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merged 2 commits into from
Jul 12, 2021
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TST CLN: Styler bar tests cleanup 2/3 #42504

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attack68 Jul 12, 2021
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Merge remote-tracking branch 'upstream/master' into styler_bar_tests_…
attack68 Jul 12, 2021
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200 changes: 91 additions & 109 deletions pandas/tests/io/formats/style/test_bar.py
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Original file line number Diff line number Diff line change
Expand Up @@ -36,104 +36,22 @@ def bar_from_to(x, y, color="#d65f5f"):
)


class TestStylerBarAlign:
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
@pytest.fixture
def df_pos():
return DataFrame([[1], [2], [3]])

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
@pytest.fixture
def df_neg():
return DataFrame([[-1], [-2], [-3]])

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

@pytest.fixture
def df_mix():
return DataFrame([[-3], [1], [2]])


class TestStylerBarAlign:
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
Expand Down Expand Up @@ -181,12 +99,6 @@ def test_bar_align_zero_nans(self):
}
assert result == expected

def test_bar_bad_align_raises(self):
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"]).render()


@pytest.mark.parametrize(
"align, exp",
Expand All @@ -200,10 +112,9 @@ def test_bar_bad_align_raises(self):
(np.median, [bar_to(50), no_bar(), bar_from_to(50, 100)]),
],
)
def test_align_positive_cases(align, exp):
def test_align_positive_cases(df_pos, align, exp):
# test different align cases for all positive values
data = DataFrame([[1], [2], [3]])
result = data.style.bar(align=align)._compute().ctx
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

Expand All @@ -220,10 +131,9 @@ def test_align_positive_cases(align, exp):
(np.median, [bar_from_to(50, 100), no_bar(), bar_to(50)]),
],
)
def test_align_negative_cases(align, exp):
def test_align_negative_cases(df_neg, align, exp):
# test different align cases for all negative values
data = DataFrame([[-1], [-2], [-3]])
result = data.style.bar(align=align)._compute().ctx
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

Expand All @@ -240,10 +150,9 @@ def test_align_negative_cases(align, exp):
(np.median, [bar_to(50), no_bar(), bar_from_to(50, 62.5)]),
],
)
def test_align_mixed_cases(align, exp):
def test_align_mixed_cases(df_mix, align, exp):
# test different align cases for mixed positive and negative values
data = DataFrame([[-3], [1], [2]])
result = data.style.bar(align=align)._compute().ctx
result = df_mix.style.bar(align=align)._compute().ctx
expected = {(0, 0): exp[0], (1, 0): exp[1], (2, 0): exp[2]}
assert result == expected

Expand Down Expand Up @@ -321,6 +230,72 @@ def test_align_axis(align, exp, axis):
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"]])
Expand All @@ -342,3 +317,10 @@ 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_bad_align_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"]).render()