test_style.py

import copy
import re
import textwrap

import numpy as np
import pytest

import pandas as pd
from pandas import DataFrame
import pandas._testing as tm

jinja2 = pytest.importorskip("jinja2")
from pandas.io.formats.style import (  # isort:skip
    Styler,
    _get_level_lengths,
    _maybe_convert_css_to_tuples,
    _non_reducing_slice,
)


class TestStyler:
    def setup_method(self, method):
        np.random.seed(24)
        self.s = DataFrame({"A": np.random.permutation(range(6))})
        self.df = DataFrame({"A": [0, 1], "B": np.random.randn(2)})
        self.f = lambda x: x
        self.g = lambda x: x

        def h(x, foo="bar"):
            return pd.Series(f"color: {foo}", index=x.index, name=x.name)

        self.h = h
        self.styler = Styler(self.df)
        self.attrs = DataFrame({"A": ["color: red", "color: blue"]})
        self.dataframes = [
            self.df,
            DataFrame(
                {"f": [1.0, 2.0], "o": ["a", "b"], "c": pd.Categorical(["a", "b"])}
            ),
        ]
        self.blank_value = " "

    def test_init_non_pandas(self):
        msg = "``data`` must be a Series or DataFrame"
        with pytest.raises(TypeError, match=msg):
            Styler([1, 2, 3])

    def test_init_series(self):
        result = Styler(pd.Series([1, 2]))
        assert result.data.ndim == 2

    def test_repr_html_ok(self):
        self.styler._repr_html_()

    def test_repr_html_mathjax(self):
        # gh-19824
        assert "tex2jax_ignore" not in self.styler._repr_html_()

        with pd.option_context("display.html.use_mathjax", False):
            assert "tex2jax_ignore" in self.styler._repr_html_()

    def test_update_ctx(self):
        self.styler._update_ctx(self.attrs)
        expected = {(0, 0): [("color", "red")], (1, 0): [("color", "blue")]}
        assert self.styler.ctx == expected

    def test_update_ctx_flatten_multi_and_trailing_semi(self):
        attrs = DataFrame({"A": ["color: red; foo: bar", "color:blue ; foo: baz;"]})
        self.styler._update_ctx(attrs)
        expected = {
            (0, 0): [("color", "red"), ("foo", "bar")],
            (1, 0): [("color", "blue"), ("foo", "baz")],
        }
        assert self.styler.ctx == expected

    @pytest.mark.parametrize("do_changes", [True, False])
    @pytest.mark.parametrize("do_render", [True, False])
    def test_copy(self, do_changes, do_render):
        # Updated in GH39708
        # Change some defaults (to check later if the new values are copied)
        if do_changes:
            self.styler.set_table_styles(
                [{"selector": "th", "props": [("foo", "bar")]}]
            )
            self.styler.set_table_attributes('class="foo" data-bar')
            self.styler.hidden_index = not self.styler.hidden_index
            self.styler.hide_columns("A")
            classes = DataFrame(
                [["favorite-val red", ""], [None, "blue my-val"]],
                index=self.df.index,
                columns=self.df.columns,
            )
            self.styler.set_td_classes(classes)
            ttips = DataFrame(
                data=[["Favorite", ""], [np.nan, "my"]],
                columns=self.df.columns,
                index=self.df.index,
            )
            self.styler.set_tooltips(ttips)
            self.styler.cell_ids = not self.styler.cell_ids

        if do_render:
            self.styler.render()

        s_copy = copy.copy(self.styler)
        s_deepcopy = copy.deepcopy(self.styler)

        assert self.styler is not s_copy
        assert self.styler is not s_deepcopy

        # Check for identity
        assert self.styler.ctx is s_copy.ctx
        assert self.styler._todo is s_copy._todo
        assert self.styler.table_styles is s_copy.table_styles
        assert self.styler.hidden_columns is s_copy.hidden_columns
        assert self.styler.cell_context is s_copy.cell_context
        assert self.styler.tooltips is s_copy.tooltips
        if do_changes:  # self.styler.tooltips is not None
            assert self.styler.tooltips.tt_data is s_copy.tooltips.tt_data
            assert (
                self.styler.tooltips.class_properties
                is s_copy.tooltips.class_properties
            )
            assert self.styler.tooltips.table_styles is s_copy.tooltips.table_styles

        # Check for non-identity
        assert self.styler.ctx is not s_deepcopy.ctx
        assert self.styler._todo is not s_deepcopy._todo
        assert self.styler.hidden_columns is not s_deepcopy.hidden_columns
        assert self.styler.cell_context is not s_deepcopy.cell_context
        if do_changes:  # self.styler.table_style is not None
            assert self.styler.table_styles is not s_deepcopy.table_styles
        if do_changes:  # self.styler.tooltips is not None
            assert self.styler.tooltips is not s_deepcopy.tooltips
            assert self.styler.tooltips.tt_data is not s_deepcopy.tooltips.tt_data
            assert (
                self.styler.tooltips.class_properties
                is not s_deepcopy.tooltips.class_properties
            )
            assert (
                self.styler.tooltips.table_styles
                is not s_deepcopy.tooltips.table_styles
            )

        self.styler._update_ctx(self.attrs)
        self.styler.highlight_max()
        assert self.styler.ctx == s_copy.ctx
        assert self.styler.ctx != s_deepcopy.ctx
        assert self.styler._todo == s_copy._todo
        assert self.styler._todo != s_deepcopy._todo
        assert s_deepcopy._todo == []

        equal_attributes = [
            "table_styles",
            "table_attributes",
            "cell_ids",
            "hidden_index",
            "hidden_columns",
            "cell_context",
        ]
        for s2 in [s_copy, s_deepcopy]:
            for att in equal_attributes:
                assert self.styler.__dict__[att] == s2.__dict__[att]
            if do_changes:  # self.styler.tooltips is not None
                tm.assert_frame_equal(self.styler.tooltips.tt_data, s2.tooltips.tt_data)
                assert (
                    self.styler.tooltips.class_properties
                    == s2.tooltips.class_properties
                )
                assert self.styler.tooltips.table_styles == s2.tooltips.table_styles

    def test_clear(self):
        # updated in GH 39396
        tt = DataFrame({"A": [None, "tt"]})
        css = DataFrame({"A": [None, "cls-a"]})
        s = self.df.style.highlight_max().set_tooltips(tt).set_td_classes(css)
        # _todo, tooltips and cell_context items added to..
        assert len(s._todo) > 0
        assert s.tooltips
        assert len(s.cell_context) > 0

        s = s._compute()
        # ctx item affected when a render takes place. _todo is maintained
        assert len(s.ctx) > 0
        assert len(s._todo) > 0

        s.clear()
        # ctx, _todo, tooltips and cell_context items all revert to null state.
        assert len(s.ctx) == 0
        assert len(s._todo) == 0
        assert not s.tooltips
        assert len(s.cell_context) == 0

    def test_render(self):
        df = DataFrame({"A": [0, 1]})
        style = lambda x: pd.Series(["color: red", "color: blue"], name=x.name)
        s = Styler(df, uuid="AB").apply(style)
        s.render()
        # it worked?

    def test_multiple_render(self):
        # GH 39396
        s = Styler(self.df, uuid_len=0).applymap(lambda x: "color: red;", subset=["A"])
        s.render()  # do 2 renders to ensure css styles not duplicated
        assert (
            '<style type="text/css">\n#T__row0_col0, #T__row1_col0 {\n'
            "  color: red;\n}\n</style>" in s.render()
        )

    def test_render_empty_dfs(self):
        empty_df = DataFrame()
        es = Styler(empty_df)
        es.render()
        # An index but no columns
        DataFrame(columns=["a"]).style.render()
        # A column but no index
        DataFrame(index=["a"]).style.render()
        # No IndexError raised?

    def test_render_double(self):
        df = DataFrame({"A": [0, 1]})
        style = lambda x: pd.Series(
            ["color: red; border: 1px", "color: blue; border: 2px"], name=x.name
        )
        s = Styler(df, uuid="AB").apply(style)
        s.render()
        # it worked?

    def test_set_properties(self):
        df = DataFrame({"A": [0, 1]})
        result = df.style.set_properties(color="white", size="10px")._compute().ctx
        # order is deterministic
        v = [("color", "white"), ("size", "10px")]
        expected = {(0, 0): v, (1, 0): v}
        assert result.keys() == expected.keys()
        for v1, v2 in zip(result.values(), expected.values()):
            assert sorted(v1) == sorted(v2)

    def test_set_properties_subset(self):
        df = DataFrame({"A": [0, 1]})
        result = (
            df.style.set_properties(subset=pd.IndexSlice[0, "A"], color="white")
            ._compute()
            .ctx
        )
        expected = {(0, 0): [("color", "white")]}
        assert result == expected

    def test_empty_index_name_doesnt_display(self):
        # https://github.com/pandas-dev/pandas/pull/12090#issuecomment-180695902
        df = DataFrame({"A": [1, 2], "B": [3, 4], "C": [5, 6]})
        result = df.style._translate()

        expected = [
            [
                {
                    "class": "blank level0",
                    "type": "th",
                    "value": self.blank_value,
                    "is_visible": True,
                    "display_value": self.blank_value,
                },
                {
                    "class": "col_heading level0 col0",
                    "display_value": "A",
                    "type": "th",
                    "value": "A",
                    "is_visible": True,
                },
                {
                    "class": "col_heading level0 col1",
                    "display_value": "B",
                    "type": "th",
                    "value": "B",
                    "is_visible": True,
                },
                {
                    "class": "col_heading level0 col2",
                    "display_value": "C",
                    "type": "th",
                    "value": "C",
                    "is_visible": True,
                },
            ]
        ]

        assert result["head"] == expected

    def test_index_name(self):
        # https://github.com/pandas-dev/pandas/issues/11655
        df = DataFrame({"A": [1, 2], "B": [3, 4], "C": [5, 6]})
        result = df.set_index("A").style._translate()

        expected = [
            [
                {
                    "class": "blank level0",
                    "type": "th",
                    "value": self.blank_value,
                    "display_value": self.blank_value,
                    "is_visible": True,
                },
                {
                    "class": "col_heading level0 col0",
                    "type": "th",
                    "value": "B",
                    "display_value": "B",
                    "is_visible": True,
                },
                {
                    "class": "col_heading level0 col1",
                    "type": "th",
                    "value": "C",
                    "display_value": "C",
                    "is_visible": True,
                },
            ],
            [
                {"class": "index_name level0", "type": "th", "value": "A"},
                {"class": "blank col0", "type": "th", "value": self.blank_value},
                {"class": "blank col1", "type": "th", "value": self.blank_value},
            ],
        ]

        assert result["head"] == expected

    def test_multiindex_name(self):
        # https://github.com/pandas-dev/pandas/issues/11655
        df = DataFrame({"A": [1, 2], "B": [3, 4], "C": [5, 6]})
        result = df.set_index(["A", "B"]).style._translate()

        expected = [
            [
                {
                    "class": "blank",
                    "type": "th",
                    "value": self.blank_value,
                    "display_value": self.blank_value,
                    "is_visible": True,
                },
                {
                    "class": "blank level0",
                    "type": "th",
                    "value": self.blank_value,
                    "display_value": self.blank_value,
                    "is_visible": True,
                },
                {
                    "class": "col_heading level0 col0",
                    "type": "th",
                    "value": "C",
                    "display_value": "C",
                    "is_visible": True,
                },
            ],
            [
                {"class": "index_name level0", "type": "th", "value": "A"},
                {"class": "index_name level1", "type": "th", "value": "B"},
                {"class": "blank col0", "type": "th", "value": self.blank_value},
            ],
        ]

        assert result["head"] == expected

    def test_numeric_columns(self):
        # https://github.com/pandas-dev/pandas/issues/12125
        # smoke test for _translate
        df = DataFrame({0: [1, 2, 3]})
        df.style._translate()

    def test_apply_axis(self):
        df = DataFrame({"A": [0, 0], "B": [1, 1]})
        f = lambda x: [f"val: {x.max()}" for v in x]
        result = df.style.apply(f, axis=1)
        assert len(result._todo) == 1
        assert len(result.ctx) == 0
        result._compute()
        expected = {
            (0, 0): [("val", "1")],
            (0, 1): [("val", "1")],
            (1, 0): [("val", "1")],
            (1, 1): [("val", "1")],
        }
        assert result.ctx == expected

        result = df.style.apply(f, axis=0)
        expected = {
            (0, 0): [("val", "0")],
            (0, 1): [("val", "1")],
            (1, 0): [("val", "0")],
            (1, 1): [("val", "1")],
        }
        result._compute()
        assert result.ctx == expected
        result = df.style.apply(f)  # default
        result._compute()
        assert result.ctx == expected

    @pytest.mark.parametrize(
        "slice_",
        [
            pd.IndexSlice[:],
            pd.IndexSlice[:, ["A"]],
            pd.IndexSlice[[1], :],
            pd.IndexSlice[[1], ["A"]],
            pd.IndexSlice[:2, ["A", "B"]],
        ],
    )
    @pytest.mark.parametrize("axis", [0, 1])
    def test_apply_subset(self, slice_, axis):
        result = (
            self.df.style.apply(self.h, axis=axis, subset=slice_, foo="baz")
            ._compute()
            .ctx
        )
        expected = {
            (r, c): [("color", "baz")]
            for r, row in enumerate(self.df.index)
            for c, col in enumerate(self.df.columns)
            if row in self.df.loc[slice_].index and col in self.df.loc[slice_].columns
        }
        assert result == expected

    @pytest.mark.parametrize(
        "slice_",
        [
            pd.IndexSlice[:],
            pd.IndexSlice[:, ["A"]],
            pd.IndexSlice[[1], :],
            pd.IndexSlice[[1], ["A"]],
            pd.IndexSlice[:2, ["A", "B"]],
        ],
    )
    def test_applymap_subset(self, slice_):
        result = (
            self.df.style.applymap(lambda x: "color:baz;", subset=slice_)._compute().ctx
        )
        expected = {
            (r, c): [("color", "baz")]
            for r, row in enumerate(self.df.index)
            for c, col in enumerate(self.df.columns)
            if row in self.df.loc[slice_].index and col in self.df.loc[slice_].columns
        }
        assert result == expected

    @pytest.mark.parametrize(
        "slice_",
        [
            pd.IndexSlice[:, pd.IndexSlice["x", "A"]],
            pd.IndexSlice[:, pd.IndexSlice[:, "A"]],
            pd.IndexSlice[:, pd.IndexSlice[:, ["A", "C"]]],  # missing col element
            pd.IndexSlice[pd.IndexSlice["a", 1], :],
            pd.IndexSlice[pd.IndexSlice[:, 1], :],
            pd.IndexSlice[pd.IndexSlice[:, [1, 3]], :],  # missing row element
            pd.IndexSlice[:, ("x", "A")],
            pd.IndexSlice[("a", 1), :],
        ],
    )
    def test_applymap_subset_multiindex(self, slice_):
        # GH 19861
        # edited for GH 33562
        idx = pd.MultiIndex.from_product([["a", "b"], [1, 2]])
        col = pd.MultiIndex.from_product([["x", "y"], ["A", "B"]])
        df = DataFrame(np.random.rand(4, 4), columns=col, index=idx)
        df.style.applymap(lambda x: "color: red;", subset=slice_).render()

    def test_applymap_subset_multiindex_code(self):
        # https://github.com/pandas-dev/pandas/issues/25858
        # Checks styler.applymap works with multindex when codes are provided
        codes = np.array([[0, 0, 1, 1], [0, 1, 0, 1]])
        columns = pd.MultiIndex(
            levels=[["a", "b"], ["%", "#"]], codes=codes, names=["", ""]
        )
        df = DataFrame(
            [[1, -1, 1, 1], [-1, 1, 1, 1]], index=["hello", "world"], columns=columns
        )
        pct_subset = pd.IndexSlice[:, pd.IndexSlice[:, "%":"%"]]

        def color_negative_red(val):
            color = "red" if val < 0 else "black"
            return f"color: {color}"

        df.loc[pct_subset]
        df.style.applymap(color_negative_red, subset=pct_subset)

    def test_where_with_one_style(self):
        # GH 17474
        def f(x):
            return x > 0.5

        style1 = "foo: bar"

        result = self.df.style.where(f, style1)._compute().ctx
        expected = {
            (r, c): [("foo", "bar")]
            for r, row in enumerate(self.df.index)
            for c, col in enumerate(self.df.columns)
            if f(self.df.loc[row, col])
        }
        assert result == expected

    @pytest.mark.parametrize(
        "slice_",
        [
            pd.IndexSlice[:],
            pd.IndexSlice[:, ["A"]],
            pd.IndexSlice[[1], :],
            pd.IndexSlice[[1], ["A"]],
            pd.IndexSlice[:2, ["A", "B"]],
        ],
    )
    def test_where_subset(self, slice_):
        # GH 17474
        def f(x):
            return x > 0.5

        style1 = "foo: bar"
        style2 = "baz: foo"

        result = self.df.style.where(f, style1, style2, subset=slice_)._compute().ctx
        expected = {
            (r, c): [("foo", "bar") if f(self.df.loc[row, col]) else ("baz", "foo")]
            for r, row in enumerate(self.df.index)
            for c, col in enumerate(self.df.columns)
            if row in self.df.loc[slice_].index and col in self.df.loc[slice_].columns
        }
        assert result == expected

    def test_where_subset_compare_with_applymap(self):
        # GH 17474
        def f(x):
            return x > 0.5

        style1 = "foo: bar"
        style2 = "baz: foo"

        def g(x):
            return style1 if f(x) else style2

        slices = [
            pd.IndexSlice[:],
            pd.IndexSlice[:, ["A"]],
            pd.IndexSlice[[1], :],
            pd.IndexSlice[[1], ["A"]],
            pd.IndexSlice[:2, ["A", "B"]],
        ]

        for slice_ in slices:
            result = (
                self.df.style.where(f, style1, style2, subset=slice_)._compute().ctx
            )
            expected = self.df.style.applymap(g, subset=slice_)._compute().ctx
            assert result == expected

    def test_empty(self):
        df = DataFrame({"A": [1, 0]})
        s = df.style
        s.ctx = {(0, 0): [("color", "red")], (1, 0): [("", "")]}

        result = s._translate()["cellstyle"]
        expected = [
            {"props": [("color", "red")], "selectors": ["row0_col0"]},
            {"props": [("", "")], "selectors": ["row1_col0"]},
        ]
        assert result == expected

    def test_duplicate(self):
        df = DataFrame({"A": [1, 0]})
        s = df.style
        s.ctx = {(0, 0): [("color", "red")], (1, 0): [("color", "red")]}

        result = s._translate()["cellstyle"]
        expected = [
            {"props": [("color", "red")], "selectors": ["row0_col0", "row1_col0"]}
        ]
        assert result == expected

    def test_format_with_na_rep(self):
        # GH 21527 28358
        df = DataFrame([[None, None], [1.1, 1.2]], columns=["A", "B"])

        ctx = df.style.format(None, na_rep="-")._translate()
        assert ctx["body"][0][1]["display_value"] == "-"
        assert ctx["body"][0][2]["display_value"] == "-"

        ctx = df.style.format("{:.2%}", na_rep="-")._translate()
        assert ctx["body"][0][1]["display_value"] == "-"
        assert ctx["body"][0][2]["display_value"] == "-"
        assert ctx["body"][1][1]["display_value"] == "110.00%"
        assert ctx["body"][1][2]["display_value"] == "120.00%"

        ctx = df.style.format("{:.2%}", na_rep="-", subset=["B"])._translate()
        assert ctx["body"][0][2]["display_value"] == "-"
        assert ctx["body"][1][2]["display_value"] == "120.00%"

    def test_init_with_na_rep(self):
        # GH 21527 28358
        df = DataFrame([[None, None], [1.1, 1.2]], columns=["A", "B"])

        ctx = Styler(df, na_rep="NA")._translate()
        assert ctx["body"][0][1]["display_value"] == "NA"
        assert ctx["body"][0][2]["display_value"] == "NA"

    def test_set_na_rep(self):
        # GH 21527 28358
        df = DataFrame([[None, None], [1.1, 1.2]], columns=["A", "B"])

        with tm.assert_produces_warning(FutureWarning):
            ctx = df.style.set_na_rep("NA")._translate()
        assert ctx["body"][0][1]["display_value"] == "NA"
        assert ctx["body"][0][2]["display_value"] == "NA"

        with tm.assert_produces_warning(FutureWarning):
            ctx = (
                df.style.set_na_rep("NA")
                .format(None, na_rep="-", subset=["B"])
                ._translate()
            )
        assert ctx["body"][0][1]["display_value"] == "NA"
        assert ctx["body"][0][2]["display_value"] == "-"

    def test_format_non_numeric_na(self):
        # GH 21527 28358
        df = DataFrame(
            {
                "object": [None, np.nan, "foo"],
                "datetime": [None, pd.NaT, pd.Timestamp("20120101")],
            }
        )

        with tm.assert_produces_warning(FutureWarning):
            ctx = df.style.set_na_rep("NA")._translate()
        assert ctx["body"][0][1]["display_value"] == "NA"
        assert ctx["body"][0][2]["display_value"] == "NA"
        assert ctx["body"][1][1]["display_value"] == "NA"
        assert ctx["body"][1][2]["display_value"] == "NA"

        ctx = df.style.format(None, na_rep="-")._translate()
        assert ctx["body"][0][1]["display_value"] == "-"
        assert ctx["body"][0][2]["display_value"] == "-"
        assert ctx["body"][1][1]["display_value"] == "-"
        assert ctx["body"][1][2]["display_value"] == "-"

    def test_format_clear(self):
        assert (0, 0) not in self.styler._display_funcs  # using default
        self.styler.format("{:.2f")
        assert (0, 0) in self.styler._display_funcs  # formatter is specified
        self.styler.format()
        assert (0, 0) not in self.styler._display_funcs  # formatter cleared to default

    def test_format_escape(self):
        df = DataFrame([['<>&"']])
        s = Styler(df, uuid_len=0).format("X&{0}>X", escape=False)
        expected = '<td id="T__row0_col0" class="data row0 col0" >X&<>&">X</td>'
        assert expected in s.render()

        # only the value should be escaped before passing to the formatter
        s = Styler(df, uuid_len=0).format("X&{0}>X", escape=True)
        ex = '<td id="T__row0_col0" class="data row0 col0" >X&&lt;&gt;&amp;&#34;>X</td>'
        assert ex in s.render()

    def test_format_escape_na_rep(self):
        # tests the na_rep is not escaped
        df = DataFrame([['<>&"', None]])
        s = Styler(df, uuid_len=0).format("X&{0}>X", escape=True, na_rep="&")
        ex = '<td id="T__row0_col0" class="data row0 col0" >X&&lt;&gt;&amp;&#34;>X</td>'
        expected2 = '<td id="T__row0_col1" class="data row0 col1" >&</td>'
        assert ex in s.render()
        assert expected2 in s.render()

    def test_format_escape_floats(self):
        # test given formatter for number format is not impacted by escape
        s = self.df.style.format("{:.1f}", escape=True)
        for expected in [">0.0<", ">1.0<", ">-1.2<", ">-0.6<"]:
            assert expected in s.render()
        # tests precision of floats is not impacted by escape
        s = self.df.style.format(precision=1, escape=True)
        for expected in [">0<", ">1<", ">-1.2<", ">-0.6<"]:
            assert expected in s.render()

    @pytest.mark.parametrize("formatter", [None, "{:,.1f}"])
    @pytest.mark.parametrize("decimal", [".", "*"])
    @pytest.mark.parametrize("precision", [None, 2])
    def test_format_thousands(self, formatter, decimal, precision):
        s = DataFrame([[1000000.123456789]]).style  # test float
        result = s.format(
            thousands="_", formatter=formatter, decimal=decimal, precision=precision
        )._translate()
        assert "1_000_000" in result["body"][0][1]["display_value"]

        s = DataFrame([[1000000]]).style  # test int
        result = s.format(
            thousands="_", formatter=formatter, decimal=decimal, precision=precision
        )._translate()
        assert "1_000_000" in result["body"][0][1]["display_value"]

        s = DataFrame([[1 + 1000000.123456789j]]).style  # test complex
        result = s.format(
            thousands="_", formatter=formatter, decimal=decimal, precision=precision
        )._translate()
        assert "1_000_000" in result["body"][0][1]["display_value"]

    @pytest.mark.parametrize("formatter", [None, "{:,.4f}"])
    @pytest.mark.parametrize("thousands", [None, ",", "*"])
    @pytest.mark.parametrize("precision", [None, 4])
    def test_format_decimal(self, formatter, thousands, precision):
        s = DataFrame([[1000000.123456789]]).style  # test float
        result = s.format(
            decimal="_", formatter=formatter, thousands=thousands, precision=precision
        )._translate()
        assert "000_123" in result["body"][0][1]["display_value"]

        s = DataFrame([[1 + 1000000.123456789j]]).style  # test complex
        result = s.format(
            decimal="_", formatter=formatter, thousands=thousands, precision=precision
        )._translate()
        assert "000_123" in result["body"][0][1]["display_value"]

    def test_nonunique_raises(self):
        df = DataFrame([[1, 2]], columns=["A", "A"])
        msg = "style is not supported for non-unique indices."
        with pytest.raises(ValueError, match=msg):
            df.style

        with pytest.raises(ValueError, match=msg):
            Styler(df)

    def test_caption(self):
        styler = Styler(self.df, caption="foo")
        result = styler.render()
        assert all(["caption" in result, "foo" in result])

        styler = self.df.style
        result = styler.set_caption("baz")
        assert styler is result
        assert styler.caption == "baz"

    def test_uuid(self):
        styler = Styler(self.df, uuid="abc123")
        result = styler.render()
        assert "abc123" in result

        styler = self.df.style
        result = styler.set_uuid("aaa")
        assert result is styler
        assert result.uuid == "aaa"

    def test_unique_id(self):
        # See https://github.com/pandas-dev/pandas/issues/16780
        df = DataFrame({"a": [1, 3, 5, 6], "b": [2, 4, 12, 21]})
        result = df.style.render(uuid="test")
        assert "test" in result
        ids = re.findall('id="(.*?)"', result)
        assert np.unique(ids).size == len(ids)

    def test_table_styles(self):
        style = [{"selector": "th", "props": [("foo", "bar")]}]  # default format
        styler = Styler(self.df, table_styles=style)
        result = " ".join(styler.render().split())
        assert "th { foo: bar; }" in result

        styler = self.df.style
        result = styler.set_table_styles(style)
        assert styler is result
        assert styler.table_styles == style

        # GH 39563
        style = [{"selector": "th", "props": "foo:bar;"}]  # css string format
        styler = self.df.style.set_table_styles(style)
        result = " ".join(styler.render().split())
        assert "th { foo: bar; }" in result

    def test_table_styles_multiple(self):
        ctx = self.df.style.set_table_styles(
            [
                {"selector": "th,td", "props": "color:red;"},
                {"selector": "tr", "props": "color:green;"},
            ]
        )._translate()["table_styles"]
        assert ctx == [
            {"selector": "th", "props": [("color", "red")]},
            {"selector": "td", "props": [("color", "red")]},
            {"selector": "tr", "props": [("color", "green")]},
        ]

    def test_maybe_convert_css_to_tuples(self):
        expected = [("a", "b"), ("c", "d e")]
        assert _maybe_convert_css_to_tuples("a:b;c:d e;") == expected
        assert _maybe_convert_css_to_tuples("a: b ;c:  d e  ") == expected
        expected = []
        assert _maybe_convert_css_to_tuples("") == expected

    def test_maybe_convert_css_to_tuples_err(self):
        msg = "Styles supplied as string must follow CSS rule formats"
        with pytest.raises(ValueError, match=msg):
            _maybe_convert_css_to_tuples("err")

    def test_table_attributes(self):
        attributes = 'class="foo" data-bar'
        styler = Styler(self.df, table_attributes=attributes)
        result = styler.render()
        assert 'class="foo" data-bar' in result

        result = self.df.style.set_table_attributes(attributes).render()
        assert 'class="foo" data-bar' in result

    def test_precision(self):
        s = Styler(self.df, precision=2)
        assert s.precision == 2

        with tm.assert_produces_warning(FutureWarning):
            s2 = s.set_precision(4)
        assert s is s2
        assert s.precision == 4

    def test_apply_none(self):
        def f(x):
            return DataFrame(
                np.where(x == x.max(), "color: red", ""),
                index=x.index,
                columns=x.columns,
            )

        result = DataFrame([[1, 2], [3, 4]]).style.apply(f, axis=None)._compute().ctx
        assert result[(1, 1)] == [("color", "red")]

    def test_trim(self):
        result = self.df.style.render()  # trim=True
        assert result.count("#") == 0

        result = self.df.style.highlight_max().render()
        assert result.count("#") == len(self.df.columns)

    def test_export(self):
        f = lambda x: "color: red" if x > 0 else "color: blue"
        g = lambda x, z: f"color: {z}" if x > 0 else f"color: {z}"
        style1 = self.styler
        style1.applymap(f).applymap(g, z="b").highlight_max()._compute()  # = render
        result = style1.export()
        style2 = self.df.style
        style2.use(result)
        assert style1._todo == style2._todo
        style2.render()

    def test_display_format(self):
        df = DataFrame(np.random.random(size=(2, 2)))
        ctx = df.style.format("{:0.1f}")._translate()

        assert all(["display_value" in c for c in row] for row in ctx["body"])
        assert all(
            [len(c["display_value"]) <= 3 for c in row[1:]] for row in ctx["body"]
        )
        assert len(ctx["body"][0][1]["display_value"].lstrip("-")) <= 3

    @pytest.mark.parametrize("formatter", [5, True, [2.0]])
    def test_format_raises(self, formatter):
        with pytest.raises(TypeError, match="expected str or callable"):
            self.df.style.format(formatter)

    def test_format_with_precision(self):
        # Issue #13257
        df = DataFrame(data=[[1.0, 2.0090], [3.2121, 4.566]], columns=["a", "b"])
        s = Styler(df)

        ctx = s.format(precision=1)._translate()
        assert ctx["body"][0][1]["display_value"] == "1.0"
        assert ctx["body"][0][2]["display_value"] == "2.0"
        assert ctx["body"][1][1]["display_value"] == "3.2"
        assert ctx["body"][1][2]["display_value"] == "4.6"

        ctx = s.format(precision=2)._translate()
        assert ctx["body"][0][1]["display_value"] == "1.00"
        assert ctx["body"][0][2]["display_value"] == "2.01"
        assert ctx["body"][1][1]["display_value"] == "3.21"
        assert ctx["body"][1][2]["display_value"] == "4.57"

        ctx = s.format(precision=3)._translate()
        assert ctx["body"][0][1]["display_value"] == "1.000"
        assert ctx["body"][0][2]["display_value"] == "2.009"
        assert ctx["body"][1][1]["display_value"] == "3.212"
        assert ctx["body"][1][2]["display_value"] == "4.566"

    def test_format_subset(self):
        df = DataFrame([[0.1234, 0.1234], [1.1234, 1.1234]], columns=["a", "b"])
        ctx = df.style.format(
            {"a": "{:0.1f}", "b": "{0:.2%}"}, subset=pd.IndexSlice[0, :]
        )._translate()
        expected = "0.1"
        raw_11 = "1.123400"
        assert ctx["body"][0][1]["display_value"] == expected
        assert ctx["body"][1][1]["display_value"] == raw_11
        assert ctx["body"][0][2]["display_value"] == "12.34%"

        ctx = df.style.format("{:0.1f}", subset=pd.IndexSlice[0, :])._translate()
        assert ctx["body"][0][1]["display_value"] == expected
        assert ctx["body"][1][1]["display_value"] == raw_11

        ctx = df.style.format("{:0.1f}", subset=pd.IndexSlice["a"])._translate()
        assert ctx["body"][0][1]["display_value"] == expected
        assert ctx["body"][0][2]["display_value"] == "0.123400"

        ctx = df.style.format("{:0.1f}", subset=pd.IndexSlice[0, "a"])._translate()
        assert ctx["body"][0][1]["display_value"] == expected
        assert ctx["body"][1][1]["display_value"] == raw_11

        ctx = df.style.format(
            "{:0.1f}", subset=pd.IndexSlice[[0, 1], ["a"]]
        )._translate()
        assert ctx["body"][0][1]["display_value"] == expected
        assert ctx["body"][1][1]["display_value"] == "1.1"
        assert ctx["body"][0][2]["display_value"] == "0.123400"
        assert ctx["body"][1][2]["display_value"] == raw_11

    def test_format_dict(self):
        df = DataFrame([[0.1234, 0.1234], [1.1234, 1.1234]], columns=["a", "b"])
        ctx = df.style.format({"a": "{:0.1f}", "b": "{0:.2%}"})._translate()
        assert ctx["body"][0][1]["display_value"] == "0.1"
        assert ctx["body"][0][2]["display_value"] == "12.34%"
        df["c"] = ["aaa", "bbb"]
        ctx = df.style.format({"a": "{:0.1f}", "c": str.upper})._translate()
        assert ctx["body"][0][1]["display_value"] == "0.1"
        assert ctx["body"][0][3]["display_value"] == "AAA"

    def test_bad_apply_shape(self):
        df = DataFrame([[1, 2], [3, 4]])
        msg = "returned the wrong shape"
        with pytest.raises(ValueError, match=msg):
            df.style._apply(lambda x: "x", subset=pd.IndexSlice[[0, 1], :])

        with pytest.raises(ValueError, match=msg):
            df.style._apply(lambda x: [""], subset=pd.IndexSlice[[0, 1], :])

        with pytest.raises(ValueError, match=msg):
            df.style._apply(lambda x: ["", "", "", ""])

        with pytest.raises(ValueError, match=msg):
            df.style._apply(lambda x: ["", "", ""], subset=1)

        msg = "Length mismatch: Expected axis has 3 elements"
        with pytest.raises(ValueError, match=msg):
            df.style._apply(lambda x: ["", "", ""], axis=1)

        msg = "returned ndarray with wrong shape"
        with pytest.raises(ValueError, match=msg):
            df.style._apply(lambda x: np.array([[""], [""]]), axis=None)

    def test_apply_bad_return(self):
        def f(x):
            return ""

        df = DataFrame([[1, 2], [3, 4]])
        msg = (
            "must return a DataFrame or ndarray when passed to `Styler.apply` "
            "with axis=None"
        )
        with pytest.raises(TypeError, match=msg):
            df.style._apply(f, axis=None)

    def test_apply_bad_labels(self):
        def f(x):
            return DataFrame(index=[1, 2], columns=["a", "b"])

        df = DataFrame([[1, 2], [3, 4]])
        msg = "must have identical index and columns as the input"
        with pytest.raises(ValueError, match=msg):
            df.style._apply(f, axis=None)

    def test_get_level_lengths(self):
        index = pd.MultiIndex.from_product([["a", "b"], [0, 1, 2]])
        expected = {
            (0, 0): 3,
            (0, 3): 3,
            (1, 0): 1,
            (1, 1): 1,
            (1, 2): 1,
            (1, 3): 1,
            (1, 4): 1,
            (1, 5): 1,
        }
        result = _get_level_lengths(index)
        tm.assert_dict_equal(result, expected)

    def test_get_level_lengths_un_sorted(self):
        index = pd.MultiIndex.from_arrays([[1, 1, 2, 1], ["a", "b", "b", "d"]])
        expected = {
            (0, 0): 2,
            (0, 2): 1,
            (0, 3): 1,
            (1, 0): 1,
            (1, 1): 1,
            (1, 2): 1,
            (1, 3): 1,
        }
        result = _get_level_lengths(index)
        tm.assert_dict_equal(result, expected)

    def test_mi_sparse(self):
        df = DataFrame(
            {"A": [1, 2]}, index=pd.MultiIndex.from_arrays([["a", "a"], [0, 1]])
        )

        result = df.style._translate()
        body_0 = result["body"][0][0]
        expected_0 = {
            "value": "a",
            "display_value": "a",
            "is_visible": True,
            "type": "th",
            "attributes": 'rowspan="2"',
            "class": "row_heading level0 row0",
            "id": "level0_row0",
        }
        tm.assert_dict_equal(body_0, expected_0)

        body_1 = result["body"][0][1]
        expected_1 = {
            "value": 0,
            "display_value": 0,
            "is_visible": True,
            "type": "th",
            "class": "row_heading level1 row0",
            "id": "level1_row0",
        }
        tm.assert_dict_equal(body_1, expected_1)

        body_10 = result["body"][1][0]
        expected_10 = {
            "value": "a",
            "display_value": "a",
            "is_visible": False,
            "type": "th",
            "class": "row_heading level0 row1",
            "id": "level0_row1",
        }
        tm.assert_dict_equal(body_10, expected_10)

        head = result["head"][0]
        expected = [
            {
                "type": "th",
                "class": "blank",
                "value": self.blank_value,
                "is_visible": True,
                "display_value": self.blank_value,
            },
            {
                "type": "th",
                "class": "blank level0",
                "value": self.blank_value,
                "is_visible": True,
                "display_value": self.blank_value,
            },
            {
                "type": "th",
                "class": "col_heading level0 col0",
                "value": "A",
                "is_visible": True,
                "display_value": "A",
            },
        ]
        assert head == expected

    def test_mi_sparse_disabled(self):
        with pd.option_context("display.multi_sparse", False):
            df = DataFrame(
                {"A": [1, 2]}, index=pd.MultiIndex.from_arrays([["a", "a"], [0, 1]])
            )
            result = df.style._translate()
        body = result["body"]
        for row in body:
            assert "attributes" not in row[0]

    def test_mi_sparse_index_names(self):
        df = DataFrame(
            {"A": [1, 2]},
            index=pd.MultiIndex.from_arrays(
                [["a", "a"], [0, 1]], names=["idx_level_0", "idx_level_1"]
            ),
        )
        result = df.style._translate()
        head = result["head"][1]
        expected = [
            {"class": "index_name level0", "value": "idx_level_0", "type": "th"},
            {"class": "index_name level1", "value": "idx_level_1", "type": "th"},
            {"class": "blank col0", "value": self.blank_value, "type": "th"},
        ]

        assert head == expected

    def test_mi_sparse_column_names(self):
        df = DataFrame(
            np.arange(16).reshape(4, 4),
            index=pd.MultiIndex.from_arrays(
                [["a", "a", "b", "a"], [0, 1, 1, 2]],
                names=["idx_level_0", "idx_level_1"],
            ),
            columns=pd.MultiIndex.from_arrays(
                [["C1", "C1", "C2", "C2"], [1, 0, 1, 0]], names=["col_0", "col_1"]
            ),
        )
        result = df.style._translate()
        head = result["head"][1]
        expected = [
            {
                "class": "blank",
                "value": self.blank_value,
                "display_value": self.blank_value,
                "type": "th",
                "is_visible": True,
            },
            {
                "class": "index_name level1",
                "value": "col_1",
                "display_value": "col_1",
                "is_visible": True,
                "type": "th",
            },
            {
                "class": "col_heading level1 col0",
                "display_value": 1,
                "is_visible": True,
                "type": "th",
                "value": 1,
            },
            {
                "class": "col_heading level1 col1",
                "display_value": 0,
                "is_visible": True,
                "type": "th",
                "value": 0,
            },
            {
                "class": "col_heading level1 col2",
                "display_value": 1,
                "is_visible": True,
                "type": "th",
                "value": 1,
            },
            {
                "class": "col_heading level1 col3",
                "display_value": 0,
                "is_visible": True,
                "type": "th",
                "value": 0,
            },
        ]
        assert head == expected

    def test_hide_single_index(self):
        # GH 14194
        # single unnamed index
        ctx = self.df.style._translate()
        assert ctx["body"][0][0]["is_visible"]
        assert ctx["head"][0][0]["is_visible"]
        ctx2 = self.df.style.hide_index()._translate()
        assert not ctx2["body"][0][0]["is_visible"]
        assert not ctx2["head"][0][0]["is_visible"]

        # single named index
        ctx3 = self.df.set_index("A").style._translate()
        assert ctx3["body"][0][0]["is_visible"]
        assert len(ctx3["head"]) == 2  # 2 header levels
        assert ctx3["head"][0][0]["is_visible"]

        ctx4 = self.df.set_index("A").style.hide_index()._translate()
        assert not ctx4["body"][0][0]["is_visible"]
        assert len(ctx4["head"]) == 1  # only 1 header levels
        assert not ctx4["head"][0][0]["is_visible"]

    def test_hide_multiindex(self):
        # GH 14194
        df = DataFrame(
            {"A": [1, 2]},
            index=pd.MultiIndex.from_arrays(
                [["a", "a"], [0, 1]], names=["idx_level_0", "idx_level_1"]
            ),
        )
        ctx1 = df.style._translate()
        # tests for 'a' and '0'
        assert ctx1["body"][0][0]["is_visible"]
        assert ctx1["body"][0][1]["is_visible"]
        # check for blank header rows
        assert ctx1["head"][0][0]["is_visible"]
        assert ctx1["head"][0][1]["is_visible"]

        ctx2 = df.style.hide_index()._translate()
        # tests for 'a' and '0'
        assert not ctx2["body"][0][0]["is_visible"]
        assert not ctx2["body"][0][1]["is_visible"]
        # check for blank header rows
        assert not ctx2["head"][0][0]["is_visible"]
        assert not ctx2["head"][0][1]["is_visible"]

    def test_hide_columns_single_level(self):
        # GH 14194
        # test hiding single column
        ctx = self.df.style._translate()
        assert ctx["head"][0][1]["is_visible"]
        assert ctx["head"][0][1]["display_value"] == "A"
        assert ctx["head"][0][2]["is_visible"]
        assert ctx["head"][0][2]["display_value"] == "B"
        assert ctx["body"][0][1]["is_visible"]  # col A, row 1
        assert ctx["body"][1][2]["is_visible"]  # col B, row 1

        ctx = self.df.style.hide_columns("A")._translate()
        assert not ctx["head"][0][1]["is_visible"]
        assert not ctx["body"][0][1]["is_visible"]  # col A, row 1
        assert ctx["body"][1][2]["is_visible"]  # col B, row 1

        # test hiding mulitiple columns
        ctx = self.df.style.hide_columns(["A", "B"])._translate()
        assert not ctx["head"][0][1]["is_visible"]
        assert not ctx["head"][0][2]["is_visible"]
        assert not ctx["body"][0][1]["is_visible"]  # col A, row 1
        assert not ctx["body"][1][2]["is_visible"]  # col B, row 1

    def test_hide_columns_mult_levels(self):
        # GH 14194
        # setup dataframe with multiple column levels and indices
        i1 = pd.MultiIndex.from_arrays(
            [["a", "a"], [0, 1]], names=["idx_level_0", "idx_level_1"]
        )
        i2 = pd.MultiIndex.from_arrays(
            [["b", "b"], [0, 1]], names=["col_level_0", "col_level_1"]
        )
        df = DataFrame([[1, 2], [3, 4]], index=i1, columns=i2)
        ctx = df.style._translate()
        # column headers
        assert ctx["head"][0][2]["is_visible"]
        assert ctx["head"][1][2]["is_visible"]
        assert ctx["head"][1][3]["display_value"] == 1
        # indices
        assert ctx["body"][0][0]["is_visible"]
        # data
        assert ctx["body"][1][2]["is_visible"]
        assert ctx["body"][1][2]["display_value"] == 3
        assert ctx["body"][1][3]["is_visible"]
        assert ctx["body"][1][3]["display_value"] == 4

        # hide top column level, which hides both columns
        ctx = df.style.hide_columns("b")._translate()
        assert not ctx["head"][0][2]["is_visible"]  # b
        assert not ctx["head"][1][2]["is_visible"]  # 0
        assert not ctx["body"][1][2]["is_visible"]  # 3
        assert ctx["body"][0][0]["is_visible"]  # index

        # hide first column only
        ctx = df.style.hide_columns([("b", 0)])._translate()
        assert ctx["head"][0][2]["is_visible"]  # b
        assert not ctx["head"][1][2]["is_visible"]  # 0
        assert not ctx["body"][1][2]["is_visible"]  # 3
        assert ctx["body"][1][3]["is_visible"]
        assert ctx["body"][1][3]["display_value"] == 4

        # hide second column and index
        ctx = df.style.hide_columns([("b", 1)]).hide_index()._translate()
        assert not ctx["body"][0][0]["is_visible"]  # index
        assert ctx["head"][0][2]["is_visible"]  # b
        assert ctx["head"][1][2]["is_visible"]  # 0
        assert not ctx["head"][1][3]["is_visible"]  # 1
        assert not ctx["body"][1][3]["is_visible"]  # 4
        assert ctx["body"][1][2]["is_visible"]
        assert ctx["body"][1][2]["display_value"] == 3

    def test_pipe(self):
        def set_caption_from_template(styler, a, b):
            return styler.set_caption(f"Dataframe with a = {a} and b = {b}")

        styler = self.df.style.pipe(set_caption_from_template, "A", b="B")
        assert "Dataframe with a = A and b = B" in styler.render()

        # Test with an argument that is a (callable, keyword_name) pair.
        def f(a, b, styler):
            return (a, b, styler)

        styler = self.df.style
        result = styler.pipe((f, "styler"), a=1, b=2)
        assert result == (1, 2, styler)

    def test_no_cell_ids(self):
        # GH 35588
        # GH 35663
        df = DataFrame(data=[[0]])
        styler = Styler(df, uuid="_", cell_ids=False)
        styler.render()
        s = styler.render()  # render twice to ensure ctx is not updated
        assert s.find('<td  class="data row0 col0" >') != -1

    @pytest.mark.parametrize(
        "classes",
        [
            DataFrame(
                data=[["", "test-class"], [np.nan, None]],
                columns=["A", "B"],
                index=["a", "b"],
            ),
            DataFrame(data=[["test-class"]], columns=["B"], index=["a"]),
            DataFrame(data=[["test-class", "unused"]], columns=["B", "C"], index=["a"]),
        ],
    )
    def test_set_data_classes(self, classes):
        # GH 36159
        df = DataFrame(data=[[0, 1], [2, 3]], columns=["A", "B"], index=["a", "b"])
        s = Styler(df, uuid_len=0, cell_ids=False).set_td_classes(classes).render()
        assert '<td  class="data row0 col0" >0</td>' in s
        assert '<td  class="data row0 col1 test-class" >1</td>' in s
        assert '<td  class="data row1 col0" >2</td>' in s
        assert '<td  class="data row1 col1" >3</td>' in s
        # GH 39317
        s = Styler(df, uuid_len=0, cell_ids=True).set_td_classes(classes).render()
        assert '<td id="T__row0_col0" class="data row0 col0" >0</td>' in s
        assert '<td id="T__row0_col1" class="data row0 col1 test-class" >1</td>' in s
        assert '<td id="T__row1_col0" class="data row1 col0" >2</td>' in s
        assert '<td id="T__row1_col1" class="data row1 col1" >3</td>' in s

    def test_set_data_classes_reindex(self):
        # GH 39317
        df = DataFrame(
            data=[[0, 1, 2], [3, 4, 5], [6, 7, 8]], columns=[0, 1, 2], index=[0, 1, 2]
        )
        classes = DataFrame(
            data=[["mi", "ma"], ["mu", "mo"]],
            columns=[0, 2],
            index=[0, 2],
        )
        s = Styler(df, uuid_len=0).set_td_classes(classes).render()
        assert '<td id="T__row0_col0" class="data row0 col0 mi" >0</td>' in s
        assert '<td id="T__row0_col2" class="data row0 col2 ma" >2</td>' in s
        assert '<td id="T__row1_col1" class="data row1 col1" >4</td>' in s
        assert '<td id="T__row2_col0" class="data row2 col0 mu" >6</td>' in s
        assert '<td id="T__row2_col2" class="data row2 col2 mo" >8</td>' in s

    def test_chaining_table_styles(self):
        # GH 35607
        df = DataFrame(data=[[0, 1], [1, 2]], columns=["A", "B"])
        styler = df.style.set_table_styles(
            [{"selector": "", "props": [("background-color", "yellow")]}]
        ).set_table_styles(
            [{"selector": ".col0", "props": [("background-color", "blue")]}],
            overwrite=False,
        )
        assert len(styler.table_styles) == 2

    def test_column_and_row_styling(self):
        # GH 35607
        df = DataFrame(data=[[0, 1], [1, 2]], columns=["A", "B"])
        s = Styler(df, uuid_len=0)
        s = s.set_table_styles({"A": [{"selector": "", "props": [("color", "blue")]}]})
        assert "#T__ .col0 {\n  color: blue;\n}" in s.render()
        s = s.set_table_styles(
            {0: [{"selector": "", "props": [("color", "blue")]}]}, axis=1
        )
        assert "#T__ .row0 {\n  color: blue;\n}" in s.render()

    def test_colspan_w3(self):
        # GH 36223
        df = DataFrame(data=[[1, 2]], columns=[["l0", "l0"], ["l1a", "l1b"]])
        s = Styler(df, uuid="_", cell_ids=False)
        assert '<th class="col_heading level0 col0" colspan="2">l0</th>' in s.render()

    def test_rowspan_w3(self):
        # GH 38533
        df = DataFrame(data=[[1, 2]], index=[["l0", "l0"], ["l1a", "l1b"]])
        s = Styler(df, uuid="_", cell_ids=False)
        assert (
            '<th id="T___level0_row0" class="row_heading '
            'level0 row0" rowspan="2">l0</th>' in s.render()
        )

    @pytest.mark.parametrize("len_", [1, 5, 32, 33, 100])
    def test_uuid_len(self, len_):
        # GH 36345
        df = DataFrame(data=[["A"]])
        s = Styler(df, uuid_len=len_, cell_ids=False).render()
        strt = s.find('id="T_')
        end = s[strt + 6 :].find('"')
        if len_ > 32:
            assert end == 32 + 1
        else:
            assert end == len_ + 1

    @pytest.mark.parametrize("len_", [-2, "bad", None])
    def test_uuid_len_raises(self, len_):
        # GH 36345
        df = DataFrame(data=[["A"]])
        msg = "``uuid_len`` must be an integer in range \\[0, 32\\]."
        with pytest.raises(TypeError, match=msg):
            Styler(df, uuid_len=len_, cell_ids=False).render()

    def test_w3_html_format(self):
        s = (
            Styler(
                DataFrame([[2.61], [2.69]], index=["a", "b"], columns=["A"]),
                uuid_len=0,
            )
            .set_table_styles([{"selector": "th", "props": "att2:v2;"}])
            .applymap(lambda x: "att1:v1;")
            .set_table_attributes('class="my-cls1" style="attr3:v3;"')
            .set_td_classes(DataFrame(["my-cls2"], index=["a"], columns=["A"]))
            .format("{:.1f}")
            .set_caption("A comprehensive test")
        )
        expected = """<style type="text/css">
#T__ th {
  att2: v2;
}
#T__row0_col0, #T__row1_col0 {
  att1: v1;
}
</style>
<table id="T__" class="my-cls1" style="attr3:v3;">
  <caption>A comprehensive test</caption>
  <thead>
    <tr>
      <th class="blank level0" >&nbsp;</th>
      <th class="col_heading level0 col0" >A</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th id="T__level0_row0" class="row_heading level0 row0" >a</th>
      <td id="T__row0_col0" class="data row0 col0 my-cls2" >2.6</td>
    </tr>
    <tr>
      <th id="T__level0_row1" class="row_heading level0 row1" >b</th>
      <td id="T__row1_col0" class="data row1 col0" >2.7</td>
    </tr>
  </tbody>
</table>
"""
        assert expected == s.render()

    @pytest.mark.parametrize(
        "slc",
        [
            pd.IndexSlice[:, :],
            pd.IndexSlice[:, 1],
            pd.IndexSlice[1, :],
            pd.IndexSlice[[1], [1]],
            pd.IndexSlice[1, [1]],
            pd.IndexSlice[[1], 1],
            pd.IndexSlice[1],
            pd.IndexSlice[1, 1],
            slice(None, None, None),
            [0, 1],
            np.array([0, 1]),
            pd.Series([0, 1]),
        ],
    )
    def test_non_reducing_slice(self, slc):
        df = DataFrame([[0, 1], [2, 3]])

        tslice_ = _non_reducing_slice(slc)
        assert isinstance(df.loc[tslice_], DataFrame)

    @pytest.mark.parametrize("box", [list, pd.Series, np.array])
    def test_list_slice(self, box):
        # like dataframe getitem
        subset = box(["A"])

        df = DataFrame({"A": [1, 2], "B": [3, 4]}, index=["A", "B"])
        expected = pd.IndexSlice[:, ["A"]]

        result = _non_reducing_slice(subset)
        tm.assert_frame_equal(df.loc[result], df.loc[expected])

    def test_non_reducing_slice_on_multiindex(self):
        # GH 19861
        dic = {
            ("a", "d"): [1, 4],
            ("a", "c"): [2, 3],
            ("b", "c"): [3, 2],
            ("b", "d"): [4, 1],
        }
        df = DataFrame(dic, index=[0, 1])
        idx = pd.IndexSlice
        slice_ = idx[:, idx["b", "d"]]
        tslice_ = _non_reducing_slice(slice_)

        result = df.loc[tslice_]
        expected = DataFrame({("b", "d"): [4, 1]})
        tm.assert_frame_equal(result, expected)

    @pytest.mark.parametrize(
        "slice_",
        [
            pd.IndexSlice[:, :],
            # check cols
            pd.IndexSlice[:, pd.IndexSlice[["a"]]],  # inferred deeper need list
            pd.IndexSlice[:, pd.IndexSlice[["a"], ["c"]]],  # inferred deeper need list
            pd.IndexSlice[:, pd.IndexSlice["a", "c", :]],
            pd.IndexSlice[:, pd.IndexSlice["a", :, "e"]],
            pd.IndexSlice[:, pd.IndexSlice[:, "c", "e"]],
            pd.IndexSlice[:, pd.IndexSlice["a", ["c", "d"], :]],  # check list
            pd.IndexSlice[:, pd.IndexSlice["a", ["c", "d", "-"], :]],  # allow missing
            pd.IndexSlice[:, pd.IndexSlice["a", ["c", "d", "-"], "e"]],  # no slice
            # check rows
            pd.IndexSlice[pd.IndexSlice[["U"]], :],  # inferred deeper need list
            pd.IndexSlice[pd.IndexSlice[["U"], ["W"]], :],  # inferred deeper need list
            pd.IndexSlice[pd.IndexSlice["U", "W", :], :],
            pd.IndexSlice[pd.IndexSlice["U", :, "Y"], :],
            pd.IndexSlice[pd.IndexSlice[:, "W", "Y"], :],
            pd.IndexSlice[pd.IndexSlice[:, "W", ["Y", "Z"]], :],  # check list
            pd.IndexSlice[pd.IndexSlice[:, "W", ["Y", "Z", "-"]], :],  # allow missing
            pd.IndexSlice[pd.IndexSlice["U", "W", ["Y", "Z", "-"]], :],  # no slice
            # check simultaneous
            pd.IndexSlice[pd.IndexSlice[:, "W", "Y"], pd.IndexSlice["a", "c", :]],
        ],
    )
    def test_non_reducing_multi_slice_on_multiindex(self, slice_):
        # GH 33562
        cols = pd.MultiIndex.from_product([["a", "b"], ["c", "d"], ["e", "f"]])
        idxs = pd.MultiIndex.from_product([["U", "V"], ["W", "X"], ["Y", "Z"]])
        df = DataFrame(np.arange(64).reshape(8, 8), columns=cols, index=idxs)

        expected = df.loc[slice_]
        result = df.loc[_non_reducing_slice(slice_)]
        tm.assert_frame_equal(result, expected)


def test_block_names():
    # catch accidental removal of a block
    expected = {
        "before_style",
        "style",
        "table_styles",
        "before_cellstyle",
        "cellstyle",
        "before_table",
        "table",
        "caption",
        "thead",
        "tbody",
        "after_table",
        "before_head_rows",
        "head_tr",
        "after_head_rows",
        "before_rows",
        "tr",
        "after_rows",
    }
    result = set(Styler.template.blocks)
    assert result == expected


def test_from_custom_template(tmpdir):
    p = tmpdir.mkdir("templates").join("myhtml.tpl")
    p.write(
        textwrap.dedent(
            """\
        {% extends "html.tpl" %}
        {% block table %}
        <h1>{{ table_title|default("My Table") }}</h1>
        {{ super() }}
        {% endblock table %}"""
        )
    )
    result = Styler.from_custom_template(str(tmpdir.join("templates")), "myhtml.tpl")
    assert issubclass(result, Styler)
    assert result.env is not Styler.env
    assert result.template is not Styler.template
    styler = result(DataFrame({"A": [1, 2]}))
    assert styler.render()