def test_table_attributes(self):
attributes = 'class="foo" data-bar'
styler = Styler(self.df, table_attributes=attributes)
result = styler.render()
self.assertTrue('class="foo" data-bar' in result)
result = self.df.style.set_table_attributes(attributes).render()
self.assertTrue('class="foo" data-bar' in result)
def test_table_attributes(self):
attributes = 'class="foo" data-bar'
styler = Styler(self.df, table_attributes=attributes)
result = styler.render()
self.assertTrue('class="foo" data-bar' in result)
result = self.df.style.set_table_attributes(attributes).render()
self.assertTrue('class="foo" data-bar' in result)
def test_uuid(self):
styler = Styler(self.df, uuid='abc123')
result = styler.render()
self.assertTrue('abc123' in result)
styler = self.df.style
result = styler.set_uuid('aaa')
self.assertTrue(result is styler)
self.assertEqual(result.uuid, 'aaa')
def test_caption(self):
styler = Styler(self.df, caption='foo')
result = styler.render()
self.assertTrue(all(['caption' in result, 'foo' in result]))
styler = self.df.style
result = styler.set_caption('baz')
self.assertTrue(styler is result)
self.assertEqual(styler.caption, 'baz')
def test_caption(self):
styler = Styler(self.df, caption='foo')
result = styler.render()
self.assertTrue(all(['caption' in result, 'foo' in result]))
styler = self.df.style
result = styler.set_caption('baz')
self.assertTrue(styler is result)
self.assertEqual(styler.caption, 'baz')
def test_uuid(self):
styler = Styler(self.df, uuid='abc123')
result = styler.render()
self.assertTrue('abc123' in result)
styler = self.df.style
result = styler.set_uuid('aaa')
self.assertTrue(result is styler)
self.assertEqual(result.uuid, 'aaa')
def test_table_styles(self):
style = [{'selector': 'th', 'props': [('foo', 'bar')]}]
styler = Styler(self.df, table_styles=style)
result = ' '.join(styler.render().split())
self.assertTrue('th { foo: bar; }' in result)
styler = self.df.style
result = styler.set_table_styles(style)
self.assertTrue(styler is result)
self.assertEqual(styler.table_styles, style)
def test_precision(self):
with pd.option_context('display.precision', 10):
s = Styler(self.df)
self.assertEqual(s.precision, 10)
s = Styler(self.df, precision=2)
self.assertEqual(s.precision, 2)
s2 = s.set_precision(4)
self.assertTrue(s is s2)
self.assertEqual(s.precision, 4)
def test_precision(self):
with pd.option_context('display.precision', 10):
s = Styler(self.df)
self.assertEqual(s.precision, 10)
s = Styler(self.df, precision=2)
self.assertEqual(s.precision, 2)
s2 = s.set_precision(4)
self.assertTrue(s is s2)
self.assertEqual(s.precision, 4)
def test_table_styles(self):
style = [{'selector': 'th', 'props': [('foo', 'bar')]}]
styler = Styler(self.df, table_styles=style)
result = ' '.join(styler.render().split())
self.assertTrue('th { foo: bar; }' in result)
styler = self.df.style
result = styler.set_table_styles(style)
self.assertTrue(styler is result)
self.assertEqual(styler.table_styles, style)
def test_nonunique_raises(self):
df = pd.DataFrame([[1, 2]], columns=['A', 'A'])
with tm.assertRaises(ValueError):
df.style
with tm.assertRaises(ValueError):
Styler(df)
def setUp(self):
np.random.seed(24)
self.s = DataFrame({'A': np.random.permutation(range(6))})
self.df = DataFrame({'A': [0, 1], 'B': np.random.randn(2)})
self.f = lambda x: x
self.g = lambda x: x
def h(x, foo='bar'):
return pd.Series(['color: %s' % foo], index=x.index, name=x.name)
self.h = h
self.styler = Styler(self.df)
self.attrs = pd.DataFrame({'A': ['color: red', 'color: blue']})
self.dataframes = [
self.df,
pd.DataFrame({'f': [1., 2.], 'o': ['a', 'b'],
'c': pd.Categorical(['a', 'b'])})
]
def setUp(self):
np.random.seed(24)
self.s = DataFrame({'A': np.random.permutation(range(6))})
self.df = DataFrame({'A': [0, 1], 'B': np.random.randn(2)})
self.f = lambda x: x
self.g = lambda x: x
def h(x, foo='bar'):
return pd.Series(['color: %s' % foo], index=x.index, name=x.name)
self.h = h
self.styler = Styler(self.df)
self.attrs = pd.DataFrame({'A': ['color: red', 'color: blue']})
self.dataframes = [
self.df,
pd.DataFrame({'f': [1., 2.], 'o': ['a', 'b'],
'c': pd.Categorical(['a', 'b'])})
]
class TestStyler(TestCase):
def setUp(self):
np.random.seed(24)
self.s = DataFrame({'A': np.random.permutation(range(6))})
self.df = DataFrame({'A': [0, 1], 'B': np.random.randn(2)})
self.f = lambda x: x
self.g = lambda x: x
def h(x, foo='bar'):
return pd.Series(['color: %s' % foo], index=x.index, name=x.name)
self.h = h
self.styler = Styler(self.df)
self.attrs = pd.DataFrame({'A': ['color: red', 'color: blue']})
self.dataframes = [
self.df,
pd.DataFrame({'f': [1., 2.], 'o': ['a', 'b'],
'c': pd.Categorical(['a', 'b'])})
]
def test_update_ctx(self):
self.styler._update_ctx(self.attrs)
expected = {(0, 0): ['color: red'],
(1, 0): ['color: blue']}
self.assertEqual(self.styler.ctx, expected)
def test_update_ctx_flatten_multi(self):
attrs = DataFrame({"A": ['color: red; foo: bar',
'color: blue; foo: baz']})
self.styler._update_ctx(attrs)
expected = {(0, 0): ['color: red', ' foo: bar'],
(1, 0): ['color: blue', ' foo: baz']}
self.assertEqual(self.styler.ctx, expected)
def test_update_ctx_flatten_multi_traliing_semi(self):
attrs = DataFrame({"A": ['color: red; foo: bar;',
'color: blue; foo: baz;']})
self.styler._update_ctx(attrs)
expected = {(0, 0): ['color: red', ' foo: bar'],
(1, 0): ['color: blue', ' foo: baz']}
self.assertEqual(self.styler.ctx, expected)
def test_copy(self):
s2 = copy.copy(self.styler)
self.assertTrue(self.styler is not s2)
self.assertTrue(self.styler.ctx is s2.ctx) # shallow
self.assertTrue(self.styler._todo is s2._todo)
self.styler._update_ctx(self.attrs)
self.styler.highlight_max()
self.assertEqual(self.styler.ctx, s2.ctx)
self.assertEqual(self.styler._todo, s2._todo)
def test_deepcopy(self):
s2 = copy.deepcopy(self.styler)
self.assertTrue(self.styler is not s2)
self.assertTrue(self.styler.ctx is not s2.ctx)
self.assertTrue(self.styler._todo is not s2._todo)
self.styler._update_ctx(self.attrs)
self.styler.highlight_max()
self.assertNotEqual(self.styler.ctx, s2.ctx)
self.assertEqual(s2._todo, [])
self.assertNotEqual(self.styler._todo, s2._todo)
def test_clear(self):
s = self.df.style.highlight_max()._compute()
self.assertTrue(len(s.ctx) > 0)
self.assertTrue(len(s._todo) > 0)
s.clear()
self.assertTrue(len(s.ctx) == 0)
self.assertTrue(len(s._todo) == 0)
def test_render(self):
df = pd.DataFrame({"A": [0, 1]})
style = lambda x: pd.Series(["color: red", "color: blue"], name=x.name)
s = Styler(df, uuid='AB').apply(style).apply(style, axis=1)
s.render()
# it worked?
def test_render_double(self):
df = pd.DataFrame({"A": [0, 1]})
style = lambda x: pd.Series(["color: red; border: 1px",
"color: blue; border: 2px"], name=x.name)
s = Styler(df, uuid='AB').apply(style)
s.render()
# it worked?
def test_set_properties(self):
df = pd.DataFrame({"A": [0, 1]})
result = df.style.set_properties(color='white',
size='10px')._compute().ctx
# order is deterministic
v = ["color: white", "size: 10px"]
expected = {(0, 0): v, (1, 0): v}
self.assertEqual(result.keys(), expected.keys())
for v1, v2 in zip(result.values(), expected.values()):
self.assertEqual(sorted(v1), sorted(v2))
def test_set_properties_subset(self):
df = pd.DataFrame({'A': [0, 1]})
result = df.style.set_properties(subset=pd.IndexSlice[0, 'A'],
color='white')._compute().ctx
expected = {(0, 0): ['color: white']}
self.assertEqual(result, expected)
def test_index_name(self):
# https://github.com/pydata/pandas/issues/11655
df = pd.DataFrame({'A': [1, 2], 'B': [3, 4], 'C': [5, 6]})
result = df.set_index('A').style._translate()
expected = [[{'class': 'blank', 'type': 'th', 'value': ''},
{'class': 'col_heading level0 col0', 'type': 'th',
'value': 'B'},
{'class': 'col_heading level0 col1', 'type': 'th',
'value': 'C'}],
[{'class': 'col_heading level2 col0', 'type': 'th',
'value': 'A'},
{'class': 'blank', 'type': 'th', 'value': ''},
{'class': 'blank', 'type': 'th', 'value': ''}]]
self.assertEqual(result['head'], expected)
def test_multiindex_name(self):
# https://github.com/pydata/pandas/issues/11655
df = pd.DataFrame({'A': [1, 2], 'B': [3, 4], 'C': [5, 6]})
result = df.set_index(['A', 'B']).style._translate()
expected = [[{'class': 'blank', 'type': 'th', 'value': ''},
{'class': 'blank', 'type': 'th', 'value': ''},
{'class': 'col_heading level0 col0', 'type': 'th',
'value': 'C'}],
[{'class': 'col_heading level2 col0', 'type': 'th',
'value': 'A'},
{'class': 'col_heading level2 col1', 'type': 'th',
'value': 'B'},
{'class': 'blank', 'type': 'th', 'value': ''}]]
self.assertEqual(result['head'], expected)
def test_apply_axis(self):
df = pd.DataFrame({'A': [0, 0], 'B': [1, 1]})
f = lambda x: ['val: %s' % x.max() for v in x]
result = df.style.apply(f, axis=1)
self.assertEqual(len(result._todo), 1)
self.assertEqual(len(result.ctx), 0)
result._compute()
expected = {(0, 0): ['val: 1'], (0, 1): ['val: 1'],
(1, 0): ['val: 1'], (1, 1): ['val: 1']}
self.assertEqual(result.ctx, expected)
result = df.style.apply(f, axis=0)
expected = {(0, 0): ['val: 0'], (0, 1): ['val: 1'],
(1, 0): ['val: 0'], (1, 1): ['val: 1']}
result._compute()
self.assertEqual(result.ctx, expected)
result = df.style.apply(f) # default
result._compute()
self.assertEqual(result.ctx, expected)
def test_apply_subset(self):
axes = [0, 1]
slices = [pd.IndexSlice[:], pd.IndexSlice[:, ['A']],
pd.IndexSlice[[1], :], pd.IndexSlice[[1], ['A']],
pd.IndexSlice[:2, ['A', 'B']]]
for ax in axes:
for slice_ in slices:
result = self.df.style.apply(self.h, axis=ax, subset=slice_,
foo='baz')._compute().ctx
expected = dict(((r, c), ['color: baz'])
for r, row in enumerate(self.df.index)
for c, col in enumerate(self.df.columns)
if row in self.df.loc[slice_].index and
col in self.df.loc[slice_].columns)
self.assertEqual(result, expected)
def test_applymap_subset(self):
def f(x):
return 'foo: bar'
slices = [pd.IndexSlice[:], pd.IndexSlice[:, ['A']],
pd.IndexSlice[[1], :], pd.IndexSlice[[1], ['A']],
pd.IndexSlice[:2, ['A', 'B']]]
for slice_ in slices:
result = self.df.style.applymap(f, subset=slice_)._compute().ctx
expected = dict(((r, c), ['foo: bar'])
for r, row in enumerate(self.df.index)
for c, col in enumerate(self.df.columns)
if row in self.df.loc[slice_].index and
col in self.df.loc[slice_].columns)
self.assertEqual(result, expected)
def test_empty(self):
df = pd.DataFrame({'A': [1, 0]})
s = df.style
s.ctx = {(0, 0): ['color: red'],
(1, 0): ['']}
result = s._translate()['cellstyle']
expected = [{'props': [['color', ' red']], 'selector': 'row0_col0'},
{'props': [['', '']], 'selector': 'row1_col0'}]
self.assertEqual(result, expected)
def test_bar(self):
df = pd.DataFrame({'A': [0, 1, 2]})
result = df.style.bar()._compute().ctx
expected = {
(0, 0): ['width: 10em', ' height: 80%'],
(1, 0): ['width: 10em', ' height: 80%',
'background: linear-gradient('
'90deg,#d65f5f 50.0%, transparent 0%)'],
(2, 0): ['width: 10em', ' height: 80%',
'background: linear-gradient('
'90deg,#d65f5f 100.0%, transparent 0%)']
}
self.assertEqual(result, expected)
result = df.style.bar(color='red', width=50)._compute().ctx
expected = {
(0, 0): ['width: 10em', ' height: 80%'],
(1, 0): ['width: 10em', ' height: 80%',
'background: linear-gradient('
'90deg,red 25.0%, transparent 0%)'],
(2, 0): ['width: 10em', ' height: 80%',
'background: linear-gradient('
'90deg,red 50.0%, transparent 0%)']
}
self.assertEqual(result, expected)
df['C'] = ['a'] * len(df)
result = df.style.bar(color='red', width=50)._compute().ctx
self.assertEqual(result, expected)
df['C'] = df['C'].astype('category')
result = df.style.bar(color='red', width=50)._compute().ctx
self.assertEqual(result, expected)
def test_bar_0points(self):
df = pd.DataFrame([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
result = df.style.bar()._compute().ctx
expected = {
(0, 0): ['width: 10em', ' height: 80%'],
(0, 1): ['width: 10em', ' height: 80%'],
(0, 2): ['width: 10em', ' height: 80%'],
(1, 0): ['width: 10em', ' height: 80%',
'background: linear-gradient(90deg,#d65f5f 50.0%, '
'transparent 0%)'],
(1, 1): ['width: 10em', ' height: 80%',
'background: linear-gradient(90deg,#d65f5f 50.0%, '
'transparent 0%)'],
(1, 2): ['width: 10em', ' height: 80%',
'background: linear-gradient(90deg,#d65f5f 50.0%, '
'transparent 0%)'],
(2, 0): ['width: 10em', ' height: 80%',
'background: linear-gradient(90deg,#d65f5f 100.0%, '
'transparent 0%)'],
(2, 1): ['width: 10em', ' height: 80%',
'background: linear-gradient(90deg,#d65f5f 100.0%, '
'transparent 0%)'],
(2, 2): ['width: 10em', ' height: 80%',
'background: linear-gradient(90deg,#d65f5f 100.0%, '
'transparent 0%)']}
self.assertEqual(result, expected)
result = df.style.bar(axis=1)._compute().ctx
expected = {
(0, 0): ['width: 10em', ' height: 80%'],
(0, 1): ['width: 10em', ' height: 80%',
'background: linear-gradient(90deg,#d65f5f 50.0%, '
'transparent 0%)'],
(0, 2): ['width: 10em', ' height: 80%',
'background: linear-gradient(90deg,#d65f5f 100.0%, '
'transparent 0%)'],
(1, 0): ['width: 10em', ' height: 80%'],
(1, 1): ['width: 10em', ' height: 80%',
'background: linear-gradient(90deg,#d65f5f 50.0%, '
'transparent 0%)'],
(1, 2): ['width: 10em', ' height: 80%',
'background: linear-gradient(90deg,#d65f5f 100.0%, '
'transparent 0%)'],
(2, 0): ['width: 10em', ' height: 80%'],
(2, 1): ['width: 10em', ' height: 80%',
'background: linear-gradient(90deg,#d65f5f 50.0%, '
'transparent 0%)'],
(2, 2): ['width: 10em', ' height: 80%',
'background: linear-gradient(90deg,#d65f5f 100.0%, '
'transparent 0%)']}
self.assertEqual(result, expected)
def test_highlight_null(self, null_color='red'):
df = pd.DataFrame({'A': [0, np.nan]})
result = df.style.highlight_null()._compute().ctx
expected = {(0, 0): [''],
(1, 0): ['background-color: red']}
self.assertEqual(result, expected)
def test_nonunique_raises(self):
df = pd.DataFrame([[1, 2]], columns=['A', 'A'])
with tm.assertRaises(ValueError):
df.style
with tm.assertRaises(ValueError):
Styler(df)
def test_caption(self):
styler = Styler(self.df, caption='foo')
result = styler.render()
self.assertTrue(all(['caption' in result, 'foo' in result]))
styler = self.df.style
result = styler.set_caption('baz')
self.assertTrue(styler is result)
self.assertEqual(styler.caption, 'baz')
def test_uuid(self):
styler = Styler(self.df, uuid='abc123')
result = styler.render()
self.assertTrue('abc123' in result)
styler = self.df.style
result = styler.set_uuid('aaa')
self.assertTrue(result is styler)
self.assertEqual(result.uuid, 'aaa')
def test_table_styles(self):
style = [{'selector': 'th', 'props': [('foo', 'bar')]}]
styler = Styler(self.df, table_styles=style)
result = ' '.join(styler.render().split())
self.assertTrue('th { foo: bar; }' in result)
styler = self.df.style
result = styler.set_table_styles(style)
self.assertTrue(styler is result)
self.assertEqual(styler.table_styles, style)
def test_table_attributes(self):
attributes = 'class="foo" data-bar'
styler = Styler(self.df, table_attributes=attributes)
result = styler.render()
self.assertTrue('class="foo" data-bar' in result)
result = self.df.style.set_table_attributes(attributes).render()
self.assertTrue('class="foo" data-bar' in result)
def test_precision(self):
with pd.option_context('display.precision', 10):
s = Styler(self.df)
self.assertEqual(s.precision, 10)
s = Styler(self.df, precision=2)
self.assertEqual(s.precision, 2)
s2 = s.set_precision(4)
self.assertTrue(s is s2)
self.assertEqual(s.precision, 4)
def test_apply_none(self):
def f(x):
return pd.DataFrame(np.where(x == x.max(), 'color: red', ''),
index=x.index, columns=x.columns)
result = (pd.DataFrame([[1, 2], [3, 4]])
.style.apply(f, axis=None)._compute().ctx)
self.assertEqual(result[(1, 1)], ['color: red'])
def test_trim(self):
result = self.df.style.render() # trim=True
self.assertEqual(result.count('#'), 0)
result = self.df.style.highlight_max().render()
self.assertEqual(result.count('#'), len(self.df.columns))
def test_highlight_max(self):
df = pd.DataFrame([[1, 2], [3, 4]], columns=['A', 'B'])
# 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
self.assertEqual(result[(1, 1)], ['background-color: yellow'])
result = getattr(df.style, attr)(color='green')._compute().ctx
self.assertEqual(result[(1, 1)], ['background-color: green'])
result = getattr(df.style, attr)(subset='A')._compute().ctx
self.assertEqual(result[(1, 0)], ['background-color: yellow'])
result = getattr(df.style, attr)(axis=0)._compute().ctx
expected = {(1, 0): ['background-color: yellow'],
(1, 1): ['background-color: yellow'],
(0, 1): [''], (0, 0): ['']}
self.assertEqual(result, expected)
result = getattr(df.style, attr)(axis=1)._compute().ctx
expected = {(0, 1): ['background-color: yellow'],
(1, 1): ['background-color: yellow'],
(0, 0): [''], (1, 0): ['']}
self.assertEqual(result, expected)
# separate since we cant negate the strs
df['C'] = ['a', 'b']
result = df.style.highlight_max()._compute().ctx
expected = {(1, 1): ['background-color: yellow']}
result = df.style.highlight_min()._compute().ctx
expected = {(0, 0): ['background-color: yellow']}
def test_export(self):
f = lambda x: 'color: red' if x > 0 else 'color: blue'
g = lambda x, y, z: 'color: %s' if x > 0 else 'color: %s' % z
style1 = self.styler
style1.applymap(f)\
.applymap(g, y='a', z='b')\
.highlight_max()
result = style1.export()
style2 = self.df.style
style2.use(result)
self.assertEqual(style1._todo, style2._todo)
style2.render()
class TestStyler(TestCase):
def setUp(self):
np.random.seed(24)
self.s = DataFrame({'A': np.random.permutation(range(6))})
self.df = DataFrame({'A': [0, 1], 'B': np.random.randn(2)})
self.f = lambda x: x
self.g = lambda x: x
def h(x, foo='bar'):
return pd.Series(['color: %s' % foo], index=x.index, name=x.name)
self.h = h
self.styler = Styler(self.df)
self.attrs = pd.DataFrame({'A': ['color: red', 'color: blue']})
self.dataframes = [
self.df,
pd.DataFrame({
'f': [1., 2.],
'o': ['a', 'b'],
'c': pd.Categorical(['a', 'b'])
})
]
def test_update_ctx(self):
self.styler._update_ctx(self.attrs)
expected = {(0, 0): ['color: red'], (1, 0): ['color: blue']}
self.assertEqual(self.styler.ctx, expected)
def test_update_ctx_flatten_multi(self):
attrs = DataFrame(
{"A": ['color: red; foo: bar', 'color: blue; foo: baz']})
self.styler._update_ctx(attrs)
expected = {
(0, 0): ['color: red', ' foo: bar'],
(1, 0): ['color: blue', ' foo: baz']
}
self.assertEqual(self.styler.ctx, expected)
def test_update_ctx_flatten_multi_traliing_semi(self):
attrs = DataFrame(
{"A": ['color: red; foo: bar;', 'color: blue; foo: baz;']})
self.styler._update_ctx(attrs)
expected = {
(0, 0): ['color: red', ' foo: bar'],
(1, 0): ['color: blue', ' foo: baz']
}
self.assertEqual(self.styler.ctx, expected)
def test_copy(self):
s2 = copy.copy(self.styler)
self.assertTrue(self.styler is not s2)
self.assertTrue(self.styler.ctx is s2.ctx) # shallow
self.assertTrue(self.styler._todo is s2._todo)
self.styler._update_ctx(self.attrs)
self.styler.highlight_max()
self.assertEqual(self.styler.ctx, s2.ctx)
self.assertEqual(self.styler._todo, s2._todo)
def test_deepcopy(self):
s2 = copy.deepcopy(self.styler)
self.assertTrue(self.styler is not s2)
self.assertTrue(self.styler.ctx is not s2.ctx)
self.assertTrue(self.styler._todo is not s2._todo)
self.styler._update_ctx(self.attrs)
self.styler.highlight_max()
self.assertNotEqual(self.styler.ctx, s2.ctx)
self.assertEqual(s2._todo, [])
self.assertNotEqual(self.styler._todo, s2._todo)
def test_clear(self):
s = self.df.style.highlight_max()._compute()
self.assertTrue(len(s.ctx) > 0)
self.assertTrue(len(s._todo) > 0)
s.clear()
self.assertTrue(len(s.ctx) == 0)
self.assertTrue(len(s._todo) == 0)
def test_render(self):
df = pd.DataFrame({"A": [0, 1]})
style = lambda x: pd.Series(["color: red", "color: blue"], name=x.name)
s = Styler(df, uuid='AB').apply(style).apply(style, axis=1)
s.render()
# it worked?
def test_render_double(self):
df = pd.DataFrame({"A": [0, 1]})
style = lambda x: pd.Series(
["color: red; border: 1px", "color: blue; border: 2px"],
name=x.name)
s = Styler(df, uuid='AB').apply(style)
s.render()
# it worked?
def test_set_properties(self):
df = pd.DataFrame({"A": [0, 1]})
result = df.style.set_properties(color='white',
size='10px')._compute().ctx
# order is deterministic
v = ["color: white", "size: 10px"]
expected = {(0, 0): v, (1, 0): v}
self.assertEqual(result.keys(), expected.keys())
for v1, v2 in zip(result.values(), expected.values()):
self.assertEqual(sorted(v1), sorted(v2))
def test_set_properties_subset(self):
df = pd.DataFrame({'A': [0, 1]})
result = df.style.set_properties(subset=pd.IndexSlice[0, 'A'],
color='white')._compute().ctx
expected = {(0, 0): ['color: white']}
self.assertEqual(result, expected)
def test_index_name(self):
# https://github.com/pydata/pandas/issues/11655
df = pd.DataFrame({'A': [1, 2], 'B': [3, 4], 'C': [5, 6]})
result = df.set_index('A').style._translate()
expected = [[{
'class': 'blank',
'type': 'th',
'value': ''
}, {
'class': 'col_heading level0 col0',
'type': 'th',
'value': 'B',
'display_value': 'B'
}, {
'class': 'col_heading level0 col1',
'type': 'th',
'value': 'C',
'display_value': 'C'
}],
[{
'class': 'col_heading level2 col0',
'type': 'th',
'value': 'A'
}, {
'class': 'blank',
'type': 'th',
'value': ''
}, {
'class': 'blank',
'type': 'th',
'value': ''
}]]
self.assertEqual(result['head'], expected)
def test_multiindex_name(self):
# https://github.com/pydata/pandas/issues/11655
df = pd.DataFrame({'A': [1, 2], 'B': [3, 4], 'C': [5, 6]})
result = df.set_index(['A', 'B']).style._translate()
expected = [[{
'class': 'blank',
'type': 'th',
'value': ''
}, {
'class': 'blank',
'type': 'th',
'value': ''
}, {
'class': 'col_heading level0 col0',
'type': 'th',
'value': 'C',
'display_value': 'C'
}],
[{
'class': 'col_heading level2 col0',
'type': 'th',
'value': 'A'
}, {
'class': 'col_heading level2 col1',
'type': 'th',
'value': 'B'
}, {
'class': 'blank',
'type': 'th',
'value': ''
}]]
self.assertEqual(result['head'], expected)
def test_numeric_columns(self):
# https://github.com/pydata/pandas/issues/12125
# smoke test for _translate
df = pd.DataFrame({0: [1, 2, 3]})
df.style._translate()
def test_apply_axis(self):
df = pd.DataFrame({'A': [0, 0], 'B': [1, 1]})
f = lambda x: ['val: %s' % x.max() for v in x]
result = df.style.apply(f, axis=1)
self.assertEqual(len(result._todo), 1)
self.assertEqual(len(result.ctx), 0)
result._compute()
expected = {
(0, 0): ['val: 1'],
(0, 1): ['val: 1'],
(1, 0): ['val: 1'],
(1, 1): ['val: 1']
}
self.assertEqual(result.ctx, expected)
result = df.style.apply(f, axis=0)
expected = {
(0, 0): ['val: 0'],
(0, 1): ['val: 1'],
(1, 0): ['val: 0'],
(1, 1): ['val: 1']
}
result._compute()
self.assertEqual(result.ctx, expected)
result = df.style.apply(f) # default
result._compute()
self.assertEqual(result.ctx, expected)
def test_apply_subset(self):
axes = [0, 1]
slices = [
pd.IndexSlice[:], pd.IndexSlice[:, ['A']], pd.IndexSlice[[1], :],
pd.IndexSlice[[1], ['A']], pd.IndexSlice[:2, ['A', 'B']]
]
for ax in axes:
for slice_ in slices:
result = self.df.style.apply(self.h,
axis=ax,
subset=slice_,
foo='baz')._compute().ctx
expected = dict(((r, c), ['color: baz'])
for r, row in enumerate(self.df.index)
for c, col in enumerate(self.df.columns)
if row in self.df.loc[slice_].index
and col in self.df.loc[slice_].columns)
self.assertEqual(result, expected)
def test_applymap_subset(self):
def f(x):
return 'foo: bar'
slices = [
pd.IndexSlice[:], pd.IndexSlice[:, ['A']], pd.IndexSlice[[1], :],
pd.IndexSlice[[1], ['A']], pd.IndexSlice[:2, ['A', 'B']]
]
for slice_ in slices:
result = self.df.style.applymap(f, subset=slice_)._compute().ctx
expected = dict(((r, c), ['foo: bar'])
for r, row in enumerate(self.df.index)
for c, col in enumerate(self.df.columns)
if row in self.df.loc[slice_].index
and col in self.df.loc[slice_].columns)
self.assertEqual(result, expected)
def test_empty(self):
df = pd.DataFrame({'A': [1, 0]})
s = df.style
s.ctx = {(0, 0): ['color: red'], (1, 0): ['']}
result = s._translate()['cellstyle']
expected = [{
'props': [['color', ' red']],
'selector': 'row0_col0'
}, {
'props': [['', '']],
'selector': 'row1_col0'
}]
self.assertEqual(result, expected)
def test_bar(self):
df = pd.DataFrame({'A': [0, 1, 2]})
result = df.style.bar()._compute().ctx
expected = {
(0, 0): ['width: 10em', ' height: 80%'],
(1, 0): [
'width: 10em', ' height: 80%', 'background: linear-gradient('
'90deg,#d65f5f 50.0%, transparent 0%)'
],
(2, 0): [
'width: 10em', ' height: 80%', 'background: linear-gradient('
'90deg,#d65f5f 100.0%, transparent 0%)'
]
}
self.assertEqual(result, expected)
result = df.style.bar(color='red', width=50)._compute().ctx
expected = {
(0, 0): ['width: 10em', ' height: 80%'],
(1, 0): [
'width: 10em', ' height: 80%', 'background: linear-gradient('
'90deg,red 25.0%, transparent 0%)'
],
(2, 0): [
'width: 10em', ' height: 80%', 'background: linear-gradient('
'90deg,red 50.0%, transparent 0%)'
]
}
self.assertEqual(result, expected)
df['C'] = ['a'] * len(df)
result = df.style.bar(color='red', width=50)._compute().ctx
self.assertEqual(result, expected)
df['C'] = df['C'].astype('category')
result = df.style.bar(color='red', width=50)._compute().ctx
self.assertEqual(result, expected)
def test_bar_0points(self):
df = pd.DataFrame([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
result = df.style.bar()._compute().ctx
expected = {
(0, 0): ['width: 10em', ' height: 80%'],
(0, 1): ['width: 10em', ' height: 80%'],
(0, 2): ['width: 10em', ' height: 80%'],
(1, 0): [
'width: 10em', ' height: 80%',
'background: linear-gradient(90deg,#d65f5f 50.0%,'
' transparent 0%)'
],
(1, 1): [
'width: 10em', ' height: 80%',
'background: linear-gradient(90deg,#d65f5f 50.0%,'
' transparent 0%)'
],
(1, 2): [
'width: 10em', ' height: 80%',
'background: linear-gradient(90deg,#d65f5f 50.0%,'
' transparent 0%)'
],
(2, 0): [
'width: 10em', ' height: 80%',
'background: linear-gradient(90deg,#d65f5f 100.0%'
', transparent 0%)'
],
(2, 1): [
'width: 10em', ' height: 80%',
'background: linear-gradient(90deg,#d65f5f 100.0%'
', transparent 0%)'
],
(2, 2): [
'width: 10em', ' height: 80%',
'background: linear-gradient(90deg,#d65f5f 100.0%'
', transparent 0%)'
]
}
self.assertEqual(result, expected)
result = df.style.bar(axis=1)._compute().ctx
expected = {
(0, 0): ['width: 10em', ' height: 80%'],
(0, 1): [
'width: 10em', ' height: 80%',
'background: linear-gradient(90deg,#d65f5f 50.0%,'
' transparent 0%)'
],
(0, 2): [
'width: 10em', ' height: 80%',
'background: linear-gradient(90deg,#d65f5f 100.0%'
', transparent 0%)'
],
(1, 0): ['width: 10em', ' height: 80%'],
(1, 1): [
'width: 10em', ' height: 80%',
'background: linear-gradient(90deg,#d65f5f 50.0%'
', transparent 0%)'
],
(1, 2): [
'width: 10em', ' height: 80%',
'background: linear-gradient(90deg,#d65f5f 100.0%'
', transparent 0%)'
],
(2, 0): ['width: 10em', ' height: 80%'],
(2, 1): [
'width: 10em', ' height: 80%',
'background: linear-gradient(90deg,#d65f5f 50.0%'
', transparent 0%)'
],
(2, 2): [
'width: 10em', ' height: 80%',
'background: linear-gradient(90deg,#d65f5f 100.0%'
', transparent 0%)'
]
}
self.assertEqual(result, expected)
def test_highlight_null(self, null_color='red'):
df = pd.DataFrame({'A': [0, np.nan]})
result = df.style.highlight_null()._compute().ctx
expected = {(0, 0): [''], (1, 0): ['background-color: red']}
self.assertEqual(result, expected)
def test_nonunique_raises(self):
df = pd.DataFrame([[1, 2]], columns=['A', 'A'])
with tm.assertRaises(ValueError):
df.style
with tm.assertRaises(ValueError):
Styler(df)
def test_caption(self):
styler = Styler(self.df, caption='foo')
result = styler.render()
self.assertTrue(all(['caption' in result, 'foo' in result]))
styler = self.df.style
result = styler.set_caption('baz')
self.assertTrue(styler is result)
self.assertEqual(styler.caption, 'baz')
def test_uuid(self):
styler = Styler(self.df, uuid='abc123')
result = styler.render()
self.assertTrue('abc123' in result)
styler = self.df.style
result = styler.set_uuid('aaa')
self.assertTrue(result is styler)
self.assertEqual(result.uuid, 'aaa')
def test_table_styles(self):
style = [{'selector': 'th', 'props': [('foo', 'bar')]}]
styler = Styler(self.df, table_styles=style)
result = ' '.join(styler.render().split())
self.assertTrue('th { foo: bar; }' in result)
styler = self.df.style
result = styler.set_table_styles(style)
self.assertTrue(styler is result)
self.assertEqual(styler.table_styles, style)
def test_table_attributes(self):
attributes = 'class="foo" data-bar'
styler = Styler(self.df, table_attributes=attributes)
result = styler.render()
self.assertTrue('class="foo" data-bar' in result)
result = self.df.style.set_table_attributes(attributes).render()
self.assertTrue('class="foo" data-bar' in result)
def test_precision(self):
with pd.option_context('display.precision', 10):
s = Styler(self.df)
self.assertEqual(s.precision, 10)
s = Styler(self.df, precision=2)
self.assertEqual(s.precision, 2)
s2 = s.set_precision(4)
self.assertTrue(s is s2)
self.assertEqual(s.precision, 4)
def test_apply_none(self):
def f(x):
return pd.DataFrame(np.where(x == x.max(), 'color: red', ''),
index=x.index,
columns=x.columns)
result = (pd.DataFrame([[1, 2],
[3, 4]]).style.apply(f,
axis=None)._compute().ctx)
self.assertEqual(result[(1, 1)], ['color: red'])
def test_trim(self):
result = self.df.style.render() # trim=True
self.assertEqual(result.count('#'), 0)
result = self.df.style.highlight_max().render()
self.assertEqual(result.count('#'), len(self.df.columns))
def test_highlight_max(self):
df = pd.DataFrame([[1, 2], [3, 4]], columns=['A', 'B'])
# 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
self.assertEqual(result[(1, 1)], ['background-color: yellow'])
result = getattr(df.style, attr)(color='green')._compute().ctx
self.assertEqual(result[(1, 1)], ['background-color: green'])
result = getattr(df.style, attr)(subset='A')._compute().ctx
self.assertEqual(result[(1, 0)], ['background-color: yellow'])
result = getattr(df.style, attr)(axis=0)._compute().ctx
expected = {
(1, 0): ['background-color: yellow'],
(1, 1): ['background-color: yellow'],
(0, 1): [''],
(0, 0): ['']
}
self.assertEqual(result, expected)
result = getattr(df.style, attr)(axis=1)._compute().ctx
expected = {
(0, 1): ['background-color: yellow'],
(1, 1): ['background-color: yellow'],
(0, 0): [''],
(1, 0): ['']
}
self.assertEqual(result, expected)
# separate since we cant negate the strs
df['C'] = ['a', 'b']
result = df.style.highlight_max()._compute().ctx
expected = {(1, 1): ['background-color: yellow']}
result = df.style.highlight_min()._compute().ctx
expected = {(0, 0): ['background-color: yellow']}
def test_export(self):
f = lambda x: 'color: red' if x > 0 else 'color: blue'
g = lambda x, y, z: 'color: %s' if x > 0 else 'color: %s' % z
style1 = self.styler
style1.applymap(f)\
.applymap(g, y='a', z='b')\
.highlight_max()
result = style1.export()
style2 = self.df.style
style2.use(result)
self.assertEqual(style1._todo, style2._todo)
style2.render()
def test_display_format(self):
df = pd.DataFrame(np.random.random(size=(2, 2)))
ctx = df.style.format("{:0.1f}")._translate()
self.assertTrue(
all(['display_value' in c for c in row] for row in ctx['body']))
self.assertTrue(
all([len(c['display_value']) <= 3 for c in row[1:]]
for row in ctx['body']))
self.assertTrue(
len(ctx['body'][0][1]['display_value'].lstrip('-')) <= 3)
def test_display_format_raises(self):
df = pd.DataFrame(np.random.randn(2, 2))
with tm.assertRaises(TypeError):
df.style.format(5)
with tm.assertRaises(TypeError):
df.style.format(True)
def test_display_subset(self):
df = pd.DataFrame([[.1234, .1234], [1.1234, 1.1234]],
columns=['a', 'b'])
ctx = df.style.format({
"a": "{:0.1f}",
"b": "{0:.2%}"
},
subset=pd.IndexSlice[0, :])._translate()
expected = '0.1'
self.assertEqual(ctx['body'][0][1]['display_value'], expected)
self.assertEqual(ctx['body'][1][1]['display_value'], '1.1234')
self.assertEqual(ctx['body'][0][2]['display_value'], '12.34%')
raw_11 = '1.1234'
ctx = df.style.format("{:0.1f}",
subset=pd.IndexSlice[0, :])._translate()
self.assertEqual(ctx['body'][0][1]['display_value'], expected)
self.assertEqual(ctx['body'][1][1]['display_value'], raw_11)
ctx = df.style.format("{:0.1f}",
subset=pd.IndexSlice[0, :])._translate()
self.assertEqual(ctx['body'][0][1]['display_value'], expected)
self.assertEqual(ctx['body'][1][1]['display_value'], raw_11)
ctx = df.style.format("{:0.1f}",
subset=pd.IndexSlice['a'])._translate()
self.assertEqual(ctx['body'][0][1]['display_value'], expected)
self.assertEqual(ctx['body'][0][2]['display_value'], '0.1234')
ctx = df.style.format("{:0.1f}",
subset=pd.IndexSlice[0, 'a'])._translate()
self.assertEqual(ctx['body'][0][1]['display_value'], expected)
self.assertEqual(ctx['body'][1][1]['display_value'], raw_11)
ctx = df.style.format("{:0.1f}",
subset=pd.IndexSlice[[0, 1],
['a']])._translate()
self.assertEqual(ctx['body'][0][1]['display_value'], expected)
self.assertEqual(ctx['body'][1][1]['display_value'], '1.1')
self.assertEqual(ctx['body'][0][2]['display_value'], '0.1234')
self.assertEqual(ctx['body'][1][2]['display_value'], '1.1234')
def test_display_dict(self):
df = pd.DataFrame([[.1234, .1234], [1.1234, 1.1234]],
columns=['a', 'b'])
ctx = df.style.format({"a": "{:0.1f}", "b": "{0:.2%}"})._translate()
self.assertEqual(ctx['body'][0][1]['display_value'], '0.1')
self.assertEqual(ctx['body'][0][2]['display_value'], '12.34%')
df['c'] = ['aaa', 'bbb']
ctx = df.style.format({"a": "{:0.1f}", "c": str.upper})._translate()
self.assertEqual(ctx['body'][0][1]['display_value'], '0.1')
self.assertEqual(ctx['body'][0][3]['display_value'], 'AAA')
def test_render(self):
df = pd.DataFrame({"A": [0, 1]})
style = lambda x: pd.Series(["color: red", "color: blue"], name=x.name)
s = Styler(df, uuid='AB').apply(style).apply(style, axis=1)
s.render()
def test_render(self):
df = pd.DataFrame({"A": [0, 1]})
style = lambda x: pd.Series(["color: red", "color: blue"], name=x.name)
s = Styler(df, uuid='AB').apply(style).apply(style, axis=1)
s.render()
def test_render_double(self):
df = pd.DataFrame({"A": [0, 1]})
style = lambda x: pd.Series(["color: red; border: 1px",
"color: blue; border: 2px"], name=x.name)
s = Styler(df, uuid='AB').apply(style)
s.render()
def test_render_double(self):
df = pd.DataFrame({"A": [0, 1]})
style = lambda x: pd.Series(["color: red; border: 1px",
"color: blue; border: 2px"], name=x.name)
s = Styler(df, uuid='AB').apply(style)
s.render()
