diff --git a/pandas/tests/io/formats/style/__init__.py b/pandas/tests/io/formats/style/__init__.py
new file mode 100644
index 0000000000000..e69de29bb2d1d
diff --git a/pandas/tests/io/formats/style/test_align.py b/pandas/tests/io/formats/style/test_align.py
new file mode 100644
index 0000000000000..f81c1fbd6d85e
--- /dev/null
+++ b/pandas/tests/io/formats/style/test_align.py
@@ -0,0 +1,406 @@
+import pytest
+
+from pandas import DataFrame
+
+pytest.importorskip("jinja2")
+
+
+def bar_grad(a=None, b=None, c=None, d=None):
+ """Used in multiple tests to simplify formatting of expected result"""
+ ret = [("width", "10em"), ("height", "80%")]
+ if all(x is None for x in [a, b, c, d]):
+ return ret
+ return ret + [
+ (
+ "background",
+ f"linear-gradient(90deg,{','.join(x for x in [a, b, c, d] if x)})",
+ )
+ ]
+
+
+class TestStylerBarAlign:
+ def test_bar_align_left(self):
+ df = DataFrame({"A": [0, 1, 2]})
+ result = df.style.bar()._compute().ctx
+ expected = {
+ (0, 0): bar_grad(),
+ (1, 0): bar_grad("#d65f5f 50.0%", " transparent 50.0%"),
+ (2, 0): bar_grad("#d65f5f 100.0%", " transparent 100.0%"),
+ }
+ assert result == expected
+
+ result = df.style.bar(color="red", width=50)._compute().ctx
+ expected = {
+ (0, 0): bar_grad(),
+ (1, 0): bar_grad("red 25.0%", " transparent 25.0%"),
+ (2, 0): bar_grad("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 = DataFrame([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
+ result = df.style.bar()._compute().ctx
+ expected = {
+ (0, 0): bar_grad(),
+ (0, 1): bar_grad(),
+ (0, 2): bar_grad(),
+ (1, 0): bar_grad("#d65f5f 50.0%", " transparent 50.0%"),
+ (1, 1): bar_grad("#d65f5f 50.0%", " transparent 50.0%"),
+ (1, 2): bar_grad("#d65f5f 50.0%", " transparent 50.0%"),
+ (2, 0): bar_grad("#d65f5f 100.0%", " transparent 100.0%"),
+ (2, 1): bar_grad("#d65f5f 100.0%", " transparent 100.0%"),
+ (2, 2): bar_grad("#d65f5f 100.0%", " transparent 100.0%"),
+ }
+ assert result == expected
+
+ result = df.style.bar(axis=1)._compute().ctx
+ expected = {
+ (0, 0): bar_grad(),
+ (0, 1): bar_grad("#d65f5f 50.0%", " transparent 50.0%"),
+ (0, 2): bar_grad("#d65f5f 100.0%", " transparent 100.0%"),
+ (1, 0): bar_grad(),
+ (1, 1): bar_grad("#d65f5f 50.0%", " transparent 50.0%"),
+ (1, 2): bar_grad("#d65f5f 100.0%", " transparent 100.0%"),
+ (2, 0): bar_grad(),
+ (2, 1): bar_grad("#d65f5f 50.0%", " transparent 50.0%"),
+ (2, 2): bar_grad("#d65f5f 100.0%", " transparent 100.0%"),
+ }
+ assert result == expected
+
+ def test_bar_align_mid_pos_and_neg(self):
+ df = DataFrame({"A": [-10, 0, 20, 90]})
+ result = df.style.bar(align="mid", color=["#d65f5f", "#5fba7d"])._compute().ctx
+ expected = {
+ (0, 0): bar_grad(
+ "#d65f5f 10.0%",
+ " transparent 10.0%",
+ ),
+ (1, 0): bar_grad(),
+ (2, 0): bar_grad(
+ " transparent 10.0%",
+ " #5fba7d 10.0%",
+ " #5fba7d 30.0%",
+ " transparent 30.0%",
+ ),
+ (3, 0): bar_grad(
+ " transparent 10.0%",
+ " #5fba7d 10.0%",
+ " #5fba7d 100.0%",
+ " transparent 100.0%",
+ ),
+ }
+ assert result == expected
+
+ def test_bar_align_mid_all_pos(self):
+ df = DataFrame({"A": [10, 20, 50, 100]})
+
+ result = df.style.bar(align="mid", color=["#d65f5f", "#5fba7d"])._compute().ctx
+
+ expected = {
+ (0, 0): bar_grad(
+ "#5fba7d 10.0%",
+ " transparent 10.0%",
+ ),
+ (1, 0): bar_grad(
+ "#5fba7d 20.0%",
+ " transparent 20.0%",
+ ),
+ (2, 0): bar_grad(
+ "#5fba7d 50.0%",
+ " transparent 50.0%",
+ ),
+ (3, 0): bar_grad(
+ "#5fba7d 100.0%",
+ " transparent 100.0%",
+ ),
+ }
+
+ assert result == expected
+
+ def test_bar_align_mid_all_neg(self):
+ df = DataFrame({"A": [-100, -60, -30, -20]})
+
+ result = df.style.bar(align="mid", color=["#d65f5f", "#5fba7d"])._compute().ctx
+
+ expected = {
+ (0, 0): bar_grad(
+ "#d65f5f 100.0%",
+ " transparent 100.0%",
+ ),
+ (1, 0): bar_grad(
+ " transparent 40.0%",
+ " #d65f5f 40.0%",
+ " #d65f5f 100.0%",
+ " transparent 100.0%",
+ ),
+ (2, 0): bar_grad(
+ " transparent 70.0%",
+ " #d65f5f 70.0%",
+ " #d65f5f 100.0%",
+ " transparent 100.0%",
+ ),
+ (3, 0): bar_grad(
+ " 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 = DataFrame({"A": [-10, 0, 20, 90]})
+
+ result = (
+ df.style.bar(align="zero", color=["#d65f5f", "#5fba7d"], width=90)
+ ._compute()
+ .ctx
+ )
+ expected = {
+ (0, 0): bar_grad(
+ " transparent 40.0%",
+ " #d65f5f 40.0%",
+ " #d65f5f 45.0%",
+ " transparent 45.0%",
+ ),
+ (1, 0): bar_grad(),
+ (2, 0): bar_grad(
+ " transparent 45.0%",
+ " #5fba7d 45.0%",
+ " #5fba7d 55.0%",
+ " transparent 55.0%",
+ ),
+ (3, 0): bar_grad(
+ " transparent 45.0%",
+ " #5fba7d 45.0%",
+ " #5fba7d 90.0%",
+ " transparent 90.0%",
+ ),
+ }
+ assert result == expected
+
+ def test_bar_align_left_axis_none(self):
+ df = DataFrame({"A": [0, 1], "B": [2, 4]})
+ result = df.style.bar(axis=None)._compute().ctx
+ expected = {
+ (0, 0): bar_grad(),
+ (1, 0): bar_grad(
+ "#d65f5f 25.0%",
+ " transparent 25.0%",
+ ),
+ (0, 1): bar_grad(
+ "#d65f5f 50.0%",
+ " transparent 50.0%",
+ ),
+ (1, 1): bar_grad(
+ "#d65f5f 100.0%",
+ " transparent 100.0%",
+ ),
+ }
+ assert result == expected
+
+ def test_bar_align_zero_axis_none(self):
+ df = DataFrame({"A": [0, 1], "B": [-2, 4]})
+ result = df.style.bar(align="zero", axis=None)._compute().ctx
+ expected = {
+ (0, 0): bar_grad(),
+ (1, 0): bar_grad(
+ " transparent 50.0%",
+ " #d65f5f 50.0%",
+ " #d65f5f 62.5%",
+ " transparent 62.5%",
+ ),
+ (0, 1): bar_grad(
+ " transparent 25.0%",
+ " #d65f5f 25.0%",
+ " #d65f5f 50.0%",
+ " transparent 50.0%",
+ ),
+ (1, 1): bar_grad(
+ " transparent 50.0%",
+ " #d65f5f 50.0%",
+ " #d65f5f 100.0%",
+ " transparent 100.0%",
+ ),
+ }
+ assert result == expected
+
+ def test_bar_align_mid_axis_none(self):
+ df = DataFrame({"A": [0, 1], "B": [-2, 4]})
+ result = df.style.bar(align="mid", axis=None)._compute().ctx
+ expected = {
+ (0, 0): bar_grad(),
+ (1, 0): bar_grad(
+ " transparent 33.3%",
+ " #d65f5f 33.3%",
+ " #d65f5f 50.0%",
+ " transparent 50.0%",
+ ),
+ (0, 1): bar_grad(
+ "#d65f5f 33.3%",
+ " transparent 33.3%",
+ ),
+ (1, 1): bar_grad(
+ " transparent 33.3%",
+ " #d65f5f 33.3%",
+ " #d65f5f 100.0%",
+ " transparent 100.0%",
+ ),
+ }
+ assert result == expected
+
+ def test_bar_align_mid_vmin(self):
+ df = DataFrame({"A": [0, 1], "B": [-2, 4]})
+ result = df.style.bar(align="mid", axis=None, vmin=-6)._compute().ctx
+ expected = {
+ (0, 0): bar_grad(),
+ (1, 0): bar_grad(
+ " transparent 60.0%",
+ " #d65f5f 60.0%",
+ " #d65f5f 70.0%",
+ " transparent 70.0%",
+ ),
+ (0, 1): bar_grad(
+ " transparent 40.0%",
+ " #d65f5f 40.0%",
+ " #d65f5f 60.0%",
+ " transparent 60.0%",
+ ),
+ (1, 1): bar_grad(
+ " transparent 60.0%",
+ " #d65f5f 60.0%",
+ " #d65f5f 100.0%",
+ " transparent 100.0%",
+ ),
+ }
+ assert result == expected
+
+ def test_bar_align_mid_vmax(self):
+ df = DataFrame({"A": [0, 1], "B": [-2, 4]})
+ result = df.style.bar(align="mid", axis=None, vmax=8)._compute().ctx
+ expected = {
+ (0, 0): bar_grad(),
+ (1, 0): bar_grad(
+ " transparent 20.0%",
+ " #d65f5f 20.0%",
+ " #d65f5f 30.0%",
+ " transparent 30.0%",
+ ),
+ (0, 1): bar_grad(
+ "#d65f5f 20.0%",
+ " transparent 20.0%",
+ ),
+ (1, 1): bar_grad(
+ " 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 = DataFrame({"A": [0, 1], "B": [-2, 4]})
+ result = df.style.bar(align="mid", axis=None, vmin=-3, vmax=7)._compute().ctx
+ expected = {
+ (0, 0): bar_grad(),
+ (1, 0): bar_grad(
+ " transparent 30.0%",
+ " #d65f5f 30.0%",
+ " #d65f5f 40.0%",
+ " transparent 40.0%",
+ ),
+ (0, 1): bar_grad(
+ " transparent 10.0%",
+ " #d65f5f 10.0%",
+ " #d65f5f 30.0%",
+ " transparent 30.0%",
+ ),
+ (1, 1): bar_grad(
+ " 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 = DataFrame({"A": [0, 1], "B": [-2, 4]})
+ result = df.style.bar(align="mid", axis=None, vmin=-1, vmax=3)._compute().ctx
+ expected = {
+ (0, 0): bar_grad(),
+ (1, 0): bar_grad(
+ " transparent 25.0%",
+ " #d65f5f 25.0%",
+ " #d65f5f 50.0%",
+ " transparent 50.0%",
+ ),
+ (0, 1): bar_grad("#d65f5f 25.0%", " transparent 25.0%"),
+ (1, 1): bar_grad(
+ " transparent 25.0%",
+ " #d65f5f 25.0%",
+ " #d65f5f 100.0%",
+ " transparent 100.0%",
+ ),
+ }
+ assert result == expected
+
+ def test_bar_align_mid_nans(self):
+ df = DataFrame({"A": [1, None], "B": [-1, 3]})
+ result = df.style.bar(align="mid", axis=None)._compute().ctx
+ expected = {
+ (0, 0): bar_grad(
+ " transparent 25.0%",
+ " #d65f5f 25.0%",
+ " #d65f5f 50.0%",
+ " transparent 50.0%",
+ ),
+ (0, 1): bar_grad("#d65f5f 25.0%", " transparent 25.0%"),
+ (1, 1): bar_grad(
+ " transparent 25.0%",
+ " #d65f5f 25.0%",
+ " #d65f5f 100.0%",
+ " transparent 100.0%",
+ ),
+ }
+ assert result == expected
+
+ def test_bar_align_zero_nans(self):
+ df = DataFrame({"A": [1, None], "B": [-1, 2]})
+ result = df.style.bar(align="zero", axis=None)._compute().ctx
+ expected = {
+ (0, 0): bar_grad(
+ " transparent 50.0%",
+ " #d65f5f 50.0%",
+ " #d65f5f 75.0%",
+ " transparent 75.0%",
+ ),
+ (0, 1): bar_grad(
+ " transparent 25.0%",
+ " #d65f5f 25.0%",
+ " #d65f5f 50.0%",
+ " transparent 50.0%",
+ ),
+ (1, 1): bar_grad(
+ " transparent 50.0%",
+ " #d65f5f 50.0%",
+ " #d65f5f 100.0%",
+ " transparent 100.0%",
+ ),
+ }
+ assert result == expected
+
+ def test_bar_bad_align_raises(self):
+ df = 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"])
diff --git a/pandas/tests/io/formats/style/test_highlight.py b/pandas/tests/io/formats/style/test_highlight.py
new file mode 100644
index 0000000000000..e02e1f012c662
--- /dev/null
+++ b/pandas/tests/io/formats/style/test_highlight.py
@@ -0,0 +1,70 @@
+import numpy as np
+import pytest
+
+from pandas import DataFrame
+
+pytest.importorskip("jinja2")
+
+
+class TestStylerHighlight:
+ def test_highlight_null(self):
+ df = 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 = 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_highlight_max(self):
+ df = DataFrame([[1, 2], [3, 4]], columns=["A", "B"])
+ css_seq = [("background-color", "yellow")]
+ # 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)] == css_seq
+
+ 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)] == css_seq
+
+ result = getattr(df.style, attr)(axis=0)._compute().ctx
+ expected = {
+ (1, 0): css_seq,
+ (1, 1): css_seq,
+ }
+ assert result == expected
+
+ result = getattr(df.style, attr)(axis=1)._compute().ctx
+ expected = {
+ (0, 1): css_seq,
+ (1, 1): css_seq,
+ }
+ 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): css_seq}
+
+ result = df.style.highlight_min()._compute().ctx
+ expected = {(0, 0): css_seq}
diff --git a/pandas/tests/io/formats/style/test_matplotlib.py b/pandas/tests/io/formats/style/test_matplotlib.py
new file mode 100644
index 0000000000000..f01e818e40b22
--- /dev/null
+++ b/pandas/tests/io/formats/style/test_matplotlib.py
@@ -0,0 +1,108 @@
+import numpy as np
+import pytest
+
+from pandas import (
+ DataFrame,
+ IndexSlice,
+ Series,
+)
+
+pytest.importorskip("matplotlib")
+pytest.importorskip("jinja2")
+
+
+class TestStylerMatplotlibDep:
+ def test_background_gradient(self):
+ df = 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][1] 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=IndexSlice[1, "A"])._compute().ctx
+
+ assert result[(1, 0)] == [("background-color", "#fff7fb"), ("color", "#000000")]
+
+ @pytest.mark.parametrize(
+ "cmap, expected",
+ [
+ (
+ "PuBu",
+ {
+ (4, 5): [("background-color", "#86b0d3"), ("color", "#000000")],
+ (4, 6): [("background-color", "#83afd3"), ("color", "#f1f1f1")],
+ },
+ ),
+ (
+ "YlOrRd",
+ {
+ (4, 8): [("background-color", "#fd913e"), ("color", "#000000")],
+ (4, 9): [("background-color", "#fd8f3d"), ("color", "#f1f1f1")],
+ },
+ ),
+ (
+ None,
+ {
+ (7, 0): [("background-color", "#48c16e"), ("color", "#f1f1f1")],
+ (7, 1): [("background-color", "#4cc26c"), ("color", "#000000")],
+ },
+ ),
+ ],
+ )
+ def test_text_color_threshold(self, cmap, expected):
+ df = DataFrame(np.arange(100).reshape(10, 10))
+ result = df.style.background_gradient(cmap=cmap, axis=None)._compute().ctx
+ for k in expected.keys():
+ assert result[k] == expected[k]
+
+ @pytest.mark.parametrize("text_color_threshold", [1.1, "1", -1, [2, 2]])
+ def test_text_color_threshold_raises(self, text_color_threshold):
+ df = 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()
+
+ def test_background_gradient_axis(self):
+ df = 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 = 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 = Series(range(3)).to_frame()
+ df2 = 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)]
diff --git a/pandas/tests/io/formats/test_style.py b/pandas/tests/io/formats/style/test_style.py
similarity index 69%
rename from pandas/tests/io/formats/test_style.py
rename to pandas/tests/io/formats/style/test_style.py
index 66dc1cd4adddd..01ed234f6e248 100644
--- a/pandas/tests/io/formats/test_style.py
+++ b/pandas/tests/io/formats/style/test_style.py
@@ -5,8 +5,6 @@
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
@@ -20,19 +18,6 @@
)
-def bar_grad(a=None, b=None, c=None, d=None):
- """Used in multiple tests to simplify formatting of expected result"""
- ret = [("width", "10em"), ("height", "80%")]
- if all(x is None for x in [a, b, c, d]):
- return ret
- return ret + [
- (
- "background",
- f"linear-gradient(90deg,{','.join(x for x in [a, b, c, d] if x)})",
- )
- ]
-
-
class TestStyler:
def setup_method(self, method):
np.random.seed(24)
@@ -517,392 +502,6 @@ def test_duplicate(self):
]
assert result == expected
- def test_bar_align_left(self):
- df = DataFrame({"A": [0, 1, 2]})
- result = df.style.bar()._compute().ctx
- expected = {
- (0, 0): bar_grad(),
- (1, 0): bar_grad("#d65f5f 50.0%", " transparent 50.0%"),
- (2, 0): bar_grad("#d65f5f 100.0%", " transparent 100.0%"),
- }
- assert result == expected
-
- result = df.style.bar(color="red", width=50)._compute().ctx
- expected = {
- (0, 0): bar_grad(),
- (1, 0): bar_grad("red 25.0%", " transparent 25.0%"),
- (2, 0): bar_grad("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 = DataFrame([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
- result = df.style.bar()._compute().ctx
- expected = {
- (0, 0): bar_grad(),
- (0, 1): bar_grad(),
- (0, 2): bar_grad(),
- (1, 0): bar_grad("#d65f5f 50.0%", " transparent 50.0%"),
- (1, 1): bar_grad("#d65f5f 50.0%", " transparent 50.0%"),
- (1, 2): bar_grad("#d65f5f 50.0%", " transparent 50.0%"),
- (2, 0): bar_grad("#d65f5f 100.0%", " transparent 100.0%"),
- (2, 1): bar_grad("#d65f5f 100.0%", " transparent 100.0%"),
- (2, 2): bar_grad("#d65f5f 100.0%", " transparent 100.0%"),
- }
- assert result == expected
-
- result = df.style.bar(axis=1)._compute().ctx
- expected = {
- (0, 0): bar_grad(),
- (0, 1): bar_grad("#d65f5f 50.0%", " transparent 50.0%"),
- (0, 2): bar_grad("#d65f5f 100.0%", " transparent 100.0%"),
- (1, 0): bar_grad(),
- (1, 1): bar_grad("#d65f5f 50.0%", " transparent 50.0%"),
- (1, 2): bar_grad("#d65f5f 100.0%", " transparent 100.0%"),
- (2, 0): bar_grad(),
- (2, 1): bar_grad("#d65f5f 50.0%", " transparent 50.0%"),
- (2, 2): bar_grad("#d65f5f 100.0%", " transparent 100.0%"),
- }
- assert result == expected
-
- def test_bar_align_mid_pos_and_neg(self):
- df = DataFrame({"A": [-10, 0, 20, 90]})
- result = df.style.bar(align="mid", color=["#d65f5f", "#5fba7d"])._compute().ctx
- expected = {
- (0, 0): bar_grad(
- "#d65f5f 10.0%",
- " transparent 10.0%",
- ),
- (1, 0): bar_grad(),
- (2, 0): bar_grad(
- " transparent 10.0%",
- " #5fba7d 10.0%",
- " #5fba7d 30.0%",
- " transparent 30.0%",
- ),
- (3, 0): bar_grad(
- " transparent 10.0%",
- " #5fba7d 10.0%",
- " #5fba7d 100.0%",
- " transparent 100.0%",
- ),
- }
- assert result == expected
-
- def test_bar_align_mid_all_pos(self):
- df = DataFrame({"A": [10, 20, 50, 100]})
-
- result = df.style.bar(align="mid", color=["#d65f5f", "#5fba7d"])._compute().ctx
-
- expected = {
- (0, 0): bar_grad(
- "#5fba7d 10.0%",
- " transparent 10.0%",
- ),
- (1, 0): bar_grad(
- "#5fba7d 20.0%",
- " transparent 20.0%",
- ),
- (2, 0): bar_grad(
- "#5fba7d 50.0%",
- " transparent 50.0%",
- ),
- (3, 0): bar_grad(
- "#5fba7d 100.0%",
- " transparent 100.0%",
- ),
- }
-
- assert result == expected
-
- def test_bar_align_mid_all_neg(self):
- df = DataFrame({"A": [-100, -60, -30, -20]})
-
- result = df.style.bar(align="mid", color=["#d65f5f", "#5fba7d"])._compute().ctx
-
- expected = {
- (0, 0): bar_grad(
- "#d65f5f 100.0%",
- " transparent 100.0%",
- ),
- (1, 0): bar_grad(
- " transparent 40.0%",
- " #d65f5f 40.0%",
- " #d65f5f 100.0%",
- " transparent 100.0%",
- ),
- (2, 0): bar_grad(
- " transparent 70.0%",
- " #d65f5f 70.0%",
- " #d65f5f 100.0%",
- " transparent 100.0%",
- ),
- (3, 0): bar_grad(
- " 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 = DataFrame({"A": [-10, 0, 20, 90]})
-
- result = (
- df.style.bar(align="zero", color=["#d65f5f", "#5fba7d"], width=90)
- ._compute()
- .ctx
- )
- expected = {
- (0, 0): bar_grad(
- " transparent 40.0%",
- " #d65f5f 40.0%",
- " #d65f5f 45.0%",
- " transparent 45.0%",
- ),
- (1, 0): bar_grad(),
- (2, 0): bar_grad(
- " transparent 45.0%",
- " #5fba7d 45.0%",
- " #5fba7d 55.0%",
- " transparent 55.0%",
- ),
- (3, 0): bar_grad(
- " transparent 45.0%",
- " #5fba7d 45.0%",
- " #5fba7d 90.0%",
- " transparent 90.0%",
- ),
- }
- assert result == expected
-
- def test_bar_align_left_axis_none(self):
- df = DataFrame({"A": [0, 1], "B": [2, 4]})
- result = df.style.bar(axis=None)._compute().ctx
- expected = {
- (0, 0): bar_grad(),
- (1, 0): bar_grad(
- "#d65f5f 25.0%",
- " transparent 25.0%",
- ),
- (0, 1): bar_grad(
- "#d65f5f 50.0%",
- " transparent 50.0%",
- ),
- (1, 1): bar_grad(
- "#d65f5f 100.0%",
- " transparent 100.0%",
- ),
- }
- assert result == expected
-
- def test_bar_align_zero_axis_none(self):
- df = DataFrame({"A": [0, 1], "B": [-2, 4]})
- result = df.style.bar(align="zero", axis=None)._compute().ctx
- expected = {
- (0, 0): bar_grad(),
- (1, 0): bar_grad(
- " transparent 50.0%",
- " #d65f5f 50.0%",
- " #d65f5f 62.5%",
- " transparent 62.5%",
- ),
- (0, 1): bar_grad(
- " transparent 25.0%",
- " #d65f5f 25.0%",
- " #d65f5f 50.0%",
- " transparent 50.0%",
- ),
- (1, 1): bar_grad(
- " transparent 50.0%",
- " #d65f5f 50.0%",
- " #d65f5f 100.0%",
- " transparent 100.0%",
- ),
- }
- assert result == expected
-
- def test_bar_align_mid_axis_none(self):
- df = DataFrame({"A": [0, 1], "B": [-2, 4]})
- result = df.style.bar(align="mid", axis=None)._compute().ctx
- expected = {
- (0, 0): bar_grad(),
- (1, 0): bar_grad(
- " transparent 33.3%",
- " #d65f5f 33.3%",
- " #d65f5f 50.0%",
- " transparent 50.0%",
- ),
- (0, 1): bar_grad(
- "#d65f5f 33.3%",
- " transparent 33.3%",
- ),
- (1, 1): bar_grad(
- " transparent 33.3%",
- " #d65f5f 33.3%",
- " #d65f5f 100.0%",
- " transparent 100.0%",
- ),
- }
- assert result == expected
-
- def test_bar_align_mid_vmin(self):
- df = DataFrame({"A": [0, 1], "B": [-2, 4]})
- result = df.style.bar(align="mid", axis=None, vmin=-6)._compute().ctx
- expected = {
- (0, 0): bar_grad(),
- (1, 0): bar_grad(
- " transparent 60.0%",
- " #d65f5f 60.0%",
- " #d65f5f 70.0%",
- " transparent 70.0%",
- ),
- (0, 1): bar_grad(
- " transparent 40.0%",
- " #d65f5f 40.0%",
- " #d65f5f 60.0%",
- " transparent 60.0%",
- ),
- (1, 1): bar_grad(
- " transparent 60.0%",
- " #d65f5f 60.0%",
- " #d65f5f 100.0%",
- " transparent 100.0%",
- ),
- }
- assert result == expected
-
- def test_bar_align_mid_vmax(self):
- df = DataFrame({"A": [0, 1], "B": [-2, 4]})
- result = df.style.bar(align="mid", axis=None, vmax=8)._compute().ctx
- expected = {
- (0, 0): bar_grad(),
- (1, 0): bar_grad(
- " transparent 20.0%",
- " #d65f5f 20.0%",
- " #d65f5f 30.0%",
- " transparent 30.0%",
- ),
- (0, 1): bar_grad(
- "#d65f5f 20.0%",
- " transparent 20.0%",
- ),
- (1, 1): bar_grad(
- " 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 = DataFrame({"A": [0, 1], "B": [-2, 4]})
- result = df.style.bar(align="mid", axis=None, vmin=-3, vmax=7)._compute().ctx
- expected = {
- (0, 0): bar_grad(),
- (1, 0): bar_grad(
- " transparent 30.0%",
- " #d65f5f 30.0%",
- " #d65f5f 40.0%",
- " transparent 40.0%",
- ),
- (0, 1): bar_grad(
- " transparent 10.0%",
- " #d65f5f 10.0%",
- " #d65f5f 30.0%",
- " transparent 30.0%",
- ),
- (1, 1): bar_grad(
- " 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 = DataFrame({"A": [0, 1], "B": [-2, 4]})
- result = df.style.bar(align="mid", axis=None, vmin=-1, vmax=3)._compute().ctx
- expected = {
- (0, 0): bar_grad(),
- (1, 0): bar_grad(
- " transparent 25.0%",
- " #d65f5f 25.0%",
- " #d65f5f 50.0%",
- " transparent 50.0%",
- ),
- (0, 1): bar_grad("#d65f5f 25.0%", " transparent 25.0%"),
- (1, 1): bar_grad(
- " transparent 25.0%",
- " #d65f5f 25.0%",
- " #d65f5f 100.0%",
- " transparent 100.0%",
- ),
- }
- assert result == expected
-
- def test_bar_align_mid_nans(self):
- df = DataFrame({"A": [1, None], "B": [-1, 3]})
- result = df.style.bar(align="mid", axis=None)._compute().ctx
- expected = {
- (0, 0): bar_grad(
- " transparent 25.0%",
- " #d65f5f 25.0%",
- " #d65f5f 50.0%",
- " transparent 50.0%",
- ),
- (0, 1): bar_grad("#d65f5f 25.0%", " transparent 25.0%"),
- (1, 1): bar_grad(
- " transparent 25.0%",
- " #d65f5f 25.0%",
- " #d65f5f 100.0%",
- " transparent 100.0%",
- ),
- }
- assert result == expected
-
- def test_bar_align_zero_nans(self):
- df = DataFrame({"A": [1, None], "B": [-1, 2]})
- result = df.style.bar(align="zero", axis=None)._compute().ctx
- expected = {
- (0, 0): bar_grad(
- " transparent 50.0%",
- " #d65f5f 50.0%",
- " #d65f5f 75.0%",
- " transparent 75.0%",
- ),
- (0, 1): bar_grad(
- " transparent 25.0%",
- " #d65f5f 25.0%",
- " #d65f5f 50.0%",
- " transparent 50.0%",
- ),
- (1, 1): bar_grad(
- " transparent 50.0%",
- " #d65f5f 50.0%",
- " #d65f5f 100.0%",
- " transparent 100.0%",
- ),
- }
- assert result == expected
-
- def test_bar_bad_align_raises(self):
- df = 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 = DataFrame([[None, None], [1.1, 1.2]], columns=["A", "B"])
@@ -973,27 +572,6 @@ def test_format_with_bad_na_rep(self):
with pytest.raises(TypeError, match=msg):
df.style.format(None, na_rep=-1)
- def test_highlight_null(self, null_color="red"):
- df = 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 = 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 = DataFrame([[1, 2]], columns=["A", "A"])
msg = "style is not supported for non-unique indices."
@@ -1098,47 +676,6 @@ def test_trim(self):
result = self.df.style.highlight_max().render()
assert result.count("#") == len(self.df.columns)
- def test_highlight_max(self):
- df = DataFrame([[1, 2], [3, 4]], columns=["A", "B"])
- css_seq = [("background-color", "yellow")]
- # 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)] == css_seq
-
- 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)] == css_seq
-
- result = getattr(df.style, attr)(axis=0)._compute().ctx
- expected = {
- (1, 0): css_seq,
- (1, 1): css_seq,
- }
- assert result == expected
-
- result = getattr(df.style, attr)(axis=1)._compute().ctx
- expected = {
- (0, 1): css_seq,
- (1, 1): css_seq,
- }
- 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): css_seq}
-
- result = df.style.highlight_min()._compute().ctx
- expected = {(0, 0): css_seq}
-
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}"
@@ -1704,96 +1241,6 @@ def test_uuid_len_raises(self, len_):
with pytest.raises(TypeError, match=msg):
Styler(df, uuid_len=len_, cell_ids=False).render()
- @pytest.mark.parametrize(
- "ttips",
- [
- DataFrame(
- data=[["Min", "Max"], [np.nan, ""]],
- columns=["A", "B"],
- index=["a", "b"],
- ),
- DataFrame(data=[["Max", "Min"]], columns=["B", "A"], index=["a"]),
- DataFrame(
- data=[["Min", "Max", None]], columns=["A", "B", "C"], index=["a"]
- ),
- ],
- )
- def test_tooltip_render(self, ttips):
- # GH 21266
- df = DataFrame(data=[[0, 3], [1, 2]], columns=["A", "B"], index=["a", "b"])
- s = Styler(df, uuid_len=0).set_tooltips(ttips).render()
-
- # test tooltip table level class
- assert "#T__ .pd-t {\n visibility: hidden;\n" in s
-
- # test 'Min' tooltip added
- assert (
- "#T__ #T__row0_col0:hover .pd-t {\n visibility: visible;\n}\n"
- + '#T__ #T__row0_col0 .pd-t::after {\n content: "Min";\n}'
- in s
- )
- assert (
- '| 0'
- + " | "
- in s
- )
-
- # test 'Max' tooltip added
- assert (
- "#T__ #T__row0_col1:hover .pd-t {\n visibility: visible;\n}\n"
- + '#T__ #T__row0_col1 .pd-t::after {\n content: "Max";\n}'
- in s
- )
- assert (
- '3'
- + " | "
- in s
- )
-
- def test_tooltip_reindex(self):
- # GH 39317
- df = DataFrame(
- data=[[0, 1, 2], [3, 4, 5], [6, 7, 8]], columns=[0, 1, 2], index=[0, 1, 2]
- )
- ttips = DataFrame(
- data=[["Mi", "Ma"], ["Mu", "Mo"]],
- columns=[0, 2],
- index=[0, 2],
- )
- s = Styler(df, uuid_len=0).set_tooltips(DataFrame(ttips)).render()
- assert '#T__ #T__row0_col0 .pd-t::after {\n content: "Mi";\n}' in s
- assert '#T__ #T__row0_col2 .pd-t::after {\n content: "Ma";\n}' in s
- assert '#T__ #T__row2_col0 .pd-t::after {\n content: "Mu";\n}' in s
- assert '#T__ #T__row2_col2 .pd-t::after {\n content: "Mo";\n}' in s
-
- def test_tooltip_ignored(self):
- # GH 21266
- df = DataFrame(data=[[0, 1], [2, 3]])
- s = Styler(df).set_tooltips_class("pd-t").render() # no set_tooltips()
- assert '' in s
- assert '' not in s
-
- def test_tooltip_class(self):
- # GH 21266
- df = DataFrame(data=[[0, 1], [2, 3]])
- s = (
- Styler(df, uuid_len=0)
- .set_tooltips(DataFrame([["tooltip"]]))
- .set_tooltips_class(name="other-class", properties=[("color", "green")])
- .render()
- )
- assert "#T__ .other-class {\n color: green;\n" in s
- assert '#T__ #T__row0_col0 .other-class::after {\n content: "tooltip";\n' in s
-
- # GH 39563
- s = (
- Styler(df, uuid_len=0)
- .set_tooltips(DataFrame([["tooltip"]]))
- .set_tooltips_class(name="other-class", properties="color:green;color:red;")
- .render()
- )
- assert "#T__ .other-class {\n color: green;\n color: red;\n}" in s
-
def test_w3_html_format(self):
s = (
Styler(
@@ -1925,107 +1372,6 @@ def test_non_reducing_multi_slice_on_multiindex(self, slice_):
tm.assert_frame_equal(result, expected)
-@td.skip_if_no_mpl
-class TestStylerMatplotlibDep:
- def test_background_gradient(self):
- df = 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][1] 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(
- "cmap, expected",
- [
- (
- "PuBu",
- {
- (4, 5): [("background-color", "#86b0d3"), ("color", "#000000")],
- (4, 6): [("background-color", "#83afd3"), ("color", "#f1f1f1")],
- },
- ),
- (
- "YlOrRd",
- {
- (4, 8): [("background-color", "#fd913e"), ("color", "#000000")],
- (4, 9): [("background-color", "#fd8f3d"), ("color", "#f1f1f1")],
- },
- ),
- (
- None,
- {
- (7, 0): [("background-color", "#48c16e"), ("color", "#f1f1f1")],
- (7, 1): [("background-color", "#4cc26c"), ("color", "#000000")],
- },
- ),
- ],
- )
- def test_text_color_threshold(self, cmap, expected):
- df = DataFrame(np.arange(100).reshape(10, 10))
- result = df.style.background_gradient(cmap=cmap, axis=None)._compute().ctx
- for k in expected.keys():
- assert result[k] == expected[k]
-
- @pytest.mark.parametrize("text_color_threshold", [1.1, "1", -1, [2, 2]])
- def test_text_color_threshold_raises(self, text_color_threshold):
- df = 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 = 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 = 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 = {
diff --git a/pandas/tests/io/formats/style/test_tooltip.py b/pandas/tests/io/formats/style/test_tooltip.py
new file mode 100644
index 0000000000000..7aebb6fadc7b1
--- /dev/null
+++ b/pandas/tests/io/formats/style/test_tooltip.py
@@ -0,0 +1,99 @@
+import numpy as np
+import pytest
+
+from pandas import DataFrame
+
+pytest.importorskip("jinja2")
+from pandas.io.formats.style import Styler
+
+
+class TestStylerTooltip:
+ @pytest.mark.parametrize(
+ "ttips",
+ [
+ DataFrame(
+ data=[["Min", "Max"], [np.nan, ""]],
+ columns=["A", "B"],
+ index=["a", "b"],
+ ),
+ DataFrame(data=[["Max", "Min"]], columns=["B", "A"], index=["a"]),
+ DataFrame(
+ data=[["Min", "Max", None]], columns=["A", "B", "C"], index=["a"]
+ ),
+ ],
+ )
+ def test_tooltip_render(self, ttips):
+ # GH 21266
+ df = DataFrame(data=[[0, 3], [1, 2]], columns=["A", "B"], index=["a", "b"])
+ s = Styler(df, uuid_len=0).set_tooltips(ttips).render()
+
+ # test tooltip table level class
+ assert "#T__ .pd-t {\n visibility: hidden;\n" in s
+
+ # test 'Min' tooltip added
+ assert (
+ "#T__ #T__row0_col0:hover .pd-t {\n visibility: visible;\n}\n"
+ + '#T__ #T__row0_col0 .pd-t::after {\n content: "Min";\n}'
+ in s
+ )
+ assert (
+ '0'
+ + " | "
+ in s
+ )
+
+ # test 'Max' tooltip added
+ assert (
+ "#T__ #T__row0_col1:hover .pd-t {\n visibility: visible;\n}\n"
+ + '#T__ #T__row0_col1 .pd-t::after {\n content: "Max";\n}'
+ in s
+ )
+ assert (
+ '3'
+ + " | "
+ in s
+ )
+
+ def test_tooltip_reindex(self):
+ # GH 39317
+ df = DataFrame(
+ data=[[0, 1, 2], [3, 4, 5], [6, 7, 8]], columns=[0, 1, 2], index=[0, 1, 2]
+ )
+ ttips = DataFrame(
+ data=[["Mi", "Ma"], ["Mu", "Mo"]],
+ columns=[0, 2],
+ index=[0, 2],
+ )
+ s = Styler(df, uuid_len=0).set_tooltips(DataFrame(ttips)).render()
+ assert '#T__ #T__row0_col0 .pd-t::after {\n content: "Mi";\n}' in s
+ assert '#T__ #T__row0_col2 .pd-t::after {\n content: "Ma";\n}' in s
+ assert '#T__ #T__row2_col0 .pd-t::after {\n content: "Mu";\n}' in s
+ assert '#T__ #T__row2_col2 .pd-t::after {\n content: "Mo";\n}' in s
+
+ def test_tooltip_ignored(self):
+ # GH 21266
+ df = DataFrame(data=[[0, 1], [2, 3]])
+ s = Styler(df).set_tooltips_class("pd-t").render() # no set_tooltips()
+ assert '' in s
+ assert '' not in s
+
+ def test_tooltip_css_class(self):
+ # GH 21266
+ df = DataFrame(data=[[0, 1], [2, 3]])
+ s = (
+ Styler(df, uuid_len=0)
+ .set_tooltips(DataFrame([["tooltip"]]))
+ .set_tooltips_class(name="other-class", properties=[("color", "green")])
+ .render()
+ )
+ assert "#T__ .other-class {\n color: green;\n" in s
+ assert '#T__ #T__row0_col0 .other-class::after {\n content: "tooltip";\n' in s
+
+ # GH 39563
+ s = (
+ Styler(df, uuid_len=0)
+ .set_tooltips(DataFrame([["tooltip"]]))
+ .set_tooltips_class(name="other-class", properties="color:green;color:red;")
+ .render()
+ )
+ assert "#T__ .other-class {\n color: green;\n color: red;\n}" in s