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TST CLN: test_bar.py cleanup 3/3: NaN cases #42510

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Jul 12, 2021
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TST CLN: test_bar.py cleanup 3/3: NaN cases #42510

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60 changes: 8 additions & 52 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 @@ -51,55 +51,6 @@ 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
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, 0): bar_grad(),
(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, 0): bar_grad(),
(1, 1): bar_grad(
" transparent 50.0%",
" #d65f5f 50.0%",
" #d65f5f 100.0%",
" transparent 100.0%",
),
}
assert result == expected


@pytest.mark.parametrize(
"align, exp",
[
Expand Down Expand Up @@ -147,13 +98,18 @@ def test_align_negative_cases(df_neg, align, exp):
("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.median, [bar_to(50), no_bar(), bar_from_to(50, 62.5)]),
(np.nanmedian, [bar_to(50), no_bar(), bar_from_to(50, 62.5)]),
],
)
def test_align_mixed_cases(df_mix, align, exp):
@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
result = df_mix.style.bar(align=align)._compute().ctx
# 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


Expand Down