"""
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_repr_min_rows(self):
        df = pd.DataFrame({"a": range(20)})

        # default setting no truncation even if above min_rows
        assert ".." not in repr(df)
        assert ".." not in df._repr_html_()

        df = pd.DataFrame({"a": range(61)})

        # default of max_rows 60 triggers truncation if above
        assert ".." in repr(df)
        assert ".." in df._repr_html_()

        with option_context("display.max_rows", 10, "display.min_rows", 4):
            # truncated after first two rows
            assert ".." in repr(df)
            assert "2  " not in repr(df)
            assert "..." in df._repr_html_()
            assert "<td>2</td>" not in df._repr_html_()

        with option_context("display.max_rows", 12, "display.min_rows", None):
            # when set to None, follow value of max_rows
            assert "5    5" in repr(df)
            assert "<td>5</td>" in df._repr_html_()

        with option_context("display.max_rows", 10, "display.min_rows", 12):
            # when set value higher as max_rows, use the minimum
            assert "5    5" not in repr(df)
            assert "<td>5</td>" not in df._repr_html_()

        with option_context("display.max_rows", None, "display.min_rows", 12):
            # max_rows of None -> never truncate
            assert ".." not in repr(df)
            assert ".." not in df._repr_html_()

    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(10.0)})

        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 aligned 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  \\\n 11   \n 22   \n 33   \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, 12e45, 1.64e6, 1.7e8, 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\n0  3234.000\n1     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\n0  1.000000e+009\n1  2.512000e-001"
        else:
            expected = "              x\n0  1.000000e+09\n1  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\n0  0\n1  0\n2 -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\n1.5  0\n2.0  1\n3.0  2\n4.0  3\n5.0  4"
        assert result == expected

    def test_to_string_complex_float_formatting(self):
        # GH #25514, 25745
        with pd.option_context("display.precision", 5):
            df = DataFrame(
                {
                    "x": [
                        (0.4467846931321966 + 0.0715185102060818j),
                        (0.2739442392974528 + 0.23515228785438969j),
                        (0.26974928742135185 + 0.3250604054898979j),
                        (-1j),
                    ]
                }
            )
            result = df.to_string()
            expected = (
                "                  x\n0  0.44678+0.07152j\n"
                "1  0.27394+0.23515j\n"
                "2  0.26975+0.32506j\n"
                "3 -0.00000-1.00000j"
            )
            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\n0 -15\n1  20\n2  25\n3 -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       \n0  3234.000\n1     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, -1.0, -2.0, 3.0, 4.0],
                "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, -1.0, -2.0, 3.0, 4.0],
                "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 = [
            2.08430917305e10,
            3.52205017305e10,
            2.30674817305e10,
            2.03954217305e10,
            5.59897817305e10,
        ]
        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([0.0, 1.5678, 2.0, -3.0, 4.0])
        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\nddd    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 = [
            2.08430917305e10,
            3.52205017305e10,
            2.30674817305e10,
            2.03954217305e10,
            5.59897817305e10,
        ]
        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_repr_min_rows(self):
        s = pd.Series(range(20))

        # default setting no truncation even if above min_rows
        assert ".." not in repr(s)

        s = pd.Series(range(61))

        # default of max_rows 60 triggers truncation if above
        assert ".." in repr(s)

        with option_context("display.max_rows", 10, "display.min_rows", 4):
            # truncated after first two rows
            assert ".." in repr(s)
            assert "2  " not in repr(s)

        with option_context("display.max_rows", 12, "display.min_rows", None):
            # when set to None, follow value of max_rows
            assert "5      5" in repr(s)

        with option_context("display.max_rows", 10, "display.min_rows", 12):
            # when set value higher as max_rows, use the minimum
            assert "5      5" not in repr(s)

        with option_context("display.max_rows", None, "display.min_rows", 12):
            # max_rows of None -> never truncate
            assert ".." 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

    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"])


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
