# -*- coding: utf-8 -*-

from __future__ import print_function

from datetime import datetime, timedelta
import functools
import itertools

import nose

from numpy.random import randn

import numpy as np
import numpy.ma as ma
import numpy.ma.mrecords as mrecords

from pandas.types.common import is_integer_dtype
from pandas.compat import (lmap, long, zip, range, lrange, lzip,
                           OrderedDict, is_platform_little_endian)
from pandas import compat
from pandas import (DataFrame, Index, Series, isnull,
                    MultiIndex, Timedelta, Timestamp,
                    date_range)
from pandas.core.common import PandasError
import pandas as pd
import pandas.core.common as com
import pandas.lib as lib
import pandas.util.testing as tm

from pandas.tests.frame.common import TestData


MIXED_FLOAT_DTYPES = ['float16', 'float32', 'float64']
MIXED_INT_DTYPES = ['uint8', 'uint16', 'uint32', 'uint64', 'int8', 'int16',
                    'int32', 'int64']


class TestDataFrameConstructors(tm.TestCase, TestData):

    _multiprocess_can_split_ = True

    def test_constructor(self):
        df = DataFrame()
        self.assertEqual(len(df.index), 0)

        df = DataFrame(data={})
        self.assertEqual(len(df.index), 0)

    def test_constructor_mixed(self):
        index, data = tm.getMixedTypeDict()

        # TODO(wesm), incomplete test?
        indexed_frame = DataFrame(data, index=index)  # noqa
        unindexed_frame = DataFrame(data)  # noqa

        self.assertEqual(self.mixed_frame['foo'].dtype, np.object_)

    def test_constructor_cast_failure(self):
        foo = DataFrame({'a': ['a', 'b', 'c']}, dtype=np.float64)
        self.assertEqual(foo['a'].dtype, object)

        # GH 3010, constructing with odd arrays
        df = DataFrame(np.ones((4, 2)))

        # this is ok
        df['foo'] = np.ones((4, 2)).tolist()

        # this is not ok
        self.assertRaises(ValueError, df.__setitem__, tuple(['test']),
                          np.ones((4, 2)))

        # this is ok
        df['foo2'] = np.ones((4, 2)).tolist()

    def test_constructor_dtype_copy(self):
        orig_df = DataFrame({
            'col1': [1.],
            'col2': [2.],
            'col3': [3.]})

        new_df = pd.DataFrame(orig_df, dtype=float, copy=True)

        new_df['col1'] = 200.
        self.assertEqual(orig_df['col1'][0], 1.)

    def test_constructor_dtype_nocast_view(self):
        df = DataFrame([[1, 2]])
        should_be_view = DataFrame(df, dtype=df[0].dtype)
        should_be_view[0][0] = 99
        self.assertEqual(df.values[0, 0], 99)

        should_be_view = DataFrame(df.values, dtype=df[0].dtype)
        should_be_view[0][0] = 97
        self.assertEqual(df.values[0, 0], 97)

    def test_constructor_dtype_list_data(self):
        df = DataFrame([[1, '2'],
                        [None, 'a']], dtype=object)
        self.assertIsNone(df.ix[1, 0])
        self.assertEqual(df.ix[0, 1], '2')

    def test_constructor_list_frames(self):

        # GH 3243
        result = DataFrame([DataFrame([])])
        self.assertEqual(result.shape, (1, 0))

        result = DataFrame([DataFrame(dict(A=lrange(5)))])
        tm.assertIsInstance(result.iloc[0, 0], DataFrame)

    def test_constructor_mixed_dtypes(self):

        def _make_mixed_dtypes_df(typ, ad=None):

            if typ == 'int':
                dtypes = MIXED_INT_DTYPES
                arrays = [np.array(np.random.rand(10), dtype=d)
                          for d in dtypes]
            elif typ == 'float':
                dtypes = MIXED_FLOAT_DTYPES
                arrays = [np.array(np.random.randint(
                    10, size=10), dtype=d) for d in dtypes]

            zipper = lzip(dtypes, arrays)
            for d, a in zipper:
                assert(a.dtype == d)
            if ad is None:
                ad = dict()
            ad.update(dict([(d, a) for d, a in zipper]))
            return DataFrame(ad)

        def _check_mixed_dtypes(df, dtypes=None):
            if dtypes is None:
                dtypes = MIXED_FLOAT_DTYPES + MIXED_INT_DTYPES
            for d in dtypes:
                if d in df:
                    assert(df.dtypes[d] == d)

        # mixed floating and integer coexinst in the same frame
        df = _make_mixed_dtypes_df('float')
        _check_mixed_dtypes(df)

        # add lots of types
        df = _make_mixed_dtypes_df('float', dict(A=1, B='foo', C='bar'))
        _check_mixed_dtypes(df)

        # GH 622
        df = _make_mixed_dtypes_df('int')
        _check_mixed_dtypes(df)

    def test_constructor_complex_dtypes(self):
        # GH10952
        a = np.random.rand(10).astype(np.complex64)
        b = np.random.rand(10).astype(np.complex128)

        df = DataFrame({'a': a, 'b': b})
        self.assertEqual(a.dtype, df.a.dtype)
        self.assertEqual(b.dtype, df.b.dtype)

    def test_constructor_rec(self):
        rec = self.frame.to_records(index=False)

        # Assigning causes segfault in NumPy < 1.5.1
        # rec.dtype.names = list(rec.dtype.names)[::-1]

        index = self.frame.index

        df = DataFrame(rec)
        self.assert_index_equal(df.columns, pd.Index(rec.dtype.names))

        df2 = DataFrame(rec, index=index)
        self.assert_index_equal(df2.columns, pd.Index(rec.dtype.names))
        self.assert_index_equal(df2.index, index)

        rng = np.arange(len(rec))[::-1]
        df3 = DataFrame(rec, index=rng, columns=['C', 'B'])
        expected = DataFrame(rec, index=rng).reindex(columns=['C', 'B'])
        tm.assert_frame_equal(df3, expected)

    def test_constructor_bool(self):
        df = DataFrame({0: np.ones(10, dtype=bool),
                        1: np.zeros(10, dtype=bool)})
        self.assertEqual(df.values.dtype, np.bool_)

    def test_constructor_overflow_int64(self):
        values = np.array([2 ** 64 - i for i in range(1, 10)],
                          dtype=np.uint64)

        result = DataFrame({'a': values})
        self.assertEqual(result['a'].dtype, object)

        # #2355
        data_scores = [(6311132704823138710, 273), (2685045978526272070, 23),
                       (8921811264899370420, 45),
                       (long(17019687244989530680), 270),
                       (long(9930107427299601010), 273)]
        dtype = [('uid', 'u8'), ('score', 'u8')]
        data = np.zeros((len(data_scores),), dtype=dtype)
        data[:] = data_scores
        df_crawls = DataFrame(data)
        self.assertEqual(df_crawls['uid'].dtype, object)

    def test_constructor_ordereddict(self):
        import random
        nitems = 100
        nums = lrange(nitems)
        random.shuffle(nums)
        expected = ['A%d' % i for i in nums]
        df = DataFrame(OrderedDict(zip(expected, [[0]] * nitems)))
        self.assertEqual(expected, list(df.columns))

    def test_constructor_dict(self):
        frame = DataFrame({'col1': self.ts1,
                           'col2': self.ts2})

        # col2 is padded with NaN
        self.assertEqual(len(self.ts1), 30)
        self.assertEqual(len(self.ts2), 25)

        tm.assert_series_equal(self.ts1, frame['col1'], check_names=False)

        exp = pd.Series(np.concatenate([[np.nan] * 5, self.ts2.values]),
                        index=self.ts1.index, name='col2')
        tm.assert_series_equal(exp, frame['col2'])

        frame = DataFrame({'col1': self.ts1,
                           'col2': self.ts2},
                          columns=['col2', 'col3', 'col4'])

        self.assertEqual(len(frame), len(self.ts2))
        self.assertNotIn('col1', frame)
        self.assertTrue(isnull(frame['col3']).all())

        # Corner cases
        self.assertEqual(len(DataFrame({})), 0)

        # mix dict and array, wrong size - no spec for which error should raise
        # first
        with tm.assertRaises(ValueError):
            DataFrame({'A': {'a': 'a', 'b': 'b'}, 'B': ['a', 'b', 'c']})

        # Length-one dict micro-optimization
        frame = DataFrame({'A': {'1': 1, '2': 2}})
        self.assert_index_equal(frame.index, pd.Index(['1', '2']))

        # empty dict plus index
        idx = Index([0, 1, 2])
        frame = DataFrame({}, index=idx)
        self.assertIs(frame.index, idx)

        # empty with index and columns
        idx = Index([0, 1, 2])
        frame = DataFrame({}, index=idx, columns=idx)
        self.assertIs(frame.index, idx)
        self.assertIs(frame.columns, idx)
        self.assertEqual(len(frame._series), 3)

        # with dict of empty list and Series
        frame = DataFrame({'A': [], 'B': []}, columns=['A', 'B'])
        self.assert_index_equal(frame.index, Index([], dtype=np.int64))

        # GH 14381
        # Dict with None value
        frame_none = DataFrame(dict(a=None), index=[0])
        frame_none_list = DataFrame(dict(a=[None]), index=[0])
        tm.assert_equal(frame_none.get_value(0, 'a'), None)
        tm.assert_equal(frame_none_list.get_value(0, 'a'), None)
        tm.assert_frame_equal(frame_none, frame_none_list)

        # GH10856
        # dict with scalar values should raise error, even if columns passed
        with tm.assertRaises(ValueError):
            DataFrame({'a': 0.7})

        with tm.assertRaises(ValueError):
            DataFrame({'a': 0.7}, columns=['a'])

        with tm.assertRaises(ValueError):
            DataFrame({'a': 0.7}, columns=['b'])

    def test_constructor_multi_index(self):
        # GH 4078
        # construction error with mi and all-nan frame
        tuples = [(2, 3), (3, 3), (3, 3)]
        mi = MultiIndex.from_tuples(tuples)
        df = DataFrame(index=mi, columns=mi)
        self.assertTrue(pd.isnull(df).values.ravel().all())

        tuples = [(3, 3), (2, 3), (3, 3)]
        mi = MultiIndex.from_tuples(tuples)
        df = DataFrame(index=mi, columns=mi)
        self.assertTrue(pd.isnull(df).values.ravel().all())

    def test_constructor_error_msgs(self):
        msg = "Empty data passed with indices specified."
        # passing an empty array with columns specified.
        with tm.assertRaisesRegexp(ValueError, msg):
            DataFrame(np.empty(0), columns=list('abc'))

        msg = "Mixing dicts with non-Series may lead to ambiguous ordering."
        # mix dict and array, wrong size
        with tm.assertRaisesRegexp(ValueError, msg):
            DataFrame({'A': {'a': 'a', 'b': 'b'},
                       'B': ['a', 'b', 'c']})

        # wrong size ndarray, GH 3105
        msg = "Shape of passed values is \(3, 4\), indices imply \(3, 3\)"
        with tm.assertRaisesRegexp(ValueError, msg):
            DataFrame(np.arange(12).reshape((4, 3)),
                      columns=['foo', 'bar', 'baz'],
                      index=pd.date_range('2000-01-01', periods=3))

        # higher dim raise exception
        with tm.assertRaisesRegexp(ValueError, 'Must pass 2-d input'):
            DataFrame(np.zeros((3, 3, 3)), columns=['A', 'B', 'C'], index=[1])

        # wrong size axis labels
        with tm.assertRaisesRegexp(ValueError, "Shape of passed values is "
                                   "\(3, 2\), indices imply \(3, 1\)"):
            DataFrame(np.random.rand(2, 3), columns=['A', 'B', 'C'], index=[1])

        with tm.assertRaisesRegexp(ValueError, "Shape of passed values is "
                                   "\(3, 2\), indices imply \(2, 2\)"):
            DataFrame(np.random.rand(2, 3), columns=['A', 'B'], index=[1, 2])

        with tm.assertRaisesRegexp(ValueError, 'If using all scalar values, '
                                   'you must pass an index'):
            DataFrame({'a': False, 'b': True})

    def test_constructor_with_embedded_frames(self):

        # embedded data frames
        df1 = DataFrame({'a': [1, 2, 3], 'b': [3, 4, 5]})
        df2 = DataFrame([df1, df1 + 10])

        df2.dtypes
        str(df2)

        result = df2.loc[0, 0]
        tm.assert_frame_equal(result, df1)

        result = df2.loc[1, 0]
        tm.assert_frame_equal(result, df1 + 10)

    def test_constructor_subclass_dict(self):
        # Test for passing dict subclass to constructor
        data = {'col1': tm.TestSubDict((x, 10.0 * x) for x in range(10)),
                'col2': tm.TestSubDict((x, 20.0 * x) for x in range(10))}
        df = DataFrame(data)
        refdf = DataFrame(dict((col, dict(compat.iteritems(val)))
                               for col, val in compat.iteritems(data)))
        tm.assert_frame_equal(refdf, df)

        data = tm.TestSubDict(compat.iteritems(data))
        df = DataFrame(data)
        tm.assert_frame_equal(refdf, df)

        # try with defaultdict
        from collections import defaultdict
        data = {}
        self.frame['B'][:10] = np.nan
        for k, v in compat.iteritems(self.frame):
            dct = defaultdict(dict)
            dct.update(v.to_dict())
            data[k] = dct
        frame = DataFrame(data)
        tm.assert_frame_equal(self.frame.sort_index(), frame)

    def test_constructor_dict_block(self):
        expected = np.array([[4., 3., 2., 1.]])
        df = DataFrame({'d': [4.], 'c': [3.], 'b': [2.], 'a': [1.]},
                       columns=['d', 'c', 'b', 'a'])
        tm.assert_numpy_array_equal(df.values, expected)

    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)
        self.assertEqual(len(frame), 3)
        self.assertEqual(frame['B'].dtype, np.float64)
        self.assertEqual(frame['A'].dtype, np.float64)

        frame = DataFrame(test_data)
        self.assertEqual(len(frame), 3)
        self.assertEqual(frame['B'].dtype, np.object_)
        self.assertEqual(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), randn(15)))
        }
        frame = DataFrame(test_data, dtype=float)
        self.assertEqual(len(frame), 20)
        self.assertEqual(frame['A'].dtype, np.object_)
        self.assertEqual(frame['B'].dtype, np.float64)

    def test_constructor_dict_dont_upcast(self):
        d = {'Col1': {'Row1': 'A String', 'Row2': np.nan}}
        df = DataFrame(d)
        tm.assertIsInstance(df['Col1']['Row2'], float)

        dm = DataFrame([[1, 2], ['a', 'b']], index=[1, 2], columns=[1, 2])
        tm.assertIsInstance(dm[1][1], int)

    def test_constructor_dict_of_tuples(self):
        # GH #1491
        data = {'a': (1, 2, 3), 'b': (4, 5, 6)}

        result = DataFrame(data)
        expected = DataFrame(dict((k, list(v))
                                  for k, v in compat.iteritems(data)))
        tm.assert_frame_equal(result, expected, check_dtype=False)

    def test_constructor_dict_multiindex(self):
        check = lambda result, expected: tm.assert_frame_equal(
            result, expected, check_dtype=True, check_index_type=True,
            check_column_type=True, check_names=True)
        d = {('a', 'a'): {('i', 'i'): 0, ('i', 'j'): 1, ('j', 'i'): 2},
             ('b', 'a'): {('i', 'i'): 6, ('i', 'j'): 5, ('j', 'i'): 4},
             ('b', 'c'): {('i', 'i'): 7, ('i', 'j'): 8, ('j', 'i'): 9}}
        _d = sorted(d.items())
        df = DataFrame(d)
        expected = DataFrame(
            [x[1] for x in _d],
            index=MultiIndex.from_tuples([x[0] for x in _d])).T
        expected.index = MultiIndex.from_tuples(expected.index)
        check(df, expected)

        d['z'] = {'y': 123., ('i', 'i'): 111, ('i', 'j'): 111, ('j', 'i'): 111}
        _d.insert(0, ('z', d['z']))
        expected = DataFrame(
            [x[1] for x in _d],
            index=Index([x[0] for x in _d], tupleize_cols=False)).T
        expected.index = Index(expected.index, tupleize_cols=False)
        df = DataFrame(d)
        df = df.reindex(columns=expected.columns, index=expected.index)
        check(df, expected)

    def test_constructor_dict_datetime64_index(self):
        # GH 10160
        dates_as_str = ['1984-02-19', '1988-11-06', '1989-12-03', '1990-03-15']

        def create_data(constructor):
            return dict((i, {constructor(s): 2 * i})
                        for i, s in enumerate(dates_as_str))

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

        expected = DataFrame([{0: 0, 1: None, 2: None, 3: None},
                              {0: None, 1: 2, 2: None, 3: None},
                              {0: None, 1: None, 2: 4, 3: None},
                              {0: None, 1: None, 2: None, 3: 6}],
                             index=[Timestamp(dt) for dt in dates_as_str])

        result_datetime64 = DataFrame(data_datetime64)
        result_datetime = DataFrame(data_datetime)
        result_Timestamp = DataFrame(data_Timestamp)
        tm.assert_frame_equal(result_datetime64, expected)
        tm.assert_frame_equal(result_datetime, expected)
        tm.assert_frame_equal(result_Timestamp, expected)

    def test_constructor_dict_timedelta64_index(self):
        # GH 10160
        td_as_int = [1, 2, 3, 4]

        def create_data(constructor):
            return dict((i, {constructor(s): 2 * i})
                        for i, s in enumerate(td_as_int))

        data_timedelta64 = create_data(lambda x: np.timedelta64(x, 'D'))
        data_timedelta = create_data(lambda x: timedelta(days=x))
        data_Timedelta = create_data(lambda x: Timedelta(x, 'D'))

        expected = DataFrame([{0: 0, 1: None, 2: None, 3: None},
                              {0: None, 1: 2, 2: None, 3: None},
                              {0: None, 1: None, 2: 4, 3: None},
                              {0: None, 1: None, 2: None, 3: 6}],
                             index=[Timedelta(td, 'D') for td in td_as_int])

        result_timedelta64 = DataFrame(data_timedelta64)
        result_timedelta = DataFrame(data_timedelta)
        result_Timedelta = DataFrame(data_Timedelta)
        tm.assert_frame_equal(result_timedelta64, expected)
        tm.assert_frame_equal(result_timedelta, expected)
        tm.assert_frame_equal(result_Timedelta, expected)

    def test_constructor_period(self):
        # PeriodIndex
        a = pd.PeriodIndex(['2012-01', 'NaT', '2012-04'], freq='M')
        b = pd.PeriodIndex(['2012-02-01', '2012-03-01', 'NaT'], freq='D')
        df = pd.DataFrame({'a': a, 'b': b})
        self.assertEqual(df['a'].dtype, 'object')
        self.assertEqual(df['b'].dtype, 'object')

        # list of periods
        df = pd.DataFrame({'a': a.asobject.tolist(),
                           'b': b.asobject.tolist()})
        self.assertEqual(df['a'].dtype, 'object')
        self.assertEqual(df['b'].dtype, 'object')

    def test_nested_dict_frame_constructor(self):
        rng = pd.period_range('1/1/2000', periods=5)
        df = DataFrame(randn(10, 5), columns=rng)

        data = {}
        for col in df.columns:
            for row in df.index:
                data.setdefault(col, {})[row] = df.get_value(row, col)

        result = DataFrame(data, columns=rng)
        tm.assert_frame_equal(result, df)

        data = {}
        for col in df.columns:
            for row in df.index:
                data.setdefault(row, {})[col] = df.get_value(row, col)

        result = DataFrame(data, index=rng).T
        tm.assert_frame_equal(result, df)

    def _check_basic_constructor(self, empty):
        # mat: 2d matrix with shpae (3, 2) to input. empty - makes sized
        # objects
        mat = empty((2, 3), dtype=float)
        # 2-D input
        frame = DataFrame(mat, columns=['A', 'B', 'C'], index=[1, 2])

        self.assertEqual(len(frame.index), 2)
        self.assertEqual(len(frame.columns), 3)

        # 1-D input
        frame = DataFrame(empty((3,)), columns=['A'], index=[1, 2, 3])
        self.assertEqual(len(frame.index), 3)
        self.assertEqual(len(frame.columns), 1)

        # cast type
        frame = DataFrame(mat, columns=['A', 'B', 'C'],
                          index=[1, 2], dtype=np.int64)
        self.assertEqual(frame.values.dtype, np.int64)

        # wrong size axis labels
        msg = r'Shape of passed values is \(3, 2\), indices imply \(3, 1\)'
        with tm.assertRaisesRegexp(ValueError, msg):
            DataFrame(mat, columns=['A', 'B', 'C'], index=[1])
        msg = r'Shape of passed values is \(3, 2\), indices imply \(2, 2\)'
        with tm.assertRaisesRegexp(ValueError, msg):
            DataFrame(mat, columns=['A', 'B'], index=[1, 2])

        # higher dim raise exception
        with tm.assertRaisesRegexp(ValueError, 'Must pass 2-d input'):
            DataFrame(empty((3, 3, 3)), columns=['A', 'B', 'C'],
                      index=[1])

        # automatic labeling
        frame = DataFrame(mat)
        self.assert_index_equal(frame.index, pd.Index(lrange(2)))
        self.assert_index_equal(frame.columns, pd.Index(lrange(3)))

        frame = DataFrame(mat, index=[1, 2])
        self.assert_index_equal(frame.columns, pd.Index(lrange(3)))

        frame = DataFrame(mat, columns=['A', 'B', 'C'])
        self.assert_index_equal(frame.index, pd.Index(lrange(2)))

        # 0-length axis
        frame = DataFrame(empty((0, 3)))
        self.assertEqual(len(frame.index), 0)

        frame = DataFrame(empty((3, 0)))
        self.assertEqual(len(frame.columns), 0)

    def test_constructor_ndarray(self):
        self._check_basic_constructor(np.ones)

        frame = DataFrame(['foo', 'bar'], index=[0, 1], columns=['A'])
        self.assertEqual(len(frame), 2)

    def test_constructor_maskedarray(self):
        self._check_basic_constructor(ma.masked_all)

        # Check non-masked values
        mat = ma.masked_all((2, 3), dtype=float)
        mat[0, 0] = 1.0
        mat[1, 2] = 2.0
        frame = DataFrame(mat, columns=['A', 'B', 'C'], index=[1, 2])
        self.assertEqual(1.0, frame['A'][1])
        self.assertEqual(2.0, frame['C'][2])

        # what is this even checking??
        mat = ma.masked_all((2, 3), dtype=float)
        frame = DataFrame(mat, columns=['A', 'B', 'C'], index=[1, 2])
        self.assertTrue(np.all(~np.asarray(frame == frame)))

    def test_constructor_maskedarray_nonfloat(self):
        # masked int promoted to float
        mat = ma.masked_all((2, 3), dtype=int)
        # 2-D input
        frame = DataFrame(mat, columns=['A', 'B', 'C'], index=[1, 2])

        self.assertEqual(len(frame.index), 2)
        self.assertEqual(len(frame.columns), 3)
        self.assertTrue(np.all(~np.asarray(frame == frame)))

        # cast type
        frame = DataFrame(mat, columns=['A', 'B', 'C'],
                          index=[1, 2], dtype=np.float64)
        self.assertEqual(frame.values.dtype, np.float64)

        # Check non-masked values
        mat2 = ma.copy(mat)
        mat2[0, 0] = 1
        mat2[1, 2] = 2
        frame = DataFrame(mat2, columns=['A', 'B', 'C'], index=[1, 2])
        self.assertEqual(1, frame['A'][1])
        self.assertEqual(2, frame['C'][2])

        # masked np.datetime64 stays (use lib.NaT as null)
        mat = ma.masked_all((2, 3), dtype='M8[ns]')
        # 2-D input
        frame = DataFrame(mat, columns=['A', 'B', 'C'], index=[1, 2])

        self.assertEqual(len(frame.index), 2)
        self.assertEqual(len(frame.columns), 3)
        self.assertTrue(isnull(frame).values.all())

        # cast type
        frame = DataFrame(mat, columns=['A', 'B', 'C'],
                          index=[1, 2], dtype=np.int64)
        self.assertEqual(frame.values.dtype, np.int64)

        # Check non-masked values
        mat2 = ma.copy(mat)
        mat2[0, 0] = 1
        mat2[1, 2] = 2
        frame = DataFrame(mat2, columns=['A', 'B', 'C'], index=[1, 2])
        self.assertEqual(1, frame['A'].view('i8')[1])
        self.assertEqual(2, frame['C'].view('i8')[2])

        # masked bool promoted to object
        mat = ma.masked_all((2, 3), dtype=bool)
        # 2-D input
        frame = DataFrame(mat, columns=['A', 'B', 'C'], index=[1, 2])

        self.assertEqual(len(frame.index), 2)
        self.assertEqual(len(frame.columns), 3)
        self.assertTrue(np.all(~np.asarray(frame == frame)))

        # cast type
        frame = DataFrame(mat, columns=['A', 'B', 'C'],
                          index=[1, 2], dtype=object)
        self.assertEqual(frame.values.dtype, object)

        # Check non-masked values
        mat2 = ma.copy(mat)
        mat2[0, 0] = True
        mat2[1, 2] = False
        frame = DataFrame(mat2, columns=['A', 'B', 'C'], index=[1, 2])
        self.assertEqual(True, frame['A'][1])
        self.assertEqual(False, frame['C'][2])

    def test_constructor_mrecarray(self):
        # Ensure mrecarray produces frame identical to dict of masked arrays
        # from GH3479

        assert_fr_equal = functools.partial(tm.assert_frame_equal,
                                            check_index_type=True,
                                            check_column_type=True,
                                            check_frame_type=True)
        arrays = [
            ('float', np.array([1.5, 2.0])),
            ('int', np.array([1, 2])),
            ('str', np.array(['abc', 'def'])),
        ]
        for name, arr in arrays[:]:
            arrays.append(('masked1_' + name,
                           np.ma.masked_array(arr, mask=[False, True])))
        arrays.append(('masked_all', np.ma.masked_all((2,))))
        arrays.append(('masked_none',
                       np.ma.masked_array([1.0, 2.5], mask=False)))

        # call assert_frame_equal for all selections of 3 arrays
        for comb in itertools.combinations(arrays, 3):
            names, data = zip(*comb)
            mrecs = mrecords.fromarrays(data, names=names)

            # fill the comb
            comb = dict([(k, v.filled()) if hasattr(
                v, 'filled') else (k, v) for k, v in comb])

            expected = DataFrame(comb, columns=names)
            result = DataFrame(mrecs)
            assert_fr_equal(result, expected)

            # specify columns
            expected = DataFrame(comb, columns=names[::-1])
            result = DataFrame(mrecs, columns=names[::-1])
            assert_fr_equal(result, expected)

            # specify index
            expected = DataFrame(comb, columns=names, index=[1, 2])
            result = DataFrame(mrecs, index=[1, 2])
            assert_fr_equal(result, expected)

    def test_constructor_corner(self):
        df = DataFrame(index=[])
        self.assertEqual(df.values.shape, (0, 0))

        # empty but with specified dtype
        df = DataFrame(index=lrange(10), columns=['a', 'b'], dtype=object)
        self.assertEqual(df.values.dtype, np.object_)

        # does not error but ends up float
        df = DataFrame(index=lrange(10), columns=['a', 'b'], dtype=int)
        self.assertEqual(df.values.dtype, np.object_)

        # #1783 empty dtype object
        df = DataFrame({}, columns=['foo', 'bar'])
        self.assertEqual(df.values.dtype, np.object_)

        df = DataFrame({'b': 1}, index=lrange(10), columns=list('abc'),
                       dtype=int)
        self.assertEqual(df.values.dtype, np.object_)

    def test_constructor_scalar_inference(self):
        data = {'int': 1, 'bool': True,
                'float': 3., 'complex': 4j, 'object': 'foo'}
        df = DataFrame(data, index=np.arange(10))

        self.assertEqual(df['int'].dtype, np.int64)
        self.assertEqual(df['bool'].dtype, np.bool_)
        self.assertEqual(df['float'].dtype, np.float64)
        self.assertEqual(df['complex'].dtype, np.complex128)
        self.assertEqual(df['object'].dtype, np.object_)

    def test_constructor_arrays_and_scalars(self):
        df = DataFrame({'a': randn(10), 'b': True})
        exp = DataFrame({'a': df['a'].values, 'b': [True] * 10})

        tm.assert_frame_equal(df, exp)
        with tm.assertRaisesRegexp(ValueError, 'must pass an index'):
            DataFrame({'a': False, 'b': True})

    def test_constructor_DataFrame(self):
        df = DataFrame(self.frame)
        tm.assert_frame_equal(df, self.frame)

        df_casted = DataFrame(self.frame, dtype=np.int64)
        self.assertEqual(df_casted.values.dtype, np.int64)

    def test_constructor_more(self):
        # used to be in test_matrix.py
        arr = randn(10)
        dm = DataFrame(arr, columns=['A'], index=np.arange(10))
        self.assertEqual(dm.values.ndim, 2)

        arr = randn(0)
        dm = DataFrame(arr)
        self.assertEqual(dm.values.ndim, 2)
        self.assertEqual(dm.values.ndim, 2)

        # no data specified
        dm = DataFrame(columns=['A', 'B'], index=np.arange(10))
        self.assertEqual(dm.values.shape, (10, 2))

        dm = DataFrame(columns=['A', 'B'])
        self.assertEqual(dm.values.shape, (0, 2))

        dm = DataFrame(index=np.arange(10))
        self.assertEqual(dm.values.shape, (10, 0))

        # corner, silly
        # TODO: Fix this Exception to be better...
        with tm.assertRaisesRegexp(PandasError, 'constructor not '
                                   'properly called'):
            DataFrame((1, 2, 3))

        # can't cast
        mat = np.array(['foo', 'bar'], dtype=object).reshape(2, 1)
        with tm.assertRaisesRegexp(ValueError, 'cast'):
            DataFrame(mat, index=[0, 1], columns=[0], dtype=float)

        dm = DataFrame(DataFrame(self.frame._series))
        tm.assert_frame_equal(dm, self.frame)

        # int cast
        dm = DataFrame({'A': np.ones(10, dtype=int),
                        'B': np.ones(10, dtype=np.float64)},
                       index=np.arange(10))

        self.assertEqual(len(dm.columns), 2)
        self.assertEqual(dm.values.dtype, np.float64)

    def test_constructor_empty_list(self):
        df = DataFrame([], index=[])
        expected = DataFrame(index=[])
        tm.assert_frame_equal(df, expected)

        # GH 9939
        df = DataFrame([], columns=['A', 'B'])
        expected = DataFrame({}, columns=['A', 'B'])
        tm.assert_frame_equal(df, expected)

        # Empty generator: list(empty_gen()) == []
        def empty_gen():
            return
            yield

        df = DataFrame(empty_gen(), columns=['A', 'B'])
        tm.assert_frame_equal(df, expected)

    def test_constructor_list_of_lists(self):
        # GH #484
        l = [[1, 'a'], [2, 'b']]
        df = DataFrame(data=l, columns=["num", "str"])
        self.assertTrue(is_integer_dtype(df['num']))
        self.assertEqual(df['str'].dtype, np.object_)

        # GH 4851
        # list of 0-dim ndarrays
        expected = DataFrame({0: range(10)})
        data = [np.array(x) for x in range(10)]
        result = DataFrame(data)
        tm.assert_frame_equal(result, expected)

    def test_constructor_sequence_like(self):
        # GH 3783
        # collections.Squence like
        import collections

        class DummyContainer(collections.Sequence):

            def __init__(self, lst):
                self._lst = lst

            def __getitem__(self, n):
                return self._lst.__getitem__(n)

            def __len__(self, n):
                return self._lst.__len__()

        l = [DummyContainer([1, 'a']), DummyContainer([2, 'b'])]
        columns = ["num", "str"]
        result = DataFrame(l, columns=columns)
        expected = DataFrame([[1, 'a'], [2, 'b']], columns=columns)
        tm.assert_frame_equal(result, expected, check_dtype=False)

        # GH 4297
        # support Array
        import array
        result = DataFrame.from_items([('A', array.array('i', range(10)))])
        expected = DataFrame({'A': list(range(10))})
        tm.assert_frame_equal(result, expected, check_dtype=False)

        expected = DataFrame([list(range(10)), list(range(10))])
        result = DataFrame([array.array('i', range(10)),
                            array.array('i', range(10))])
        tm.assert_frame_equal(result, expected, check_dtype=False)

    def test_constructor_iterator(self):

        expected = DataFrame([list(range(10)), list(range(10))])
        result = DataFrame([range(10), range(10)])
        tm.assert_frame_equal(result, expected)

    def test_constructor_generator(self):
        # related #2305

        gen1 = (i for i in range(10))
        gen2 = (i for i in range(10))

        expected = DataFrame([list(range(10)), list(range(10))])
        result = DataFrame([gen1, gen2])
        tm.assert_frame_equal(result, expected)

        gen = ([i, 'a'] for i in range(10))
        result = DataFrame(gen)
        expected = DataFrame({0: range(10), 1: 'a'})
        tm.assert_frame_equal(result, expected, check_dtype=False)

    def test_constructor_list_of_dicts(self):
        data = [OrderedDict([['a', 1.5], ['b', 3], ['c', 4], ['d', 6]]),
                OrderedDict([['a', 1.5], ['b', 3], ['d', 6]]),
                OrderedDict([['a', 1.5], ['d', 6]]),
                OrderedDict(),
                OrderedDict([['a', 1.5], ['b', 3], ['c', 4]]),
                OrderedDict([['b', 3], ['c', 4], ['d', 6]])]

        result = DataFrame(data)
        expected = DataFrame.from_dict(dict(zip(range(len(data)), data)),
                                       orient='index')
        tm.assert_frame_equal(result, expected.reindex(result.index))

        result = DataFrame([{}])
        expected = DataFrame(index=[0])
        tm.assert_frame_equal(result, expected)

    def test_constructor_ordered_dict_preserve_order(self):
        # see gh-13304
        expected = DataFrame([[2, 1]], columns=['b', 'a'])

        data = OrderedDict()
        data['b'] = [2]
        data['a'] = [1]

        result = DataFrame(data)
        tm.assert_frame_equal(result, expected)

        data = OrderedDict()
        data['b'] = 2
        data['a'] = 1

        result = DataFrame([data])
        tm.assert_frame_equal(result, expected)

    def test_constructor_ordered_dict_conflicting_orders(self):
        # the first dict element sets the ordering for the DataFrame,
        # even if there are conflicting orders from subsequent ones
        row_one = OrderedDict()
        row_one['b'] = 2
        row_one['a'] = 1

        row_two = OrderedDict()
        row_two['a'] = 1
        row_two['b'] = 2

        row_three = {'b': 2, 'a': 1}

        expected = DataFrame([[2, 1], [2, 1]], columns=['b', 'a'])
        result = DataFrame([row_one, row_two])
        tm.assert_frame_equal(result, expected)

        expected = DataFrame([[2, 1], [2, 1], [2, 1]], columns=['b', 'a'])
        result = DataFrame([row_one, row_two, row_three])
        tm.assert_frame_equal(result, expected)

    def test_constructor_list_of_series(self):
        data = [OrderedDict([['a', 1.5], ['b', 3.0], ['c', 4.0]]),
                OrderedDict([['a', 1.5], ['b', 3.0], ['c', 6.0]])]
        sdict = OrderedDict(zip(['x', 'y'], data))
        idx = Index(['a', 'b', 'c'])

        # all named
        data2 = [Series([1.5, 3, 4], idx, dtype='O', name='x'),
                 Series([1.5, 3, 6], idx, name='y')]
        result = DataFrame(data2)
        expected = DataFrame.from_dict(sdict, orient='index')
        tm.assert_frame_equal(result, expected)

        # some unnamed
        data2 = [Series([1.5, 3, 4], idx, dtype='O', name='x'),
                 Series([1.5, 3, 6], idx)]
        result = DataFrame(data2)

        sdict = OrderedDict(zip(['x', 'Unnamed 0'], data))
        expected = DataFrame.from_dict(sdict, orient='index')
        tm.assert_frame_equal(result.sort_index(), expected)

        # none named
        data = [OrderedDict([['a', 1.5], ['b', 3], ['c', 4], ['d', 6]]),
                OrderedDict([['a', 1.5], ['b', 3], ['d', 6]]),
                OrderedDict([['a', 1.5], ['d', 6]]),
                OrderedDict(),
                OrderedDict([['a', 1.5], ['b', 3], ['c', 4]]),
                OrderedDict([['b', 3], ['c', 4], ['d', 6]])]
        data = [Series(d) for d in data]

        result = DataFrame(data)
        sdict = OrderedDict(zip(range(len(data)), data))
        expected = DataFrame.from_dict(sdict, orient='index')
        tm.assert_frame_equal(result, expected.reindex(result.index))

        result2 = DataFrame(data, index=np.arange(6))
        tm.assert_frame_equal(result, result2)

        result = DataFrame([Series({})])
        expected = DataFrame(index=[0])
        tm.assert_frame_equal(result, expected)

        data = [OrderedDict([['a', 1.5], ['b', 3.0], ['c', 4.0]]),
                OrderedDict([['a', 1.5], ['b', 3.0], ['c', 6.0]])]
        sdict = OrderedDict(zip(range(len(data)), data))

        idx = Index(['a', 'b', 'c'])
        data2 = [Series([1.5, 3, 4], idx, dtype='O'),
                 Series([1.5, 3, 6], idx)]
        result = DataFrame(data2)
        expected = DataFrame.from_dict(sdict, orient='index')
        tm.assert_frame_equal(result, expected)

    def test_constructor_list_of_derived_dicts(self):
        class CustomDict(dict):
            pass
        d = {'a': 1.5, 'b': 3}

        data_custom = [CustomDict(d)]
        data = [d]

        result_custom = DataFrame(data_custom)
        result = DataFrame(data)
        tm.assert_frame_equal(result, result_custom)

    def test_constructor_ragged(self):
        data = {'A': randn(10),
                'B': randn(8)}
        with tm.assertRaisesRegexp(ValueError,
                                   'arrays must all be same length'):
            DataFrame(data)

    def test_constructor_scalar(self):
        idx = Index(lrange(3))
        df = DataFrame({"a": 0}, index=idx)
        expected = DataFrame({"a": [0, 0, 0]}, index=idx)
        tm.assert_frame_equal(df, expected, check_dtype=False)

    def test_constructor_Series_copy_bug(self):
        df = DataFrame(self.frame['A'], index=self.frame.index, columns=['A'])
        df.copy()

    def test_constructor_mixed_dict_and_Series(self):
        data = {}
        data['A'] = {'foo': 1, 'bar': 2, 'baz': 3}
        data['B'] = Series([4, 3, 2, 1], index=['bar', 'qux', 'baz', 'foo'])

        result = DataFrame(data)
        self.assertTrue(result.index.is_monotonic)

        # ordering ambiguous, raise exception
        with tm.assertRaisesRegexp(ValueError, 'ambiguous ordering'):
            DataFrame({'A': ['a', 'b'], 'B': {'a': 'a', 'b': 'b'}})

        # this is OK though
        result = DataFrame({'A': ['a', 'b'],
                            'B': Series(['a', 'b'], index=['a', 'b'])})
        expected = DataFrame({'A': ['a', 'b'], 'B': ['a', 'b']},
                             index=['a', 'b'])
        tm.assert_frame_equal(result, expected)

    def test_constructor_tuples(self):
        result = DataFrame({'A': [(1, 2), (3, 4)]})
        expected = DataFrame({'A': Series([(1, 2), (3, 4)])})
        tm.assert_frame_equal(result, expected)

    def test_constructor_namedtuples(self):
        # GH11181
        from collections import namedtuple
        named_tuple = namedtuple("Pandas", list('ab'))
        tuples = [named_tuple(1, 3), named_tuple(2, 4)]
        expected = DataFrame({'a': [1, 2], 'b': [3, 4]})
        result = DataFrame(tuples)
        tm.assert_frame_equal(result, expected)

        # with columns
        expected = DataFrame({'y': [1, 2], 'z': [3, 4]})
        result = DataFrame(tuples, columns=['y', 'z'])
        tm.assert_frame_equal(result, expected)

    def test_constructor_orient(self):
        data_dict = self.mixed_frame.T._series
        recons = DataFrame.from_dict(data_dict, orient='index')
        expected = self.mixed_frame.sort_index()
        tm.assert_frame_equal(recons, expected)

        # dict of sequence
        a = {'hi': [32, 3, 3],
             'there': [3, 5, 3]}
        rs = DataFrame.from_dict(a, orient='index')
        xp = DataFrame.from_dict(a).T.reindex(list(a.keys()))
        tm.assert_frame_equal(rs, xp)

    def test_constructor_Series_named(self):
        a = Series([1, 2, 3], index=['a', 'b', 'c'], name='x')
        df = DataFrame(a)
        self.assertEqual(df.columns[0], 'x')
        self.assert_index_equal(df.index, a.index)

        # ndarray like
        arr = np.random.randn(10)
        s = Series(arr, name='x')
        df = DataFrame(s)
        expected = DataFrame(dict(x=s))
        tm.assert_frame_equal(df, expected)

        s = Series(arr, index=range(3, 13))
        df = DataFrame(s)
        expected = DataFrame({0: s})
        tm.assert_frame_equal(df, expected)

        self.assertRaises(ValueError, DataFrame, s, columns=[1, 2])

        # #2234
        a = Series([], name='x')
        df = DataFrame(a)
        self.assertEqual(df.columns[0], 'x')

        # series with name and w/o
        s1 = Series(arr, name='x')
        df = DataFrame([s1, arr]).T
        expected = DataFrame({'x': s1, 'Unnamed 0': arr},
                             columns=['x', 'Unnamed 0'])
        tm.assert_frame_equal(df, expected)

        # this is a bit non-intuitive here; the series collapse down to arrays
        df = DataFrame([arr, s1]).T
        expected = DataFrame({1: s1, 0: arr}, columns=[0, 1])
        tm.assert_frame_equal(df, expected)

    def test_constructor_Series_differently_indexed(self):
        # name
        s1 = Series([1, 2, 3], index=['a', 'b', 'c'], name='x')

        # no name
        s2 = Series([1, 2, 3], index=['a', 'b', 'c'])

        other_index = Index(['a', 'b'])

        df1 = DataFrame(s1, index=other_index)
        exp1 = DataFrame(s1.reindex(other_index))
        self.assertEqual(df1.columns[0], 'x')
        tm.assert_frame_equal(df1, exp1)

        df2 = DataFrame(s2, index=other_index)
        exp2 = DataFrame(s2.reindex(other_index))
        self.assertEqual(df2.columns[0], 0)
        self.assert_index_equal(df2.index, other_index)
        tm.assert_frame_equal(df2, exp2)

    def test_constructor_manager_resize(self):
        index = list(self.frame.index[:5])
        columns = list(self.frame.columns[:3])

        result = DataFrame(self.frame._data, index=index,
                           columns=columns)
        self.assert_index_equal(result.index, Index(index))
        self.assert_index_equal(result.columns, Index(columns))

    def test_constructor_from_items(self):
        items = [(c, self.frame[c]) for c in self.frame.columns]
        recons = DataFrame.from_items(items)
        tm.assert_frame_equal(recons, self.frame)

        # pass some columns
        recons = DataFrame.from_items(items, columns=['C', 'B', 'A'])
        tm.assert_frame_equal(recons, self.frame.ix[:, ['C', 'B', 'A']])

        # orient='index'

        row_items = [(idx, self.mixed_frame.xs(idx))
                     for idx in self.mixed_frame.index]

        recons = DataFrame.from_items(row_items,
                                      columns=self.mixed_frame.columns,
                                      orient='index')
        tm.assert_frame_equal(recons, self.mixed_frame)
        self.assertEqual(recons['A'].dtype, np.float64)

        with tm.assertRaisesRegexp(TypeError,
                                   "Must pass columns with orient='index'"):
            DataFrame.from_items(row_items, orient='index')

        # orient='index', but thar be tuples
        arr = lib.list_to_object_array(
            [('bar', 'baz')] * len(self.mixed_frame))
        self.mixed_frame['foo'] = arr
        row_items = [(idx, list(self.mixed_frame.xs(idx)))
                     for idx in self.mixed_frame.index]
        recons = DataFrame.from_items(row_items,
                                      columns=self.mixed_frame.columns,
                                      orient='index')
        tm.assert_frame_equal(recons, self.mixed_frame)
        tm.assertIsInstance(recons['foo'][0], tuple)

        rs = DataFrame.from_items([('A', [1, 2, 3]), ('B', [4, 5, 6])],
                                  orient='index',
                                  columns=['one', 'two', 'three'])
        xp = DataFrame([[1, 2, 3], [4, 5, 6]], index=['A', 'B'],
                       columns=['one', 'two', 'three'])
        tm.assert_frame_equal(rs, xp)

    def test_constructor_mix_series_nonseries(self):
        df = DataFrame({'A': self.frame['A'],
                        'B': list(self.frame['B'])}, columns=['A', 'B'])
        tm.assert_frame_equal(df, self.frame.ix[:, ['A', 'B']])

        with tm.assertRaisesRegexp(ValueError, 'does not match index length'):
            DataFrame({'A': self.frame['A'], 'B': list(self.frame['B'])[:-2]})

    def test_constructor_miscast_na_int_dtype(self):
        df = DataFrame([[np.nan, 1], [1, 0]], dtype=np.int64)
        expected = DataFrame([[np.nan, 1], [1, 0]])
        tm.assert_frame_equal(df, expected)

    def test_constructor_iterator_failure(self):
        with tm.assertRaisesRegexp(TypeError, 'iterator'):
            df = DataFrame(iter([1, 2, 3]))  # noqa

    def test_constructor_column_duplicates(self):
        # it works! #2079
        df = DataFrame([[8, 5]], columns=['a', 'a'])
        edf = DataFrame([[8, 5]])
        edf.columns = ['a', 'a']

        tm.assert_frame_equal(df, edf)

        idf = DataFrame.from_items(
            [('a', [8]), ('a', [5])], columns=['a', 'a'])
        tm.assert_frame_equal(idf, edf)

        self.assertRaises(ValueError, DataFrame.from_items,
                          [('a', [8]), ('a', [5]), ('b', [6])],
                          columns=['b', 'a', 'a'])

    def test_constructor_empty_with_string_dtype(self):
        # GH 9428
        expected = DataFrame(index=[0, 1], columns=[0, 1], dtype=object)

        df = DataFrame(index=[0, 1], columns=[0, 1], dtype=str)
        tm.assert_frame_equal(df, expected)
        df = DataFrame(index=[0, 1], columns=[0, 1], dtype=np.str_)
        tm.assert_frame_equal(df, expected)
        df = DataFrame(index=[0, 1], columns=[0, 1], dtype=np.unicode_)
        tm.assert_frame_equal(df, expected)
        df = DataFrame(index=[0, 1], columns=[0, 1], dtype='U5')
        tm.assert_frame_equal(df, expected)

    def test_constructor_single_value(self):
        # expecting single value upcasting here
        df = DataFrame(0., index=[1, 2, 3], columns=['a', 'b', 'c'])
        tm.assert_frame_equal(df,
                              DataFrame(np.zeros(df.shape).astype('float64'),
                                        df.index, df.columns))

        df = DataFrame(0, index=[1, 2, 3], columns=['a', 'b', 'c'])
        tm.assert_frame_equal(df, DataFrame(np.zeros(df.shape).astype('int64'),
                                            df.index, df.columns))

        df = DataFrame('a', index=[1, 2], columns=['a', 'c'])
        tm.assert_frame_equal(df, DataFrame(np.array([['a', 'a'], ['a', 'a']],
                                                     dtype=object),
                                            index=[1, 2], columns=['a', 'c']))

        self.assertRaises(com.PandasError, DataFrame, 'a', [1, 2])
        self.assertRaises(com.PandasError, DataFrame, 'a', columns=['a', 'c'])
        with tm.assertRaisesRegexp(TypeError, 'incompatible data and dtype'):
            DataFrame('a', [1, 2], ['a', 'c'], float)

    def test_constructor_with_datetimes(self):
        intname = np.dtype(np.int_).name
        floatname = np.dtype(np.float_).name
        datetime64name = np.dtype('M8[ns]').name
        objectname = np.dtype(np.object_).name

        # single item
        df = DataFrame({'A': 1, 'B': 'foo', 'C': 'bar',
                        'D': Timestamp("20010101"),
                        'E': datetime(2001, 1, 2, 0, 0)},
                       index=np.arange(10))
        result = df.get_dtype_counts()
        expected = Series({'int64': 1, datetime64name: 2, objectname: 2})
        result.sort_index()
        expected.sort_index()
        tm.assert_series_equal(result, expected)

        # check with ndarray construction ndim==0 (e.g. we are passing a ndim 0
        # ndarray with a dtype specified)
        df = DataFrame({'a': 1., 'b': 2, 'c': 'foo',
                        floatname: np.array(1., dtype=floatname),
                        intname: np.array(1, dtype=intname)},
                       index=np.arange(10))
        result = df.get_dtype_counts()
        expected = {objectname: 1}
        if intname == 'int64':
            expected['int64'] = 2
        else:
            expected['int64'] = 1
            expected[intname] = 1
        if floatname == 'float64':
            expected['float64'] = 2
        else:
            expected['float64'] = 1
            expected[floatname] = 1

        result.sort_index()
        expected = Series(expected)
        expected.sort_index()
        tm.assert_series_equal(result, expected)

        # check with ndarray construction ndim>0
        df = DataFrame({'a': 1., 'b': 2, 'c': 'foo',
                        floatname: np.array([1.] * 10, dtype=floatname),
                        intname: np.array([1] * 10, dtype=intname)},
                       index=np.arange(10))
        result = df.get_dtype_counts()
        result.sort_index()
        tm.assert_series_equal(result, expected)

        # GH 2809
        ind = date_range(start="2000-01-01", freq="D", periods=10)
        datetimes = [ts.to_pydatetime() for ts in ind]
        datetime_s = Series(datetimes)
        self.assertEqual(datetime_s.dtype, 'M8[ns]')
        df = DataFrame({'datetime_s': datetime_s})
        result = df.get_dtype_counts()
        expected = Series({datetime64name: 1})
        result.sort_index()
        expected.sort_index()
        tm.assert_series_equal(result, expected)

        # GH 2810
        ind = date_range(start="2000-01-01", freq="D", periods=10)
        datetimes = [ts.to_pydatetime() for ts in ind]
        dates = [ts.date() for ts in ind]
        df = DataFrame({'datetimes': datetimes, 'dates': dates})
        result = df.get_dtype_counts()
        expected = Series({datetime64name: 1, objectname: 1})
        result.sort_index()
        expected.sort_index()
        tm.assert_series_equal(result, expected)

        # GH 7594
        # don't coerce tz-aware
        import pytz
        tz = pytz.timezone('US/Eastern')
        dt = tz.localize(datetime(2012, 1, 1))

        df = DataFrame({'End Date': dt}, index=[0])
        self.assertEqual(df.iat[0, 0], dt)
        tm.assert_series_equal(df.dtypes, Series(
            {'End Date': 'datetime64[ns, US/Eastern]'}))

        df = DataFrame([{'End Date': dt}])
        self.assertEqual(df.iat[0, 0], dt)
        tm.assert_series_equal(df.dtypes, Series(
            {'End Date': 'datetime64[ns, US/Eastern]'}))

        # tz-aware (UTC and other tz's)
        # GH 8411
        dr = date_range('20130101', periods=3)
        df = DataFrame({'value': dr})
        self.assertTrue(df.iat[0, 0].tz is None)
        dr = date_range('20130101', periods=3, tz='UTC')
        df = DataFrame({'value': dr})
        self.assertTrue(str(df.iat[0, 0].tz) == 'UTC')
        dr = date_range('20130101', periods=3, tz='US/Eastern')
        df = DataFrame({'value': dr})
        self.assertTrue(str(df.iat[0, 0].tz) == 'US/Eastern')

        # GH 7822
        # preserver an index with a tz on dict construction
        i = date_range('1/1/2011', periods=5, freq='10s', tz='US/Eastern')

        expected = DataFrame(
            {'a': i.to_series(keep_tz=True).reset_index(drop=True)})
        df = DataFrame()
        df['a'] = i
        tm.assert_frame_equal(df, expected)

        df = DataFrame({'a': i})
        tm.assert_frame_equal(df, expected)

        # multiples
        i_no_tz = date_range('1/1/2011', periods=5, freq='10s')
        df = DataFrame({'a': i, 'b': i_no_tz})
        expected = DataFrame({'a': i.to_series(keep_tz=True)
                              .reset_index(drop=True), 'b': i_no_tz})
        tm.assert_frame_equal(df, expected)

    def test_constructor_for_list_with_dtypes(self):
        # TODO(wesm): unused
        intname = np.dtype(np.int_).name  # noqa
        floatname = np.dtype(np.float_).name  # noqa
        datetime64name = np.dtype('M8[ns]').name
        objectname = np.dtype(np.object_).name

        # test list of lists/ndarrays
        df = DataFrame([np.arange(5) for x in range(5)])
        result = df.get_dtype_counts()
        expected = Series({'int64': 5})

        df = DataFrame([np.array(np.arange(5), dtype='int32')
                        for x in range(5)])
        result = df.get_dtype_counts()
        expected = Series({'int32': 5})

        # overflow issue? (we always expecte int64 upcasting here)
        df = DataFrame({'a': [2 ** 31, 2 ** 31 + 1]})
        result = df.get_dtype_counts()
        expected = Series({'int64': 1})
        tm.assert_series_equal(result, expected)

        # GH #2751 (construction with no index specified), make sure we cast to
        # platform values
        df = DataFrame([1, 2])
        result = df.get_dtype_counts()
        expected = Series({'int64': 1})
        tm.assert_series_equal(result, expected)

        df = DataFrame([1., 2.])
        result = df.get_dtype_counts()
        expected = Series({'float64': 1})
        tm.assert_series_equal(result, expected)

        df = DataFrame({'a': [1, 2]})
        result = df.get_dtype_counts()
        expected = Series({'int64': 1})
        tm.assert_series_equal(result, expected)

        df = DataFrame({'a': [1., 2.]})
        result = df.get_dtype_counts()
        expected = Series({'float64': 1})
        tm.assert_series_equal(result, expected)

        df = DataFrame({'a': 1}, index=lrange(3))
        result = df.get_dtype_counts()
        expected = Series({'int64': 1})
        tm.assert_series_equal(result, expected)

        df = DataFrame({'a': 1.}, index=lrange(3))
        result = df.get_dtype_counts()
        expected = Series({'float64': 1})
        tm.assert_series_equal(result, expected)

        # with object list
        df = DataFrame({'a': [1, 2, 4, 7], 'b': [1.2, 2.3, 5.1, 6.3],
                        'c': list('abcd'),
                        'd': [datetime(2000, 1, 1) for i in range(4)],
                        'e': [1., 2, 4., 7]})
        result = df.get_dtype_counts()
        expected = Series(
            {'int64': 1, 'float64': 2, datetime64name: 1, objectname: 1})
        result.sort_index()
        expected.sort_index()
        tm.assert_series_equal(result, expected)

    def test_constructor_frame_copy(self):
        cop = DataFrame(self.frame, copy=True)
        cop['A'] = 5
        self.assertTrue((cop['A'] == 5).all())
        self.assertFalse((self.frame['A'] == 5).all())

    def test_constructor_ndarray_copy(self):
        df = DataFrame(self.frame.values)

        self.frame.values[5] = 5
        self.assertTrue((df.values[5] == 5).all())

        df = DataFrame(self.frame.values, copy=True)
        self.frame.values[6] = 6
        self.assertFalse((df.values[6] == 6).all())

    def test_constructor_series_copy(self):
        series = self.frame._series

        df = DataFrame({'A': series['A']})
        df['A'][:] = 5

        self.assertFalse((series['A'] == 5).all())

    def test_constructor_with_nas(self):
        # GH 5016
        # na's in indicies

        def check(df):
            for i in range(len(df.columns)):
                df.iloc[:, i]

            # allow single nans to succeed
            indexer = np.arange(len(df.columns))[isnull(df.columns)]

            if len(indexer) == 1:
                tm.assert_series_equal(df.iloc[:, indexer[0]],
                                       df.loc[:, np.nan])

            # multiple nans should fail
            else:

                def f():
                    df.loc[:, np.nan]
                self.assertRaises(TypeError, f)

        df = DataFrame([[1, 2, 3], [4, 5, 6]], index=[1, np.nan])
        check(df)

        df = DataFrame([[1, 2, 3], [4, 5, 6]], columns=[1.1, 2.2, np.nan])
        check(df)

        df = DataFrame([[0, 1, 2, 3], [4, 5, 6, 7]],
                       columns=[np.nan, 1.1, 2.2, np.nan])
        check(df)

        df = DataFrame([[0.0, 1, 2, 3.0], [4, 5, 6, 7]],
                       columns=[np.nan, 1.1, 2.2, np.nan])
        check(df)

    def test_constructor_lists_to_object_dtype(self):
        # from #1074
        d = DataFrame({'a': [np.nan, False]})
        self.assertEqual(d['a'].dtype, np.object_)
        self.assertFalse(d['a'][1])

    def test_from_records_to_records(self):
        # from numpy documentation
        arr = np.zeros((2,), dtype=('i4,f4,a10'))
        arr[:] = [(1, 2., 'Hello'), (2, 3., "World")]

        # TODO(wesm): unused
        frame = DataFrame.from_records(arr)  # noqa

        index = pd.Index(np.arange(len(arr))[::-1])
        indexed_frame = DataFrame.from_records(arr, index=index)
        self.assert_index_equal(indexed_frame.index, index)

        # without names, it should go to last ditch
        arr2 = np.zeros((2, 3))
        tm.assert_frame_equal(DataFrame.from_records(arr2), DataFrame(arr2))

        # wrong length
        msg = r'Shape of passed values is \(3, 2\), indices imply \(3, 1\)'
        with tm.assertRaisesRegexp(ValueError, msg):
            DataFrame.from_records(arr, index=index[:-1])

        indexed_frame = DataFrame.from_records(arr, index='f1')

        # what to do?
        records = indexed_frame.to_records()
        self.assertEqual(len(records.dtype.names), 3)

        records = indexed_frame.to_records(index=False)
        self.assertEqual(len(records.dtype.names), 2)
        self.assertNotIn('index', records.dtype.names)

    def test_from_records_nones(self):
        tuples = [(1, 2, None, 3),
                  (1, 2, None, 3),
                  (None, 2, 5, 3)]

        df = DataFrame.from_records(tuples, columns=['a', 'b', 'c', 'd'])
        self.assertTrue(np.isnan(df['c'][0]))

    def test_from_records_iterator(self):
        arr = np.array([(1.0, 1.0, 2, 2), (3.0, 3.0, 4, 4), (5., 5., 6, 6),
                        (7., 7., 8, 8)],
                       dtype=[('x', np.float64), ('u', np.float32),
                              ('y', np.int64), ('z', np.int32)])
        df = DataFrame.from_records(iter(arr), nrows=2)
        xp = DataFrame({'x': np.array([1.0, 3.0], dtype=np.float64),
                        'u': np.array([1.0, 3.0], dtype=np.float32),
                        'y': np.array([2, 4], dtype=np.int64),
                        'z': np.array([2, 4], dtype=np.int32)})
        tm.assert_frame_equal(df.reindex_like(xp), xp)

        # no dtypes specified here, so just compare with the default
        arr = [(1.0, 2), (3.0, 4), (5., 6), (7., 8)]
        df = DataFrame.from_records(iter(arr), columns=['x', 'y'],
                                    nrows=2)
        tm.assert_frame_equal(df, xp.reindex(columns=['x', 'y']),
                              check_dtype=False)

    def test_from_records_tuples_generator(self):
        def tuple_generator(length):
            for i in range(length):
                letters = 'ABCDEFGHIJKLMNOPQRSTUVWXYZ'
                yield (i, letters[i % len(letters)], i / length)

        columns_names = ['Integer', 'String', 'Float']
        columns = [[i[j] for i in tuple_generator(
            10)] for j in range(len(columns_names))]
        data = {'Integer': columns[0],
                'String': columns[1], 'Float': columns[2]}
        expected = DataFrame(data, columns=columns_names)

        generator = tuple_generator(10)
        result = DataFrame.from_records(generator, columns=columns_names)
        tm.assert_frame_equal(result, expected)

    def test_from_records_lists_generator(self):
        def list_generator(length):
            for i in range(length):
                letters = 'ABCDEFGHIJKLMNOPQRSTUVWXYZ'
                yield [i, letters[i % len(letters)], i / length]

        columns_names = ['Integer', 'String', 'Float']
        columns = [[i[j] for i in list_generator(
            10)] for j in range(len(columns_names))]
        data = {'Integer': columns[0],
                'String': columns[1], 'Float': columns[2]}
        expected = DataFrame(data, columns=columns_names)

        generator = list_generator(10)
        result = DataFrame.from_records(generator, columns=columns_names)
        tm.assert_frame_equal(result, expected)

    def test_from_records_columns_not_modified(self):
        tuples = [(1, 2, 3),
                  (1, 2, 3),
                  (2, 5, 3)]

        columns = ['a', 'b', 'c']
        original_columns = list(columns)

        df = DataFrame.from_records(tuples, columns=columns, index='a')  # noqa

        self.assertEqual(columns, original_columns)

    def test_from_records_decimal(self):
        from decimal import Decimal

        tuples = [(Decimal('1.5'),), (Decimal('2.5'),), (None,)]

        df = DataFrame.from_records(tuples, columns=['a'])
        self.assertEqual(df['a'].dtype, object)

        df = DataFrame.from_records(tuples, columns=['a'], coerce_float=True)
        self.assertEqual(df['a'].dtype, np.float64)
        self.assertTrue(np.isnan(df['a'].values[-1]))

    def test_from_records_duplicates(self):
        result = DataFrame.from_records([(1, 2, 3), (4, 5, 6)],
                                        columns=['a', 'b', 'a'])

        expected = DataFrame([(1, 2, 3), (4, 5, 6)],
                             columns=['a', 'b', 'a'])

        tm.assert_frame_equal(result, expected)

    def test_from_records_set_index_name(self):
        def create_dict(order_id):
            return {'order_id': order_id, 'quantity': np.random.randint(1, 10),
                    'price': np.random.randint(1, 10)}
        documents = [create_dict(i) for i in range(10)]
        # demo missing data
        documents.append({'order_id': 10, 'quantity': 5})

        result = DataFrame.from_records(documents, index='order_id')
        self.assertEqual(result.index.name, 'order_id')

        # MultiIndex
        result = DataFrame.from_records(documents,
                                        index=['order_id', 'quantity'])
        self.assertEqual(result.index.names, ('order_id', 'quantity'))

    def test_from_records_misc_brokenness(self):
        # #2179

        data = {1: ['foo'], 2: ['bar']}

        result = DataFrame.from_records(data, columns=['a', 'b'])
        exp = DataFrame(data, columns=['a', 'b'])
        tm.assert_frame_equal(result, exp)

        # overlap in index/index_names

        data = {'a': [1, 2, 3], 'b': [4, 5, 6]}

        result = DataFrame.from_records(data, index=['a', 'b', 'c'])
        exp = DataFrame(data, index=['a', 'b', 'c'])
        tm.assert_frame_equal(result, exp)

        # GH 2623
        rows = []
        rows.append([datetime(2010, 1, 1), 1])
        rows.append([datetime(2010, 1, 2), 'hi'])  # test col upconverts to obj
        df2_obj = DataFrame.from_records(rows, columns=['date', 'test'])
        results = df2_obj.get_dtype_counts()
        expected = Series({'datetime64[ns]': 1, 'object': 1})

        rows = []
        rows.append([datetime(2010, 1, 1), 1])
        rows.append([datetime(2010, 1, 2), 1])
        df2_obj = DataFrame.from_records(rows, columns=['date', 'test'])
        results = df2_obj.get_dtype_counts()
        expected = Series({'datetime64[ns]': 1, 'int64': 1})
        tm.assert_series_equal(results, expected)

    def test_from_records_empty(self):
        # 3562
        result = DataFrame.from_records([], columns=['a', 'b', 'c'])
        expected = DataFrame(columns=['a', 'b', 'c'])
        tm.assert_frame_equal(result, expected)

        result = DataFrame.from_records([], columns=['a', 'b', 'b'])
        expected = DataFrame(columns=['a', 'b', 'b'])
        tm.assert_frame_equal(result, expected)

    def test_from_records_empty_with_nonempty_fields_gh3682(self):
        a = np.array([(1, 2)], dtype=[('id', np.int64), ('value', np.int64)])
        df = DataFrame.from_records(a, index='id')
        tm.assert_index_equal(df.index, Index([1], name='id'))
        self.assertEqual(df.index.name, 'id')
        tm.assert_index_equal(df.columns, Index(['value']))

        b = np.array([], dtype=[('id', np.int64), ('value', np.int64)])
        df = DataFrame.from_records(b, index='id')
        tm.assert_index_equal(df.index, Index([], name='id'))
        self.assertEqual(df.index.name, 'id')

    def test_from_records_with_datetimes(self):

        # this may fail on certain platforms because of a numpy issue
        # related GH6140
        if not is_platform_little_endian():
            raise nose.SkipTest("known failure of test on non-little endian")

        # construction with a null in a recarray
        # GH 6140
        expected = DataFrame({'EXPIRY': [datetime(2005, 3, 1, 0, 0), None]})

        arrdata = [np.array([datetime(2005, 3, 1, 0, 0), None])]
        dtypes = [('EXPIRY', '<M8[ns]')]

        try:
            recarray = np.core.records.fromarrays(arrdata, dtype=dtypes)
        except (ValueError):
            raise nose.SkipTest("known failure of numpy rec array creation")

        result = DataFrame.from_records(recarray)
        tm.assert_frame_equal(result, expected)

        # coercion should work too
        arrdata = [np.array([datetime(2005, 3, 1, 0, 0), None])]
        dtypes = [('EXPIRY', '<M8[m]')]
        recarray = np.core.records.fromarrays(arrdata, dtype=dtypes)
        result = DataFrame.from_records(recarray)
        tm.assert_frame_equal(result, expected)

    def test_from_records_sequencelike(self):
        df = DataFrame({'A': np.array(np.random.randn(6), dtype=np.float64),
                        'A1': np.array(np.random.randn(6), dtype=np.float64),
                        'B': np.array(np.arange(6), dtype=np.int64),
                        'C': ['foo'] * 6,
                        'D': np.array([True, False] * 3, dtype=bool),
                        'E': np.array(np.random.randn(6), dtype=np.float32),
                        'E1': np.array(np.random.randn(6), dtype=np.float32),
                        'F': np.array(np.arange(6), dtype=np.int32)})

        # this is actually tricky to create the recordlike arrays and
        # have the dtypes be intact
        blocks = df.blocks
        tuples = []
        columns = []
        dtypes = []
        for dtype, b in compat.iteritems(blocks):
            columns.extend(b.columns)
            dtypes.extend([(c, np.dtype(dtype).descr[0][1])
                           for c in b.columns])
        for i in range(len(df.index)):
            tup = []
            for _, b in compat.iteritems(blocks):
                tup.extend(b.iloc[i].values)
            tuples.append(tuple(tup))

        recarray = np.array(tuples, dtype=dtypes).view(np.recarray)
        recarray2 = df.to_records()
        lists = [list(x) for x in tuples]

        # tuples (lose the dtype info)
        result = (DataFrame.from_records(tuples, columns=columns)
                  .reindex(columns=df.columns))

        # created recarray and with to_records recarray (have dtype info)
        result2 = (DataFrame.from_records(recarray, columns=columns)
                   .reindex(columns=df.columns))
        result3 = (DataFrame.from_records(recarray2, columns=columns)
                   .reindex(columns=df.columns))

        # list of tupels (no dtype info)
        result4 = (DataFrame.from_records(lists, columns=columns)
                   .reindex(columns=df.columns))

        tm.assert_frame_equal(result, df, check_dtype=False)
        tm.assert_frame_equal(result2, df)
        tm.assert_frame_equal(result3, df)
        tm.assert_frame_equal(result4, df, check_dtype=False)

        # tuples is in the order of the columns
        result = DataFrame.from_records(tuples)
        tm.assert_index_equal(result.columns, pd.Index(lrange(8)))

        # test exclude parameter & we are casting the results here (as we don't
        # have dtype info to recover)
        columns_to_test = [columns.index('C'), columns.index('E1')]

        exclude = list(set(range(8)) - set(columns_to_test))
        result = DataFrame.from_records(tuples, exclude=exclude)
        result.columns = [columns[i] for i in sorted(columns_to_test)]
        tm.assert_series_equal(result['C'], df['C'])
        tm.assert_series_equal(result['E1'], df['E1'].astype('float64'))

        # empty case
        result = DataFrame.from_records([], columns=['foo', 'bar', 'baz'])
        self.assertEqual(len(result), 0)
        self.assert_index_equal(result.columns,
                                pd.Index(['foo', 'bar', 'baz']))

        result = DataFrame.from_records([])
        self.assertEqual(len(result), 0)
        self.assertEqual(len(result.columns), 0)

    def test_from_records_dictlike(self):

        # test the dict methods
        df = DataFrame({'A': np.array(np.random.randn(6), dtype=np.float64),
                        'A1': np.array(np.random.randn(6), dtype=np.float64),
                        'B': np.array(np.arange(6), dtype=np.int64),
                        'C': ['foo'] * 6,
                        'D': np.array([True, False] * 3, dtype=bool),
                        'E': np.array(np.random.randn(6), dtype=np.float32),
                        'E1': np.array(np.random.randn(6), dtype=np.float32),
                        'F': np.array(np.arange(6), dtype=np.int32)})

        # columns is in a different order here than the actual items iterated
        # from the dict
        columns = []
        for dtype, b in compat.iteritems(df.blocks):
            columns.extend(b.columns)

        asdict = dict((x, y) for x, y in compat.iteritems(df))
        asdict2 = dict((x, y.values) for x, y in compat.iteritems(df))

        # dict of series & dict of ndarrays (have dtype info)
        results = []
        results.append(DataFrame.from_records(
            asdict).reindex(columns=df.columns))
        results.append(DataFrame.from_records(asdict, columns=columns)
                       .reindex(columns=df.columns))
        results.append(DataFrame.from_records(asdict2, columns=columns)
                       .reindex(columns=df.columns))

        for r in results:
            tm.assert_frame_equal(r, df)

    def test_from_records_with_index_data(self):
        df = DataFrame(np.random.randn(10, 3), columns=['A', 'B', 'C'])

        data = np.random.randn(10)
        df1 = DataFrame.from_records(df, index=data)
        tm.assert_index_equal(df1.index, Index(data))

    def test_from_records_bad_index_column(self):
        df = DataFrame(np.random.randn(10, 3), columns=['A', 'B', 'C'])

        # should pass
        df1 = DataFrame.from_records(df, index=['C'])
        tm.assert_index_equal(df1.index, Index(df.C))

        df1 = DataFrame.from_records(df, index='C')
        tm.assert_index_equal(df1.index, Index(df.C))

        # should fail
        self.assertRaises(ValueError, DataFrame.from_records, df, index=[2])
        self.assertRaises(KeyError, DataFrame.from_records, df, index=2)

    def test_from_records_non_tuple(self):
        class Record(object):

            def __init__(self, *args):
                self.args = args

            def __getitem__(self, i):
                return self.args[i]

            def __iter__(self):
                return iter(self.args)

        recs = [Record(1, 2, 3), Record(4, 5, 6), Record(7, 8, 9)]
        tups = lmap(tuple, recs)

        result = DataFrame.from_records(recs)
        expected = DataFrame.from_records(tups)
        tm.assert_frame_equal(result, expected)

    def test_from_records_len0_with_columns(self):
        # #2633
        result = DataFrame.from_records([], index='foo',
                                        columns=['foo', 'bar'])

        self.assertTrue(np.array_equal(result.columns, ['bar']))
        self.assertEqual(len(result), 0)
        self.assertEqual(result.index.name, 'foo')


class TestDataFrameConstructorWithDatetimeTZ(tm.TestCase, TestData):

    _multiprocess_can_split_ = True

    def test_from_dict(self):

        # 8260
        # support datetime64 with tz

        idx = Index(date_range('20130101', periods=3, tz='US/Eastern'),
                    name='foo')
        dr = date_range('20130110', periods=3)

        # construction
        df = DataFrame({'A': idx, 'B': dr})
        self.assertTrue(df['A'].dtype, 'M8[ns, US/Eastern')
        self.assertTrue(df['A'].name == 'A')
        tm.assert_series_equal(df['A'], Series(idx, name='A'))
        tm.assert_series_equal(df['B'], Series(dr, name='B'))

    def test_from_index(self):

        # from index
        idx2 = date_range('20130101', periods=3, tz='US/Eastern', name='foo')
        df2 = DataFrame(idx2)
        tm.assert_series_equal(df2['foo'], Series(idx2, name='foo'))
        df2 = DataFrame(Series(idx2))
        tm.assert_series_equal(df2['foo'], Series(idx2, name='foo'))

        idx2 = date_range('20130101', periods=3, tz='US/Eastern')
        df2 = DataFrame(idx2)
        tm.assert_series_equal(df2[0], Series(idx2, name=0))
        df2 = DataFrame(Series(idx2))
        tm.assert_series_equal(df2[0], Series(idx2, name=0))

if __name__ == '__main__':
    import nose  # noqa

    nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'],
                   exit=False)
