Python pandas.compat方法代碼示例

# 需要導入模塊: import pandas [as 別名]
# 或者: from pandas import compat [as 別名]
def write_legacy_pickles(output_dir):

    # make sure we are < 0.13 compat (in py3)
    try:
        from pandas.compat import zip, cPickle as pickle  # noqa
    except ImportError:
        import pickle

    version = pandas.__version__

    print("This script generates a storage file for the current arch, system, "
          "and python version")
    print("  pandas version: {0}".format(version))
    print("  output dir    : {0}".format(output_dir))
    print("  storage format: pickle")

    pth = '{0}.pickle'.format(platform_name())

    fh = open(os.path.join(output_dir, pth), 'wb')
    pickle.dump(create_pickle_data(), fh, pickle.HIGHEST_PROTOCOL)
    fh.close()

    print("created pickle file: %s" % pth) 

# 需要導入模塊: import pandas [as 別名]
# 或者: from pandas import compat [as 別名]
def setup_method(self, method):
        super(TestEncoding, self).setup_method(method)
        data = {
            'A': [compat.u('\u2019')] * 1000,
            'B': np.arange(1000, dtype=np.int32),
            'C': list(100 * 'abcdefghij'),
            'D': date_range(datetime.datetime(2015, 4, 1), periods=1000),
            'E': [datetime.timedelta(days=x) for x in range(1000)],
            'G': [400] * 1000
        }
        self.frame = {
            'float': DataFrame({k: data[k] for k in ['A', 'A']}),
            'int': DataFrame({k: data[k] for k in ['B', 'B']}),
            'mixed': DataFrame(data),
        }
        self.utf_encodings = ['utf8', 'utf16', 'utf32'] 

# 需要導入模塊: import pandas [as 別名]
# 或者: from pandas import compat [as 別名]
def write_legacy_pickles(output_dir):

    # make sure we are < 0.13 compat (in py3)
    try:
        from pandas.compat import zip, cPickle as pickle  # noqa
    except:
        import pickle

    version = pandas.__version__

    print("This script generates a storage file for the current arch, system, "
          "and python version")
    print("  pandas version: {0}".format(version))
    print("  output dir    : {0}".format(output_dir))
    print("  storage format: pickle")

    pth = '{0}.pickle'.format(platform_name())

    fh = open(os.path.join(output_dir, pth), 'wb')
    pickle.dump(create_pickle_data(), fh, pickle.HIGHEST_PROTOCOL)
    fh.close()

    print("created pickle file: %s" % pth) 

# 需要導入模塊: import pandas [as 別名]
# 或者: from pandas import compat [as 別名]
def test_msgpacks_legacy(self, current_packers_data, all_packers_data,
                             legacy_packer, datapath):

        version = os.path.basename(os.path.dirname(legacy_packer))

        # GH12142 0.17 files packed in P2 can't be read in P3
        if (compat.PY3 and version.startswith('0.17.') and
                legacy_packer.split('.')[-4][-1] == '2'):
            msg = "Files packed in Py2 can't be read in Py3 ({})"
            pytest.skip(msg.format(version))
        try:
            with catch_warnings(record=True):
                self.compare(current_packers_data, all_packers_data,
                             legacy_packer, version)
        except ImportError:
            # blosc not installed
            pass 

# 需要導入模塊: import pandas [as 別名]
# 或者: from pandas import compat [as 別名]
def compare(self, vf):

        # py3 compat when reading py2 pickle
        try:
            data = pandas.read_pickle(vf)
        except (ValueError) as detail:
            # trying to read a py3 pickle in py2
            return

        for typ, dv in data.items():
            for dt, result in dv.items():
                try:
                    expected = self.data[typ][dt]
                except (KeyError):
                    continue

                self.compare_element(typ, result, expected) 

# 需要導入模塊: import pandas [as 別名]
# 或者: from pandas import compat [as 別名]
def test_constructor_dtypes_datetime(self):

        for tz in [None, 'UTC', 'US/Eastern', 'Asia/Tokyo']:
            idx = pd.date_range('2011-01-01', periods=5, tz=tz)
            dtype = idx.dtype

            # pass values without timezone, as DatetimeIndex localizes it
            for values in [pd.date_range('2011-01-01', periods=5).values,
                           pd.date_range('2011-01-01', periods=5).asi8]:

                for res in [pd.Index(values, tz=tz),
                            pd.Index(values, dtype=dtype),
                            pd.Index(list(values), tz=tz),
                            pd.Index(list(values), dtype=dtype)]:
                    tm.assert_index_equal(res, idx)

                # check compat with DatetimeIndex
                for res in [pd.DatetimeIndex(values, tz=tz),
                            pd.DatetimeIndex(values, dtype=dtype),
                            pd.DatetimeIndex(list(values), tz=tz),
                            pd.DatetimeIndex(list(values), dtype=dtype)]:
                    tm.assert_index_equal(res, idx) 

# 需要導入模塊: import pandas [as 別名]
# 或者: from pandas import compat [as 別名]
def test_equals_op_multiindex(self):
        # GH9785
        # test comparisons of multiindex
        from pandas.compat import StringIO
        df = pd.read_csv(StringIO('a,b,c\n1,2,3\n4,5,6'), index_col=[0, 1])
        tm.assert_numpy_array_equal(df.index == df.index,
                                    np.array([True, True]))

        mi1 = MultiIndex.from_tuples([(1, 2), (4, 5)])
        tm.assert_numpy_array_equal(df.index == mi1, np.array([True, True]))
        mi2 = MultiIndex.from_tuples([(1, 2), (4, 6)])
        tm.assert_numpy_array_equal(df.index == mi2, np.array([True, False]))
        mi3 = MultiIndex.from_tuples([(1, 2), (4, 5), (8, 9)])
        with tm.assert_raises_regex(ValueError, "Lengths must match"):
            df.index == mi3

        index_a = Index(['foo', 'bar', 'baz'])
        with tm.assert_raises_regex(ValueError, "Lengths must match"):
            df.index == index_a
        tm.assert_numpy_array_equal(index_a == mi3,
                                    np.array([False, False, False])) 

# 需要導入模塊: import pandas [as 別名]
# 或者: from pandas import compat [as 別名]
def setup_method(self, method):
        super(TestEncoding, self).setup_method(method)
        data = {
            'A': [compat.u('\u2019')] * 1000,
            'B': np.arange(1000, dtype=np.int32),
            'C': list(100 * 'abcdefghij'),
            'D': date_range(datetime.datetime(2015, 4, 1), periods=1000),
            'E': [datetime.timedelta(days=x) for x in range(1000)],
            'G': [400] * 1000
        }
        self.frame = {
            'float': DataFrame(dict((k, data[k]) for k in ['A', 'A'])),
            'int': DataFrame(dict((k, data[k]) for k in ['B', 'B'])),
            'mixed': DataFrame(data),
        }
        self.utf_encodings = ['utf8', 'utf16', 'utf32'] 

# 需要導入模塊: import pandas [as 別名]
# 或者: from pandas import compat [as 別名]
def from_csv(infile, dtype=None):
        """Load marginals from file.

        Args:
            infile (unicode): path to csv
            dtype (dict {unicode -> type}): pandas dtype dict, sets for each column label (dict key)
                 its data type (dict value). Since pandas automatically interprets numbers as
                 numeric types, columns containing codes (e.g. PUMA and state codes) must be set as
                 text types to preserve leading zeros.

        Returns:
            Marginals: marginals fetched from a csv file

        """
        if dtype is None:
            from pandas.compat import text_type
            dtype = {
                column: text_type for column in [inputs.PUMA.pums_name, inputs.STATE.pums_name]
            }
        data = pandas.read_csv(infile, dtype=dtype)
        return PumsData(data) 

# 需要導入模塊: import pandas [as 別名]
# 或者: from pandas import compat [as 別名]
def test_string_io(self):

        df = DataFrame(np.random.randn(10, 2))
        s = df.to_msgpack(None)
        result = read_msgpack(s)
        tm.assert_frame_equal(result, df)

        s = df.to_msgpack()
        result = read_msgpack(s)
        tm.assert_frame_equal(result, df)

        s = df.to_msgpack()
        result = read_msgpack(compat.BytesIO(s))
        tm.assert_frame_equal(result, df)

        s = to_msgpack(None, df)
        result = read_msgpack(s)
        tm.assert_frame_equal(result, df)

        with ensure_clean(self.path) as p:

            s = df.to_msgpack()
            with open(p, 'wb') as fh:
                fh.write(s)
            result = read_msgpack(p)
            tm.assert_frame_equal(result, df) 

# 需要導入模塊: import pandas [as 別名]
# 或者: from pandas import compat [as 別名]
def test_utf(self):
        # GH10581
        for encoding in self.utf_encodings:
            for frame in compat.itervalues(self.frame):
                result = self.encode_decode(frame, encoding=encoding)
                assert_frame_equal(result, frame) 

# 需要導入模塊: import pandas [as 別名]
# 或者: from pandas import compat [as 別名]
def test_default_encoding(self):
        for frame in compat.itervalues(self.frame):
            result = frame.to_msgpack()
            expected = frame.to_msgpack(encoding='utf8')
            assert result == expected
            result = self.encode_decode(frame)
            assert_frame_equal(result, frame) 

# 需要導入模塊: import pandas [as 別名]
# 或者: from pandas import compat [as 別名]
def compare(data, vf, version):

    # py3 compat when reading py2 pickle
    try:
        data = pandas.read_pickle(vf)
    except (ValueError) as e:
        if 'unsupported pickle protocol:' in str(e):
            # trying to read a py3 pickle in py2
            return
        else:
            raise

    m = globals()
    for typ, dv in data.items():
        for dt, result in dv.items():
            try:
                expected = data[typ][dt]
            except (KeyError):
                if version in ('0.10.1', '0.11.0') and dt == 'reg':
                    break
                else:
                    raise

            # use a specific comparator
            # if available
            comparator = "compare_{typ}_{dt}".format(typ=typ, dt=dt)

            comparator = m.get(comparator, m['compare_element'])
            comparator(result, expected, typ, version)
    return data 

# 需要導入模塊: import pandas [as 別名]
# 或者: from pandas import compat [as 別名]
def compress_file(self, src_path, dest_path, compression):
        if compression is None:
            shutil.copyfile(src_path, dest_path)
            return

        if compression == 'gzip':
            import gzip
            f = gzip.open(dest_path, "w")
        elif compression == 'bz2':
            import bz2
            f = bz2.BZ2File(dest_path, "w")
        elif compression == 'zip':
            import zipfile
            zip_file = zipfile.ZipFile(dest_path, "w",
                                       compression=zipfile.ZIP_DEFLATED)
            zip_file.write(src_path, os.path.basename(src_path))
        elif compression == 'xz':
            lzma = pandas.compat.import_lzma()
            f = lzma.LZMAFile(dest_path, "w")
        else:
            msg = 'Unrecognized compression type: {}'.format(compression)
            raise ValueError(msg)

        if compression != "zip":
            with open(src_path, "rb") as fh:
                f.write(fh.read())
            f.close() 

# 需要導入模塊: import pandas [as 別名]
# 或者: from pandas import compat [as 別名]
def test_string_io(self):

        df = DataFrame(np.random.randn(10, 2))
        s = df.to_msgpack(None)
        result = read_msgpack(s)
        tm.assert_frame_equal(result, df)

        s = df.to_msgpack()
        result = read_msgpack(s)
        tm.assert_frame_equal(result, df)

        s = df.to_msgpack()
        result = read_msgpack(compat.BytesIO(s))
        tm.assert_frame_equal(result, df)

        s = to_msgpack(None, df)
        result = read_msgpack(s)
        tm.assert_frame_equal(result, df)

        with ensure_clean(self.path) as p:

            s = df.to_msgpack()
            fh = open(p, 'wb')
            fh.write(s)
            fh.close()
            result = read_msgpack(p)
            tm.assert_frame_equal(result, df)