# -*- coding: utf-8 -*-
# pylint: disable=E1101

import datetime as dt
import os
import struct
import sys
import warnings
from datetime import datetime
from distutils.version import LooseVersion

import nose
import numpy as np
from pandas.tslib import NaT

import pandas as pd
import pandas.util.testing as tm
from pandas import compat
from pandas.compat import iterkeys
from pandas.core.frame import DataFrame, Series
from pandas.io.parsers import read_csv
from pandas.io.stata import (read_stata, StataReader, InvalidColumnName,
                             PossiblePrecisionLoss, StataMissingValue)
from pandas.types.common import is_categorical_dtype


class TestStata(tm.TestCase):

    def setUp(self):
        self.dirpath = tm.get_data_path()
        self.dta1_114 = os.path.join(self.dirpath, 'stata1_114.dta')
        self.dta1_117 = os.path.join(self.dirpath, 'stata1_117.dta')

        self.dta2_113 = os.path.join(self.dirpath, 'stata2_113.dta')
        self.dta2_114 = os.path.join(self.dirpath, 'stata2_114.dta')
        self.dta2_115 = os.path.join(self.dirpath, 'stata2_115.dta')
        self.dta2_117 = os.path.join(self.dirpath, 'stata2_117.dta')

        self.dta3_113 = os.path.join(self.dirpath, 'stata3_113.dta')
        self.dta3_114 = os.path.join(self.dirpath, 'stata3_114.dta')
        self.dta3_115 = os.path.join(self.dirpath, 'stata3_115.dta')
        self.dta3_117 = os.path.join(self.dirpath, 'stata3_117.dta')
        self.csv3 = os.path.join(self.dirpath, 'stata3.csv')

        self.dta4_113 = os.path.join(self.dirpath, 'stata4_113.dta')
        self.dta4_114 = os.path.join(self.dirpath, 'stata4_114.dta')
        self.dta4_115 = os.path.join(self.dirpath, 'stata4_115.dta')
        self.dta4_117 = os.path.join(self.dirpath, 'stata4_117.dta')

        self.dta_encoding = os.path.join(self.dirpath, 'stata1_encoding.dta')

        self.csv14 = os.path.join(self.dirpath, 'stata5.csv')
        self.dta14_113 = os.path.join(self.dirpath, 'stata5_113.dta')
        self.dta14_114 = os.path.join(self.dirpath, 'stata5_114.dta')
        self.dta14_115 = os.path.join(self.dirpath, 'stata5_115.dta')
        self.dta14_117 = os.path.join(self.dirpath, 'stata5_117.dta')

        self.csv15 = os.path.join(self.dirpath, 'stata6.csv')
        self.dta15_113 = os.path.join(self.dirpath, 'stata6_113.dta')
        self.dta15_114 = os.path.join(self.dirpath, 'stata6_114.dta')
        self.dta15_115 = os.path.join(self.dirpath, 'stata6_115.dta')
        self.dta15_117 = os.path.join(self.dirpath, 'stata6_117.dta')

        self.dta16_115 = os.path.join(self.dirpath, 'stata7_115.dta')
        self.dta16_117 = os.path.join(self.dirpath, 'stata7_117.dta')

        self.dta17_113 = os.path.join(self.dirpath, 'stata8_113.dta')
        self.dta17_115 = os.path.join(self.dirpath, 'stata8_115.dta')
        self.dta17_117 = os.path.join(self.dirpath, 'stata8_117.dta')

        self.dta18_115 = os.path.join(self.dirpath, 'stata9_115.dta')
        self.dta18_117 = os.path.join(self.dirpath, 'stata9_117.dta')

        self.dta19_115 = os.path.join(self.dirpath, 'stata10_115.dta')
        self.dta19_117 = os.path.join(self.dirpath, 'stata10_117.dta')

        self.dta20_115 = os.path.join(self.dirpath, 'stata11_115.dta')
        self.dta20_117 = os.path.join(self.dirpath, 'stata11_117.dta')

        self.dta21_117 = os.path.join(self.dirpath, 'stata12_117.dta')

        self.dta22_118 = os.path.join(self.dirpath, 'stata14_118.dta')
        self.dta23 = os.path.join(self.dirpath, 'stata15.dta')

        self.dta24_111 = os.path.join(self.dirpath, 'stata7_111.dta')

    def read_dta(self, file):
        # Legacy default reader configuration
        return read_stata(file, convert_dates=True)

    def read_csv(self, file):
        return read_csv(file, parse_dates=True)

    def test_read_empty_dta(self):
        empty_ds = DataFrame(columns=['unit'])
        # GH 7369, make sure can read a 0-obs dta file
        with tm.ensure_clean() as path:
            empty_ds.to_stata(path, write_index=False)
            empty_ds2 = read_stata(path)
            tm.assert_frame_equal(empty_ds, empty_ds2)

    def test_data_method(self):
        # Minimal testing of legacy data method
        with StataReader(self.dta1_114) as rdr:
            with warnings.catch_warnings(record=True) as w:  # noqa
                parsed_114_data = rdr.data()

        with StataReader(self.dta1_114) as rdr:
            parsed_114_read = rdr.read()
        tm.assert_frame_equal(parsed_114_data, parsed_114_read)

    def test_read_dta1(self):

        parsed_114 = self.read_dta(self.dta1_114)
        parsed_117 = self.read_dta(self.dta1_117)

        # Pandas uses np.nan as missing value.
        # Thus, all columns will be of type float, regardless of their name.
        expected = DataFrame([(np.nan, np.nan, np.nan, np.nan, np.nan)],
                             columns=['float_miss', 'double_miss', 'byte_miss',
                                      'int_miss', 'long_miss'])

        # this is an oddity as really the nan should be float64, but
        # the casting doesn't fail so need to match stata here
        expected['float_miss'] = expected['float_miss'].astype(np.float32)

        tm.assert_frame_equal(parsed_114, expected)
        tm.assert_frame_equal(parsed_117, expected)

    def test_read_dta2(self):
        if LooseVersion(sys.version) < '2.7':
            raise nose.SkipTest('datetime interp under 2.6 is faulty')

        expected = DataFrame.from_records(
            [
                (
                    datetime(2006, 11, 19, 23, 13, 20),
                    1479596223000,
                    datetime(2010, 1, 20),
                    datetime(2010, 1, 8),
                    datetime(2010, 1, 1),
                    datetime(1974, 7, 1),
                    datetime(2010, 1, 1),
                    datetime(2010, 1, 1)
                ),
                (
                    datetime(1959, 12, 31, 20, 3, 20),
                    -1479590,
                    datetime(1953, 10, 2),
                    datetime(1948, 6, 10),
                    datetime(1955, 1, 1),
                    datetime(1955, 7, 1),
                    datetime(1955, 1, 1),
                    datetime(2, 1, 1)
                ),
                (
                    pd.NaT,
                    pd.NaT,
                    pd.NaT,
                    pd.NaT,
                    pd.NaT,
                    pd.NaT,
                    pd.NaT,
                    pd.NaT,
                )
            ],
            columns=['datetime_c', 'datetime_big_c', 'date', 'weekly_date',
                     'monthly_date', 'quarterly_date', 'half_yearly_date',
                     'yearly_date']
        )
        expected['yearly_date'] = expected['yearly_date'].astype('O')

        with warnings.catch_warnings(record=True) as w:
            warnings.simplefilter("always")
            parsed_114 = self.read_dta(self.dta2_114)
            parsed_115 = self.read_dta(self.dta2_115)
            parsed_117 = self.read_dta(self.dta2_117)
            # 113 is buggy due to limits of date format support in Stata
            # parsed_113 = self.read_dta(self.dta2_113)

            # Remove resource warnings
            w = [x for x in w if x.category is UserWarning]

            # should get warning for each call to read_dta
            self.assertEqual(len(w), 3)

        # buggy test because of the NaT comparison on certain platforms
        # Format 113 test fails since it does not support tc and tC formats
        # tm.assert_frame_equal(parsed_113, expected)
        tm.assert_frame_equal(parsed_114, expected,
                              check_datetimelike_compat=True)
        tm.assert_frame_equal(parsed_115, expected,
                              check_datetimelike_compat=True)
        tm.assert_frame_equal(parsed_117, expected,
                              check_datetimelike_compat=True)

    def test_read_dta3(self):
        parsed_113 = self.read_dta(self.dta3_113)
        parsed_114 = self.read_dta(self.dta3_114)
        parsed_115 = self.read_dta(self.dta3_115)
        parsed_117 = self.read_dta(self.dta3_117)

        # match stata here
        expected = self.read_csv(self.csv3)
        expected = expected.astype(np.float32)
        expected['year'] = expected['year'].astype(np.int16)
        expected['quarter'] = expected['quarter'].astype(np.int8)

        tm.assert_frame_equal(parsed_113, expected)
        tm.assert_frame_equal(parsed_114, expected)
        tm.assert_frame_equal(parsed_115, expected)
        tm.assert_frame_equal(parsed_117, expected)

    def test_read_dta4(self):
        parsed_113 = self.read_dta(self.dta4_113)
        parsed_114 = self.read_dta(self.dta4_114)
        parsed_115 = self.read_dta(self.dta4_115)
        parsed_117 = self.read_dta(self.dta4_117)

        expected = DataFrame.from_records(
            [
                ["one", "ten", "one", "one", "one"],
                ["two", "nine", "two", "two", "two"],
                ["three", "eight", "three", "three", "three"],
                ["four", "seven", 4, "four", "four"],
                ["five", "six", 5, np.nan, "five"],
                ["six", "five", 6, np.nan, "six"],
                ["seven", "four", 7, np.nan, "seven"],
                ["eight", "three", 8, np.nan, "eight"],
                ["nine", "two", 9, np.nan, "nine"],
                ["ten", "one", "ten", np.nan, "ten"]
            ],
            columns=['fully_labeled', 'fully_labeled2', 'incompletely_labeled',
                     'labeled_with_missings', 'float_labelled'])

        # these are all categoricals
        expected = pd.concat([expected[col].astype('category')
                              for col in expected], axis=1)

        # stata doesn't save .category metadata
        tm.assert_frame_equal(parsed_113, expected, check_categorical=False)
        tm.assert_frame_equal(parsed_114, expected, check_categorical=False)
        tm.assert_frame_equal(parsed_115, expected, check_categorical=False)
        tm.assert_frame_equal(parsed_117, expected, check_categorical=False)

    # File containing strls
    def test_read_dta12(self):
        parsed_117 = self.read_dta(self.dta21_117)
        expected = DataFrame.from_records(
            [
                [1, "abc", "abcdefghi"],
                [3, "cba", "qwertywertyqwerty"],
                [93, "", "strl"],
            ],
            columns=['x', 'y', 'z'])

        tm.assert_frame_equal(parsed_117, expected, check_dtype=False)

    def test_read_dta18(self):
        parsed_118 = self.read_dta(self.dta22_118)
        parsed_118["Bytes"] = parsed_118["Bytes"].astype('O')
        expected = DataFrame.from_records(
            [['Cat', 'Bogota', u'Bogotá', 1, 1.0, u'option b Ünicode', 1.0],
             ['Dog', 'Boston', u'Uzunköprü', np.nan, np.nan, np.nan, np.nan],
             ['Plane', 'Rome', u'Tromsø', 0, 0.0, 'option a', 0.0],
             ['Potato', 'Tokyo', u'Elâzığ', -4, 4.0, 4, 4],
             ['', '', '', 0, 0.3332999, 'option a', 1 / 3.]
             ],
            columns=['Things', 'Cities', 'Unicode_Cities_Strl',
                     'Ints', 'Floats', 'Bytes', 'Longs'])
        expected["Floats"] = expected["Floats"].astype(np.float32)
        for col in parsed_118.columns:
            tm.assert_almost_equal(parsed_118[col], expected[col])

        with StataReader(self.dta22_118) as rdr:
            vl = rdr.variable_labels()
            vl_expected = {u'Unicode_Cities_Strl':
                           u'Here are some strls with Ünicode chars',
                           u'Longs': u'long data',
                           u'Things': u'Here are some things',
                           u'Bytes': u'byte data',
                           u'Ints': u'int data',
                           u'Cities': u'Here are some cities',
                           u'Floats': u'float data'}
            tm.assert_dict_equal(vl, vl_expected)

            self.assertEqual(rdr.data_label, u'This is a  Ünicode data label')

    def test_read_write_dta5(self):
        original = DataFrame([(np.nan, np.nan, np.nan, np.nan, np.nan)],
                             columns=['float_miss', 'double_miss', 'byte_miss',
                                      'int_miss', 'long_miss'])
        original.index.name = 'index'

        with tm.ensure_clean() as path:
            original.to_stata(path, None)
            written_and_read_again = self.read_dta(path)
            tm.assert_frame_equal(written_and_read_again.set_index('index'),
                                  original)

    def test_write_dta6(self):
        original = self.read_csv(self.csv3)
        original.index.name = 'index'
        original.index = original.index.astype(np.int32)
        original['year'] = original['year'].astype(np.int32)
        original['quarter'] = original['quarter'].astype(np.int32)

        with tm.ensure_clean() as path:
            original.to_stata(path, None)
            written_and_read_again = self.read_dta(path)
            tm.assert_frame_equal(written_and_read_again.set_index('index'),
                                  original, check_index_type=False)

    def test_read_write_dta10(self):
        original = DataFrame(data=[["string", "object", 1, 1.1,
                                    np.datetime64('2003-12-25')]],
                             columns=['string', 'object', 'integer',
                                      'floating', 'datetime'])
        original["object"] = Series(original["object"], dtype=object)
        original.index.name = 'index'
        original.index = original.index.astype(np.int32)
        original['integer'] = original['integer'].astype(np.int32)

        with tm.ensure_clean() as path:
            original.to_stata(path, {'datetime': 'tc'})
            written_and_read_again = self.read_dta(path)
            # original.index is np.int32, readed index is np.int64
            tm.assert_frame_equal(written_and_read_again.set_index('index'),
                                  original, check_index_type=False)

    def test_stata_doc_examples(self):
        with tm.ensure_clean() as path:
            df = DataFrame(np.random.randn(10, 2), columns=list('AB'))
            df.to_stata(path)

    def test_write_preserves_original(self):
        # 9795
        np.random.seed(423)
        df = pd.DataFrame(np.random.randn(5, 4), columns=list('abcd'))
        df.ix[2, 'a':'c'] = np.nan
        df_copy = df.copy()
        with tm.ensure_clean() as path:
            df.to_stata(path, write_index=False)
        tm.assert_frame_equal(df, df_copy)

    def test_encoding(self):

        # GH 4626, proper encoding handling
        raw = read_stata(self.dta_encoding)
        encoded = read_stata(self.dta_encoding, encoding="latin-1")
        result = encoded.kreis1849[0]

        if compat.PY3:
            expected = raw.kreis1849[0]
            self.assertEqual(result, expected)
            self.assertIsInstance(result, compat.string_types)
        else:
            expected = raw.kreis1849.str.decode("latin-1")[0]
            self.assertEqual(result, expected)
            self.assertIsInstance(result, unicode)  # noqa

        with tm.ensure_clean() as path:
            encoded.to_stata(path, encoding='latin-1', write_index=False)
            reread_encoded = read_stata(path, encoding='latin-1')
            tm.assert_frame_equal(encoded, reread_encoded)

    def test_read_write_dta11(self):
        original = DataFrame([(1, 2, 3, 4)],
                             columns=['good', compat.u('b\u00E4d'), '8number',
                                      'astringwithmorethan32characters______'])
        formatted = DataFrame([(1, 2, 3, 4)],
                              columns=['good', 'b_d', '_8number',
                                       'astringwithmorethan32characters_'])
        formatted.index.name = 'index'
        formatted = formatted.astype(np.int32)

        with tm.ensure_clean() as path:
            with warnings.catch_warnings(record=True) as w:
                original.to_stata(path, None)
                # should get a warning for that format.
            self.assertEqual(len(w), 1)

            written_and_read_again = self.read_dta(path)
            tm.assert_frame_equal(
                written_and_read_again.set_index('index'), formatted)

    def test_read_write_dta12(self):
        original = DataFrame([(1, 2, 3, 4, 5, 6)],
                             columns=['astringwithmorethan32characters_1',
                                      'astringwithmorethan32characters_2',
                                      '+',
                                      '-',
                                      'short',
                                      'delete'])
        formatted = DataFrame([(1, 2, 3, 4, 5, 6)],
                              columns=['astringwithmorethan32characters_',
                                       '_0astringwithmorethan32character',
                                       '_',
                                       '_1_',
                                       '_short',
                                       '_delete'])
        formatted.index.name = 'index'
        formatted = formatted.astype(np.int32)

        with tm.ensure_clean() as path:
            with warnings.catch_warnings(record=True) as w:
                original.to_stata(path, None)
                # should get a warning for that format.
                self.assertEqual(len(w), 1)

            written_and_read_again = self.read_dta(path)
            tm.assert_frame_equal(
                written_and_read_again.set_index('index'), formatted)

    def test_read_write_dta13(self):
        s1 = Series(2 ** 9, dtype=np.int16)
        s2 = Series(2 ** 17, dtype=np.int32)
        s3 = Series(2 ** 33, dtype=np.int64)
        original = DataFrame({'int16': s1, 'int32': s2, 'int64': s3})
        original.index.name = 'index'

        formatted = original
        formatted['int64'] = formatted['int64'].astype(np.float64)

        with tm.ensure_clean() as path:
            original.to_stata(path)
            written_and_read_again = self.read_dta(path)
            tm.assert_frame_equal(written_and_read_again.set_index('index'),
                                  formatted)

    def test_read_write_reread_dta14(self):
        expected = self.read_csv(self.csv14)
        cols = ['byte_', 'int_', 'long_', 'float_', 'double_']
        for col in cols:
            expected[col] = expected[col]._convert(datetime=True, numeric=True)
        expected['float_'] = expected['float_'].astype(np.float32)
        expected['date_td'] = pd.to_datetime(
            expected['date_td'], errors='coerce')

        parsed_113 = self.read_dta(self.dta14_113)
        parsed_113.index.name = 'index'
        parsed_114 = self.read_dta(self.dta14_114)
        parsed_114.index.name = 'index'
        parsed_115 = self.read_dta(self.dta14_115)
        parsed_115.index.name = 'index'
        parsed_117 = self.read_dta(self.dta14_117)
        parsed_117.index.name = 'index'

        tm.assert_frame_equal(parsed_114, parsed_113)
        tm.assert_frame_equal(parsed_114, parsed_115)
        tm.assert_frame_equal(parsed_114, parsed_117)

        with tm.ensure_clean() as path:
            parsed_114.to_stata(path, {'date_td': 'td'})
            written_and_read_again = self.read_dta(path)
            tm.assert_frame_equal(
                written_and_read_again.set_index('index'), parsed_114)

    def test_read_write_reread_dta15(self):
        expected = self.read_csv(self.csv15)
        expected['byte_'] = expected['byte_'].astype(np.int8)
        expected['int_'] = expected['int_'].astype(np.int16)
        expected['long_'] = expected['long_'].astype(np.int32)
        expected['float_'] = expected['float_'].astype(np.float32)
        expected['double_'] = expected['double_'].astype(np.float64)
        expected['date_td'] = expected['date_td'].apply(
            datetime.strptime, args=('%Y-%m-%d',))

        parsed_113 = self.read_dta(self.dta15_113)
        parsed_114 = self.read_dta(self.dta15_114)
        parsed_115 = self.read_dta(self.dta15_115)
        parsed_117 = self.read_dta(self.dta15_117)

        tm.assert_frame_equal(expected, parsed_114)
        tm.assert_frame_equal(parsed_113, parsed_114)
        tm.assert_frame_equal(parsed_114, parsed_115)
        tm.assert_frame_equal(parsed_114, parsed_117)

    def test_timestamp_and_label(self):
        original = DataFrame([(1,)], columns=['var'])
        time_stamp = datetime(2000, 2, 29, 14, 21)
        data_label = 'This is a data file.'
        with tm.ensure_clean() as path:
            original.to_stata(path, time_stamp=time_stamp,
                              data_label=data_label)

            with StataReader(path) as reader:
                parsed_time_stamp = dt.datetime.strptime(
                    reader.time_stamp, ('%d %b %Y %H:%M'))
                assert parsed_time_stamp == time_stamp
                assert reader.data_label == data_label

    def test_numeric_column_names(self):
        original = DataFrame(np.reshape(np.arange(25.0), (5, 5)))
        original.index.name = 'index'
        with tm.ensure_clean() as path:
            # should get a warning for that format.
            with tm.assert_produces_warning(InvalidColumnName):
                original.to_stata(path)

            written_and_read_again = self.read_dta(path)
            written_and_read_again = written_and_read_again.set_index('index')
            columns = list(written_and_read_again.columns)
            convert_col_name = lambda x: int(x[1])
            written_and_read_again.columns = map(convert_col_name, columns)
            tm.assert_frame_equal(original, written_and_read_again)

    def test_nan_to_missing_value(self):
        s1 = Series(np.arange(4.0), dtype=np.float32)
        s2 = Series(np.arange(4.0), dtype=np.float64)
        s1[::2] = np.nan
        s2[1::2] = np.nan
        original = DataFrame({'s1': s1, 's2': s2})
        original.index.name = 'index'
        with tm.ensure_clean() as path:
            original.to_stata(path)
            written_and_read_again = self.read_dta(path)
            written_and_read_again = written_and_read_again.set_index('index')
            tm.assert_frame_equal(written_and_read_again, original)

    def test_no_index(self):
        columns = ['x', 'y']
        original = DataFrame(np.reshape(np.arange(10.0), (5, 2)),
                             columns=columns)
        original.index.name = 'index_not_written'
        with tm.ensure_clean() as path:
            original.to_stata(path, write_index=False)
            written_and_read_again = self.read_dta(path)
            tm.assertRaises(
                KeyError, lambda: written_and_read_again['index_not_written'])

    def test_string_no_dates(self):
        s1 = Series(['a', 'A longer string'])
        s2 = Series([1.0, 2.0], dtype=np.float64)
        original = DataFrame({'s1': s1, 's2': s2})
        original.index.name = 'index'
        with tm.ensure_clean() as path:
            original.to_stata(path)
            written_and_read_again = self.read_dta(path)
            tm.assert_frame_equal(written_and_read_again.set_index('index'),
                                  original)

    def test_large_value_conversion(self):
        s0 = Series([1, 99], dtype=np.int8)
        s1 = Series([1, 127], dtype=np.int8)
        s2 = Series([1, 2 ** 15 - 1], dtype=np.int16)
        s3 = Series([1, 2 ** 63 - 1], dtype=np.int64)
        original = DataFrame({'s0': s0, 's1': s1, 's2': s2, 's3': s3})
        original.index.name = 'index'
        with tm.ensure_clean() as path:
            with tm.assert_produces_warning(PossiblePrecisionLoss):
                original.to_stata(path)

            written_and_read_again = self.read_dta(path)
            modified = original.copy()
            modified['s1'] = Series(modified['s1'], dtype=np.int16)
            modified['s2'] = Series(modified['s2'], dtype=np.int32)
            modified['s3'] = Series(modified['s3'], dtype=np.float64)
            tm.assert_frame_equal(written_and_read_again.set_index('index'),
                                  modified)

    def test_dates_invalid_column(self):
        original = DataFrame([datetime(2006, 11, 19, 23, 13, 20)])
        original.index.name = 'index'
        with tm.ensure_clean() as path:
            with tm.assert_produces_warning(InvalidColumnName):
                original.to_stata(path, {0: 'tc'})

            written_and_read_again = self.read_dta(path)
            modified = original.copy()
            modified.columns = ['_0']
            tm.assert_frame_equal(written_and_read_again.set_index('index'),
                                  modified)

    def test_105(self):
        # Data obtained from:
        # http://go.worldbank.org/ZXY29PVJ21
        dpath = os.path.join(self.dirpath, 'S4_EDUC1.dta')
        df = pd.read_stata(dpath)
        df0 = [[1, 1, 3, -2], [2, 1, 2, -2], [4, 1, 1, -2]]
        df0 = pd.DataFrame(df0)
        df0.columns = ["clustnum", "pri_schl", "psch_num", "psch_dis"]
        df0['clustnum'] = df0["clustnum"].astype(np.int16)
        df0['pri_schl'] = df0["pri_schl"].astype(np.int8)
        df0['psch_num'] = df0["psch_num"].astype(np.int8)
        df0['psch_dis'] = df0["psch_dis"].astype(np.float32)
        tm.assert_frame_equal(df.head(3), df0)

    def test_date_export_formats(self):
        columns = ['tc', 'td', 'tw', 'tm', 'tq', 'th', 'ty']
        conversions = dict(((c, c) for c in columns))
        data = [datetime(2006, 11, 20, 23, 13, 20)] * len(columns)
        original = DataFrame([data], columns=columns)
        original.index.name = 'index'
        expected_values = [datetime(2006, 11, 20, 23, 13, 20),  # Time
                           datetime(2006, 11, 20),  # Day
                           datetime(2006, 11, 19),  # Week
                           datetime(2006, 11, 1),  # Month
                           datetime(2006, 10, 1),  # Quarter year
                           datetime(2006, 7, 1),  # Half year
                           datetime(2006, 1, 1)]  # Year

        expected = DataFrame([expected_values], columns=columns)
        expected.index.name = 'index'
        with tm.ensure_clean() as path:
            original.to_stata(path, conversions)
            written_and_read_again = self.read_dta(path)
            tm.assert_frame_equal(written_and_read_again.set_index('index'),
                                  expected)

    def test_write_missing_strings(self):
        original = DataFrame([["1"], [None]], columns=["foo"])
        expected = DataFrame([["1"], [""]], columns=["foo"])
        expected.index.name = 'index'
        with tm.ensure_clean() as path:
            original.to_stata(path)
            written_and_read_again = self.read_dta(path)
            tm.assert_frame_equal(written_and_read_again.set_index('index'),
                                  expected)

    def test_bool_uint(self):
        s0 = Series([0, 1, True], dtype=np.bool)
        s1 = Series([0, 1, 100], dtype=np.uint8)
        s2 = Series([0, 1, 255], dtype=np.uint8)
        s3 = Series([0, 1, 2 ** 15 - 100], dtype=np.uint16)
        s4 = Series([0, 1, 2 ** 16 - 1], dtype=np.uint16)
        s5 = Series([0, 1, 2 ** 31 - 100], dtype=np.uint32)
        s6 = Series([0, 1, 2 ** 32 - 1], dtype=np.uint32)

        original = DataFrame({'s0': s0, 's1': s1, 's2': s2, 's3': s3,
                              's4': s4, 's5': s5, 's6': s6})
        original.index.name = 'index'
        expected = original.copy()
        expected_types = (np.int8, np.int8, np.int16, np.int16, np.int32,
                          np.int32, np.float64)
        for c, t in zip(expected.columns, expected_types):
            expected[c] = expected[c].astype(t)

        with tm.ensure_clean() as path:
            original.to_stata(path)
            written_and_read_again = self.read_dta(path)
            written_and_read_again = written_and_read_again.set_index('index')
            tm.assert_frame_equal(written_and_read_again, expected)

    def test_variable_labels(self):
        with StataReader(self.dta16_115) as rdr:
            sr_115 = rdr.variable_labels()
        with StataReader(self.dta16_117) as rdr:
            sr_117 = rdr.variable_labels()
        keys = ('var1', 'var2', 'var3')
        labels = ('label1', 'label2', 'label3')
        for k, v in compat.iteritems(sr_115):
            self.assertTrue(k in sr_117)
            self.assertTrue(v == sr_117[k])
            self.assertTrue(k in keys)
            self.assertTrue(v in labels)

    def test_minimal_size_col(self):
        str_lens = (1, 100, 244)
        s = {}
        for str_len in str_lens:
            s['s' + str(str_len)] = Series(['a' * str_len,
                                            'b' * str_len, 'c' * str_len])
        original = DataFrame(s)
        with tm.ensure_clean() as path:
            original.to_stata(path, write_index=False)

            with StataReader(path) as sr:
                typlist = sr.typlist
                variables = sr.varlist
                formats = sr.fmtlist
                for variable, fmt, typ in zip(variables, formats, typlist):
                    self.assertTrue(int(variable[1:]) == int(fmt[1:-1]))
                    self.assertTrue(int(variable[1:]) == typ)

    def test_excessively_long_string(self):
        str_lens = (1, 244, 500)
        s = {}
        for str_len in str_lens:
            s['s' + str(str_len)] = Series(['a' * str_len,
                                            'b' * str_len, 'c' * str_len])
        original = DataFrame(s)
        with tm.assertRaises(ValueError):
            with tm.ensure_clean() as path:
                original.to_stata(path)

    def test_missing_value_generator(self):
        types = ('b', 'h', 'l')
        df = DataFrame([[0.0]], columns=['float_'])
        with tm.ensure_clean() as path:
            df.to_stata(path)
            with StataReader(path) as rdr:
                valid_range = rdr.VALID_RANGE
        expected_values = ['.' + chr(97 + i) for i in range(26)]
        expected_values.insert(0, '.')
        for t in types:
            offset = valid_range[t][1]
            for i in range(0, 27):
                val = StataMissingValue(offset + 1 + i)
                self.assertTrue(val.string == expected_values[i])

        # Test extremes for floats
        val = StataMissingValue(struct.unpack('<f', b'\x00\x00\x00\x7f')[0])
        self.assertTrue(val.string == '.')
        val = StataMissingValue(struct.unpack('<f', b'\x00\xd0\x00\x7f')[0])
        self.assertTrue(val.string == '.z')

        # Test extremes for floats
        val = StataMissingValue(struct.unpack(
            '<d', b'\x00\x00\x00\x00\x00\x00\xe0\x7f')[0])
        self.assertTrue(val.string == '.')
        val = StataMissingValue(struct.unpack(
            '<d', b'\x00\x00\x00\x00\x00\x1a\xe0\x7f')[0])
        self.assertTrue(val.string == '.z')

    def test_missing_value_conversion(self):
        columns = ['int8_', 'int16_', 'int32_', 'float32_', 'float64_']
        smv = StataMissingValue(101)
        keys = [key for key in iterkeys(smv.MISSING_VALUES)]
        keys.sort()
        data = []
        for i in range(27):
            row = [StataMissingValue(keys[i + (j * 27)]) for j in range(5)]
            data.append(row)
        expected = DataFrame(data, columns=columns)

        parsed_113 = read_stata(self.dta17_113, convert_missing=True)
        parsed_115 = read_stata(self.dta17_115, convert_missing=True)
        parsed_117 = read_stata(self.dta17_117, convert_missing=True)

        tm.assert_frame_equal(expected, parsed_113)
        tm.assert_frame_equal(expected, parsed_115)
        tm.assert_frame_equal(expected, parsed_117)

    def test_big_dates(self):
        yr = [1960, 2000, 9999, 100, 2262, 1677]
        mo = [1, 1, 12, 1, 4, 9]
        dd = [1, 1, 31, 1, 22, 23]
        hr = [0, 0, 23, 0, 0, 0]
        mm = [0, 0, 59, 0, 0, 0]
        ss = [0, 0, 59, 0, 0, 0]
        expected = []
        for i in range(len(yr)):
            row = []
            for j in range(7):
                if j == 0:
                    row.append(
                        datetime(yr[i], mo[i], dd[i], hr[i], mm[i], ss[i]))
                elif j == 6:
                    row.append(datetime(yr[i], 1, 1))
                else:
                    row.append(datetime(yr[i], mo[i], dd[i]))
            expected.append(row)
        expected.append([NaT] * 7)
        columns = ['date_tc', 'date_td', 'date_tw', 'date_tm', 'date_tq',
                   'date_th', 'date_ty']

        # Fixes for weekly, quarterly,half,year
        expected[2][2] = datetime(9999, 12, 24)
        expected[2][3] = datetime(9999, 12, 1)
        expected[2][4] = datetime(9999, 10, 1)
        expected[2][5] = datetime(9999, 7, 1)
        expected[4][2] = datetime(2262, 4, 16)
        expected[4][3] = expected[4][4] = datetime(2262, 4, 1)
        expected[4][5] = expected[4][6] = datetime(2262, 1, 1)
        expected[5][2] = expected[5][3] = expected[
            5][4] = datetime(1677, 10, 1)
        expected[5][5] = expected[5][6] = datetime(1678, 1, 1)

        expected = DataFrame(expected, columns=columns, dtype=np.object)
        parsed_115 = read_stata(self.dta18_115)
        parsed_117 = read_stata(self.dta18_117)
        tm.assert_frame_equal(expected, parsed_115,
                              check_datetimelike_compat=True)
        tm.assert_frame_equal(expected, parsed_117,
                              check_datetimelike_compat=True)

        date_conversion = dict((c, c[-2:]) for c in columns)
        # {c : c[-2:] for c in columns}
        with tm.ensure_clean() as path:
            expected.index.name = 'index'
            expected.to_stata(path, date_conversion)
            written_and_read_again = self.read_dta(path)
            tm.assert_frame_equal(written_and_read_again.set_index('index'),
                                  expected,
                                  check_datetimelike_compat=True)

    def test_dtype_conversion(self):
        expected = self.read_csv(self.csv15)
        expected['byte_'] = expected['byte_'].astype(np.int8)
        expected['int_'] = expected['int_'].astype(np.int16)
        expected['long_'] = expected['long_'].astype(np.int32)
        expected['float_'] = expected['float_'].astype(np.float32)
        expected['double_'] = expected['double_'].astype(np.float64)
        expected['date_td'] = expected['date_td'].apply(datetime.strptime,
                                                        args=('%Y-%m-%d',))

        no_conversion = read_stata(self.dta15_117,
                                   convert_dates=True)
        tm.assert_frame_equal(expected, no_conversion)

        conversion = read_stata(self.dta15_117,
                                convert_dates=True,
                                preserve_dtypes=False)

        # read_csv types are the same
        expected = self.read_csv(self.csv15)
        expected['date_td'] = expected['date_td'].apply(datetime.strptime,
                                                        args=('%Y-%m-%d',))

        tm.assert_frame_equal(expected, conversion)

    def test_drop_column(self):
        expected = self.read_csv(self.csv15)
        expected['byte_'] = expected['byte_'].astype(np.int8)
        expected['int_'] = expected['int_'].astype(np.int16)
        expected['long_'] = expected['long_'].astype(np.int32)
        expected['float_'] = expected['float_'].astype(np.float32)
        expected['double_'] = expected['double_'].astype(np.float64)
        expected['date_td'] = expected['date_td'].apply(datetime.strptime,
                                                        args=('%Y-%m-%d',))

        columns = ['byte_', 'int_', 'long_']
        expected = expected[columns]
        dropped = read_stata(self.dta15_117, convert_dates=True,
                             columns=columns)

        tm.assert_frame_equal(expected, dropped)

        # See PR 10757
        columns = ['int_', 'long_', 'byte_']
        expected = expected[columns]
        reordered = read_stata(self.dta15_117, convert_dates=True,
                               columns=columns)
        tm.assert_frame_equal(expected, reordered)

        with tm.assertRaises(ValueError):
            columns = ['byte_', 'byte_']
            read_stata(self.dta15_117, convert_dates=True, columns=columns)

        with tm.assertRaises(ValueError):
            columns = ['byte_', 'int_', 'long_', 'not_found']
            read_stata(self.dta15_117, convert_dates=True, columns=columns)

    def test_categorical_writing(self):
        original = DataFrame.from_records(
            [
                ["one", "ten", "one", "one", "one", 1],
                ["two", "nine", "two", "two", "two", 2],
                ["three", "eight", "three", "three", "three", 3],
                ["four", "seven", 4, "four", "four", 4],
                ["five", "six", 5, np.nan, "five", 5],
                ["six", "five", 6, np.nan, "six", 6],
                ["seven", "four", 7, np.nan, "seven", 7],
                ["eight", "three", 8, np.nan, "eight", 8],
                ["nine", "two", 9, np.nan, "nine", 9],
                ["ten", "one", "ten", np.nan, "ten", 10]
            ],
            columns=['fully_labeled', 'fully_labeled2', 'incompletely_labeled',
                     'labeled_with_missings', 'float_labelled', 'unlabeled'])
        expected = original.copy()

        # these are all categoricals
        original = pd.concat([original[col].astype('category')
                              for col in original], axis=1)

        expected['incompletely_labeled'] = expected[
            'incompletely_labeled'].apply(str)
        expected['unlabeled'] = expected['unlabeled'].apply(str)
        expected = pd.concat([expected[col].astype('category')
                              for col in expected], axis=1)
        expected.index.name = 'index'

        with tm.ensure_clean() as path:
            with warnings.catch_warnings(record=True) as w:  # noqa
                # Silence warnings
                original.to_stata(path)
                written_and_read_again = self.read_dta(path)
                res = written_and_read_again.set_index('index')
                tm.assert_frame_equal(res, expected, check_categorical=False)

    def test_categorical_warnings_and_errors(self):
        # Warning for non-string labels
        # Error for labels too long
        original = pd.DataFrame.from_records(
            [['a' * 10000],
             ['b' * 10000],
             ['c' * 10000],
             ['d' * 10000]],
            columns=['Too_long'])

        original = pd.concat([original[col].astype('category')
                              for col in original], axis=1)
        with tm.ensure_clean() as path:
            tm.assertRaises(ValueError, original.to_stata, path)

        original = pd.DataFrame.from_records(
            [['a'],
             ['b'],
             ['c'],
             ['d'],
             [1]],
            columns=['Too_long'])
        original = pd.concat([original[col].astype('category')
                              for col in original], axis=1)

        with warnings.catch_warnings(record=True) as w:
            original.to_stata(path)
            # should get a warning for mixed content
            self.assertEqual(len(w), 1)

    def test_categorical_with_stata_missing_values(self):
        values = [['a' + str(i)] for i in range(120)]
        values.append([np.nan])
        original = pd.DataFrame.from_records(values, columns=['many_labels'])
        original = pd.concat([original[col].astype('category')
                              for col in original], axis=1)
        original.index.name = 'index'
        with tm.ensure_clean() as path:
            original.to_stata(path)
            written_and_read_again = self.read_dta(path)
            res = written_and_read_again.set_index('index')
            tm.assert_frame_equal(res, original, check_categorical=False)

    def test_categorical_order(self):
        # Directly construct using expected codes
        # Format is is_cat, col_name, labels (in order), underlying data
        expected = [(True, 'ordered', ['a', 'b', 'c', 'd', 'e'], np.arange(5)),
                    (True, 'reverse', ['a', 'b', 'c',
                                       'd', 'e'], np.arange(5)[::-1]),
                    (True, 'noorder', ['a', 'b', 'c', 'd',
                                       'e'], np.array([2, 1, 4, 0, 3])),
                    (True, 'floating', [
                     'a', 'b', 'c', 'd', 'e'], np.arange(0, 5)),
                    (True, 'float_missing', [
                     'a', 'd', 'e'], np.array([0, 1, 2, -1, -1])),
                    (False, 'nolabel', [
                     1.0, 2.0, 3.0, 4.0, 5.0], np.arange(5)),
                    (True, 'int32_mixed', ['d', 2, 'e', 'b', 'a'],
                     np.arange(5))]
        cols = []
        for is_cat, col, labels, codes in expected:
            if is_cat:
                cols.append((col, pd.Categorical.from_codes(codes, labels)))
            else:
                cols.append((col, pd.Series(labels, dtype=np.float32)))
        expected = DataFrame.from_items(cols)

        # Read with and with out categoricals, ensure order is identical
        parsed_115 = read_stata(self.dta19_115)
        parsed_117 = read_stata(self.dta19_117)
        tm.assert_frame_equal(expected, parsed_115, check_categorical=False)
        tm.assert_frame_equal(expected, parsed_117, check_categorical=False)

        # Check identity of codes
        for col in expected:
            if is_categorical_dtype(expected[col]):
                tm.assert_series_equal(expected[col].cat.codes,
                                       parsed_115[col].cat.codes)
                tm.assert_index_equal(expected[col].cat.categories,
                                      parsed_115[col].cat.categories)

    def test_categorical_sorting(self):
        parsed_115 = read_stata(self.dta20_115)
        parsed_117 = read_stata(self.dta20_117)
        # Sort based on codes, not strings
        parsed_115 = parsed_115.sort_values("srh")
        parsed_117 = parsed_117.sort_values("srh")
        # Don't sort index
        parsed_115.index = np.arange(parsed_115.shape[0])
        parsed_117.index = np.arange(parsed_117.shape[0])
        codes = [-1, -1, 0, 1, 1, 1, 2, 2, 3, 4]
        categories = ["Poor", "Fair", "Good", "Very good", "Excellent"]
        cat = pd.Categorical.from_codes(codes=codes, categories=categories)
        expected = pd.Series(cat, name='srh')
        tm.assert_series_equal(expected, parsed_115["srh"],
                               check_categorical=False)
        tm.assert_series_equal(expected, parsed_117["srh"],
                               check_categorical=False)

    def test_categorical_ordering(self):
        parsed_115 = read_stata(self.dta19_115)
        parsed_117 = read_stata(self.dta19_117)

        parsed_115_unordered = read_stata(self.dta19_115,
                                          order_categoricals=False)
        parsed_117_unordered = read_stata(self.dta19_117,
                                          order_categoricals=False)
        for col in parsed_115:
            if not is_categorical_dtype(parsed_115[col]):
                continue
            self.assertEqual(True, parsed_115[col].cat.ordered)
            self.assertEqual(True, parsed_117[col].cat.ordered)
            self.assertEqual(False, parsed_115_unordered[col].cat.ordered)
            self.assertEqual(False, parsed_117_unordered[col].cat.ordered)

    def test_read_chunks_117(self):
        files_117 = [self.dta1_117, self.dta2_117, self.dta3_117,
                     self.dta4_117, self.dta14_117, self.dta15_117,
                     self.dta16_117, self.dta17_117, self.dta18_117,
                     self.dta19_117, self.dta20_117]

        for fname in files_117:
            for chunksize in 1, 2:
                for convert_categoricals in False, True:
                    for convert_dates in False, True:

                        with warnings.catch_warnings(record=True) as w:
                            warnings.simplefilter("always")
                            parsed = read_stata(
                                fname,
                                convert_categoricals=convert_categoricals,
                                convert_dates=convert_dates)
                        itr = read_stata(
                            fname, iterator=True,
                            convert_categoricals=convert_categoricals,
                            convert_dates=convert_dates)

                        pos = 0
                        for j in range(5):
                            with warnings.catch_warnings(record=True) as w:  # noqa
                                warnings.simplefilter("always")
                                try:
                                    chunk = itr.read(chunksize)
                                except StopIteration:
                                    break
                            from_frame = parsed.iloc[pos:pos + chunksize, :]
                            tm.assert_frame_equal(
                                from_frame, chunk, check_dtype=False,
                                check_datetimelike_compat=True,
                                check_categorical=False)

                            pos += chunksize
                        itr.close()

    def test_iterator(self):

        fname = self.dta3_117

        parsed = read_stata(fname)

        with read_stata(fname, iterator=True) as itr:
            chunk = itr.read(5)
            tm.assert_frame_equal(parsed.iloc[0:5, :], chunk)

        with read_stata(fname, chunksize=5) as itr:
            chunk = list(itr)
            tm.assert_frame_equal(parsed.iloc[0:5, :], chunk[0])

        with read_stata(fname, iterator=True) as itr:
            chunk = itr.get_chunk(5)
            tm.assert_frame_equal(parsed.iloc[0:5, :], chunk)

        with read_stata(fname, chunksize=5) as itr:
            chunk = itr.get_chunk()
            tm.assert_frame_equal(parsed.iloc[0:5, :], chunk)

        # GH12153
        from_chunks = pd.concat(read_stata(fname, chunksize=4))
        tm.assert_frame_equal(parsed, from_chunks)

    def test_read_chunks_115(self):
        files_115 = [self.dta2_115, self.dta3_115, self.dta4_115,
                     self.dta14_115, self.dta15_115, self.dta16_115,
                     self.dta17_115, self.dta18_115, self.dta19_115,
                     self.dta20_115]

        for fname in files_115:
            for chunksize in 1, 2:
                for convert_categoricals in False, True:
                    for convert_dates in False, True:

                        # Read the whole file
                        with warnings.catch_warnings(record=True) as w:
                            warnings.simplefilter("always")
                            parsed = read_stata(
                                fname,
                                convert_categoricals=convert_categoricals,
                                convert_dates=convert_dates)

                        # Compare to what we get when reading by chunk
                        itr = read_stata(
                            fname, iterator=True,
                            convert_dates=convert_dates,
                            convert_categoricals=convert_categoricals)
                        pos = 0
                        for j in range(5):
                            with warnings.catch_warnings(record=True) as w:  # noqa
                                warnings.simplefilter("always")
                                try:
                                    chunk = itr.read(chunksize)
                                except StopIteration:
                                    break
                            from_frame = parsed.iloc[pos:pos + chunksize, :]
                            tm.assert_frame_equal(
                                from_frame, chunk, check_dtype=False,
                                check_datetimelike_compat=True,
                                check_categorical=False)

                            pos += chunksize
                        itr.close()

    def test_read_chunks_columns(self):
        fname = self.dta3_117
        columns = ['quarter', 'cpi', 'm1']
        chunksize = 2

        parsed = read_stata(fname, columns=columns)
        with read_stata(fname, iterator=True) as itr:
            pos = 0
            for j in range(5):
                chunk = itr.read(chunksize, columns=columns)
                if chunk is None:
                    break
                from_frame = parsed.iloc[pos:pos + chunksize, :]
                tm.assert_frame_equal(from_frame, chunk, check_dtype=False)
                pos += chunksize

    def test_write_variable_labels(self):
        # GH 13631, add support for writing variable labels
        original = pd.DataFrame({'a': [1, 2, 3, 4],
                                 'b': [1.0, 3.0, 27.0, 81.0],
                                 'c': ['Atlanta', 'Birmingham',
                                       'Cincinnati', 'Detroit']})
        original.index.name = 'index'
        variable_labels = {'a': 'City Rank', 'b': 'City Exponent', 'c': 'City'}
        with tm.ensure_clean() as path:
            original.to_stata(path, variable_labels=variable_labels)
            with StataReader(path) as sr:
                read_labels = sr.variable_labels()
            expected_labels = {'index': '',
                               'a': 'City Rank',
                               'b': 'City Exponent',
                               'c': 'City'}
            tm.assert_equal(read_labels, expected_labels)

        variable_labels['index'] = 'The Index'
        with tm.ensure_clean() as path:
            original.to_stata(path, variable_labels=variable_labels)
            with StataReader(path) as sr:
                read_labels = sr.variable_labels()
            tm.assert_equal(read_labels, variable_labels)

    def test_write_variable_label_errors(self):
        original = pd.DataFrame({'a': [1, 2, 3, 4],
                                 'b': [1.0, 3.0, 27.0, 81.0],
                                 'c': ['Atlanta', 'Birmingham',
                                       'Cincinnati', 'Detroit']})
        values = [u'\u03A1', u'\u0391',
                  u'\u039D', u'\u0394',
                  u'\u0391', u'\u03A3']

        variable_labels_utf8 = {'a': 'City Rank',
                                'b': 'City Exponent',
                                'c': u''.join(values)}

        with tm.assertRaises(ValueError):
            with tm.ensure_clean() as path:
                original.to_stata(path, variable_labels=variable_labels_utf8)

        variable_labels_long = {'a': 'City Rank',
                                'b': 'City Exponent',
                                'c': 'A very, very, very long variable label '
                                     'that is too long for Stata which means '
                                     'that it has more than 80 characters'}

        with tm.assertRaises(ValueError):
            with tm.ensure_clean() as path:
                original.to_stata(path, variable_labels=variable_labels_long)

    def test_default_date_conversion(self):
        # GH 12259
        dates = [dt.datetime(1999, 12, 31, 12, 12, 12, 12000),
                 dt.datetime(2012, 12, 21, 12, 21, 12, 21000),
                 dt.datetime(1776, 7, 4, 7, 4, 7, 4000)]
        original = pd.DataFrame({'nums': [1.0, 2.0, 3.0],
                                 'strs': ['apple', 'banana', 'cherry'],
                                 'dates': dates})

        with tm.ensure_clean() as path:
            original.to_stata(path, write_index=False)
            reread = read_stata(path, convert_dates=True)
            tm.assert_frame_equal(original, reread)

            original.to_stata(path,
                              write_index=False,
                              convert_dates={'dates': 'tc'})
            direct = read_stata(path, convert_dates=True)
            tm.assert_frame_equal(reread, direct)

    def test_unsupported_type(self):
        original = pd.DataFrame({'a': [1 + 2j, 2 + 4j]})

        with tm.assertRaises(NotImplementedError):
            with tm.ensure_clean() as path:
                original.to_stata(path)

    def test_unsupported_datetype(self):
        dates = [dt.datetime(1999, 12, 31, 12, 12, 12, 12000),
                 dt.datetime(2012, 12, 21, 12, 21, 12, 21000),
                 dt.datetime(1776, 7, 4, 7, 4, 7, 4000)]
        original = pd.DataFrame({'nums': [1.0, 2.0, 3.0],
                                 'strs': ['apple', 'banana', 'cherry'],
                                 'dates': dates})

        with tm.assertRaises(NotImplementedError):
            with tm.ensure_clean() as path:
                original.to_stata(path, convert_dates={'dates': 'tC'})

        dates = pd.date_range('1-1-1990', periods=3, tz='Asia/Hong_Kong')
        original = pd.DataFrame({'nums': [1.0, 2.0, 3.0],
                                 'strs': ['apple', 'banana', 'cherry'],
                                 'dates': dates})
        with tm.assertRaises(NotImplementedError):
            with tm.ensure_clean() as path:
                original.to_stata(path)

    def test_repeated_column_labels(self):
        # GH 13923
        with tm.assertRaises(ValueError) as cm:
            read_stata(self.dta23, convert_categoricals=True)
            tm.assertTrue('wolof' in cm.exception)

    def test_stata_111(self):
        # 111 is an old version but still used by current versions of
        # SAS when exporting to Stata format. We do not know of any
        # on-line documentation for this version.
        df = read_stata(self.dta24_111)
        original = pd.DataFrame({'y': [1, 1, 1, 1, 1, 0, 0, np.NaN, 0, 0],
                                 'x': [1, 2, 1, 3, np.NaN, 4, 3, 5, 1, 6],
                                 'w': [2, np.NaN, 5, 2, 4, 4, 3, 1, 2, 3],
                                 'z': ['a', 'b', 'c', 'd', 'e', '', 'g', 'h',
                                       'i', 'j']})
        original = original[['y', 'x', 'w', 'z']]
        tm.assert_frame_equal(original, df)


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