Python compat.PY2属性代码示例

# 需要导入模块: from pandas import compat [as 别名]
# 或者: from pandas.compat import PY2 [as 别名]
def test_iteration_open_handle(all_parsers):
    parser = all_parsers
    kwargs = dict(squeeze=True, header=None)

    with tm.ensure_clean() as path:
        with open(path, "wb" if compat.PY2 else "w") as f:
            f.write("AAA\nBBB\nCCC\nDDD\nEEE\nFFF\nGGG")

        with open(path, "rb" if compat.PY2 else "r") as f:
            for line in f:
                if "CCC" in line:
                    break

            if parser.engine == "c" and compat.PY2:
                msg = "Mixing iteration and read methods would lose data"
                with pytest.raises(ValueError, match=msg):
                    parser.read_csv(f, **kwargs)
            else:
                result = parser.read_csv(f, **kwargs)
                expected = Series(["DDD", "EEE", "FFF", "GGG"], name=0)
                tm.assert_series_equal(result, expected) 

# 需要导入模块: from pandas import compat [as 别名]
# 或者: from pandas.compat import PY2 [as 别名]
def setup_method(self, datapath):
        self.dirpath = datapath("io", "sas", "data")
        self.data = []
        self.test_ix = [list(range(1, 16)), [16]]
        for j in 1, 2:
            fname = os.path.join(
                self.dirpath, "test_sas7bdat_{j}.csv".format(j=j))
            df = pd.read_csv(fname)
            epoch = pd.datetime(1960, 1, 1)
            t1 = pd.to_timedelta(df["Column4"], unit='d')
            df["Column4"] = epoch + t1
            t2 = pd.to_timedelta(df["Column12"], unit='d')
            df["Column12"] = epoch + t2
            for k in range(df.shape[1]):
                col = df.iloc[:, k]
                if col.dtype == np.int64:
                    df.iloc[:, k] = df.iloc[:, k].astype(np.float64)
                elif col.dtype == np.dtype('O'):
                    if PY2:
                        f = lambda x: (x.decode('utf-8') if
                                       isinstance(x, str) else x)
                        df.iloc[:, k] = df.iloc[:, k].apply(f)
            self.data.append(df) 

# 需要导入模块: from pandas import compat [as 别名]
# 或者: from pandas.compat import PY2 [as 别名]
def test_cant_compare_tz_naive_w_aware_explicit_pytz(self):
        # see gh-1404
        a = Timestamp('3/12/2012')
        b = Timestamp('3/12/2012', tz=utc)

        pytest.raises(Exception, a.__eq__, b)
        pytest.raises(Exception, a.__ne__, b)
        pytest.raises(Exception, a.__lt__, b)
        pytest.raises(Exception, a.__gt__, b)
        pytest.raises(Exception, b.__eq__, a)
        pytest.raises(Exception, b.__ne__, a)
        pytest.raises(Exception, b.__lt__, a)
        pytest.raises(Exception, b.__gt__, a)

        if PY2:
            pytest.raises(Exception, a.__eq__, b.to_pydatetime())
            pytest.raises(Exception, a.to_pydatetime().__eq__, b)
        else:
            assert not a == b.to_pydatetime()
            assert not a.to_pydatetime() == b 

# 需要导入模块: from pandas import compat [as 别名]
# 或者: from pandas.compat import PY2 [as 别名]
def test_cant_compare_tz_naive_w_aware_dateutil(self):
        # see gh-1404
        a = Timestamp('3/12/2012')
        b = Timestamp('3/12/2012', tz=tzutc())

        pytest.raises(Exception, a.__eq__, b)
        pytest.raises(Exception, a.__ne__, b)
        pytest.raises(Exception, a.__lt__, b)
        pytest.raises(Exception, a.__gt__, b)
        pytest.raises(Exception, b.__eq__, a)
        pytest.raises(Exception, b.__ne__, a)
        pytest.raises(Exception, b.__lt__, a)
        pytest.raises(Exception, b.__gt__, a)

        if PY2:
            pytest.raises(Exception, a.__eq__, b.to_pydatetime())
            pytest.raises(Exception, a.to_pydatetime().__eq__, b)
        else:
            assert not a == b.to_pydatetime()
            assert not a.to_pydatetime() == b 

# 需要导入模块: from pandas import compat [as 别名]
# 或者: from pandas.compat import PY2 [as 别名]
def to_latex(self, column_format=None, longtable=False, encoding=None,
                 multicolumn=False, multicolumn_format=None, multirow=False):
        """
        Render a DataFrame to a LaTeX tabular/longtable environment output.
        """

        from pandas.io.formats.latex import LatexFormatter
        latex_renderer = LatexFormatter(self, column_format=column_format,
                                        longtable=longtable,
                                        multicolumn=multicolumn,
                                        multicolumn_format=multicolumn_format,
                                        multirow=multirow)

        if encoding is None:
            encoding = 'ascii' if compat.PY2 else 'utf-8'

        if hasattr(self.buf, 'write'):
            latex_renderer.write_result(self.buf)
        elif isinstance(self.buf, compat.string_types):
            import codecs
            with codecs.open(self.buf, 'w', encoding=encoding) as f:
                latex_renderer.write_result(f)
        else:
            raise TypeError('buf is not a file name and it has no write '
                            'method') 

# 需要导入模块: from pandas import compat [as 别名]
# 或者: from pandas.compat import PY2 [as 别名]
def to_latex(self, column_format=None, longtable=False, encoding=None,
                 multicolumn=False, multicolumn_format=None, multirow=False):
        """
        Render a DataFrame to a LaTeX tabular/longtable environment output.
        """

        latex_renderer = LatexFormatter(self, column_format=column_format,
                                        longtable=longtable,
                                        multicolumn=multicolumn,
                                        multicolumn_format=multicolumn_format,
                                        multirow=multirow)

        if encoding is None:
            encoding = 'ascii' if compat.PY2 else 'utf-8'

        if hasattr(self.buf, 'write'):
            latex_renderer.write_result(self.buf)
        elif isinstance(self.buf, compat.string_types):
            import codecs
            with codecs.open(self.buf, 'w', encoding=encoding) as f:
                latex_renderer.write_result(f)
        else:
            raise TypeError('buf is not a file name and it has no write '
                            'method') 

# 需要导入模块: from pandas import compat [as 别名]
# 或者: from pandas.compat import PY2 [as 别名]
def setup_method(self, method):
        self.dirpath = tm.get_data_path()
        self.data = []
        self.test_ix = [list(range(1, 16)), [16]]
        for j in 1, 2:
            fname = os.path.join(self.dirpath, "test_sas7bdat_%d.csv" % j)
            df = pd.read_csv(fname)
            epoch = pd.datetime(1960, 1, 1)
            t1 = pd.to_timedelta(df["Column4"], unit='d')
            df["Column4"] = epoch + t1
            t2 = pd.to_timedelta(df["Column12"], unit='d')
            df["Column12"] = epoch + t2
            for k in range(df.shape[1]):
                col = df.iloc[:, k]
                if col.dtype == np.int64:
                    df.iloc[:, k] = df.iloc[:, k].astype(np.float64)
                elif col.dtype == np.dtype('O'):
                    if PY2:
                        f = lambda x: (x.decode('utf-8') if
                                       isinstance(x, str) else x)
                        df.iloc[:, k] = df.iloc[:, k].apply(f)
            self.data.append(df) 

# 需要导入模块: from pandas import compat [as 别名]
# 或者: from pandas.compat import PY2 [as 别名]
def test_arith_series_with_scalar(self, data, all_arithmetic_operators):
        if (compat.PY2 and
                all_arithmetic_operators in {'__div__', '__rdiv__'}):
            raise pytest.skip(
                "Matching NumPy int / int -> float behavior."
            )
        super(TestArithmetics, self).test_arith_series_with_scalar(
            data, all_arithmetic_operators
        ) 

# 需要导入模块: from pandas import compat [as 别名]
# 或者: from pandas.compat import PY2 [as 别名]
def test_arith_series_with_array(self, data, all_arithmetic_operators):
        if (compat.PY2 and
                all_arithmetic_operators in {'__div__', '__rdiv__'}):
            raise pytest.skip(
                "Matching NumPy int / int -> float behavior."
            )
        super(TestArithmetics, self).test_arith_series_with_array(
            data, all_arithmetic_operators
        ) 

# 需要导入模块: from pandas import compat [as 别名]
# 或者: from pandas.compat import PY2 [as 别名]
def test_concat_order(self):
        # GH 17344
        dfs = [pd.DataFrame(index=range(3), columns=['a', 1, None])]
        dfs += [pd.DataFrame(index=range(3), columns=[None, 1, 'a'])
                for i in range(100)]

        result = pd.concat(dfs, sort=True).columns

        if PY2:
            # Different sort order between incomparable objects between
            # python 2 and python3 via Index.union.
            expected = dfs[1].columns
        else:
            expected = dfs[0].columns
        tm.assert_index_equal(result, expected) 

# 需要导入模块: from pandas import compat [as 别名]
# 或者: from pandas.compat import PY2 [as 别名]
def any_allowed_skipna_inferred_dtype(request):
    """
    Fixture for all (inferred) dtypes allowed in StringMethods.__init__

    The covered (inferred) types are:
    * 'string'
    * 'unicode' (if PY2)
    * 'empty'
    * 'bytes' (if PY3)
    * 'mixed'
    * 'mixed-integer'

    Returns
    -------
    inferred_dtype : str
        The string for the inferred dtype from _libs.lib.infer_dtype
    values : np.ndarray
        An array of object dtype that will be inferred to have
        `inferred_dtype`

    Examples
    --------
    >>> import pandas._libs.lib as lib
    >>>
    >>> def test_something(any_allowed_skipna_inferred_dtype):
    ...     inferred_dtype, values = any_allowed_skipna_inferred_dtype
    ...     # will pass
    ...     assert lib.infer_dtype(values, skipna=True) == inferred_dtype
    """
    inferred_dtype, values = request.param
    values = np.array(values, dtype=object)  # object dtype to avoid casting

    # correctness of inference tested in tests/dtypes/test_inference.py
    return inferred_dtype, values 

# 需要导入模块: from pandas import compat [as 别名]
# 或者: from pandas.compat import PY2 [as 别名]
def test_replace_callable(self):
        # GH 15055
        values = Series(['fooBAD__barBAD', NA])

        # test with callable
        repl = lambda m: m.group(0).swapcase()
        result = values.str.replace('[a-z][A-Z]{2}', repl, n=2)
        exp = Series(['foObaD__baRbaD', NA])
        tm.assert_series_equal(result, exp)

        # test with wrong number of arguments, raising an error
        if compat.PY2:
            p_err = r'takes (no|(exactly|at (least|most)) ?\d+) arguments?'
        else:
            p_err = (r'((takes)|(missing)) (?(2)from \d+ to )?\d+ '
                     r'(?(3)required )positional arguments?')

        repl = lambda: None
        with pytest.raises(TypeError, match=p_err):
            values.str.replace('a', repl)

        repl = lambda m, x: None
        with pytest.raises(TypeError, match=p_err):
            values.str.replace('a', repl)

        repl = lambda m, x, y=None: None
        with pytest.raises(TypeError, match=p_err):
            values.str.replace('a', repl)

        # test regex named groups
        values = Series(['Foo Bar Baz', NA])
        pat = r"(?P<first>\w+) (?P<middle>\w+) (?P<last>\w+)"
        repl = lambda m: m.group('middle').swapcase()
        result = values.str.replace(pat, repl)
        exp = Series(['bAR', NA])
        tm.assert_series_equal(result, exp) 

# 需要导入模块: from pandas import compat [as 别名]
# 或者: from pandas.compat import PY2 [as 别名]
def test_infer_dtype_bytes(self):
        compare = 'string' if PY2 else 'bytes'

        # string array of bytes
        arr = np.array(list('abc'), dtype='S1')
        assert lib.infer_dtype(arr, skipna=True) == compare

        # object array of bytes
        arr = arr.astype(object)
        assert lib.infer_dtype(arr, skipna=True) == compare

        # object array of bytes with missing values
        assert lib.infer_dtype([b'a', np.nan, b'c'], skipna=True) == compare 

# 需要导入模块: from pandas import compat [as 别名]
# 或者: from pandas.compat import PY2 [as 别名]
def test_unicode(self):
        arr = [u'a', np.nan, u'c']
        result = lib.infer_dtype(arr, skipna=False)
        assert result == 'mixed'

        arr = [u'a', np.nan, u'c']
        result = lib.infer_dtype(arr, skipna=True)
        expected = 'unicode' if PY2 else 'string'
        assert result == expected 

# 需要导入模块: from pandas import compat [as 别名]
# 或者: from pandas.compat import PY2 [as 别名]
def test_cant_compare_tz_naive_w_aware(self, utc_fixture):
        # see GH#1404
        a = Timestamp('3/12/2012')
        b = Timestamp('3/12/2012', tz=utc_fixture)

        with pytest.raises(TypeError):
            a == b
        with pytest.raises(TypeError):
            a != b
        with pytest.raises(TypeError):
            a < b
        with pytest.raises(TypeError):
            a <= b
        with pytest.raises(TypeError):
            a > b
        with pytest.raises(TypeError):
            a >= b

        with pytest.raises(TypeError):
            b == a
        with pytest.raises(TypeError):
            b != a
        with pytest.raises(TypeError):
            b < a
        with pytest.raises(TypeError):
            b <= a
        with pytest.raises(TypeError):
            b > a
        with pytest.raises(TypeError):
            b >= a

        if PY2:
            with pytest.raises(TypeError):
                a == b.to_pydatetime()
            with pytest.raises(TypeError):
                a.to_pydatetime() == b
        else:
            assert not a == b.to_pydatetime()
            assert not a.to_pydatetime() == b