test_constructors.py

# coding=utf-8
# pylint: disable-msg=E1101,W0612

from collections import OrderedDict
from datetime import datetime, timedelta

import numpy as np
from numpy import nan
import numpy.ma as ma
import pytest

from pandas._libs import lib
from pandas._libs.tslib import iNaT
from pandas.compat import PY36, long, lrange, range, zip

from pandas.core.dtypes.common import (
    is_categorical_dtype, is_datetime64tz_dtype)

import pandas as pd
from pandas import (
    Categorical, DataFrame, Index, IntervalIndex, MultiIndex, NaT, Series,
    Timestamp, date_range, isna, period_range, timedelta_range)
from pandas.api.types import CategoricalDtype
from pandas.core.arrays import period_array
import pandas.util.testing as tm
from pandas.util.testing import assert_series_equal


class TestSeriesConstructors():

    def test_invalid_dtype(self):
        # GH15520
        msg = 'not understood'
        invalid_list = [pd.Timestamp, 'pd.Timestamp', list]
        for dtype in invalid_list:
            with pytest.raises(TypeError, match=msg):
                Series([], name='time', dtype=dtype)

    def test_scalar_conversion(self):

        # Pass in scalar is disabled
        scalar = Series(0.5)
        assert not isinstance(scalar, float)

        # Coercion
        assert float(Series([1.])) == 1.0
        assert int(Series([1.])) == 1
        assert long(Series([1.])) == 1

    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
        pytest.raises(Exception, 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]])
        pytest.raises(NotImplementedError, Series, m)

    @pytest.mark.parametrize('input_class', [list, dict, OrderedDict])
    def test_constructor_empty(self, input_class):
        empty = Series()
        empty2 = Series(input_class())

        # these are Index() and RangeIndex() which don't compare type equal
        # but are just .equals
        assert_series_equal(empty, empty2, check_index_type=False)

        # With explicit dtype:
        empty = Series(dtype='float64')
        empty2 = Series(input_class(), dtype='float64')
        assert_series_equal(empty, empty2, check_index_type=False)

        # GH 18515 : with dtype=category:
        empty = Series(dtype='category')
        empty2 = Series(input_class(), dtype='category')
        assert_series_equal(empty, empty2, check_index_type=False)

        if input_class is not list:
            # With index:
            empty = Series(index=lrange(10))
            empty2 = Series(input_class(), index=lrange(10))
            assert_series_equal(empty, empty2)

            # With index and dtype float64:
            empty = Series(np.nan, index=lrange(10))
            empty2 = Series(input_class(), index=lrange(10), dtype='float64')
            assert_series_equal(empty, empty2)

            # GH 19853 : with empty string, index and dtype str
            empty = Series('', dtype=str, index=range(3))
            empty2 = Series('', index=range(3))
            assert_series_equal(empty, empty2)

    @pytest.mark.parametrize('input_arg', [np.nan, float('nan')])
    def test_constructor_nan(self, input_arg):
        empty = Series(dtype='float64', index=lrange(10))
        empty2 = Series(input_arg, index=lrange(10))

        assert_series_equal(empty, empty2, check_index_type=False)

    @pytest.mark.parametrize('dtype', [
        'f8', 'i8', 'M8[ns]', 'm8[ns]', 'category', 'object',
        'datetime64[ns, UTC]',
    ])
    @pytest.mark.parametrize('index', [None, pd.Index([])])
    def test_constructor_dtype_only(self, dtype, index):
        # GH-20865
        result = pd.Series(dtype=dtype, index=index)
        assert result.dtype == dtype
        assert len(result) == 0

    def test_constructor_no_data_index_order(self):
        result = pd.Series(index=['b', 'a', 'c'])
        assert result.index.tolist() == ['b', 'a', 'c']

    def test_constructor_no_data_string_type(self):
        # GH 22477
        result = pd.Series(index=[1], dtype=str)
        assert np.isnan(result.iloc[0])

    @pytest.mark.parametrize('item', ['13'])
    def test_constructor_single_element_string_type(self, item):
        # GH 22477
        result = pd.Series(int(item), index=[1], dtype=str)
        assert result.iloc[0] == item

    @pytest.mark.parametrize('item', ['entry', 'ѐ'])
    def test_constructor_string_element_string_type(self, item):
        # GH 22477
        result = pd.Series(item, index=[1], dtype=str)
        assert result.iloc[0] == item

    def test_constructor_dtype_str_na_values(self, string_dtype):
        # https://github.com/pandas-dev/pandas/issues/21083
        ser = Series(['x', None], dtype=string_dtype)
        result = ser.isna()
        expected = Series([False, True])
        tm.assert_series_equal(result, expected)
        assert ser.iloc[1] is None

        ser = Series(['x', np.nan], dtype=string_dtype)
        assert np.isnan(ser.iloc[1])

    def test_constructor_series(self):
        index1 = ['d', 'b', 'a', 'c']
        index2 = sorted(index1)
        s1 = Series([4, 7, -5, 3], index=index1)
        s2 = Series(s1, index=index2)

        assert_series_equal(s2, s1.sort_index())

    def test_constructor_iterable(self):
        # GH 21987
        class Iter():
            def __iter__(self):
                for i in range(10):
                    yield i

        expected = Series(list(range(10)), dtype='int64')
        result = Series(Iter(), dtype='int64')
        assert_series_equal(result, expected)

    def test_constructor_sequence(self):
        # GH 21987
        expected = Series(list(range(10)), dtype='int64')
        result = Series(range(10), dtype='int64')
        assert_series_equal(result, expected)

    def test_constructor_single_str(self):
        # GH 21987
        expected = Series(['abc'])
        result = Series('abc')
        assert_series_equal(result, expected)

    def test_constructor_list_like(self):

        # make sure that we are coercing different
        # list-likes to standard dtypes and not
        # platform specific
        expected = Series([1, 2, 3], dtype='int64')
        for obj in [[1, 2, 3], (1, 2, 3),
                    np.array([1, 2, 3], dtype='int64')]:
            result = Series(obj, index=[0, 1, 2])
            assert_series_equal(result, expected)

    @pytest.mark.parametrize('input_vals', [
        ([1, 2]),
        (['1', '2']),
        (list(pd.date_range('1/1/2011', periods=2, freq='H'))),
        (list(pd.date_range('1/1/2011', periods=2, freq='H',
                            tz='US/Eastern'))),
        ([pd.Interval(left=0, right=5)]),
    ])
    def test_constructor_list_str(self, input_vals, string_dtype):
        # GH 16605
        # Ensure that data elements from a list are converted to strings
        # when dtype is str, 'str', or 'U'
        result = Series(input_vals, dtype=string_dtype)
        expected = Series(input_vals).astype(string_dtype)
        assert_series_equal(result, expected)

    def test_constructor_list_str_na(self, string_dtype):
        result = Series([1.0, 2.0, np.nan], dtype=string_dtype)
        expected = Series(['1.0', '2.0', np.nan], dtype=object)
        assert_series_equal(result, expected)
        assert np.isnan(result[2])

    def test_constructor_generator(self):
        gen = (i for i in range(10))

        result = Series(gen)
        exp = Series(lrange(10))
        assert_series_equal(result, exp)

        gen = (i for i in range(10))
        result = Series(gen, index=lrange(10, 20))
        exp.index = lrange(10, 20)
        assert_series_equal(result, exp)

    def test_constructor_map(self):
        # GH8909
        m = map(lambda x: x, range(10))

        result = Series(m)
        exp = Series(lrange(10))
        assert_series_equal(result, exp)

        m = map(lambda x: x, range(10))
        result = Series(m, index=lrange(10, 20))
        exp.index = lrange(10, 20)
        assert_series_equal(result, exp)

    def test_constructor_categorical(self):
        cat = pd.Categorical([0, 1, 2, 0, 1, 2], ['a', 'b', 'c'],
                             fastpath=True)
        res = Series(cat)
        tm.assert_categorical_equal(res.values, cat)

        # can cast to a new dtype
        result = Series(pd.Categorical([1, 2, 3]),
                        dtype='int64')
        expected = pd.Series([1, 2, 3], dtype='int64')
        tm.assert_series_equal(result, expected)

        # GH12574
        cat = Series(pd.Categorical([1, 2, 3]), dtype='category')
        assert is_categorical_dtype(cat)
        assert is_categorical_dtype(cat.dtype)
        s = Series([1, 2, 3], dtype='category')
        assert is_categorical_dtype(s)
        assert is_categorical_dtype(s.dtype)

    def test_constructor_categorical_with_coercion(self):
        factor = Categorical(['a', 'b', 'b', 'a', 'a', 'c', 'c', 'c'])
        # test basic creation / coercion of categoricals
        s = Series(factor, name='A')
        assert s.dtype == 'category'
        assert len(s) == len(factor)
        str(s.values)
        str(s)

        # in a frame
        df = DataFrame({'A': factor})
        result = df['A']
        tm.assert_series_equal(result, s)
        result = df.iloc[:, 0]
        tm.assert_series_equal(result, s)
        assert len(df) == len(factor)
        str(df.values)
        str(df)

        df = DataFrame({'A': s})
        result = df['A']
        tm.assert_series_equal(result, s)
        assert len(df) == len(factor)
        str(df.values)
        str(df)

        # multiples
        df = DataFrame({'A': s, 'B': s, 'C': 1})
        result1 = df['A']
        result2 = df['B']
        tm.assert_series_equal(result1, s)
        tm.assert_series_equal(result2, s, check_names=False)
        assert result2.name == 'B'
        assert len(df) == len(factor)
        str(df.values)
        str(df)

        # GH8623
        x = DataFrame([[1, 'John P. Doe'], [2, 'Jane Dove'],
                       [1, 'John P. Doe']],
                      columns=['person_id', 'person_name'])
        x['person_name'] = Categorical(x.person_name
                                       )  # doing this breaks transform

        expected = x.iloc[0].person_name
        result = x.person_name.iloc[0]
        assert result == expected

        result = x.person_name[0]
        assert result == expected

        result = x.person_name.loc[0]
        assert result == expected

    def test_constructor_categorical_dtype(self):
        result = pd.Series(['a', 'b'],
                           dtype=CategoricalDtype(['a', 'b', 'c'],
                                                  ordered=True))
        assert is_categorical_dtype(result) is True
        tm.assert_index_equal(result.cat.categories, pd.Index(['a', 'b', 'c']))
        assert result.cat.ordered

        result = pd.Series(['a', 'b'], dtype=CategoricalDtype(['b', 'a']))
        assert is_categorical_dtype(result)
        tm.assert_index_equal(result.cat.categories, pd.Index(['b', 'a']))
        assert result.cat.ordered is False

        # GH 19565 - Check broadcasting of scalar with Categorical dtype
        result = Series('a', index=[0, 1],
                        dtype=CategoricalDtype(['a', 'b'], ordered=True))
        expected = Series(['a', 'a'], index=[0, 1],
                          dtype=CategoricalDtype(['a', 'b'], ordered=True))
        tm.assert_series_equal(result, expected, check_categorical=True)

    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)

    def test_unordered_compare_equal(self):
        left = pd.Series(['a', 'b', 'c'],
                         dtype=CategoricalDtype(['a', 'b']))
        right = pd.Series(pd.Categorical(['a', 'b', np.nan],
                                         categories=['a', 'b']))
        tm.assert_series_equal(left, right)

    def test_constructor_maskedarray(self):
        data = ma.masked_all((3, ), dtype=float)
        result = Series(data)
        expected = Series([nan, nan, nan])
        assert_series_equal(result, expected)

        data[0] = 0.0
        data[2] = 2.0
        index = ['a', 'b', 'c']
        result = Series(data, index=index)
        expected = Series([0.0, nan, 2.0], index=index)
        assert_series_equal(result, expected)

        data[1] = 1.0
        result = Series(data, index=index)
        expected = Series([0.0, 1.0, 2.0], index=index)
        assert_series_equal(result, expected)

        data = ma.masked_all((3, ), dtype=int)
        result = Series(data)
        expected = Series([nan, nan, nan], dtype=float)
        assert_series_equal(result, expected)

        data[0] = 0
        data[2] = 2
        index = ['a', 'b', 'c']
        result = Series(data, index=index)
        expected = Series([0, nan, 2], index=index, dtype=float)
        assert_series_equal(result, expected)

        data[1] = 1
        result = Series(data, index=index)
        expected = Series([0, 1, 2], index=index, dtype=int)
        assert_series_equal(result, expected)

        data = ma.masked_all((3, ), dtype=bool)
        result = Series(data)
        expected = Series([nan, nan, nan], dtype=object)
        assert_series_equal(result, expected)

        data[0] = True
        data[2] = False
        index = ['a', 'b', 'c']
        result = Series(data, index=index)
        expected = Series([True, nan, False], index=index, dtype=object)
        assert_series_equal(result, expected)

        data[1] = True
        result = Series(data, index=index)
        expected = Series([True, True, False], index=index, dtype=bool)
        assert_series_equal(result, expected)

        data = ma.masked_all((3, ), dtype='M8[ns]')
        result = Series(data)
        expected = Series([iNaT, iNaT, iNaT], dtype='M8[ns]')
        assert_series_equal(result, expected)

        data[0] = datetime(2001, 1, 1)
        data[2] = datetime(2001, 1, 3)
        index = ['a', 'b', 'c']
        result = Series(data, index=index)
        expected = Series([datetime(2001, 1, 1), iNaT,
                           datetime(2001, 1, 3)], index=index, dtype='M8[ns]')
        assert_series_equal(result, expected)

        data[1] = datetime(2001, 1, 2)
        result = Series(data, index=index)
        expected = Series([datetime(2001, 1, 1), datetime(2001, 1, 2),
                           datetime(2001, 1, 3)], index=index, dtype='M8[ns]')
        assert_series_equal(result, expected)

    def test_series_ctor_plus_datetimeindex(self):
        rng = date_range('20090415', '20090519', freq='B')
        data = {k: 1 for k in rng}

        result = Series(data, index=rng)
        assert result.index is rng

    def test_constructor_default_index(self):
        s = Series([0, 1, 2])
        tm.assert_index_equal(s.index, pd.Index(np.arange(3)))

    @pytest.mark.parametrize('input', [[1, 2, 3],
                                       (1, 2, 3),
                                       list(range(3)),
                                       pd.Categorical(['a', 'b', 'a']),
                                       (i for i in range(3)),
                                       map(lambda x: x, range(3))])
    def test_constructor_index_mismatch(self, input):
        # GH 19342
        # test that construction of a Series with an index of different length
        # raises an error
        msg = 'Length of passed values is 3, index implies 4'
        with pytest.raises(ValueError, match=msg):
            Series(input, index=np.arange(4))

    def test_constructor_numpy_scalar(self):
        # GH 19342
        # construction with a numpy scalar
        # should not raise
        result = Series(np.array(100), index=np.arange(4), dtype='int64')
        expected = Series(100, index=np.arange(4), dtype='int64')
        tm.assert_series_equal(result, expected)

    def test_constructor_broadcast_list(self):
        # GH 19342
        # construction with single-element container and index
        # should raise
        pytest.raises(ValueError, Series, ['foo'], index=['a', 'b', 'c'])

    def test_constructor_corner(self):
        df = tm.makeTimeDataFrame()
        objs = [df, df]
        s = Series(objs, index=[0, 1])
        assert isinstance(s, Series)

    def test_constructor_sanitize(self):
        s = Series(np.array([1., 1., 8.]), dtype='i8')
        assert s.dtype == np.dtype('i8')

        s = Series(np.array([1., 1., np.nan]), copy=True, dtype='i8')
        assert s.dtype == np.dtype('f8')

    def test_constructor_copy(self):
        # GH15125
        # test dtype parameter has no side effects on copy=True
        for data in [[1.], np.array([1.])]:
            x = Series(data)
            y = pd.Series(x, copy=True, dtype=float)

            # copy=True maintains original data in Series
            tm.assert_series_equal(x, y)

            # changes to origin of copy does not affect the copy
            x[0] = 2.
            assert not x.equals(y)
            assert x[0] == 2.
            assert y[0] == 1.

    @pytest.mark.parametrize(
        "index",
        [
            pd.date_range('20170101', periods=3, tz='US/Eastern'),
            pd.date_range('20170101', periods=3),
            pd.timedelta_range('1 day', periods=3),
            pd.period_range('2012Q1', periods=3, freq='Q'),
            pd.Index(list('abc')),
            pd.Int64Index([1, 2, 3]),
            pd.RangeIndex(0, 3)],
        ids=lambda x: type(x).__name__)
    def test_constructor_limit_copies(self, index):
        # GH 17449
        # limit copies of input
        s = pd.Series(index)

        # we make 1 copy; this is just a smoke test here
        assert s._data.blocks[0].values is not index

    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)

    def test_constructor_pass_nan_nat(self):
        # GH 13467
        exp = Series([np.nan, np.nan], dtype=np.float64)
        assert exp.dtype == np.float64
        tm.assert_series_equal(Series([np.nan, np.nan]), exp)
        tm.assert_series_equal(Series(np.array([np.nan, np.nan])), exp)

        exp = Series([pd.NaT, pd.NaT])
        assert exp.dtype == 'datetime64[ns]'
        tm.assert_series_equal(Series([pd.NaT, pd.NaT]), exp)
        tm.assert_series_equal(Series(np.array([pd.NaT, pd.NaT])), exp)

        tm.assert_series_equal(Series([pd.NaT, np.nan]), exp)
        tm.assert_series_equal(Series(np.array([pd.NaT, np.nan])), exp)

        tm.assert_series_equal(Series([np.nan, pd.NaT]), exp)
        tm.assert_series_equal(Series(np.array([np.nan, pd.NaT])), exp)

    def test_constructor_cast(self):
        msg = "could not convert string to float"
        with pytest.raises(ValueError, match=msg):
            Series(["a", "b", "c"], dtype=float)

    def test_constructor_unsigned_dtype_overflow(self, uint_dtype):
        # see gh-15832
        msg = 'Trying to coerce negative values to unsigned integers'
        with pytest.raises(OverflowError, match=msg):
            Series([-1], dtype=uint_dtype)

    def test_constructor_coerce_float_fail(self, any_int_dtype):
        # see gh-15832
        msg = "Trying to coerce float values to integers"
        with pytest.raises(ValueError, match=msg):
            Series([1, 2, 3.5], dtype=any_int_dtype)

    def test_constructor_coerce_float_valid(self, float_dtype):
        s = Series([1, 2, 3.5], dtype=float_dtype)
        expected = Series([1, 2, 3.5]).astype(float_dtype)
        assert_series_equal(s, expected)

    def test_constructor_dtype_no_cast(self):
        # see gh-1572
        s = Series([1, 2, 3])
        s2 = Series(s, dtype=np.int64)

        s2[1] = 5
        assert s[1] == 5

    def test_constructor_datelike_coercion(self):

        # GH 9477
        # incorrectly inferring on dateimelike looking when object dtype is
        # specified
        s = Series([Timestamp('20130101'), 'NOV'], dtype=object)
        assert s.iloc[0] == Timestamp('20130101')
        assert s.iloc[1] == 'NOV'
        assert s.dtype == object

        # the dtype was being reset on the slicing and re-inferred to datetime
        # even thought the blocks are mixed
        belly = '216 3T19'.split()
        wing1 = '2T15 4H19'.split()
        wing2 = '416 4T20'.split()
        mat = pd.to_datetime('2016-01-22 2019-09-07'.split())
        df = pd.DataFrame(
            {'wing1': wing1,
             'wing2': wing2,
             'mat': mat}, index=belly)

        result = df.loc['3T19']
        assert result.dtype == object
        result = df.loc['216']
        assert result.dtype == object

    def test_constructor_datetimes_with_nulls(self):
        # gh-15869
        for arr in [np.array([None, None, None, None,
                              datetime.now(), None]),
                    np.array([None, None, datetime.now(), None])]:
            result = Series(arr)
            assert result.dtype == 'M8[ns]'

    def test_constructor_dtype_datetime64(self):

        s = Series(iNaT, dtype='M8[ns]', index=lrange(5))
        assert isna(s).all()

        # in theory this should be all nulls, but since
        # we are not specifying a dtype is ambiguous
        s = Series(iNaT, index=lrange(5))
        assert not isna(s).all()

        s = Series(nan, dtype='M8[ns]', index=lrange(5))
        assert isna(s).all()

        s = Series([datetime(2001, 1, 2, 0, 0), iNaT], dtype='M8[ns]')
        assert isna(s[1])
        assert s.dtype == 'M8[ns]'

        s = Series([datetime(2001, 1, 2, 0, 0), nan], dtype='M8[ns]')
        assert isna(s[1])
        assert s.dtype == 'M8[ns]'

        # GH3416
        dates = [
            np.datetime64(datetime(2013, 1, 1)),
            np.datetime64(datetime(2013, 1, 2)),
            np.datetime64(datetime(2013, 1, 3)),
        ]

        s = Series(dates)
        assert s.dtype == 'M8[ns]'

        s.iloc[0] = np.nan
        assert s.dtype == 'M8[ns]'

        # GH3414 related
        pytest.raises(TypeError, lambda x: Series(
            Series(dates).astype('int') / 1000000, dtype='M8[ms]'))
        pytest.raises(TypeError,
                      lambda x: Series(dates, dtype='datetime64'))

        # invalid dates can be help as object
        result = Series([datetime(2, 1, 1)])
        assert result[0] == datetime(2, 1, 1, 0, 0)

        result = Series([datetime(3000, 1, 1)])
        assert result[0] == datetime(3000, 1, 1, 0, 0)

        # don't mix types
        result = Series([Timestamp('20130101'), 1], index=['a', 'b'])
        assert result['a'] == Timestamp('20130101')
        assert result['b'] == 1

        # GH6529
        # coerce datetime64 non-ns properly
        dates = date_range('01-Jan-2015', '01-Dec-2015', freq='M')
        values2 = dates.view(np.ndarray).astype('datetime64[ns]')
        expected = Series(values2, index=dates)

        for dtype in ['s', 'D', 'ms', 'us', 'ns']:
            values1 = dates.view(np.ndarray).astype('M8[{0}]'.format(dtype))
            result = Series(values1, dates)
            assert_series_equal(result, expected)

        # GH 13876
        # coerce to non-ns to object properly
        expected = Series(values2, index=dates, dtype=object)
        for dtype in ['s', 'D', 'ms', 'us', 'ns']:
            values1 = dates.view(np.ndarray).astype('M8[{0}]'.format(dtype))
            result = Series(values1, index=dates, dtype=object)
            assert_series_equal(result, expected)

        # leave datetime.date alone
        dates2 = np.array([d.date() for d in dates.to_pydatetime()],
                          dtype=object)
        series1 = Series(dates2, dates)
        tm.assert_numpy_array_equal(series1.values, dates2)
        assert series1.dtype == object

        # these will correctly infer a datetime
        s = Series([None, pd.NaT, '2013-08-05 15:30:00.000001'])
        assert s.dtype == 'datetime64[ns]'
        s = Series([np.nan, pd.NaT, '2013-08-05 15:30:00.000001'])
        assert s.dtype == 'datetime64[ns]'
        s = Series([pd.NaT, None, '2013-08-05 15:30:00.000001'])
        assert s.dtype == 'datetime64[ns]'
        s = Series([pd.NaT, np.nan, '2013-08-05 15:30:00.000001'])
        assert s.dtype == 'datetime64[ns]'

        # tz-aware (UTC and other tz's)
        # GH 8411
        dr = date_range('20130101', periods=3)
        assert Series(dr).iloc[0].tz is None
        dr = date_range('20130101', periods=3, tz='UTC')
        assert str(Series(dr).iloc[0].tz) == 'UTC'
        dr = date_range('20130101', periods=3, tz='US/Eastern')
        assert str(Series(dr).iloc[0].tz) == 'US/Eastern'

        # non-convertible
        s = Series([1479596223000, -1479590, pd.NaT])
        assert s.dtype == 'object'
        assert s[2] is pd.NaT
        assert 'NaT' in str(s)

        # if we passed a NaT it remains
        s = Series([datetime(2010, 1, 1), datetime(2, 1, 1), pd.NaT])
        assert s.dtype == 'object'
        assert s[2] is pd.NaT
        assert 'NaT' in str(s)

        # if we passed a nan it remains
        s = Series([datetime(2010, 1, 1), datetime(2, 1, 1), np.nan])
        assert s.dtype == 'object'
        assert s[2] is np.nan
        assert 'NaN' in str(s)

    def test_constructor_with_datetime_tz(self):

        # 8260
        # support datetime64 with tz

        dr = date_range('20130101', periods=3, tz='US/Eastern')
        s = Series(dr)
        assert s.dtype.name == 'datetime64[ns, US/Eastern]'
        assert s.dtype == 'datetime64[ns, US/Eastern]'
        assert is_datetime64tz_dtype(s.dtype)
        assert 'datetime64[ns, US/Eastern]' in str(s)

        # export
        result = s.values
        assert isinstance(result, np.ndarray)
        assert result.dtype == 'datetime64[ns]'

        exp = pd.DatetimeIndex(result)
        exp = exp.tz_localize('UTC').tz_convert(tz=s.dt.tz)
        tm.assert_index_equal(dr, exp)

        # indexing
        result = s.iloc[0]
        assert result == Timestamp('2013-01-01 00:00:00-0500',
                                   tz='US/Eastern', freq='D')
        result = s[0]
        assert result == Timestamp('2013-01-01 00:00:00-0500',
                                   tz='US/Eastern', freq='D')

        result = s[Series([True, True, False], index=s.index)]
        assert_series_equal(result, s[0:2])

        result = s.iloc[0:1]
        assert_series_equal(result, Series(dr[0:1]))

        # concat
        result = pd.concat([s.iloc[0:1], s.iloc[1:]])
        assert_series_equal(result, s)

        # short str
        assert 'datetime64[ns, US/Eastern]' in str(s)

        # formatting with NaT
        result = s.shift()
        assert 'datetime64[ns, US/Eastern]' in str(result)
        assert 'NaT' in str(result)

        # long str
        t = Series(date_range('20130101', periods=1000, tz='US/Eastern'))
        assert 'datetime64[ns, US/Eastern]' in str(t)

        result = pd.DatetimeIndex(s, freq='infer')
        tm.assert_index_equal(result, dr)

        # inference
        s = Series([pd.Timestamp('2013-01-01 13:00:00-0800', tz='US/Pacific'),
                    pd.Timestamp('2013-01-02 14:00:00-0800', tz='US/Pacific')])
        assert s.dtype == 'datetime64[ns, US/Pacific]'
        assert lib.infer_dtype(s) == 'datetime64'

        s = Series([pd.Timestamp('2013-01-01 13:00:00-0800', tz='US/Pacific'),
                    pd.Timestamp('2013-01-02 14:00:00-0800', tz='US/Eastern')])
        assert s.dtype == 'object'
        assert lib.infer_dtype(s) == 'datetime'

        # with all NaT
        s = Series(pd.NaT, index=[0, 1], dtype='datetime64[ns, US/Eastern]')
        expected = Series(pd.DatetimeIndex(['NaT', 'NaT'], tz='US/Eastern'))
        assert_series_equal(s, expected)

    @pytest.mark.parametrize("arr_dtype", [np.int64, np.float64])
    @pytest.mark.parametrize("dtype", ["M8", "m8"])
    @pytest.mark.parametrize("unit", ['ns', 'us', 'ms', 's', 'h', 'm', 'D'])
    def test_construction_to_datetimelike_unit(self, arr_dtype, dtype, unit):
        # tests all units
        # gh-19223
        dtype = "{}[{}]".format(dtype, unit)
        arr = np.array([1, 2, 3], dtype=arr_dtype)
        s = Series(arr)
        result = s.astype(dtype)
        expected = Series(arr.astype(dtype))

        tm.assert_series_equal(result, expected)

    @pytest.mark.parametrize('arg',
                             ['2013-01-01 00:00:00', pd.NaT, np.nan, None])
    def test_constructor_with_naive_string_and_datetimetz_dtype(self, arg):
        # GH 17415: With naive string
        result = Series([arg], dtype='datetime64[ns, CET]')
        expected = Series(pd.Timestamp(arg)).dt.tz_localize('CET')
        assert_series_equal(result, expected)

    def test_construction_interval(self):
        # construction from interval & array of intervals
        index = IntervalIndex.from_breaks(np.arange(3), closed='right')
        result = Series(index)
        repr(result)
        str(result)
        tm.assert_index_equal(Index(result.values), index)

        result = Series(index.values)
        tm.assert_index_equal(Index(result.values), index)

    def test_construction_consistency(self):

        # make sure that we are not re-localizing upon construction
        # GH 14928
        s = Series(pd.date_range('20130101', periods=3, tz='US/Eastern'))

        result = Series(s, dtype=s.dtype)
        tm.assert_series_equal(result, s)

        result = Series(s.dt.tz_convert('UTC'), dtype=s.dtype)
        tm.assert_series_equal(result, s)

        result = Series(s.values, dtype=s.dtype)
        tm.assert_series_equal(result, s)

    def test_constructor_infer_period(self):
        data = [pd.Period('2000', 'D'), pd.Period('2001', 'D'), None]
        result = pd.Series(data)
        expected = pd.Series(period_array(data))
        tm.assert_series_equal(result, expected)
        assert result.dtype == 'Period[D]'

        data = np.asarray(data, dtype=object)
        tm.assert_series_equal(result, expected)
        assert result.dtype == 'Period[D]'

    def test_constructor_period_incompatible_frequency(self):
        data = [pd.Period('2000', 'D'), pd.Period('2001', 'A')]
        result = pd.Series(data)
        assert result.dtype == object
        assert result.tolist() == data

    def test_constructor_periodindex(self):
        # GH7932
        # converting a PeriodIndex when put in a Series

        pi = period_range('20130101', periods=5, freq='D')
        s = Series(pi)
        assert s.dtype == 'Period[D]'
        expected = Series(pi.astype(object))
        assert_series_equal(s, expected)

    def test_constructor_dict(self):
        d = {'a': 0., 'b': 1., 'c': 2.}
        result = Series(d, index=['b', 'c', 'd', 'a'])
        expected = Series([1, 2, nan, 0], index=['b', 'c', 'd', 'a'])
        assert_series_equal(result, expected)

        pidx = tm.makePeriodIndex(100)
        d = {pidx[0]: 0, pidx[1]: 1}
        result = Series(d, index=pidx)
        expected = Series(np.nan, pidx)
        expected.iloc[0] = 0
        expected.iloc[1] = 1
        assert_series_equal(result, expected)

    def test_constructor_dict_order(self):
        # GH19018
        # initialization ordering: by insertion order if python>= 3.6, else
        # order by value
        d = {'b': 1, 'a': 0, 'c': 2}
        result = Series(d)
        if PY36:
            expected = Series([1, 0, 2], index=list('bac'))
        else:
            expected = Series([0, 1, 2], index=list('abc'))
        tm.assert_series_equal(result, expected)

    @pytest.mark.parametrize("value", [2, np.nan, None, float('nan')])
    def test_constructor_dict_nan_key(self, value):
        # GH 18480
        d = {1: 'a', value: 'b', float('nan'): 'c', 4: 'd'}
        result = Series(d).sort_values()
        expected = Series(['a', 'b', 'c', 'd'], index=[1, value, np.nan, 4])
        assert_series_equal(result, expected)

        # MultiIndex:
        d = {(1, 1): 'a', (2, np.nan): 'b', (3, value): 'c'}
        result = Series(d).sort_values()
        expected = Series(['a', 'b', 'c'],
                          index=Index([(1, 1), (2, np.nan), (3, value)]))
        assert_series_equal(result, expected)

    def test_constructor_dict_datetime64_index(self):
        # GH 9456

        dates_as_str = ['1984-02-19', '1988-11-06', '1989-12-03', '1990-03-15']
        values = [42544017.198965244, 1234565, 40512335.181958228, -1]

        def create_data(constructor):
            return dict(zip((constructor(x) for x in dates_as_str), values))

        data_datetime64 = create_data(np.datetime64)
        data_datetime = create_data(lambda x: datetime.strptime(x, '%Y-%m-%d'))
        data_Timestamp = create_data(Timestamp)

        expected = Series(values, (Timestamp(x) for x in dates_as_str))

        result_datetime64 = Series(data_datetime64)
        result_datetime = Series(data_datetime)
        result_Timestamp = Series(data_Timestamp)

        assert_series_equal(result_datetime64, expected)
        assert_series_equal(result_datetime, expected)
        assert_series_equal(result_Timestamp, expected)

    def test_constructor_list_of_tuples(self):
        data = [(1, 1), (2, 2), (2, 3)]
        s = Series(data)
        assert list(s) == data

    def test_constructor_tuple_of_tuples(self):
        data = ((1, 1), (2, 2), (2, 3))
        s = Series(data)
        assert tuple(s) == data

    def test_constructor_dict_of_tuples(self):
        data = {(1, 2): 3,
                (None, 5): 6}
        result = Series(data).sort_values()
        expected = Series([3, 6],
                          index=MultiIndex.from_tuples([(1, 2), (None, 5)]))
        tm.assert_series_equal(result, expected)

    def test_constructor_set(self):
        values = {1, 2, 3, 4, 5}
        pytest.raises(TypeError, Series, values)
        values = frozenset(values)
        pytest.raises(TypeError, Series, values)

    # https://github.com/pandas-dev/pandas/issues/22698
    @pytest.mark.filterwarnings("ignore:elementwise comparison:FutureWarning")
    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

    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)

    def test_constructor_dtype_timedelta64(self):

        # basic
        td = Series([timedelta(days=i) for i in range(3)])
        assert td.dtype == 'timedelta64[ns]'

        td = Series([timedelta(days=1)])
        assert td.dtype == 'timedelta64[ns]'

        td = Series([timedelta(days=1), timedelta(days=2), np.timedelta64(
            1, 's')])

        assert td.dtype == 'timedelta64[ns]'

        # mixed with NaT
        td = Series([timedelta(days=1), NaT], dtype='m8[ns]')
        assert td.dtype == 'timedelta64[ns]'

        td = Series([timedelta(days=1), np.nan], dtype='m8[ns]')
        assert td.dtype == 'timedelta64[ns]'

        td = Series([np.timedelta64(300000000), pd.NaT], dtype='m8[ns]')
        assert td.dtype == 'timedelta64[ns]'

        # improved inference
        # GH5689
        td = Series([np.timedelta64(300000000), NaT])
        assert td.dtype == 'timedelta64[ns]'

        # because iNaT is int, not coerced to timedelta
        td = Series([np.timedelta64(300000000), iNaT])
        assert td.dtype == 'object'

        td = Series([np.timedelta64(300000000), np.nan])
        assert td.dtype == 'timedelta64[ns]'

        td = Series([pd.NaT, np.timedelta64(300000000)])
        assert td.dtype == 'timedelta64[ns]'

        td = Series([np.timedelta64(1, 's')])
        assert td.dtype == 'timedelta64[ns]'

        # these are frequency conversion astypes
        # for t in ['s', 'D', 'us', 'ms']:
        #    pytest.raises(TypeError, td.astype, 'm8[%s]' % t)

        # valid astype
        td.astype('int64')

        # invalid casting
        pytest.raises(TypeError, td.astype, 'int32')

        # this is an invalid casting
        def f():
            Series([timedelta(days=1), 'foo'], dtype='m8[ns]')

        pytest.raises(Exception, f)

        # leave as object here
        td = Series([timedelta(days=i) for i in range(3)] + ['foo'])
        assert td.dtype == 'object'

        # these will correctly infer a timedelta
        s = Series([None, pd.NaT, '1 Day'])
        assert s.dtype == 'timedelta64[ns]'
        s = Series([np.nan, pd.NaT, '1 Day'])
        assert s.dtype == 'timedelta64[ns]'
        s = Series([pd.NaT, None, '1 Day'])
        assert s.dtype == 'timedelta64[ns]'
        s = Series([pd.NaT, np.nan, '1 Day'])
        assert s.dtype == 'timedelta64[ns]'

    # GH 16406
    def test_constructor_mixed_tz(self):
        s = Series([Timestamp('20130101'),
                    Timestamp('20130101', tz='US/Eastern')])
        expected = Series([Timestamp('20130101'),
                           Timestamp('20130101', tz='US/Eastern')],
                          dtype='object')
        assert_series_equal(s, expected)

    def test_NaT_scalar(self):
        series = Series([0, 1000, 2000, iNaT], dtype='M8[ns]')

        val = series[3]
        assert isna(val)

        series[2] = val
        assert isna(series[2])

    def test_NaT_cast(self):
        # GH10747
        result = Series([np.nan]).astype('M8[ns]')
        expected = Series([NaT])
        assert_series_equal(result, expected)

    def test_constructor_name_hashable(self):
        for n in [777, 777., 'name', datetime(2001, 11, 11), (1, ), u"\u05D0"]:
            for data in [[1, 2, 3], np.ones(3), {'a': 0, 'b': 1}]:
                s = Series(data, name=n)
                assert s.name == n

    def test_constructor_name_unhashable(self):
        for n in [['name_list'], np.ones(2), {1: 2}]:
            for data in [['name_list'], np.ones(2), {1: 2}]:
                pytest.raises(TypeError, Series, data, name=n)

    def test_auto_conversion(self):
        series = Series(list(date_range('1/1/2000', periods=10)))
        assert series.dtype == 'M8[ns]'

    def test_convert_non_ns(self):
        # convert from a numpy array of non-ns timedelta64
        arr = np.array([1, 2, 3], dtype='timedelta64[s]')
        s = Series(arr)
        expected = Series(pd.timedelta_range('00:00:01', periods=3, freq='s'))
        assert_series_equal(s, expected)

        # convert from a numpy array of non-ns datetime64
        # note that creating a numpy datetime64 is in LOCAL time!!!!
        # seems to work for M8[D], but not for M8[s]

        s = Series(np.array(['2013-01-01', '2013-01-02',
                             '2013-01-03'], dtype='datetime64[D]'))
        assert_series_equal(s, Series(date_range('20130101', periods=3,
                                                 freq='D')))

        # s = Series(np.array(['2013-01-01 00:00:01','2013-01-01
        # 00:00:02','2013-01-01 00:00:03'],dtype='datetime64[s]'))

        # assert_series_equal(s,date_range('20130101
        # 00:00:01',period=3,freq='s'))

    @pytest.mark.parametrize(
        "index",
        [
            date_range('1/1/2000', periods=10),
            timedelta_range('1 day', periods=10),
            period_range('2000-Q1', periods=10, freq='Q')],
        ids=lambda x: type(x).__name__)
    def test_constructor_cant_cast_datetimelike(self, index):

        # floats are not ok
        msg = "Cannot cast {}.*? to ".format(
            # strip Index to convert PeriodIndex -> Period
            # We don't care whether the error message says
            # PeriodIndex or PeriodArray
            type(index).__name__.rstrip("Index")
        )
        with pytest.raises(TypeError, match=msg):
            Series(index, dtype=float)

        # ints are ok
        # we test with np.int64 to get similar results on
        # windows / 32-bit platforms
        result = Series(index, dtype=np.int64)
        expected = Series(index.astype(np.int64))
        tm.assert_series_equal(result, expected)

    @pytest.mark.parametrize(
        "index",
        [
            date_range('1/1/2000', periods=10),
            timedelta_range('1 day', periods=10),
            period_range('2000-Q1', periods=10, freq='Q')],
        ids=lambda x: type(x).__name__)
    def test_constructor_cast_object(self, index):
        s = Series(index, dtype=object)
        exp = Series(index).astype(object)
        tm.assert_series_equal(s, exp)

        s = Series(pd.Index(index, dtype=object), dtype=object)
        exp = Series(index).astype(object)
        tm.assert_series_equal(s, exp)

        s = Series(index.astype(object), dtype=object)
        exp = Series(index).astype(object)
        tm.assert_series_equal(s, exp)

    @pytest.mark.parametrize("dtype", [
        np.datetime64,
        np.timedelta64,
    ])
    def test_constructor_generic_timestamp_no_frequency(self, dtype):
        # see gh-15524, gh-15987
        msg = "dtype has no unit. Please pass in"

        with pytest.raises(ValueError, match=msg):
            Series([], dtype=dtype)

    @pytest.mark.parametrize("dtype,msg", [
        ("m8[ps]", "cannot convert timedeltalike"),
        ("M8[ps]", "cannot convert datetimelike"),
    ])
    def test_constructor_generic_timestamp_bad_frequency(self, dtype, msg):
        # see gh-15524, gh-15987

        with pytest.raises(TypeError, match=msg):
            Series([], dtype=dtype)

    @pytest.mark.parametrize('dtype', [None, 'uint8', 'category'])
    def test_constructor_range_dtype(self, dtype):
        # GH 16804
        expected = Series([0, 1, 2, 3, 4], dtype=dtype or 'int64')
        result = Series(range(5), dtype=dtype)
        tm.assert_series_equal(result, expected)

    def test_constructor_tz_mixed_data(self):
        # GH 13051
        dt_list = [Timestamp('2016-05-01 02:03:37'),
                   Timestamp('2016-04-30 19:03:37-0700', tz='US/Pacific')]
        result = Series(dt_list)
        expected = Series(dt_list, dtype=object)
        tm.assert_series_equal(result, expected)