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CLN TST: Refactor Styler.bar tests through parametrisation (#42472)
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‎pandas/tests/io/formats/style/test_align.py

Lines changed: 104 additions & 246 deletions
Original file line numberDiff line numberDiff line change
@@ -23,258 +23,20 @@ def no_bar():
2323
return bar_grad()
2424

2525

26-
def bar_to(x):
27-
return bar_grad(f" #d65f5f {x:.1f}%", f" transparent {x:.1f}%")
26+
def bar_to(x, color="#d65f5f"):
27+
return bar_grad(f" {color} {x:.1f}%", f" transparent {x:.1f}%")
2828

2929

30-
def bar_from_to(x, y):
30+
def bar_from_to(x, y, color="#d65f5f"):
3131
return bar_grad(
3232
f" transparent {x:.1f}%",
33-
f" #d65f5f {x:.1f}%",
34-
f" #d65f5f {y:.1f}%",
33+
f" {color} {x:.1f}%",
34+
f" {color} {y:.1f}%",
3535
f" transparent {y:.1f}%",
3636
)
3737

3838

3939
class TestStylerBarAlign:
40-
def test_bar_align_left(self):
41-
df = DataFrame({"A": [0, 1, 2]})
42-
result = df.style.bar(align="left")._compute().ctx
43-
expected = {
44-
(0, 0): bar_grad(),
45-
(1, 0): bar_grad(" #d65f5f 50.0%", " transparent 50.0%"),
46-
(2, 0): bar_grad(" #d65f5f 100.0%", " transparent 100.0%"),
47-
}
48-
assert result == expected
49-
50-
result = df.style.bar(color="red", width=50, align="left")._compute().ctx
51-
expected = {
52-
(0, 0): bar_grad(),
53-
(1, 0): bar_grad(" red 25.0%", " transparent 25.0%"),
54-
(2, 0): bar_grad(" red 50.0%", " transparent 50.0%"),
55-
}
56-
assert result == expected
57-
58-
df["C"] = ["a"] * len(df)
59-
result = df.style.bar(color="red", width=50, align="left")._compute().ctx
60-
assert result == expected
61-
df["C"] = df["C"].astype("category")
62-
result = df.style.bar(color="red", width=50, align="left")._compute().ctx
63-
assert result == expected
64-
65-
def test_bar_align_left_0points(self):
66-
df = DataFrame([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
67-
result = df.style.bar(align="left")._compute().ctx
68-
expected = {
69-
(0, 0): bar_grad(),
70-
(0, 1): bar_grad(),
71-
(0, 2): bar_grad(),
72-
(1, 0): bar_grad(" #d65f5f 50.0%", " transparent 50.0%"),
73-
(1, 1): bar_grad(" #d65f5f 50.0%", " transparent 50.0%"),
74-
(1, 2): bar_grad(" #d65f5f 50.0%", " transparent 50.0%"),
75-
(2, 0): bar_grad(" #d65f5f 100.0%", " transparent 100.0%"),
76-
(2, 1): bar_grad(" #d65f5f 100.0%", " transparent 100.0%"),
77-
(2, 2): bar_grad(" #d65f5f 100.0%", " transparent 100.0%"),
78-
}
79-
assert result == expected
80-
81-
result = df.style.bar(axis=1, align="left")._compute().ctx
82-
expected = {
83-
(0, 0): bar_grad(),
84-
(0, 1): bar_grad(" #d65f5f 50.0%", " transparent 50.0%"),
85-
(0, 2): bar_grad(" #d65f5f 100.0%", " transparent 100.0%"),
86-
(1, 0): bar_grad(),
87-
(1, 1): bar_grad(" #d65f5f 50.0%", " transparent 50.0%"),
88-
(1, 2): bar_grad(" #d65f5f 100.0%", " transparent 100.0%"),
89-
(2, 0): bar_grad(),
90-
(2, 1): bar_grad(" #d65f5f 50.0%", " transparent 50.0%"),
91-
(2, 2): bar_grad(" #d65f5f 100.0%", " transparent 100.0%"),
92-
}
93-
assert result == expected
94-
95-
def test_bar_align_mid_pos_and_neg(self):
96-
df = DataFrame({"A": [-10, 0, 20, 90]})
97-
result = df.style.bar(align="mid", color=["#d65f5f", "#5fba7d"])._compute().ctx
98-
expected = {
99-
(0, 0): bar_grad(
100-
" #d65f5f 10.0%",
101-
" transparent 10.0%",
102-
),
103-
(1, 0): bar_grad(),
104-
(2, 0): bar_grad(
105-
" transparent 10.0%",
106-
" #5fba7d 10.0%",
107-
" #5fba7d 30.0%",
108-
" transparent 30.0%",
109-
),
110-
(3, 0): bar_grad(
111-
" transparent 10.0%",
112-
" #5fba7d 10.0%",
113-
" #5fba7d 100.0%",
114-
" transparent 100.0%",
115-
),
116-
}
117-
assert result == expected
118-
119-
def test_bar_align_mid_all_pos(self):
120-
df = DataFrame({"A": [10, 20, 50, 100]})
121-
122-
result = df.style.bar(align="mid", color=["#d65f5f", "#5fba7d"])._compute().ctx
123-
124-
expected = {
125-
(0, 0): bar_grad(
126-
" #5fba7d 10.0%",
127-
" transparent 10.0%",
128-
),
129-
(1, 0): bar_grad(
130-
" #5fba7d 20.0%",
131-
" transparent 20.0%",
132-
),
133-
(2, 0): bar_grad(
134-
" #5fba7d 50.0%",
135-
" transparent 50.0%",
136-
),
137-
(3, 0): bar_grad(
138-
" #5fba7d 100.0%",
139-
" transparent 100.0%",
140-
),
141-
}
142-
143-
assert result == expected
144-
145-
def test_bar_align_mid_all_neg(self):
146-
df = DataFrame({"A": [-100, -60, -30, -20]})
147-
148-
result = df.style.bar(align="mid", color=["#d65f5f", "#5fba7d"])._compute().ctx
149-
150-
expected = {
151-
(0, 0): bar_grad(
152-
" #d65f5f 100.0%",
153-
" transparent 100.0%",
154-
),
155-
(1, 0): bar_grad(
156-
" transparent 40.0%",
157-
" #d65f5f 40.0%",
158-
" #d65f5f 100.0%",
159-
" transparent 100.0%",
160-
),
161-
(2, 0): bar_grad(
162-
" transparent 70.0%",
163-
" #d65f5f 70.0%",
164-
" #d65f5f 100.0%",
165-
" transparent 100.0%",
166-
),
167-
(3, 0): bar_grad(
168-
" transparent 80.0%",
169-
" #d65f5f 80.0%",
170-
" #d65f5f 100.0%",
171-
" transparent 100.0%",
172-
),
173-
}
174-
assert result == expected
175-
176-
def test_bar_align_zero_pos_and_neg(self):
177-
# See https://github.com/pandas-dev/pandas/pull/14757
178-
df = DataFrame({"A": [-10, 0, 20, 90]})
179-
180-
result = (
181-
df.style.bar(align="zero", color=["#d65f5f", "#5fba7d"], width=90)
182-
._compute()
183-
.ctx
184-
)
185-
expected = {
186-
(0, 0): bar_grad(
187-
" transparent 40.0%",
188-
" #d65f5f 40.0%",
189-
" #d65f5f 45.0%",
190-
" transparent 45.0%",
191-
),
192-
(1, 0): bar_grad(),
193-
(2, 0): bar_grad(
194-
" transparent 45.0%",
195-
" #5fba7d 45.0%",
196-
" #5fba7d 55.0%",
197-
" transparent 55.0%",
198-
),
199-
(3, 0): bar_grad(
200-
" transparent 45.0%",
201-
" #5fba7d 45.0%",
202-
" #5fba7d 90.0%",
203-
" transparent 90.0%",
204-
),
205-
}
206-
assert result == expected
207-
208-
def test_bar_align_left_axis_none(self):
209-
df = DataFrame({"A": [0, 1], "B": [2, 4]})
210-
result = df.style.bar(axis=None, align="left")._compute().ctx
211-
expected = {
212-
(0, 0): bar_grad(),
213-
(1, 0): bar_grad(
214-
" #d65f5f 25.0%",
215-
" transparent 25.0%",
216-
),
217-
(0, 1): bar_grad(
218-
" #d65f5f 50.0%",
219-
" transparent 50.0%",
220-
),
221-
(1, 1): bar_grad(
222-
" #d65f5f 100.0%",
223-
" transparent 100.0%",
224-
),
225-
}
226-
assert result == expected
227-
228-
def test_bar_align_zero_axis_none(self):
229-
df = DataFrame({"A": [0, 1], "B": [-2, 4]})
230-
result = df.style.bar(align="zero", axis=None)._compute().ctx
231-
expected = {
232-
(0, 0): bar_grad(),
233-
(1, 0): bar_grad(
234-
" transparent 50.0%",
235-
" #d65f5f 50.0%",
236-
" #d65f5f 62.5%",
237-
" transparent 62.5%",
238-
),
239-
(0, 1): bar_grad(
240-
" transparent 25.0%",
241-
" #d65f5f 25.0%",
242-
" #d65f5f 50.0%",
243-
" transparent 50.0%",
244-
),
245-
(1, 1): bar_grad(
246-
" transparent 50.0%",
247-
" #d65f5f 50.0%",
248-
" #d65f5f 100.0%",
249-
" transparent 100.0%",
250-
),
251-
}
252-
assert result == expected
253-
254-
def test_bar_align_mid_axis_none(self):
255-
df = DataFrame({"A": [0, 1], "B": [-2, 4]})
256-
result = df.style.bar(align="mid", axis=None)._compute().ctx
257-
expected = {
258-
(0, 0): bar_grad(),
259-
(1, 0): bar_grad(
260-
" transparent 33.3%",
261-
" #d65f5f 33.3%",
262-
" #d65f5f 50.0%",
263-
" transparent 50.0%",
264-
),
265-
(0, 1): bar_grad(
266-
" #d65f5f 33.3%",
267-
" transparent 33.3%",
268-
),
269-
(1, 1): bar_grad(
270-
" transparent 33.3%",
271-
" #d65f5f 33.3%",
272-
" #d65f5f 100.0%",
273-
" transparent 100.0%",
274-
),
275-
}
276-
assert result == expected
277-
27840
def test_bar_align_mid_vmin(self):
27941
df = DataFrame({"A": [0, 1], "B": [-2, 4]})
28042
result = df.style.bar(align="mid", axis=None, vmin=-6)._compute().ctx
@@ -438,7 +200,7 @@ def test_bar_bad_align_raises(self):
438200
(np.median, [bar_to(50), no_bar(), bar_from_to(50, 100)]),
439201
],
440202
)
441-
def test_bar_align_positive_cases(align, exp):
203+
def test_align_positive_cases(align, exp):
442204
# test different align cases for all positive values
443205
data = DataFrame([[1], [2], [3]])
444206
result = data.style.bar(align=align)._compute().ctx
@@ -458,7 +220,7 @@ def test_bar_align_positive_cases(align, exp):
458220
(np.median, [bar_from_to(50, 100), no_bar(), bar_to(50)]),
459221
],
460222
)
461-
def test_bar_align_negative_cases(align, exp):
223+
def test_align_negative_cases(align, exp):
462224
# test different align cases for all negative values
463225
data = DataFrame([[-1], [-2], [-3]])
464226
result = data.style.bar(align=align)._compute().ctx
@@ -478,9 +240,105 @@ def test_bar_align_negative_cases(align, exp):
478240
(np.median, [bar_to(50), no_bar(), bar_from_to(50, 62.5)]),
479241
],
480242
)
481-
def test_bar_align_mixed_cases(align, exp):
243+
def test_align_mixed_cases(align, exp):
482244
# test different align cases for mixed positive and negative values
483245
data = DataFrame([[-3], [1], [2]])
484246
result = data.style.bar(align=align)._compute().ctx
485247
expected = {(0, 0): exp[0], (1, 0): exp[1], (2, 0): exp[2]}
486248
assert result == expected
249+
250+
251+
@pytest.mark.parametrize(
252+
"align, exp",
253+
[
254+
(
255+
"left",
256+
{
257+
"index": [[no_bar(), no_bar()], [bar_to(100), bar_to(100)]],
258+
"columns": [[no_bar(), bar_to(100)], [no_bar(), bar_to(100)]],
259+
"none": [[no_bar(), bar_to(33.33)], [bar_to(66.66), bar_to(100)]],
260+
},
261+
),
262+
(
263+
"mid",
264+
{
265+
"index": [[bar_to(33.33), bar_to(50)], [bar_to(100), bar_to(100)]],
266+
"columns": [[bar_to(50), bar_to(100)], [bar_to(75), bar_to(100)]],
267+
"none": [[bar_to(25), bar_to(50)], [bar_to(75), bar_to(100)]],
268+
},
269+
),
270+
(
271+
"zero",
272+
{
273+
"index": [
274+
[bar_from_to(50, 66.66), bar_from_to(50, 75)],
275+
[bar_from_to(50, 100), bar_from_to(50, 100)],
276+
],
277+
"columns": [
278+
[bar_from_to(50, 75), bar_from_to(50, 100)],
279+
[bar_from_to(50, 87.5), bar_from_to(50, 100)],
280+
],
281+
"none": [
282+
[bar_from_to(50, 62.5), bar_from_to(50, 75)],
283+
[bar_from_to(50, 87.5), bar_from_to(50, 100)],
284+
],
285+
},
286+
),
287+
(
288+
2,
289+
{
290+
"index": [
291+
[bar_to(50), no_bar()],
292+
[bar_from_to(50, 100), bar_from_to(50, 100)],
293+
],
294+
"columns": [
295+
[bar_to(50), no_bar()],
296+
[bar_from_to(50, 75), bar_from_to(50, 100)],
297+
],
298+
"none": [
299+
[bar_from_to(25, 50), no_bar()],
300+
[bar_from_to(50, 75), bar_from_to(50, 100)],
301+
],
302+
},
303+
),
304+
],
305+
)
306+
@pytest.mark.parametrize("axis", ["index", "columns", "none"])
307+
def test_align_axis(align, exp, axis):
308+
# test all axis combinations with positive values and different aligns
309+
data = DataFrame([[1, 2], [3, 4]])
310+
result = (
311+
data.style.bar(align=align, axis=None if axis == "none" else axis)
312+
._compute()
313+
.ctx
314+
)
315+
expected = {
316+
(0, 0): exp[axis][0][0],
317+
(0, 1): exp[axis][0][1],
318+
(1, 0): exp[axis][1][0],
319+
(1, 1): exp[axis][1][1],
320+
}
321+
assert result == expected
322+
323+
324+
def test_numerics():
325+
# test data is pre-selected for numeric values
326+
data = DataFrame([[1, "a"], [2, "b"]])
327+
result = data.style.bar()._compute().ctx
328+
assert (0, 1) not in result
329+
assert (1, 1) not in result
330+
331+
332+
@pytest.mark.parametrize(
333+
"align, exp",
334+
[
335+
("left", [no_bar(), bar_to(100, "green")]),
336+
("right", [bar_to(100, "red"), no_bar()]),
337+
("mid", [bar_to(25, "red"), bar_from_to(25, 100, "green")]),
338+
("zero", [bar_from_to(33.33, 50, "red"), bar_from_to(50, 100, "green")]),
339+
],
340+
)
341+
def test_colors_mixed(align, exp):
342+
data = DataFrame([[-1], [3]])
343+
result = data.style.bar(align=align, color=["red", "green"])._compute().ctx
344+
assert result == {(0, 0): exp[0], (1, 0): exp[1]}

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