code: from datetime import datetime, time, timedelta
  
  import numpy as np
  import pytest
  import pytz
  
  import pandas.compat as compat
  
  import pandas as pd
  from pandas import DatetimeIndex, Index, Timestamp, date_range, notna
  import pandas.util.testing as tm
  
  from pandas.tseries.offsets import BDay, CDay
  
  START, END = datetime(2009, 1, 1), datetime(2010, 1, 1)
  
  
  class TestGetItem(object):
      def test_getitem(self):
          idx1 = pd.date_range('2011-01-01', '2011-01-31', freq='D', name='idx')
          idx2 = pd.date_range('2011-01-01', '2011-01-31', freq='D',
                               tz='Asia/Tokyo', name='idx')
  
          for idx in [idx1, idx2]:
              result = idx[0]
              assert result == Timestamp('2011-01-01', tz=idx.tz)
  
              result = idx[0:5]
              expected = pd.date_range('2011-01-01', '2011-01-05', freq='D',
                                       tz=idx.tz, name='idx')
              tm.assert_index_equal(result, expected)
              assert result.freq == expected.freq
  
              result = idx[0:10:2]
              expected = pd.date_range('2011-01-01', '2011-01-09', freq='2D',
                                       tz=idx.tz, name='idx')
              tm.assert_index_equal(result, expected)
              assert result.freq == expected.freq
  
              result = idx[-20:-5:3]
              expected = pd.date_range('2011-01-12', '2011-01-24', freq='3D',
                                       tz=idx.tz, name='idx')
              tm.assert_index_equal(result, expected)
              assert result.freq == expected.freq
  
              result = idx[4::-1]
              expected = DatetimeIndex(['2011-01-05', '2011-01-04', '2011-01-03',
                                        '2011-01-02', '2011-01-01'],
                                       freq='-1D', tz=idx.tz, name='idx')
              tm.assert_index_equal(result, expected)
              assert result.freq == expected.freq
  
      def test_dti_business_getitem(self):
          rng = pd.bdate_range(START, END)
          smaller = rng[:5]
          exp = DatetimeIndex(rng.view(np.ndarray)[:5])
          tm.assert_index_equal(smaller, exp)
  
          assert smaller.freq == rng.freq
  
          sliced = rng[::5]
          assert sliced.freq == BDay() * 5
  
          fancy_indexed = rng[[4, 3, 2, 1, 0]]
          assert len(fancy_indexed) == 5
          assert isinstance(fancy_indexed, DatetimeIndex)
          assert fancy_indexed.freq is None
  
          # 32-bit vs. 64-bit platforms
          assert rng[4] == rng[np.int_(4)]
  
      def test_dti_business_getitem_matplotlib_hackaround(self):
          rng = pd.bdate_range(START, END)
          values = rng[:, None]
          expected = rng.values[:, None]
          tm.assert_numpy_array_equal(values, expected)
  
      def test_dti_custom_getitem(self):
          rng = pd.bdate_range(START, END, freq='C')
          smaller = rng[:5]
          exp = DatetimeIndex(rng.view(np.ndarray)[:5])
          tm.assert_index_equal(smaller, exp)
          assert smaller.freq == rng.freq
  
          sliced = rng[::5]
          assert sliced.freq == CDay() * 5
  
          fancy_indexed = rng[[4, 3, 2, 1, 0]]
          assert len(fancy_indexed) == 5
          assert isinstance(fancy_indexed, DatetimeIndex)
          assert fancy_indexed.freq is None
  
          # 32-bit vs. 64-bit platforms
          assert rng[4] == rng[np.int_(4)]
  
      def test_dti_custom_getitem_matplotlib_hackaround(self):
          rng = pd.bdate_range(START, END, freq='C')
          values = rng[:, None]
          expected = rng.values[:, None]
          tm.assert_numpy_array_equal(values, expected)
  
  
  class TestWhere(object):
      def test_where_other(self):
          # other is ndarray or Index
          i = pd.date_range('20130101', periods=3, tz='US/Eastern')
  
          for arr in [np.nan, pd.NaT]:
              result = i.where(notna(i), other=np.nan)
              expected = i
              tm.assert_index_equal(result, expected)
  
          i2 = i.copy()
          i2 = Index([pd.NaT, pd.NaT] + i[2:].tolist())
          result = i.where(notna(i2), i2)
          tm.assert_index_equal(result, i2)
  
          i2 = i.copy()
          i2 = Index([pd.NaT, pd.NaT] + i[2:].tolist())
          result = i.where(notna(i2), i2.values)
          tm.assert_index_equal(result, i2)
  
      def test_where_tz(self):
          i = pd.date_range('20130101', periods=3, tz='US/Eastern')
          result = i.where(notna(i))
          expected = i
          tm.assert_index_equal(result, expected)
  
          i2 = i.copy()
          i2 = Index([pd.NaT, pd.NaT] + i[2:].tolist())
          result = i.where(notna(i2))
          expected = i2
          tm.assert_index_equal(result, expected)
  
  
  class TestTake(object):
      def test_take(self):
          # GH#10295
          idx1 = pd.date_range('2011-01-01', '2011-01-31', freq='D', name='idx')
          idx2 = pd.date_range('2011-01-01', '2011-01-31', freq='D',
                               tz='Asia/Tokyo', name='idx')
  
          for idx in [idx1, idx2]:
              result = idx.take([0])
              assert result == Timestamp('2011-01-01', tz=idx.tz)
  
              result = idx.take([0, 1, 2])
              expected = pd.date_range('2011-01-01', '2011-01-03', freq='D',
                                       tz=idx.tz, name='idx')
              tm.assert_index_equal(result, expected)
              assert result.freq == expected.freq
  
              result = idx.take([0, 2, 4])
              expected = pd.date_range('2011-01-01', '2011-01-05', freq='2D',
                                       tz=idx.tz, name='idx')
              tm.assert_index_equal(result, expected)
              assert result.freq == expected.freq
  
              result = idx.take([7, 4, 1])
              expected = pd.date_range('2011-01-08', '2011-01-02', freq='-3D',
                                       tz=idx.tz, name='idx')
              tm.assert_index_equal(result, expected)
              assert result.freq == expected.freq
  
              result = idx.take([3, 2, 5])
              expected = DatetimeIndex(['2011-01-04', '2011-01-03',
                                        '2011-01-06'],
                                       freq=None, tz=idx.tz, name='idx')
              tm.assert_index_equal(result, expected)
              assert result.freq is None
  
              result = idx.take([-3, 2, 5])
              expected = DatetimeIndex(['2011-01-29', '2011-01-03',
                                        '2011-01-06'],
                                       freq=None, tz=idx.tz, name='idx')
              tm.assert_index_equal(result, expected)
              assert result.freq is None
  
      def test_take_invalid_kwargs(self):
          idx = pd.date_range('2011-01-01', '2011-01-31', freq='D', name='idx')
          indices = [1, 6, 5, 9, 10, 13, 15, 3]
  
          msg = r"take\(\) got an unexpected keyword argument 'foo'"
          with pytest.raises(TypeError, match=msg):
              idx.take(indices, foo=2)
  
          msg = "the 'out' parameter is not supported"
          with pytest.raises(ValueError, match=msg):
              idx.take(indices, out=indices)
  
          msg = "the 'mode' parameter is not supported"
          with pytest.raises(ValueError, match=msg):
              idx.take(indices, mode='clip')
  
      # TODO: This method came from test_datetime; de-dup with version above
      @pytest.mark.parametrize('tz', [None, 'US/Eastern', 'Asia/Tokyo'])
      def test_take2(self, tz):
          dates = [datetime(2010, 1, 1, 14), datetime(2010, 1, 1, 15),
                   datetime(2010, 1, 1, 17), datetime(2010, 1, 1, 21)]
  
          idx = DatetimeIndex(start='2010-01-01 09:00',
                              end='2010-02-01 09:00', freq='H', tz=tz,
                              name='idx')
          expected = DatetimeIndex(dates, freq=None, name='idx', tz=tz)
  
          taken1 = idx.take([5, 6, 8, 12])
          taken2 = idx[[5, 6, 8, 12]]
  
          for taken in [taken1, taken2]:
              tm.assert_index_equal(taken, expected)
              assert isinstance(taken, DatetimeIndex)
              assert taken.freq is None
              assert taken.tz == expected.tz
              assert taken.name == expected.name
  
      def test_take_fill_value(self):
          # GH#12631
          idx = pd.DatetimeIndex(['2011-01-01', '2011-02-01', '2011-03-01'],
                                 name='xxx')
          result = idx.take(np.array([1, 0, -1]))
          expected = pd.DatetimeIndex(['2011-02-01', '2011-01-01', '2011-03-01'],
                                      name='xxx')
          tm.assert_index_equal(result, expected)
  
          # fill_value
          result = idx.take(np.array([1, 0, -1]), fill_value=True)
          expected = pd.DatetimeIndex(['2011-02-01', '2011-01-01', 'NaT'],
                                      name='xxx')
          tm.assert_index_equal(result, expected)
  
          # allow_fill=False
          result = idx.take(np.array([1, 0, -1]), allow_fill=False,
                            fill_value=True)
          expected = pd.DatetimeIndex(['2011-02-01', '2011-01-01', '2011-03-01'],
                                      name='xxx')
          tm.assert_index_equal(result, expected)
  
          msg = ('When allow_fill=True and fill_value is not None, '
                 'all indices must be >= -1')
          with pytest.raises(ValueError, match=msg):
              idx.take(np.array([1, 0, -2]), fill_value=True)
          with pytest.raises(ValueError, match=msg):
              idx.take(np.array([1, 0, -5]), fill_value=True)
  
          with pytest.raises(IndexError):
              idx.take(np.array([1, -5]))
  
      def test_take_fill_value_with_timezone(self):
          idx = pd.DatetimeIndex(['2011-01-01', '2011-02-01', '2011-03-01'],
                                 name='xxx', tz='US/Eastern')
          result = idx.take(np.array([1, 0, -1]))
          expected = pd.DatetimeIndex(['2011-02-01', '2011-01-01', '2011-03-01'],
                                      name='xxx', tz='US/Eastern')
          tm.assert_index_equal(result, expected)
  
          # fill_value
          result = idx.take(np.array([1, 0, -1]), fill_value=True)
          expected = pd.DatetimeIndex(['2011-02-01', '2011-01-01', 'NaT'],
                                      name='xxx', tz='US/Eastern')
          tm.assert_index_equal(result, expected)
  
          # allow_fill=False
          result = idx.take(np.array([1, 0, -1]), allow_fill=False,
                            fill_value=True)
          expected = pd.DatetimeIndex(['2011-02-01', '2011-01-01', '2011-03-01'],
                                      name='xxx', tz='US/Eastern')
          tm.assert_index_equal(result, expected)
  
          msg = ('When allow_fill=True and fill_value is not None, '
                 'all indices must be >= -1')
          with pytest.raises(ValueError, match=msg):
              idx.take(np.array([1, 0, -2]), fill_value=True)
          with pytest.raises(ValueError, match=msg):
              idx.take(np.array([1, 0, -5]), fill_value=True)
  
          with pytest.raises(IndexError):
              idx.take(np.array([1, -5]))
  
  
  class TestDatetimeIndex(object):
      @pytest.mark.parametrize('null', [None, np.nan, pd.NaT])
      @pytest.mark.parametrize('tz', [None, 'UTC', 'US/Eastern'])
      def test_insert_nat(self, tz, null):
          # GH#16537, GH#18295 (test missing)
          idx = pd.DatetimeIndex(['2017-01-01'], tz=tz)
          expected = pd.DatetimeIndex(['NaT', '2017-01-01'], tz=tz)
          res = idx.insert(0, null)
          tm.assert_index_equal(res, expected)
  
      def test_insert(self):
          idx = DatetimeIndex(
              ['2000-01-04', '2000-01-01', '2000-01-02'], name='idx')
  
          result = idx.insert(2, datetime(2000, 1, 5))
          exp = DatetimeIndex(['2000-01-04', '2000-01-01', '2000-01-05',
                               '2000-01-02'], name='idx')
          tm.assert_index_equal(result, exp)
  
          # insertion of non-datetime should coerce to object index
          result = idx.insert(1, 'inserted')
          expected = Index([datetime(2000, 1, 4), 'inserted',
                            datetime(2000, 1, 1),
                            datetime(2000, 1, 2)], name='idx')
          assert not isinstance(result, DatetimeIndex)
          tm.assert_index_equal(result, expected)
          assert result.name == expected.name
  
          idx = date_range('1/1/2000', periods=3, freq='M', name='idx')
  
          # preserve freq
          expected_0 = DatetimeIndex(['1999-12-31', '2000-01-31', '2000-02-29',
                                      '2000-03-31'], name='idx', freq='M')
          expected_3 = DatetimeIndex(['2000-01-31', '2000-02-29', '2000-03-31',
                                      '2000-04-30'], name='idx', freq='M')
  
          # reset freq to None
          expected_1_nofreq = DatetimeIndex(['2000-01-31', '2000-01-31',
                                             '2000-02-29',
                                             '2000-03-31'], name='idx',
                                            freq=None)
          expected_3_nofreq = DatetimeIndex(['2000-01-31', '2000-02-29',
                                             '2000-03-31',
                                             '2000-01-02'], name='idx',
                                            freq=None)
  
          cases = [(0, datetime(1999, 12, 31), expected_0),
                   (-3, datetime(1999, 12, 31), expected_0),
                   (3, datetime(2000, 4, 30), expected_3),
                   (1, datetime(2000, 1, 31), expected_1_nofreq),
                   (3, datetime(2000, 1, 2), expected_3_nofreq)]
  
          for n, d, expected in cases:
              result = idx.insert(n, d)
              tm.assert_index_equal(result, expected)
              assert result.name == expected.name
              assert result.freq == expected.freq
  
          # reset freq to None
          result = idx.insert(3, datetime(2000, 1, 2))
          expected = DatetimeIndex(['2000-01-31', '2000-02-29', '2000-03-31',
                                    '2000-01-02'], name='idx', freq=None)
          tm.assert_index_equal(result, expected)
          assert result.name == expected.name
          assert result.freq is None
  
          # see gh-7299
          idx = date_range('1/1/2000', periods=3, freq='D', tz='Asia/Tokyo',
                           name='idx')
          with pytest.raises(ValueError):
              idx.insert(3, pd.Timestamp('2000-01-04'))
          with pytest.raises(ValueError):
              idx.insert(3, datetime(2000, 1, 4))
          with pytest.raises(ValueError):
              idx.insert(3, pd.Timestamp('2000-01-04', tz='US/Eastern'))
          with pytest.raises(ValueError):
              idx.insert(3, datetime(2000, 1, 4,
                                     tzinfo=pytz.timezone('US/Eastern')))
  
          for tz in ['US/Pacific', 'Asia/Singapore']:
              idx = date_range('1/1/2000 09:00', periods=6, freq='H', tz=tz,
                               name='idx')
              # preserve freq
              expected = date_range('1/1/2000 09:00', periods=7, freq='H', tz=tz,
                                    name='idx')
              for d in [pd.Timestamp('2000-01-01 15:00', tz=tz),
                        pytz.timezone(tz).localize(datetime(2000, 1, 1, 15))]:
  
                  result = idx.insert(6, d)
                  tm.assert_index_equal(result, expected)
                  assert result.name == expected.name
                  assert result.freq == expected.freq
                  assert result.tz == expected.tz
  
              expected = DatetimeIndex(['2000-01-01 09:00', '2000-01-01 10:00',
                                        '2000-01-01 11:00',
                                        '2000-01-01 12:00', '2000-01-01 13:00',
                                        '2000-01-01 14:00',
                                        '2000-01-01 10:00'], name='idx',
                                       tz=tz, freq=None)
              # reset freq to None
              for d in [pd.Timestamp('2000-01-01 10:00', tz=tz),
                        pytz.timezone(tz).localize(datetime(2000, 1, 1, 10))]:
                  result = idx.insert(6, d)
                  tm.assert_index_equal(result, expected)
                  assert result.name == expected.name
                  assert result.tz == expected.tz
                  assert result.freq is None
  
      def test_delete(self):
          idx = date_range(start='2000-01-01', periods=5, freq='M', name='idx')
  
          # prserve freq
          expected_0 = date_range(start='2000-02-01', periods=4, freq='M',
                                  name='idx')
          expected_4 = date_range(start='2000-01-01', periods=4, freq='M',
                                  name='idx')
  
          # reset freq to None
          expected_1 = DatetimeIndex(['2000-01-31', '2000-03-31', '2000-04-30',
                                      '2000-05-31'], freq=None, name='idx')
  
          cases = {0: expected_0,
                   -5: expected_0,
                   -1: expected_4,
                   4: expected_4,
                   1: expected_1}
          for n, expected in compat.iteritems(cases):
              result = idx.delete(n)
              tm.assert_index_equal(result, expected)
              assert result.name == expected.name
              assert result.freq == expected.freq
  
          with pytest.raises((IndexError, ValueError)):
              # either depending on numpy version
              idx.delete(5)
  
          for tz in [None, 'Asia/Tokyo', 'US/Pacific']:
              idx = date_range(start='2000-01-01 09:00', periods=10, freq='H',
                               name='idx', tz=tz)
  
              expected = date_range(start='2000-01-01 10:00', periods=9,
                                    freq='H', name='idx', tz=tz)
              result = idx.delete(0)
              tm.assert_index_equal(result, expected)
              assert result.name == expected.name
              assert result.freqstr == 'H'
              assert result.tz == expected.tz
  
              expected = date_range(start='2000-01-01 09:00', periods=9,
                                    freq='H', name='idx', tz=tz)
              result = idx.delete(-1)
              tm.assert_index_equal(result, expected)
              assert result.name == expected.name
              assert result.freqstr == 'H'
              assert result.tz == expected.tz
  
      def test_delete_slice(self):
          idx = date_range(start='2000-01-01', periods=10, freq='D', name='idx')
  
          # prserve freq
          expected_0_2 = date_range(start='2000-01-04', periods=7, freq='D',
                                    name='idx')
          expected_7_9 = date_range(start='2000-01-01', periods=7, freq='D',
                                    name='idx')
  
          # reset freq to None
          expected_3_5 = DatetimeIndex(['2000-01-01', '2000-01-02', '2000-01-03',
                                        '2000-01-07', '2000-01-08', '2000-01-09',
                                        '2000-01-10'], freq=None, name='idx')
  
          cases = {(0, 1, 2): expected_0_2,
                   (7, 8, 9): expected_7_9,
                   (3, 4, 5): expected_3_5}
          for n, expected in compat.iteritems(cases):
              result = idx.delete(n)
              tm.assert_index_equal(result, expected)
              assert result.name == expected.name
              assert result.freq == expected.freq
  
              result = idx.delete(slice(n[0], n[-1] + 1))
              tm.assert_index_equal(result, expected)
              assert result.name == expected.name
              assert result.freq == expected.freq
  
          for tz in [None, 'Asia/Tokyo', 'US/Pacific']:
              ts = pd.Series(1, index=pd.date_range(
                  '2000-01-01 09:00', periods=10, freq='H', name='idx', tz=tz))
              # preserve freq
              result = ts.drop(ts.index[:5]).index
              expected = pd.date_range('2000-01-01 14:00', periods=5, freq='H',
                                       name='idx', tz=tz)
              tm.assert_index_equal(result, expected)
              assert result.name == expected.name
              assert result.freq == expected.freq
              assert result.tz == expected.tz
  
              # reset freq to None
              result = ts.drop(ts.index[[1, 3, 5, 7, 9]]).index
              expected = DatetimeIndex(['2000-01-01 09:00', '2000-01-01 11:00',
                                        '2000-01-01 13:00',
                                        '2000-01-01 15:00', '2000-01-01 17:00'],
                                       freq=None, name='idx', tz=tz)
              tm.assert_index_equal(result, expected)
              assert result.name == expected.name
              assert result.freq == expected.freq
              assert result.tz == expected.tz
  
      def test_get_loc(self):
          idx = pd.date_range('2000-01-01', periods=3)
  
          for method in [None, 'pad', 'backfill', 'nearest']:
              assert idx.get_loc(idx[1], method) == 1
              assert idx.get_loc(idx[1].to_pydatetime(), method) == 1
              assert idx.get_loc(str(idx[1]), method) == 1
  
              if method is not None:
                  assert idx.get_loc(idx[1], method,
                                     tolerance=pd.Timedelta('0 days')) == 1
  
          assert idx.get_loc('2000-01-01', method='nearest') == 0
          assert idx.get_loc('2000-01-01T12', method='nearest') == 1
  
          assert idx.get_loc('2000-01-01T12', method='nearest',
                             tolerance='1 day') == 1
          assert idx.get_loc('2000-01-01T12', method='nearest',
                             tolerance=pd.Timedelta('1D')) == 1
          assert idx.get_loc('2000-01-01T12', method='nearest',
                             tolerance=np.timedelta64(1, 'D')) == 1
          assert idx.get_loc('2000-01-01T12', method='nearest',
                             tolerance=timedelta(1)) == 1
          with pytest.raises(ValueError, match='unit abbreviation w/o a number'):
              idx.get_loc('2000-01-01T12', method='nearest', tolerance='foo')
          with pytest.raises(KeyError):
              idx.get_loc('2000-01-01T03', method='nearest', tolerance='2 hours')
          with pytest.raises(
                  ValueError,
                  match='tolerance size must match target index size'):
              idx.get_loc('2000-01-01', method='nearest',
                          tolerance=[pd.Timedelta('1day').to_timedelta64(),
                                     pd.Timedelta('1day').to_timedelta64()])
  
          assert idx.get_loc('2000', method='nearest') == slice(0, 3)
          assert idx.get_loc('2000-01', method='nearest') == slice(0, 3)
  
          assert idx.get_loc('1999', method='nearest') == 0
          assert idx.get_loc('2001', method='nearest') == 2
  
          with pytest.raises(KeyError):
              idx.get_loc('1999', method='pad')
          with pytest.raises(KeyError):
              idx.get_loc('2001', method='backfill')
  
          with pytest.raises(KeyError):
              idx.get_loc('foobar')
          with pytest.raises(TypeError):
              idx.get_loc(slice(2))
  
          idx = pd.to_datetime(['2000-01-01', '2000-01-04'])
          assert idx.get_loc('2000-01-02', method='nearest') == 0
          assert idx.get_loc('2000-01-03', method='nearest') == 1
          assert idx.get_loc('2000-01', method='nearest') == slice(0, 2)
  
          # time indexing
          idx = pd.date_range('2000-01-01', periods=24, freq='H')
          tm.assert_numpy_array_equal(idx.get_loc(time(12)),
                                      np.array([12]), check_dtype=False)
          tm.assert_numpy_array_equal(idx.get_loc(time(12, 30)),
                                      np.array([]), check_dtype=False)
          with pytest.raises(NotImplementedError):
              idx.get_loc(time(12, 30), method='pad')
  
      def test_get_indexer(self):
          idx = pd.date_range('2000-01-01', periods=3)
          exp = np.array([0, 1, 2], dtype=np.intp)
          tm.assert_numpy_array_equal(idx.get_indexer(idx), exp)
  
          target = idx[0] + pd.to_timedelta(['-1 hour', '12 hours',
                                             '1 day 1 hour'])
          tm.assert_numpy_array_equal(idx.get_indexer(target, 'pad'),
                                      np.array([-1, 0, 1], dtype=np.intp))
          tm.assert_numpy_array_equal(idx.get_indexer(target, 'backfill'),
                                      np.array([0, 1, 2], dtype=np.intp))
          tm.assert_numpy_array_equal(idx.get_indexer(target, 'nearest'),
                                      np.array([0, 1, 1], dtype=np.intp))
          tm.assert_numpy_array_equal(
              idx.get_indexer(target, 'nearest',
                              tolerance=pd.Timedelta('1 hour')),
              np.array([0, -1, 1], dtype=np.intp))
          tol_raw = [pd.Timedelta('1 hour'),
                     pd.Timedelta('1 hour'),
                     pd.Timedelta('1 hour').to_timedelta64(), ]
          tm.assert_numpy_array_equal(
              idx.get_indexer(target, 'nearest',
                              tolerance=[np.timedelta64(x) for x in tol_raw]),
              np.array([0, -1, 1], dtype=np.intp))
          tol_bad = [pd.Timedelta('2 hour').to_timedelta64(),
                     pd.Timedelta('1 hour').to_timedelta64(),
                     'foo', ]
          with pytest.raises(
                  ValueError, match='abbreviation w/o a number'):
              idx.get_indexer(target, 'nearest', tolerance=tol_bad)
          with pytest.raises(ValueError):
              idx.get_indexer(idx[[0]], method='nearest', tolerance='foo')
  
      def test_reasonable_key_error(self):
          # GH#1062
          index = DatetimeIndex(['1/3/2000'])
          with pytest.raises(KeyError, match='2000'):
              index.get_loc('1/1/2000')
  
      @pytest.mark.parametrize('key', [pd.Timedelta(0),
                                       pd.Timedelta(1),
                                       timedelta(0)])
      def test_timedelta_invalid_key(self, key):
          # GH#20464
          dti = pd.date_range('1970-01-01', periods=10)
          with pytest.raises(TypeError):
              dti.get_loc(key)
  
      def test_get_loc_nat(self):
          # GH#20464
          index = DatetimeIndex(['1/3/2000', 'NaT'])
          assert index.get_loc(pd.NaT) == 1
   """
  Module contains tools for processing files into DataFrames or other objects
  """
  from __future__ import print_function
  
  from collections import defaultdict
  import csv
  import datetime
  import re
  import sys
  from textwrap import fill
  import warnings
  
  import numpy as np
  
  import pandas._libs.lib as lib
  import pandas._libs.ops as libops
  import pandas._libs.parsers as parsers
  from pandas._libs.tslibs import parsing
  import pandas.compat as compat
  from pandas.compat import (
      PY3, StringIO, lrange, lzip, map, range, string_types, u, zip)
  from pandas.errors import (
      AbstractMethodError, EmptyDataError, ParserError, ParserWarning)
  from pandas.util._decorators import Appender
  
  from pandas.core.dtypes.cast import astype_nansafe
  from pandas.core.dtypes.common import (
      ensure_object, is_categorical_dtype, is_dtype_equal, is_float, is_integer,
      is_integer_dtype, is_list_like, is_object_dtype, is_scalar,
      is_string_dtype)
  from pandas.core.dtypes.dtypes import CategoricalDtype
  from pandas.core.dtypes.missing import isna
  
  from pandas.core import algorithms
  from pandas.core.arrays import Categorical
  from pandas.core.frame import DataFrame
  from pandas.core.index import (
      Index, MultiIndex, RangeIndex, ensure_index_from_sequences)
  from pandas.core.series import Series
  from pandas.core.tools import datetimes as tools
  
  from pandas.io.common import (
      _NA_VALUES, BaseIterator, UnicodeReader, UTF8Recoder, _get_handle,
      _infer_compression, _validate_header_arg, get_filepath_or_buffer,
      is_file_like)
  from pandas.io.date_converters import generic_parser
  
  # BOM character (byte order mark)
  # This exists at the beginning of a file to indicate endianness
  # of a file (stream). Unfortunately, this marker screws up parsing,
  # so we need to remove it if we see it.
  _BOM = u('\ufeff')
  
  _parser_params = r"""Also supports optionally iterating or breaking of the file
  into chunks.
  
  Additional help can be found in the `online docs for IO Tools
  <http://pandas.pydata.org/pandas-docs/stable/io.html>`_.
  
  Parameters
  ----------
  filepath_or_buffer : str, path object, or file-like object
      Any valid string path is acceptable. The string could be a URL. Valid
      URL schemes include http, ftp, s3, and file. For file URLs, a host is
      expected. A local file could be: file://localhost/path/to/table.csv.
  
      If you want to pass in a path object, pandas accepts either
      ``pathlib.Path`` or ``py._path.local.LocalPath``.
  
      By file-like object, we refer to objects with a ``read()`` method, such as
      a file handler (e.g. via builtin ``open`` function) or ``StringIO``.
  %s
  delim_whitespace : boolean, default False
      Specifies whether or not whitespace (e.g. ``' '`` or ``'\t'``) will be
      used as the sep. Equivalent to setting ``sep='\s+'``. If this option
      is set to True, nothing should be passed in for the ``delimiter``
      parameter.
  
      .. versionadded:: 0.18.1 support for the Python parser.
  
  header : int or list of ints, default 'infer'
      Row number(s) to use as the column names, and the start of the
      data.  Default behavior is to infer the column names: if no names
      are passed the behavior is identical to ``header=0`` and column
      names are inferred from the first line of the file, if column
      names are passed explicitly then the behavior is identical to
      ``header=None``. Explicitly pass ``header=0`` to be able to
      replace existing names. The header can be a list of integers that
      specify row locations for a multi-index on the columns
      e.g. [0,1,3]. Intervening rows that are not specified will be
      skipped (e.g. 2 in this example is skipped). Note that this
      parameter ignores commented lines and empty lines if
      ``skip_blank_lines=True``, so header=0 denotes the first line of
      data rather than the first line of the file.
  names : array-like, default None
      List of column names to use. If file contains no header row, then you
      should explicitly pass header=None. Duplicates in this list will cause
      a ``UserWarning`` to be issued.
  index_col : int or sequence or False, default None
      Column to use as the row labels of the DataFrame. If a sequence is given, a
      MultiIndex is used. If you have a malformed file with delimiters at the end
      of each line, you might consider index_col=False to force pandas to _not_
      use the first column as the index (row names)
  usecols : list-like or callable, default None
      Return a subset of the columns. If list-like, all elements must either
      be positional (i.e. integer indices into the document columns) or strings
      that correspond to column names provided either by the user in `names` or
      inferred from the document header row(s). For example, a valid list-like
      `usecols` parameter would be [0, 1, 2] or ['foo', 'bar', 'baz']. Element
      order is ignored, so ``usecols=[0, 1]`` is the same as ``[1, 0]``.
      To instantiate a DataFrame from ``data`` with element order preserved use
      ``pd.read_csv(data, usecols=['foo', 'bar'])[['foo', 'bar']]`` for columns
      in ``['foo', 'bar']`` order or
      ``pd.read_csv(data, usecols=['foo', 'bar'])[['bar', 'foo']]``
      for ``['bar', 'foo']`` order.
  
      If callable, the callable function will be evaluated against the column
      names, returning names where the callable function evaluates to True. An
      example of a valid callable argument would be ``lambda x: x.upper() in
      ['AAA', 'BBB', 'DDD']``. Using this parameter results in much faster
      parsing time and lower memory usage.
  squeeze : boolean, default False
      If the parsed data only contains one column then return a Series
  prefix : str, default None
      Prefix to add to column numbers when no header, e.g. 'X' for X0, X1, ...
  mangle_dupe_cols : boolean, default True
      Duplicate columns will be specified as 'X', 'X.1', ...'X.N', rather than
      'X'...'X'. Passing in False will cause data to be overwritten if there
      are duplicate names in the columns.
  dtype : Type name or dict of column -> type, default None
      Data type for data or columns. E.g. {'a': np.float64, 'b': np.int32}
      Use `str` or `object` together with suitable `na_values` settings
      to preserve and not interpret dtype.
      If converters are specified, they will be applied INSTEAD
      of dtype conversion.
  %s
  converters : dict, default None
      Dict of functions for converting values in certain columns. Keys can either
      be integers or column labels
  true_values : list, default None
      Values to consider as True
  false_values : list, default None
      Values to consider as False
  skipinitialspace : boolean, default False
      Skip spaces after delimiter.
  skiprows : list-like or integer or callable, default None
      Line numbers to skip (0-indexed) or number of lines to skip (int)
      at the start of the file.
  
      If callable, the callable function will be evaluated against the row
      indices, returning True if the row should be skipped and False otherwise.
      An example of a valid callable argument would be ``lambda x: x in [0, 2]``.
  skipfooter : int, default 0
      Number of lines at bottom of file to skip (Unsupported with engine='c')
  nrows : int, default None
      Number of rows of file to read. Useful for reading pieces of large files
  na_values : scalar, str, list-like, or dict, default None
      Additional strings to recognize as NA/NaN. If dict passed, specific
      per-column NA values.  By default the following values are interpreted as
      NaN: '""" + fill("', '".join(sorted(_NA_VALUES)),
                       70, subsequent_indent="    ") + """'.
  keep_default_na : bool, default True
      Whether or not to include the default NaN values when parsing the data.
      Depending on whether `na_values` is passed in, the behavior is as follows:
  
      * If `keep_default_na` is True, and `na_values` are specified, `na_values`
        is appended to the default NaN values used for parsing.
      * If `keep_default_na` is True, and `na_values` are not specified, only
        the default NaN values are used for parsing.
      * If `keep_default_na` is False, and `na_values` are specified, only
        the NaN values specified `na_values` are used for parsing.
      * If `keep_default_na` is False, and `na_values` are not specified, no
        strings will be parsed as NaN.
  
      Note that if `na_filter` is passed in as False, the `keep_default_na` and
      `na_values` parameters will be ignored.
  na_filter : boolean, default True
      Detect missing value markers (empty strings and the value of na_values). In
      data without any NAs, passing na_filter=False can improve the performance
      of reading a large file
  verbose : boolean, default False
      Indicate number of NA values placed in non-numeric columns
  skip_blank_lines : boolean, default True
      If True, skip over blank lines rather than interpreting as NaN values
  parse_dates : boolean or list of ints or names or list of lists or dict, \
  default False
  
      * boolean. If True -> try parsing the index.
      * list of ints or names. e.g. If [1, 2, 3] -> try parsing columns 1, 2, 3
        each as a separate date column.
      * list of lists. e.g.  If [[1, 3]] -> combine columns 1 and 3 and parse as
        a single date column.
      * dict, e.g. {'foo' : [1, 3]} -> parse columns 1, 3 as date and call result
        'foo'
  
      If a column or index contains an unparseable date, the entire column or
      index will be returned unaltered as an object data type. For non-standard
      datetime parsing, use ``pd.to_datetime`` after ``pd.read_csv``
  
      Note: A fast-path exists for iso8601-formatted dates.
  infer_datetime_format : boolean, default False
      If True and `parse_dates` is enabled, pandas will attempt to infer the
      format of the datetime strings in the columns, and if it can be inferred,
      switch to a faster method of parsing them. In some cases this can increase
      the parsing speed by 5-10x.
  keep_date_col : boolean, default False
      If True and `parse_dates` specifies combining multiple columns then
      keep the original columns.
  date_parser : function, default None
      Function to use for converting a sequence of string columns to an array of
      datetime instances. The default uses ``dateutil.parser.parser`` to do the
      conversion. Pandas will try to call `date_parser` in three different ways,
      advancing to the next if an exception occurs: 1) Pass one or more arrays
      (as defined by `parse_dates`) as arguments; 2) concatenate (row-wise) the
      string values from the columns defined by `parse_dates` into a single array
      and pass that; and 3) call `date_parser` once for each row using one or
      more strings (corresponding to the columns defined by `parse_dates`) as
      arguments.
  dayfirst : boolean, default False
      DD/MM format dates, international and European format
  iterator : boolean, default False
      Return TextFileReader object for iteration or getting chunks with
      ``get_chunk()``.
  chunksize : int, default None
      Return TextFileReader object for iteration.
      See the `IO Tools docs
      <http://pandas.pydata.org/pandas-docs/stable/io.html#io-chunking>`_
      for more information on ``iterator`` and ``chunksize``.
  compression : {'infer', 'gzip', 'bz2', 'zip', 'xz', None}, default 'infer'
      For on-the-fly decompression of on-disk data. If 'infer' and
      `filepath_or_buffer` is path-like, then detect compression from the
      following extensions: '.gz', '.bz2', '.zip', or '.xz' (otherwise no
      decompression). If using 'zip', the ZIP file must contain only one data
      file to be read in. Set to None for no decompression.
  
      .. versionadded:: 0.18.1 support for 'zip' and 'xz' compression.
  
  thousands : str, default None
      Thousands separator
  decimal : str, default '.'
      Character to recognize as decimal point (e.g. use ',' for European data).
  float_precision : string, default None
      Specifies which converter the C engine should use for floating-point
      values. The options are `None` for the ordinary converter,
      `high` for the high-precision converter, and `round_trip` for the
      round-trip converter.
  lineterminator : str (length 1), default None
      Character to break file into lines. Only valid with C parser.
  quotechar : str (length 1), optional
      The character used to denote the start and end of a quoted item. Quoted
      items can include the delimiter and it will be ignored.
  quoting : int or csv.QUOTE_* instance, default 0
      Control field quoting behavior per ``csv.QUOTE_*`` constants. Use one of
      QUOTE_MINIMAL (0), QUOTE_ALL (1), QUOTE_NONNUMERIC (2) or QUOTE_NONE (3).
  doublequote : boolean, default ``True``
     When quotechar is specified and quoting is not ``QUOTE_NONE``, indicate
     whether or not to interpret two consecutive quotechar elements INSIDE a
     field as a single ``quotechar`` element.
  escapechar : str (length 1), default None
      One-character string used to escape delimiter when quoting is QUOTE_NONE.
  comment : str, default None
      Indicates remainder of line should not be parsed. If found at the beginning
      of a line, the line will be ignored altogether. This parameter must be a
      single character. Like empty lines (as long as ``skip_blank_lines=True``),
      fully commented lines are ignored by the parameter `header` but not by
      `skiprows`. For example, if ``comment='#'``, parsing
      ``#empty\\na,b,c\\n1,2,3`` with ``header=0`` will result in 'a,b,c' being
      treated as the header.
  encoding : str, default None
      Encoding to use for UTF when reading/writing (ex. 'utf-8'). `List of Python
      standard encodings
      <https://docs.python.org/3/library/codecs.html#standard-encodings>`_
  dialect : str or csv.Dialect instance, default None
      If provided, this parameter will override values (default or not) for the
      following parameters: `delimiter`, `doublequote`, `escapechar`,
      `skipinitialspace`, `quotechar`, and `quoting`. If it is necessary to
      override values, a ParserWarning will be issued. See csv.Dialect
      documentation for more details.
  tupleize_cols : boolean, default False
      .. deprecated:: 0.21.0
         This argument will be removed and will always convert to MultiIndex
  
      Leave a list of tuples on columns as is (default is to convert to
      a MultiIndex on the columns)
  error_bad_lines : boolean, default True
      Lines with too many fields (e.g. a csv line with too many commas) will by
      default cause an exception to be raised, and no DataFrame will be returned.
      If False, then these "bad lines" will dropped from the DataFrame that is
      returned.
  warn_bad_lines : boolean, default True
      If error_bad_lines is False, and warn_bad_lines is True, a warning for each
      "bad line" will be output.
  low_memory : boolean, default True
      Internally process the file in chunks, resulting in lower memory use
      while parsing, but possibly mixed type inference.  To ensure no mixed
      types either set False, or specify the type with the `dtype` parameter.
      Note that the entire file is read into a single DataFrame regardless,
      use the `chunksize` or `iterator` parameter to return the data in chunks.
      (Only valid with C parser)
  memory_map : boolean, default False
      If a filepath is provided for `filepath_or_buffer`, map the file object
      directly onto memory and access the data directly from there. Using this
      option can improve performance because there is no longer any I/O overhead.
  
  Returns
  -------
  result : DataFrame or TextParser
  """
  
  # engine is not used in read_fwf() so is factored out of the shared docstring
  _engine_doc = """engine : {'c', 'python'}, optional
      Parser engine to use. The C engine is faster while the python engine is
      currently more feature-complete."""
  
  _sep_doc = r"""sep : str, default {default}
      Delimiter to use. If sep is None, the C engine cannot automatically detect
      the separator, but the Python parsing engine can, meaning the latter will
      be used and automatically detect the separator by Python's builtin sniffer
      tool, ``csv.Sniffer``. In addition, separators longer than 1 character and
      different from ``'\s+'`` will be interpreted as regular expressions and
      will also force the use of the Python parsing engine. Note that regex
      delimiters are prone to ignoring quoted data. Regex example: ``'\r\t'``
  delimiter : str, default ``None``
      Alternative argument name for sep."""
  
  _read_csv_doc = """
  Read CSV (comma-separated) file into DataFrame
  
  %s
  """ % (_parser_params % (_sep_doc.format(default="','"), _engine_doc))
  
  _read_table_doc = """
  
  .. deprecated:: 0.24.0
     Use :func:`pandas.read_csv` instead, passing ``sep='\t'`` if necessary.
  
  Read general delimited file into DataFrame
  
  %s
  """ % (_parser_params % (_sep_doc.format(default="\\t (tab-stop)"),
                           _engine_doc))
  
  _fwf_widths = """\
  colspecs : list of pairs (int, int) or 'infer'. optional
      A list of pairs (tuples) giving the extents of the fixed-width
      fields of each line as half-open intervals (i.e.,  [from, to[ ).
      String value 'infer' can be used to instruct the parser to try
      detecting the column specifications from the first 100 rows of
      the data which are not being skipped via skiprows (default='infer').
  widths : list of ints. optional
      A list of field widths which can be used instead of 'colspecs' if
      the intervals are contiguous.
  delimiter : str, default ``'\t' + ' '``
      Characters to consider as filler characters in the fixed-width file.
      Can be used to specify the filler character of the fields
      if it is not spaces (e.g., '~').
  """
  
  _read_fwf_doc = """
  Read a table of fixed-width formatted lines into DataFrame
  
  %s
  """ % (_parser_params % (_fwf_widths, ''))
  
  
  def _validate_integer(name, val, min_val=0):
      """
      Checks whether the 'name' parameter for parsing is either
      an integer OR float that can SAFELY be cast to an integer
      without losing accuracy. Raises a ValueError if that is
      not the case.
  
      Parameters
      ----------
      name : string
          Parameter name (used for error reporting)
      val : int or float
          The value to check
      min_val : int
          Minimum allowed value (val < min_val will result in a ValueError)
      """
      msg = "'{name:s}' must be an integer >={min_val:d}".format(name=name,
                                                                 min_val=min_val)
  
      if val is not None:
          if is_float(val):
              if int(val) != val:
                  raise ValueError(msg)
              val = int(val)
          elif not (is_integer(val) and val >= min_val):
              raise ValueError(msg)
  
      return val
  
  
  def _validate_names(names):
      """
      Check if the `names` parameter contains duplicates.
  
      If duplicates are found, we issue a warning before returning.
  
      Parameters
      ----------
      names : array-like or None
          An array containing a list of the names used for the output DataFrame.
  
      Returns
      -------
      names : array-like or None
          The original `names` parameter.
      """
  
      if names is not None:
          if len(names) != len(set(names)):
              msg = ("Duplicate names specified. This "
                     "will raise an error in the future.")
              warnings.warn(msg, UserWarning, stacklevel=3)
  
      return names
  
  
  def _read(filepath_or_buffer, kwds):
      """Generic reader of line files."""
      encoding = kwds.get('encoding', None)
      if encoding is not None:
          encoding = re.sub('_', '-', encoding).lower()
          kwds['encoding'] = encoding
  
      compression = kwds.get('compression')
      compression = _infer_compression(filepath_or_buffer, compression)
      filepath_or_buffer, _, compression, should_close = get_filepath_or_buffer(
          filepath_or_buffer, encoding, compression)
      kwds['compression'] = compression
  
      if kwds.get('date_parser', None) is not None:
          if isinstance(kwds['parse_dates'], bool):
              kwds['parse_dates'] = True
  
      # Extract some of the arguments (pass chunksize on).
      iterator = kwds.get('iterator', False)
      chunksize = _validate_integer('chunksize', kwds.get('chunksize', None), 1)
      nrows = kwds.get('nrows', None)
  
      # Check for duplicates in names.
      _validate_names(kwds.get("names", None))
  
      # Create the parser.
      parser = TextFileReader(filepath_or_buffer, **kwds)
  
      if chunksize or iterator:
          return parser
  
      try:
          data = parser.read(nrows)
      finally:
          parser.close()
  
      if should_close:
          try:
              filepath_or_buffer.close()
          except ValueError:
              pass
  
      return data
  
  
  _parser_defaults = {
      'delimiter': None,
  
      'doublequote': True,
      'escapechar': None,
      'quotechar': '"',
      'quoting': csv.QUOTE_MINIMAL,
      'skipinitialspace': False,
      'lineterminator': None,
  
      'header': 'infer',
      'index_col': None,
      'names': None,
      'prefix': None,
      'skiprows': None,
      'na_values': None,
      'true_values': None,
      'false_values': None,
      'converters': None,
      'dtype': None,
      'skipfooter': 0,
  
      'keep_default_na': True,
      'thousands': None,
      'comment': None,
      'decimal': b'.',
  
      # 'engine': 'c',
      'parse_dates': False,
      'keep_date_col': False,
      'dayfirst': False,
      'date_parser': None,
  
      'usecols': None,
  
      'nrows': None,
      # 'iterator': False,
      'chunksize': None,
      'verbose': False,
      'encoding': None,
      'squeeze': False,
      'compression': None,
      'mangle_dupe_cols': True,
      'tupleize_cols': False,
      'infer_datetime_format': False,
      'skip_blank_lines': True
  }
  
  
  _c_parser_defaults = {
      'delim_whitespace': False,
      'na_filter': True,
      'low_memory': True,
      'memory_map': False,
      'error_bad_lines': True,
      'warn_bad_lines': True,
      'tupleize_cols': False,
      'float_precision': None
  }
  
  _fwf_defaults = {
      'colspecs': 'infer',
      'widths': None,
  }
  
  _c_unsupported = {'skipfooter'}
  _python_unsupported = {
      'low_memory',
      'float_precision',
  }
  
  _deprecated_defaults = {
      'tupleize_cols': None
  }
  _deprecated_args = {
      'tupleize_cols',
  }
  
  
  def _make_parser_function(name, default_sep=','):
  
      # prepare read_table deprecation
      if name == "read_table":
          sep = False
      else:
          sep = default_sep
  
      def parser_f(filepath_or_buffer,
                   sep=sep,
                   delimiter=None,
  
                   # Column and Index Locations and Names
                   header='infer',
                   names=None,
                   index_col=None,
                   usecols=None,
                   squeeze=False,
                   prefix=None,
                   mangle_dupe_cols=True,
  
                   # General Parsing Configuration
                   dtype=None,
                   engine=None,
                   converters=None,
                   true_values=None,
                   false_values=None,
                   skipinitialspace=False,
                   skiprows=None,
                   nrows=None,
  
                   # NA and Missing Data Handling
                   na_values=None,
                   keep_default_na=True,
                   na_filter=True,
                   verbose=False,
                   skip_blank_lines=True,
  
                   # Datetime Handling
                   parse_dates=False,
                   infer_datetime_format=False,
                   keep_date_col=False,
                   date_parser=None,
                   dayfirst=False,
  
                   # Iteration
                   iterator=False,
                   chunksize=None,
  
                   # Quoting, Compression, and File Format
                   compression='infer',
                   thousands=None,
                   decimal=b'.',
                   lineterminator=None,
                   quotechar='"',
                   quoting=csv.QUOTE_MINIMAL,
                   escapechar=None,
                   comment=None,
                   encoding=None,
                   dialect=None,
                   tupleize_cols=None,
  
                   # Error Handling
                   error_bad_lines=True,
                   warn_bad_lines=True,
  
                   skipfooter=0,
  
                   # Internal
                   doublequote=True,
                   delim_whitespace=False,
                   low_memory=_c_parser_defaults['low_memory'],
                   memory_map=False,
                   float_precision=None):
  
          # deprecate read_table GH21948
          if name == "read_table":
              if sep is False and delimiter is None:
                  warnings.warn("read_table is deprecated, use read_csv "
                                "instead, passing sep='\\t'.",
                                FutureWarning, stacklevel=2)
              else:
                  warnings.warn("read_table is deprecated, use read_csv "
                                "instead.",
                                FutureWarning, stacklevel=2)
              if sep is False:
                  sep = default_sep
  
          # Alias sep -> delimiter.
          if delimiter is None:
              delimiter = sep
  
          if delim_whitespace and delimiter != default_sep:
              raise ValueError("Specified a delimiter with both sep and"
                               " delim_whitespace=True; you can only"
                               " specify one.")
  
          if engine is not None:
              engine_specified = True
          else:
              engine = 'c'
              engine_specified = False
  
          kwds = dict(delimiter=delimiter,
                      engine=engine,
                      dialect=dialect,
                      compression=compression,
                      engine_specified=engine_specified,
  
                      doublequote=doublequote,
                      escapechar=escapechar,
                      quotechar=quotechar,
                      quoting=quoting,
                      skipinitialspace=skipinitialspace,
                      lineterminator=lineterminator,
  
                      header=header,
                      index_col=index_col,
                      names=names,
                      prefix=prefix,
                      skiprows=skiprows,
                      na_values=na_values,
                      true_values=true_values,
                      false_values=false_values,
                      keep_default_na=keep_default_na,
                      thousands=thousands,
                      comment=comment,
                      decimal=decimal,
  
                      parse_dates=parse_dates,
                      keep_date_col=keep_date_col,
                      dayfirst=dayfirst,
                      date_parser=date_parser,
  
                      nrows=nrows,
                      iterator=iterator,
                      chunksize=chunksize,
                      skipfooter=skipfooter,
                      converters=converters,
                      dtype=dtype,
                      usecols=usecols,
                      verbose=verbose,
                      encoding=encoding,
                      squeeze=squeeze,
                      memory_map=memory_map,
                      float_precision=float_precision,
  
                      na_filter=na_filter,
                      delim_whitespace=delim_whitespace,
                      warn_bad_lines=warn_bad_lines,
                      error_bad_lines=error_bad_lines,
                      low_memory=low_memory,
                      mangle_dupe_cols=mangle_dupe_cols,
                      tupleize_cols=tupleize_cols,
                      infer_datetime_format=infer_datetime_format,
                      skip_blank_lines=skip_blank_lines)
  
          return _read(filepath_or_buffer, kwds)
  
      parser_f.__name__ = name
  
      return parser_f
  
  
  read_csv = _make_parser_function('read_csv', default_sep=',')
  read_csv = Appender(_read_csv_doc)(read_csv)
  
  read_table = _make_parser_function('read_table', default_sep='\t')
  read_table = Appender(_read_table_doc)(read_table)
  
  
  @Appender(_read_fwf_doc)
  def read_fwf(filepath_or_buffer, colspecs='infer', widths=None, **kwds):
      # Check input arguments.
      if colspecs is None and widths is None:
          raise ValueError("Must specify either colspecs or widths")
      elif colspecs not in (None, 'infer') and widths is not None:
          raise ValueError("You must specify only one of 'widths' and "
                           "'colspecs'")
  
      # Compute 'colspecs' from 'widths', if specified.
      if widths is not None:
          colspecs, col = [], 0
          for w in widths:
              colspecs.append((col, col + w))
              col += w
  
      kwds['colspecs'] = colspecs
      kwds['engine'] = 'python-fwf'
      return _read(filepath_or_buffer, kwds)
  
  
  class TextFileReader(BaseIterator):
      """
  
      Passed dialect overrides any of the related parser options
  
      """
  
      def __init__(self, f, engine=None, **kwds):
  
          self.f = f
  
          if engine is not None:
              engine_specified = True
          else:
              engine = 'python'
              engine_specified = False
  
          self._engine_specified = kwds.get('engine_specified', engine_specified)
  
          if kwds.get('dialect') is not None:
              dialect = kwds['dialect']
              if dialect in csv.list_dialects():
                  dialect = csv.get_dialect(dialect)
  
              # Any valid dialect should have these attributes.
              # If any are missing, we will raise automatically.
              for param in ('delimiter', 'doublequote', 'escapechar',
                            'skipinitialspace', 'quotechar', 'quoting'):
                  try:
                      dialect_val = getattr(dialect, param)
                  except AttributeError:
                      raise ValueError("Invalid dialect '{dialect}' provided"
                                       .format(dialect=kwds['dialect']))
                  provided = kwds.get(param, _parser_defaults[param])
  
                  # Messages for conflicting values between the dialect instance
                  # and the actual parameters provided.
                  conflict_msgs = []
  
                  if dialect_val != provided:
                      conflict_msgs.append((
                          "Conflicting values for '{param}': '{val}' was "
                          "provided, but the dialect specifies '{diaval}'. "
                          "Using the dialect-specified value.".format(
                              param=param, val=provided, diaval=dialect_val)))
  
                  if conflict_msgs:
                      warnings.warn('\n\n'.join(conflict_msgs), ParserWarning,
                                    stacklevel=2)
                  kwds[param] = dialect_val
  
          if kwds.get("skipfooter"):
              if kwds.get("iterator") or kwds.get("chunksize"):
                  raise ValueError("'skipfooter' not supported for 'iteration'")
              if kwds.get("nrows"):
                  raise ValueError("'skipfooter' not supported with 'nrows'")
  
          if kwds.get('header', 'infer') == 'infer':
              kwds['header'] = 0 if kwds.get('names') is None else None
  
          self.orig_options = kwds
  
          # miscellanea
          self.engine = engine
          self._engine = None
          self._currow = 0
  
          options = self._get_options_with_defaults(engine)
  
          self.chunksize = options.pop('chunksize', None)
          self.nrows = options.pop('nrows', None)
          self.squeeze = options.pop('squeeze', False)
  
          # might mutate self.engine
          self.engine = self._check_file_or_buffer(f, engine)
          self.options, self.engine = self._clean_options(options, engine)
  
          if 'has_index_names' in kwds:
              self.options['has_index_names'] = kwds['has_index_names']
  
          self._make_engine(self.engine)
  
      def close(self):
          self._engine.close()
  
      def _get_options_with_defaults(self, engine):
          kwds = self.orig_options
  
          options = {}
  
          for argname, default in compat.iteritems(_parser_defaults):
              value = kwds.get(argname, default)
  
              # see gh-12935
              if argname == 'mangle_dupe_cols' and not value:
                  raise ValueError('Setting mangle_dupe_cols=False is '
                                   'not supported yet')
              else:
                  options[argname] = value
  
          for argname, default in compat.iteritems(_c_parser_defaults):
              if argname in kwds:
                  value = kwds[argname]
  
                  if engine != 'c' and value != default:
                      if ('python' in engine and
                              argname not in _python_unsupported):
                          pass
                      elif value == _deprecated_defaults.get(argname, default):
                          pass
                      else:
                          raise ValueError(
                              'The %r option is not supported with the'
                              ' %r engine' % (argname, engine))
              else:
                  value = _deprecated_defaults.get(argname, default)
              options[argname] = value
  
          if engine == 'python-fwf':
              for argname, default in compat.iteritems(_fwf_defaults):
                  options[argname] = kwds.get(argname, default)
  
          return options
  
      def _check_file_or_buffer(self, f, engine):
          # see gh-16530
          if is_file_like(f):
              next_attr = "__next__" if PY3 else "next"
  
              # The C engine doesn't need the file-like to have the "next" or
              # "__next__" attribute. However, the Python engine explicitly calls
              # "next(...)" when iterating through such an object, meaning it
              # needs to have that attribute ("next" for Python 2.x, "__next__"
              # for Python 3.x)
              if engine != "c" and not hasattr(f, next_attr):
                  msg = ("The 'python' engine cannot iterate "
                         "through this file buffer.")
                  raise ValueError(msg)
  
          return engine
  
      def _clean_options(self, options, engine):
          result = options.copy()
  
          engine_specified = self._engine_specified
          fallback_reason = None
  
          sep = options['delimiter']
          delim_whitespace = options['delim_whitespace']
  
          # C engine not supported yet
          if engine == 'c':
              if options['skipfooter'] > 0:
                  fallback_reason = ("the 'c' engine does not support"
                                     " skipfooter")
                  engine = 'python'
  
          encoding = sys.getfilesystemencoding() or 'utf-8'
          if sep is None and not delim_whitespace:
              if engine == 'c':
                  fallback_reason = ("the 'c' engine does not support"
                                     " sep=None with delim_whitespace=False")
                  engine = 'python'
          elif sep is not None and len(sep) > 1:
              if engine == 'c' and sep == r'\s+':
                  result['delim_whitespace'] = True
                  del result['delimiter']
              elif engine not in ('python', 'python-fwf'):
                  # wait until regex engine integrated
                  fallback_reason = ("the 'c' engine does not support"
                                     " regex separators (separators > 1 char and"
                                     r" different from '\s+' are"
                                     " interpreted as regex)")
                  engine = 'python'
          elif delim_whitespace:
              if 'python' in engine:
                  result['delimiter'] = r'\s+'
          elif sep is not None:
              encodeable = True
              try:
                  if len(sep.encode(encoding)) > 1:
                      encodeable = False
              except UnicodeDecodeError:
                  encodeable = False
              if not encodeable and engine not in ('python', 'python-fwf'):
                  fallback_reason = ("the separator encoded in {encoding}"
                                     " is > 1 char long, and the 'c' engine"
                                     " does not support such separators"
                                     .format(encoding=encoding))
                  engine = 'python'
  
          quotechar = options['quotechar']
          if (quotechar is not None and
                  isinstance(quotechar, (str, compat.text_type, bytes))):
              if (len(quotechar) == 1 and ord(quotechar) > 127 and
                      engine not in ('python', 'python-fwf')):
                  fallback_reason = ("ord(quotechar) > 127, meaning the "
                                     "quotechar is larger than one byte, "
                                     "and the 'c' engine does not support "
                                     "such quotechars")
                  engine = 'python'
  
          if fallback_reason and engine_specified:
              raise ValueError(fallback_reason)
  
          if engine == 'c':
              for arg in _c_unsupported:
                  del result[arg]
  
          if 'python' in engine:
              for arg in _python_unsupported:
                  if fallback_reason and result[arg] != _c_parser_defaults[arg]:
                      msg = ("Falling back to the 'python' engine because"
                             " {reason}, but this causes {option!r} to be"
                             " ignored as it is not supported by the 'python'"
                             " engine.").format(reason=fallback_reason,
                                                option=arg)
                      raise ValueError(msg)
                  del result[arg]
  
          if fallback_reason:
              warnings.warn(("Falling back to the 'python' engine because"
                             " {0}; you can avoid this warning by specifying"
                             " engine='python'.").format(fallback_reason),
                            ParserWarning, stacklevel=5)
  
          index_col = options['index_col']
          names = options['names']
          converters = options['converters']
          na_values = options['na_values']
          skiprows = options['skiprows']
  
          _validate_header_arg(options['header'])
  
          depr_warning = ''
  
          for arg in _deprecated_args:
              parser_default = _c_parser_defaults[arg]
              depr_default = _deprecated_defaults[arg]
  
              msg = ("The '{arg}' argument has been deprecated "
                     "and will be removed in a future version."
                     .format(arg=arg))
  
              if arg == 'tupleize_cols':
                  msg += (' Column tuples will then '
                          'always be converted to MultiIndex.')
  
              if result.get(arg, depr_default) != depr_default:
                  # raise Exception(result.get(arg, depr_default), depr_default)
                  depr_warning += msg + '\n\n'
              else:
                  result[arg] = parser_default
  
          if depr_warning != '':
              warnings.warn(depr_warning, FutureWarning, stacklevel=2)
  
          if index_col is True:
              raise ValueError("The value of index_col couldn't be 'True'")
          if _is_index_col(index_col):
              if not isinstance(index_col, (list, tuple, np.ndarray)):
                  index_col = [index_col]
          result['index_col'] = index_col
  
          names = list(names) if names is not None else names
  
          # type conversion-related
          if converters is not None:
              if not isinstance(converters, dict):
                  raise TypeError('Type converters must be a dict or'
                                  ' subclass, input was '
                                  'a {0!r}'.format(type(converters).__name__))
          else:
              converters = {}
  
          # Converting values to NA
          keep_default_na = options['keep_default_na']
          na_values, na_fvalues = _clean_na_values(na_values, keep_default_na)
  
          # handle skiprows; this is internally handled by the
          # c-engine, so only need for python parsers
          if engine != 'c':
              if is_integer(skiprows):
                  skiprows = lrange(skiprows)
              if skiprows is None:
                  skiprows = set()
              elif not callable(skiprows):
                  skiprows = set(skiprows)
  
          # put stuff back
          result['names'] = names
          result['converters'] = converters
          result['na_values'] = na_values
          result['na_fvalues'] = na_fvalues
          result['skiprows'] = skiprows
  
          return result, engine
  
      def __next__(self):
          try:
              return self.get_chunk()
          except StopIteration:
              self.close()
              raise
  
      def _make_engine(self, engine='c'):
          if engine == 'c':
              self._engine = CParserWrapper(self.f, **self.options)
          else:
              if engine == 'python':
                  klass = PythonParser
              elif engine == 'python-fwf':
                  klass = FixedWidthFieldParser
              else:
                  raise ValueError('Unknown engine: {engine} (valid options are'
                                   ' "c", "python", or' ' "python-fwf")'.format(
                                       engine=engine))
              self._engine = klass(self.f, **self.options)
  
      def _failover_to_python(self):
          raise AbstractMethodError(self)
  
      def read(self, nrows=None):
          nrows = _validate_integer('nrows', nrows)
          ret = self._engine.read(nrows)
  
          # May alter columns / col_dict
          index, columns, col_dict = self._create_index(ret)
  
          if index is None:
              if col_dict:
                  # Any column is actually fine:
                  new_rows = len(compat.next(compat.itervalues(col_dict)))
                  index = RangeIndex(self._currow, self._currow + new_rows)
              else:
                  new_rows = 0
          else:
              new_rows = len(index)
  
          df = DataFrame(col_dict, columns=columns, index=index)
  
          self._currow += new_rows
  
          if self.squeeze and len(df.columns) == 1:
              return df[df.columns[0]].copy()
          return df
  
      def _create_index(self, ret):
          index, columns, col_dict = ret
          return index, columns, col_dict
  
      def get_chunk(self, size=None):
          if size is None:
              size = self.chunksize
          if self.nrows is not None:
              if self._currow >= self.nrows:
                  raise StopIteration
              size = min(size, self.nrows - self._currow)
          return self.read(nrows=size)
  
  
  def _is_index_col(col):
      return col is not None and col is not False
  
  
  def _is_potential_multi_index(columns):
      """
      Check whether or not the `columns` parameter
      could be converted into a MultiIndex.
  
      Parameters
      ----------
      columns : array-like
          Object which may or may not be convertible into a MultiIndex
  
      Returns
      -------
      boolean : Whether or not columns could become a MultiIndex
      """
      return (len(columns) and not isinstance(columns, MultiIndex) and
              all(isinstance(c, tuple) for c in columns))
  
  
  def _evaluate_usecols(usecols, names):
      """
      Check whether or not the 'usecols' parameter
      is a callable.  If so, enumerates the 'names'
      parameter and returns a set of indices for
      each entry in 'names' that evaluates to True.
      If not a callable, returns 'usecols'.
      """
      if callable(usecols):
          return {i for i, name in enumerate(names) if usecols(name)}
      return usecols
  
  
  def _validate_usecols_names(usecols, names):
      """
      Validates that all usecols are present in a given
      list of names. If not, raise a ValueError that
      shows what usecols are missing.
  
      Parameters
      ----------
      usecols : iterable of usecols
          The columns to validate are present in names.
      names : iterable of names
          The column names to check against.
  
      Returns
      -------
      usecols : iterable of usecols
          The `usecols` parameter if the validation succeeds.
  
      Raises
      ------
      ValueError : Columns were missing. Error message will list them.
      """
      missing = [c for c in usecols if c not in names]
      if len(missing) > 0:
          raise ValueError(
              "Usecols do not match columns, "
              "columns expected but not found: {missing}".format(missing=missing)
          )
  
      return usecols
  
  
  def _validate_skipfooter_arg(skipfooter):
      """
      Validate the 'skipfooter' parameter.
  
      Checks whether 'skipfooter' is a non-negative integer.
      Raises a ValueError if that is not the case.
  
      Parameters
      ----------
      skipfooter : non-negative integer
          The number of rows to skip at the end of the file.
  
      Returns
      -------
      validated_skipfooter : non-negative integer
          The original input if the validation succeeds.
  
      Raises
      ------
      ValueError : 'skipfooter' was not a non-negative integer.
      """
  
      if not is_integer(skipfooter):
          raise ValueError("skipfooter must be an integer")
  
      if skipfooter < 0:
          raise ValueError("skipfooter cannot be negative")
  
      return skipfooter
  
  
  def _validate_usecols_arg(usecols):
      """
      Validate the 'usecols' parameter.
  
      Checks whether or not the 'usecols' parameter contains all integers
      (column selection by index), strings (column by name) or is a callable.
      Raises a ValueError if that is not the case.
  
      Parameters
      ----------
      usecols : list-like, callable, or None
          List of columns to use when parsing or a callable that can be used
          to filter a list of table columns.
  
      Returns
      -------
      usecols_tuple : tuple
          A tuple of (verified_usecols, usecols_dtype).
  
          'verified_usecols' is either a set if an array-like is passed in or
          'usecols' if a callable or None is passed in.
  
          'usecols_dtype` is the inferred dtype of 'usecols' if an array-like
          is passed in or None if a callable or None is passed in.
      """
      msg = ("'usecols' must either be list-like of all strings, all unicode, "
             "all integers or a callable.")
      if usecols is not None:
          if callable(usecols):
              return usecols, None
          # GH20529, ensure is iterable container but not string.
          elif not is_list_like(usecols):
              raise ValueError(msg)
          else:
              usecols_dtype = lib.infer_dtype(usecols)
              if usecols_dtype not in ('empty', 'integer',
                                       'string', 'unicode'):
                  raise ValueError(msg)
          return set(usecols), usecols_dtype
      return usecols, None
  
  
  def _validate_parse_dates_arg(parse_dates):
      """
      Check whether or not the 'parse_dates' parameter
      is a non-boolean scalar. Raises a ValueError if
      that is the case.
      """
      msg = ("Only booleans, lists, and "
             "dictionaries are accepted "
             "for the 'parse_dates' parameter")
  
      if parse_dates is not None:
          if is_scalar(parse_dates):
              if not lib.is_bool(parse_dates):
                  raise TypeError(msg)
  
          elif not isinstance(parse_dates, (list, dict)):
              raise TypeError(msg)
  
      return parse_dates
  
  
  class ParserBase(object):
  
      def __init__(self, kwds):
          self.names = kwds.get('names')
          self.orig_names = None
          self.prefix = kwds.pop('prefix', None)
  
          self.index_col = kwds.get('index_col', None)
          self.unnamed_cols = set()
          self.index_names = None
          self.col_names = None
  
          self.parse_dates = _validate_parse_dates_arg(
              kwds.pop('parse_dates', False))
          self.date_parser = kwds.pop('date_parser', None)
          self.dayfirst = kwds.pop('dayfirst', False)
          self.keep_date_col = kwds.pop('keep_date_col', False)
  
          self.na_values = kwds.get('na_values')
          self.na_fvalues = kwds.get('na_fvalues')
          self.na_filter = kwds.get('na_filter', False)
          self.keep_default_na = kwds.get('keep_default_na', True)
  
          self.true_values = kwds.get('true_values')
          self.false_values = kwds.get('false_values')
          self.tupleize_cols = kwds.get('tupleize_cols', False)
          self.mangle_dupe_cols = kwds.get('mangle_dupe_cols', True)
          self.infer_datetime_format = kwds.pop('infer_datetime_format', False)
  
          self._date_conv = _make_date_converter(
              date_parser=self.date_parser,
              dayfirst=self.dayfirst,
              infer_datetime_format=self.infer_datetime_format
          )
  
          # validate header options for mi
          self.header = kwds.get('header')
          if isinstance(self.header, (list, tuple, np.ndarray)):
              if not all(map(is_integer, self.header)):
                  raise ValueError("header must be integer or list of integers")
              if kwds.get('usecols'):
                  raise ValueError("cannot specify usecols when "
                                   "specifying a multi-index header")
              if kwds.get('names'):
                  raise ValueError("cannot specify names when "
                                   "specifying a multi-index header")
  
              # validate index_col that only contains integers
              if self.index_col is not None:
                  is_sequence = isinstance(self.index_col, (list, tuple,
                                                            np.ndarray))
                  if not (is_sequence and
                          all(map(is_integer, self.index_col)) or
                          is_integer(self.index_col)):
                      raise ValueError("index_col must only contain row numbers "
                                       "when specifying a multi-index header")
  
          # GH 16338
          elif self.header is not None and not is_integer(self.header):
              raise ValueError("header must be integer or list of integers")
  
          self._name_processed = False
  
          self._first_chunk = True
  
          # GH 13932
          # keep references to file handles opened by the parser itself
          self.handles = []
  
      def close(self):
          for f in self.handles:
              f.close()
  
      @property
      def _has_complex_date_col(self):
          return (isinstance(self.parse_dates, dict) or
                  (isinstance(self.parse_dates, list) and
                   len(self.parse_dates) > 0 and
                   isinstance(self.parse_dates[0], list)))
  
      def _should_parse_dates(self, i):
          if isinstance(self.parse_dates, bool):
              return self.parse_dates
          else:
              if self.index_names is not None:
                  name = self.index_names[i]
              else:
                  name = None
              j = self.index_col[i]
  
              if is_scalar(self.parse_dates):
                  return ((j == self.parse_dates) or
                          (name is not None and name == self.parse_dates))
              else:
                  return ((j in self.parse_dates) or
                          (name is not None and name in self.parse_dates))
  
      def _extract_multi_indexer_columns(self, header, index_names, col_names,
                                         passed_names=False):
          """ extract and return the names, index_names, col_names
              header is a list-of-lists returned from the parsers """
          if len(header) < 2:
              return header[0], index_names, col_names, passed_names
  
          # the names are the tuples of the header that are not the index cols
          # 0 is the name of the index, assuming index_col is a list of column
          # numbers
          ic = self.index_col
          if ic is None:
              ic = []
  
          if not isinstance(ic, (list, tuple, np.ndarray)):
              ic = [ic]
          sic = set(ic)
  
          # clean the index_names
          index_names = header.pop(-1)
          index_names, names, index_col = _clean_index_names(index_names,
                                                             self.index_col,
                                                             self.unnamed_cols)
  
          # extract the columns
          field_count = len(header[0])
  
          def extract(r):
              return tuple(r[i] for i in range(field_count) if i not in sic)
  
          columns = lzip(*[extract(r) for r in header])
          names = ic + columns
  
          # If we find unnamed columns all in a single
          # level, then our header was too long.
          for n in range(len(columns[0])):
              if all(compat.to_str(c[n]) in self.unnamed_cols for c in columns):
                  raise ParserError(
                      "Passed header=[%s] are too many rows for this "
                      "multi_index of columns"
                      % ','.join(str(x) for x in self.header)
                  )
  
          # Clean the column names (if we have an index_col).
          if len(ic):
              col_names = [r[0] if (len(r[0]) and
                                    r[0] not in self.unnamed_cols) else None
                           for r in header]
          else:
              col_names = [None] * len(header)
  
          passed_names = True
  
          return names, index_names, col_names, passed_names
  
      def _maybe_dedup_names(self, names):
          # see gh-7160 and gh-9424: this helps to provide
          # immediate alleviation of the duplicate names
          # issue and appears to be satisfactory to users,
          # but ultimately, not needing to butcher the names
          # would be nice!
          if self.mangle_dupe_cols:
              names = list(names)  # so we can index
              counts = defaultdict(int)
              is_potential_mi = _is_potential_multi_index(names)
  
              for i, col in enumerate(names):
                  cur_count = counts[col]
  
                  while cur_count > 0:
                      counts[col] = cur_count + 1
  
                      if is_potential_mi:
                          col = col[:-1] + ('%s.%d' % (col[-1], cur_count),)
                      else:
                          col = '%s.%d' % (col, cur_count)
                      cur_count = counts[col]
  
                  names[i] = col
                  counts[col] = cur_count + 1
  
          return names
  
      def _maybe_make_multi_index_columns(self, columns, col_names=None):
          # possibly create a column mi here
          if _is_potential_multi_index(columns):
              columns = MultiIndex.from_tuples(columns, names=col_names)
          return columns
  
      def _make_index(self, data, alldata, columns, indexnamerow=False):
          if not _is_index_col(self.index_col) or not self.index_col:
              index = None
  
          elif not self._has_complex_date_col:
              index = self._get_simple_index(alldata, columns)
              index = self._agg_index(index)
          elif self._has_complex_date_col:
              if not self._name_processed:
                  (self.index_names, _,
                   self.index_col) = _clean_index_names(list(columns),
                                                        self.index_col,
                                                        self.unnamed_cols)
                  self._name_processed = True
              index = self._get_complex_date_index(data, columns)
              index = self._agg_index(index, try_parse_dates=False)
  
          # add names for the index
          if indexnamerow:
              coffset = len(indexnamerow) - len(columns)
              index = index.set_names(indexnamerow[:coffset])
  
          # maybe create a mi on the columns
          columns = self._maybe_make_multi_index_columns(columns, self.col_names)
  
          return index, columns
  
      _implicit_index = False
  
      def _get_simple_index(self, data, columns):
          def ix(col):
              if not isinstance(col, compat.string_types):
                  return col
              raise ValueError('Index %s invalid' % col)
  
          to_remove = []
          index = []
          for idx in self.index_col:
              i = ix(idx)
              to_remove.append(i)
              index.append(data[i])
  
          # remove index items from content and columns, don't pop in
          # loop
          for i in reversed(sorted(to_remove)):
              data.pop(i)
              if not self._implicit_index:
                  columns.pop(i)
  
          return index
  
      def _get_complex_date_index(self, data, col_names):
          def _get_name(icol):
              if isinstance(icol, compat.string_types):
                  return icol
  
              if col_names is None:
                  raise ValueError(('Must supply column order to use %s as '
                                    'index') % str(icol))
  
              for i, c in enumerate(col_names):
                  if i == icol:
                      return c
  
          to_remove = []
          index = []
          for idx in self.index_col:
              name = _get_name(idx)
              to_remove.append(name)
              index.append(data[name])
  
          # remove index items from content and columns, don't pop in
          # loop
          for c in reversed(sorted(to_remove)):
              data.pop(c)
              col_names.remove(c)
  
          return index
  
      def _agg_index(self, index, try_parse_dates=True):
          arrays = []
  
          for i, arr in enumerate(index):
  
              if try_parse_dates and self._should_parse_dates(i):
                  arr = self._date_conv(arr)
  
              if self.na_filter:
                  col_na_values = self.na_values
                  col_na_fvalues = self.na_fvalues
              else:
                  col_na_values = set()
                  col_na_fvalues = set()
  
              if isinstance(self.na_values, dict):
                  col_name = self.index_names[i]
                  if col_name is not None:
                      col_na_values, col_na_fvalues = _get_na_values(
                          col_name, self.na_values, self.na_fvalues,
                          self.keep_default_na)
  
              arr, _ = self._infer_types(arr, col_na_values | col_na_fvalues)
              arrays.append(arr)
  
          names = self.index_names
          index = ensure_index_from_sequences(arrays, names)
  
          return index
  
      def _convert_to_ndarrays(self, dct, na_values, na_fvalues, verbose=False,
                               converters=None, dtypes=None):
          result = {}
          for c, values in compat.iteritems(dct):
              conv_f = None if converters is None else converters.get(c, None)
              if isinstance(dtypes, dict):
                  cast_type = dtypes.get(c, None)
              else:
                  # single dtype or None
                  cast_type = dtypes
  
              if self.na_filter:
                  col_na_values, col_na_fvalues = _get_na_values(
                      c, na_values, na_fvalues, self.keep_default_na)
              else:
                  col_na_values, col_na_fvalues = set(), set()
  
              if conv_f is not None:
                  # conv_f applied to data before inference
                  if cast_type is not None:
                      warnings.warn(("Both a converter and dtype were specified "
                                     "for column {0} - only the converter will "
                                     "be used").format(c), ParserWarning,
                                    stacklevel=7)
  
                  try:
                      values = lib.map_infer(values, conv_f)
                  except ValueError:
                      mask = algorithms.isin(
                          values, list(na_values)).view(np.uint8)
                      values = lib.map_infer_mask(values, conv_f, mask)
  
                  cvals, na_count = self._infer_types(
                      values, set(col_na_values) | col_na_fvalues,
                      try_num_bool=False)
              else:
                  # skip inference if specified dtype is object
                  try_num_bool = not (cast_type and is_string_dtype(cast_type))
  
                  # general type inference and conversion
                  cvals, na_count = self._infer_types(
                      values, set(col_na_values) | col_na_fvalues,
                      try_num_bool)
  
                  # type specified in dtype param
                  if cast_type and not is_dtype_equal(cvals, cast_type):
                      cvals = self._cast_types(cvals, cast_type, c)
  
              result[c] = cvals
              if verbose and na_count:
                  print('Filled %d NA values in column %s' % (na_count, str(c)))
          return result
  
      def _infer_types(self, values, na_values, try_num_bool=True):
          """
          Infer types of values, possibly casting
  
          Parameters
          ----------
          values : ndarray
          na_values : set
          try_num_bool : bool, default try
             try to cast values to numeric (first preference) or boolean
  
          Returns:
          --------
          converted : ndarray
          na_count : int
          """
          na_count = 0
          if issubclass(values.dtype.type, (np.number, np.bool_)):
              mask = algorithms.isin(values, list(na_values))
              na_count = mask.sum()
              if na_count > 0:
                  if is_integer_dtype(values):
                      values = values.astype(np.float64)
                  np.putmask(values, mask, np.nan)
              return values, na_count
  
          if try_num_bool:
              try:
                  result = lib.maybe_convert_numeric(values, na_values, False)
                  na_count = isna(result).sum()
              except Exception:
                  result = values
                  if values.dtype == np.object_:
                      na_count = parsers.sanitize_objects(result,
                                                          na_values, False)
          else:
              result = values
              if values.dtype == np.object_:
                  na_count = parsers.sanitize_objects(values, na_values, False)
  
          if result.dtype == np.object_ and try_num_bool:
              result = libops.maybe_convert_bool(np.asarray(values),
                                                 true_values=self.true_values,
                                                 false_values=self.false_values)
  
          return result, na_count
  
      def _cast_types(self, values, cast_type, column):
          """
          Cast values to specified type
  
          Parameters
          ----------
          values : ndarray
          cast_type : string or np.dtype
             dtype to cast values to
          column : string
              column name - used only for error reporting
  
          Returns
          -------
          converted : ndarray
          """
  
          if is_categorical_dtype(cast_type):
              known_cats = (isinstance(cast_type, CategoricalDtype) and
                            cast_type.categories is not None)
  
              if not is_object_dtype(values) and not known_cats:
                  # XXX this is for consistency with
                  # c-parser which parses all categories
                  # as strings
                  values = astype_nansafe(values, str)
  
              cats = Index(values).unique().dropna()
              values = Categorical._from_inferred_categories(
                  cats, cats.get_indexer(values), cast_type
              )
  
          else:
              try:
                  values = astype_nansafe(values, cast_type,
                                          copy=True, skipna=True)
              except ValueError:
                  raise ValueError("Unable to convert column %s to "
                                   "type %s" % (column, cast_type))
          return values
  
      def _do_date_conversions(self, names, data):
          # returns data, columns
  
          if self.parse_dates is not None:
              data, names = _process_date_conversion(
                  data, self._date_conv, self.parse_dates, self.index_col,
                  self.index_names, names, keep_date_col=self.keep_date_col)
  
          return names, data
  
  
  class CParserWrapper(ParserBase):
      """
  
      """
  
      def __init__(self, src, **kwds):
          self.kwds = kwds
          kwds = kwds.copy()
  
          ParserBase.__init__(self, kwds)
  
          if (kwds.get('compression') is None
             and 'utf-16' in (kwds.get('encoding') or '')):
              # if source is utf-16 plain text, convert source to utf-8
              if isinstance(src, compat.string_types):
                  src = open(src, 'rb')
                  self.handles.append(src)
              src = UTF8Recoder(src, kwds['encoding'])
              kwds['encoding'] = 'utf-8'
  
          # #2442
          kwds['allow_leading_cols'] = self.index_col is not False
  
          # GH20529, validate usecol arg before TextReader
          self.usecols, self.usecols_dtype = _validate_usecols_arg(
              kwds['usecols'])
          kwds['usecols'] = self.usecols
  
          self._reader = parsers.TextReader(src, **kwds)
          self.unnamed_cols = self._reader.unnamed_cols
  
          passed_names = self.names is None
  
          if self._reader.header is None:
              self.names = None
          else:
              if len(self._reader.header) > 1:
                  # we have a multi index in the columns
                  self.names, self.index_names, self.col_names, passed_names = (
                      self._extract_multi_indexer_columns(
                          self._reader.header, self.index_names, self.col_names,
                          passed_names
                      )
                  )
              else:
                  self.names = list(self._reader.header[0])
  
          if self.names is None:
              if self.prefix:
                  self.names = ['%s%d' % (self.prefix, i)
                                for i in range(self._reader.table_width)]
              else:
                  self.names = lrange(self._reader.table_width)
  
          # gh-9755
          #
          # need to set orig_names here first
          # so that proper indexing can be done
          # with _set_noconvert_columns
          #
          # once names has been filtered, we will
          # then set orig_names again to names
          self.orig_names = self.names[:]
  
          if self.usecols:
              usecols = _evaluate_usecols(self.usecols, self.orig_names)
  
              # GH 14671
              if (self.usecols_dtype == 'string' and
                      not set(usecols).issubset(self.orig_names)):
                  _validate_usecols_names(usecols, self.orig_names)
  
              if len(self.names) > len(usecols):
                  self.names = [n for i, n in enumerate(self.names)
                                if (i in usecols or n in usecols)]
  
              if len(self.names) < len(usecols):
                  _validate_usecols_names(usecols, self.names)
  
          self._set_noconvert_columns()
  
          self.orig_names = self.names
  
          if not self._has_complex_date_col:
              if (self._reader.leading_cols == 0 and
                      _is_index_col(self.index_col)):
  
                  self._name_processed = True
                  (index_names, self.names,
                   self.index_col) = _clean_index_names(self.names,
                                                        self.index_col,
                                                        self.unnamed_cols)
  
                  if self.index_names is None:
                      self.index_names = index_names
  
              if self._reader.header is None and not passed_names:
                  self.index_names = [None] * len(self.index_names)
  
          self._implicit_index = self._reader.leading_cols > 0
  
      def close(self):
          for f in self.handles:
              f.close()
  
          # close additional handles opened by C parser (for compression)
          try:
              self._reader.close()
          except ValueError:
              pass
  
      def _set_noconvert_columns(self):
          """
          Set the columns that should not undergo dtype conversions.
  
          Currently, any column that is involved with date parsing will not
          undergo such conversions.
          """
          names = self.orig_names
          if self.usecols_dtype == 'integer':
              # A set of integers will be converted to a list in
              # the correct order every single time.
              usecols = list(self.usecols)
              usecols.sort()
          elif (callable(self.usecols) or
                  self.usecols_dtype not in ('empty', None)):
              # The names attribute should have the correct columns
              # in the proper order for indexing with parse_dates.
              usecols = self.names[:]
          else:
              # Usecols is empty.
              usecols = None
  
          def _set(x):
              if usecols is not None and is_integer(x):
                  x = usecols[x]
  
              if not is_integer(x):
                  x = names.index(x)
  
              self._reader.set_noconvert(x)
  
          if isinstance(self.parse_dates, list):
              for val in self.parse_dates:
                  if isinstance(val, list):
                      for k in val:
                          _set(k)
                  else:
                      _set(val)
  
          elif isinstance(self.parse_dates, dict):
              for val in self.parse_dates.values():
                  if isinstance(val, list):
                      for k in val:
                          _set(k)
                  else:
                      _set(val)
  
          elif self.parse_dates:
              if isinstance(self.index_col, list):
                  for k in self.index_col:
                      _set(k)
              elif self.index_col is not None:
                  _set(self.index_col)
  
      def set_error_bad_lines(self, status):
          self._reader.set_error_bad_lines(int(status))
  
      def read(self, nrows=None):
          try:
              data = self._reader.read(nrows)
          except StopIteration:
              if self._first_chunk:
                  self._first_chunk = False
                  names = self._maybe_dedup_names(self.orig_names)
                  index, columns, col_dict = _get_empty_meta(
                      names, self.index_col, self.index_names,
                      dtype=self.kwds.get('dtype'))
                  columns = self._maybe_make_multi_index_columns(
                      columns, self.col_names)
  
                  if self.usecols is not None:
                      columns = self._filter_usecols(columns)
  
                  col_dict = dict(filter(lambda item: item[0] in columns,
                                         col_dict.items()))
  
                  return index, columns, col_dict
  
              else:
                  raise
  
          # Done with first read, next time raise StopIteration
          self._first_chunk = False
  
          names = self.names
  
          if self._reader.leading_cols:
              if self._has_complex_date_col:
                  raise NotImplementedError('file structure not yet supported')
  
              # implicit index, no index names
              arrays = []
  
              for i in range(self._reader.leading_cols):
                  if self.index_col is None:
                      values = data.pop(i)
                  else:
                      values = data.pop(self.index_col[i])
  
                  values = self._maybe_parse_dates(values, i,
                                                   try_parse_dates=True)
                  arrays.append(values)
  
              index = ensure_index_from_sequences(arrays)
  
              if self.usecols is not None:
                  names = self._filter_usecols(names)
  
              names = self._maybe_dedup_names(names)
  
              # rename dict keys
              data = sorted(data.items())
              data = {k: v for k, (i, v) in zip(names, data)}
  
              names, data = self._do_date_conversions(names, data)
  
          else:
              # rename dict keys
              data = sorted(data.items())
  
              # ugh, mutation
              names = list(self.orig_names)
              names = self._maybe_dedup_names(names)
  
              if self.usecols is not None:
                  names = self._filter_usecols(names)
  
              # columns as list
              alldata = [x[1] for x in data]
  
              data = {k: v for k, (i, v) in zip(names, data)}
  
              names, data = self._do_date_conversions(names, data)
              index, names = self._make_index(data, alldata, names)
  
          # maybe create a mi on the columns
          names = self._maybe_make_multi_index_columns(names, self.col_names)
  
          return index, names, data
  
      def _filter_usecols(self, names):
          # hackish
          usecols = _evaluate_usecols(self.usecols, names)
          if usecols is not None and len(names) != len(usecols):
              names = [name for i, name in enumerate(names)
                       if i in usecols or name in usecols]
          return names
  
      def _get_index_names(self):
          names = list(self._reader.header[0])
          idx_names = None
  
          if self._reader.leading_cols == 0 and self.index_col is not None:
              (idx_names, names,
               self.index_col) = _clean_index_names(names, self.index_col,
                                                    self.unnamed_cols)
  
          return names, idx_names
  
      def _maybe_parse_dates(self, values, index, try_parse_dates=True):
          if try_parse_dates and self._should_parse_dates(index):
              values = self._date_conv(values)
          return values
  
  
  def TextParser(*args, **kwds):
      """
      Converts lists of lists/tuples into DataFrames with proper type inference
      and optional (e.g. string to datetime) conversion. Also enables iterating
      lazily over chunks of large files
  
      Parameters
      ----------
      data : file-like object or list
      delimiter : separator character to use
      dialect : str or csv.Dialect instance, default None
          Ignored if delimiter is longer than 1 character
      names : sequence, default
      header : int, default 0
          Row to use to parse column labels. Defaults to the first row. Prior
          rows will be discarded
      index_col : int or list, default None
          Column or columns to use as the (possibly hierarchical) index
      has_index_names: boolean, default False
          True if the cols defined in index_col have an index name and are
          not in the header
      na_values : scalar, str, list-like, or dict, default None
          Additional strings to recognize as NA/NaN.
      keep_default_na : bool, default True
      thousands : str, default None
          Thousands separator
      comment : str, default None
          Comment out remainder of line
      parse_dates : boolean, default False
      keep_date_col : boolean, default False
      date_parser : function, default None
      skiprows : list of integers
          Row numbers to skip
      skipfooter : int
          Number of line at bottom of file to skip
      converters : dict, default None
          Dict of functions for converting values in certain columns. Keys can
          either be integers or column labels, values are functions that take one
          input argument, the cell (not column) content, and return the
          transformed content.
      encoding : string, default None
          Encoding to use for UTF when reading/writing (ex. 'utf-8')
      squeeze : boolean, default False
          returns Series if only one column
      infer_datetime_format: boolean, default False
          If True and `parse_dates` is True for a column, try to infer the
          datetime format based on the first datetime string. If the format
          can be inferred, there often will be a large parsing speed-up.
      float_precision : string, default None
          Specifies which converter the C engine should use for floating-point
          values. The options are None for the ordinary converter,
          'high' for the high-precision converter, and 'round_trip' for the
          round-trip converter.
      """
      kwds['engine'] = 'python'
      return TextFileReader(*args, **kwds)
  
  
  def count_empty_vals(vals):
      return sum(1 for v in vals if v == '' or v is None)
  
  
  class PythonParser(ParserBase):
  
      def __init__(self, f, **kwds):
          """
          Workhorse function for processing nested list into DataFrame
  
          Should be replaced by np.genfromtxt eventually?
          """
          ParserBase.__init__(self, kwds)
  
          self.data = None
          self.buf = []
          self.pos = 0
          self.line_pos = 0
  
          self.encoding = kwds['encoding']
          self.compression = kwds['compression']
          self.memory_map = kwds['memory_map']
          self.skiprows = kwds['skiprows']
  
          if callable(self.skiprows):
              self.skipfunc = self.skiprows
          else:
              self.skipfunc = lambda x: x in self.skiprows
  
          self.skipfooter = _validate_skipfooter_arg(kwds['skipfooter'])
          self.delimiter = kwds['delimiter']
  
          self.quotechar = kwds['quotechar']
          if isinstance(self.quotechar, compat.text_type):
              self.quotechar = str(self.quotechar)
  
          self.escapechar = kwds['escapechar']
          self.doublequote = kwds['doublequote']
          self.skipinitialspace = kwds['skipinitialspace']
          self.lineterminator = kwds['lineterminator']
          self.quoting = kwds['quoting']
          self.usecols, _ = _validate_usecols_arg(kwds['usecols'])
          self.skip_blank_lines = kwds['skip_blank_lines']
  
          self.warn_bad_lines = kwds['warn_bad_lines']
          self.error_bad_lines = kwds['error_bad_lines']
  
          self.names_passed = kwds['names'] or None
  
          self.has_index_names = False
          if 'has_index_names' in kwds:
              self.has_index_names = kwds['has_index_names']
  
          self.verbose = kwds['verbose']
          self.converters = kwds['converters']
          self.dtype = kwds['dtype']
  
          self.thousands = kwds['thousands']
          self.decimal = kwds['decimal']
  
          self.comment = kwds['comment']
          self._comment_lines = []
  
          mode = 'r' if PY3 else 'rb'
          f, handles = _get_handle(f, mode, encoding=self.encoding,
                                   compression=self.compression,
                                   memory_map=self.memory_map)
          self.handles.extend(handles)
  
          # Set self.data to something that can read lines.
          if hasattr(f, 'readline'):
              self._make_reader(f)
          else:
              self.data = f
  
          # Get columns in two steps: infer from data, then
          # infer column indices from self.usecols if it is specified.
          self._col_indices = None
          (self.columns, self.num_original_columns,
           self.unnamed_cols) = self._infer_columns()
  
          # Now self.columns has the set of columns that we will process.
          # The original set is stored in self.original_columns.
          if len(self.columns) > 1:
              # we are processing a multi index column
              self.columns, self.index_names, self.col_names, _ = (
                  self._extract_multi_indexer_columns(
                      self.columns, self.index_names, self.col_names
                  )
              )
              # Update list of original names to include all indices.
              self.num_original_columns = len(self.columns)
          else:
              self.columns = self.columns[0]
  
          # get popped off for index
          self.orig_names = list(self.columns)
  
          # needs to be cleaned/refactored
          # multiple date column thing turning into a real spaghetti factory
  
          if not self._has_complex_date_col:
              (index_names, self.orig_names, self.columns) = (
                  self._get_index_name(self.columns))
              self._name_processed = True
              if self.index_names is None:
                  self.index_names = index_names
  
          if self.parse_dates:
              self._no_thousands_columns = self._set_no_thousands_columns()
          else:
              self._no_thousands_columns = None
  
          if len(self.decimal) != 1:
              raise ValueError('Only length-1 decimal markers supported')
  
          if self.thousands is None:
              self.nonnum = re.compile('[^-^0-9^%s]+' % self.decimal)
          else:
              self.nonnum = re.compile('[^-^0-9^%s^%s]+' % (self.thousands,
                                                            self.decimal))
  
      def _set_no_thousands_columns(self):
          # Create a set of column ids that are not to be stripped of thousands
          # operators.
          noconvert_columns = set()
  
          def _set(x):
              if is_integer(x):
                  noconvert_columns.add(x)
              else:
                  noconvert_columns.add(self.columns.index(x))
  
          if isinstance(self.parse_dates, list):
              for val in self.parse_dates:
                  if isinstance(val, list):
                      for k in val:
                          _set(k)
                  else:
                      _set(val)
  
          elif isinstance(self.parse_dates, dict):
              for val in self.parse_dates.values():
                  if isinstance(val, list):
                      for k in val:
                          _set(k)
                  else:
                      _set(val)
  
          elif self.parse_dates:
              if isinstance(self.index_col, list):
                  for k in self.index_col:
                      _set(k)
              elif self.index_col is not None:
                  _set(self.index_col)
  
          return noconvert_columns
  
      def _make_reader(self, f):
          sep = self.delimiter
  
          if sep is None or len(sep) == 1:
              if self.lineterminator:
                  raise ValueError('Custom line terminators not supported in '
                                   'python parser (yet)')
  
              class MyDialect(csv.Dialect):
                  delimiter = self.delimiter
                  quotechar = self.quotechar
                  escapechar = self.escapechar
                  doublequote = self.doublequote
                  skipinitialspace = self.skipinitialspace
                  quoting = self.quoting
                  lineterminator = '\n'
  
              dia = MyDialect
  
              sniff_sep = True
  
              if sep is not None:
                  sniff_sep = False
                  dia.delimiter = sep
              # attempt to sniff the delimiter
              if sniff_sep:
                  line = f.readline()
                  while self.skipfunc(self.pos):
                      self.pos += 1
                      line = f.readline()
  
                  line = self._check_comments([line])[0]
  
                  self.pos += 1
                  self.line_pos += 1
                  sniffed = csv.Sniffer().sniff(line)
                  dia.delimiter = sniffed.delimiter
                  if self.encoding is not None:
                      self.buf.extend(list(
                          UnicodeReader(StringIO(line),
                                        dialect=dia,
                                        encoding=self.encoding)))
                  else:
                      self.buf.extend(list(csv.reader(StringIO(line),
                                                      dialect=dia)))
  
              if self.encoding is not None:
                  reader = UnicodeReader(f, dialect=dia,
                                         encoding=self.encoding,
                                         strict=True)
              else:
                  reader = csv.reader(f, dialect=dia,
                                      strict=True)
  
          else:
              def _read():
                  line = f.readline()
  
                  if compat.PY2 and self.encoding:
                      line = line.decode(self.encoding)
  
                  pat = re.compile(sep)
                  yield pat.split(line.strip())
                  for line in f:
                      yield pat.split(line.strip())
              reader = _read()
  
          self.data = reader
  
      def read(self, rows=None):
          try:
              content = self._get_lines(rows)
          except StopIteration:
              if self._first_chunk:
                  content = []
              else:
                  raise
  
          # done with first read, next time raise StopIteration
          self._first_chunk = False
  
          columns = list(self.orig_names)
          if not len(content):  # pragma: no cover
              # DataFrame with the right metadata, even though it's length 0
              names = self._maybe_dedup_names(self.orig_names)
              index, columns, col_dict = _get_empty_meta(
                  names, self.index_col, self.index_names, self.dtype)
              columns = self._maybe_make_multi_index_columns(
                  columns, self.col_names)
              return index, columns, col_dict
  
          # handle new style for names in index
          count_empty_content_vals = count_empty_vals(content[0])
          indexnamerow = None
          if self.has_index_names and count_empty_content_vals == len(columns):
              indexnamerow = content[0]
              content = content[1:]
  
          alldata = self._rows_to_cols(content)
          data = self._exclude_implicit_index(alldata)
  
          columns = self._maybe_dedup_names(self.columns)
          columns, data = self._do_date_conversions(columns, data)
  
          data = self._convert_data(data)
          index, columns = self._make_index(data, alldata, columns, indexnamerow)
  
          return index, columns, data
  
      def _exclude_implicit_index(self, alldata):
          names = self._maybe_dedup_names(self.orig_names)
  
          if self._implicit_index:
              excl_indices = self.index_col
  
              data = {}
              offset = 0
              for i, col in enumerate(names):
                  while i + offset in excl_indices:
                      offset += 1
                  data[col] = alldata[i + offset]
          else:
              data = {k: v for k, v in zip(names, alldata)}
  
          return data
  
      # legacy
      def get_chunk(self, size=None):
          if size is None:
              size = self.chunksize
          return self.read(rows=size)
  
      def _convert_data(self, data):
          # apply converters
          def _clean_mapping(mapping):
              "converts col numbers to names"
              clean = {}
              for col, v in compat.iteritems(mapping):
                  if isinstance(col, int) and col not in self.orig_names:
                      col = self.orig_names[col]
                  clean[col] = v
              return clean
  
          clean_conv = _clean_mapping(self.converters)
          if not isinstance(self.dtype, dict):
              # handles single dtype applied to all columns
              clean_dtypes = self.dtype
          else:
              clean_dtypes = _clean_mapping(self.dtype)
  
          # Apply NA values.
          clean_na_values = {}
          clean_na_fvalues = {}
  
          if isinstance(self.na_values, dict):
              for col in self.na_values:
                  na_value = self.na_values[col]
                  na_fvalue = self.na_fvalues[col]
  
                  if isinstance(col, int) and col not in self.orig_names:
                      col = self.orig_names[col]
  
                  clean_na_values[col] = na_value
                  clean_na_fvalues[col] = na_fvalue
          else:
              clean_na_values = self.na_values
              clean_na_fvalues = self.na_fvalues
  
          return self._convert_to_ndarrays(data, clean_na_values,
                                           clean_na_fvalues, self.verbose,
                                           clean_conv, clean_dtypes)
  
      def _infer_columns(self):
          names = self.names
          num_original_columns = 0
          clear_buffer = True
          unnamed_cols = set()
  
          if self.header is not None:
              header = self.header
  
              if isinstance(header, (list, tuple, np.ndarray)):
                  have_mi_columns = len(header) > 1
                  # we have a mi columns, so read an extra line
                  if have_mi_columns:
                      header = list(header) + [header[-1] + 1]
              else:
                  have_mi_columns = False
                  header = [header]
  
              columns = []
              for level, hr in enumerate(header):
                  try:
                      line = self._buffered_line()
  
                      while self.line_pos <= hr:
                          line = self._next_line()
  
                  except StopIteration:
                      if self.line_pos < hr:
                          raise ValueError(
                              'Passed header=%s but only %d lines in file'
                              % (hr, self.line_pos + 1))
  
                      # We have an empty file, so check
                      # if columns are provided. That will
                      # serve as the 'line' for parsing
                      if have_mi_columns and hr > 0:
                          if clear_buffer:
                              self._clear_buffer()
                          columns.append([None] * len(columns[-1]))
                          return columns, num_original_columns, unnamed_cols
  
                      if not self.names:
                          raise EmptyDataError(
                              "No columns to parse from file")
  
                      line = self.names[:]
  
                  this_columns = []
                  this_unnamed_cols = []
  
                  for i, c in enumerate(line):
                      if c == '':
                          if have_mi_columns:
                              col_name = ("Unnamed: {i}_level_{level}"
                                          .format(i=i, level=level))
                          else:
                              col_name = "Unnamed: {i}".format(i=i)
  
                          this_unnamed_cols.append(i)
                          this_columns.append(col_name)
                      else:
                          this_columns.append(c)
  
                  if not have_mi_columns and self.mangle_dupe_cols:
                      counts = defaultdict(int)
  
                      for i, col in enumerate(this_columns):
                          cur_count = counts[col]
  
                          while cur_count > 0:
                              counts[col] = cur_count + 1
                              col = "%s.%d" % (col, cur_count)
                              cur_count = counts[col]
  
                          this_columns[i] = col
                          counts[col] = cur_count + 1
                  elif have_mi_columns:
  
                      # if we have grabbed an extra line, but its not in our
                      # format so save in the buffer, and create an blank extra
                      # line for the rest of the parsing code
                      if hr == header[-1]:
                          lc = len(this_columns)
                          ic = (len(self.index_col)
                                if self.index_col is not None else 0)
                          unnamed_count = len(this_unnamed_cols)
  
                          if lc != unnamed_count and lc - ic > unnamed_count:
                              clear_buffer = False
                              this_columns = [None] * lc
                              self.buf = [self.buf[-1]]
  
                  columns.append(this_columns)
                  unnamed_cols.update({this_columns[i]
                                       for i in this_unnamed_cols})
  
                  if len(columns) == 1:
                      num_original_columns = len(this_columns)
  
              if clear_buffer:
                  self._clear_buffer()
  
              if names is not None:
                  if ((self.usecols is not None and
                       len(names) != len(self.usecols)) or
                      (self.usecols is None and
                       len(names) != len(columns[0]))):
                      raise ValueError('Number of passed names did not match '
                                       'number of header fields in the file')
                  if len(columns) > 1:
                      raise TypeError('Cannot pass names with multi-index '
                                      'columns')
  
                  if self.usecols is not None:
                      # Set _use_cols. We don't store columns because they are
                      # overwritten.
                      self._handle_usecols(columns, names)
                  else:
                      self._col_indices = None
                      num_original_columns = len(names)
                  columns = [names]
              else:
                  columns = self._handle_usecols(columns, columns[0])
          else:
              try:
                  line = self._buffered_line()
  
              except StopIteration:
                  if not names:
                      raise EmptyDataError(
                          "No columns to parse from file")
  
                  line = names[:]
  
              ncols = len(line)
              num_original_columns = ncols
  
              if not names:
                  if self.prefix:
                      columns = [['%s%d' % (self.prefix, i)
                                  for i in range(ncols)]]
                  else:
                      columns = [lrange(ncols)]
                  columns = self._handle_usecols(columns, columns[0])
              else:
                  if self.usecols is None or len(names) >= num_original_columns:
                      columns = self._handle_usecols([names], names)
                      num_original_columns = len(names)
                  else:
                      if (not callable(self.usecols) and
                              len(names) != len(self.usecols)):
                          raise ValueError(
                              'Number of passed names did not match number of '
                              'header fields in the file'
                          )
                      # Ignore output but set used columns.
                      self._handle_usecols([names], names)
                      columns = [names]
                      num_original_columns = ncols
  
          return columns, num_original_columns, unnamed_cols
  
      def _handle_usecols(self, columns, usecols_key):
          """
          Sets self._col_indices
  
          usecols_key is used if there are string usecols.
          """
          if self.usecols is not None:
              if callable(self.usecols):
                  col_indices = _evaluate_usecols(self.usecols, usecols_key)
              elif any(isinstance(u, string_types) for u in self.usecols):
                  if len(columns) > 1:
                      raise ValueError("If using multiple headers, usecols must "
                                       "be integers.")
                  col_indices = []
  
                  for col in self.usecols:
                      if isinstance(col, string_types):
                          try:
                              col_indices.append(usecols_key.index(col))
                          except ValueError:
                              _validate_usecols_names(self.usecols, usecols_key)
                      else:
                          col_indices.append(col)
              else:
                  col_indices = self.usecols
  
              columns = [[n for i, n in enumerate(column) if i in col_indices]
                         for column in columns]
              self._col_indices = col_indices
          return columns
  
      def _buffered_line(self):
          """
          Return a line from buffer, filling buffer if required.
          """
          if len(self.buf) > 0:
              return self.buf[0]
          else:
              return self._next_line()
  
      def _check_for_bom(self, first_row):
          """
          Checks whether the file begins with the BOM character.
          If it does, remove it. In addition, if there is quoting
          in the field subsequent to the BOM, remove it as well
          because it technically takes place at the beginning of
          the name, not the middle of it.
          """
          # first_row will be a list, so we need to check
          # that that list is not empty before proceeding.
          if not first_row:
              return first_row
  
          # The first element of this row is the one that could have the
          # BOM that we want to remove. Check that the first element is a
          # string before proceeding.
          if not isinstance(first_row[0], compat.string_types):
              return first_row
  
          # Check that the string is not empty, as that would
          # obviously not have a BOM at the start of it.
          if not first_row[0]:
              return first_row
  
          # Since the string is non-empty, check that it does
          # in fact begin with a BOM.
          first_elt = first_row[0][0]
  
          # This is to avoid warnings we get in Python 2.x if
          # we find ourselves comparing with non-Unicode
          if compat.PY2 and not isinstance(first_elt, unicode):  # noqa
              try:
                  first_elt = u(first_elt)
              except UnicodeDecodeError:
                  return first_row
  
          if first_elt != _BOM:
              return first_row
  
          first_row = first_row[0]
  
          if len(first_row) > 1 and first_row[1] == self.quotechar:
              start = 2
              quote = first_row[1]
              end = first_row[2:].index(quote) + 2
  
              # Extract the data between the quotation marks
              new_row = first_row[start:end]
  
              # Extract any remaining data after the second
              # quotation mark.
              if len(first_row) > end + 1:
                  new_row += first_row[end + 1:]
              return [new_row]
          elif len(first_row) > 1:
              return [first_row[1:]]
          else:
              # First row is just the BOM, so we
              # return an empty string.
              return [""]
  
      def _is_line_empty(self, line):
          """
          Check if a line is empty or not.
  
          Parameters
          ----------
          line : str, array-like
              The line of data to check.
  
          Returns
          -------
          boolean : Whether or not the line is empty.
          """
          return not line or all(not x for x in line)
  
      def _next_line(self):
          if isinstance(self.data, list):
              while self.skipfunc(self.pos):
                  self.pos += 1
  
              while True:
                  try:
                      line = self._check_comments([self.data[self.pos]])[0]
                      self.pos += 1
                      # either uncommented or blank to begin with
                      if (not self.skip_blank_lines and
                              (self._is_line_empty(
                                  self.data[self.pos - 1]) or line)):
                          break
                      elif self.skip_blank_lines:
                          ret = self._remove_empty_lines([line])
                          if ret:
                              line = ret[0]
                              break
                  except IndexError:
                      raise StopIteration
          else:
              while self.skipfunc(self.pos):
                  self.pos += 1
                  next(self.data)
  
              while True:
                  orig_line = self._next_iter_line(row_num=self.pos + 1)
                  self.pos += 1
  
                  if orig_line is not None:
                      line = self._check_comments([orig_line])[0]
  
                      if self.skip_blank_lines:
                          ret = self._remove_empty_lines([line])
  
                          if ret:
                              line = ret[0]
                              break
                      elif self._is_line_empty(orig_line) or line:
                          break
  
          # This was the first line of the file,
          # which could contain the BOM at the
          # beginning of it.
          if self.pos == 1:
              line = self._check_for_bom(line)
  
          self.line_pos += 1
          self.buf.append(line)
          return line
  
      def _alert_malformed(self, msg, row_num):
          """
          Alert a user about a malformed row.
  
          If `self.error_bad_lines` is True, the alert will be `ParserError`.
          If `self.warn_bad_lines` is True, the alert will be printed out.
  
          Parameters
          ----------
          msg : The error message to display.
          row_num : The row number where the parsing error occurred.
                    Because this row number is displayed, we 1-index,
                    even though we 0-index internally.
          """
  
          if self.error_bad_lines:
              raise ParserError(msg)
          elif self.warn_bad_lines:
              base = 'Skipping line {row_num}: '.format(row_num=row_num)
              sys.stderr.write(base + msg + '\n')
  
      def _next_iter_line(self, row_num):
          """
          Wrapper around iterating through `self.data` (CSV source).
  
          When a CSV error is raised, we check for specific
          error messages that allow us to customize the
          error message displayed to the user.
  
          Parameters
          ----------
          row_num : The row number of the line being parsed.
          """
  
          try:
              return next(self.data)
          except csv.Error as e:
              if self.warn_bad_lines or self.error_bad_lines:
                  msg = str(e)
  
                  if 'NULL byte' in msg:
                      msg = ('NULL byte detected. This byte '
                             'cannot be processed in Python\'s '
                             'native csv library at the moment, '
                             'so please pass in engine=\'c\' instead')
  
                  if self.skipfooter > 0:
                      reason = ('Error could possibly be due to '
                                'parsing errors in the skipped footer rows '
                                '(the skipfooter keyword is only applied '
                                'after Python\'s csv library has parsed '
                                'all rows).')
                      msg += '. ' + reason
  
                  self._alert_malformed(msg, row_num)
              return None
  
      def _check_comments(self, lines):
          if self.comment is None:
              return lines
          ret = []
          for l in lines:
              rl = []
              for x in l:
                  if (not isinstance(x, compat.string_types) or
                          self.comment not in x):
                      rl.append(x)
                  else:
                      x = x[:x.find(self.comment)]
                      if len(x) > 0:
                          rl.append(x)
                      break
              ret.append(rl)
          return ret
  
      def _remove_empty_lines(self, lines):
          """
          Iterate through the lines and remove any that are
          either empty or contain only one whitespace value
  
          Parameters
          ----------
          lines : array-like
              The array of lines that we are to filter.
  
          Returns
          -------
          filtered_lines : array-like
              The same array of lines with the "empty" ones removed.
          """
  
          ret = []
          for l in lines:
              # Remove empty lines and lines with only one whitespace value
              if (len(l) > 1 or len(l) == 1 and
                      (not isinstance(l[0], compat.string_types) or
                       l[0].strip())):
                  ret.append(l)
          return ret
  
      def _check_thousands(self, lines):
          if self.thousands is None:
              return lines
  
          return self._search_replace_num_columns(lines=lines,
                                                  search=self.thousands,
                                                  replace='')
  
      def _search_replace_num_columns(self, lines, search, replace):
          ret = []
          for l in lines:
              rl = []
              for i, x in enumerate(l):
                  if (not isinstance(x, compat.string_types) or
                      search not in x or
                      (self._no_thousands_columns and
                       i in self._no_thousands_columns) or
                          self.nonnum.search(x.strip())):
                      rl.append(x)
                  else:
                      rl.append(x.replace(search, replace))
              ret.append(rl)
          return ret
  
      def _check_decimal(self, lines):
          if self.decimal == _parser_defaults['decimal']:
              return lines
  
          return self._search_replace_num_columns(lines=lines,
                                                  search=self.decimal,
                                                  replace='.')
  
      def _clear_buffer(self):
          self.buf = []
  
      _implicit_index = False
  
      def _get_index_name(self, columns):
          """
          Try several cases to get lines:
  
          0) There are headers on row 0 and row 1 and their
          total summed lengths equals the length of the next line.
          Treat row 0 as columns and row 1 as indices
          1) Look for implicit index: there are more columns
          on row 1 than row 0. If this is true, assume that row
          1 lists index columns and row 0 lists normal columns.
          2) Get index from the columns if it was listed.
          """
          orig_names = list(columns)
          columns = list(columns)
  
          try:
              line = self._next_line()
          except StopIteration:
              line = None
  
          try:
              next_line = self._next_line()
          except StopIteration:
              next_line = None
  
          # implicitly index_col=0 b/c 1 fewer column names
          implicit_first_cols = 0
          if line is not None:
              # leave it 0, #2442
              # Case 1
              if self.index_col is not False:
                  implicit_first_cols = len(line) - self.num_original_columns
  
              # Case 0
              if next_line is not None:
                  if len(next_line) == len(line) + self.num_original_columns:
                      # column and index names on diff rows
                      self.index_col = lrange(len(line))
                      self.buf = self.buf[1:]
  
                      for c in reversed(line):
                          columns.insert(0, c)
  
                      # Update list of original names to include all indices.
                      orig_names = list(columns)
                      self.num_original_columns = len(columns)
                      return line, orig_names, columns
  
          if implicit_first_cols > 0:
              # Case 1
              self._implicit_index = True
              if self.index_col is None:
                  self.index_col = lrange(implicit_first_cols)
  
              index_name = None
  
          else:
              # Case 2
              (index_name, columns_,
               self.index_col) = _clean_index_names(columns, self.index_col,
                                                    self.unnamed_cols)
  
          return index_name, orig_names, columns
  
      def _rows_to_cols(self, content):
          col_len = self.num_original_columns
  
          if self._implicit_index:
              col_len += len(self.index_col)
  
          max_len = max(len(row) for row in content)
  
          # Check that there are no rows with too many
          # elements in their row (rows with too few
          # elements are padded with NaN).
          if (max_len > col_len and
                  self.index_col is not False and
                  self.usecols is None):
  
              footers = self.skipfooter if self.skipfooter else 0
              bad_lines = []
  
              iter_content = enumerate(content)
              content_len = len(content)
              content = []
  
              for (i, l) in iter_content:
                  actual_len = len(l)
  
                  if actual_len > col_len:
                      if self.error_bad_lines or self.warn_bad_lines:
                          row_num = self.pos - (content_len - i + footers)
                          bad_lines.append((row_num, actual_len))
  
                          if self.error_bad_lines:
                              break
                  else:
                      content.append(l)
  
              for row_num, actual_len in bad_lines:
                  msg = ('Expected %d fields in line %d, saw %d' %
                         (col_len, row_num + 1, actual_len))
                  if (self.delimiter and
                          len(self.delimiter) > 1 and
                          self.quoting != csv.QUOTE_NONE):
                      # see gh-13374
                      reason = ('Error could possibly be due to quotes being '
                                'ignored when a multi-char delimiter is used.')
                      msg += '. ' + reason
  
                  self._alert_malformed(msg, row_num + 1)
  
          # see gh-13320
          zipped_content = list(lib.to_object_array(
              content, min_width=col_len).T)
  
          if self.usecols:
              if self._implicit_index:
                  zipped_content = [
                      a for i, a in enumerate(zipped_content)
                      if (i < len(self.index_col) or
                          i - len(self.index_col) in self._col_indices)]
              else:
                  zipped_content = [a for i, a in enumerate(zipped_content)
                                    if i in self._col_indices]
          return zipped_content
  
      def _get_lines(self, rows=None):
          lines = self.buf
          new_rows = None
  
          # already fetched some number
          if rows is not None:
              # we already have the lines in the buffer
              if len(self.buf) >= rows:
                  new_rows, self.buf = self.buf[:rows], self.buf[rows:]
  
              # need some lines
              else:
                  rows -= len(self.buf)
  
          if new_rows is None:
              if isinstance(self.data, list):
                  if self.pos > len(self.data):
                      raise StopIteration
                  if rows is None:
                      new_rows = self.data[self.pos:]
                      new_pos = len(self.data)
                  else:
                      new_rows = self.data[self.pos:self.pos + rows]
                      new_pos = self.pos + rows
  
                  # Check for stop rows. n.b.: self.skiprows is a set.
                  if self.skiprows:
                      new_rows = [row for i, row in enumerate(new_rows)
                                  if not self.skipfunc(i + self.pos)]
  
                  lines.extend(new_rows)
                  self.pos = new_pos
  
              else:
                  new_rows = []
                  try:
                      if rows is not None:
                          for _ in range(rows):
                              new_rows.append(next(self.data))
                          lines.extend(new_rows)
                      else:
                          rows = 0
  
                          while True:
                              new_row = self._next_iter_line(
                                  row_num=self.pos + rows + 1)
                              rows += 1
  
                              if new_row is not None:
                                  new_rows.append(new_row)
  
                  except StopIteration:
                      if self.skiprows:
                          new_rows = [row for i, row in enumerate(new_rows)
                                      if not self.skipfunc(i + self.pos)]
                      lines.extend(new_rows)
                      if len(lines) == 0:
                          raise
                  self.pos += len(new_rows)
  
              self.buf = []
          else:
              lines = new_rows
  
          if self.skipfooter:
              lines = lines[:-self.skipfooter]
  
          lines = self._check_comments(lines)
          if self.skip_blank_lines:
              lines = self._remove_empty_lines(lines)
          lines = self._check_thousands(lines)
          return self._check_decimal(lines)
  
  
  def _make_date_converter(date_parser=None, dayfirst=False,
                           infer_datetime_format=False):
      def converter(*date_cols):
          if date_parser is None:
              strs = _concat_date_cols(date_cols)
  
              try:
                  return tools.to_datetime(
                      ensure_object(strs),
                      utc=None,
                      box=False,
                      dayfirst=dayfirst,
                      errors='ignore',
                      infer_datetime_format=infer_datetime_format
                  )
              except ValueError:
                  return tools.to_datetime(
                      parsing.try_parse_dates(strs, dayfirst=dayfirst))
          else:
              try:
                  result = tools.to_datetime(
                      date_parser(*date_cols), errors='ignore')
                  if isinstance(result, datetime.datetime):
                      raise Exception('scalar parser')
                  return result
              except Exception:
                  try:
                      return tools.to_datetime(
                          parsing.try_parse_dates(_concat_date_cols(date_cols),
                                                  parser=date_parser,
                                                  dayfirst=dayfirst),
                          errors='ignore')
                  except Exception:
                      return generic_parser(date_parser, *date_cols)
  
      return converter
  
  
  def _process_date_conversion(data_dict, converter, parse_spec,
                               index_col, index_names, columns,
                               keep_date_col=False):
      def _isindex(colspec):
          return ((isinstance(index_col, list) and
                   colspec in index_col) or
                  (isinstance(index_names, list) and
                   colspec in index_names))
  
      new_cols = []
      new_data = {}
  
      orig_names = columns
      columns = list(columns)
  
      date_cols = set()
  
      if parse_spec is None or isinstance(parse_spec, bool):
          return data_dict, columns
  
      if isinstance(parse_spec, list):
          # list of column lists
          for colspec in parse_spec:
              if is_scalar(colspec):
                  if isinstance(colspec, int) and colspec not in data_dict:
                      colspec = orig_names[colspec]
                  if _isindex(colspec):
                      continue
                  data_dict[colspec] = converter(data_dict[colspec])
              else:
                  new_name, col, old_names = _try_convert_dates(
                      converter, colspec, data_dict, orig_names)
                  if new_name in data_dict:
                      raise ValueError('New date column already in dict %s' %
                                       new_name)
                  new_data[new_name] = col
                  new_cols.append(new_name)
                  date_cols.update(old_names)
  
      elif isinstance(parse_spec, dict):
          # dict of new name to column list
          for new_name, colspec in compat.iteritems(parse_spec):
              if new_name in data_dict:
                  raise ValueError('Date column %s already in dict' %
                                   new_name)
  
              _, col, old_names = _try_convert_dates(converter, colspec,
                                                     data_dict, orig_names)
  
              new_data[new_name] = col
              new_cols.append(new_name)
              date_cols.update(old_names)
  
      data_dict.update(new_data)
      new_cols.extend(columns)
  
      if not keep_date_col:
          for c in list(date_cols):
              data_dict.pop(c)
              new_cols.remove(c)
  
      return data_dict, new_cols
  
  
  def _try_convert_dates(parser, colspec, data_dict, columns):
      colset = set(columns)
      colnames = []
  
      for c in colspec:
          if c in colset:
              colnames.append(c)
          elif isinstance(c, int) and c not in columns:
              colnames.append(columns[c])
          else:
              colnames.append(c)
  
      new_name = '_'.join(str(x) for x in colnames)
      to_parse = [data_dict[c] for c in colnames if c in data_dict]
  
      new_col = parser(*to_parse)
      return new_name, new_col, colnames
  
  
  def _clean_na_values(na_values, keep_default_na=True):
  
      if na_values is None:
          if keep_default_na:
              na_values = _NA_VALUES
          else:
              na_values = set()
          na_fvalues = set()
      elif isinstance(na_values, dict):
          old_na_values = na_values.copy()
          na_values = {}  # Prevent aliasing.
  
          # Convert the values in the na_values dictionary
          # into array-likes for further use. This is also
          # where we append the default NaN values, provided
          # that `keep_default_na=True`.
          for k, v in compat.iteritems(old_na_values):
              if not is_list_like(v):
                  v = [v]
  
              if keep_default_na:
                  v = set(v) | _NA_VALUES
  
              na_values[k] = v
          na_fvalues = {k: _floatify_na_values(v) for k, v in na_values.items()}
      else:
          if not is_list_like(na_values):
              na_values = [na_values]
          na_values = _stringify_na_values(na_values)
          if keep_default_na:
              na_values = na_values | _NA_VALUES
  
          na_fvalues = _floatify_na_values(na_values)
  
      return na_values, na_fvalues
  
  
  def _clean_index_names(columns, index_col, unnamed_cols):
      if not _is_index_col(index_col):
          return None, columns, index_col
  
      columns = list(columns)
  
      cp_cols = list(columns)
      index_names = []
  
      # don't mutate
      index_col = list(index_col)
  
      for i, c in enumerate(index_col):
          if isinstance(c, compat.string_types):
              index_names.append(c)
              for j, name in enumerate(cp_cols):
                  if name == c:
                      index_col[i] = j
                      columns.remove(name)
                      break
          else:
              name = cp_cols[c]
              columns.remove(name)
              index_names.append(name)
  
      # Only clean index names that were placeholders.
      for i, name in enumerate(index_names):
          if isinstance(name, compat.string_types) and name in unnamed_cols:
              index_names[i] = None
  
      return index_names, columns, index_col
  
  
  def _get_empty_meta(columns, index_col, index_names, dtype=None):
      columns = list(columns)
  
      # Convert `dtype` to a defaultdict of some kind.
      # This will enable us to write `dtype[col_name]`
      # without worrying about KeyError issues later on.
      if not isinstance(dtype, dict):
          # if dtype == None, default will be np.object.
          default_dtype = dtype or np.object
          dtype = defaultdict(lambda: default_dtype)
      else:
          # Save a copy of the dictionary.
          _dtype = dtype.copy()
          dtype = defaultdict(lambda: np.object)
  
          # Convert column indexes to column names.
          for k, v in compat.iteritems(_dtype):
              col = columns[k] if is_integer(k) else k
              dtype[col] = v
  
      # Even though we have no data, the "index" of the empty DataFrame
      # could for example still be an empty MultiIndex. Thus, we need to
      # check whether we have any index columns specified, via either:
      #
      # 1) index_col (column indices)
      # 2) index_names (column names)
      #
      # Both must be non-null to ensure a successful construction. Otherwise,
      # we have to create a generic emtpy Index.
      if (index_col is None or index_col is False) or index_names is None:
          index = Index([])
      else:
          data = [Series([], dtype=dtype[name]) for name in index_names]
          index = ensure_index_from_sequences(data, names=index_names)
          index_col.sort()
  
          for i, n in enumerate(index_col):
              columns.pop(n - i)
  
      col_dict = {col_name: Series([], dtype=dtype[col_name])
                  for col_name in columns}
  
      return index, columns, col_dict
  
  
  def _floatify_na_values(na_values):
      # create float versions of the na_values
      result = set()
      for v in na_values:
          try:
              v = float(v)
              if not np.isnan(v):
                  result.add(v)
          except (TypeError, ValueError, OverflowError):
              pass
      return result
  
  
  def _stringify_na_values(na_values):
      """ return a stringified and numeric for these values """
      result = []
      for x in na_values:
          result.append(str(x))
          result.append(x)
          try:
              v = float(x)
  
              # we are like 999 here
              if v == int(v):
                  v = int(v)
                  result.append("%s.0" % v)
                  result.append(str(v))
  
              result.append(v)
          except (TypeError, ValueError, OverflowError):
              pass
          try:
              result.append(int(x))
          except (TypeError, ValueError, OverflowError):
              pass
      return set(result)
  
  
  def _get_na_values(col, na_values, na_fvalues, keep_default_na):
      """
      Get the NaN values for a given column.
  
      Parameters
      ----------
      col : str
          The name of the column.
      na_values : array-like, dict
          The object listing the NaN values as strings.
      na_fvalues : array-like, dict
          The object listing the NaN values as floats.
      keep_default_na : bool
          If `na_values` is a dict, and the column is not mapped in the
          dictionary, whether to return the default NaN values or the empty set.
  
      Returns
      -------
      nan_tuple : A length-two tuple composed of
  
          1) na_values : the string NaN values for that column.
          2) na_fvalues : the float NaN values for that column.
      """
  
      if isinstance(na_values, dict):
          if col in na_values:
              return na_values[col], na_fvalues[col]
          else:
              if keep_default_na:
                  return _NA_VALUES, set()
  
              return set(), set()
      else:
          return na_values, na_fvalues
  
  
  def _get_col_names(colspec, columns):
      colset = set(columns)
      colnames = []
      for c in colspec:
          if c in colset:
              colnames.append(c)
          elif isinstance(c, int):
              colnames.append(columns[c])
      return colnames
  
  
  def _concat_date_cols(date_cols):
      if len(date_cols) == 1:
          if compat.PY3:
              return np.array([compat.text_type(x) for x in date_cols[0]],
                              dtype=object)
          else:
              return np.array([
                  str(x) if not isinstance(x, compat.string_types) else x
                  for x in date_cols[0]
              ], dtype=object)
  
      rs = np.array([' '.join(compat.text_type(y) for y in x)
                     for x in zip(*date_cols)], dtype=object)
      return rs
  
  
  class FixedWidthReader(BaseIterator):
      """
      A reader of fixed-width lines.
      """
  
      def __init__(self, f, colspecs, delimiter, comment, skiprows=None):
          self.f = f
          self.buffer = None
          self.delimiter = '\r\n' + delimiter if delimiter else '\n\r\t '
          self.comment = comment
          if colspecs == 'infer':
              self.colspecs = self.detect_colspecs(skiprows=skiprows)
          else:
              self.colspecs = colspecs
  
          if not isinstance(self.colspecs, (tuple, list)):
              raise TypeError("column specifications must be a list or tuple, "
                              "input was a %r" % type(colspecs).__name__)
  
          for colspec in self.colspecs:
              if not (isinstance(colspec, (tuple, list)) and
                      len(colspec) == 2 and
                      isinstance(colspec[0], (int, np.integer, type(None))) and
                      isinstance(colspec[1], (int, np.integer, type(None)))):
                  raise TypeError('Each column specification must be '
                                  '2 element tuple or list of integers')
  
      def get_rows(self, n, skiprows=None):
          """
          Read rows from self.f, skipping as specified.
  
          We distinguish buffer_rows (the first <= n lines)
          from the rows returned to detect_colspecs because
          it's simpler to leave the other locations with
          skiprows logic alone than to modify them to deal
          with the fact we skipped some rows here as well.
  
          Parameters
          ----------
          n : int
              Number of rows to read from self.f, not counting
              rows that are skipped.
          skiprows: set, optional
              Indices of rows to skip.
  
          Returns
          -------
          detect_rows : list of str
              A list containing the rows to read.
  
          """
          if skiprows is None:
              skiprows = set()
          buffer_rows = []
          detect_rows = []
          for i, row in enumerate(self.f):
              if i not in skiprows:
                  detect_rows.append(row)
              buffer_rows.append(row)
              if len(detect_rows) >= n:
                  break
          self.buffer = iter(buffer_rows)
          return detect_rows
  
      def detect_colspecs(self, n=100, skiprows=None):
          # Regex escape the delimiters
          delimiters = ''.join(r'\%s' % x for x in self.delimiter)
          pattern = re.compile('([^%s]+)' % delimiters)
          rows = self.get_rows(n, skiprows)
          if not rows:
              raise EmptyDataError("No rows from which to infer column width")
          max_len = max(map(len, rows))
          mask = np.zeros(max_len + 1, dtype=int)
          if self.comment is not None:
              rows = [row.partition(self.comment)[0] for row in rows]
          for row in rows:
              for m in pattern.finditer(row):
                  mask[m.start():m.end()] = 1
          shifted = np.roll(mask, 1)
          shifted[0] = 0
          edges = np.where((mask ^ shifted) == 1)[0]
          edge_pairs = list(zip(edges[::2], edges[1::2]))
          return edge_pairs
  
      def __next__(self):
          if self.buffer is not None:
              try:
                  line = next(self.buffer)
              except StopIteration:
                  self.buffer = None
                  line = next(self.f)
          else:
              line = next(self.f)
          # Note: 'colspecs' is a sequence of half-open intervals.
          return [line[fromm:to].strip(self.delimiter)
                  for (fromm, to) in self.colspecs]
  
  
  class FixedWidthFieldParser(PythonParser):
      """
      Specialization that Converts fixed-width fields into DataFrames.
      See PythonParser for details.
      """
  
      def __init__(self, f, **kwds):
          # Support iterators, convert to a list.
          self.colspecs = kwds.pop('colspecs')
          PythonParser.__init__(self, f, **kwds)
  
      def _make_reader(self, f):
          self.data = FixedWidthReader(f, self.colspecs, self.delimiter,
                                       self.comment, self.skiprows)
  
apis: pandas.DatetimeIndex pandas.compat.iteritems pandas.date_range pandas.util.testing.assert_numpy_array_equal pandas.notna numpy.timedelta64 pandas.bdate_range pandas.tseries.offsets.CDay numpy.int_ pandas.Timedelta pandas.to_datetime pandas.util.testing.assert_index_equal pandas.tseries.offsets.BDay numpy.array pandas.to_timedelta pandas.Timestamp
apis: pandas.errors.EmptyDataError pandas.compat.iteritems pandas.io.common._infer_compression pandas.compat.map numpy.asarray pandas.compat.to_str pandas.compat.zip pandas.core.dtypes.common.is_integer pandas._libs.parsers.TextReader pandas._libs.tslibs.parsing.try_parse_dates pandas.core.dtypes.common.is_list_like pandas.io.common._get_handle pandas.io.common.is_file_like pandas.core.dtypes.common.is_float pandas._libs.lib.map_infer pandas.compat.itervalues pandas.errors.ParserError pandas.core.dtypes.common.is_categorical_dtype pandas._libs.parsers.sanitize_objects pandas.compat.StringIO pandas.core.index.RangeIndex pandas.core.dtypes.cast.astype_nansafe pandas._libs.lib.maybe_convert_numeric numpy.isnan pandas.io.date_converters.generic_parser numpy.where pandas.io.common.UTF8Recoder pandas.core.dtypes.common.ensure_object pandas._libs.lib.infer_dtype numpy.zeros pandas._libs.lib.to_object_array pandas._libs.lib.is_bool pandas.core.index.ensure_index_from_sequences pandas.io.common._validate_header_arg numpy.putmask pandas.core.dtypes.common.is_scalar pandas.core.dtypes.missing.isna pandas.io.common.get_filepath_or_buffer pandas.compat.u pandas.core.dtypes.common.is_string_dtype pandas.util._decorators.Appender numpy.roll pandas._libs.lib.map_infer_mask pandas.core.series.Series pandas.io.common.UnicodeReader pandas.core.index.MultiIndex.from_tuples pandas.core.dtypes.common.is_dtype_equal pandas.core.index.Index pandas.core.dtypes.common.is_object_dtype pandas.errors.AbstractMethodError pandas.compat.range pandas.compat.text_type pandas.compat.lrange pandas.core.frame.DataFrame pandas.core.dtypes.common.is_integer_dtype