Python numpy.object_() Examples

The following are 30 code examples of numpy.object_(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module numpy , or try the search function .
Example #1
Source File: test_analytics.py    From recruit with Apache License 2.0 6 votes vote down vote up
def test_stat_operators_attempt_obj_array(self, method):
        # GH 676
        data = {
            'a': [-0.00049987540199591344, -0.0016467257772919831,
                  0.00067695870775883013],
            'b': [-0, -0, 0.0],
            'c': [0.00031111847529610595, 0.0014902627951905339,
                  -0.00094099200035979691]
        }
        df1 = DataFrame(data, index=['foo', 'bar', 'baz'], dtype='O')

        df2 = DataFrame({0: [np.nan, 2], 1: [np.nan, 3],
                         2: [np.nan, 4]}, dtype=object)

        for df in [df1, df2]:
            assert df.values.dtype == np.object_
            result = getattr(df, method)(1)
            expected = getattr(df.astype('f8'), method)(1)

            if method in ['sum', 'prod']:
                tm.assert_series_equal(result, expected) 
Example #2
Source File: test_dtypes.py    From recruit with Apache License 2.0 6 votes vote down vote up
def test_astype_datetime(self):
        s = Series(iNaT, dtype='M8[ns]', index=lrange(5))

        s = s.astype('O')
        assert s.dtype == np.object_

        s = Series([datetime(2001, 1, 2, 0, 0)])

        s = s.astype('O')
        assert s.dtype == np.object_

        s = Series([datetime(2001, 1, 2, 0, 0) for i in range(3)])

        s[1] = np.nan
        assert s.dtype == 'M8[ns]'

        s = s.astype('O')
        assert s.dtype == np.object_ 
Example #3
Source File: test_constructors.py    From recruit with Apache License 2.0 6 votes vote down vote up
def test_constructor_dict_cast(self):
        # cast float tests
        test_data = {
            'A': {'1': 1, '2': 2},
            'B': {'1': '1', '2': '2', '3': '3'},
        }
        frame = DataFrame(test_data, dtype=float)
        assert len(frame) == 3
        assert frame['B'].dtype == np.float64
        assert frame['A'].dtype == np.float64

        frame = DataFrame(test_data)
        assert len(frame) == 3
        assert frame['B'].dtype == np.object_
        assert frame['A'].dtype == np.float64

        # can't cast to float
        test_data = {
            'A': dict(zip(range(20), tm.makeStringIndex(20))),
            'B': dict(zip(range(15), np.random.randn(15)))
        }
        frame = DataFrame(test_data, dtype=float)
        assert len(frame) == 20
        assert frame['A'].dtype == np.object_
        assert frame['B'].dtype == np.float64 
Example #4
Source File: test_constructors.py    From recruit with Apache License 2.0 6 votes vote down vote up
def test_fromDict(self):
        data = {'a': 0, 'b': 1, 'c': 2, 'd': 3}

        series = Series(data)
        assert tm.is_sorted(series.index)

        data = {'a': 0, 'b': '1', 'c': '2', 'd': datetime.now()}
        series = Series(data)
        assert series.dtype == np.object_

        data = {'a': 0, 'b': '1', 'c': '2', 'd': '3'}
        series = Series(data)
        assert series.dtype == np.object_

        data = {'a': '0', 'b': '1'}
        series = Series(data, dtype=float)
        assert series.dtype == np.float64 
Example #5
Source File: test_constructors.py    From recruit with Apache License 2.0 6 votes vote down vote up
def test_fromValue(self, datetime_series):

        nans = Series(np.NaN, index=datetime_series.index)
        assert nans.dtype == np.float_
        assert len(nans) == len(datetime_series)

        strings = Series('foo', index=datetime_series.index)
        assert strings.dtype == np.object_
        assert len(strings) == len(datetime_series)

        d = datetime.now()
        dates = Series(d, index=datetime_series.index)
        assert dates.dtype == 'M8[ns]'
        assert len(dates) == len(datetime_series)

        # GH12336
        # Test construction of categorical series from value
        categorical = Series(0, index=datetime_series.index, dtype="category")
        expected = Series(0, index=datetime_series.index).astype("category")
        assert categorical.dtype == 'category'
        assert len(categorical) == len(datetime_series)
        tm.assert_series_equal(categorical, expected) 
Example #6
Source File: test_dtypes.py    From recruit with Apache License 2.0 6 votes vote down vote up
def test_is_dtype(self):
        assert PeriodDtype.is_dtype(self.dtype)
        assert PeriodDtype.is_dtype('period[D]')
        assert PeriodDtype.is_dtype('period[3D]')
        assert PeriodDtype.is_dtype(PeriodDtype('3D'))
        assert PeriodDtype.is_dtype('period[U]')
        assert PeriodDtype.is_dtype('period[S]')
        assert PeriodDtype.is_dtype(PeriodDtype('U'))
        assert PeriodDtype.is_dtype(PeriodDtype('S'))

        assert not PeriodDtype.is_dtype('D')
        assert not PeriodDtype.is_dtype('3D')
        assert not PeriodDtype.is_dtype('U')
        assert not PeriodDtype.is_dtype('S')
        assert not PeriodDtype.is_dtype('foo')
        assert not PeriodDtype.is_dtype(np.object_)
        assert not PeriodDtype.is_dtype(np.int64)
        assert not PeriodDtype.is_dtype(np.float64) 
Example #7
Source File: test_dtypes.py    From recruit with Apache License 2.0 6 votes vote down vote up
def test_is_dtype(self):
        assert IntervalDtype.is_dtype(self.dtype)
        assert IntervalDtype.is_dtype('interval')
        assert IntervalDtype.is_dtype(IntervalDtype('float64'))
        assert IntervalDtype.is_dtype(IntervalDtype('int64'))
        assert IntervalDtype.is_dtype(IntervalDtype(np.int64))

        assert not IntervalDtype.is_dtype('D')
        assert not IntervalDtype.is_dtype('3D')
        assert not IntervalDtype.is_dtype('U')
        assert not IntervalDtype.is_dtype('S')
        assert not IntervalDtype.is_dtype('foo')
        assert not IntervalDtype.is_dtype('IntervalA')
        assert not IntervalDtype.is_dtype(np.object_)
        assert not IntervalDtype.is_dtype(np.int64)
        assert not IntervalDtype.is_dtype(np.float64) 
Example #8
Source File: test_base.py    From recruit with Apache License 2.0 6 votes vote down vote up
def test_constructor_from_index_dtlike(self, cast_as_obj, index):
        if cast_as_obj:
            result = pd.Index(index.astype(object))
        else:
            result = pd.Index(index)

        tm.assert_index_equal(result, index)

        if isinstance(index, pd.DatetimeIndex):
            assert result.tz == index.tz
            if cast_as_obj:
                # GH#23524 check that Index(dti, dtype=object) does not
                #  incorrectly raise ValueError, and that nanoseconds are not
                #  dropped
                index += pd.Timedelta(nanoseconds=50)
                result = pd.Index(index, dtype=object)
                assert result.dtype == np.object_
                assert list(result) == list(index) 
Example #9
Source File: test_strings.py    From recruit with Apache License 2.0 6 votes vote down vote up
def test_zfill(self):
        values = Series(['1', '22', 'aaa', '333', '45678'])

        result = values.str.zfill(5)
        expected = Series(['00001', '00022', '00aaa', '00333', '45678'])
        tm.assert_series_equal(result, expected)
        expected = np.array([v.zfill(5) for v in values.values],
                            dtype=np.object_)
        tm.assert_numpy_array_equal(result.values, expected)

        result = values.str.zfill(3)
        expected = Series(['001', '022', 'aaa', '333', '45678'])
        tm.assert_series_equal(result, expected)
        expected = np.array([v.zfill(3) for v in values.values],
                            dtype=np.object_)
        tm.assert_numpy_array_equal(result.values, expected)

        values = Series(['1', np.nan, 'aaa', np.nan, '45678'])
        result = values.str.zfill(5)
        expected = Series(['00001', np.nan, '00aaa', np.nan, '45678'])
        tm.assert_series_equal(result, expected) 
Example #10
Source File: test_groupby.py    From recruit with Apache License 2.0 6 votes vote down vote up
def test_convert_objects_leave_decimal_alone():

    s = Series(lrange(5))
    labels = np.array(['a', 'b', 'c', 'd', 'e'], dtype='O')

    def convert_fast(x):
        return Decimal(str(x.mean()))

    def convert_force_pure(x):
        # base will be length 0
        assert (len(x.values.base) > 0)
        return Decimal(str(x.mean()))

    grouped = s.groupby(labels)

    result = grouped.agg(convert_fast)
    assert result.dtype == np.object_
    assert isinstance(result[0], Decimal)

    result = grouped.agg(convert_force_pure)
    assert result.dtype == np.object_
    assert isinstance(result[0], Decimal) 
Example #11
Source File: test_regression.py    From recruit with Apache License 2.0 6 votes vote down vote up
def test_object_array_refcount_self_assign(self):
        # Ticket #711
        class VictimObject(object):
            deleted = False

            def __del__(self):
                self.deleted = True

        d = VictimObject()
        arr = np.zeros(5, dtype=np.object_)
        arr[:] = d
        del d
        arr[:] = arr  # refcount of 'd' might hit zero here
        assert_(not arr[0].deleted)
        arr[:] = arr  # trying to induce a segfault by doing it again...
        assert_(not arr[0].deleted) 
Example #12
Source File: test_textreader.py    From recruit with Apache License 2.0 5 votes vote down vote up
def test_embedded_newline(self):
        data = 'a\n"hello\nthere"\nthis'

        reader = TextReader(StringIO(data), header=None)
        result = reader.read()

        expected = np.array(['a', 'hello\nthere', 'this'], dtype=np.object_)
        tm.assert_numpy_array_equal(result[0], expected) 
Example #13
Source File: test_dtypes.py    From recruit with Apache License 2.0 5 votes vote down vote up
def test_basic_dtype(self):
        assert is_interval_dtype('interval[int64]')
        assert is_interval_dtype(IntervalIndex.from_tuples([(0, 1)]))
        assert is_interval_dtype(IntervalIndex.from_breaks(np.arange(4)))
        assert is_interval_dtype(IntervalIndex.from_breaks(
            date_range('20130101', periods=3)))
        assert not is_interval_dtype('U')
        assert not is_interval_dtype('S')
        assert not is_interval_dtype('foo')
        assert not is_interval_dtype(np.object_)
        assert not is_interval_dtype(np.int64)
        assert not is_interval_dtype(np.float64) 
Example #14
Source File: core.py    From esmlab with Apache License 2.0 5 votes vote down vote up
def isdecoded(self, obj):
        return obj.dtype.type in {np.str_, np.object_, np.datetime64} 
Example #15
Source File: strings.py    From recruit with Apache License 2.0 5 votes vote down vote up
def _validate(data):
        from pandas.core.index import Index

        if (isinstance(data, ABCSeries) and
                not ((is_categorical_dtype(data.dtype) and
                      is_object_dtype(data.values.categories)) or
                     (is_object_dtype(data.dtype)))):
            # it's neither a string series not a categorical series with
            # strings inside the categories.
            # this really should exclude all series with any non-string values
            # (instead of test for object dtype), but that isn't practical for
            # performance reasons until we have a str dtype (GH 9343)
            raise AttributeError("Can only use .str accessor with string "
                                 "values, which use np.object_ dtype in "
                                 "pandas")
        elif isinstance(data, Index):
            # can't use ABCIndex to exclude non-str

            # see src/inference.pyx which can contain string values
            allowed_types = ('string', 'unicode', 'mixed', 'mixed-integer')
            if is_categorical_dtype(data.dtype):
                inf_type = data.categories.inferred_type
            else:
                inf_type = data.inferred_type
            if inf_type not in allowed_types:
                message = ("Can only use .str accessor with string values "
                           "(i.e. inferred_type is 'string', 'unicode' or "
                           "'mixed')")
                raise AttributeError(message)
            if data.nlevels > 1:
                message = ("Can only use .str accessor with Index, not "
                           "MultiIndex")
                raise AttributeError(message) 
Example #16
Source File: cast.py    From recruit with Apache License 2.0 5 votes vote down vote up
def maybe_cast_item(obj, item, dtype):
    chunk = obj[item]

    if chunk.values.dtype != dtype:
        if dtype in (np.object_, np.bool_):
            obj[item] = chunk.astype(np.object_)
        elif not issubclass(dtype, (np.integer, np.bool_)):  # pragma: no cover
            raise ValueError("Unexpected dtype encountered: {dtype}"
                             .format(dtype=dtype)) 
Example #17
Source File: cast.py    From recruit with Apache License 2.0 5 votes vote down vote up
def maybe_convert_platform(values):
    """ try to do platform conversion, allow ndarray or list here """

    if isinstance(values, (list, tuple)):
        values = construct_1d_object_array_from_listlike(list(values))
    if getattr(values, 'dtype', None) == np.object_:
        if hasattr(values, '_values'):
            values = values._values
        values = lib.maybe_convert_objects(values)

    return values 
Example #18
Source File: ops.py    From recruit with Apache License 2.0 5 votes vote down vote up
def _comp_method_OBJECT_ARRAY(op, x, y):
    if isinstance(y, list):
        y = construct_1d_object_array_from_listlike(y)
    if isinstance(y, (np.ndarray, ABCSeries, ABCIndex)):
        if not is_object_dtype(y.dtype):
            y = y.astype(np.object_)

        if isinstance(y, (ABCSeries, ABCIndex)):
            y = y.values

        result = libops.vec_compare(x, y, op)
    else:
        result = libops.scalar_compare(x, y, op)
    return result 
Example #19
Source File: timedeltas.py    From recruit with Apache License 2.0 5 votes vote down vote up
def objects_to_td64ns(data, unit="ns", errors="raise"):
    """
    Convert a object-dtyped or string-dtyped array into an
    timedelta64[ns]-dtyped array.

    Parameters
    ----------
    data : ndarray or Index
    unit : str, default "ns"
        The timedelta unit to treat integers as multiples of.
    errors : {"raise", "coerce", "ignore"}, default "raise"
        How to handle elements that cannot be converted to timedelta64[ns].
        See ``pandas.to_timedelta`` for details.

    Returns
    -------
    numpy.ndarray : timedelta64[ns] array converted from data

    Raises
    ------
    ValueError : Data cannot be converted to timedelta64[ns].

    Notes
    -----
    Unlike `pandas.to_timedelta`, if setting `errors=ignore` will not cause
    errors to be ignored; they are caught and subsequently ignored at a
    higher level.
    """
    # coerce Index to np.ndarray, converting string-dtype if necessary
    values = np.array(data, dtype=np.object_, copy=False)

    result = array_to_timedelta64(values,
                                  unit=unit, errors=errors)
    return result.view('timedelta64[ns]') 
Example #20
Source File: construction.py    From recruit with Apache License 2.0 5 votes vote down vote up
def _list_of_series_to_arrays(data, columns, coerce_float=False, dtype=None):
    if columns is None:
        columns = _get_objs_combined_axis(data, sort=False)

    indexer_cache = {}

    aligned_values = []
    for s in data:
        index = getattr(s, 'index', None)
        if index is None:
            index = ibase.default_index(len(s))

        if id(index) in indexer_cache:
            indexer = indexer_cache[id(index)]
        else:
            indexer = indexer_cache[id(index)] = index.get_indexer(columns)

        values = com.values_from_object(s)
        aligned_values.append(algorithms.take_1d(values, indexer))

    values = np.vstack(aligned_values)

    if values.dtype == np.object_:
        content = list(values.T)
        return _convert_object_array(content, columns, dtype=dtype,
                                     coerce_float=coerce_float)
    else:
        return values.T, columns 
Example #21
Source File: test_apply.py    From recruit with Apache License 2.0 5 votes vote down vote up
def test_map_decimal(self, string_series):
        from decimal import Decimal

        result = string_series.map(lambda x: Decimal(str(x)))
        assert result.dtype == np.object_
        assert isinstance(result[0], Decimal) 
Example #22
Source File: test_indexing.py    From recruit with Apache License 2.0 5 votes vote down vote up
def test_xs(self):
        idx = self.frame.index[5]
        xs = self.frame.xs(idx)
        for item, value in compat.iteritems(xs):
            if np.isnan(value):
                assert np.isnan(self.frame[item][idx])
            else:
                assert value == self.frame[item][idx]

        # mixed-type xs
        test_data = {
            'A': {'1': 1, '2': 2},
            'B': {'1': '1', '2': '2', '3': '3'},
        }
        frame = DataFrame(test_data)
        xs = frame.xs('1')
        assert xs.dtype == np.object_
        assert xs['A'] == 1
        assert xs['B'] == '1'

        with pytest.raises(KeyError):
            self.tsframe.xs(self.tsframe.index[0] - BDay())

        # xs get column
        series = self.frame.xs('A', axis=1)
        expected = self.frame['A']
        assert_series_equal(series, expected)

        # view is returned if possible
        series = self.frame.xs('A', axis=1)
        series[:] = 5
        assert (expected == 5).all() 
Example #23
Source File: test_indexing.py    From recruit with Apache License 2.0 5 votes vote down vote up
def test_lookup(self):
        def alt(df, rows, cols, dtype):
            with tm.assert_produces_warning(FutureWarning,
                                            check_stacklevel=False):
                result = [df.get_value(r, c) for r, c in zip(rows, cols)]
            return np.array(result, dtype=dtype)

        def testit(df):
            rows = list(df.index) * len(df.columns)
            cols = list(df.columns) * len(df.index)
            result = df.lookup(rows, cols)
            expected = alt(df, rows, cols, dtype=np.object_)
            tm.assert_almost_equal(result, expected, check_dtype=False)

        testit(self.mixed_frame)
        testit(self.frame)

        df = DataFrame({'label': ['a', 'b', 'a', 'c'],
                        'mask_a': [True, True, False, True],
                        'mask_b': [True, False, False, False],
                        'mask_c': [False, True, False, True]})
        df['mask'] = df.lookup(df.index, 'mask_' + df['label'])
        exp_mask = alt(df, df.index, 'mask_' + df['label'], dtype=np.bool_)
        tm.assert_series_equal(df['mask'], pd.Series(exp_mask, name='mask'))
        assert df['mask'].dtype == np.bool_

        with pytest.raises(KeyError):
            self.frame.lookup(['xyz'], ['A'])

        with pytest.raises(KeyError):
            self.frame.lookup([self.frame.index[0]], ['xyz'])

        with pytest.raises(ValueError, match='same size'):
            self.frame.lookup(['a', 'b', 'c'], ['a']) 
Example #24
Source File: test_axis_select_reindex.py    From recruit with Apache License 2.0 5 votes vote down vote up
def test_reindex_boolean(self):
        frame = DataFrame(np.ones((10, 2), dtype=bool),
                          index=np.arange(0, 20, 2),
                          columns=[0, 2])

        reindexed = frame.reindex(np.arange(10))
        assert reindexed.values.dtype == np.object_
        assert isna(reindexed[0][1])

        reindexed = frame.reindex(columns=lrange(3))
        assert reindexed.values.dtype == np.object_
        assert isna(reindexed[1]).all() 
Example #25
Source File: test_strings.py    From recruit with Apache License 2.0 5 votes vote down vote up
def test_endswith(self):
        values = Series(['om', NA, 'foo_nom', 'nom', 'bar_foo', NA, 'foo'])

        result = values.str.endswith('foo')
        exp = Series([False, NA, False, False, True, NA, True])
        tm.assert_series_equal(result, exp)

        # mixed
        mixed = ['a', NA, 'b', True, datetime.today(), 'foo', None, 1, 2.]
        rs = strings.str_endswith(mixed, 'f')
        xp = np.array([False, NA, False, NA, NA, False, NA, NA, NA],
                      dtype=np.object_)
        tm.assert_numpy_array_equal(rs, xp)

        rs = Series(mixed).str.endswith('f')
        xp = Series([False, NA, False, NA, NA, False, NA, NA, NA])
        assert isinstance(rs, Series)
        tm.assert_series_equal(rs, xp)

        # unicode
        values = Series([u('om'), NA, u('foo_nom'), u('nom'), u('bar_foo'), NA,
                         u('foo')])

        result = values.str.endswith('foo')
        exp = Series([False, NA, False, False, True, NA, True])
        tm.assert_series_equal(result, exp)

        result = values.str.endswith('foo', na=False)
        tm.assert_series_equal(result, exp.fillna(False).astype(bool)) 
Example #26
Source File: test_strings.py    From recruit with Apache License 2.0 5 votes vote down vote up
def test_startswith(self):
        values = Series(['om', NA, 'foo_nom', 'nom', 'bar_foo', NA, 'foo'])

        result = values.str.startswith('foo')
        exp = Series([False, NA, True, False, False, NA, True])
        tm.assert_series_equal(result, exp)

        # mixed
        mixed = np.array(['a', NA, 'b', True, datetime.today(),
                          'foo', None, 1, 2.], dtype=np.object_)
        rs = strings.str_startswith(mixed, 'f')
        xp = np.array([False, NA, False, NA, NA, True, NA, NA, NA],
                      dtype=np.object_)
        tm.assert_numpy_array_equal(rs, xp)

        rs = Series(mixed).str.startswith('f')
        assert isinstance(rs, Series)
        xp = Series([False, NA, False, NA, NA, True, NA, NA, NA])
        tm.assert_series_equal(rs, xp)

        # unicode
        values = Series([u('om'), NA, u('foo_nom'), u('nom'), u('bar_foo'), NA,
                         u('foo')])

        result = values.str.startswith('foo')
        exp = Series([False, NA, True, False, False, NA, True])
        tm.assert_series_equal(result, exp)

        result = values.str.startswith('foo', na=True)
        tm.assert_series_equal(result, exp.fillna(True).astype(bool)) 
Example #27
Source File: test_constructors.py    From recruit with Apache License 2.0 5 votes vote down vote up
def test_constructor_pass_none(self):
        s = Series(None, index=lrange(5))
        assert s.dtype == np.float64

        s = Series(None, index=lrange(5), dtype=object)
        assert s.dtype == np.object_

        # GH 7431
        # inference on the index
        s = Series(index=np.array([None]))
        expected = Series(index=Index([None]))
        assert_series_equal(s, expected) 
Example #28
Source File: test_constructors.py    From recruit with Apache License 2.0 5 votes vote down vote up
def test_categorical_sideeffects_free(self):
        # Passing a categorical to a Series and then changing values in either
        # the series or the categorical should not change the values in the
        # other one, IF you specify copy!
        cat = Categorical(["a", "b", "c", "a"])
        s = Series(cat, copy=True)
        assert s.cat is not cat
        s.cat.categories = [1, 2, 3]
        exp_s = np.array([1, 2, 3, 1], dtype=np.int64)
        exp_cat = np.array(["a", "b", "c", "a"], dtype=np.object_)
        tm.assert_numpy_array_equal(s.__array__(), exp_s)
        tm.assert_numpy_array_equal(cat.__array__(), exp_cat)

        # setting
        s[0] = 2
        exp_s2 = np.array([2, 2, 3, 1], dtype=np.int64)
        tm.assert_numpy_array_equal(s.__array__(), exp_s2)
        tm.assert_numpy_array_equal(cat.__array__(), exp_cat)

        # however, copy is False by default
        # so this WILL change values
        cat = Categorical(["a", "b", "c", "a"])
        s = Series(cat)
        assert s.values is cat
        s.cat.categories = [1, 2, 3]
        exp_s = np.array([1, 2, 3, 1], dtype=np.int64)
        tm.assert_numpy_array_equal(s.__array__(), exp_s)
        tm.assert_numpy_array_equal(cat.__array__(), exp_s)

        s[0] = 2
        exp_s2 = np.array([2, 2, 3, 1], dtype=np.int64)
        tm.assert_numpy_array_equal(s.__array__(), exp_s2)
        tm.assert_numpy_array_equal(cat.__array__(), exp_s2) 
Example #29
Source File: test_constructors.py    From recruit with Apache License 2.0 5 votes vote down vote up
def test_constructor(self, datetime_series, empty_series):
        assert datetime_series.index.is_all_dates

        # Pass in Series
        derived = Series(datetime_series)
        assert derived.index.is_all_dates

        assert tm.equalContents(derived.index, datetime_series.index)
        # Ensure new index is not created
        assert id(datetime_series.index) == id(derived.index)

        # Mixed type Series
        mixed = Series(['hello', np.NaN], index=[0, 1])
        assert mixed.dtype == np.object_
        assert mixed[1] is np.NaN

        assert not empty_series.index.is_all_dates
        assert not Series({}).index.is_all_dates

        # exception raised is of type Exception
        with pytest.raises(Exception, match="Data must be 1-dimensional"):
            Series(np.random.randn(3, 3), index=np.arange(3))

        mixed.name = 'Series'
        rs = Series(mixed).name
        xp = 'Series'
        assert rs == xp

        # raise on MultiIndex GH4187
        m = MultiIndex.from_arrays([[1, 2], [3, 4]])
        msg = "initializing a Series from a MultiIndex is not supported"
        with pytest.raises(NotImplementedError, match=msg):
            Series(m) 
Example #30
Source File: date_converters.py    From recruit with Apache License 2.0 5 votes vote down vote up
def _maybe_cast(arr):
    if not arr.dtype.type == np.object_:
        arr = np.array(arr, dtype=object)
    return arr