content: # -*- coding: utf-8 -*-
  import collections
  from datetime import datetime
  import re
  
  import nose
  from nose.tools import assert_equal, assert_true
  import numpy as np
  import pandas as pd
  from pandas.tslib import iNaT, NaT
  from pandas import Series, DataFrame, date_range, DatetimeIndex, Timestamp, Float64Index
  from pandas import compat
  from pandas.compat import range, long, lrange, lmap, u
  from pandas.core.common import notnull, isnull, array_equivalent
  import pandas.core.common as com
  import pandas.core.convert as convert
  import pandas.core.format as fmt
  import pandas.util.testing as tm
  import pandas.core.config as cf
  
  _multiprocess_can_split_ = True
  
  
  def test_mut_exclusive():
      msg = "mutually exclusive arguments: '[ab]' and '[ab]'"
      with tm.assertRaisesRegexp(TypeError, msg):
          com._mut_exclusive(a=1, b=2)
      assert com._mut_exclusive(a=1, b=None) == 1
      assert com._mut_exclusive(major=None, major_axis=None) is None
  
  
  def test_is_sequence():
      is_seq = com.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_get_callable_name():
      from functools import partial
      getname = com._get_callable_name
  
      def fn(x):
          return x
      lambda_ = lambda x: x
      part1 = partial(fn)
      part2 = partial(part1)
  
      class somecall(object):
          def __call__(self):
              return x
  
      assert getname(fn) == 'fn'
      assert getname(lambda_)
      assert getname(part1) == 'fn'
      assert getname(part2) == 'fn'
      assert getname(somecall()) == 'somecall'
      assert getname(1) is None
  
  #Issue 10859
  class TestABCClasses(tm.TestCase):
      tuples = [[1, 2, 2], ['red', 'blue', 'red']]
      multi_index = pd.MultiIndex.from_arrays(tuples, names=('number', 'color'))
      datetime_index = pd.to_datetime(['2000/1/1', '2010/1/1'])
      timedelta_index = pd.to_timedelta(np.arange(5), unit='s')
      period_index = pd.period_range('2000/1/1', '2010/1/1/', freq='M')
      categorical = pd.Categorical([1, 2, 3], categories=[2, 3, 1])
      categorical_df = pd.DataFrame({"values": [1, 2, 3]}, index=categorical)
      df = pd.DataFrame({'names': ['a', 'b', 'c']}, index=multi_index)
      sparse_series = pd.Series([1, 2, 3]).to_sparse()
      sparse_array = pd.SparseArray(np.random.randn(10))
  
      def test_abc_types(self):
          self.assertIsInstance(pd.Index(['a', 'b', 'c']), com.ABCIndex)
          self.assertIsInstance(pd.Int64Index([1, 2, 3]), com.ABCInt64Index)
          self.assertIsInstance(pd.Float64Index([1, 2, 3]), com.ABCFloat64Index)
          self.assertIsInstance(self.multi_index, com.ABCMultiIndex)
          self.assertIsInstance(self.datetime_index, com.ABCDatetimeIndex)
          self.assertIsInstance(self.timedelta_index, com.ABCTimedeltaIndex)
          self.assertIsInstance(self.period_index, com.ABCPeriodIndex)
          self.assertIsInstance(self.categorical_df.index, com.ABCCategoricalIndex)
          self.assertIsInstance(pd.Index(['a', 'b', 'c']), com.ABCIndexClass)
          self.assertIsInstance(pd.Int64Index([1, 2, 3]), com.ABCIndexClass)
          self.assertIsInstance(pd.Series([1, 2, 3]), com.ABCSeries)
          self.assertIsInstance(self.df, com.ABCDataFrame)
          self.assertIsInstance(self.df.to_panel(), com.ABCPanel)
          self.assertIsInstance(self.sparse_series, com.ABCSparseSeries)
          self.assertIsInstance(self.sparse_array, com.ABCSparseArray)
          self.assertIsInstance(self.categorical, com.ABCCategorical)
          self.assertIsInstance(pd.Period('2012', freq='A-DEC'), com.ABCPeriod)
  
  
  class TestInferDtype(tm.TestCase):
  
      def test_infer_dtype_from_scalar(self):
          # Test that _infer_dtype_from_scalar is returning correct dtype for int and float.
  
          for dtypec in [ np.uint8, np.int8,
                          np.uint16, np.int16,
                          np.uint32, np.int32,
                          np.uint64, np.int64 ]:
              data = dtypec(12)
              dtype, val = com._infer_dtype_from_scalar(data)
              self.assertEqual(dtype, type(data))
  
          data = 12
          dtype, val = com._infer_dtype_from_scalar(data)
          self.assertEqual(dtype, np.int64)
  
          for dtypec in [ np.float16, np.float32, np.float64 ]:
              data = dtypec(12)
              dtype, val = com._infer_dtype_from_scalar(data)
              self.assertEqual(dtype, dtypec)
  
          data = np.float(12)
          dtype, val = com._infer_dtype_from_scalar(data)
          self.assertEqual(dtype, np.float64)
  
          for data in [ True, False ]:
              dtype, val = com._infer_dtype_from_scalar(data)
              self.assertEqual(dtype, np.bool_)
  
          for data in [ np.complex64(1), np.complex128(1) ]:
              dtype, val = com._infer_dtype_from_scalar(data)
              self.assertEqual(dtype, np.complex_)
  
          import datetime
          for data in [ np.datetime64(1,'ns'),
                        pd.Timestamp(1),
                        datetime.datetime(2000,1,1,0,0)
                        ]:
              dtype, val = com._infer_dtype_from_scalar(data)
              self.assertEqual(dtype, 'M8[ns]')
  
          for data in [ np.timedelta64(1,'ns'),
                        pd.Timedelta(1),
                        datetime.timedelta(1)
                        ]:
              dtype, val = com._infer_dtype_from_scalar(data)
              self.assertEqual(dtype, 'm8[ns]')
  
          for data in [ datetime.date(2000,1,1),
                        pd.Timestamp(1,tz='US/Eastern'),
                        'foo'
                        ]:
              dtype, val = com._infer_dtype_from_scalar(data)
              self.assertEqual(dtype, np.object_)
  
  def test_notnull():
      assert notnull(1.)
      assert not notnull(None)
      assert not notnull(np.NaN)
  
      with cf.option_context("mode.use_inf_as_null", False):
          assert notnull(np.inf)
          assert notnull(-np.inf)
  
          arr = np.array([1.5, np.inf, 3.5, -np.inf])
          result = notnull(arr)
          assert result.all()
  
      with cf.option_context("mode.use_inf_as_null", True):
          assert not notnull(np.inf)
          assert not notnull(-np.inf)
  
          arr = np.array([1.5, np.inf, 3.5, -np.inf])
          result = notnull(arr)
          assert result.sum() == 2
  
      with cf.option_context("mode.use_inf_as_null", False):
          for s in [tm.makeFloatSeries(),tm.makeStringSeries(),
                    tm.makeObjectSeries(),tm.makeTimeSeries(),tm.makePeriodSeries()]:
              assert(isinstance(isnull(s), Series))
  
  def test_isnull():
      assert not isnull(1.)
      assert isnull(None)
      assert isnull(np.NaN)
      assert not isnull(np.inf)
      assert not isnull(-np.inf)
  
      # series
      for s in [tm.makeFloatSeries(),tm.makeStringSeries(),
                tm.makeObjectSeries(),tm.makeTimeSeries(),tm.makePeriodSeries()]:
          assert(isinstance(isnull(s), Series))
  
      # frame
      for df in [tm.makeTimeDataFrame(),tm.makePeriodFrame(),tm.makeMixedDataFrame()]:
          result = isnull(df)
          expected = df.apply(isnull)
          tm.assert_frame_equal(result, expected)
  
      # panel
      for p in [ tm.makePanel(), tm.makePeriodPanel(), tm.add_nans(tm.makePanel()) ]:
          result = isnull(p)
          expected = p.apply(isnull)
          tm.assert_panel_equal(result, expected)
  
      # panel 4d
      for p in [ tm.makePanel4D(), tm.add_nans_panel4d(tm.makePanel4D()) ]:
          result = isnull(p)
          expected = p.apply(isnull)
          tm.assert_panel4d_equal(result, expected)
  
  def test_isnull_lists():
      result = isnull([[False]])
      exp = np.array([[False]])
      assert(np.array_equal(result, exp))
  
      result = isnull([[1], [2]])
      exp = np.array([[False], [False]])
      assert(np.array_equal(result, exp))
  
      # list of strings / unicode
      result = isnull(['foo', 'bar'])
      assert(not result.any())
  
      result = isnull([u('foo'), u('bar')])
      assert(not result.any())
  
  def test_isnull_nat():
      result = isnull([NaT])
      exp = np.array([True])
      assert(np.array_equal(result, exp))
  
      result = isnull(np.array([NaT], dtype=object))
      exp = np.array([True])
      assert(np.array_equal(result, exp))
  
  def test_isnull_numpy_nat():
      arr = np.array([NaT, np.datetime64('NaT'), np.timedelta64('NaT'),
                      np.datetime64('NaT', 's')])
      result = isnull(arr)
      expected = np.array([True] * 4)
      tm.assert_numpy_array_equal(result, expected)
  
  def test_isnull_datetime():
      assert (not isnull(datetime.now()))
      assert notnull(datetime.now())
  
      idx = date_range('1/1/1990', periods=20)
      assert(notnull(idx).all())
  
      idx = np.asarray(idx)
      idx[0] = iNaT
      idx = DatetimeIndex(idx)
      mask = isnull(idx)
      assert(mask[0])
      assert(not mask[1:].any())
  
      # GH 9129
      pidx = idx.to_period(freq='M')
      mask = isnull(pidx)
      assert(mask[0])
      assert(not mask[1:].any())
  
      mask = isnull(pidx[1:])
      assert(not mask.any())
  
  
  class TestIsNull(tm.TestCase):
      def test_0d_array(self):
          self.assertTrue(isnull(np.array(np.nan)))
          self.assertFalse(isnull(np.array(0.0)))
          self.assertFalse(isnull(np.array(0)))
          # test object dtype
          self.assertTrue(isnull(np.array(np.nan, dtype=object)))
          self.assertFalse(isnull(np.array(0.0, dtype=object)))
          self.assertFalse(isnull(np.array(0, dtype=object)))
  
  
  def test_downcast_conv():
      # test downcasting
  
      arr = np.array([8.5, 8.6, 8.7, 8.8, 8.9999999999995])
      result = com._possibly_downcast_to_dtype(arr, 'infer')
      assert (np.array_equal(result, arr))
  
      arr = np.array([8., 8., 8., 8., 8.9999999999995])
      result = com._possibly_downcast_to_dtype(arr, 'infer')
      expected = np.array([8, 8, 8, 8, 9])
      assert (np.array_equal(result, expected))
  
      arr = np.array([8., 8., 8., 8., 9.0000000000005])
      result = com._possibly_downcast_to_dtype(arr, 'infer')
      expected = np.array([8, 8, 8, 8, 9])
      assert (np.array_equal(result, expected))
  
      # conversions
  
      expected = np.array([1,2])
      for dtype in [np.float64,object,np.int64]:
          arr = np.array([1.0,2.0],dtype=dtype)
          result = com._possibly_downcast_to_dtype(arr,'infer')
          tm.assert_almost_equal(result, expected)
  
      expected = np.array([1.0,2.0,np.nan])
      for dtype in [np.float64,object]:
          arr = np.array([1.0,2.0,np.nan],dtype=dtype)
          result = com._possibly_downcast_to_dtype(arr,'infer')
          tm.assert_almost_equal(result, expected)
  
      # empties
      for dtype in [np.int32,np.float64,np.float32,np.bool_,np.int64,object]:
          arr = np.array([],dtype=dtype)
          result = com._possibly_downcast_to_dtype(arr,'int64')
          tm.assert_almost_equal(result, np.array([],dtype=np.int64))
          assert result.dtype == np.int64
  
  def test_array_equivalent():
      assert array_equivalent(np.array([np.nan, np.nan]),
                              np.array([np.nan, np.nan]))
      assert array_equivalent(np.array([np.nan, 1, np.nan]),
                              np.array([np.nan, 1, np.nan]))
      assert array_equivalent(np.array([np.nan, None], dtype='object'),
                              np.array([np.nan, None], dtype='object'))
      assert array_equivalent(np.array([np.nan, 1+1j], dtype='complex'),
                              np.array([np.nan, 1+1j], dtype='complex'))
      assert not array_equivalent(np.array([np.nan, 1+1j], dtype='complex'),
                                  np.array([np.nan, 1+2j], dtype='complex'))
      assert not array_equivalent(np.array([np.nan, 1, np.nan]),
                                  np.array([np.nan, 2, np.nan]))
      assert not array_equivalent(np.array(['a', 'b', 'c', 'd']), np.array(['e', 'e']))
      assert array_equivalent(Float64Index([0, np.nan]), Float64Index([0, np.nan]))
      assert not array_equivalent(Float64Index([0, np.nan]), Float64Index([1, np.nan]))
      assert array_equivalent(DatetimeIndex([0, np.nan]), DatetimeIndex([0, np.nan]))
      assert not array_equivalent(DatetimeIndex([0, np.nan]), DatetimeIndex([1, np.nan]))
  
  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 == 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_any_none():
      assert(com._any_none(1, 2, 3, None))
      assert(not com._any_none(1, 2, 3, 4))
  
  
  def test_all_not_none():
      assert(com._all_not_none(1, 2, 3, 4))
      assert(not com._all_not_none(1, 2, 3, None))
      assert(not com._all_not_none(None, None, None, None))
  
  
  def test_repr_binary_type():
      import string
      letters = string.ascii_letters
      btype = compat.binary_type
      try:
          raw = btype(letters, encoding=cf.get_option('display.encoding'))
      except TypeError:
          raw = btype(letters)
      b = compat.text_type(compat.bytes_to_str(raw))
      res = com.pprint_thing(b, quote_strings=True)
      assert_equal(res, repr(b))
      res = com.pprint_thing(b, quote_strings=False)
      assert_equal(res, b)
  
  
  def test_adjoin():
      data = [['a', 'b', 'c'],
              ['dd', 'ee', 'ff'],
              ['ggg', 'hhh', 'iii']]
      expected = 'a  dd  ggg\nb  ee  hhh\nc  ff  iii'
  
      adjoined = com.adjoin(2, *data)
  
      assert(adjoined == expected)
  
  
  
  class TestFormattBase(tm.TestCase):
  
      def test_adjoin(self):
          data = [['a', 'b', 'c'],
                  ['dd', 'ee', 'ff'],
                  ['ggg', 'hhh', 'iii']]
          expected = 'a  dd  ggg\nb  ee  hhh\nc  ff  iii'
  
          adjoined = com.adjoin(2, *data)
  
          self.assertEqual(adjoined, expected)
  
      def test_adjoin_unicode(self):
          data = [[u'あ', 'b', 'c'],
                  ['dd', u'ええ', 'ff'],
                  ['ggg', 'hhh', u'いいい']]
          expected = u'あ  dd  ggg\nb  ええ  hhh\nc  ff  いいい'
          adjoined = com.adjoin(2, *data)
          self.assertEqual(adjoined, expected)
  
          adj = fmt.EastAsianTextAdjustment()
  
          expected = u"""あ  dd    ggg
  b   ええ  hhh
  c   ff    いいい"""
          adjoined = adj.adjoin(2, *data)
          self.assertEqual(adjoined, expected)
          cols = adjoined.split('\n')
          self.assertEqual(adj.len(cols[0]), 13)
          self.assertEqual(adj.len(cols[1]), 13)
          self.assertEqual(adj.len(cols[2]), 16)
  
          expected = u"""あ       dd         ggg
  b        ええ       hhh
  c        ff         いいい"""
          adjoined = adj.adjoin(7, *data)
          self.assertEqual(adjoined, expected)
          cols = adjoined.split('\n')
          self.assertEqual(adj.len(cols[0]), 23)
          self.assertEqual(adj.len(cols[1]), 23)
          self.assertEqual(adj.len(cols[2]), 26)
  
      def test_justify(self):
          adj = fmt.EastAsianTextAdjustment()
  
          def just(x, *args, **kwargs):
              # wrapper to test single str
              return adj.justify([x], *args, **kwargs)[0]
  
          self.assertEqual(just('abc', 5, mode='left'), 'abc  ')
          self.assertEqual(just('abc', 5, mode='center'), ' abc ')
          self.assertEqual(just('abc', 5, mode='right'), '  abc')
          self.assertEqual(just(u'abc', 5, mode='left'), 'abc  ')
          self.assertEqual(just(u'abc', 5, mode='center'), ' abc ')
          self.assertEqual(just(u'abc', 5, mode='right'), '  abc')
  
          self.assertEqual(just(u'パンダ', 5, mode='left'), u'パンダ')
          self.assertEqual(just(u'パンダ', 5, mode='center'), u'パンダ')
          self.assertEqual(just(u'パンダ', 5, mode='right'), u'パンダ')
  
          self.assertEqual(just(u'パンダ', 10, mode='left'), u'パンダ    ')
          self.assertEqual(just(u'パンダ', 10, mode='center'), u'  パンダ  ')
          self.assertEqual(just(u'パンダ', 10, mode='right'), u'    パンダ')
  
      def test_east_asian_len(self):
          adj = fmt.EastAsianTextAdjustment()
  
          self.assertEqual(adj.len('abc'), 3)
          self.assertEqual(adj.len(u'abc'), 3)
  
          self.assertEqual(adj.len(u'パンダ'), 6)
          self.assertEqual(adj.len(u'ﾊﾟﾝﾀﾞ'), 5)
          self.assertEqual(adj.len(u'パンダpanda'), 11)
          self.assertEqual(adj.len(u'ﾊﾟﾝﾀﾞpanda'), 10)
  
  
      def test_ambiguous_width(self):
          adj = fmt.EastAsianTextAdjustment()
          self.assertEqual(adj.len(u'¡¡ab'), 4)
  
          with cf.option_context('display.unicode.ambiguous_as_wide', True):
              adj = fmt.EastAsianTextAdjustment()
              self.assertEqual(adj.len(u'¡¡ab'), 6)
  
          data = [[u'あ', 'b', 'c'],
                  ['dd', u'ええ', 'ff'],
                  ['ggg', u'¡¡ab', u'いいい']]
          expected = u'あ  dd    ggg \nb   ええ  ¡¡ab\nc   ff    いいい'
          adjoined = adj.adjoin(2, *data)
          self.assertEqual(adjoined, expected)
  
  
  def test_iterpairs():
      data = [1, 2, 3, 4]
      expected = [(1, 2),
                  (2, 3),
                  (3, 4)]
  
      result = list(com.iterpairs(data))
  
      assert(result == expected)
  
  
  def test_split_ranges():
      def _bin(x, width):
          "return int(x) as a base2 string of given width"
          return ''.join(str((x >> i) & 1) for i in range(width - 1, -1, -1))
  
      def test_locs(mask):
          nfalse = sum(np.array(mask) == 0)
  
          remaining = 0
          for s, e in com.split_ranges(mask):
              remaining += e - s
  
              assert 0 not in mask[s:e]
  
          # make sure the total items covered by the ranges are a complete cover
          assert remaining + nfalse == len(mask)
  
      # exhaustively test all possible mask sequences of length 8
      ncols = 8
      for i in range(2 ** ncols):
          cols = lmap(int, list(_bin(i, ncols)))  # count up in base2
          mask = [cols[i] == 1 for i in range(len(cols))]
          test_locs(mask)
  
      # base cases
      test_locs([])
      test_locs([0])
      test_locs([1])
  
  
  def test_indent():
      s = 'a b c\nd e f'
      result = com.indent(s, spaces=6)
  
      assert(result == '      a b c\n      d e f')
  
  
  def test_banner():
      ban = com.banner('hi')
      assert(ban == ('%s\nhi\n%s' % ('=' * 80, '=' * 80)))
  
  
  def test_map_indices_py():
      data = [4, 3, 2, 1]
      expected = {4: 0, 3: 1, 2: 2, 1: 3}
  
      result = com.map_indices_py(data)
  
      assert(result == expected)
  
  
  def test_union():
      a = [1, 2, 3]
      b = [4, 5, 6]
  
      union = sorted(com.union(a, b))
  
      assert((a + b) == union)
  
  
  def test_difference():
      a = [1, 2, 3]
      b = [1, 2, 3, 4, 5, 6]
  
      inter = sorted(com.difference(b, a))
  
      assert([4, 5, 6] == inter)
  
  
  def test_intersection():
      a = [1, 2, 3]
      b = [1, 2, 3, 4, 5, 6]
  
      inter = sorted(com.intersection(a, b))
  
      assert(a == inter)
  
  
  def test_groupby():
      values = ['foo', 'bar', 'baz', 'baz2', 'qux', 'foo3']
      expected = {'f': ['foo', 'foo3'],
                  'b': ['bar', 'baz', 'baz2'],
                  'q': ['qux']}
  
      grouped = com.groupby(values, lambda x: x[0])
  
      for k, v in grouped:
          assert v == expected[k]
  
  
  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 com.is_list_like(p)
  
      for f in fails:
          assert not com.is_list_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 com.is_named_tuple(p)
  
      for f in fails:
          assert not com.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 com.is_hashable(i)
      for i in not_hashable:
          assert not com.is_hashable(i)
      for i in abc_hashable_not_really_hashable:
          assert not com.is_hashable(i)
  
      # numpy.array is no longer collections.Hashable as of
      # https://github.com/numpy/numpy/pull/5326, just test
      # pandas.common.is_hashable()
      assert not com.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 compat.PY2:
          class OldStyleClass():
              pass
          c = OldStyleClass()
          assert not isinstance(c, collections.Hashable)
          assert com.is_hashable(c)
          hash(c)  # this will not raise
  
  
  def test_ensure_int32():
      values = np.arange(10, dtype=np.int32)
      result = com._ensure_int32(values)
      assert(result.dtype == np.int32)
  
      values = np.arange(10, dtype=np.int64)
      result = com._ensure_int32(values)
      assert(result.dtype == np.int32)
  
  
  def test_ensure_platform_int():
  
      # verify that when we create certain types of indices
      # they remain the correct type under platform conversions
      from pandas.core.index import Int64Index
  
      # int64
      x = Int64Index([1, 2, 3], dtype='int64')
      assert(x.dtype == np.int64)
  
      pi = com._ensure_platform_int(x)
      assert(pi.dtype == np.int_)
  
      # int32
      x = Int64Index([1, 2, 3], dtype='int32')
      assert(x.dtype == np.int32)
  
      pi = com._ensure_platform_int(x)
      assert(pi.dtype == np.int_)
  
  # TODO: fix this broken test
  
  # def test_console_encode():
  #     """
  #     On Python 2, if sys.stdin.encoding is None (IPython with zmq frontend)
  #     common.console_encode should encode things as utf-8.
  #     """
  #     if compat.PY3:
  #         raise nose.SkipTest
  
  #     with tm.stdin_encoding(encoding=None):
  #         result = com.console_encode(u"\u05d0")
  #         expected = u"\u05d0".encode('utf-8')
  #         assert (result == expected)
  
  
  def test_is_re():
      passes = re.compile('ad'),
      fails = 'x', 2, 3, object()
  
      for p in passes:
          assert com.is_re(p)
  
      for f in fails:
          assert not com.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 com.is_re_compilable(p)
  
      for f in fails:
          assert not com.is_re_compilable(f)
  
  def test_random_state():
      import numpy.random as npr
      # Check with seed
      state = com._random_state(5)
      assert_equal(state.uniform(), npr.RandomState(5).uniform())
  
      # Check with random state object
      state2 = npr.RandomState(10)
      assert_equal(com._random_state(state2).uniform(), npr.RandomState(10).uniform())
  
      # check with no arg random state
      assert isinstance(com._random_state(), npr.RandomState)
  
      # Error for floats or strings
      with tm.assertRaises(ValueError):
          com._random_state('test')
  
      with tm.assertRaises(ValueError):
          com._random_state(5.5)
  
  
  def test_maybe_match_name():
  
      matched = com._maybe_match_name(Series([1], name='x'), Series([2], name='x'))
      assert(matched == 'x')
  
      matched = com._maybe_match_name(Series([1], name='x'), Series([2], name='y'))
      assert(matched is None)
  
      matched = com._maybe_match_name(Series([1]), Series([2], name='x'))
      assert(matched is None)
  
      matched = com._maybe_match_name(Series([1], name='x'), Series([2]))
      assert(matched is None)
  
      matched = com._maybe_match_name(Series([1], name='x'), [2])
      assert(matched == 'x')
  
      matched = com._maybe_match_name([1], Series([2], name='y'))
      assert(matched == 'y')
  
  
  class TestTake(tm.TestCase):
      # standard incompatible fill error
      fill_error = re.compile("Incompatible type for fill_value")
  
      _multiprocess_can_split_ = True
  
      def test_1d_with_out(self):
          def _test_dtype(dtype, can_hold_na):
              data = np.random.randint(0, 2, 4).astype(dtype)
  
              indexer = [2, 1, 0, 1]
              out = np.empty(4, dtype=dtype)
              com.take_1d(data, indexer, out=out)
              expected = data.take(indexer)
              tm.assert_almost_equal(out, expected)
  
              indexer = [2, 1, 0, -1]
              out = np.empty(4, dtype=dtype)
              if can_hold_na:
                  com.take_1d(data, indexer, out=out)
                  expected = data.take(indexer)
                  expected[3] = np.nan
                  tm.assert_almost_equal(out, expected)
              else:
                  with tm.assertRaisesRegexp(TypeError, self.fill_error):
                      com.take_1d(data, indexer, out=out)
                  # no exception o/w
                  data.take(indexer, out=out)
  
          _test_dtype(np.float64, True)
          _test_dtype(np.float32, True)
          _test_dtype(np.uint64, False)
          _test_dtype(np.uint32, False)
          _test_dtype(np.uint16, False)
          _test_dtype(np.uint8, False)
          _test_dtype(np.int64, False)
          _test_dtype(np.int32, False)
          _test_dtype(np.int16, False)
          _test_dtype(np.int8, False)
          _test_dtype(np.object_, True)
          _test_dtype(np.bool, False)
  
      def test_1d_fill_nonna(self):
          def _test_dtype(dtype, fill_value, out_dtype):
              data = np.random.randint(0, 2, 4).astype(dtype)
  
              indexer = [2, 1, 0, -1]
  
              result = com.take_1d(data, indexer, fill_value=fill_value)
              assert((result[[0, 1, 2]] == data[[2, 1, 0]]).all())
              assert(result[3] == fill_value)
              assert(result.dtype == out_dtype)
  
              indexer = [2, 1, 0, 1]
  
              result = com.take_1d(data, indexer, fill_value=fill_value)
              assert((result[[0, 1, 2, 3]] == data[indexer]).all())
              assert(result.dtype == dtype)
  
          _test_dtype(np.int8, np.int16(127), np.int8)
          _test_dtype(np.int8, np.int16(128), np.int16)
          _test_dtype(np.int32, 1, np.int32)
          _test_dtype(np.int32, 2.0, np.float64)
          _test_dtype(np.int32, 3.0 + 4.0j, np.complex128)
          _test_dtype(np.int32, True, np.object_)
          _test_dtype(np.int32, '', np.object_)
          _test_dtype(np.float64, 1, np.float64)
          _test_dtype(np.float64, 2.0, np.float64)
          _test_dtype(np.float64, 3.0 + 4.0j, np.complex128)
          _test_dtype(np.float64, True, np.object_)
          _test_dtype(np.float64, '', np.object_)
          _test_dtype(np.complex128, 1, np.complex128)
          _test_dtype(np.complex128, 2.0, np.complex128)
          _test_dtype(np.complex128, 3.0 + 4.0j, np.complex128)
          _test_dtype(np.complex128, True, np.object_)
          _test_dtype(np.complex128, '', np.object_)
          _test_dtype(np.bool_, 1, np.object_)
          _test_dtype(np.bool_, 2.0, np.object_)
          _test_dtype(np.bool_, 3.0 + 4.0j, np.object_)
          _test_dtype(np.bool_, True, np.bool_)
          _test_dtype(np.bool_, '', np.object_)
  
      def test_2d_with_out(self):
          def _test_dtype(dtype, can_hold_na, writeable=True):
              data = np.random.randint(0, 2, (5, 3)).astype(dtype)
              data.flags.writeable = writeable
  
              indexer = [2, 1, 0, 1]
              out0 = np.empty((4, 3), dtype=dtype)
              out1 = np.empty((5, 4), dtype=dtype)
              com.take_nd(data, indexer, out=out0, axis=0)
              com.take_nd(data, indexer, out=out1, axis=1)
              expected0 = data.take(indexer, axis=0)
              expected1 = data.take(indexer, axis=1)
              tm.assert_almost_equal(out0, expected0)
              tm.assert_almost_equal(out1, expected1)
  
              indexer = [2, 1, 0, -1]
              out0 = np.empty((4, 3), dtype=dtype)
              out1 = np.empty((5, 4), dtype=dtype)
              if can_hold_na:
                  com.take_nd(data, indexer, out=out0, axis=0)
                  com.take_nd(data, indexer, out=out1, axis=1)
                  expected0 = data.take(indexer, axis=0)
                  expected1 = data.take(indexer, axis=1)
                  expected0[3, :] = np.nan
                  expected1[:, 3] = np.nan
                  tm.assert_almost_equal(out0, expected0)
                  tm.assert_almost_equal(out1, expected1)
              else:
                  for i, out in enumerate([out0, out1]):
                      with tm.assertRaisesRegexp(TypeError, self.fill_error):
                          com.take_nd(data, indexer, out=out, axis=i)
                      # no exception o/w
                      data.take(indexer, out=out, axis=i)
  
          for writeable in [True, False]:
              # Check that take_nd works both with writeable arrays (in which
              # case fast typed memoryviews implementation) and read-only
              # arrays alike.
              _test_dtype(np.float64, True, writeable=writeable)
              _test_dtype(np.float32, True, writeable=writeable)
              _test_dtype(np.uint64, False, writeable=writeable)
              _test_dtype(np.uint32, False, writeable=writeable)
              _test_dtype(np.uint16, False, writeable=writeable)
              _test_dtype(np.uint8, False, writeable=writeable)
              _test_dtype(np.int64, False, writeable=writeable)
              _test_dtype(np.int32, False, writeable=writeable)
              _test_dtype(np.int16, False, writeable=writeable)
              _test_dtype(np.int8, False, writeable=writeable)
              _test_dtype(np.object_, True, writeable=writeable)
              _test_dtype(np.bool, False, writeable=writeable)
  
      def test_2d_fill_nonna(self):
          def _test_dtype(dtype, fill_value, out_dtype):
              data = np.random.randint(0, 2, (5, 3)).astype(dtype)
  
              indexer = [2, 1, 0, -1]
  
              result = com.take_nd(data, indexer, axis=0, fill_value=fill_value)
              assert((result[[0, 1, 2], :] == data[[2, 1, 0], :]).all())
              assert((result[3, :] == fill_value).all())
              assert(result.dtype == out_dtype)
  
              result = com.take_nd(data, indexer, axis=1, fill_value=fill_value)
              assert((result[:, [0, 1, 2]] == data[:, [2, 1, 0]]).all())
              assert((result[:, 3] == fill_value).all())
              assert(result.dtype == out_dtype)
  
              indexer = [2, 1, 0, 1]
  
              result = com.take_nd(data, indexer, axis=0, fill_value=fill_value)
              assert((result[[0, 1, 2, 3], :] == data[indexer, :]).all())
              assert(result.dtype == dtype)
  
              result = com.take_nd(data, indexer, axis=1, fill_value=fill_value)
              assert((result[:, [0, 1, 2, 3]] == data[:, indexer]).all())
              assert(result.dtype == dtype)
  
          _test_dtype(np.int8, np.int16(127), np.int8)
          _test_dtype(np.int8, np.int16(128), np.int16)
          _test_dtype(np.int32, 1, np.int32)
          _test_dtype(np.int32, 2.0, np.float64)
          _test_dtype(np.int32, 3.0 + 4.0j, np.complex128)
          _test_dtype(np.int32, True, np.object_)
          _test_dtype(np.int32, '', np.object_)
          _test_dtype(np.float64, 1, np.float64)
          _test_dtype(np.float64, 2.0, np.float64)
          _test_dtype(np.float64, 3.0 + 4.0j, np.complex128)
          _test_dtype(np.float64, True, np.object_)
          _test_dtype(np.float64, '', np.object_)
          _test_dtype(np.complex128, 1, np.complex128)
          _test_dtype(np.complex128, 2.0, np.complex128)
          _test_dtype(np.complex128, 3.0 + 4.0j, np.complex128)
          _test_dtype(np.complex128, True, np.object_)
          _test_dtype(np.complex128, '', np.object_)
          _test_dtype(np.bool_, 1, np.object_)
          _test_dtype(np.bool_, 2.0, np.object_)
          _test_dtype(np.bool_, 3.0 + 4.0j, np.object_)
          _test_dtype(np.bool_, True, np.bool_)
          _test_dtype(np.bool_, '', np.object_)
  
      def test_3d_with_out(self):
          def _test_dtype(dtype, can_hold_na):
              data = np.random.randint(0, 2, (5, 4, 3)).astype(dtype)
  
              indexer = [2, 1, 0, 1]
              out0 = np.empty((4, 4, 3), dtype=dtype)
              out1 = np.empty((5, 4, 3), dtype=dtype)
              out2 = np.empty((5, 4, 4), dtype=dtype)
              com.take_nd(data, indexer, out=out0, axis=0)
              com.take_nd(data, indexer, out=out1, axis=1)
              com.take_nd(data, indexer, out=out2, axis=2)
              expected0 = data.take(indexer, axis=0)
              expected1 = data.take(indexer, axis=1)
              expected2 = data.take(indexer, axis=2)
              tm.assert_almost_equal(out0, expected0)
              tm.assert_almost_equal(out1, expected1)
              tm.assert_almost_equal(out2, expected2)
  
              indexer = [2, 1, 0, -1]
              out0 = np.empty((4, 4, 3), dtype=dtype)
              out1 = np.empty((5, 4, 3), dtype=dtype)
              out2 = np.empty((5, 4, 4), dtype=dtype)
              if can_hold_na:
                  com.take_nd(data, indexer, out=out0, axis=0)
                  com.take_nd(data, indexer, out=out1, axis=1)
                  com.take_nd(data, indexer, out=out2, axis=2)
                  expected0 = data.take(indexer, axis=0)
                  expected1 = data.take(indexer, axis=1)
                  expected2 = data.take(indexer, axis=2)
                  expected0[3, :, :] = np.nan
                  expected1[:, 3, :] = np.nan
                  expected2[:, :, 3] = np.nan
                  tm.assert_almost_equal(out0, expected0)
                  tm.assert_almost_equal(out1, expected1)
                  tm.assert_almost_equal(out2, expected2)
              else:
                  for i, out in enumerate([out0, out1, out2]):
                      with tm.assertRaisesRegexp(TypeError, self.fill_error):
                          com.take_nd(data, indexer, out=out, axis=i)
                      # no exception o/w
                      data.take(indexer, out=out, axis=i)
  
          _test_dtype(np.float64, True)
          _test_dtype(np.float32, True)
          _test_dtype(np.uint64, False)
          _test_dtype(np.uint32, False)
          _test_dtype(np.uint16, False)
          _test_dtype(np.uint8, False)
          _test_dtype(np.int64, False)
          _test_dtype(np.int32, False)
          _test_dtype(np.int16, False)
          _test_dtype(np.int8, False)
          _test_dtype(np.object_, True)
          _test_dtype(np.bool, False)
  
      def test_3d_fill_nonna(self):
          def _test_dtype(dtype, fill_value, out_dtype):
              data = np.random.randint(0, 2, (5, 4, 3)).astype(dtype)
  
              indexer = [2, 1, 0, -1]
  
              result = com.take_nd(data, indexer, axis=0, fill_value=fill_value)
              assert((result[[0, 1, 2], :, :] == data[[2, 1, 0], :, :]).all())
              assert((result[3, :, :] == fill_value).all())
              assert(result.dtype == out_dtype)
  
              result = com.take_nd(data, indexer, axis=1, fill_value=fill_value)
              assert((result[:, [0, 1, 2], :] == data[:, [2, 1, 0], :]).all())
              assert((result[:, 3, :] == fill_value).all())
              assert(result.dtype == out_dtype)
  
              result = com.take_nd(data, indexer, axis=2, fill_value=fill_value)
              assert((result[:, :, [0, 1, 2]] == data[:, :, [2, 1, 0]]).all())
              assert((result[:, :, 3] == fill_value).all())
              assert(result.dtype == out_dtype)
  
              indexer = [2, 1, 0, 1]
  
              result = com.take_nd(data, indexer, axis=0, fill_value=fill_value)
              assert((result[[0, 1, 2, 3], :, :] == data[indexer, :, :]).all())
              assert(result.dtype == dtype)
  
              result = com.take_nd(data, indexer, axis=1, fill_value=fill_value)
              assert((result[:, [0, 1, 2, 3], :] == data[:, indexer, :]).all())
              assert(result.dtype == dtype)
  
              result = com.take_nd(data, indexer, axis=2, fill_value=fill_value)
              assert((result[:, :, [0, 1, 2, 3]] == data[:, :, indexer]).all())
              assert(result.dtype == dtype)
  
          _test_dtype(np.int8, np.int16(127), np.int8)
          _test_dtype(np.int8, np.int16(128), np.int16)
          _test_dtype(np.int32, 1, np.int32)
          _test_dtype(np.int32, 2.0, np.float64)
          _test_dtype(np.int32, 3.0 + 4.0j, np.complex128)
          _test_dtype(np.int32, True, np.object_)
          _test_dtype(np.int32, '', np.object_)
          _test_dtype(np.float64, 1, np.float64)
          _test_dtype(np.float64, 2.0, np.float64)
          _test_dtype(np.float64, 3.0 + 4.0j, np.complex128)
          _test_dtype(np.float64, True, np.object_)
          _test_dtype(np.float64, '', np.object_)
          _test_dtype(np.complex128, 1, np.complex128)
          _test_dtype(np.complex128, 2.0, np.complex128)
          _test_dtype(np.complex128, 3.0 + 4.0j, np.complex128)
          _test_dtype(np.complex128, True, np.object_)
          _test_dtype(np.complex128, '', np.object_)
          _test_dtype(np.bool_, 1, np.object_)
          _test_dtype(np.bool_, 2.0, np.object_)
          _test_dtype(np.bool_, 3.0 + 4.0j, np.object_)
          _test_dtype(np.bool_, True, np.bool_)
          _test_dtype(np.bool_, '', np.object_)
  
      def test_1d_other_dtypes(self):
          arr = np.random.randn(10).astype(np.float32)
  
          indexer = [1, 2, 3, -1]
          result = com.take_1d(arr, indexer)
          expected = arr.take(indexer)
          expected[-1] = np.nan
          tm.assert_almost_equal(result, expected)
  
      def test_2d_other_dtypes(self):
          arr = np.random.randn(10, 5).astype(np.float32)
  
          indexer = [1, 2, 3, -1]
  
          # axis=0
          result = com.take_nd(arr, indexer, axis=0)
          expected = arr.take(indexer, axis=0)
          expected[-1] = np.nan
          tm.assert_almost_equal(result, expected)
  
          # axis=1
          result = com.take_nd(arr, indexer, axis=1)
          expected = arr.take(indexer, axis=1)
          expected[:, -1] = np.nan
          tm.assert_almost_equal(result, expected)
  
      def test_1d_bool(self):
          arr = np.array([0, 1, 0], dtype=bool)
  
          result = com.take_1d(arr, [0, 2, 2, 1])
          expected = arr.take([0, 2, 2, 1])
          self.assert_numpy_array_equal(result, expected)
  
          result = com.take_1d(arr, [0, 2, -1])
          self.assertEqual(result.dtype, np.object_)
  
      def test_2d_bool(self):
          arr = np.array([[0, 1, 0],
                          [1, 0, 1],
                          [0, 1, 1]], dtype=bool)
  
          result = com.take_nd(arr, [0, 2, 2, 1])
          expected = arr.take([0, 2, 2, 1], axis=0)
          self.assert_numpy_array_equal(result, expected)
  
          result = com.take_nd(arr, [0, 2, 2, 1], axis=1)
          expected = arr.take([0, 2, 2, 1], axis=1)
          self.assert_numpy_array_equal(result, expected)
  
          result = com.take_nd(arr, [0, 2, -1])
          self.assertEqual(result.dtype, np.object_)
  
      def test_2d_float32(self):
          arr = np.random.randn(4, 3).astype(np.float32)
          indexer = [0, 2, -1, 1, -1]
  
          # axis=0
          result = com.take_nd(arr, indexer, axis=0)
          result2 = np.empty_like(result)
          com.take_nd(arr, indexer, axis=0, out=result2)
          tm.assert_almost_equal(result, result2)
  
          expected = arr.take(indexer, axis=0)
          expected[[2, 4], :] = np.nan
          tm.assert_almost_equal(result, expected)
  
          #### this now accepts a float32! # test with float64 out buffer
          out = np.empty((len(indexer), arr.shape[1]), dtype='float32')
          com.take_nd(arr, indexer, out=out)  # it works!
  
          # axis=1
          result = com.take_nd(arr, indexer, axis=1)
          result2 = np.empty_like(result)
          com.take_nd(arr, indexer, axis=1, out=result2)
          tm.assert_almost_equal(result, result2)
  
          expected = arr.take(indexer, axis=1)
          expected[:, [2, 4]] = np.nan
          tm.assert_almost_equal(result, expected)
  
      def test_2d_datetime64(self):
          # 2005/01/01 - 2006/01/01
          arr = np.random.randint(long(11045376), long(11360736), (5, 3))*100000000000
          arr = arr.view(dtype='datetime64[ns]')
          indexer = [0, 2, -1, 1, -1]
  
          # axis=0
          result = com.take_nd(arr, indexer, axis=0)
          result2 = np.empty_like(result)
          com.take_nd(arr, indexer, axis=0, out=result2)
          tm.assert_almost_equal(result, result2)
  
          expected = arr.take(indexer, axis=0)
          expected.view(np.int64)[[2, 4], :] = iNaT
          tm.assert_almost_equal(result, expected)
  
          result = com.take_nd(arr, indexer, axis=0,
                               fill_value=datetime(2007, 1, 1))
          result2 = np.empty_like(result)
          com.take_nd(arr, indexer, out=result2, axis=0,
                      fill_value=datetime(2007, 1, 1))
          tm.assert_almost_equal(result, result2)
  
          expected = arr.take(indexer, axis=0)
          expected[[2, 4], :] = datetime(2007, 1, 1)
          tm.assert_almost_equal(result, expected)
  
          # axis=1
          result = com.take_nd(arr, indexer, axis=1)
          result2 = np.empty_like(result)
          com.take_nd(arr, indexer, axis=1, out=result2)
          tm.assert_almost_equal(result, result2)
  
          expected = arr.take(indexer, axis=1)
          expected.view(np.int64)[:, [2, 4]] = iNaT
          tm.assert_almost_equal(result, expected)
  
          result = com.take_nd(arr, indexer, axis=1,
                               fill_value=datetime(2007, 1, 1))
          result2 = np.empty_like(result)
          com.take_nd(arr, indexer, out=result2, axis=1,
                      fill_value=datetime(2007, 1, 1))
          tm.assert_almost_equal(result, result2)
  
          expected = arr.take(indexer, axis=1)
          expected[:, [2, 4]] = datetime(2007, 1, 1)
          tm.assert_almost_equal(result, expected)
  
  
  class TestMaybe(tm.TestCase):
  
      def test_maybe_convert_string_to_array(self):
          result = com._maybe_convert_string_to_object('x')
          tm.assert_numpy_array_equal(result, np.array(['x'], dtype=object))
          self.assertTrue(result.dtype == object)
  
          result = com._maybe_convert_string_to_object(1)
          self.assertEqual(result, 1)
  
          arr = np.array(['x', 'y'], dtype=str)
          result = com._maybe_convert_string_to_object(arr)
          tm.assert_numpy_array_equal(result, np.array(['x', 'y'], dtype=object))
          self.assertTrue(result.dtype == object)
  
          # unicode
          arr = np.array(['x', 'y']).astype('U')
          result = com._maybe_convert_string_to_object(arr)
          tm.assert_numpy_array_equal(result, np.array(['x', 'y'], dtype=object))
          self.assertTrue(result.dtype == object)
  
          # object
          arr = np.array(['x', 2], dtype=object)
          result = com._maybe_convert_string_to_object(arr)
          tm.assert_numpy_array_equal(result, np.array(['x', 2], dtype=object))
          self.assertTrue(result.dtype == object)
  
  def test_possibly_convert_objects_copy():
      values = np.array([1, 2])
  
      out = convert._possibly_convert_objects(values, copy=False)
      assert_true(values is out)
  
      out = convert._possibly_convert_objects(values, copy=True)
      assert_true(values is not out)
  
      values = np.array(['apply','banana'])
      out = convert._possibly_convert_objects(values, copy=False)
      assert_true(values is out)
  
      out = convert._possibly_convert_objects(values, copy=True)
      assert_true(values is not out)
  
  
  def test_dict_compat():
      data_datetime64 = {np.datetime64('1990-03-15'): 1,
                         np.datetime64('2015-03-15'): 2}
      data_unchanged = {1: 2, 3: 4, 5: 6}
      expected = {Timestamp('1990-3-15'): 1, Timestamp('2015-03-15'): 2}
      assert(com._dict_compat(data_datetime64) == expected)
      assert(com._dict_compat(expected) == expected)
      assert(com._dict_compat(data_unchanged) == data_unchanged)
  
  
  if __name__ == '__main__':
      nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'],
                     exit=False)
  