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| import numpy as np
| 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")]
| 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])})",
| )
| ]
|
|
| def no_bar():
| return bar_grad()
|
|
| def bar_to(x, color="#d65f5f"):
| return bar_grad(f" {color} {x:.1f}%", f" transparent {x:.1f}%")
|
|
| def bar_from_to(x, y, color="#d65f5f"):
| return bar_grad(
| f" transparent {x:.1f}%",
| f" {color} {x:.1f}%",
| f" {color} {y:.1f}%",
| f" transparent {y:.1f}%",
| )
|
|
| @pytest.fixture
| def df_pos():
| return DataFrame([[1], [2], [3]])
|
|
| @pytest.fixture
| def df_neg():
| return DataFrame([[-1], [-2], [-3]])
|
|
| @pytest.fixture
| def df_mix():
| return DataFrame([[-3], [1], [2]])
|
|
| @pytest.mark.parametrize(
| "align, exp",
| [
| ("left", [no_bar(), bar_to(50), bar_to(100)]),
| ("right", [bar_to(100), bar_from_to(50, 100), no_bar()]),
| ("mid", [bar_to(33.33), bar_to(66.66), bar_to(100)]),
| ("zero", [bar_from_to(50, 66.7), bar_from_to(50, 83.3), bar_from_to(50, 100)]),
| ("mean", [bar_to(50), no_bar(), bar_from_to(50, 100)]),
| (2.0, [bar_to(50), no_bar(), bar_from_to(50, 100)]),
| (np.median, [bar_to(50), no_bar(), bar_from_to(50, 100)]),
| ],
| )
| def test_align_positive_cases(df_pos, align, exp):
| # test different align cases for all positive values
| result = df_pos.style.bar(align=align)._compute().ctx
| expected = {(0, 0): exp[0], (1, 0): exp[1], (2, 0): exp[2]}
| assert result == expected
|
|
| @pytest.mark.parametrize(
| "align, exp",
| [
| ("left", [bar_to(100), bar_to(50), no_bar()]),
| ("right", [no_bar(), bar_from_to(50, 100), bar_to(100)]),
| ("mid", [bar_from_to(66.66, 100), bar_from_to(33.33, 100), bar_to(100)]),
| ("zero", [bar_from_to(33.33, 50), bar_from_to(16.66, 50), bar_to(50)]),
| ("mean", [bar_from_to(50, 100), no_bar(), bar_to(50)]),
| (-2.0, [bar_from_to(50, 100), no_bar(), bar_to(50)]),
| (np.median, [bar_from_to(50, 100), no_bar(), bar_to(50)]),
| ],
| )
| def test_align_negative_cases(df_neg, align, exp):
| # test different align cases for all negative values
| result = df_neg.style.bar(align=align)._compute().ctx
| expected = {(0, 0): exp[0], (1, 0): exp[1], (2, 0): exp[2]}
| assert result == expected
|
|
| @pytest.mark.parametrize(
| "align, exp",
| [
| ("left", [no_bar(), bar_to(80), bar_to(100)]),
| ("right", [bar_to(100), bar_from_to(80, 100), no_bar()]),
| ("mid", [bar_to(60), bar_from_to(60, 80), bar_from_to(60, 100)]),
| ("zero", [bar_to(50), bar_from_to(50, 66.66), bar_from_to(50, 83.33)]),
| ("mean", [bar_to(50), bar_from_to(50, 66.66), bar_from_to(50, 83.33)]),
| (-0.0, [bar_to(50), bar_from_to(50, 66.66), bar_from_to(50, 83.33)]),
| (np.nanmedian, [bar_to(50), no_bar(), bar_from_to(50, 62.5)]),
| ],
| )
| @pytest.mark.parametrize("nans", [True, False])
| def test_align_mixed_cases(df_mix, align, exp, nans):
| # test different align cases for mixed positive and negative values
| # also test no impact of NaNs and no_bar
| expected = {(0, 0): exp[0], (1, 0): exp[1], (2, 0): exp[2]}
| if nans:
| df_mix.loc[3, :] = np.nan
| expected.update({(3, 0): no_bar()})
| result = df_mix.style.bar(align=align)._compute().ctx
| assert result == expected
|
|
| @pytest.mark.parametrize(
| "align, exp",
| [
| (
| "left",
| {
| "index": [[no_bar(), no_bar()], [bar_to(100), bar_to(100)]],
| "columns": [[no_bar(), bar_to(100)], [no_bar(), bar_to(100)]],
| "none": [[no_bar(), bar_to(33.33)], [bar_to(66.66), bar_to(100)]],
| },
| ),
| (
| "mid",
| {
| "index": [[bar_to(33.33), bar_to(50)], [bar_to(100), bar_to(100)]],
| "columns": [[bar_to(50), bar_to(100)], [bar_to(75), bar_to(100)]],
| "none": [[bar_to(25), bar_to(50)], [bar_to(75), bar_to(100)]],
| },
| ),
| (
| "zero",
| {
| "index": [
| [bar_from_to(50, 66.66), bar_from_to(50, 75)],
| [bar_from_to(50, 100), bar_from_to(50, 100)],
| ],
| "columns": [
| [bar_from_to(50, 75), bar_from_to(50, 100)],
| [bar_from_to(50, 87.5), bar_from_to(50, 100)],
| ],
| "none": [
| [bar_from_to(50, 62.5), bar_from_to(50, 75)],
| [bar_from_to(50, 87.5), bar_from_to(50, 100)],
| ],
| },
| ),
| (
| 2,
| {
| "index": [
| [bar_to(50), no_bar()],
| [bar_from_to(50, 100), bar_from_to(50, 100)],
| ],
| "columns": [
| [bar_to(50), no_bar()],
| [bar_from_to(50, 75), bar_from_to(50, 100)],
| ],
| "none": [
| [bar_from_to(25, 50), no_bar()],
| [bar_from_to(50, 75), bar_from_to(50, 100)],
| ],
| },
| ),
| ],
| )
| @pytest.mark.parametrize("axis", ["index", "columns", "none"])
| def test_align_axis(align, exp, axis):
| # test all axis combinations with positive values and different aligns
| data = DataFrame([[1, 2], [3, 4]])
| result = (
| data.style.bar(align=align, axis=None if axis == "none" else axis)
| ._compute()
| .ctx
| )
| expected = {
| (0, 0): exp[axis][0][0],
| (0, 1): exp[axis][0][1],
| (1, 0): exp[axis][1][0],
| (1, 1): exp[axis][1][1],
| }
| assert result == expected
|
|
| @pytest.mark.parametrize(
| "values, vmin, vmax",
| [
| ("positive", 1.5, 2.5),
| ("negative", -2.5, -1.5),
| ("mixed", -2.5, 1.5),
| ],
| )
| @pytest.mark.parametrize("nullify", [None, "vmin", "vmax"]) # test min/max separately
| @pytest.mark.parametrize("align", ["left", "right", "zero", "mid"])
| def test_vmin_vmax_clipping(df_pos, df_neg, df_mix, values, vmin, vmax, nullify, align):
| # test that clipping occurs if any vmin > data_values or vmax < data_values
| if align == "mid": # mid acts as left or right in each case
| if values == "positive":
| align = "left"
| elif values == "negative":
| align = "right"
| df = {"positive": df_pos, "negative": df_neg, "mixed": df_mix}[values]
| vmin = None if nullify == "vmin" else vmin
| vmax = None if nullify == "vmax" else vmax
|
| clip_df = df.where(df <= (vmax if vmax else 999), other=vmax)
| clip_df = clip_df.where(clip_df >= (vmin if vmin else -999), other=vmin)
|
| result = (
| df.style.bar(align=align, vmin=vmin, vmax=vmax, color=["red", "green"])
| ._compute()
| .ctx
| )
| expected = clip_df.style.bar(align=align, color=["red", "green"])._compute().ctx
| assert result == expected
|
|
| @pytest.mark.parametrize(
| "values, vmin, vmax",
| [
| ("positive", 0.5, 4.5),
| ("negative", -4.5, -0.5),
| ("mixed", -4.5, 4.5),
| ],
| )
| @pytest.mark.parametrize("nullify", [None, "vmin", "vmax"]) # test min/max separately
| @pytest.mark.parametrize("align", ["left", "right", "zero", "mid"])
| def test_vmin_vmax_widening(df_pos, df_neg, df_mix, values, vmin, vmax, nullify, align):
| # test that widening occurs if any vmax > data_values or vmin < data_values
| if align == "mid": # mid acts as left or right in each case
| if values == "positive":
| align = "left"
| elif values == "negative":
| align = "right"
| df = {"positive": df_pos, "negative": df_neg, "mixed": df_mix}[values]
| vmin = None if nullify == "vmin" else vmin
| vmax = None if nullify == "vmax" else vmax
|
| expand_df = df.copy()
| expand_df.loc[3, :], expand_df.loc[4, :] = vmin, vmax
|
| result = (
| df.style.bar(align=align, vmin=vmin, vmax=vmax, color=["red", "green"])
| ._compute()
| .ctx
| )
| expected = expand_df.style.bar(align=align, color=["red", "green"])._compute().ctx
| assert result.items() <= expected.items()
|
|
| def test_numerics():
| # test data is pre-selected for numeric values
| data = DataFrame([[1, "a"], [2, "b"]])
| result = data.style.bar()._compute().ctx
| assert (0, 1) not in result
| assert (1, 1) not in result
|
|
| @pytest.mark.parametrize(
| "align, exp",
| [
| ("left", [no_bar(), bar_to(100, "green")]),
| ("right", [bar_to(100, "red"), no_bar()]),
| ("mid", [bar_to(25, "red"), bar_from_to(25, 100, "green")]),
| ("zero", [bar_from_to(33.33, 50, "red"), bar_from_to(50, 100, "green")]),
| ],
| )
| def test_colors_mixed(align, exp):
| data = DataFrame([[-1], [3]])
| result = data.style.bar(align=align, color=["red", "green"])._compute().ctx
| assert result == {(0, 0): exp[0], (1, 0): exp[1]}
|
|
| def test_bar_align_height():
| # test when keyword height is used 'no-repeat center' and 'background-size' present
| data = DataFrame([[1], [2]])
| result = data.style.bar(align="left", height=50)._compute().ctx
| bg_s = "linear-gradient(90deg, #d65f5f 100.0%, transparent 100.0%) no-repeat center"
| expected = {
| (0, 0): [("width", "10em")],
| (1, 0): [
| ("width", "10em"),
| ("background", bg_s),
| ("background-size", "100% 50.0%"),
| ],
| }
| assert result == expected
|
|
| def test_bar_value_error_raises():
| df = DataFrame({"A": [-100, -60, -30, -20]})
|
| msg = "`align` should be in {'left', 'right', 'mid', 'mean', 'zero'} or"
| with pytest.raises(ValueError, match=msg):
| df.style.bar(align="poorly", color=["#d65f5f", "#5fba7d"]).to_html()
|
| msg = r"`width` must be a value in \[0, 100\]"
| with pytest.raises(ValueError, match=msg):
| df.style.bar(width=200).to_html()
|
| msg = r"`height` must be a value in \[0, 100\]"
| with pytest.raises(ValueError, match=msg):
| df.style.bar(height=200).to_html()
|
|
