter = read_xml_iterparse(\n xml,\n parser=parser,\n parse_dates={"date_end": ["year", "month", "day"]},\n iterparse={"row": ["shape", "degrees", "sides", "year", "month", "day"]},\n )\n\n df_expected = DataFrame(\n {\n "date_end": to_datetime(["2020-12-31", "2021-12-31", "2022-12-31"]),\n "shape": ["square", "circle", "triangle"],\n "degrees": [360, 360, 180],\n "sides": [4.0, float("nan"), 3.0],\n }\n )\n\n tm.assert_frame_equal(df_result, df_expected)\n tm.assert_frame_equal(df_iter, df_expected)\n\n\ndef test_day_first_parse_dates(parser):\n xml = """\\n<?xml version='1.0' encoding='utf-8'?>\n<data>\n <row>\n <shape>square</shape>\n <degrees>00360</degrees>\n <sides>4.0</sides>\n <date>31/12/2020</date>\n </row>\n <row>\n <shape>circle</shape>\n <degrees>00360</degrees>\n <sides/>\n <date>31/12/2021</date>\n </row>\n <row>\n <shape>triangle</shape>\n <degrees>00180</degrees>\n <sides>3.0</sides>\n <date>31/12/2022</date>\n </row>\n</data>"""\n\n df_expected = DataFrame(\n {\n "shape": ["square", "circle", "triangle"],\n "degrees": [360, 360, 180],\n "sides": [4.0, float("nan"), 3.0],\n "date": to_datetime(["2020-12-31", "2021-12-31", "2022-12-31"]),\n }\n )\n\n with tm.assert_produces_warning(\n UserWarning, match="Parsing dates in %d/%m/%Y format"\n ):\n df_result = read_xml(StringIO(xml), parse_dates=["date"], parser=parser)\n df_iter = read_xml_iterparse(\n xml,\n parse_dates=["date"],\n parser=parser,\n iterparse={"row": ["shape", "degrees", "sides", "date"]},\n )\n\n tm.assert_frame_equal(df_result, df_expected)\n tm.assert_frame_equal(df_iter, df_expected)\n\n\ndef test_wrong_parse_dates_type(xml_books, parser, iterparse):\n with pytest.raises(\n TypeError, match=("Only booleans, lists, and dictionaries are accepted")\n ):\n read_xml(xml_books, parse_dates={"date"}, parser=parser, iterparse=iterparse)\n | .venv\Lib\site-packages\pandas\tests\io\xml\test_xml_dtypes.py | test_xml_dtypes.py | Python | 13,266 | 0.95 | 0.043299 | 0.0075 | vue-tools | 41 | 2024-03-06T00:23:39.670811 | MIT | true | 8e8d1c436f7e224a7584a6f32a205a50 |
\n\n | .venv\Lib\site-packages\pandas\tests\io\xml\__pycache__\conftest.cpython-313.pyc | conftest.cpython-313.pyc | Other | 1,822 | 0.7 | 0 | 0 | vue-tools | 923 | 2025-02-21T15:05:30.968083 | GPL-3.0 | true | c6f013458c63fe5d22b88fe8059b157e |
\n\n | .venv\Lib\site-packages\pandas\tests\io\xml\__pycache__\test_to_xml.cpython-313.pyc | test_to_xml.cpython-313.pyc | Other | 42,615 | 0.95 | 0.014493 | 0.002484 | vue-tools | 727 | 2025-02-05T19:46:09.853119 | GPL-3.0 | true | 2ab72c078c4ef732bd6dac69139633bc |
\n\n | .venv\Lib\site-packages\pandas\tests\io\xml\__pycache__\test_xml.cpython-313.pyc | test_xml.cpython-313.pyc | Other | 65,548 | 0.75 | 0.00736 | 0.005623 | react-lib | 931 | 2025-05-06T00:28:37.393378 | GPL-3.0 | true | ad2e7d8f7f6936d5633651e405bccf26 |
\n\n | .venv\Lib\site-packages\pandas\tests\io\xml\__pycache__\test_xml_dtypes.cpython-313.pyc | test_xml_dtypes.cpython-313.pyc | Other | 14,752 | 0.8 | 0 | 0.008811 | awesome-app | 802 | 2023-12-19T04:44:46.881241 | BSD-3-Clause | true | 6326853836466233659cbd8716cfe370 |
\n\n | .venv\Lib\site-packages\pandas\tests\io\xml\__pycache__\__init__.cpython-313.pyc | __init__.cpython-313.pyc | Other | 194 | 0.7 | 0 | 0 | node-utils | 758 | 2025-04-06T00:12:56.286717 | Apache-2.0 | true | 5d95c8d653ba66a3b8b878df408b606c |
\n\n | .venv\Lib\site-packages\pandas\tests\io\__pycache__\conftest.cpython-313.pyc | conftest.cpython-313.pyc | Other | 8,242 | 0.95 | 0.021505 | 0 | react-lib | 869 | 2024-09-13T15:49:04.354027 | MIT | true | e8a5b3f354b3af8fe37c50bf2810c084 |
\n\n | .venv\Lib\site-packages\pandas\tests\io\__pycache__\generate_legacy_storage_files.cpython-313.pyc | generate_legacy_storage_files.cpython-313.pyc | Other | 13,378 | 0.8 | 0.016484 | 0 | node-utils | 725 | 2024-02-29T01:21:34.184715 | Apache-2.0 | true | bf529c05d7aa769ecece2d292b1dd1f4 |
\n\n | .venv\Lib\site-packages\pandas\tests\io\__pycache__\test_clipboard.cpython-313.pyc | test_clipboard.cpython-313.pyc | Other | 19,099 | 0.95 | 0.012146 | 0.008368 | awesome-app | 662 | 2023-10-28T00:34:06.790785 | BSD-3-Clause | true | 8adeada72f8d8386787b8c908289ec90 |
\n\n | .venv\Lib\site-packages\pandas\tests\io\__pycache__\test_common.cpython-313.pyc | test_common.cpython-313.pyc | Other | 38,968 | 0.95 | 0.004016 | 0.008247 | python-kit | 797 | 2024-06-27T01:26:56.577740 | GPL-3.0 | true | 4b0983c3cd8d4ae0c242f6af2290d82d |
\n\n | .venv\Lib\site-packages\pandas\tests\io\__pycache__\test_compression.cpython-313.pyc | test_compression.cpython-313.pyc | Other | 19,627 | 0.95 | 0.010101 | 0.020906 | awesome-app | 146 | 2025-01-07T06:35:03.570317 | GPL-3.0 | true | 79e81cc341e70b5f9849752c4f900258 |
\n\n | .venv\Lib\site-packages\pandas\tests\io\__pycache__\test_feather.cpython-313.pyc | test_feather.cpython-313.pyc | Other | 17,417 | 0.95 | 0 | 0 | react-lib | 69 | 2024-03-23T05:54:16.827809 | BSD-3-Clause | true | cdb5798968fe899197bea0fafc3ba82c |
\n\n | .venv\Lib\site-packages\pandas\tests\io\__pycache__\test_fsspec.cpython-313.pyc | test_fsspec.cpython-313.pyc | Other | 16,277 | 0.95 | 0.010989 | 0.011494 | react-lib | 406 | 2024-10-22T18:27:12.967087 | GPL-3.0 | true | 04f347e2228e5a6df8d1250957c0156c |
\n\n | .venv\Lib\site-packages\pandas\tests\io\__pycache__\test_gbq.cpython-313.pyc | test_gbq.cpython-313.pyc | Other | 1,424 | 0.8 | 0 | 0 | react-lib | 707 | 2025-02-02T10:34:19.261613 | MIT | true | 4eb8c293357e2fb398277f9a75e5d950 |
\n\n | .venv\Lib\site-packages\pandas\tests\io\__pycache__\test_gcs.cpython-313.pyc | test_gcs.cpython-313.pyc | Other | 11,300 | 0.95 | 0.006944 | 0.008 | node-utils | 117 | 2024-07-05T05:40:03.772073 | BSD-3-Clause | true | 6a34622534b2d4c854f40429f39a8ab8 |
\n\n | .venv\Lib\site-packages\pandas\tests\io\__pycache__\test_html.cpython-313.pyc | test_html.cpython-313.pyc | Other | 71,594 | 0.75 | 0.061845 | 0.001066 | react-lib | 458 | 2023-09-09T22:58:19.976870 | BSD-3-Clause | true | 931721239d3621fd553bd11faff9e88e |
\n\n | .venv\Lib\site-packages\pandas\tests\io\__pycache__\test_http_headers.cpython-313.pyc | test_http_headers.cpython-313.pyc | Other | 7,328 | 0.95 | 0.013514 | 0 | vue-tools | 555 | 2024-10-15T10:08:32.974045 | Apache-2.0 | true | 758b50cf657304cac75ad43510dc42e3 |
\n\n | .venv\Lib\site-packages\pandas\tests\io\__pycache__\test_orc.cpython-313.pyc | test_orc.cpython-313.pyc | Other | 19,714 | 0.95 | 0 | 0 | react-lib | 115 | 2024-02-09T13:43:35.292435 | Apache-2.0 | true | 9440bef261cc7cf4acd6ba8e67b03eb8 |
\n\n | .venv\Lib\site-packages\pandas\tests\io\__pycache__\test_parquet.cpython-313.pyc | test_parquet.cpython-313.pyc | Other | 72,306 | 0.75 | 0.004932 | 0 | vue-tools | 718 | 2025-01-24T14:39:28.513253 | GPL-3.0 | true | e98eb0b479dc38d7bfd1a1fa4aeab4a4 |
\n\n | .venv\Lib\site-packages\pandas\tests\io\__pycache__\test_pickle.cpython-313.pyc | test_pickle.cpython-313.pyc | Other | 35,924 | 0.95 | 0.002439 | 0.05641 | node-utils | 489 | 2024-05-14T02:39:30.479553 | MIT | true | 731fc34c3b032b0ce9a3340447d6b8c4 |
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