/pandas/tests/types/test_inference.py

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

"""
These the test the public routines exposed in types/common.py
related to inference and not otherwise tested in types/test_common.py

"""

import nose
import collections
import re
from datetime import datetime, date, timedelta, time
import numpy as np

import pandas as pd
from pandas import lib, tslib
from pandas import (Series, Index, DataFrame, Timedelta,
                    DatetimeIndex, TimedeltaIndex, Timestamp,
                    Panel, Period)
from pandas.compat import u, PY2, lrange
from pandas.types import inference
from pandas.types.common import (is_timedelta64_dtype,
                                 is_timedelta64_ns_dtype,
                                 is_number,
                                 is_integer,
                                 is_float,
                                 is_bool,
                                 is_scalar,
                                 _ensure_int32)
from pandas.types.missing import isnull
from pandas.util import testing as tm

_multiprocess_can_split_ = True


def test_is_sequence():
    is_seq = inference.is_sequence
    assert (is_seq((1, 2)))
    assert (is_seq([1, 2]))
    assert (not is_seq("abcd"))
    assert (not is_seq(u("abcd")))
    assert (not is_seq(np.int64))

    class A(object):

        def __getitem__(self):
            return 1

    assert (not is_seq(A()))


def test_is_list_like():
    passes = ([], [1], (1, ), (1, 2), {'a': 1}, set([1, 'a']), Series([1]),
              Series([]), Series(['a']).str)
    fails = (1, '2', object())

    for p in passes:
        assert inference.is_list_like(p)

    for f in fails:
        assert not inference.is_list_like(f)


def test_is_dict_like():
    passes = [{}, {'A': 1}, Series([1])]
    fails = ['1', 1, [1, 2], (1, 2), range(2), Index([1])]

    for p in passes:
        assert inference.is_dict_like(p)

    for f in fails:
        assert not inference.is_dict_like(f)


def test_is_named_tuple():
    passes = (collections.namedtuple('Test', list('abc'))(1, 2, 3), )
    fails = ((1, 2, 3), 'a', Series({'pi': 3.14}))

    for p in passes:
        assert inference.is_named_tuple(p)

    for f in fails:
        assert not inference.is_named_tuple(f)


def test_is_hashable():

    # all new-style classes are hashable by default
    class HashableClass(object):
        pass

    class UnhashableClass1(object):
        __hash__ = None

    class UnhashableClass2(object):

        def __hash__(self):
            raise TypeError("Not hashable")

    hashable = (1,
                3.14,
                np.float64(3.14),
                'a',
                tuple(),
                (1, ),
                HashableClass(), )
    not_hashable = ([], UnhashableClass1(), )
    abc_hashable_not_really_hashable = (([], ), UnhashableClass2(), )

    for i in hashable:
        assert inference.is_hashable(i)
    for i in not_hashable:
        assert not inference.is_hashable(i)
    for i in abc_hashable_not_really_hashable:
        assert not inference.is_hashable(i)

    # numpy.array is no longer collections.Hashable as of
    # https://github.com/numpy/numpy/pull/5326, just test
    # is_hashable()
    assert not inference.is_hashable(np.array([]))

    # old-style classes in Python 2 don't appear hashable to
    # collections.Hashable but also seem to support hash() by default
    if PY2:

        class OldStyleClass():
            pass

        c = OldStyleClass()
        assert not isinstance(c, collections.Hashable)
        assert inference.is_hashable(c)
        hash(c)  # this will not raise


def test_is_re():
    passes = re.compile('ad'),
    fails = 'x', 2, 3, object()

    for p in passes:
        assert inference.is_re(p)

    for f in fails:
        assert not inference.is_re(f)


def test_is_recompilable():
    passes = (r'a', u('x'), r'asdf', re.compile('adsf'), u(r'\u2233\s*'),
              re.compile(r''))
    fails = 1, [], object()

    for p in passes:
        assert inference.is_re_compilable(p)

    for f in fails:
        assert not inference.is_re_compilable(f)


class TestInference(tm.TestCase):

    def test_infer_dtype_bytes(self):
        compare = 'string' if PY2 else 'bytes'

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

        # object array of bytes
        arr = arr.astype(object)
        self.assertEqual(lib.infer_dtype(arr), compare)

    def test_isinf_scalar(self):
        # GH 11352
        self.assertTrue(lib.isposinf_scalar(float('inf')))
        self.assertTrue(lib.isposinf_scalar(np.inf))
        self.assertFalse(lib.isposinf_scalar(-np.inf))
        self.assertFalse(lib.isposinf_scalar(1))
        self.assertFalse(lib.isposinf_scalar('a'))

        self.assertTrue(lib.isneginf_scalar(float('-inf')))
        self.assertTrue(lib.isneginf_scalar(-np.inf))
        self.assertFalse(lib.isneginf_scalar(np.inf))
        self.assertFalse(lib.isneginf_scalar(1))
        self.assertFalse(lib.isneginf_scalar('a'))

    def test_maybe_convert_numeric_infinities(self):
        # see gh-13274
        infinities = ['inf', 'inF', 'iNf', 'Inf',
                      'iNF', 'InF', 'INf', 'INF']
        na_values = set(['', 'NULL', 'nan'])

        pos = np.array(['inf'], dtype=np.float64)
        neg = np.array(['-inf'], dtype=np.float64)

        msg = "Unable to parse string"

        for infinity in infinities:
            for maybe_int in (True, False):
                out = lib.maybe_convert_numeric(
                    np.array([infinity], dtype=object),
                    na_values, maybe_int)
                tm.assert_numpy_array_equal(out, pos)

                out = lib.maybe_convert_numeric(
                    np.array(['-' + infinity], dtype=object),
                    na_values, maybe_int)
                tm.assert_numpy_array_equal(out, neg)

                out = lib.maybe_convert_numeric(
                    np.array([u(infinity)], dtype=object),
                    na_values, maybe_int)
                tm.assert_numpy_array_equal(out, pos)

                out = lib.maybe_convert_numeric(
                    np.array(['+' + infinity], dtype=object),
                    na_values, maybe_int)
                tm.assert_numpy_array_equal(out, pos)

                # too many characters
                with tm.assertRaisesRegexp(ValueError, msg):
                    lib.maybe_convert_numeric(
                        np.array(['foo_' + infinity], dtype=object),
                        na_values, maybe_int)

    def test_maybe_convert_numeric_post_floatify_nan(self):
        # see gh-13314
        data = np.array(['1.200', '-999.000', '4.500'], dtype=object)
        expected = np.array([1.2, np.nan, 4.5], dtype=np.float64)
        nan_values = set([-999, -999.0])

        for coerce_type in (True, False):
            out = lib.maybe_convert_numeric(data, nan_values, coerce_type)
            tm.assert_numpy_array_equal(out, expected)

    def test_convert_infs(self):
        arr = np.array(['inf', 'inf', 'inf'], dtype='O')
        result = lib.maybe_convert_numeric(arr, set(), False)
        self.assertTrue(result.dtype == np.float64)

        arr = np.array(['-inf', '-inf', '-inf'], dtype='O')
        result = lib.maybe_convert_numeric(arr, set(), False)
        self.assertTrue(result.dtype == np.float64)

    def test_scientific_no_exponent(self):
        # See PR 12215
        arr = np.array(['42E', '2E', '99e', '6e'], dtype='O')
        result = lib.maybe_convert_numeric(arr, set(), False, True)
        self.assertTrue(np.all(np.isnan(result)))

    def test_convert_non_hashable(self):
        # GH13324
        # make sure that we are handing non-hashables
        arr = np.array([[10.0, 2], 1.0, 'apple'])
        result = lib.maybe_convert_numeric(arr, set(), False, True)
        tm.assert_numpy_array_equal(result, np.array([np.nan, 1.0, np.nan]))


class TestTypeInference(tm.TestCase):
    _multiprocess_can_split_ = True

    def test_length_zero(self):
        result = lib.infer_dtype(np.array([], dtype='i4'))
        self.assertEqual(result, 'integer')

        result = lib.infer_dtype([])
        self.assertEqual(result, 'empty')

    def test_integers(self):
        arr = np.array([1, 2, 3, np.int64(4), np.int32(5)], dtype='O')
        result = lib.infer_dtype(arr)
        self.assertEqual(result, 'integer')

        arr = np.array([1, 2, 3, np.int64(4), np.int32(5), 'foo'], dtype='O')
        result = lib.infer_dtype(arr)
        self.assertEqual(result, 'mixed-integer')

        arr = np.array([1, 2, 3, 4, 5], dtype='i4')
        result = lib.infer_dtype(arr)
        self.assertEqual(result, 'integer')

    def test_bools(self):
        arr = np.array([True, False, True, True, True], dtype='O')
        result = lib.infer_dtype(arr)
        self.assertEqual(result, 'boolean')

        arr = np.array([np.bool_(True), np.bool_(False)], dtype='O')
        result = lib.infer_dtype(arr)
        self.assertEqual(result, 'boolean')

        arr = np.array([True, False, True, 'foo'], dtype='O')
        result = lib.infer_dtype(arr)
        self.assertEqual(result, 'mixed')

        arr = np.array([True, False, True], dtype=bool)
        result = lib.infer_dtype(arr)
        self.assertEqual(result, 'boolean')

    def test_floats(self):
        arr = np.array([1., 2., 3., np.float64(4), np.float32(5)], dtype='O')
        result = lib.infer_dtype(arr)
        self.assertEqual(result, 'floating')

        arr = np.array([1, 2, 3, np.float64(4), np.float32(5), 'foo'],
                       dtype='O')
        result = lib.infer_dtype(arr)
        self.assertEqual(result, 'mixed-integer')

        arr = np.array([1, 2, 3, 4, 5], dtype='f4')
        result = lib.infer_dtype(arr)
        self.assertEqual(result, 'floating')

        arr = np.array([1, 2, 3, 4, 5], dtype='f8')
        result = lib.infer_dtype(arr)
        self.assertEqual(result, 'floating')

    def test_string(self):
        pass

    def test_unicode(self):
        pass

    def test_datetime(self):

        dates = [datetime(2012, 1, x) for x in range(1, 20)]
        index = Index(dates)
        self.assertEqual(index.inferred_type, 'datetime64')

    def test_infer_dtype_datetime(self):

        arr = np.array([Timestamp('2011-01-01'),
                        Timestamp('2011-01-02')])
        self.assertEqual(lib.infer_dtype(arr), 'datetime')

        arr = np.array([np.datetime64('2011-01-01'),
                        np.datetime64('2011-01-01')], dtype=object)
        self.assertEqual(lib.infer_dtype(arr), 'datetime64')

        arr = np.array([datetime(2011, 1, 1), datetime(2012, 2, 1)])
        self.assertEqual(lib.infer_dtype(arr), 'datetime')

        # starts with nan
        for n in [pd.NaT, np.nan]:
            arr = np.array([n, pd.Timestamp('2011-01-02')])
            self.assertEqual(lib.infer_dtype(arr), 'datetime')

            arr = np.array([n, np.datetime64('2011-01-02')])
            self.assertEqual(lib.infer_dtype(arr), 'datetime64')

            arr = np.array([n, datetime(2011, 1, 1)])
            self.assertEqual(lib.infer_dtype(arr), 'datetime')

            arr = np.array([n, pd.Timestamp('2011-01-02'), n])
            self.assertEqual(lib.infer_dtype(arr), 'datetime')

            arr = np.array([n, np.datetime64('2011-01-02'), n])
            self.assertEqual(lib.infer_dtype(arr), 'datetime64')

            arr = np.array([n, datetime(2011, 1, 1), n])
            self.assertEqual(lib.infer_dtype(arr), 'datetime')

        # different type of nat
        arr = np.array([np.timedelta64('nat'),
                        np.datetime64('2011-01-02')], dtype=object)
        self.assertEqual(lib.infer_dtype(arr), 'mixed')

        arr = np.array([np.datetime64('2011-01-02'),
                        np.timedelta64('nat')], dtype=object)
        self.assertEqual(lib.infer_dtype(arr), 'mixed')

        # mixed datetime
        arr = np.array([datetime(2011, 1, 1),
                        pd.Timestamp('2011-01-02')])
        self.assertEqual(lib.infer_dtype(arr), 'datetime')

        # should be datetime?
        arr = np.array([np.datetime64('2011-01-01'),
                        pd.Timestamp('2011-01-02')])
        self.assertEqual(lib.infer_dtype(arr), 'mixed')

        arr = np.array([pd.Timestamp('2011-01-02'),
                        np.datetime64('2011-01-01')])
        self.assertEqual(lib.infer_dtype(arr), 'mixed')

        arr = np.array([np.nan, pd.Timestamp('2011-01-02'), 1])
        self.assertEqual(lib.infer_dtype(arr), 'mixed-integer')

        arr = np.array([np.nan, pd.Timestamp('2011-01-02'), 1.1])
        self.assertEqual(lib.infer_dtype(arr), 'mixed')

        arr = np.array([np.nan, '2011-01-01', pd.Timestamp('2011-01-02')])
        self.assertEqual(lib.infer_dtype(arr), 'mixed')

    def test_infer_dtype_timedelta(self):

        arr = np.array([pd.Timedelta('1 days'),
                        pd.Timedelta('2 days')])
        self.assertEqual(lib.infer_dtype(arr), 'timedelta')

        arr = np.array([np.timedelta64(1, 'D'),
                        np.timedelta64(2, 'D')], dtype=object)
        self.assertEqual(lib.infer_dtype(arr), 'timedelta')

        arr = np.array([timedelta(1), timedelta(2)])
        self.assertEqual(lib.infer_dtype(arr), 'timedelta')

        # starts with nan
        for n in [pd.NaT, np.nan]:
            arr = np.array([n, Timedelta('1 days')])
            self.assertEqual(lib.infer_dtype(arr), 'timedelta')

            arr = np.array([n, np.timedelta64(1, 'D')])
            self.assertEqual(lib.infer_dtype(arr), 'timedelta')

            arr = np.array([n, timedelta(1)])
            self.assertEqual(lib.infer_dtype(arr), 'timedelta')

            arr = np.array([n, pd.Timedelta('1 days'), n])
            self.assertEqual(lib.infer_dtype(arr), 'timedelta')

            arr = np.array([n, np.timedelta64(1, 'D'), n])
            self.assertEqual(lib.infer_dtype(arr), 'timedelta')

            arr = np.array([n, timedelta(1), n])
            self.assertEqual(lib.infer_dtype(arr), 'timedelta')

        # different type of nat
        arr = np.array([np.datetime64('nat'), np.timedelta64(1, 'D')],
                       dtype=object)
        self.assertEqual(lib.infer_dtype(arr), 'mixed')

        arr = np.array([np.timedelta64(1, 'D'), np.datetime64('nat')],
                       dtype=object)
        self.assertEqual(lib.infer_dtype(arr), 'mixed')

    def test_infer_dtype_period(self):
        # GH 13664
        arr = np.array([pd.Period('2011-01', freq='D'),
                        pd.Period('2011-02', freq='D')])
        self.assertEqual(pd.lib.infer_dtype(arr), 'period')

        arr = np.array([pd.Period('2011-01', freq='D'),
                        pd.Period('2011-02', freq='M')])
        self.assertEqual(pd.lib.infer_dtype(arr), 'period')

        # starts with nan
        for n in [pd.NaT, np.nan]:
            arr = np.array([n, pd.Period('2011-01', freq='D')])
            self.assertEqual(pd.lib.infer_dtype(arr), 'period')

            arr = np.array([n, pd.Period('2011-01', freq='D'), n])
            self.assertEqual(pd.lib.infer_dtype(arr), 'period')

        # different type of nat
        arr = np.array([np.datetime64('nat'), pd.Period('2011-01', freq='M')],
                       dtype=object)
        self.assertEqual(pd.lib.infer_dtype(arr), 'mixed')

        arr = np.array([pd.Period('2011-01', freq='M'), np.datetime64('nat')],
                       dtype=object)
        self.assertEqual(pd.lib.infer_dtype(arr), 'mixed')

    def test_infer_dtype_all_nan_nat_like(self):
        arr = np.array([np.nan, np.nan])
        self.assertEqual(lib.infer_dtype(arr), 'floating')

        # nan and None mix are result in mixed
        arr = np.array([np.nan, np.nan, None])
        self.assertEqual(lib.infer_dtype(arr), 'mixed')

        arr = np.array([None, np.nan, np.nan])
        self.assertEqual(lib.infer_dtype(arr), 'mixed')

        # pd.NaT
        arr = np.array([pd.NaT])
        self.assertEqual(lib.infer_dtype(arr), 'datetime')

        arr = np.array([pd.NaT, np.nan])
        self.assertEqual(lib.infer_dtype(arr), 'datetime')

        arr = np.array([np.nan, pd.NaT])
        self.assertEqual(lib.infer_dtype(arr), 'datetime')

        arr = np.array([np.nan, pd.NaT, np.nan])
        self.assertEqual(lib.infer_dtype(arr), 'datetime')

        arr = np.array([None, pd.NaT, None])
        self.assertEqual(lib.infer_dtype(arr), 'datetime')

        # np.datetime64(nat)
        arr = np.array([np.datetime64('nat')])
        self.assertEqual(lib.infer_dtype(arr), 'datetime64')

        for n in [np.nan, pd.NaT, None]:
            arr = np.array([n, np.datetime64('nat'), n])
            self.assertEqual(lib.infer_dtype(arr), 'datetime64')

            arr = np.array([pd.NaT, n, np.datetime64('nat'), n])
            self.assertEqual(lib.infer_dtype(arr), 'datetime64')

        arr = np.array([np.timedelta64('nat')], dtype=object)
        self.assertEqual(lib.infer_dtype(arr), 'timedelta')

        for n in [np.nan, pd.NaT, None]:
            arr = np.array([n, np.timedelta64('nat'), n])
            self.assertEqual(lib.infer_dtype(arr), 'timedelta')

            arr = np.array([pd.NaT, n, np.timedelta64('nat'), n])
            self.assertEqual(lib.infer_dtype(arr), 'timedelta')

        # datetime / timedelta mixed
        arr = np.array([pd.NaT, np.datetime64('nat'),
                        np.timedelta64('nat'), np.nan])
        self.assertEqual(lib.infer_dtype(arr), 'mixed')

        arr = np.array([np.timedelta64('nat'), np.datetime64('nat')],
                       dtype=object)
        self.assertEqual(lib.infer_dtype(arr), 'mixed')

    def test_is_datetimelike_array_all_nan_nat_like(self):
        arr = np.array([np.nan, pd.NaT, np.datetime64('nat')])
        self.assertTrue(lib.is_datetime_array(arr))
        self.assertTrue(lib.is_datetime64_array(arr))
        self.assertFalse(lib.is_timedelta_array(arr))
        self.assertFalse(lib.is_timedelta64_array(arr))
        self.assertFalse(lib.is_timedelta_or_timedelta64_array(arr))

        arr = np.array([np.nan, pd.NaT, np.timedelta64('nat')])
        self.assertFalse(lib.is_datetime_array(arr))
        self.assertFalse(lib.is_datetime64_array(arr))
        self.assertTrue(lib.is_timedelta_array(arr))
        self.assertTrue(lib.is_timedelta64_array(arr))
        self.assertTrue(lib.is_timedelta_or_timedelta64_array(arr))

        arr = np.array([np.nan, pd.NaT, np.datetime64('nat'),
                        np.timedelta64('nat')])
        self.assertFalse(lib.is_datetime_array(arr))
        self.assertFalse(lib.is_datetime64_array(arr))
        self.assertFalse(lib.is_timedelta_array(arr))
        self.assertFalse(lib.is_timedelta64_array(arr))
        self.assertFalse(lib.is_timedelta_or_timedelta64_array(arr))

        arr = np.array([np.nan, pd.NaT])
        self.assertTrue(lib.is_datetime_array(arr))
        self.assertTrue(lib.is_datetime64_array(arr))
        self.assertTrue(lib.is_timedelta_array(arr))
        self.assertTrue(lib.is_timedelta64_array(arr))
        self.assertTrue(lib.is_timedelta_or_timedelta64_array(arr))

        arr = np.array([np.nan, np.nan], dtype=object)
        self.assertFalse(lib.is_datetime_array(arr))
        self.assertFalse(lib.is_datetime64_array(arr))
        self.assertFalse(lib.is_timedelta_array(arr))
        self.assertFalse(lib.is_timedelta64_array(arr))
        self.assertFalse(lib.is_timedelta_or_timedelta64_array(arr))

    def test_date(self):

        dates = [date(2012, 1, x) for x in range(1, 20)]
        index = Index(dates)
        self.assertEqual(index.inferred_type, 'date')

    def test_to_object_array_tuples(self):
        r = (5, 6)
        values = [r]
        result = lib.to_object_array_tuples(values)

        try:
            # make sure record array works
            from collections import namedtuple
            record = namedtuple('record', 'x y')
            r = record(5, 6)
            values = [r]
            result = lib.to_object_array_tuples(values)  # noqa
        except ImportError:
            pass

    def test_object(self):

        # GH 7431
        # cannot infer more than this as only a single element
        arr = np.array([None], dtype='O')
        result = lib.infer_dtype(arr)
        self.assertEqual(result, 'mixed')

    def test_to_object_array_width(self):
        # see gh-13320
        rows = [[1, 2, 3], [4, 5, 6]]

        expected = np.array(rows, dtype=object)
        out = lib.to_object_array(rows)
        tm.assert_numpy_array_equal(out, expected)

        expected = np.array(rows, dtype=object)
        out = lib.to_object_array(rows, min_width=1)
        tm.assert_numpy_array_equal(out, expected)

        expected = np.array([[1, 2, 3, None, None],
                             [4, 5, 6, None, None]], dtype=object)
        out = lib.to_object_array(rows, min_width=5)
        tm.assert_numpy_array_equal(out, expected)

    def test_is_period(self):
        self.assertTrue(lib.is_period(pd.Period('2011-01', freq='M')))
        self.assertFalse(lib.is_period(pd.PeriodIndex(['2011-01'], freq='M')))
        self.assertFalse(lib.is_period(pd.Timestamp('2011-01')))
        self.assertFalse(lib.is_period(1))
        self.assertFalse(lib.is_period(np.nan))

    def test_categorical(self):

        # GH 8974
        from pandas import Categorical, Series
        arr = Categorical(list('abc'))
        result = lib.infer_dtype(arr)
        self.assertEqual(result, 'categorical')

        result = lib.infer_dtype(Series(arr))
        self.assertEqual(result, 'categorical')

        arr = Categorical(list('abc'), categories=['cegfab'], ordered=True)
        result = lib.infer_dtype(arr)
        self.assertEqual(result, 'categorical')

        result = lib.infer_dtype(Series(arr))
        self.assertEqual(result, 'categorical')


class TestNumberScalar(tm.TestCase):

    def test_is_number(self):

        self.assertTrue(is_number(True))
        self.assertTrue(is_number(1))
        self.assertTrue(is_number(1.1))
        self.assertTrue(is_number(1 + 3j))
        self.assertTrue(is_number(np.bool(False)))
        self.assertTrue(is_number(np.int64(1)))
        self.assertTrue(is_number(np.float64(1.1)))
        self.assertTrue(is_number(np.complex128(1 + 3j)))
        self.assertTrue(is_number(np.nan))

        self.assertFalse(is_number(None))
        self.assertFalse(is_number('x'))
        self.assertFalse(is_number(datetime(2011, 1, 1)))
        self.assertFalse(is_number(np.datetime64('2011-01-01')))
        self.assertFalse(is_number(Timestamp('2011-01-01')))
        self.assertFalse(is_number(Timestamp('2011-01-01',
                                             tz='US/Eastern')))
        self.assertFalse(is_number(timedelta(1000)))
        self.assertFalse(is_number(Timedelta('1 days')))

        # questionable
        self.assertFalse(is_number(np.bool_(False)))
        self.assertTrue(is_number(np.timedelta64(1, 'D')))

    def test_is_bool(self):
        self.assertTrue(is_bool(True))
        self.assertTrue(is_bool(np.bool(False)))
        self.assertTrue(is_bool(np.bool_(False)))

        self.assertFalse(is_bool(1))
        self.assertFalse(is_bool(1.1))
        self.assertFalse(is_bool(1 + 3j))
        self.assertFalse(is_bool(np.int64(1)))
        self.assertFalse(is_bool(np.float64(1.1)))
        self.assertFalse(is_bool(np.complex128(1 + 3j)))
        self.assertFalse(is_bool(np.nan))
        self.assertFalse(is_bool(None))
        self.assertFalse(is_bool('x'))
        self.assertFalse(is_bool(datetime(2011, 1, 1)))
        self.assertFalse(is_bool(np.datetime64('2011-01-01')))
        self.assertFalse(is_bool(Timestamp('2011-01-01')))
        self.assertFalse(is_bool(Timestamp('2011-01-01',
                                           tz='US/Eastern')))
        self.assertFalse(is_bool(timedelta(1000)))
        self.assertFalse(is_bool(np.timedelta64(1, 'D')))
        self.assertFalse(is_bool(Timedelta('1 days')))

    def test_is_integer(self):
        self.assertTrue(is_integer(1))
        self.assertTrue(is_integer(np.int64(1)))

        self.assertFalse(is_integer(True))
        self.assertFalse(is_integer(1.1))
        self.assertFalse(is_integer(1 + 3j))
        self.assertFalse(is_integer(np.bool(False)))
        self.assertFalse(is_integer(np.bool_(False)))
        self.assertFalse(is_integer(np.float64(1.1)))
        self.assertFalse(is_integer(np.complex128(1 + 3j)))
        self.assertFalse(is_integer(np.nan))
        self.assertFalse(is_integer(None))
        self.assertFalse(is_integer('x'))
        self.assertFalse(is_integer(datetime(2011, 1, 1)))
        self.assertFalse(is_integer(np.datetime64('2011-01-01')))
        self.assertFalse(is_integer(Timestamp('2011-01-01')))
        self.assertFalse(is_integer(Timestamp('2011-01-01',
                                              tz='US/Eastern')))
        self.assertFalse(is_integer(timedelta(1000)))
        self.assertFalse(is_integer(Timedelta('1 days')))

        # questionable
        self.assertTrue(is_integer(np.timedelta64(1, 'D')))

    def test_is_float(self):
        self.assertTrue(is_float(1.1))
        self.assertTrue(is_float(np.float64(1.1)))
        self.assertTrue(is_float(np.nan))

        self.assertFalse(is_float(True))
        self.assertFalse(is_float(1))
        self.assertFalse(is_float(1 + 3j))
        self.assertFalse(is_float(np.bool(False)))
        self.assertFalse(is_float(np.bool_(False)))
        self.assertFalse(is_float(np.int64(1)))
        self.assertFalse(is_float(np.complex128(1 + 3j)))
        self.assertFalse(is_float(None))
        self.assertFalse(is_float('x'))
        self.assertFalse(is_float(datetime(2011, 1, 1)))
        self.assertFalse(is_float(np.datetime64('2011-01-01')))
        self.assertFalse(is_float(Timestamp('2011-01-01')))
        self.assertFalse(is_float(Timestamp('2011-01-01',
                                            tz='US/Eastern')))
        self.assertFalse(is_float(timedelta(1000)))
        self.assertFalse(is_float(np.timedelta64(1, 'D')))
        self.assertFalse(is_float(Timedelta('1 days')))

    def test_is_timedelta(self):
        self.assertTrue(is_timedelta64_dtype('timedelta64'))
        self.assertTrue(is_timedelta64_dtype('timedelta64[ns]'))
        self.assertFalse(is_timedelta64_ns_dtype('timedelta64'))
        self.assertTrue(is_timedelta64_ns_dtype('timedelta64[ns]'))

        tdi = TimedeltaIndex([1e14, 2e14], dtype='timedelta64')
        self.assertTrue(is_timedelta64_dtype(tdi))
        self.assertTrue(is_timedelta64_ns_dtype(tdi))
        self.assertTrue(is_timedelta64_ns_dtype(tdi.astype('timedelta64[ns]')))

        # Conversion to Int64Index:
        self.assertFalse(is_timedelta64_ns_dtype(tdi.astype('timedelta64')))
        self.assertFalse(is_timedelta64_ns_dtype(tdi.astype('timedelta64[h]')))


class Testisscalar(tm.TestCase):

    def test_isscalar_builtin_scalars(self):
        self.assertTrue(is_scalar(None))
        self.assertTrue(is_scalar(True))
        self.assertTrue(is_scalar(False))
        self.assertTrue(is_scalar(0.))
        self.assertTrue(is_scalar(np.nan))
        self.assertTrue(is_scalar('foobar'))
        self.assertTrue(is_scalar(b'foobar'))
        self.assertTrue(is_scalar(u('efoobar')))
        self.assertTrue(is_scalar(datetime(2014, 1, 1)))
        self.assertTrue(is_scalar(date(2014, 1, 1)))
        self.assertTrue(is_scalar(time(12, 0)))
        self.assertTrue(is_scalar(timedelta(hours=1)))
        self.assertTrue(is_scalar(pd.NaT))

    def test_isscalar_builtin_nonscalars(self):
        self.assertFalse(is_scalar({}))
        self.assertFalse(is_scalar([]))
        self.assertFalse(is_scalar([1]))
        self.assertFalse(is_scalar(()))
        self.assertFalse(is_scalar((1, )))
        self.assertFalse(is_scalar(slice(None)))
        self.assertFalse(is_scalar(Ellipsis))

    def test_isscalar_numpy_array_scalars(self):
        self.assertTrue(is_scalar(np.int64(1)))
        self.assertTrue(is_scalar(np.float64(1.)))
        self.assertTrue(is_scalar(np.int32(1)))
        self.assertTrue(is_scalar(np.object_('foobar')))
        self.assertTrue(is_scalar(np.str_('foobar')))
        self.assertTrue(is_scalar(np.unicode_(u('foobar'))))
        self.assertTrue(is_scalar(np.bytes_(b'foobar')))
        self.assertTrue(is_scalar(np.datetime64('2014-01-01')))
        self.assertTrue(is_scalar(np.timedelta64(1, 'h')))

    def test_isscalar_numpy_zerodim_arrays(self):
        for zerodim in [np.array(1), np.array('foobar'),
                        np.array(np.datetime64('2014-01-01')),
                        np.array(np.timedelta64(1, 'h')),
                        np.array(np.datetime64('NaT'))]:
            self.assertFalse(is_scalar(zerodim))
            self.assertTrue(is_scalar(lib.item_from_zerodim(zerodim)))

    def test_isscalar_numpy_arrays(self):
        self.assertFalse(is_scalar(np.array([])))
        self.assertFalse(is_scalar(np.array([[]])))
        self.assertFalse(is_scalar(np.matrix('1; 2')))

    def test_isscalar_pandas_scalars(self):
        self.assertTrue(is_scalar(Timestamp('2014-01-01')))
        self.assertTrue(is_scalar(Timedelta(hours=1)))
        self.assertTrue(is_scalar(Period('2014-01-01')))

    def test_lisscalar_pandas_containers(self):
        self.assertFalse(is_scalar(Series()))
        self.assertFalse(is_scalar(Series([1])))
        self.assertFalse(is_scalar(DataFrame()))
        self.assertFalse(is_scalar(DataFrame([[1]])))
        self.assertFalse(is_scalar(Panel()))
        self.assertFalse(is_scalar(Panel([[[1]]])))
        self.assertFalse(is_scalar(Index([])))
        self.assertFalse(is_scalar(Index([1])))


def test_datetimeindex_from_empty_datetime64_array():
    for unit in ['ms', 'us', 'ns']:
        idx = DatetimeIndex(np.array([], dtype='datetime64[%s]' % unit))
        assert (len(idx) == 0)


def test_nan_to_nat_conversions():

    df = DataFrame(dict({
        'A': np.asarray(
            lrange(10), dtype='float64'),
        'B': Timestamp('20010101')
    }))
    df.iloc[3:6, :] = np.nan
    result = df.loc[4, 'B'].value
    assert (result == tslib.iNaT)

    s = df['B'].copy()
    s._data = s._data.setitem(indexer=tuple([slice(8, 9)]), value=np.nan)
    assert (isnull(s[8]))

    # numpy < 1.7.0 is wrong
    from distutils.version import LooseVersion
    if LooseVersion(np.__version__) >= '1.7.0':
        assert (s[8].value == np.datetime64('NaT').astype(np.int64))


def test_ensure_int32():
    values = np.arange(10, dtype=np.int32)
    result = _ensure_int32(values)
    assert (result.dtype == np.int32)

    values = np.arange(10, dtype=np.int64)
    result = _ensure_int32(values)
    assert (result.dtype == np.int32)


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