import copy
import re
import textwrap

import numpy as np
import pytest

import pandas.util._test_decorators as td

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

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


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

    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(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

    def test_update_ctx_flatten_multi_traliing_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

    def test_copy(self):
        s2 = copy.copy(self.styler)
        assert self.styler is not s2
        assert self.styler.ctx is s2.ctx  # shallow
        assert self.styler._todo is s2._todo

        self.styler._update_ctx(self.attrs)
        self.styler.highlight_max()
        assert self.styler.ctx == s2.ctx
        assert self.styler._todo == s2._todo

    def test_deepcopy(self):
        s2 = copy.deepcopy(self.styler)
        assert self.styler is not s2
        assert self.styler.ctx is not s2.ctx
        assert self.styler._todo is not s2._todo

        self.styler._update_ctx(self.attrs)
        self.styler.highlight_max()
        assert self.styler.ctx != s2.ctx
        assert s2._todo == []
        assert self.styler._todo != s2._todo

    def test_clear(self):
        s = self.df.style.highlight_max()._compute()
        assert len(s.ctx) > 0
        assert len(s._todo) > 0
        s.clear()
        assert len(s.ctx) == 0
        assert len(s._todo) == 0

    def test_render(self):
        df = pd.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_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 = pd.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 = pd.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 = pd.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 = pd.DataFrame({"A": [1, 2], "B": [3, 4], "C": [5, 6]})
        result = df.style._translate()

        expected = [
            [
                {
                    "class": "blank level0",
                    "type": "th",
                    "value": "",
                    "is_visible": True,
                    "display_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 = pd.DataFrame({"A": [1, 2], "B": [3, 4], "C": [5, 6]})
        result = df.set_index("A").style._translate()

        expected = [
            [
                {
                    "class": "blank level0",
                    "type": "th",
                    "value": "",
                    "display_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", "type": "th", "value": ""},
                {"class": "blank", "type": "th", "value": ""},
            ],
        ]

        assert result["head"] == expected

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

        expected = [
            [
                {
                    "class": "blank",
                    "type": "th",
                    "value": "",
                    "display_value": "",
                    "is_visible": True,
                },
                {
                    "class": "blank level0",
                    "type": "th",
                    "value": "",
                    "display_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", "type": "th", "value": ""},
            ],
        ]

        assert result["head"] == expected

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

    def test_apply_axis(self):
        df = pd.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

    def test_apply_subset(self):
        axes = [0, 1]
        slices = [
            pd.IndexSlice[:],
            pd.IndexSlice[:, ["A"]],
            pd.IndexSlice[[1], :],
            pd.IndexSlice[[1], ["A"]],
            pd.IndexSlice[:2, ["A", "B"]],
        ]
        for ax in axes:
            for slice_ in slices:
                result = (
                    self.df.style.apply(self.h, axis=ax, 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

    def test_applymap_subset(self):
        def f(x):
            return "foo: bar"

        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.applymap(f, subset=slice_)._compute().ctx
            expected = {
                (r, c): ["foo: bar"]
                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_applymap_subset_multiindex(self):
        # GH 19861
        # Smoke test for applymap
        def color_negative_red(val):
            """
            Takes a scalar and returns a string with
            the css property `'color: red'` for negative
            strings, black otherwise.
            """
            color = "red" if val < 0 else "black"
            return f"color: {color}"

        dic = {
            ("a", "d"): [-1.12, 2.11],
            ("a", "c"): [2.78, -2.88],
            ("b", "c"): [-3.99, 3.77],
            ("b", "d"): [4.21, -1.22],
        }

        idx = pd.IndexSlice
        df = pd.DataFrame(dic, index=[0, 1])

        (df.style.applymap(color_negative_red, subset=idx[:, idx["b", "d"]]).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): [style1]
            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

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

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

        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 = {
                (r, c): [style1 if f(self.df.loc[row, col]) else style2]
                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 = pd.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 = pd.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_bar_align_left(self):
        df = pd.DataFrame({"A": [0, 1, 2]})
        result = df.style.bar()._compute().ctx
        expected = {
            (0, 0): ["width: 10em", " height: 80%"],
            (1, 0): [
                "width: 10em",
                " height: 80%",
                "background: linear-gradient("
                "90deg,#d65f5f 50.0%, transparent 50.0%)",
            ],
            (2, 0): [
                "width: 10em",
                " height: 80%",
                "background: linear-gradient("
                "90deg,#d65f5f 100.0%, transparent 100.0%)",
            ],
        }
        assert result == expected

        result = df.style.bar(color="red", width=50)._compute().ctx
        expected = {
            (0, 0): ["width: 10em", " height: 80%"],
            (1, 0): [
                "width: 10em",
                " height: 80%",
                "background: linear-gradient(90deg,red 25.0%, transparent 25.0%)",
            ],
            (2, 0): [
                "width: 10em",
                " height: 80%",
                "background: linear-gradient(90deg,red 50.0%, transparent 50.0%)",
            ],
        }
        assert result == expected

        df["C"] = ["a"] * len(df)
        result = df.style.bar(color="red", width=50)._compute().ctx
        assert result == expected
        df["C"] = df["C"].astype("category")
        result = df.style.bar(color="red", width=50)._compute().ctx
        assert result == expected

    def test_bar_align_left_0points(self):
        df = pd.DataFrame([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
        result = df.style.bar()._compute().ctx
        expected = {
            (0, 0): ["width: 10em", " height: 80%"],
            (0, 1): ["width: 10em", " height: 80%"],
            (0, 2): ["width: 10em", " height: 80%"],
            (1, 0): [
                "width: 10em",
                " height: 80%",
                "background: linear-gradient(90deg,#d65f5f 50.0%, transparent 50.0%)",
            ],
            (1, 1): [
                "width: 10em",
                " height: 80%",
                "background: linear-gradient(90deg,#d65f5f 50.0%, transparent 50.0%)",
            ],
            (1, 2): [
                "width: 10em",
                " height: 80%",
                "background: linear-gradient(90deg,#d65f5f 50.0%, transparent 50.0%)",
            ],
            (2, 0): [
                "width: 10em",
                " height: 80%",
                "background: linear-gradient(90deg,#d65f5f 100.0%"
                ", transparent 100.0%)",
            ],
            (2, 1): [
                "width: 10em",
                " height: 80%",
                "background: linear-gradient(90deg,#d65f5f 100.0%"
                ", transparent 100.0%)",
            ],
            (2, 2): [
                "width: 10em",
                " height: 80%",
                "background: linear-gradient(90deg,#d65f5f 100.0%"
                ", transparent 100.0%)",
            ],
        }
        assert result == expected

        result = df.style.bar(axis=1)._compute().ctx
        expected = {
            (0, 0): ["width: 10em", " height: 80%"],
            (0, 1): [
                "width: 10em",
                " height: 80%",
                "background: linear-gradient(90deg,#d65f5f 50.0%, transparent 50.0%)",
            ],
            (0, 2): [
                "width: 10em",
                " height: 80%",
                "background: linear-gradient(90deg,#d65f5f 100.0%"
                ", transparent 100.0%)",
            ],
            (1, 0): ["width: 10em", " height: 80%"],
            (1, 1): [
                "width: 10em",
                " height: 80%",
                "background: linear-gradient(90deg,#d65f5f 50.0%"
                ", transparent 50.0%)",
            ],
            (1, 2): [
                "width: 10em",
                " height: 80%",
                "background: linear-gradient(90deg,#d65f5f 100.0%"
                ", transparent 100.0%)",
            ],
            (2, 0): ["width: 10em", " height: 80%"],
            (2, 1): [
                "width: 10em",
                " height: 80%",
                "background: linear-gradient(90deg,#d65f5f 50.0%"
                ", transparent 50.0%)",
            ],
            (2, 2): [
                "width: 10em",
                " height: 80%",
                "background: linear-gradient(90deg,#d65f5f 100.0%"
                ", transparent 100.0%)",
            ],
        }
        assert result == expected

    def test_bar_align_mid_pos_and_neg(self):
        df = pd.DataFrame({"A": [-10, 0, 20, 90]})

        result = df.style.bar(align="mid", color=["#d65f5f", "#5fba7d"])._compute().ctx

        expected = {
            (0, 0): [
                "width: 10em",
                " height: 80%",
                "background: linear-gradient(90deg,"
                "#d65f5f 10.0%, transparent 10.0%)",
            ],
            (1, 0): ["width: 10em", " height: 80%"],
            (2, 0): [
                "width: 10em",
                " height: 80%",
                "background: linear-gradient(90deg, "
                "transparent 10.0%, #5fba7d 10.0%"
                ", #5fba7d 30.0%, transparent 30.0%)",
            ],
            (3, 0): [
                "width: 10em",
                " height: 80%",
                "background: linear-gradient(90deg, "
                "transparent 10.0%, "
                "#5fba7d 10.0%, #5fba7d 100.0%, "
                "transparent 100.0%)",
            ],
        }

        assert result == expected

    def test_bar_align_mid_all_pos(self):
        df = pd.DataFrame({"A": [10, 20, 50, 100]})

        result = df.style.bar(align="mid", color=["#d65f5f", "#5fba7d"])._compute().ctx

        expected = {
            (0, 0): [
                "width: 10em",
                " height: 80%",
                "background: linear-gradient(90deg,"
                "#5fba7d 10.0%, transparent 10.0%)",
            ],
            (1, 0): [
                "width: 10em",
                " height: 80%",
                "background: linear-gradient(90deg,"
                "#5fba7d 20.0%, transparent 20.0%)",
            ],
            (2, 0): [
                "width: 10em",
                " height: 80%",
                "background: linear-gradient(90deg,"
                "#5fba7d 50.0%, transparent 50.0%)",
            ],
            (3, 0): [
                "width: 10em",
                " height: 80%",
                "background: linear-gradient(90deg,"
                "#5fba7d 100.0%, transparent 100.0%)",
            ],
        }

        assert result == expected

    def test_bar_align_mid_all_neg(self):
        df = pd.DataFrame({"A": [-100, -60, -30, -20]})

        result = df.style.bar(align="mid", color=["#d65f5f", "#5fba7d"])._compute().ctx

        expected = {
            (0, 0): [
                "width: 10em",
                " height: 80%",
                "background: linear-gradient(90deg,"
                "#d65f5f 100.0%, transparent 100.0%)",
            ],
            (1, 0): [
                "width: 10em",
                " height: 80%",
                "background: linear-gradient(90deg, "
                "transparent 40.0%, "
                "#d65f5f 40.0%, #d65f5f 100.0%, "
                "transparent 100.0%)",
            ],
            (2, 0): [
                "width: 10em",
                " height: 80%",
                "background: linear-gradient(90deg, "
                "transparent 70.0%, "
                "#d65f5f 70.0%, #d65f5f 100.0%, "
                "transparent 100.0%)",
            ],
            (3, 0): [
                "width: 10em",
                " height: 80%",
                "background: linear-gradient(90deg, "
                "transparent 80.0%, "
                "#d65f5f 80.0%, #d65f5f 100.0%, "
                "transparent 100.0%)",
            ],
        }
        assert result == expected

    def test_bar_align_zero_pos_and_neg(self):
        # See https://github.com/pandas-dev/pandas/pull/14757
        df = pd.DataFrame({"A": [-10, 0, 20, 90]})

        result = (
            df.style.bar(align="zero", color=["#d65f5f", "#5fba7d"], width=90)
            ._compute()
            .ctx
        )
        expected = {
            (0, 0): [
                "width: 10em",
                " height: 80%",
                "background: linear-gradient(90deg, "
                "transparent 40.0%, #d65f5f 40.0%, "
                "#d65f5f 45.0%, transparent 45.0%)",
            ],
            (1, 0): ["width: 10em", " height: 80%"],
            (2, 0): [
                "width: 10em",
                " height: 80%",
                "background: linear-gradient(90deg, "
                "transparent 45.0%, #5fba7d 45.0%, "
                "#5fba7d 55.0%, transparent 55.0%)",
            ],
            (3, 0): [
                "width: 10em",
                " height: 80%",
                "background: linear-gradient(90deg, "
                "transparent 45.0%, #5fba7d 45.0%, "
                "#5fba7d 90.0%, transparent 90.0%)",
            ],
        }
        assert result == expected

    def test_bar_align_left_axis_none(self):
        df = pd.DataFrame({"A": [0, 1], "B": [2, 4]})
        result = df.style.bar(axis=None)._compute().ctx
        expected = {
            (0, 0): ["width: 10em", " height: 80%"],
            (1, 0): [
                "width: 10em",
                " height: 80%",
                "background: linear-gradient(90deg,"
                "#d65f5f 25.0%, transparent 25.0%)",
            ],
            (0, 1): [
                "width: 10em",
                " height: 80%",
                "background: linear-gradient(90deg,"
                "#d65f5f 50.0%, transparent 50.0%)",
            ],
            (1, 1): [
                "width: 10em",
                " height: 80%",
                "background: linear-gradient(90deg,"
                "#d65f5f 100.0%, transparent 100.0%)",
            ],
        }
        assert result == expected

    def test_bar_align_zero_axis_none(self):
        df = pd.DataFrame({"A": [0, 1], "B": [-2, 4]})
        result = df.style.bar(align="zero", axis=None)._compute().ctx
        expected = {
            (0, 0): ["width: 10em", " height: 80%"],
            (1, 0): [
                "width: 10em",
                " height: 80%",
                "background: linear-gradient(90deg, "
                "transparent 50.0%, #d65f5f 50.0%, "
                "#d65f5f 62.5%, transparent 62.5%)",
            ],
            (0, 1): [
                "width: 10em",
                " height: 80%",
                "background: linear-gradient(90deg, "
                "transparent 25.0%, #d65f5f 25.0%, "
                "#d65f5f 50.0%, transparent 50.0%)",
            ],
            (1, 1): [
                "width: 10em",
                " height: 80%",
                "background: linear-gradient(90deg, "
                "transparent 50.0%, #d65f5f 50.0%, "
                "#d65f5f 100.0%, transparent 100.0%)",
            ],
        }
        assert result == expected

    def test_bar_align_mid_axis_none(self):
        df = pd.DataFrame({"A": [0, 1], "B": [-2, 4]})
        result = df.style.bar(align="mid", axis=None)._compute().ctx
        expected = {
            (0, 0): ["width: 10em", " height: 80%"],
            (1, 0): [
                "width: 10em",
                " height: 80%",
                "background: linear-gradient(90deg, "
                "transparent 33.3%, #d65f5f 33.3%, "
                "#d65f5f 50.0%, transparent 50.0%)",
            ],
            (0, 1): [
                "width: 10em",
                " height: 80%",
                "background: linear-gradient(90deg,"
                "#d65f5f 33.3%, transparent 33.3%)",
            ],
            (1, 1): [
                "width: 10em",
                " height: 80%",
                "background: linear-gradient(90deg, "
                "transparent 33.3%, #d65f5f 33.3%, "
                "#d65f5f 100.0%, transparent 100.0%)",
            ],
        }
        assert result == expected

    def test_bar_align_mid_vmin(self):
        df = pd.DataFrame({"A": [0, 1], "B": [-2, 4]})
        result = df.style.bar(align="mid", axis=None, vmin=-6)._compute().ctx
        expected = {
            (0, 0): ["width: 10em", " height: 80%"],
            (1, 0): [
                "width: 10em",
                " height: 80%",
                "background: linear-gradient(90deg, "
                "transparent 60.0%, #d65f5f 60.0%, "
                "#d65f5f 70.0%, transparent 70.0%)",
            ],
            (0, 1): [
                "width: 10em",
                " height: 80%",
                "background: linear-gradient(90deg, "
                "transparent 40.0%, #d65f5f 40.0%, "
                "#d65f5f 60.0%, transparent 60.0%)",
            ],
            (1, 1): [
                "width: 10em",
                " height: 80%",
                "background: linear-gradient(90deg, "
                "transparent 60.0%, #d65f5f 60.0%, "
                "#d65f5f 100.0%, transparent 100.0%)",
            ],
        }
        assert result == expected

    def test_bar_align_mid_vmax(self):
        df = pd.DataFrame({"A": [0, 1], "B": [-2, 4]})
        result = df.style.bar(align="mid", axis=None, vmax=8)._compute().ctx
        expected = {
            (0, 0): ["width: 10em", " height: 80%"],
            (1, 0): [
                "width: 10em",
                " height: 80%",
                "background: linear-gradient(90deg, "
                "transparent 20.0%, #d65f5f 20.0%, "
                "#d65f5f 30.0%, transparent 30.0%)",
            ],
            (0, 1): [
                "width: 10em",
                " height: 80%",
                "background: linear-gradient(90deg,"
                "#d65f5f 20.0%, transparent 20.0%)",
            ],
            (1, 1): [
                "width: 10em",
                " height: 80%",
                "background: linear-gradient(90deg, "
                "transparent 20.0%, #d65f5f 20.0%, "
                "#d65f5f 60.0%, transparent 60.0%)",
            ],
        }
        assert result == expected

    def test_bar_align_mid_vmin_vmax_wide(self):
        df = pd.DataFrame({"A": [0, 1], "B": [-2, 4]})
        result = df.style.bar(align="mid", axis=None, vmin=-3, vmax=7)._compute().ctx
        expected = {
            (0, 0): ["width: 10em", " height: 80%"],
            (1, 0): [
                "width: 10em",
                " height: 80%",
                "background: linear-gradient(90deg, "
                "transparent 30.0%, #d65f5f 30.0%, "
                "#d65f5f 40.0%, transparent 40.0%)",
            ],
            (0, 1): [
                "width: 10em",
                " height: 80%",
                "background: linear-gradient(90deg, "
                "transparent 10.0%, #d65f5f 10.0%, "
                "#d65f5f 30.0%, transparent 30.0%)",
            ],
            (1, 1): [
                "width: 10em",
                " height: 80%",
                "background: linear-gradient(90deg, "
                "transparent 30.0%, #d65f5f 30.0%, "
                "#d65f5f 70.0%, transparent 70.0%)",
            ],
        }
        assert result == expected

    def test_bar_align_mid_vmin_vmax_clipping(self):
        df = pd.DataFrame({"A": [0, 1], "B": [-2, 4]})
        result = df.style.bar(align="mid", axis=None, vmin=-1, vmax=3)._compute().ctx
        expected = {
            (0, 0): ["width: 10em", " height: 80%"],
            (1, 0): [
                "width: 10em",
                " height: 80%",
                "background: linear-gradient(90deg, "
                "transparent 25.0%, #d65f5f 25.0%, "
                "#d65f5f 50.0%, transparent 50.0%)",
            ],
            (0, 1): [
                "width: 10em",
                " height: 80%",
                "background: linear-gradient(90deg,"
                "#d65f5f 25.0%, transparent 25.0%)",
            ],
            (1, 1): [
                "width: 10em",
                " height: 80%",
                "background: linear-gradient(90deg, "
                "transparent 25.0%, #d65f5f 25.0%, "
                "#d65f5f 100.0%, transparent 100.0%)",
            ],
        }
        assert result == expected

    def test_bar_align_mid_nans(self):
        df = pd.DataFrame({"A": [1, None], "B": [-1, 3]})
        result = df.style.bar(align="mid", axis=None)._compute().ctx
        expected = {
            (0, 0): [
                "width: 10em",
                " height: 80%",
                "background: linear-gradient(90deg, "
                "transparent 25.0%, #d65f5f 25.0%, "
                "#d65f5f 50.0%, transparent 50.0%)",
            ],
            (0, 1): [
                "width: 10em",
                " height: 80%",
                "background: linear-gradient(90deg,"
                "#d65f5f 25.0%, transparent 25.0%)",
            ],
            (1, 1): [
                "width: 10em",
                " height: 80%",
                "background: linear-gradient(90deg, "
                "transparent 25.0%, #d65f5f 25.0%, "
                "#d65f5f 100.0%, transparent 100.0%)",
            ],
        }
        assert result == expected

    def test_bar_align_zero_nans(self):
        df = pd.DataFrame({"A": [1, None], "B": [-1, 2]})
        result = df.style.bar(align="zero", axis=None)._compute().ctx
        expected = {
            (0, 0): [
                "width: 10em",
                " height: 80%",
                "background: linear-gradient(90deg, "
                "transparent 50.0%, #d65f5f 50.0%, "
                "#d65f5f 75.0%, transparent 75.0%)",
            ],
            (0, 1): [
                "width: 10em",
                " height: 80%",
                "background: linear-gradient(90deg, "
                "transparent 25.0%, #d65f5f 25.0%, "
                "#d65f5f 50.0%, transparent 50.0%)",
            ],
            (1, 1): [
                "width: 10em",
                " height: 80%",
                "background: linear-gradient(90deg, "
                "transparent 50.0%, #d65f5f 50.0%, "
                "#d65f5f 100.0%, transparent 100.0%)",
            ],
        }
        assert result == expected

    def test_bar_bad_align_raises(self):
        df = pd.DataFrame({"A": [-100, -60, -30, -20]})
        msg = "`align` must be one of {'left', 'zero',' mid'}"
        with pytest.raises(ValueError, match=msg):
            df.style.bar(align="poorly", color=["#d65f5f", "#5fba7d"])

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

        ctx = df.style.set_na_rep("NA")._translate()
        assert ctx["body"][0][1]["display_value"] == "NA"
        assert ctx["body"][0][2]["display_value"] == "NA"

        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 = pd.DataFrame(
            {
                "object": [None, np.nan, "foo"],
                "datetime": [None, pd.NaT, pd.Timestamp("20120101")],
            }
        )

        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_with_bad_na_rep(self):
        # GH 21527 28358
        df = pd.DataFrame([[None, None], [1.1, 1.2]], columns=["A", "B"])
        msg = "Expected a string, got -1 instead"
        with pytest.raises(TypeError, match=msg):
            df.style.format(None, na_rep=-1)

    def test_highlight_null(self, null_color="red"):
        df = pd.DataFrame({"A": [0, np.nan]})
        result = df.style.highlight_null()._compute().ctx
        expected = {(1, 0): ["background-color: red"]}
        assert result == expected

    def test_highlight_null_subset(self):
        # GH 31345
        df = pd.DataFrame({"A": [0, np.nan], "B": [0, np.nan]})
        result = (
            df.style.highlight_null(null_color="red", subset=["A"])
            .highlight_null(null_color="green", subset=["B"])
            ._compute()
            .ctx
        )
        expected = {
            (1, 0): ["background-color: red"],
            (1, 1): ["background-color: green"],
        }
        assert result == expected

    def test_nonunique_raises(self):
        df = pd.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 = pd.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")]}]
        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

    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):
        with pd.option_context("display.precision", 10):
            s = Styler(self.df)
        assert s.precision == 10
        s = Styler(self.df, precision=2)
        assert s.precision == 2

        s2 = s.set_precision(4)
        assert s is s2
        assert s.precision == 4

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

        result = pd.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_highlight_max(self):
        df = pd.DataFrame([[1, 2], [3, 4]], columns=["A", "B"])
        # max(df) = min(-df)
        for max_ in [True, False]:
            if max_:
                attr = "highlight_max"
            else:
                df = -df
                attr = "highlight_min"
            result = getattr(df.style, attr)()._compute().ctx
            assert result[(1, 1)] == ["background-color: yellow"]

            result = getattr(df.style, attr)(color="green")._compute().ctx
            assert result[(1, 1)] == ["background-color: green"]

            result = getattr(df.style, attr)(subset="A")._compute().ctx
            assert result[(1, 0)] == ["background-color: yellow"]

            result = getattr(df.style, attr)(axis=0)._compute().ctx
            expected = {
                (1, 0): ["background-color: yellow"],
                (1, 1): ["background-color: yellow"],
            }
            assert result == expected

            result = getattr(df.style, attr)(axis=1)._compute().ctx
            expected = {
                (0, 1): ["background-color: yellow"],
                (1, 1): ["background-color: yellow"],
            }
            assert result == expected

        # separate since we can't negate the strs
        df["C"] = ["a", "b"]
        result = df.style.highlight_max()._compute().ctx
        expected = {(1, 1): ["background-color: yellow"]}

        result = df.style.highlight_min()._compute().ctx
        expected = {(0, 0): ["background-color: yellow"]}

    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()
        result = style1.export()
        style2 = self.df.style
        style2.use(result)
        assert style1._todo == style2._todo
        style2.render()

    def test_display_format(self):
        df = pd.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

    def test_display_format_raises(self):
        df = pd.DataFrame(np.random.randn(2, 2))
        msg = "Expected a template string or callable, got 5 instead"
        with pytest.raises(TypeError, match=msg):
            df.style.format(5)

        msg = "Expected a template string or callable, got True instead"
        with pytest.raises(TypeError, match=msg):
            df.style.format(True)

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

        ctx = s.set_precision(1)._translate()

        assert s.precision == 1
        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.set_precision(2)._translate()
        assert s.precision == 2
        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.set_precision(3)._translate()
        assert s.precision == 3
        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_display_subset(self):
        df = pd.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_display_dict(self):
        df = pd.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 = pd.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)

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

        df = pd.DataFrame([[1, 2], [3, 4]])
        msg = "must return a DataFrame 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 pd.DataFrame(index=[1, 2], columns=["a", "b"])

        df = pd.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 = pd.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": "",
                "is_visible": True,
                "display_value": "",
            },
            {
                "type": "th",
                "class": "blank level0",
                "value": "",
                "is_visible": True,
                "display_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 = pd.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 = pd.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", "value": "", "type": "th"},
        ]

        assert head == expected

    def test_mi_sparse_column_names(self):
        df = pd.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": "",
                "display_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 = pd.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 = pd.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 = pd.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


@td.skip_if_no_mpl
class TestStylerMatplotlibDep:
    def test_background_gradient(self):
        df = pd.DataFrame([[1, 2], [2, 4]], columns=["A", "B"])

        for c_map in [None, "YlOrRd"]:
            result = df.style.background_gradient(cmap=c_map)._compute().ctx
            assert all("#" in x[0] for x in result.values())
            assert result[(0, 0)] == result[(0, 1)]
            assert result[(1, 0)] == result[(1, 1)]

        result = (
            df.style.background_gradient(subset=pd.IndexSlice[1, "A"])._compute().ctx
        )

        assert result[(1, 0)] == ["background-color: #fff7fb", "color: #000000"]

    @pytest.mark.parametrize(
        "c_map,expected",
        [
            (
                None,
                {
                    (0, 0): ["background-color: #440154", "color: #f1f1f1"],
                    (1, 0): ["background-color: #fde725", "color: #000000"],
                },
            ),
            (
                "YlOrRd",
                {
                    (0, 0): ["background-color: #ffffcc", "color: #000000"],
                    (1, 0): ["background-color: #800026", "color: #f1f1f1"],
                },
            ),
        ],
    )
    def test_text_color_threshold(self, c_map, expected):
        df = pd.DataFrame([1, 2], columns=["A"])
        result = df.style.background_gradient(cmap=c_map)._compute().ctx
        assert result == expected

    @pytest.mark.parametrize("text_color_threshold", [1.1, "1", -1, [2, 2]])
    def test_text_color_threshold_raises(self, text_color_threshold):
        df = pd.DataFrame([[1, 2], [2, 4]], columns=["A", "B"])
        msg = "`text_color_threshold` must be a value from 0 to 1."
        with pytest.raises(ValueError, match=msg):
            df.style.background_gradient(
                text_color_threshold=text_color_threshold
            )._compute()

    @td.skip_if_no_mpl
    def test_background_gradient_axis(self):
        df = pd.DataFrame([[1, 2], [2, 4]], columns=["A", "B"])

        low = ["background-color: #f7fbff", "color: #000000"]
        high = ["background-color: #08306b", "color: #f1f1f1"]
        mid = ["background-color: #abd0e6", "color: #000000"]
        result = df.style.background_gradient(cmap="Blues", axis=0)._compute().ctx
        assert result[(0, 0)] == low
        assert result[(0, 1)] == low
        assert result[(1, 0)] == high
        assert result[(1, 1)] == high

        result = df.style.background_gradient(cmap="Blues", axis=1)._compute().ctx
        assert result[(0, 0)] == low
        assert result[(0, 1)] == high
        assert result[(1, 0)] == low
        assert result[(1, 1)] == high

        result = df.style.background_gradient(cmap="Blues", axis=None)._compute().ctx
        assert result[(0, 0)] == low
        assert result[(0, 1)] == mid
        assert result[(1, 0)] == mid
        assert result[(1, 1)] == high

    def test_background_gradient_vmin_vmax(self):
        # GH 12145
        df = pd.DataFrame(range(5))
        ctx = df.style.background_gradient(vmin=1, vmax=3)._compute().ctx
        assert ctx[(0, 0)] == ctx[(1, 0)]
        assert ctx[(4, 0)] == ctx[(3, 0)]

    def test_background_gradient_int64(self):
        # GH 28869
        df1 = pd.Series(range(3)).to_frame()
        df2 = pd.Series(range(3), dtype="Int64").to_frame()
        ctx1 = df1.style.background_gradient()._compute().ctx
        ctx2 = df2.style.background_gradient()._compute().ctx
        assert ctx2[(0, 0)] == ctx1[(0, 0)]
        assert ctx2[(1, 0)] == ctx1[(1, 0)]
        assert ctx2[(2, 0)] == ctx1[(2, 0)]


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(pd.DataFrame({"A": [1, 2]}))
    assert styler.render()
