test_format.py

"""
Test output formatting for Series/DataFrame, including to_string & reprs
"""

from datetime import datetime
from io import StringIO
import itertools
from operator import methodcaller
import os
import re
from shutil import get_terminal_size
import sys
import textwrap

import dateutil
import numpy as np
import pytest
import pytz

from pandas.compat import is_platform_32bit, is_platform_windows

import pandas as pd
from pandas import (
    DataFrame, Index, MultiIndex, NaT, Series, Timestamp, date_range,
    get_option, option_context, read_csv, reset_option, set_option)
import pandas.util.testing as tm

import pandas.io.formats.format as fmt
import pandas.io.formats.printing as printing

use_32bit_repr = is_platform_windows() or is_platform_32bit()


def curpath():
    pth, _ = os.path.split(os.path.abspath(__file__))
    return pth


def has_info_repr(df):
    r = repr(df)
    c1 = r.split('\n')[0].startswith("<class")
    c2 = r.split('\n')[0].startswith(r"&lt;class")  # _repr_html_
    return c1 or c2


def has_non_verbose_info_repr(df):
    has_info = has_info_repr(df)
    r = repr(df)

    # 1. <class>
    # 2. Index
    # 3. Columns
    # 4. dtype
    # 5. memory usage
    # 6. trailing newline
    nv = len(r.split('\n')) == 6
    return has_info and nv


def has_horizontally_truncated_repr(df):
    try:  # Check header row
        fst_line = np.array(repr(df).splitlines()[0].split())
        cand_col = np.where(fst_line == '...')[0][0]
    except IndexError:
        return False
    # Make sure each row has this ... in the same place
    r = repr(df)
    for ix, l in enumerate(r.splitlines()):
        if not r.split()[cand_col] == '...':
            return False
    return True


def has_vertically_truncated_repr(df):
    r = repr(df)
    only_dot_row = False
    for row in r.splitlines():
        if re.match(r'^[\.\ ]+$', row):
            only_dot_row = True
    return only_dot_row


def has_truncated_repr(df):
    return has_horizontally_truncated_repr(
        df) or has_vertically_truncated_repr(df)


def has_doubly_truncated_repr(df):
    return has_horizontally_truncated_repr(
        df) and has_vertically_truncated_repr(df)


def has_expanded_repr(df):
    r = repr(df)
    for line in r.split('\n'):
        if line.endswith('\\'):
            return True
    return False


@pytest.mark.filterwarnings('ignore::FutureWarning:.*format')
class TestDataFrameFormatting:

    def test_repr_embedded_ndarray(self):
        arr = np.empty(10, dtype=[('err', object)])
        for i in range(len(arr)):
            arr['err'][i] = np.random.randn(i)

        df = DataFrame(arr)
        repr(df['err'])
        repr(df)
        df.to_string()

    def test_eng_float_formatter(self, float_frame):
        df = float_frame
        df.loc[5] = 0

        fmt.set_eng_float_format()
        repr(df)

        fmt.set_eng_float_format(use_eng_prefix=True)
        repr(df)

        fmt.set_eng_float_format(accuracy=0)
        repr(df)
        tm.reset_display_options()

    def test_show_null_counts(self):

        df = DataFrame(1, columns=range(10), index=range(10))
        df.iloc[1, 1] = np.nan

        def check(null_counts, result):
            buf = StringIO()
            df.info(buf=buf, null_counts=null_counts)
            assert ('non-null' in buf.getvalue()) is result

        with option_context('display.max_info_rows', 20,
                            'display.max_info_columns', 20):
            check(None, True)
            check(True, True)
            check(False, False)

        with option_context('display.max_info_rows', 5,
                            'display.max_info_columns', 5):
            check(None, False)
            check(True, False)
            check(False, False)

    def test_repr_tuples(self):
        buf = StringIO()

        df = DataFrame({'tups': list(zip(range(10), range(10)))})
        repr(df)
        df.to_string(col_space=10, buf=buf)

    def test_repr_truncation(self):
        max_len = 20
        with option_context("display.max_colwidth", max_len):
            df = DataFrame({'A': np.random.randn(10),
                            'B': [tm.rands(np.random.randint(
                                max_len - 1, max_len + 1)) for i in range(10)
            ]})
            r = repr(df)
            r = r[r.find('\n') + 1:]

            adj = fmt._get_adjustment()

            for line, value in zip(r.split('\n'), df['B']):
                if adj.len(value) + 1 > max_len:
                    assert '...' in line
                else:
                    assert '...' not in line

        with option_context("display.max_colwidth", 999999):
            assert '...' not in repr(df)

        with option_context("display.max_colwidth", max_len + 2):
            assert '...' not in repr(df)

    def test_repr_chop_threshold(self):
        df = DataFrame([[0.1, 0.5], [0.5, -0.1]])
        pd.reset_option("display.chop_threshold")  # default None
        assert repr(df) == '     0    1\n0  0.1  0.5\n1  0.5 -0.1'

        with option_context("display.chop_threshold", 0.2):
            assert repr(df) == '     0    1\n0  0.0  0.5\n1  0.5  0.0'

        with option_context("display.chop_threshold", 0.6):
            assert repr(df) == '     0    1\n0  0.0  0.0\n1  0.0  0.0'

        with option_context("display.chop_threshold", None):
            assert repr(df) == '     0    1\n0  0.1  0.5\n1  0.5 -0.1'

    def test_repr_chop_threshold_column_below(self):
        # GH 6839: validation case

        df = pd.DataFrame([[10, 20, 30, 40],
                           [8e-10, -1e-11, 2e-9, -2e-11]]).T

        with option_context("display.chop_threshold", 0):
            assert repr(df) == ('      0             1\n'
                                '0  10.0  8.000000e-10\n'
                                '1  20.0 -1.000000e-11\n'
                                '2  30.0  2.000000e-09\n'
                                '3  40.0 -2.000000e-11')

        with option_context("display.chop_threshold", 1e-8):
            assert repr(df) == ('      0             1\n'
                                '0  10.0  0.000000e+00\n'
                                '1  20.0  0.000000e+00\n'
                                '2  30.0  0.000000e+00\n'
                                '3  40.0  0.000000e+00')

        with option_context("display.chop_threshold", 5e-11):
            assert repr(df) == ('      0             1\n'
                                '0  10.0  8.000000e-10\n'
                                '1  20.0  0.000000e+00\n'
                                '2  30.0  2.000000e-09\n'
                                '3  40.0  0.000000e+00')

    def test_repr_obeys_max_seq_limit(self):
        with option_context("display.max_seq_items", 2000):
            assert len(printing.pprint_thing(list(range(1000)))) > 1000

        with option_context("display.max_seq_items", 5):
            assert len(printing.pprint_thing(list(range(1000)))) < 100

    def test_repr_set(self):
        assert printing.pprint_thing({1}) == '{1}'

    def test_repr_is_valid_construction_code(self):
        # for the case of Index, where the repr is traditional rather then
        # stylized
        idx = Index(['a', 'b'])
        res = eval("pd." + repr(idx))
        tm.assert_series_equal(Series(res), Series(idx))

    def test_repr_should_return_str(self):
        # https://docs.python.org/3/reference/datamodel.html#object.__repr__
        # "...The return value must be a string object."

        # (str on py2.x, str (unicode) on py3)

        data = [8, 5, 3, 5]
        index1 = ["\u03c3", "\u03c4", "\u03c5", "\u03c6"]
        cols = ["\u03c8"]
        df = DataFrame(data, columns=cols, index=index1)
        assert type(df.__repr__()) == str  # both py2 / 3

    def test_repr_no_backslash(self):
        with option_context('mode.sim_interactive', True):
            df = DataFrame(np.random.randn(10, 4))
            assert '\\' not in repr(df)

    def test_expand_frame_repr(self):
        df_small = DataFrame('hello', index=[0], columns=[0])
        df_wide = DataFrame('hello', index=[0], columns=range(10))
        df_tall = DataFrame('hello', index=range(30), columns=range(5))

        with option_context('mode.sim_interactive', True):
            with option_context('display.max_columns', 10, 'display.width', 20,
                                'display.max_rows', 20,
                                'display.show_dimensions', True):
                with option_context('display.expand_frame_repr', True):
                    assert not has_truncated_repr(df_small)
                    assert not has_expanded_repr(df_small)
                    assert not has_truncated_repr(df_wide)
                    assert has_expanded_repr(df_wide)
                    assert has_vertically_truncated_repr(df_tall)
                    assert has_expanded_repr(df_tall)

                with option_context('display.expand_frame_repr', False):
                    assert not has_truncated_repr(df_small)
                    assert not has_expanded_repr(df_small)
                    assert not has_horizontally_truncated_repr(df_wide)
                    assert not has_expanded_repr(df_wide)
                    assert has_vertically_truncated_repr(df_tall)
                    assert not has_expanded_repr(df_tall)

    def test_repr_non_interactive(self):
        # in non interactive mode, there can be no dependency on the
        # result of terminal auto size detection
        df = DataFrame('hello', index=range(1000), columns=range(5))

        with option_context('mode.sim_interactive', False, 'display.width', 0,
                            'display.max_rows', 5000):
            assert not has_truncated_repr(df)
            assert not has_expanded_repr(df)

    def test_repr_truncates_terminal_size(self, monkeypatch):
        # see gh-21180

        terminal_size = (118, 96)
        monkeypatch.setattr('pandas.io.formats.format.get_terminal_size',
                            lambda: terminal_size)

        index = range(5)
        columns = pd.MultiIndex.from_tuples([
            ('This is a long title with > 37 chars.', 'cat'),
            ('This is a loooooonger title with > 43 chars.', 'dog'),
        ])
        df = pd.DataFrame(1, index=index, columns=columns)

        result = repr(df)

        h1, h2 = result.split('\n')[:2]
        assert 'long' in h1
        assert 'loooooonger' in h1
        assert 'cat' in h2
        assert 'dog' in h2

        # regular columns
        df2 = pd.DataFrame({"A" * 41: [1, 2], 'B' * 41: [1, 2]})
        result = repr(df2)

        assert df2.columns[0] in result.split('\n')[0]

    def test_repr_truncates_terminal_size_full(self, monkeypatch):
        # GH 22984 ensure entire window is filled
        terminal_size = (80, 24)
        df = pd.DataFrame(np.random.rand(1, 7))

        monkeypatch.setattr('pandas.io.formats.format.get_terminal_size',
                            lambda: terminal_size)
        assert "..." not in str(df)

    def test_repr_truncation_column_size(self):
        # dataframe with last column very wide -> check it is not used to
        # determine size of truncation (...) column
        df = pd.DataFrame({'a': [108480, 30830], 'b': [12345, 12345],
                           'c': [12345, 12345], 'd': [12345, 12345],
                           'e': ['a' * 50] * 2})
        assert "..." in str(df)
        assert "    ...    " not in str(df)

    def test_repr_max_columns_max_rows(self):
        term_width, term_height = get_terminal_size()
        if term_width < 10 or term_height < 10:
            pytest.skip("terminal size too small, "
                        "{0} x {1}".format(term_width, term_height))

        def mkframe(n):
            index = ['{i:05d}'.format(i=i) for i in range(n)]
            return DataFrame(0, index, index)

        df6 = mkframe(6)
        df10 = mkframe(10)
        with option_context('mode.sim_interactive', True):
            with option_context('display.width', term_width * 2):
                with option_context('display.max_rows', 5,
                                    'display.max_columns', 5):
                    assert not has_expanded_repr(mkframe(4))
                    assert not has_expanded_repr(mkframe(5))
                    assert not has_expanded_repr(df6)
                    assert has_doubly_truncated_repr(df6)

                with option_context('display.max_rows', 20,
                                    'display.max_columns', 10):
                    # Out off max_columns boundary, but no extending
                    # since not exceeding width
                    assert not has_expanded_repr(df6)
                    assert not has_truncated_repr(df6)

                with option_context('display.max_rows', 9,
                                    'display.max_columns', 10):
                    # out vertical bounds can not result in exanded repr
                    assert not has_expanded_repr(df10)
                    assert has_vertically_truncated_repr(df10)

            # width=None in terminal, auto detection
            with option_context('display.max_columns', 100, 'display.max_rows',
                                term_width * 20, 'display.width', None):
                df = mkframe((term_width // 7) - 2)
                assert not has_expanded_repr(df)
                df = mkframe((term_width // 7) + 2)
                printing.pprint_thing(df._repr_fits_horizontal_())
                assert has_expanded_repr(df)

    def test_str_max_colwidth(self):
        # GH 7856
        df = pd.DataFrame([{'a': 'foo',
                            'b': 'bar',
                            'c': 'uncomfortably long line with lots of stuff',
                            'd': 1}, {'a': 'foo',
                                      'b': 'bar',
                                      'c': 'stuff',
                                      'd': 1}])
        df.set_index(['a', 'b', 'c'])
        assert str(df) == (
            '     a    b                                           c  d\n'
            '0  foo  bar  uncomfortably long line with lots of stuff  1\n'
            '1  foo  bar                                       stuff  1')
        with option_context('max_colwidth', 20):
            assert str(df) == ('     a    b                    c  d\n'
                               '0  foo  bar  uncomfortably lo...  1\n'
                               '1  foo  bar                stuff  1')

    def test_auto_detect(self):
        term_width, term_height = get_terminal_size()
        fac = 1.05  # Arbitrary large factor to exceed term width
        cols = range(int(term_width * fac))
        index = range(10)
        df = DataFrame(index=index, columns=cols)
        with option_context('mode.sim_interactive', True):
            with option_context('max_rows', None):
                with option_context('max_columns', None):
                    # Wrap around with None
                    assert has_expanded_repr(df)
            with option_context('max_rows', 0):
                with option_context('max_columns', 0):
                    # Truncate with auto detection.
                    assert has_horizontally_truncated_repr(df)

            index = range(int(term_height * fac))
            df = DataFrame(index=index, columns=cols)
            with option_context('max_rows', 0):
                with option_context('max_columns', None):
                    # Wrap around with None
                    assert has_expanded_repr(df)
                    # Truncate vertically
                    assert has_vertically_truncated_repr(df)

            with option_context('max_rows', None):
                with option_context('max_columns', 0):
                    assert has_horizontally_truncated_repr(df)

    def test_to_string_repr_unicode(self):
        buf = StringIO()

        unicode_values = ['\u03c3'] * 10
        unicode_values = np.array(unicode_values, dtype=object)
        df = DataFrame({'unicode': unicode_values})
        df.to_string(col_space=10, buf=buf)

        # it works!
        repr(df)

        idx = Index(['abc', '\u03c3a', 'aegdvg'])
        ser = Series(np.random.randn(len(idx)), idx)
        rs = repr(ser).split('\n')
        line_len = len(rs[0])
        for line in rs[1:]:
            try:
                line = line.decode(get_option("display.encoding"))
            except AttributeError:
                pass
            if not line.startswith('dtype:'):
                assert len(line) == line_len

        # it works even if sys.stdin in None
        _stdin = sys.stdin
        try:
            sys.stdin = None
            repr(df)
        finally:
            sys.stdin = _stdin

    def test_to_string_unicode_columns(self, float_frame):
        df = DataFrame({'\u03c3': np.arange(10.)})

        buf = StringIO()
        df.to_string(buf=buf)
        buf.getvalue()

        buf = StringIO()
        df.info(buf=buf)
        buf.getvalue()

        result = float_frame.to_string()
        assert isinstance(result, str)

    def test_to_string_utf8_columns(self):
        n = "\u05d0".encode('utf-8')

        with option_context('display.max_rows', 1):
            df = DataFrame([1, 2], columns=[n])
            repr(df)

    def test_to_string_unicode_two(self):
        dm = DataFrame({'c/\u03c3': []})
        buf = StringIO()
        dm.to_string(buf)

    def test_to_string_unicode_three(self):
        dm = DataFrame(['\xc2'])
        buf = StringIO()
        dm.to_string(buf)

    def test_to_string_with_formatters(self):
        df = DataFrame({'int': [1, 2, 3],
                        'float': [1.0, 2.0, 3.0],
                        'object': [(1, 2), True, False]},
                       columns=['int', 'float', 'object'])

        formatters = [('int', lambda x: '0x{x:x}'.format(x=x)),
                      ('float', lambda x: '[{x: 4.1f}]'.format(x=x)),
                      ('object', lambda x: '-{x!s}-'.format(x=x))]
        result = df.to_string(formatters=dict(formatters))
        result2 = df.to_string(formatters=list(zip(*formatters))[1])
        assert result == ('  int  float    object\n'
                          '0 0x1 [ 1.0]  -(1, 2)-\n'
                          '1 0x2 [ 2.0]    -True-\n'
                          '2 0x3 [ 3.0]   -False-')
        assert result == result2

    def test_to_string_with_datetime64_monthformatter(self):
        months = [datetime(2016, 1, 1), datetime(2016, 2, 2)]
        x = DataFrame({'months': months})

        def format_func(x):
            return x.strftime('%Y-%m')
        result = x.to_string(formatters={'months': format_func})
        expected = 'months\n0 2016-01\n1 2016-02'
        assert result.strip() == expected

    def test_to_string_with_datetime64_hourformatter(self):

        x = DataFrame({'hod': pd.to_datetime(['10:10:10.100', '12:12:12.120'],
                                             format='%H:%M:%S.%f')})

        def format_func(x):
            return x.strftime('%H:%M')

        result = x.to_string(formatters={'hod': format_func})
        expected = 'hod\n0 10:10\n1 12:12'
        assert result.strip() == expected

    def test_to_string_with_formatters_unicode(self):
        df = DataFrame({'c/\u03c3': [1, 2, 3]})
        result = df.to_string(
            formatters={'c/\u03c3': lambda x: '{x}'.format(x=x)})
        assert result == '  c/\u03c3\n' + '0   1\n1   2\n2   3'

    def test_east_asian_unicode_false(self):
        # not alighned properly because of east asian width

        # mid col
        df = DataFrame({'a': ['あ', 'いいい', 'う', 'ええええええ'],
                        'b': [1, 222, 33333, 4]},
                       index=['a', 'bb', 'c', 'ddd'])
        expected = ("          a      b\na         あ      1\n"
                    "bb      いいい    222\nc         う  33333\n"
                    "ddd  ええええええ      4")
        assert repr(df) == expected

        # last col
        df = DataFrame({'a': [1, 222, 33333, 4],
                        'b': ['あ', 'いいい', 'う', 'ええええええ']},
                       index=['a', 'bb', 'c', 'ddd'])
        expected = ("         a       b\na        1       あ\n"
                    "bb     222     いいい\nc    33333       う\n"
                    "ddd      4  ええええええ")
        assert repr(df) == expected

        # all col
        df = DataFrame({'a': ['あああああ', 'い', 'う', 'えええ'],
                        'b': ['あ', 'いいい', 'う', 'ええええええ']},
                       index=['a', 'bb', 'c', 'ddd'])
        expected = ("         a       b\na    あああああ       あ\n"
                    "bb       い     いいい\nc        う       う\n"
                    "ddd    えええ  ええええええ")
        assert repr(df) == expected

        # column name
        df = DataFrame({'b': ['あ', 'いいい', 'う', 'ええええええ'],
                        'あああああ': [1, 222, 33333, 4]},
                       index=['a', 'bb', 'c', 'ddd'])
        expected = ("          b  あああああ\na         あ      1\n"
                    "bb      いいい    222\nc         う  33333\n"
                    "ddd  ええええええ      4")
        assert repr(df) == expected

        # index
        df = DataFrame({'a': ['あああああ', 'い', 'う', 'えええ'],
                        'b': ['あ', 'いいい', 'う', 'ええええええ']},
                       index=['あああ', 'いいいいいい', 'うう', 'え'])
        expected = ("            a       b\nあああ     あああああ       あ\n"
                    "いいいいいい      い     いいい\nうう          う       う\n"
                    "え         えええ  ええええええ")
        assert repr(df) == expected

        # index name
        df = DataFrame({'a': ['あああああ', 'い', 'う', 'えええ'],
                        'b': ['あ', 'いいい', 'う', 'ええええええ']},
                       index=pd.Index(['あ', 'い', 'うう', 'え'],
                                      name='おおおお'))
        expected = ("          a       b\n"
                    "おおおお               \n"
                    "あ     あああああ       あ\n"
                    "い         い     いいい\n"
                    "うう        う       う\n"
                    "え       えええ  ええええええ")
        assert repr(df) == expected

        # all
        df = DataFrame({'あああ': ['あああ', 'い', 'う', 'えええええ'],
                        'いいいいい': ['あ', 'いいい', 'う', 'ええ']},
                       index=pd.Index(['あ', 'いいい', 'うう', 'え'],
                                      name='お'))
        expected = ("       あああ いいいいい\n"
                    "お               \n"
                    "あ      あああ     あ\n"
                    "いいい      い   いいい\n"
                    "うう       う     う\n"
                    "え    えええええ    ええ")
        assert repr(df) == expected

        # MultiIndex
        idx = pd.MultiIndex.from_tuples([('あ', 'いい'), ('う', 'え'), (
            'おおお', 'かかかか'), ('き', 'くく')])
        df = DataFrame({'a': ['あああああ', 'い', 'う', 'えええ'],
                        'b': ['あ', 'いいい', 'う', 'ええええええ']},
                       index=idx)
        expected = ("              a       b\n"
                    "あ   いい    あああああ       あ\n"
                    "う   え         い     いいい\n"
                    "おおお かかかか      う       う\n"
                    "き   くく      えええ  ええええええ")
        assert repr(df) == expected

        # truncate
        with option_context('display.max_rows', 3, 'display.max_columns', 3):
            df = pd.DataFrame({'a': ['あああああ', 'い', 'う', 'えええ'],
                               'b': ['あ', 'いいい', 'う', 'ええええええ'],
                               'c': ['お', 'か', 'ききき', 'くくくくくく'],
                               'ああああ': ['さ', 'し', 'す', 'せ']},
                              columns=['a', 'b', 'c', 'ああああ'])

            expected = ("        a  ... ああああ\n0   あああああ  ...    さ\n"
                        "..    ...  ...  ...\n3     えええ  ...    せ\n"
                        "\n[4 rows x 4 columns]")
            assert repr(df) == expected

            df.index = ['あああ', 'いいいい', 'う', 'aaa']
            expected = ("         a  ... ああああ\nあああ  あああああ  ...    さ\n"
                        "..     ...  ...  ...\naaa    えええ  ...    せ\n"
                        "\n[4 rows x 4 columns]")
            assert repr(df) == expected

    def test_east_asian_unicode_true(self):
        # Enable Unicode option -----------------------------------------
        with option_context('display.unicode.east_asian_width', True):

            # mid col
            df = DataFrame({'a': ['あ', 'いいい', 'う', 'ええええええ'],
                            'b': [1, 222, 33333, 4]},
                           index=['a', 'bb', 'c', 'ddd'])
            expected = ("                a      b\na              あ      1\n"
                        "bb         いいい    222\nc              う  33333\n"
                        "ddd  ええええええ      4")
            assert repr(df) == expected

            # last col
            df = DataFrame({'a': [1, 222, 33333, 4],
                            'b': ['あ', 'いいい', 'う', 'ええええええ']},
                           index=['a', 'bb', 'c', 'ddd'])
            expected = ("         a             b\na        1            あ\n"
                        "bb     222        いいい\nc    33333            う\n"
                        "ddd      4  ええええええ")
            assert repr(df) == expected

            # all col
            df = DataFrame({'a': ['あああああ', 'い', 'う', 'えええ'],
                            'b': ['あ', 'いいい', 'う', 'ええええええ']},
                           index=['a', 'bb', 'c', 'ddd'])
            expected = ("              a             b\n"
                        "a    あああああ            あ\n"
                        "bb           い        いいい\n"
                        "c            う            う\n"
                        "ddd      えええ  ええええええ")
            assert repr(df) == expected

            # column name
            df = DataFrame({'b': ['あ', 'いいい', 'う', 'ええええええ'],
                            'あああああ': [1, 222, 33333, 4]},
                           index=['a', 'bb', 'c', 'ddd'])
            expected = ("                b  あああああ\n"
                        "a              あ           1\n"
                        "bb         いいい         222\n"
                        "c              う       33333\n"
                        "ddd  ええええええ           4")
            assert repr(df) == expected

            # index
            df = DataFrame({'a': ['あああああ', 'い', 'う', 'えええ'],
                            'b': ['あ', 'いいい', 'う', 'ええええええ']},
                           index=['あああ', 'いいいいいい', 'うう', 'え'])
            expected = ("                       a             b\n"
                        "あああ        あああああ            あ\n"
                        "いいいいいい          い        いいい\n"
                        "うう                  う            う\n"
                        "え                えええ  ええええええ")
            assert repr(df) == expected

            # index name
            df = DataFrame({'a': ['あああああ', 'い', 'う', 'えええ'],
                            'b': ['あ', 'いいい', 'う', 'ええええええ']},
                           index=pd.Index(['あ', 'い', 'うう', 'え'],
                                          name='おおおお'))
            expected = ("                   a             b\n"
                        "おおおお                          \n"
                        "あ        あああああ            あ\n"
                        "い                い        いいい\n"
                        "うう              う            う\n"
                        "え            えええ  ええええええ")
            assert repr(df) == expected

            # all
            df = DataFrame({'あああ': ['あああ', 'い', 'う', 'えええええ'],
                            'いいいいい': ['あ', 'いいい', 'う', 'ええ']},
                           index=pd.Index(['あ', 'いいい', 'うう', 'え'],
                                          name='お'))
            expected = ("            あああ いいいいい\n"
                        "お                           \n"
                        "あ          あああ         あ\n"
                        "いいい          い     いいい\n"
                        "うう            う         う\n"
                        "え      えええええ       ええ")
            assert repr(df) == expected

            # MultiIndex
            idx = pd.MultiIndex.from_tuples([('あ', 'いい'), ('う', 'え'), (
                'おおお', 'かかかか'), ('き', 'くく')])
            df = DataFrame({'a': ['あああああ', 'い', 'う', 'えええ'],
                            'b': ['あ', 'いいい', 'う', 'ええええええ']},
                           index=idx)
            expected = ("                          a             b\n"
                        "あ     いい      あああああ            あ\n"
                        "う     え                い        いいい\n"
                        "おおお かかかか          う            う\n"
                        "き     くく          えええ  ええええええ")
            assert repr(df) == expected

            # truncate
            with option_context('display.max_rows', 3, 'display.max_columns',
                                3):

                df = pd.DataFrame({'a': ['あああああ', 'い', 'う', 'えええ'],
                                   'b': ['あ', 'いいい', 'う', 'ええええええ'],
                                   'c': ['お', 'か', 'ききき', 'くくくくくく'],
                                   'ああああ': ['さ', 'し', 'す', 'せ']},
                                  columns=['a', 'b', 'c', 'ああああ'])

                expected = ("             a  ... ああああ\n"
                            "0   あああああ  ...       さ\n"
                            "..         ...  ...      ...\n"
                            "3       えええ  ...       せ\n"
                            "\n[4 rows x 4 columns]")
                assert repr(df) == expected

                df.index = ['あああ', 'いいいい', 'う', 'aaa']
                expected = ("                 a  ... ああああ\n"
                            "あああ  あああああ  ...       さ\n"
                            "...            ...  ...      ...\n"
                            "aaa         えええ  ...       せ\n"
                            "\n[4 rows x 4 columns]")
                assert repr(df) == expected

            # ambiguous unicode
            df = DataFrame({'b': ['あ', 'いいい', '¡¡', 'ええええええ'],
                            'あああああ': [1, 222, 33333, 4]},
                           index=['a', 'bb', 'c', '¡¡¡'])
            expected = ("                b  あああああ\n"
                        "a              あ           1\n"
                        "bb         いいい         222\n"
                        "c              ¡¡       33333\n"
                        "¡¡¡  ええええええ           4")
            assert repr(df) == expected

    def test_to_string_buffer_all_unicode(self):
        buf = StringIO()

        empty = DataFrame({'c/\u03c3': Series()})
        nonempty = DataFrame({'c/\u03c3': Series([1, 2, 3])})

        print(empty, file=buf)
        print(nonempty, file=buf)

        # this should work
        buf.getvalue()

    def test_to_string_with_col_space(self):
        df = DataFrame(np.random.random(size=(1, 3)))
        c10 = len(df.to_string(col_space=10).split("\n")[1])
        c20 = len(df.to_string(col_space=20).split("\n")[1])
        c30 = len(df.to_string(col_space=30).split("\n")[1])
        assert c10 < c20 < c30

        # GH 8230
        # col_space wasn't being applied with header=False
        with_header = df.to_string(col_space=20)
        with_header_row1 = with_header.splitlines()[1]
        no_header = df.to_string(col_space=20, header=False)
        assert len(with_header_row1) == len(no_header)

    def test_to_string_truncate_indices(self):
        for index in [tm.makeStringIndex, tm.makeUnicodeIndex, tm.makeIntIndex,
                      tm.makeDateIndex, tm.makePeriodIndex]:
            for column in [tm.makeStringIndex]:
                for h in [10, 20]:
                    for w in [10, 20]:
                        with option_context("display.expand_frame_repr",
                                            False):
                            df = DataFrame(index=index(h), columns=column(w))
                            with option_context("display.max_rows", 15):
                                if h == 20:
                                    assert has_vertically_truncated_repr(df)
                                else:
                                    assert not has_vertically_truncated_repr(
                                        df)
                            with option_context("display.max_columns", 15):
                                if w == 20:
                                    assert has_horizontally_truncated_repr(df)
                                else:
                                    assert not (
                                        has_horizontally_truncated_repr(df))
                            with option_context("display.max_rows", 15,
                                                "display.max_columns", 15):
                                if h == 20 and w == 20:
                                    assert has_doubly_truncated_repr(df)
                                else:
                                    assert not has_doubly_truncated_repr(
                                        df)

    def test_to_string_truncate_multilevel(self):
        arrays = [['bar', 'bar', 'baz', 'baz', 'foo', 'foo', 'qux', 'qux'],
                  ['one', 'two', 'one', 'two', 'one', 'two', 'one', 'two']]
        df = DataFrame(index=arrays, columns=arrays)
        with option_context("display.max_rows", 7, "display.max_columns", 7):
            assert has_doubly_truncated_repr(df)

    def test_truncate_with_different_dtypes(self):

        # 11594, 12045
        # when truncated the dtypes of the splits can differ

        # 11594
        import datetime
        s = Series([datetime.datetime(2012, 1, 1)] * 10 +
                   [datetime.datetime(1012, 1, 2)] + [
            datetime.datetime(2012, 1, 3)] * 10)

        with pd.option_context('display.max_rows', 8):
            result = str(s)
            assert 'object' in result

        # 12045
        df = DataFrame({'text': ['some words'] + [None] * 9})

        with pd.option_context('display.max_rows', 8,
                               'display.max_columns', 3):
            result = str(df)
            assert 'None' in result
            assert 'NaN' not in result

    def test_datetimelike_frame(self):

        # GH 12211
        df = DataFrame(
            {'date': [pd.Timestamp('20130101').tz_localize('UTC')] +
                     [pd.NaT] * 5})

        with option_context("display.max_rows", 5):
            result = str(df)
            assert '2013-01-01 00:00:00+00:00' in result
            assert 'NaT' in result
            assert '...' in result
            assert '[6 rows x 1 columns]' in result

        dts = [pd.Timestamp('2011-01-01', tz='US/Eastern')] * 5 + [pd.NaT] * 5
        df = pd.DataFrame({"dt": dts,
                           "x": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]})
        with option_context('display.max_rows', 5):
            expected = ('                          dt   x\n'
                        '0  2011-01-01 00:00:00-05:00   1\n'
                        '1  2011-01-01 00:00:00-05:00   2\n'
                        '..                       ...  ..\n'
                        '8                        NaT   9\n'
                        '9                        NaT  10\n\n'
                        '[10 rows x 2 columns]')
            assert repr(df) == expected

        dts = [pd.NaT] * 5 + [pd.Timestamp('2011-01-01', tz='US/Eastern')] * 5
        df = pd.DataFrame({"dt": dts,
                           "x": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]})
        with option_context('display.max_rows', 5):
            expected = ('                          dt   x\n'
                        '0                        NaT   1\n'
                        '1                        NaT   2\n'
                        '..                       ...  ..\n'
                        '8  2011-01-01 00:00:00-05:00   9\n'
                        '9  2011-01-01 00:00:00-05:00  10\n\n'
                        '[10 rows x 2 columns]')
            assert repr(df) == expected

        dts = ([pd.Timestamp('2011-01-01', tz='Asia/Tokyo')] * 5 +
               [pd.Timestamp('2011-01-01', tz='US/Eastern')] * 5)
        df = pd.DataFrame({"dt": dts,
                           "x": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]})
        with option_context('display.max_rows', 5):
            expected = ('                           dt   x\n'
                        '0   2011-01-01 00:00:00+09:00   1\n'
                        '1   2011-01-01 00:00:00+09:00   2\n'
                        '..                        ...  ..\n'
                        '8   2011-01-01 00:00:00-05:00   9\n'
                        '9   2011-01-01 00:00:00-05:00  10\n\n'
                        '[10 rows x 2 columns]')
            assert repr(df) == expected

    @pytest.mark.parametrize('start_date', [
        '2017-01-01 23:59:59.999999999',
        '2017-01-01 23:59:59.99999999',
        '2017-01-01 23:59:59.9999999',
        '2017-01-01 23:59:59.999999',
        '2017-01-01 23:59:59.99999',
        '2017-01-01 23:59:59.9999',
    ])
    def test_datetimeindex_highprecision(self, start_date):
        # GH19030
        # Check that high-precision time values for the end of day are
        # included in repr for DatetimeIndex
        df = DataFrame({'A': date_range(start=start_date,
                                        freq='D', periods=5)})
        result = str(df)
        assert start_date in result

        dti = date_range(start=start_date,
                         freq='D', periods=5)
        df = DataFrame({'A': range(5)}, index=dti)
        result = str(df.index)
        assert start_date in result

    def test_nonunicode_nonascii_alignment(self):
        df = DataFrame([["aa\xc3\xa4\xc3\xa4", 1], ["bbbb", 2]])
        rep_str = df.to_string()
        lines = rep_str.split('\n')
        assert len(lines[1]) == len(lines[2])

    def test_unicode_problem_decoding_as_ascii(self):
        dm = DataFrame({'c/\u03c3': Series({'test': np.nan})})
        str(dm.to_string())

    def test_string_repr_encoding(self, datapath):
        filepath = datapath('io', 'parser', 'data', 'unicode_series.csv')
        df = pd.read_csv(filepath, header=None, encoding='latin1')
        repr(df)
        repr(df[1])

    def test_repr_corner(self):
        # representing infs poses no problems
        df = DataFrame({'foo': [-np.inf, np.inf]})
        repr(df)

    def test_frame_info_encoding(self):
        index = ['\'Til There Was You (1997)',
                 'ldum klaka (Cold Fever) (1994)']
        fmt.set_option('display.max_rows', 1)
        df = DataFrame(columns=['a', 'b', 'c'], index=index)
        repr(df)
        repr(df.T)
        fmt.set_option('display.max_rows', 200)

    def test_wide_repr(self):
        with option_context('mode.sim_interactive', True,
                            'display.show_dimensions', True,
                            'display.max_columns', 20):
            max_cols = get_option('display.max_columns')
            df = DataFrame(tm.rands_array(25, size=(10, max_cols - 1)))
            set_option('display.expand_frame_repr', False)
            rep_str = repr(df)

            assert "10 rows x {c} columns".format(c=max_cols - 1) in rep_str
            set_option('display.expand_frame_repr', True)
            wide_repr = repr(df)
            assert rep_str != wide_repr

            with option_context('display.width', 120):
                wider_repr = repr(df)
                assert len(wider_repr) < len(wide_repr)

        reset_option('display.expand_frame_repr')

    def test_wide_repr_wide_columns(self):
        with option_context('mode.sim_interactive', True,
                            'display.max_columns', 20):
            df = DataFrame(np.random.randn(5, 3),
                           columns=['a' * 90, 'b' * 90, 'c' * 90])
            rep_str = repr(df)

            assert len(rep_str.splitlines()) == 20

    def test_wide_repr_named(self):
        with option_context('mode.sim_interactive', True,
                            'display.max_columns', 20):
            max_cols = get_option('display.max_columns')
            df = DataFrame(tm.rands_array(25, size=(10, max_cols - 1)))
            df.index.name = 'DataFrame Index'
            set_option('display.expand_frame_repr', False)

            rep_str = repr(df)
            set_option('display.expand_frame_repr', True)
            wide_repr = repr(df)
            assert rep_str != wide_repr

            with option_context('display.width', 150):
                wider_repr = repr(df)
                assert len(wider_repr) < len(wide_repr)

            for line in wide_repr.splitlines()[1::13]:
                assert 'DataFrame Index' in line

        reset_option('display.expand_frame_repr')

    def test_wide_repr_multiindex(self):
        with option_context('mode.sim_interactive', True,
                            'display.max_columns', 20):
            midx = MultiIndex.from_arrays(tm.rands_array(5, size=(2, 10)))
            max_cols = get_option('display.max_columns')
            df = DataFrame(tm.rands_array(25, size=(10, max_cols - 1)),
                           index=midx)
            df.index.names = ['Level 0', 'Level 1']
            set_option('display.expand_frame_repr', False)
            rep_str = repr(df)
            set_option('display.expand_frame_repr', True)
            wide_repr = repr(df)
            assert rep_str != wide_repr

            with option_context('display.width', 150):
                wider_repr = repr(df)
                assert len(wider_repr) < len(wide_repr)

            for line in wide_repr.splitlines()[1::13]:
                assert 'Level 0 Level 1' in line

        reset_option('display.expand_frame_repr')

    def test_wide_repr_multiindex_cols(self):
        with option_context('mode.sim_interactive', True,
                            'display.max_columns', 20):
            max_cols = get_option('display.max_columns')
            midx = MultiIndex.from_arrays(tm.rands_array(5, size=(2, 10)))
            mcols = MultiIndex.from_arrays(
                tm.rands_array(3, size=(2, max_cols - 1)))
            df = DataFrame(tm.rands_array(25, (10, max_cols - 1)),
                           index=midx, columns=mcols)
            df.index.names = ['Level 0', 'Level 1']
            set_option('display.expand_frame_repr', False)
            rep_str = repr(df)
            set_option('display.expand_frame_repr', True)
            wide_repr = repr(df)
            assert rep_str != wide_repr

        with option_context('display.width', 150, 'display.max_columns', 20):
            wider_repr = repr(df)
            assert len(wider_repr) < len(wide_repr)

        reset_option('display.expand_frame_repr')

    def test_wide_repr_unicode(self):
        with option_context('mode.sim_interactive', True,
                            'display.max_columns', 20):
            max_cols = 20
            df = DataFrame(tm.rands_array(25, size=(10, max_cols - 1)))
            set_option('display.expand_frame_repr', False)
            rep_str = repr(df)
            set_option('display.expand_frame_repr', True)
            wide_repr = repr(df)
            assert rep_str != wide_repr

            with option_context('display.width', 150):
                wider_repr = repr(df)
                assert len(wider_repr) < len(wide_repr)

        reset_option('display.expand_frame_repr')

    def test_wide_repr_wide_long_columns(self):
        with option_context('mode.sim_interactive', True):
            df = DataFrame({'a': ['a' * 30, 'b' * 30],
                            'b': ['c' * 70, 'd' * 80]})

            result = repr(df)
            assert 'ccccc' in result
            assert 'ddddd' in result

    def test_long_series(self):
        n = 1000
        s = Series(
            np.random.randint(-50, 50, n),
            index=['s{x:04d}'.format(x=x) for x in range(n)], dtype='int64')

        import re
        str_rep = str(s)
        nmatches = len(re.findall('dtype', str_rep))
        assert nmatches == 1

    def test_index_with_nan(self):
        #  GH 2850
        df = DataFrame({'id1': {0: '1a3',
                                1: '9h4'},
                        'id2': {0: np.nan,
                                1: 'd67'},
                        'id3': {0: '78d',
                                1: '79d'},
                        'value': {0: 123,
                                  1: 64}})

        # multi-index
        y = df.set_index(['id1', 'id2', 'id3'])
        result = y.to_string()
        expected = ('             value\nid1 id2 id3       \n'
                    '1a3 NaN 78d    123\n9h4 d67 79d     64')
        assert result == expected

        # index
        y = df.set_index('id2')
        result = y.to_string()
        expected = ('     id1  id3  value\nid2                 \n'
                    'NaN  1a3  78d    123\nd67  9h4  79d     64')
        assert result == expected

        # with append (this failed in 0.12)
        y = df.set_index(['id1', 'id2']).set_index('id3', append=True)
        result = y.to_string()
        expected = ('             value\nid1 id2 id3       \n'
                    '1a3 NaN 78d    123\n9h4 d67 79d     64')
        assert result == expected

        # all-nan in mi
        df2 = df.copy()
        df2.loc[:, 'id2'] = np.nan
        y = df2.set_index('id2')
        result = y.to_string()
        expected = ('     id1  id3  value\nid2                 \n'
                    'NaN  1a3  78d    123\nNaN  9h4  79d     64')
        assert result == expected

        # partial nan in mi
        df2 = df.copy()
        df2.loc[:, 'id2'] = np.nan
        y = df2.set_index(['id2', 'id3'])
        result = y.to_string()
        expected = ('         id1  value\nid2 id3            \n'
                    'NaN 78d  1a3    123\n    79d  9h4     64')
        assert result == expected

        df = DataFrame({'id1': {0: np.nan,
                                1: '9h4'},
                        'id2': {0: np.nan,
                                1: 'd67'},
                        'id3': {0: np.nan,
                                1: '79d'},
                        'value': {0: 123,
                                  1: 64}})

        y = df.set_index(['id1', 'id2', 'id3'])
        result = y.to_string()
        expected = ('             value\nid1 id2 id3       \n'
                    'NaN NaN NaN    123\n9h4 d67 79d     64')
        assert result == expected

    def test_to_string(self):

        # big mixed
        biggie = DataFrame({'A': np.random.randn(200),
                            'B': tm.makeStringIndex(200)},
                           index=np.arange(200))

        biggie.loc[:20, 'A'] = np.nan
        biggie.loc[:20, 'B'] = np.nan
        s = biggie.to_string()

        buf = StringIO()
        retval = biggie.to_string(buf=buf)
        assert retval is None
        assert buf.getvalue() == s

        assert isinstance(s, str)

        # print in right order
        result = biggie.to_string(columns=['B', 'A'], col_space=17,
                                  float_format='%.5f'.__mod__)
        lines = result.split('\n')
        header = lines[0].strip().split()
        joined = '\n'.join(re.sub(r'\s+', ' ', x).strip() for x in lines[1:])
        recons = read_csv(StringIO(joined), names=header,
                          header=None, sep=' ')
        tm.assert_series_equal(recons['B'], biggie['B'])
        assert recons['A'].count() == biggie['A'].count()
        assert (np.abs(recons['A'].dropna() -
                       biggie['A'].dropna()) < 0.1).all()

        # expected = ['B', 'A']
        # assert header == expected

        result = biggie.to_string(columns=['A'], col_space=17)
        header = result.split('\n')[0].strip().split()
        expected = ['A']
        assert header == expected

        biggie.to_string(columns=['B', 'A'],
                         formatters={'A': lambda x: '{x:.1f}'.format(x=x)})

        biggie.to_string(columns=['B', 'A'], float_format=str)
        biggie.to_string(columns=['B', 'A'], col_space=12, float_format=str)

        frame = DataFrame(index=np.arange(200))
        frame.to_string()

    def test_to_string_no_header(self):
        df = DataFrame({'x': [1, 2, 3], 'y': [4, 5, 6]})

        df_s = df.to_string(header=False)
        expected = "0  1  4\n1  2  5\n2  3  6"

        assert df_s == expected

    def test_to_string_specified_header(self):
        df = DataFrame({'x': [1, 2, 3], 'y': [4, 5, 6]})

        df_s = df.to_string(header=['X', 'Y'])
        expected = '   X  Y\n0  1  4\n1  2  5\n2  3  6'

        assert df_s == expected

        with pytest.raises(ValueError):
            df.to_string(header=['X'])

    def test_to_string_no_index(self):
        # GH 16839, GH 13032
        df = DataFrame({'x': [11, 22], 'y': [33, -44], 'z': ['AAA', '   ']})

        df_s = df.to_string(index=False)
        # Leading space is expected for positive numbers.
        expected = (" x   y   z\n"
                    "11  33 AAA\n"
                    "22 -44    ")
        assert df_s == expected

        df_s = df[['y', 'x', 'z']].to_string(index=False)
        expected = ("  y  x   z\n"
                    " 33 11 AAA\n"
                    "-44 22    ")
        assert df_s == expected

    def test_to_string_line_width_no_index(self):
        # GH 13998, GH 22505
        df = DataFrame({'x': [1, 2, 3], 'y': [4, 5, 6]})

        df_s = df.to_string(line_width=1, index=False)
        expected = " x  \\\n 1   \n 2   \n 3   \n\n y  \n 4  \n 5  \n 6  "

        assert df_s == expected

        df = DataFrame({'x': [11, 22, 33], 'y': [4, 5, 6]})

        df_s = df.to_string(line_width=1, index=False)
        expected = " x  \\\n11   \n22   \n33   \n\n y  \n 4  \n 5  \n 6  "

        assert df_s == expected

        df = DataFrame({'x': [11, 22, -33], 'y': [4, 5, -6]})

        df_s = df.to_string(line_width=1, index=False)
        expected = "  x  \\\n 11   \n 22   \n-33   \n\n y  \n 4  \n 5  \n-6  "

        assert df_s == expected

    def test_to_string_float_formatting(self):
        tm.reset_display_options()
        fmt.set_option('display.precision', 5, 'display.column_space', 12,
                       'display.notebook_repr_html', False)

        df = DataFrame({'x': [0, 0.25, 3456.000, 12e+45, 1.64e+6, 1.7e+8,
                              1.253456, np.pi, -1e6]})

        df_s = df.to_string()

        if _three_digit_exp():
            expected = ('              x\n0  0.00000e+000\n1  2.50000e-001\n'
                        '2  3.45600e+003\n3  1.20000e+046\n4  1.64000e+006\n'
                        '5  1.70000e+008\n6  1.25346e+000\n7  3.14159e+000\n'
                        '8 -1.00000e+006')
        else:
            expected = ('             x\n0  0.00000e+00\n1  2.50000e-01\n'
                        '2  3.45600e+03\n3  1.20000e+46\n4  1.64000e+06\n'
                        '5  1.70000e+08\n6  1.25346e+00\n7  3.14159e+00\n'
                        '8 -1.00000e+06')
        assert df_s == expected

        df = DataFrame({'x': [3234, 0.253]})
        df_s = df.to_string()

        expected = ('          x\n' '0  3234.000\n' '1     0.253')
        assert df_s == expected

        tm.reset_display_options()
        assert get_option("display.precision") == 6

        df = DataFrame({'x': [1e9, 0.2512]})
        df_s = df.to_string()

        if _three_digit_exp():
            expected = ('               x\n'
                        '0  1.000000e+009\n'
                        '1  2.512000e-001')
        else:
            expected = ('              x\n'
                        '0  1.000000e+09\n'
                        '1  2.512000e-01')
        assert df_s == expected

    def test_to_string_float_format_no_fixed_width(self):

        # GH 21625
        df = DataFrame({'x': [0.19999]})
        expected = '      x\n0 0.200'
        assert df.to_string(float_format='%.3f') == expected

        # GH 22270
        df = DataFrame({'x': [100.0]})
        expected = '    x\n0 100'
        assert df.to_string(float_format='%.0f') == expected

    def test_to_string_small_float_values(self):
        df = DataFrame({'a': [1.5, 1e-17, -5.5e-7]})

        result = df.to_string()
        # sadness per above
        if '{x:.4g}'.format(x=1.7e8) == '1.7e+008':
            expected = ('               a\n'
                        '0  1.500000e+000\n'
                        '1  1.000000e-017\n'
                        '2 -5.500000e-007')
        else:
            expected = ('              a\n'
                        '0  1.500000e+00\n'
                        '1  1.000000e-17\n'
                        '2 -5.500000e-07')
        assert result == expected

        # but not all exactly zero
        df = df * 0
        result = df.to_string()
        expected = ('   0\n' '0  0\n' '1  0\n' '2 -0')

    def test_to_string_float_index(self):
        index = Index([1.5, 2, 3, 4, 5])
        df = DataFrame(np.arange(5), index=index)

        result = df.to_string()
        expected = ('     0\n'
                    '1.5  0\n'
                    '2.0  1\n'
                    '3.0  2\n'
                    '4.0  3\n'
                    '5.0  4')
        assert result == expected

    def test_to_string_complex_float_formatting(self):
        # GH #25514
        with pd.option_context('display.precision', 5):
            df = DataFrame({'x': [
                (0.4467846931321966 + 0.0715185102060818j),
                (0.2739442392974528 + 0.23515228785438969j),
                (0.26974928742135185 + 0.3250604054898979j)]})
            result = df.to_string()
            expected = ('                  x\n0  0.44678+0.07152j\n'
                        '1  0.27394+0.23515j\n'
                        '2  0.26975+0.32506j')
            assert result == expected

    def test_to_string_ascii_error(self):
        data = [('0  ', '                        .gitignore ', '     5 ',
                 ' \xe2\x80\xa2\xe2\x80\xa2\xe2\x80'
                 '\xa2\xe2\x80\xa2\xe2\x80\xa2')]
        df = DataFrame(data)

        # it works!
        repr(df)

    def test_to_string_int_formatting(self):
        df = DataFrame({'x': [-15, 20, 25, -35]})
        assert issubclass(df['x'].dtype.type, np.integer)

        output = df.to_string()
        expected = ('    x\n' '0 -15\n' '1  20\n' '2  25\n' '3 -35')
        assert output == expected

    def test_to_string_index_formatter(self):
        df = DataFrame([range(5), range(5, 10), range(10, 15)])

        rs = df.to_string(formatters={'__index__': lambda x: 'abc' [x]})

        xp = """\
    0   1   2   3   4
a   0   1   2   3   4
b   5   6   7   8   9
c  10  11  12  13  14\
"""

        assert rs == xp

    def test_to_string_left_justify_cols(self):
        tm.reset_display_options()
        df = DataFrame({'x': [3234, 0.253]})
        df_s = df.to_string(justify='left')
        expected = ('   x       \n' '0  3234.000\n' '1     0.253')
        assert df_s == expected

    def test_to_string_format_na(self):
        tm.reset_display_options()
        df = DataFrame({'A': [np.nan, -1, -2.1234, 3, 4],
                        'B': [np.nan, 'foo', 'foooo', 'fooooo', 'bar']})
        result = df.to_string()

        expected = ('        A       B\n'
                    '0     NaN     NaN\n'
                    '1 -1.0000     foo\n'
                    '2 -2.1234   foooo\n'
                    '3  3.0000  fooooo\n'
                    '4  4.0000     bar')
        assert result == expected

        df = DataFrame({'A': [np.nan, -1., -2., 3., 4.],
                        'B': [np.nan, 'foo', 'foooo', 'fooooo', 'bar']})
        result = df.to_string()

        expected = ('     A       B\n'
                    '0  NaN     NaN\n'
                    '1 -1.0     foo\n'
                    '2 -2.0   foooo\n'
                    '3  3.0  fooooo\n'
                    '4  4.0     bar')
        assert result == expected

    def test_to_string_format_inf(self):
        # Issue #24861
        tm.reset_display_options()
        df = DataFrame({
            'A': [-np.inf, np.inf, -1, -2.1234, 3, 4],
            'B': [-np.inf, np.inf, 'foo', 'foooo', 'fooooo', 'bar']
        })
        result = df.to_string()

        expected = ('        A       B\n'
                    '0    -inf    -inf\n'
                    '1     inf     inf\n'
                    '2 -1.0000     foo\n'
                    '3 -2.1234   foooo\n'
                    '4  3.0000  fooooo\n'
                    '5  4.0000     bar')
        assert result == expected

        df = DataFrame({
            'A': [-np.inf, np.inf, -1., -2., 3., 4.],
            'B': [-np.inf, np.inf, 'foo', 'foooo', 'fooooo', 'bar']
        })
        result = df.to_string()

        expected = ('     A       B\n'
                    '0 -inf    -inf\n'
                    '1  inf     inf\n'
                    '2 -1.0     foo\n'
                    '3 -2.0   foooo\n'
                    '4  3.0  fooooo\n'
                    '5  4.0     bar')
        assert result == expected

    def test_to_string_decimal(self):
        # Issue #23614
        df = DataFrame({'A': [6.0, 3.1, 2.2]})
        expected = '     A\n0  6,0\n1  3,1\n2  2,2'
        assert df.to_string(decimal=',') == expected

    def test_to_string_line_width(self):
        df = DataFrame(123, index=range(10, 15), columns=range(30))
        s = df.to_string(line_width=80)
        assert max(len(l) for l in s.split('\n')) == 80

    def test_show_dimensions(self):
        df = DataFrame(123, index=range(10, 15), columns=range(30))

        with option_context('display.max_rows', 10, 'display.max_columns', 40,
                            'display.width', 500, 'display.expand_frame_repr',
                            'info', 'display.show_dimensions', True):
            assert '5 rows' in str(df)
            assert '5 rows' in df._repr_html_()
        with option_context('display.max_rows', 10, 'display.max_columns', 40,
                            'display.width', 500, 'display.expand_frame_repr',
                            'info', 'display.show_dimensions', False):
            assert '5 rows' not in str(df)
            assert '5 rows' not in df._repr_html_()
        with option_context('display.max_rows', 2, 'display.max_columns', 2,
                            'display.width', 500, 'display.expand_frame_repr',
                            'info', 'display.show_dimensions', 'truncate'):
            assert '5 rows' in str(df)
            assert '5 rows' in df._repr_html_()
        with option_context('display.max_rows', 10, 'display.max_columns', 40,
                            'display.width', 500, 'display.expand_frame_repr',
                            'info', 'display.show_dimensions', 'truncate'):
            assert '5 rows' not in str(df)
            assert '5 rows' not in df._repr_html_()

    def test_repr_html(self, float_frame):
        df = float_frame
        df._repr_html_()

        fmt.set_option('display.max_rows', 1, 'display.max_columns', 1)
        df._repr_html_()

        fmt.set_option('display.notebook_repr_html', False)
        df._repr_html_()

        tm.reset_display_options()

        df = DataFrame([[1, 2], [3, 4]])
        fmt.set_option('display.show_dimensions', True)
        assert '2 rows' in df._repr_html_()
        fmt.set_option('display.show_dimensions', False)
        assert '2 rows' not in df._repr_html_()

        tm.reset_display_options()

    def test_repr_html_mathjax(self):
        df = DataFrame([[1, 2], [3, 4]])
        assert 'tex2jax_ignore' not in df._repr_html_()

        with pd.option_context('display.html.use_mathjax', False):
            assert 'tex2jax_ignore' in df._repr_html_()

    def test_repr_html_wide(self):
        max_cols = 20
        df = DataFrame(tm.rands_array(25, size=(10, max_cols - 1)))
        with option_context('display.max_rows', 60, 'display.max_columns', 20):
            assert "..." not in df._repr_html_()

        wide_df = DataFrame(tm.rands_array(25, size=(10, max_cols + 1)))
        with option_context('display.max_rows', 60, 'display.max_columns', 20):
            assert "..." in wide_df._repr_html_()

    def test_repr_html_wide_multiindex_cols(self):
        max_cols = 20

        mcols = MultiIndex.from_product([np.arange(max_cols // 2),
                                         ['foo', 'bar']],
                                        names=['first', 'second'])
        df = DataFrame(tm.rands_array(25, size=(10, len(mcols))),
                       columns=mcols)
        reg_repr = df._repr_html_()
        assert '...' not in reg_repr

        mcols = MultiIndex.from_product((np.arange(1 + (max_cols // 2)),
                                         ['foo', 'bar']),
                                        names=['first', 'second'])
        df = DataFrame(tm.rands_array(25, size=(10, len(mcols))),
                       columns=mcols)
        with option_context('display.max_rows', 60, 'display.max_columns', 20):
            assert '...' in df._repr_html_()

    def test_repr_html_long(self):
        with option_context('display.max_rows', 60):
            max_rows = get_option('display.max_rows')
            h = max_rows - 1
            df = DataFrame({'A': np.arange(1, 1 + h),
                            'B': np.arange(41, 41 + h)})
            reg_repr = df._repr_html_()
            assert '..' not in reg_repr
            assert str(41 + max_rows // 2) in reg_repr

            h = max_rows + 1
            df = DataFrame({'A': np.arange(1, 1 + h),
                            'B': np.arange(41, 41 + h)})
            long_repr = df._repr_html_()
            assert '..' in long_repr
            assert str(41 + max_rows // 2) not in long_repr
            assert '{h} rows '.format(h=h) in long_repr
            assert '2 columns' in long_repr

    def test_repr_html_float(self):
        with option_context('display.max_rows', 60):

            max_rows = get_option('display.max_rows')
            h = max_rows - 1
            df = DataFrame({'idx': np.linspace(-10, 10, h),
                            'A': np.arange(1, 1 + h),
                            'B': np.arange(41, 41 + h)}).set_index('idx')
            reg_repr = df._repr_html_()
            assert '..' not in reg_repr
            assert '<td>{val}</td>'.format(val=str(40 + h)) in reg_repr

            h = max_rows + 1
            df = DataFrame({'idx': np.linspace(-10, 10, h),
                            'A': np.arange(1, 1 + h),
                            'B': np.arange(41, 41 + h)}).set_index('idx')
            long_repr = df._repr_html_()
            assert '..' in long_repr
            assert '<td>{val}</td>'.format(val='31') not in long_repr
            assert '{h} rows '.format(h=h) in long_repr
            assert '2 columns' in long_repr

    def test_repr_html_long_multiindex(self):
        max_rows = 60
        max_L1 = max_rows // 2

        tuples = list(itertools.product(np.arange(max_L1), ['foo', 'bar']))
        idx = MultiIndex.from_tuples(tuples, names=['first', 'second'])
        df = DataFrame(np.random.randn(max_L1 * 2, 2), index=idx,
                       columns=['A', 'B'])
        with option_context('display.max_rows', 60, 'display.max_columns', 20):
            reg_repr = df._repr_html_()
        assert '...' not in reg_repr

        tuples = list(itertools.product(np.arange(max_L1 + 1), ['foo', 'bar']))
        idx = MultiIndex.from_tuples(tuples, names=['first', 'second'])
        df = DataFrame(np.random.randn((max_L1 + 1) * 2, 2), index=idx,
                       columns=['A', 'B'])
        long_repr = df._repr_html_()
        assert '...' in long_repr

    def test_repr_html_long_and_wide(self):
        max_cols = 20
        max_rows = 60

        h, w = max_rows - 1, max_cols - 1
        df = DataFrame({k: np.arange(1, 1 + h) for k in np.arange(w)})
        with option_context('display.max_rows', 60, 'display.max_columns', 20):
            assert '...' not in df._repr_html_()

        h, w = max_rows + 1, max_cols + 1
        df = DataFrame({k: np.arange(1, 1 + h) for k in np.arange(w)})
        with option_context('display.max_rows', 60, 'display.max_columns', 20):
            assert '...' in df._repr_html_()

    def test_info_repr(self):
        # GH#21746 For tests inside a terminal (i.e. not CI) we need to detect
        # the terminal size to ensure that we try to print something "too big"
        term_width, term_height = get_terminal_size()

        max_rows = 60
        max_cols = 20 + (max(term_width, 80) - 80) // 4
        # Long
        h, w = max_rows + 1, max_cols - 1
        df = DataFrame({k: np.arange(1, 1 + h) for k in np.arange(w)})
        assert has_vertically_truncated_repr(df)
        with option_context('display.large_repr', 'info'):
            assert has_info_repr(df)

        # Wide
        h, w = max_rows - 1, max_cols + 1
        df = DataFrame({k: np.arange(1, 1 + h) for k in np.arange(w)})
        assert has_horizontally_truncated_repr(df)
        with option_context('display.large_repr', 'info',
                            'display.max_columns', max_cols):
            assert has_info_repr(df)

    def test_info_repr_max_cols(self):
        # GH #6939
        df = DataFrame(np.random.randn(10, 5))
        with option_context('display.large_repr', 'info',
                            'display.max_columns', 1,
                            'display.max_info_columns', 4):
            assert has_non_verbose_info_repr(df)

        with option_context('display.large_repr', 'info',
                            'display.max_columns', 1,
                            'display.max_info_columns', 5):
            assert not has_non_verbose_info_repr(df)

        # test verbose overrides
        # fmt.set_option('display.max_info_columns', 4)  # exceeded

    def test_info_repr_html(self):
        max_rows = 60
        max_cols = 20
        # Long
        h, w = max_rows + 1, max_cols - 1
        df = DataFrame({k: np.arange(1, 1 + h) for k in np.arange(w)})
        assert r'&lt;class' not in df._repr_html_()
        with option_context('display.large_repr', 'info'):
            assert r'&lt;class' in df._repr_html_()

        # Wide
        h, w = max_rows - 1, max_cols + 1
        df = DataFrame({k: np.arange(1, 1 + h) for k in np.arange(w)})
        assert '<class' not in df._repr_html_()
        with option_context('display.large_repr', 'info',
                            'display.max_columns', max_cols):
            assert '&lt;class' in df._repr_html_()

    def test_fake_qtconsole_repr_html(self, float_frame):
        df = float_frame

        def get_ipython():
            return {'config': {'KernelApp':
                               {'parent_appname': 'ipython-qtconsole'}}}

        repstr = df._repr_html_()
        assert repstr is not None

        fmt.set_option('display.max_rows', 5, 'display.max_columns', 2)
        repstr = df._repr_html_()

        assert 'class' in repstr  # info fallback
        tm.reset_display_options()

    def test_pprint_pathological_object(self):
        """
        If the test fails, it at least won't hang.
        """

        class A:
            def __getitem__(self, key):
                return 3  # obviously simplified

        df = DataFrame([A()])
        repr(df)  # just don't die

    def test_float_trim_zeros(self):
        vals = [2.08430917305e+10, 3.52205017305e+10, 2.30674817305e+10,
                2.03954217305e+10, 5.59897817305e+10]
        skip = True
        for line in repr(DataFrame({'A': vals})).split('\n')[:-2]:
            if line.startswith('dtype:'):
                continue
            if _three_digit_exp():
                assert ('+010' in line) or skip
            else:
                assert ('+10' in line) or skip
            skip = False

    def test_dict_entries(self):
        df = DataFrame({'A': [{'a': 1, 'b': 2}]})

        val = df.to_string()
        assert "'a': 1" in val
        assert "'b': 2" in val

    def test_period(self):
        # GH 12615
        df = pd.DataFrame({'A': pd.period_range('2013-01',
                                                periods=4, freq='M'),
                           'B': [pd.Period('2011-01', freq='M'),
                                 pd.Period('2011-02-01', freq='D'),
                                 pd.Period('2011-03-01 09:00', freq='H'),
                                 pd.Period('2011-04', freq='M')],
                           'C': list('abcd')})
        exp = ("         A                 B  C\n"
               "0  2013-01           2011-01  a\n"
               "1  2013-02        2011-02-01  b\n"
               "2  2013-03  2011-03-01 09:00  c\n"
               "3  2013-04           2011-04  d")
        assert str(df) == exp


def gen_series_formatting():
    s1 = pd.Series(['a'] * 100)
    s2 = pd.Series(['ab'] * 100)
    s3 = pd.Series(['a', 'ab', 'abc', 'abcd', 'abcde', 'abcdef'])
    s4 = s3[::-1]
    test_sers = {'onel': s1, 'twol': s2, 'asc': s3, 'desc': s4}
    return test_sers


class TestSeriesFormatting:

    def setup_method(self, method):
        self.ts = tm.makeTimeSeries()

    def test_repr_unicode(self):
        s = Series(['\u03c3'] * 10)
        repr(s)

        a = Series(["\u05d0"] * 1000)
        a.name = 'title1'
        repr(a)

    def test_to_string(self):
        buf = StringIO()

        s = self.ts.to_string()

        retval = self.ts.to_string(buf=buf)
        assert retval is None
        assert buf.getvalue().strip() == s

        # pass float_format
        format = '%.4f'.__mod__
        result = self.ts.to_string(float_format=format)
        result = [x.split()[1] for x in result.split('\n')[:-1]]
        expected = [format(x) for x in self.ts]
        assert result == expected

        # empty string
        result = self.ts[:0].to_string()
        assert result == 'Series([], Freq: B)'

        result = self.ts[:0].to_string(length=0)
        assert result == 'Series([], Freq: B)'

        # name and length
        cp = self.ts.copy()
        cp.name = 'foo'
        result = cp.to_string(length=True, name=True, dtype=True)
        last_line = result.split('\n')[-1].strip()
        assert last_line == ("Freq: B, Name: foo, "
                             "Length: {cp}, dtype: float64".format(cp=len(cp)))

    def test_freq_name_separation(self):
        s = Series(np.random.randn(10),
                   index=date_range('1/1/2000', periods=10), name=0)

        result = repr(s)
        assert 'Freq: D, Name: 0' in result

    def test_to_string_mixed(self):
        s = Series(['foo', np.nan, -1.23, 4.56])
        result = s.to_string()
        expected = ('0     foo\n' + '1     NaN\n' + '2   -1.23\n' +
                    '3    4.56')
        assert result == expected

        # but don't count NAs as floats
        s = Series(['foo', np.nan, 'bar', 'baz'])
        result = s.to_string()
        expected = ('0    foo\n' + '1    NaN\n' + '2    bar\n' + '3    baz')
        assert result == expected

        s = Series(['foo', 5, 'bar', 'baz'])
        result = s.to_string()
        expected = ('0    foo\n' + '1      5\n' + '2    bar\n' + '3    baz')
        assert result == expected

    def test_to_string_float_na_spacing(self):
        s = Series([0., 1.5678, 2., -3., 4.])
        s[::2] = np.nan

        result = s.to_string()
        expected = ('0       NaN\n' + '1    1.5678\n' + '2       NaN\n' +
                    '3   -3.0000\n' + '4       NaN')
        assert result == expected

    def test_to_string_without_index(self):
        # GH 11729 Test index=False option
        s = Series([1, 2, 3, 4])
        result = s.to_string(index=False)
        expected = ('1\n' + '2\n' + '3\n' + '4')
        assert result == expected

    def test_unicode_name_in_footer(self):
        s = Series([1, 2], name='\u05e2\u05d1\u05e8\u05d9\u05ea')
        sf = fmt.SeriesFormatter(s, name='\u05e2\u05d1\u05e8\u05d9\u05ea')
        sf._get_footer()  # should not raise exception

    def test_east_asian_unicode_series(self):
        # not aligned properly because of east asian width

        # unicode index
        s = Series(['a', 'bb', 'CCC', 'D'],
                   index=['あ', 'いい', 'ううう', 'ええええ'])
        expected = ("あ         a\nいい       bb\nううう     CCC\n"
                    "ええええ      D\ndtype: object")
        assert repr(s) == expected

        # unicode values
        s = Series(['あ', 'いい', 'ううう', 'ええええ'],
                   index=['a', 'bb', 'c', 'ddd'])
        expected = ("a         あ\nbb       いい\nc       ううう\n"
                    "ddd    ええええ\ndtype: object")
        assert repr(s) == expected

        # both
        s = Series(['あ', 'いい', 'ううう', 'ええええ'],
                   index=['ああ', 'いいいい', 'う', 'えええ'])
        expected = ("ああ         あ\nいいいい      いい\nう        ううう\n"
                    "えええ     ええええ\ndtype: object")
        assert repr(s) == expected

        # unicode footer
        s = Series(['あ', 'いい', 'ううう', 'ええええ'],
                   index=['ああ', 'いいいい', 'う', 'えええ'],
                   name='おおおおおおお')
        expected = ("ああ         あ\nいいいい      いい\nう        ううう\n"
                    "えええ     ええええ\nName: おおおおおおお, dtype: object")
        assert repr(s) == expected

        # MultiIndex
        idx = pd.MultiIndex.from_tuples([('あ', 'いい'), ('う', 'え'), (
            'おおお', 'かかかか'), ('き', 'くく')])
        s = Series([1, 22, 3333, 44444], index=idx)
        expected = ("あ    いい          1\n"
                    "う    え          22\n"
                    "おおお  かかかか     3333\n"
                    "き    くく      44444\ndtype: int64")
        assert repr(s) == expected

        # object dtype, shorter than unicode repr
        s = Series([1, 22, 3333, 44444], index=[1, 'AB', np.nan, 'あああ'])
        expected = ("1          1\nAB        22\nNaN     3333\n"
                    "あああ    44444\ndtype: int64")
        assert repr(s) == expected

        # object dtype, longer than unicode repr
        s = Series([1, 22, 3333, 44444],
                   index=[1, 'AB', pd.Timestamp('2011-01-01'), 'あああ'])
        expected = ("1                          1\n"
                    "AB                        22\n"
                    "2011-01-01 00:00:00     3333\n"
                    "あああ                    44444\ndtype: int64")
        assert repr(s) == expected

        # truncate
        with option_context('display.max_rows', 3):
            s = Series(['あ', 'いい', 'ううう', 'ええええ'],
                       name='おおおおおおお')

            expected = ("0       あ\n     ... \n"
                        "3    ええええ\n"
                        "Name: おおおおおおお, Length: 4, dtype: object")
            assert repr(s) == expected

            s.index = ['ああ', 'いいいい', 'う', 'えええ']
            expected = ("ああ        あ\n       ... \n"
                        "えええ    ええええ\n"
                        "Name: おおおおおおお, Length: 4, dtype: object")
            assert repr(s) == expected

        # Emable Unicode option -----------------------------------------
        with option_context('display.unicode.east_asian_width', True):

            # unicode index
            s = Series(['a', 'bb', 'CCC', 'D'],
                       index=['あ', 'いい', 'ううう', 'ええええ'])
            expected = ("あ            a\nいい         bb\nううう      CCC\n"
                        "ええええ      D\ndtype: object")
            assert repr(s) == expected

            # unicode values
            s = Series(['あ', 'いい', 'ううう', 'ええええ'],
                       index=['a', 'bb', 'c', 'ddd'])
            expected = ("a            あ\nbb         いい\nc        ううう\n"
                        "ddd    ええええ\ndtype: object")
            assert repr(s) == expected

            # both
            s = Series(['あ', 'いい', 'ううう', 'ええええ'],
                       index=['ああ', 'いいいい', 'う', 'えええ'])
            expected = ("ああ              あ\n"
                        "いいいい        いい\n"
                        "う            ううう\n"
                        "えええ      ええええ\ndtype: object")
            assert repr(s) == expected

            # unicode footer
            s = Series(['あ', 'いい', 'ううう', 'ええええ'],
                       index=['ああ', 'いいいい', 'う', 'えええ'],
                       name='おおおおおおお')
            expected = ("ああ              あ\n"
                        "いいいい        いい\n"
                        "う            ううう\n"
                        "えええ      ええええ\n"
                        "Name: おおおおおおお, dtype: object")
            assert repr(s) == expected

            # MultiIndex
            idx = pd.MultiIndex.from_tuples([('あ', 'いい'), ('う', 'え'), (
                'おおお', 'かかかか'), ('き', 'くく')])
            s = Series([1, 22, 3333, 44444], index=idx)
            expected = ("あ      いい            1\n"
                        "う      え             22\n"
                        "おおお  かかかか     3333\n"
                        "き      くく        44444\n"
                        "dtype: int64")
            assert repr(s) == expected

            # object dtype, shorter than unicode repr
            s = Series([1, 22, 3333, 44444], index=[1, 'AB', np.nan, 'あああ'])
            expected = ("1             1\nAB           22\nNaN        3333\n"
                        "あああ    44444\ndtype: int64")
            assert repr(s) == expected

            # object dtype, longer than unicode repr
            s = Series([1, 22, 3333, 44444],
                       index=[1, 'AB', pd.Timestamp('2011-01-01'), 'あああ'])
            expected = ("1                          1\n"
                        "AB                        22\n"
                        "2011-01-01 00:00:00     3333\n"
                        "あああ                 44444\ndtype: int64")
            assert repr(s) == expected

            # truncate
            with option_context('display.max_rows', 3):
                s = Series(['あ', 'いい', 'ううう', 'ええええ'],
                           name='おおおおおおお')
                expected = ("0          あ\n       ...   \n"
                            "3    ええええ\n"
                            "Name: おおおおおおお, Length: 4, dtype: object")
                assert repr(s) == expected

                s.index = ['ああ', 'いいいい', 'う', 'えええ']
                expected = ("ああ            あ\n"
                            "            ...   \n"
                            "えええ    ええええ\n"
                            "Name: おおおおおおお, Length: 4, dtype: object")
                assert repr(s) == expected

            # ambiguous unicode
            s = Series(['¡¡', 'い¡¡', 'ううう', 'ええええ'],
                       index=['ああ', '¡¡¡¡いい', '¡¡', 'えええ'])
            expected = ("ああ              ¡¡\n"
                        "¡¡¡¡いい        い¡¡\n"
                        "¡¡            ううう\n"
                        "えええ      ええええ\ndtype: object")
            assert repr(s) == expected

    def test_float_trim_zeros(self):
        vals = [2.08430917305e+10, 3.52205017305e+10, 2.30674817305e+10,
                2.03954217305e+10, 5.59897817305e+10]
        for line in repr(Series(vals)).split('\n'):
            if line.startswith('dtype:'):
                continue
            if _three_digit_exp():
                assert '+010' in line
            else:
                assert '+10' in line

    def test_datetimeindex(self):

        index = date_range('20130102', periods=6)
        s = Series(1, index=index)
        result = s.to_string()
        assert '2013-01-02' in result

        # nat in index
        s2 = Series(2, index=[Timestamp('20130111'), NaT])
        s = s2.append(s)
        result = s.to_string()
        assert 'NaT' in result

        # nat in summary
        result = str(s2.index)
        assert 'NaT' in result

    @pytest.mark.parametrize('start_date', [
        '2017-01-01 23:59:59.999999999',
        '2017-01-01 23:59:59.99999999',
        '2017-01-01 23:59:59.9999999',
        '2017-01-01 23:59:59.999999',
        '2017-01-01 23:59:59.99999',
        '2017-01-01 23:59:59.9999'
    ])
    def test_datetimeindex_highprecision(self, start_date):
        # GH19030
        # Check that high-precision time values for the end of day are
        # included in repr for DatetimeIndex
        s1 = Series(date_range(start=start_date, freq='D', periods=5))
        result = str(s1)
        assert start_date in result

        dti = date_range(start=start_date, freq='D', periods=5)
        s2 = Series(3, index=dti)
        result = str(s2.index)
        assert start_date in result

    def test_timedelta64(self):

        from datetime import datetime, timedelta

        Series(np.array([1100, 20], dtype='timedelta64[ns]')).to_string()

        s = Series(date_range('2012-1-1', periods=3, freq='D'))

        # GH2146

        # adding NaTs
        y = s - s.shift(1)
        result = y.to_string()
        assert '1 days' in result
        assert '00:00:00' not in result
        assert 'NaT' in result

        # with frac seconds
        o = Series([datetime(2012, 1, 1, microsecond=150)] * 3)
        y = s - o
        result = y.to_string()
        assert '-1 days +23:59:59.999850' in result

        # rounding?
        o = Series([datetime(2012, 1, 1, 1)] * 3)
        y = s - o
        result = y.to_string()
        assert '-1 days +23:00:00' in result
        assert '1 days 23:00:00' in result

        o = Series([datetime(2012, 1, 1, 1, 1)] * 3)
        y = s - o
        result = y.to_string()
        assert '-1 days +22:59:00' in result
        assert '1 days 22:59:00' in result

        o = Series([datetime(2012, 1, 1, 1, 1, microsecond=150)] * 3)
        y = s - o
        result = y.to_string()
        assert '-1 days +22:58:59.999850' in result
        assert '0 days 22:58:59.999850' in result

        # neg time
        td = timedelta(minutes=5, seconds=3)
        s2 = Series(date_range('2012-1-1', periods=3, freq='D')) + td
        y = s - s2
        result = y.to_string()
        assert '-1 days +23:54:57' in result

        td = timedelta(microseconds=550)
        s2 = Series(date_range('2012-1-1', periods=3, freq='D')) + td
        y = s - td
        result = y.to_string()
        assert '2012-01-01 23:59:59.999450' in result

        # no boxing of the actual elements
        td = Series(pd.timedelta_range('1 days', periods=3))
        result = td.to_string()
        assert result == "0   1 days\n1   2 days\n2   3 days"

    def test_mixed_datetime64(self):
        df = DataFrame({'A': [1, 2], 'B': ['2012-01-01', '2012-01-02']})
        df['B'] = pd.to_datetime(df.B)

        result = repr(df.loc[0])
        assert '2012-01-01' in result

    def test_period(self):
        # GH 12615
        index = pd.period_range('2013-01', periods=6, freq='M')
        s = Series(np.arange(6, dtype='int64'), index=index)
        exp = ("2013-01    0\n"
               "2013-02    1\n"
               "2013-03    2\n"
               "2013-04    3\n"
               "2013-05    4\n"
               "2013-06    5\n"
               "Freq: M, dtype: int64")
        assert str(s) == exp

        s = Series(index)
        exp = ("0    2013-01\n"
               "1    2013-02\n"
               "2    2013-03\n"
               "3    2013-04\n"
               "4    2013-05\n"
               "5    2013-06\n"
               "dtype: period[M]")
        assert str(s) == exp

        # periods with mixed freq
        s = Series([pd.Period('2011-01', freq='M'),
                    pd.Period('2011-02-01', freq='D'),
                    pd.Period('2011-03-01 09:00', freq='H')])
        exp = ("0             2011-01\n1          2011-02-01\n"
               "2    2011-03-01 09:00\ndtype: object")
        assert str(s) == exp

    def test_max_multi_index_display(self):
        # GH 7101

        # doc example (indexing.rst)

        # multi-index
        arrays = [['bar', 'bar', 'baz', 'baz', 'foo', 'foo', 'qux', 'qux'],
                  ['one', 'two', 'one', 'two', 'one', 'two', 'one', 'two']]
        tuples = list(zip(*arrays))
        index = MultiIndex.from_tuples(tuples, names=['first', 'second'])
        s = Series(np.random.randn(8), index=index)

        with option_context("display.max_rows", 10):
            assert len(str(s).split('\n')) == 10
        with option_context("display.max_rows", 3):
            assert len(str(s).split('\n')) == 5
        with option_context("display.max_rows", 2):
            assert len(str(s).split('\n')) == 5
        with option_context("display.max_rows", 1):
            assert len(str(s).split('\n')) == 4
        with option_context("display.max_rows", 0):
            assert len(str(s).split('\n')) == 10

        # index
        s = Series(np.random.randn(8), None)

        with option_context("display.max_rows", 10):
            assert len(str(s).split('\n')) == 9
        with option_context("display.max_rows", 3):
            assert len(str(s).split('\n')) == 4
        with option_context("display.max_rows", 2):
            assert len(str(s).split('\n')) == 4
        with option_context("display.max_rows", 1):
            assert len(str(s).split('\n')) == 3
        with option_context("display.max_rows", 0):
            assert len(str(s).split('\n')) == 9

    # Make sure #8532 is fixed
    def test_consistent_format(self):
        s = pd.Series([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.9999, 1, 1] * 10)
        with option_context("display.max_rows", 10,
                            "display.show_dimensions", False):
            res = repr(s)
        exp = ('0      1.0000\n1      1.0000\n2      1.0000\n3      '
               '1.0000\n4      1.0000\n        ...  \n125    '
               '1.0000\n126    1.0000\n127    0.9999\n128    '
               '1.0000\n129    1.0000\ndtype: float64')
        assert res == exp

    def chck_ncols(self, s):
        with option_context("display.max_rows", 10):
            res = repr(s)
        lines = res.split('\n')
        lines = [line for line in repr(s).split('\n')
                 if not re.match(r'[^\.]*\.+', line)][:-1]
        ncolsizes = len({len(line.strip()) for line in lines})
        assert ncolsizes == 1

    def test_format_explicit(self):
        test_sers = gen_series_formatting()
        with option_context("display.max_rows", 4,
                            "display.show_dimensions", False):
            res = repr(test_sers['onel'])
            exp = '0     a\n1     a\n     ..\n98    a\n99    a\ndtype: object'
            assert exp == res
            res = repr(test_sers['twol'])
            exp = ('0     ab\n1     ab\n      ..\n98    ab\n99    ab\ndtype:'
                   ' object')
            assert exp == res
            res = repr(test_sers['asc'])
            exp = ('0         a\n1        ab\n      ...  \n4     abcde\n5'
                   '    abcdef\ndtype: object')
            assert exp == res
            res = repr(test_sers['desc'])
            exp = ('5    abcdef\n4     abcde\n      ...  \n1        ab\n0'
                   '         a\ndtype: object')
            assert exp == res

    def test_ncols(self):
        test_sers = gen_series_formatting()
        for s in test_sers.values():
            self.chck_ncols(s)

    def test_max_rows_eq_one(self):
        s = Series(range(10), dtype='int64')
        with option_context("display.max_rows", 1):
            strrepr = repr(s).split('\n')
        exp1 = ['0', '0']
        res1 = strrepr[0].split()
        assert exp1 == res1
        exp2 = ['..']
        res2 = strrepr[1].split()
        assert exp2 == res2

    def test_truncate_ndots(self):
        def getndots(s):
            return len(re.match(r'[^\.]*(\.*)', s).groups()[0])

        s = Series([0, 2, 3, 6])
        with option_context("display.max_rows", 2):
            strrepr = repr(s).replace('\n', '')
        assert getndots(strrepr) == 2

        s = Series([0, 100, 200, 400])
        with option_context("display.max_rows", 2):
            strrepr = repr(s).replace('\n', '')
        assert getndots(strrepr) == 3

    def test_show_dimensions(self):
        # gh-7117
        s = Series(range(5))

        assert 'Length' not in repr(s)

        with option_context("display.max_rows", 4):
            assert 'Length' in repr(s)

        with option_context("display.show_dimensions", True):
            assert 'Length' in repr(s)

        with option_context("display.max_rows", 4,
                            "display.show_dimensions", False):
            assert 'Length' not in repr(s)

    def test_to_string_name(self):
        s = Series(range(100), dtype='int64')
        s.name = 'myser'
        res = s.to_string(max_rows=2, name=True)
        exp = '0      0\n      ..\n99    99\nName: myser'
        assert res == exp
        res = s.to_string(max_rows=2, name=False)
        exp = '0      0\n      ..\n99    99'
        assert res == exp

    def test_to_string_dtype(self):
        s = Series(range(100), dtype='int64')
        res = s.to_string(max_rows=2, dtype=True)
        exp = '0      0\n      ..\n99    99\ndtype: int64'
        assert res == exp
        res = s.to_string(max_rows=2, dtype=False)
        exp = '0      0\n      ..\n99    99'
        assert res == exp

    def test_to_string_length(self):
        s = Series(range(100), dtype='int64')
        res = s.to_string(max_rows=2, length=True)
        exp = '0      0\n      ..\n99    99\nLength: 100'
        assert res == exp

    def test_to_string_na_rep(self):
        s = pd.Series(index=range(100))
        res = s.to_string(na_rep='foo', max_rows=2)
        exp = '0    foo\n      ..\n99   foo'
        assert res == exp

    def test_to_string_float_format(self):
        s = pd.Series(range(10), dtype='float64')
        res = s.to_string(float_format=lambda x: '{0:2.1f}'.format(x),
                          max_rows=2)
        exp = '0   0.0\n     ..\n9   9.0'
        assert res == exp

    def test_to_string_header(self):
        s = pd.Series(range(10), dtype='int64')
        s.index.name = 'foo'
        res = s.to_string(header=True, max_rows=2)
        exp = 'foo\n0    0\n    ..\n9    9'
        assert res == exp
        res = s.to_string(header=False, max_rows=2)
        exp = '0    0\n    ..\n9    9'
        assert res == exp

    @pytest.mark.parametrize("inputs, expected", [
        ([' a', ' b'], ' a\n b'),
        (['.1', '1'], '.1\n 1'),
        (['10', '-10'], ' 10\n-10')
    ])
    def test_to_string_index_false_corner_case(self, inputs, expected):
        s = pd.Series(inputs).to_string(index=False)
        assert s == expected

    def test_to_string_multindex_header(self):
        # GH 16718
        df = (pd.DataFrame({'a': [0], 'b': [1], 'c': [2], 'd': [3]})
              .set_index(['a', 'b']))
        res = df.to_string(header=['r1', 'r2'])
        exp = '    r1 r2\na b      \n0 1  2  3'
        assert res == exp


def _three_digit_exp():
    return '{x:.4g}'.format(x=1.7e8) == '1.7e+008'


class TestFloatArrayFormatter:

    def test_misc(self):
        obj = fmt.FloatArrayFormatter(np.array([], dtype=np.float64))
        result = obj.get_result()
        assert len(result) == 0

    def test_format(self):
        obj = fmt.FloatArrayFormatter(np.array([12, 0], dtype=np.float64))
        result = obj.get_result()
        assert result[0] == " 12.0"
        assert result[1] == "  0.0"

    def test_output_significant_digits(self):
        # Issue #9764

        # In case default display precision changes:
        with pd.option_context('display.precision', 6):
            # DataFrame example from issue #9764
            d = pd.DataFrame(
                {'col1': [9.999e-8, 1e-7, 1.0001e-7, 2e-7, 4.999e-7, 5e-7,
                          5.0001e-7, 6e-7, 9.999e-7, 1e-6, 1.0001e-6, 2e-6,
                          4.999e-6, 5e-6, 5.0001e-6, 6e-6]})

            expected_output = {
                (0, 6):
                '           col1\n'
                '0  9.999000e-08\n'
                '1  1.000000e-07\n'
                '2  1.000100e-07\n'
                '3  2.000000e-07\n'
                '4  4.999000e-07\n'
                '5  5.000000e-07',
                (1, 6):
                '           col1\n'
                '1  1.000000e-07\n'
                '2  1.000100e-07\n'
                '3  2.000000e-07\n'
                '4  4.999000e-07\n'
                '5  5.000000e-07',
                (1, 8):
                '           col1\n'
                '1  1.000000e-07\n'
                '2  1.000100e-07\n'
                '3  2.000000e-07\n'
                '4  4.999000e-07\n'
                '5  5.000000e-07\n'
                '6  5.000100e-07\n'
                '7  6.000000e-07',
                (8, 16):
                '            col1\n'
                '8   9.999000e-07\n'
                '9   1.000000e-06\n'
                '10  1.000100e-06\n'
                '11  2.000000e-06\n'
                '12  4.999000e-06\n'
                '13  5.000000e-06\n'
                '14  5.000100e-06\n'
                '15  6.000000e-06',
                (9, 16):
                '        col1\n'
                '9   0.000001\n'
                '10  0.000001\n'
                '11  0.000002\n'
                '12  0.000005\n'
                '13  0.000005\n'
                '14  0.000005\n'
                '15  0.000006'
            }

            for (start, stop), v in expected_output.items():
                assert str(d[start:stop]) == v

    def test_too_long(self):
        # GH 10451
        with pd.option_context('display.precision', 4):
            # need both a number > 1e6 and something that normally formats to
            # having length > display.precision + 6
            df = pd.DataFrame(dict(x=[12345.6789]))
            assert str(df) == '            x\n0  12345.6789'
            df = pd.DataFrame(dict(x=[2e6]))
            assert str(df) == '           x\n0  2000000.0'
            df = pd.DataFrame(dict(x=[12345.6789, 2e6]))
            assert str(df) == '            x\n0  1.2346e+04\n1  2.0000e+06'


class TestRepr_timedelta64:

    def test_none(self):
        delta_1d = pd.to_timedelta(1, unit='D')
        delta_0d = pd.to_timedelta(0, unit='D')
        delta_1s = pd.to_timedelta(1, unit='s')
        delta_500ms = pd.to_timedelta(500, unit='ms')

        drepr = lambda x: x._repr_base()
        assert drepr(delta_1d) == "1 days"
        assert drepr(-delta_1d) == "-1 days"
        assert drepr(delta_0d) == "0 days"
        assert drepr(delta_1s) == "0 days 00:00:01"
        assert drepr(delta_500ms) == "0 days 00:00:00.500000"
        assert drepr(delta_1d + delta_1s) == "1 days 00:00:01"
        assert drepr(-delta_1d + delta_1s) == "-1 days +00:00:01"
        assert drepr(delta_1d + delta_500ms) == "1 days 00:00:00.500000"
        assert drepr(-delta_1d + delta_500ms) == "-1 days +00:00:00.500000"

    def test_sub_day(self):
        delta_1d = pd.to_timedelta(1, unit='D')
        delta_0d = pd.to_timedelta(0, unit='D')
        delta_1s = pd.to_timedelta(1, unit='s')
        delta_500ms = pd.to_timedelta(500, unit='ms')

        drepr = lambda x: x._repr_base(format='sub_day')
        assert drepr(delta_1d) == "1 days"
        assert drepr(-delta_1d) == "-1 days"
        assert drepr(delta_0d) == "00:00:00"
        assert drepr(delta_1s) == "00:00:01"
        assert drepr(delta_500ms) == "00:00:00.500000"
        assert drepr(delta_1d + delta_1s) == "1 days 00:00:01"
        assert drepr(-delta_1d + delta_1s) == "-1 days +00:00:01"
        assert drepr(delta_1d + delta_500ms) == "1 days 00:00:00.500000"
        assert drepr(-delta_1d + delta_500ms) == "-1 days +00:00:00.500000"

    def test_long(self):
        delta_1d = pd.to_timedelta(1, unit='D')
        delta_0d = pd.to_timedelta(0, unit='D')
        delta_1s = pd.to_timedelta(1, unit='s')
        delta_500ms = pd.to_timedelta(500, unit='ms')

        drepr = lambda x: x._repr_base(format='long')
        assert drepr(delta_1d) == "1 days 00:00:00"
        assert drepr(-delta_1d) == "-1 days +00:00:00"
        assert drepr(delta_0d) == "0 days 00:00:00"
        assert drepr(delta_1s) == "0 days 00:00:01"
        assert drepr(delta_500ms) == "0 days 00:00:00.500000"
        assert drepr(delta_1d + delta_1s) == "1 days 00:00:01"
        assert drepr(-delta_1d + delta_1s) == "-1 days +00:00:01"
        assert drepr(delta_1d + delta_500ms) == "1 days 00:00:00.500000"
        assert drepr(-delta_1d + delta_500ms) == "-1 days +00:00:00.500000"

    def test_all(self):
        delta_1d = pd.to_timedelta(1, unit='D')
        delta_0d = pd.to_timedelta(0, unit='D')
        delta_1ns = pd.to_timedelta(1, unit='ns')

        drepr = lambda x: x._repr_base(format='all')
        assert drepr(delta_1d) == "1 days 00:00:00.000000000"
        assert drepr(-delta_1d) == "-1 days +00:00:00.000000000"
        assert drepr(delta_0d) == "0 days 00:00:00.000000000"
        assert drepr(delta_1ns) == "0 days 00:00:00.000000001"
        assert drepr(-delta_1d + delta_1ns) == "-1 days +00:00:00.000000001"


class TestTimedelta64Formatter:

    def test_days(self):
        x = pd.to_timedelta(list(range(5)) + [pd.NaT], unit='D')
        result = fmt.Timedelta64Formatter(x, box=True).get_result()
        assert result[0].strip() == "'0 days'"
        assert result[1].strip() == "'1 days'"

        result = fmt.Timedelta64Formatter(x[1:2], box=True).get_result()
        assert result[0].strip() == "'1 days'"

        result = fmt.Timedelta64Formatter(x, box=False).get_result()
        assert result[0].strip() == "0 days"
        assert result[1].strip() == "1 days"

        result = fmt.Timedelta64Formatter(x[1:2], box=False).get_result()
        assert result[0].strip() == "1 days"

    def test_days_neg(self):
        x = pd.to_timedelta(list(range(5)) + [pd.NaT], unit='D')
        result = fmt.Timedelta64Formatter(-x, box=True).get_result()
        assert result[0].strip() == "'0 days'"
        assert result[1].strip() == "'-1 days'"

    def test_subdays(self):
        y = pd.to_timedelta(list(range(5)) + [pd.NaT], unit='s')
        result = fmt.Timedelta64Formatter(y, box=True).get_result()
        assert result[0].strip() == "'00:00:00'"
        assert result[1].strip() == "'00:00:01'"

    def test_subdays_neg(self):
        y = pd.to_timedelta(list(range(5)) + [pd.NaT], unit='s')
        result = fmt.Timedelta64Formatter(-y, box=True).get_result()
        assert result[0].strip() == "'00:00:00'"
        assert result[1].strip() == "'-1 days +23:59:59'"

    def test_zero(self):
        x = pd.to_timedelta(list(range(1)) + [pd.NaT], unit='D')
        result = fmt.Timedelta64Formatter(x, box=True).get_result()
        assert result[0].strip() == "'0 days'"

        x = pd.to_timedelta(list(range(1)), unit='D')
        result = fmt.Timedelta64Formatter(x, box=True).get_result()
        assert result[0].strip() == "'0 days'"


class TestDatetime64Formatter:

    def test_mixed(self):
        x = Series([datetime(2013, 1, 1), datetime(2013, 1, 1, 12), pd.NaT])
        result = fmt.Datetime64Formatter(x).get_result()
        assert result[0].strip() == "2013-01-01 00:00:00"
        assert result[1].strip() == "2013-01-01 12:00:00"

    def test_dates(self):
        x = Series([datetime(2013, 1, 1), datetime(2013, 1, 2), pd.NaT])
        result = fmt.Datetime64Formatter(x).get_result()
        assert result[0].strip() == "2013-01-01"
        assert result[1].strip() == "2013-01-02"

    def test_date_nanos(self):
        x = Series([Timestamp(200)])
        result = fmt.Datetime64Formatter(x).get_result()
        assert result[0].strip() == "1970-01-01 00:00:00.000000200"

    def test_dates_display(self):

        # 10170
        # make sure that we are consistently display date formatting
        x = Series(date_range('20130101 09:00:00', periods=5, freq='D'))
        x.iloc[1] = np.nan
        result = fmt.Datetime64Formatter(x).get_result()
        assert result[0].strip() == "2013-01-01 09:00:00"
        assert result[1].strip() == "NaT"
        assert result[4].strip() == "2013-01-05 09:00:00"

        x = Series(date_range('20130101 09:00:00', periods=5, freq='s'))
        x.iloc[1] = np.nan
        result = fmt.Datetime64Formatter(x).get_result()
        assert result[0].strip() == "2013-01-01 09:00:00"
        assert result[1].strip() == "NaT"
        assert result[4].strip() == "2013-01-01 09:00:04"

        x = Series(date_range('20130101 09:00:00', periods=5, freq='ms'))
        x.iloc[1] = np.nan
        result = fmt.Datetime64Formatter(x).get_result()
        assert result[0].strip() == "2013-01-01 09:00:00.000"
        assert result[1].strip() == "NaT"
        assert result[4].strip() == "2013-01-01 09:00:00.004"

        x = Series(date_range('20130101 09:00:00', periods=5, freq='us'))
        x.iloc[1] = np.nan
        result = fmt.Datetime64Formatter(x).get_result()
        assert result[0].strip() == "2013-01-01 09:00:00.000000"
        assert result[1].strip() == "NaT"
        assert result[4].strip() == "2013-01-01 09:00:00.000004"

        x = Series(date_range('20130101 09:00:00', periods=5, freq='N'))
        x.iloc[1] = np.nan
        result = fmt.Datetime64Formatter(x).get_result()
        assert result[0].strip() == "2013-01-01 09:00:00.000000000"
        assert result[1].strip() == "NaT"
        assert result[4].strip() == "2013-01-01 09:00:00.000000004"

    def test_datetime64formatter_yearmonth(self):
        x = Series([datetime(2016, 1, 1), datetime(2016, 2, 2)])

        def format_func(x):
            return x.strftime('%Y-%m')

        formatter = fmt.Datetime64Formatter(x, formatter=format_func)
        result = formatter.get_result()
        assert result == ['2016-01', '2016-02']

    def test_datetime64formatter_hoursecond(self):

        x = Series(pd.to_datetime(['10:10:10.100', '12:12:12.120'],
                                  format='%H:%M:%S.%f'))

        def format_func(x):
            return x.strftime('%H:%M')

        formatter = fmt.Datetime64Formatter(x, formatter=format_func)
        result = formatter.get_result()
        assert result == ['10:10', '12:12']


class TestNaTFormatting:

    def test_repr(self):
        assert repr(pd.NaT) == "NaT"

    def test_str(self):
        assert str(pd.NaT) == "NaT"


class TestDatetimeIndexFormat:

    def test_datetime(self):
        formatted = pd.to_datetime([datetime(2003, 1, 1, 12), pd.NaT]).format()
        assert formatted[0] == "2003-01-01 12:00:00"
        assert formatted[1] == "NaT"

    def test_date(self):
        formatted = pd.to_datetime([datetime(2003, 1, 1), pd.NaT]).format()
        assert formatted[0] == "2003-01-01"
        assert formatted[1] == "NaT"

    def test_date_tz(self):
        formatted = pd.to_datetime([datetime(2013, 1, 1)], utc=True).format()
        assert formatted[0] == "2013-01-01 00:00:00+00:00"

        formatted = pd.to_datetime(
            [datetime(2013, 1, 1), pd.NaT], utc=True).format()
        assert formatted[0] == "2013-01-01 00:00:00+00:00"

    def test_date_explicit_date_format(self):
        formatted = pd.to_datetime([datetime(2003, 2, 1), pd.NaT]).format(
            date_format="%m-%d-%Y", na_rep="UT")
        assert formatted[0] == "02-01-2003"
        assert formatted[1] == "UT"


class TestDatetimeIndexUnicode:

    def test_dates(self):
        text = str(pd.to_datetime([datetime(2013, 1, 1), datetime(2014, 1, 1)
                                   ]))
        assert "['2013-01-01'," in text
        assert ", '2014-01-01']" in text

    def test_mixed(self):
        text = str(pd.to_datetime([datetime(2013, 1, 1), datetime(
            2014, 1, 1, 12), datetime(2014, 1, 1)]))
        assert "'2013-01-01 00:00:00'," in text
        assert "'2014-01-01 00:00:00']" in text


class TestStringRepTimestamp:

    def test_no_tz(self):
        dt_date = datetime(2013, 1, 2)
        assert str(dt_date) == str(Timestamp(dt_date))

        dt_datetime = datetime(2013, 1, 2, 12, 1, 3)
        assert str(dt_datetime) == str(Timestamp(dt_datetime))

        dt_datetime_us = datetime(2013, 1, 2, 12, 1, 3, 45)
        assert str(dt_datetime_us) == str(Timestamp(dt_datetime_us))

        ts_nanos_only = Timestamp(200)
        assert str(ts_nanos_only) == "1970-01-01 00:00:00.000000200"

        ts_nanos_micros = Timestamp(1200)
        assert str(ts_nanos_micros) == "1970-01-01 00:00:00.000001200"

    def test_tz_pytz(self):
        dt_date = datetime(2013, 1, 2, tzinfo=pytz.utc)
        assert str(dt_date) == str(Timestamp(dt_date))

        dt_datetime = datetime(2013, 1, 2, 12, 1, 3, tzinfo=pytz.utc)
        assert str(dt_datetime) == str(Timestamp(dt_datetime))

        dt_datetime_us = datetime(2013, 1, 2, 12, 1, 3, 45, tzinfo=pytz.utc)
        assert str(dt_datetime_us) == str(Timestamp(dt_datetime_us))

    def test_tz_dateutil(self):
        utc = dateutil.tz.tzutc()

        dt_date = datetime(2013, 1, 2, tzinfo=utc)
        assert str(dt_date) == str(Timestamp(dt_date))

        dt_datetime = datetime(2013, 1, 2, 12, 1, 3, tzinfo=utc)
        assert str(dt_datetime) == str(Timestamp(dt_datetime))

        dt_datetime_us = datetime(2013, 1, 2, 12, 1, 3, 45, tzinfo=utc)
        assert str(dt_datetime_us) == str(Timestamp(dt_datetime_us))

    def test_nat_representations(self):
        for f in (str, repr, methodcaller('isoformat')):
            assert f(pd.NaT) == 'NaT'


def test_format_percentiles():
    result = fmt.format_percentiles([0.01999, 0.02001, 0.5, 0.666666, 0.9999])
    expected = ['1.999%', '2.001%', '50%', '66.667%', '99.99%']
    assert result == expected

    result = fmt.format_percentiles([0, 0.5, 0.02001, 0.5, 0.666666, 0.9999])
    expected = ['0%', '50%', '2.0%', '50%', '66.67%', '99.99%']
    assert result == expected

    msg = r"percentiles should all be in the interval \[0,1\]"
    with pytest.raises(ValueError, match=msg):
        fmt.format_percentiles([0.1, np.nan, 0.5])
    with pytest.raises(ValueError, match=msg):
        fmt.format_percentiles([-0.001, 0.1, 0.5])
    with pytest.raises(ValueError, match=msg):
        fmt.format_percentiles([2, 0.1, 0.5])
    with pytest.raises(ValueError, match=msg):
        fmt.format_percentiles([0.1, 0.5, 'a'])


@pytest.mark.parametrize("input_array, expected", [
    ("a", "a"),
    (["a", "b"], "a\nb"),
    ([1, "a"], "1\na"),
    (1, "1"),
    ([0, -1], " 0\n-1"),
    (1.0, '1.0')
])
def test_format_remove_leading_space_series(input_array, expected):
    # GH: 24980
    s = pd.Series(input_array).to_string(index=False)
    assert s == expected


@pytest.mark.parametrize("input_array, expected", [
    ({"A": ["a"]}, "A\na"),
    ({"A": ["a", "b"], "B": ["c", "dd"]}, "A  B\na  c\nb dd"),
    ({"A": ["a", 1], "B": ["aa", 1]}, "A  B\na aa\n1  1")
])
def test_format_remove_leading_space_dataframe(input_array, expected):
    # GH: 24980
    df = pd.DataFrame(input_array).to_string(index=False)
    assert df == expected


def test_format_percentiles_integer_idx():
    # Issue #26660
    result = fmt.format_percentiles(np.linspace(0, 1, 10 + 1))
    expected = ['0%', '10%', '20%', '30%', '40%', '50%',
                '60%', '70%', '80%', '90%', '100%']
    assert result == expected


def test_repr_html_ipython_config(ip):
    code = textwrap.dedent("""\
    import pandas as pd
    df = pd.DataFrame({"A": [1, 2]})
    df._repr_html_()

    cfg = get_ipython().config
    cfg['IPKernelApp']['parent_appname']
    df._repr_html_()
    """)
    result = ip.run_cell(code)
    assert not result.error_in_exec