class TestStyler(object):
def setup_method(self, method):
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: {foo}'.format(foo=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_init_non_pandas(self):
with pytest.raises(TypeError):
Styler([1, 2, 3])
def test_init_series(self):
result = Styler(pd.Series([1, 2]))
assert result.data.ndim == 2
def test_repr_html_ok(self):
self.styler._repr_html_()
def test_update_ctx(self):
self.styler._update_ctx(self.attrs)
expected = {(0, 0): ['color: red'], (1, 0): ['color: blue']}
assert 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']
}
assert 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']
}
assert self.styler.ctx == expected
def test_copy(self):
s2 = copy.copy(self.styler)
assert self.styler is not s2
assert self.styler.ctx is s2.ctx # shallow
assert self.styler._todo is s2._todo
self.styler._update_ctx(self.attrs)
self.styler.highlight_max()
assert self.styler.ctx == s2.ctx
assert self.styler._todo == s2._todo
def test_deepcopy(self):
s2 = copy.deepcopy(self.styler)
assert self.styler is not s2
assert self.styler.ctx is not s2.ctx
assert self.styler._todo is not s2._todo
self.styler._update_ctx(self.attrs)
self.styler.highlight_max()
assert self.styler.ctx != s2.ctx
assert s2._todo == []
assert self.styler._todo != s2._todo
def test_clear(self):
s = self.df.style.highlight_max()._compute()
assert len(s.ctx) > 0
assert len(s._todo) > 0
s.clear()
assert len(s.ctx) == 0
assert 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)
s.render()
# it worked?
def test_render_empty_dfs(self):
empty_df = DataFrame()
es = Styler(empty_df)
es.render()
# An index but no columns
DataFrame(columns=['a']).style.render()
# A column but no index
DataFrame(index=['a']).style.render()
# No IndexError raised?
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}
assert result.keys() == expected.keys()
for v1, v2 in zip(result.values(), expected.values()):
assert 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']}
assert result == expected
def test_empty_index_name_doesnt_display(self):
# https://github.com/pandas-dev/pandas/pull/12090#issuecomment-180695902
df = pd.DataFrame({'A': [1, 2], 'B': [3, 4], 'C': [5, 6]})
result = df.style._translate()
expected = [[{
'class': 'blank level0',
'type': 'th',
'value': '',
'is_visible': True,
'display_value': ''
}, {
'class': 'col_heading level0 col0',
'display_value': 'A',
'type': 'th',
'value': 'A',
'is_visible': True,
}, {
'class': 'col_heading level0 col1',
'display_value': 'B',
'type': 'th',
'value': 'B',
'is_visible': True,
}, {
'class': 'col_heading level0 col2',
'display_value': 'C',
'type': 'th',
'value': 'C',
'is_visible': True,
}]]
assert result['head'] == expected
def test_index_name(self):
# https://github.com/pandas-dev/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 level0',
'type': 'th',
'value': '',
'display_value': '',
'is_visible': True
}, {
'class': 'col_heading level0 col0',
'type': 'th',
'value': 'B',
'display_value': 'B',
'is_visible': True
}, {
'class': 'col_heading level0 col1',
'type': 'th',
'value': 'C',
'display_value': 'C',
'is_visible': True
}],
[{
'class': 'index_name level0',
'type': 'th',
'value': 'A'
}, {
'class': 'blank',
'type': 'th',
'value': ''
}, {
'class': 'blank',
'type': 'th',
'value': ''
}]]
assert result['head'] == expected
def test_multiindex_name(self):
# https://github.com/pandas-dev/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': '',
'display_value': '',
'is_visible': True
}, {
'class': 'blank level0',
'type': 'th',
'value': '',
'display_value': '',
'is_visible': True
}, {
'class': 'col_heading level0 col0',
'type': 'th',
'value': 'C',
'display_value': 'C',
'is_visible': True
}],
[{
'class': 'index_name level0',
'type': 'th',
'value': 'A'
}, {
'class': 'index_name level1',
'type': 'th',
'value': 'B'
}, {
'class': 'blank',
'type': 'th',
'value': ''
}]]
assert result['head'] == expected
def test_numeric_columns(self):
# https://github.com/pandas-dev/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: {max}'.format(max=x.max()) for v in x]
result = df.style.apply(f, axis=1)
assert len(result._todo) == 1
assert len(result.ctx) == 0
result._compute()
expected = {
(0, 0): ['val: 1'],
(0, 1): ['val: 1'],
(1, 0): ['val: 1'],
(1, 1): ['val: 1']
}
assert 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()
assert result.ctx == expected
result = df.style.apply(f) # default
result._compute()
assert 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)
assert 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)
assert result == expected
def test_where_with_one_style(self):
# GH 17474
def f(x):
return x > 0.5
style1 = 'foo: bar'
result = self.df.style.where(f, style1)._compute().ctx
expected = dict(((r, c), [style1 if f(self.df.loc[row, col]) else ''])
for r, row in enumerate(self.df.index)
for c, col in enumerate(self.df.columns))
assert result == expected
def test_where_subset(self):
# GH 17474
def f(x):
return x > 0.5
style1 = 'foo: bar'
style2 = 'baz: foo'
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.where(f, style1, style2,
subset=slice_)._compute().ctx
expected = dict(
((r, c), [style1 if f(self.df.loc[row, col]) else style2])
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)
assert result == expected
def test_where_subset_compare_with_applymap(self):
# GH 17474
def f(x):
return x > 0.5
style1 = 'foo: bar'
style2 = 'baz: foo'
def g(x):
return style1 if f(x) else style2
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.where(f, style1, style2,
subset=slice_)._compute().ctx
expected = self.df.style.applymap(g, subset=slice_)._compute().ctx
assert 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'
}]
assert result == expected
def test_bar_align_left(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%)'
]
}
assert 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%)'
]
}
assert result == expected
df['C'] = ['a'] * len(df)
result = df.style.bar(color='red', width=50)._compute().ctx
assert result == expected
df['C'] = df['C'].astype('category')
result = df.style.bar(color='red', width=50)._compute().ctx
assert result == expected
def test_bar_align_left_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%)'
]
}
assert 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%)'
]
}
assert result == expected
def test_bar_align_mid_pos_and_neg(self):
df = pd.DataFrame({'A': [-10, 0, 20, 90]})
result = df.style.bar(align='mid', color=['#d65f5f',
'#5fba7d'])._compute().ctx
expected = {
(0, 0): [
'width: 10em', ' height: 80%',
'background: linear-gradient(90deg, '
'transparent 0%, transparent 0.0%, #d65f5f 0.0%, '
'#d65f5f 10.0%, transparent 10.0%)'
],
(1, 0): [
'width: 10em', ' height: 80%',
'background: linear-gradient(90deg, '
'transparent 0%, transparent 10.0%, '
'#d65f5f 10.0%, #d65f5f 10.0%, '
'transparent 10.0%)'
],
(2, 0): [
'width: 10em', ' height: 80%',
'background: linear-gradient(90deg, '
'transparent 0%, transparent 10.0%, #5fba7d 10.0%'
', #5fba7d 30.0%, transparent 30.0%)'
],
(3, 0): [
'width: 10em', ' height: 80%',
'background: linear-gradient(90deg, '
'transparent 0%, transparent 10.0%, '
'#5fba7d 10.0%, #5fba7d 100.0%, '
'transparent 100.0%)'
]
}
assert result == expected
def test_bar_align_mid_all_pos(self):
df = pd.DataFrame({'A': [10, 20, 50, 100]})
result = df.style.bar(align='mid', color=['#d65f5f',
'#5fba7d'])._compute().ctx
expected = {
(0, 0): [
'width: 10em', ' height: 80%',
'background: linear-gradient(90deg, '
'transparent 0%, transparent 0.0%, #5fba7d 0.0%, '
'#5fba7d 10.0%, transparent 10.0%)'
],
(1, 0): [
'width: 10em', ' height: 80%',
'background: linear-gradient(90deg, '
'transparent 0%, transparent 0.0%, #5fba7d 0.0%, '
'#5fba7d 20.0%, transparent 20.0%)'
],
(2, 0): [
'width: 10em', ' height: 80%',
'background: linear-gradient(90deg, '
'transparent 0%, transparent 0.0%, #5fba7d 0.0%, '
'#5fba7d 50.0%, transparent 50.0%)'
],
(3, 0): [
'width: 10em', ' height: 80%',
'background: linear-gradient(90deg, '
'transparent 0%, transparent 0.0%, #5fba7d 0.0%, '
'#5fba7d 100.0%, transparent 100.0%)'
]
}
assert result == expected
def test_bar_align_mid_all_neg(self):
df = pd.DataFrame({'A': [-100, -60, -30, -20]})
result = df.style.bar(align='mid', color=['#d65f5f',
'#5fba7d'])._compute().ctx
expected = {
(0, 0): [
'width: 10em', ' height: 80%',
'background: linear-gradient(90deg, '
'transparent 0%, transparent 0.0%, '
'#d65f5f 0.0%, #d65f5f 100.0%, '
'transparent 100.0%)'
],
(1, 0): [
'width: 10em', ' height: 80%',
'background: linear-gradient(90deg, '
'transparent 0%, transparent 40.0%, '
'#d65f5f 40.0%, #d65f5f 100.0%, '
'transparent 100.0%)'
],
(2, 0): [
'width: 10em', ' height: 80%',
'background: linear-gradient(90deg, '
'transparent 0%, transparent 70.0%, '
'#d65f5f 70.0%, #d65f5f 100.0%, '
'transparent 100.0%)'
],
(3, 0): [
'width: 10em', ' height: 80%',
'background: linear-gradient(90deg, '
'transparent 0%, transparent 80.0%, '
'#d65f5f 80.0%, #d65f5f 100.0%, '
'transparent 100.0%)'
]
}
assert result == expected
def test_bar_align_zero_pos_and_neg(self):
# See https://github.com/pandas-dev/pandas/pull/14757
df = pd.DataFrame({'A': [-10, 0, 20, 90]})
result = df.style.bar(align='zero',
color=['#d65f5f', '#5fba7d'],
width=90)._compute().ctx
expected = {
(0, 0): [
'width: 10em', ' height: 80%',
'background: linear-gradient(90deg, '
'transparent 0%, transparent 45.0%, '
'#d65f5f 45.0%, #d65f5f 50%, '
'transparent 50%)'
],
(1, 0): [
'width: 10em', ' height: 80%',
'background: linear-gradient(90deg, '
'transparent 0%, transparent 50%, '
'#5fba7d 50%, #5fba7d 50.0%, '
'transparent 50.0%)'
],
(2, 0): [
'width: 10em', ' height: 80%',
'background: linear-gradient(90deg, '
'transparent 0%, transparent 50%, #5fba7d 50%, '
'#5fba7d 60.0%, transparent 60.0%)'
],
(3, 0): [
'width: 10em', ' height: 80%',
'background: linear-gradient(90deg, '
'transparent 0%, transparent 50%, #5fba7d 50%, '
'#5fba7d 95.0%, transparent 95.0%)'
]
}
assert result == expected
def test_bar_bad_align_raises(self):
df = pd.DataFrame({'A': [-100, -60, -30, -20]})
with pytest.raises(ValueError):
df.style.bar(align='poorly', color=['#d65f5f', '#5fba7d'])
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']}
assert result == expected
def test_nonunique_raises(self):
df = pd.DataFrame([[1, 2]], columns=['A', 'A'])
with pytest.raises(ValueError):
df.style
with pytest.raises(ValueError):
Styler(df)
def test_caption(self):
styler = Styler(self.df, caption='foo')
result = styler.render()
assert all(['caption' in result, 'foo' in result])
styler = self.df.style
result = styler.set_caption('baz')
assert styler is result
assert styler.caption == 'baz'
def test_uuid(self):
styler = Styler(self.df, uuid='abc123')
result = styler.render()
assert 'abc123' in result
styler = self.df.style
result = styler.set_uuid('aaa')
assert result is styler
assert result.uuid == 'aaa'
def test_unique_id(self):
# See https://github.com/pandas-dev/pandas/issues/16780
df = pd.DataFrame({'a': [1, 3, 5, 6], 'b': [2, 4, 12, 21]})
result = df.style.render(uuid='test')
assert 'test' in result
ids = re.findall('id="(.*?)"', result)
assert np.unique(ids).size == len(ids)
def test_table_styles(self):
style = [{'selector': 'th', 'props': [('foo', 'bar')]}]
styler = Styler(self.df, table_styles=style)
result = ' '.join(styler.render().split())
assert 'th { foo: bar; }' in result
styler = self.df.style
result = styler.set_table_styles(style)
assert styler is result
assert styler.table_styles == style
def test_table_attributes(self):
attributes = 'class="foo" data-bar'
styler = Styler(self.df, table_attributes=attributes)
result = styler.render()
assert 'class="foo" data-bar' in result
result = self.df.style.set_table_attributes(attributes).render()
assert 'class="foo" data-bar' in result
def test_precision(self):
with pd.option_context('display.precision', 10):
s = Styler(self.df)
assert s.precision == 10
s = Styler(self.df, precision=2)
assert s.precision == 2
s2 = s.set_precision(4)
assert s is s2
assert 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)
assert result[(1, 1)] == ['color: red']
def test_trim(self):
result = self.df.style.render() # trim=True
assert result.count('#') == 0
result = self.df.style.highlight_max().render()
assert 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
assert result[(1, 1)] == ['background-color: yellow']
result = getattr(df.style, attr)(color='green')._compute().ctx
assert result[(1, 1)] == ['background-color: green']
result = getattr(df.style, attr)(subset='A')._compute().ctx
assert 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): ['']
}
assert 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): ['']
}
assert result == expected
# separate since we can't 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: {z}'.format(z=z) \
if x > 0 else 'color: {z}'.format(z=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)
assert 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()
assert all(['display_value' in c for c in row] for row in ctx['body'])
assert all([len(c['display_value']) <= 3 for c in row[1:]]
for row in ctx['body'])
assert len(ctx['body'][0][1]['display_value'].lstrip('-')) <= 3
def test_display_format_raises(self):
df = pd.DataFrame(np.random.randn(2, 2))
with pytest.raises(TypeError):
df.style.format(5)
with pytest.raises(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'
assert ctx['body'][0][1]['display_value'] == expected
assert ctx['body'][1][1]['display_value'] == '1.1234'
assert ctx['body'][0][2]['display_value'] == '12.34%'
raw_11 = '1.1234'
ctx = df.style.format("{:0.1f}",
subset=pd.IndexSlice[0, :])._translate()
assert ctx['body'][0][1]['display_value'] == expected
assert ctx['body'][1][1]['display_value'] == raw_11
ctx = df.style.format("{:0.1f}",
subset=pd.IndexSlice[0, :])._translate()
assert ctx['body'][0][1]['display_value'] == expected
assert ctx['body'][1][1]['display_value'] == raw_11
ctx = df.style.format("{:0.1f}",
subset=pd.IndexSlice['a'])._translate()
assert ctx['body'][0][1]['display_value'] == expected
assert ctx['body'][0][2]['display_value'] == '0.1234'
ctx = df.style.format("{:0.1f}",
subset=pd.IndexSlice[0, 'a'])._translate()
assert ctx['body'][0][1]['display_value'] == expected
assert ctx['body'][1][1]['display_value'] == raw_11
ctx = df.style.format("{:0.1f}",
subset=pd.IndexSlice[[0, 1],
['a']])._translate()
assert ctx['body'][0][1]['display_value'] == expected
assert ctx['body'][1][1]['display_value'] == '1.1'
assert ctx['body'][0][2]['display_value'] == '0.1234'
assert 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()
assert ctx['body'][0][1]['display_value'] == '0.1'
assert ctx['body'][0][2]['display_value'] == '12.34%'
df['c'] = ['aaa', 'bbb']
ctx = df.style.format({"a": "{:0.1f}", "c": str.upper})._translate()
assert ctx['body'][0][1]['display_value'] == '0.1'
assert ctx['body'][0][3]['display_value'] == 'AAA'
def test_bad_apply_shape(self):
df = pd.DataFrame([[1, 2], [3, 4]])
with pytest.raises(ValueError):
df.style._apply(lambda x: 'x', subset=pd.IndexSlice[[0, 1], :])
with pytest.raises(ValueError):
df.style._apply(lambda x: [''], subset=pd.IndexSlice[[0, 1], :])
with pytest.raises(ValueError):
df.style._apply(lambda x: ['', '', '', ''])
with pytest.raises(ValueError):
df.style._apply(lambda x: ['', '', ''], subset=1)
with pytest.raises(ValueError):
df.style._apply(lambda x: ['', '', ''], axis=1)
def test_apply_bad_return(self):
def f(x):
return ''
df = pd.DataFrame([[1, 2], [3, 4]])
with pytest.raises(TypeError):
df.style._apply(f, axis=None)
def test_apply_bad_labels(self):
def f(x):
return pd.DataFrame(index=[1, 2], columns=['a', 'b'])
df = pd.DataFrame([[1, 2], [3, 4]])
with pytest.raises(ValueError):
df.style._apply(f, axis=None)
def test_get_level_lengths(self):
index = pd.MultiIndex.from_product([['a', 'b'], [0, 1, 2]])
expected = {
(0, 0): 3,
(0, 3): 3,
(1, 0): 1,
(1, 1): 1,
(1, 2): 1,
(1, 3): 1,
(1, 4): 1,
(1, 5): 1
}
result = _get_level_lengths(index)
tm.assert_dict_equal(result, expected)
def test_get_level_lengths_un_sorted(self):
index = pd.MultiIndex.from_arrays([[1, 1, 2, 1], ['a', 'b', 'b', 'd']])
expected = {
(0, 0): 2,
(0, 2): 1,
(0, 3): 1,
(1, 0): 1,
(1, 1): 1,
(1, 2): 1,
(1, 3): 1
}
result = _get_level_lengths(index)
tm.assert_dict_equal(result, expected)
def test_mi_sparse(self):
df = pd.DataFrame({'A': [1, 2]},
index=pd.MultiIndex.from_arrays([['a', 'a'], [0,
1]]))
result = df.style._translate()
body_0 = result['body'][0][0]
expected_0 = {
"value": "a",
"display_value": "a",
"is_visible": True,
"type": "th",
"attributes": ["rowspan=2"],
"class": "row_heading level0 row0",
"id": "level0_row0"
}
tm.assert_dict_equal(body_0, expected_0)
body_1 = result['body'][0][1]
expected_1 = {
"value": 0,
"display_value": 0,
"is_visible": True,
"type": "th",
"class": "row_heading level1 row0",
"id": "level1_row0"
}
tm.assert_dict_equal(body_1, expected_1)
body_10 = result['body'][1][0]
expected_10 = {
"value": 'a',
"display_value": 'a',
"is_visible": False,
"type": "th",
"class": "row_heading level0 row1",
"id": "level0_row1"
}
tm.assert_dict_equal(body_10, expected_10)
head = result['head'][0]
expected = [{
'type': 'th',
'class': 'blank',
'value': '',
'is_visible': True,
"display_value": ''
}, {
'type': 'th',
'class': 'blank level0',
'value': '',
'is_visible': True,
'display_value': ''
}, {
'type': 'th',
'class': 'col_heading level0 col0',
'value': 'A',
'is_visible': True,
'display_value': 'A'
}]
assert head == expected
def test_mi_sparse_disabled(self):
with pd.option_context('display.multi_sparse', False):
df = pd.DataFrame({'A': [1, 2]},
index=pd.MultiIndex.from_arrays([['a', 'a'],
[0, 1]]))
result = df.style._translate()
body = result['body']
for row in body:
assert 'attributes' not in row[0]
def test_mi_sparse_index_names(self):
df = pd.DataFrame({'A': [1, 2]},
index=pd.MultiIndex.from_arrays(
[['a', 'a'], [0, 1]],
names=['idx_level_0', 'idx_level_1']))
result = df.style._translate()
head = result['head'][1]
expected = [{
'class': 'index_name level0',
'value': 'idx_level_0',
'type': 'th'
}, {
'class': 'index_name level1',
'value': 'idx_level_1',
'type': 'th'
}, {
'class': 'blank',
'value': '',
'type': 'th'
}]
assert head == expected
def test_mi_sparse_column_names(self):
df = pd.DataFrame(np.arange(16).reshape(4, 4),
index=pd.MultiIndex.from_arrays(
[['a', 'a', 'b', 'a'], [0, 1, 1, 2]],
names=['idx_level_0', 'idx_level_1']),
columns=pd.MultiIndex.from_arrays(
[['C1', 'C1', 'C2', 'C2'], [1, 0, 1, 0]],
names=['col_0', 'col_1']))
result = df.style._translate()
head = result['head'][1]
expected = [
{
'class': 'blank',
'value': '',
'display_value': '',
'type': 'th',
'is_visible': True
},
{
'class': 'index_name level1',
'value': 'col_1',
'display_value': 'col_1',
'is_visible': True,
'type': 'th'
},
{
'class': 'col_heading level1 col0',
'display_value': 1,
'is_visible': True,
'type': 'th',
'value': 1
},
{
'class': 'col_heading level1 col1',
'display_value': 0,
'is_visible': True,
'type': 'th',
'value': 0
},
{
'class': 'col_heading level1 col2',
'display_value': 1,
'is_visible': True,
'type': 'th',
'value': 1
},
{
'class': 'col_heading level1 col3',
'display_value': 0,
'is_visible': True,
'type': 'th',
'value': 0
},
]
assert head == expected
def test_hide_single_index(self):
# GH 14194
# single unnamed index
ctx = self.df.style._translate()
assert ctx['body'][0][0]['is_visible']
assert ctx['head'][0][0]['is_visible']
ctx2 = self.df.style.hide_index()._translate()
assert not ctx2['body'][0][0]['is_visible']
assert not ctx2['head'][0][0]['is_visible']
# single named index
ctx3 = self.df.set_index('A').style._translate()
assert ctx3['body'][0][0]['is_visible']
assert len(ctx3['head']) == 2 # 2 header levels
assert ctx3['head'][0][0]['is_visible']
ctx4 = self.df.set_index('A').style.hide_index()._translate()
assert not ctx4['body'][0][0]['is_visible']
assert len(ctx4['head']) == 1 # only 1 header levels
assert not ctx4['head'][0][0]['is_visible']
def test_hide_multiindex(self):
# GH 14194
df = pd.DataFrame({'A': [1, 2]},
index=pd.MultiIndex.from_arrays(
[['a', 'a'], [0, 1]],
names=['idx_level_0', 'idx_level_1']))
ctx1 = df.style._translate()
# tests for 'a' and '0'
assert ctx1['body'][0][0]['is_visible']
assert ctx1['body'][0][1]['is_visible']
# check for blank header rows
assert ctx1['head'][0][0]['is_visible']
assert ctx1['head'][0][1]['is_visible']
ctx2 = df.style.hide_index()._translate()
# tests for 'a' and '0'
assert not ctx2['body'][0][0]['is_visible']
assert not ctx2['body'][0][1]['is_visible']
# check for blank header rows
assert not ctx2['head'][0][0]['is_visible']
assert not ctx2['head'][0][1]['is_visible']
def test_hide_columns_single_level(self):
# GH 14194
# test hiding single column
ctx = self.df.style._translate()
assert ctx['head'][0][1]['is_visible']
assert ctx['head'][0][1]['display_value'] == 'A'
assert ctx['head'][0][2]['is_visible']
assert ctx['head'][0][2]['display_value'] == 'B'
assert ctx['body'][0][1]['is_visible'] # col A, row 1
assert ctx['body'][1][2]['is_visible'] # col B, row 1
ctx = self.df.style.hide_columns('A')._translate()
assert not ctx['head'][0][1]['is_visible']
assert not ctx['body'][0][1]['is_visible'] # col A, row 1
assert ctx['body'][1][2]['is_visible'] # col B, row 1
# test hiding mulitiple columns
ctx = self.df.style.hide_columns(['A', 'B'])._translate()
assert not ctx['head'][0][1]['is_visible']
assert not ctx['head'][0][2]['is_visible']
assert not ctx['body'][0][1]['is_visible'] # col A, row 1
assert not ctx['body'][1][2]['is_visible'] # col B, row 1
def test_hide_columns_mult_levels(self):
# GH 14194
# setup dataframe with multiple column levels and indices
i1 = pd.MultiIndex.from_arrays([['a', 'a'], [0, 1]],
names=['idx_level_0', 'idx_level_1'])
i2 = pd.MultiIndex.from_arrays([['b', 'b'], [0, 1]],
names=['col_level_0', 'col_level_1'])
df = pd.DataFrame([[1, 2], [3, 4]], index=i1, columns=i2)
ctx = df.style._translate()
# column headers
assert ctx['head'][0][2]['is_visible']
assert ctx['head'][1][2]['is_visible']
assert ctx['head'][1][3]['display_value'] == 1
# indices
assert ctx['body'][0][0]['is_visible']
# data
assert ctx['body'][1][2]['is_visible']
assert ctx['body'][1][2]['display_value'] == 3
assert ctx['body'][1][3]['is_visible']
assert ctx['body'][1][3]['display_value'] == 4
# hide top column level, which hides both columns
ctx = df.style.hide_columns('b')._translate()
assert not ctx['head'][0][2]['is_visible'] # b
assert not ctx['head'][1][2]['is_visible'] # 0
assert not ctx['body'][1][2]['is_visible'] # 3
assert ctx['body'][0][0]['is_visible'] # index
# hide first column only
ctx = df.style.hide_columns([('b', 0)])._translate()
assert ctx['head'][0][2]['is_visible'] # b
assert not ctx['head'][1][2]['is_visible'] # 0
assert not ctx['body'][1][2]['is_visible'] # 3
assert ctx['body'][1][3]['is_visible']
assert ctx['body'][1][3]['display_value'] == 4
# hide second column and index
ctx = df.style.hide_columns([('b', 1)]).hide_index()._translate()
assert not ctx['body'][0][0]['is_visible'] # index
assert ctx['head'][0][2]['is_visible'] # b
assert ctx['head'][1][2]['is_visible'] # 0
assert not ctx['head'][1][3]['is_visible'] # 1
assert not ctx['body'][1][3]['is_visible'] # 4
assert ctx['body'][1][2]['is_visible']
assert ctx['body'][1][2]['display_value'] == 3
class TestStyler(object):
def setup_method(self, method):
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: {foo}'.format(foo=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_init_non_pandas(self):
with pytest.raises(TypeError):
Styler([1, 2, 3])
def test_init_series(self):
result = Styler(pd.Series([1, 2]))
assert result.data.ndim == 2
def test_repr_html_ok(self):
self.styler._repr_html_()
def test_update_ctx(self):
self.styler._update_ctx(self.attrs)
expected = {(0, 0): ['color: red'],
(1, 0): ['color: blue']}
assert 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']}
assert 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']}
assert self.styler.ctx == expected
def test_copy(self):
s2 = copy.copy(self.styler)
assert self.styler is not s2
assert self.styler.ctx is s2.ctx # shallow
assert self.styler._todo is s2._todo
self.styler._update_ctx(self.attrs)
self.styler.highlight_max()
assert self.styler.ctx == s2.ctx
assert self.styler._todo == s2._todo
def test_deepcopy(self):
s2 = copy.deepcopy(self.styler)
assert self.styler is not s2
assert self.styler.ctx is not s2.ctx
assert self.styler._todo is not s2._todo
self.styler._update_ctx(self.attrs)
self.styler.highlight_max()
assert self.styler.ctx != s2.ctx
assert s2._todo == []
assert self.styler._todo != s2._todo
def test_clear(self):
s = self.df.style.highlight_max()._compute()
assert len(s.ctx) > 0
assert len(s._todo) > 0
s.clear()
assert len(s.ctx) == 0
assert 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)
s.render()
# it worked?
def test_render_empty_dfs(self):
empty_df = DataFrame()
es = Styler(empty_df)
es.render()
# An index but no columns
DataFrame(columns=['a']).style.render()
# A column but no index
DataFrame(index=['a']).style.render()
# No IndexError raised?
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}
assert result.keys() == expected.keys()
for v1, v2 in zip(result.values(), expected.values()):
assert 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']}
assert result == expected
def test_empty_index_name_doesnt_display(self):
# https://github.com/pandas-dev/pandas/pull/12090#issuecomment-180695902
df = pd.DataFrame({'A': [1, 2], 'B': [3, 4], 'C': [5, 6]})
result = df.style._translate()
expected = [[{'class': 'blank level0', 'type': 'th', 'value': '',
'is_visible': True, 'display_value': ''},
{'class': 'col_heading level0 col0',
'display_value': 'A',
'type': 'th',
'value': 'A',
'is_visible': True,
},
{'class': 'col_heading level0 col1',
'display_value': 'B',
'type': 'th',
'value': 'B',
'is_visible': True,
},
{'class': 'col_heading level0 col2',
'display_value': 'C',
'type': 'th',
'value': 'C',
'is_visible': True,
}]]
assert result['head'] == expected
def test_index_name(self):
# https://github.com/pandas-dev/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 level0', 'type': 'th', 'value': '',
'display_value': '', 'is_visible': True},
{'class': 'col_heading level0 col0', 'type': 'th',
'value': 'B', 'display_value': 'B', 'is_visible': True},
{'class': 'col_heading level0 col1', 'type': 'th',
'value': 'C', 'display_value': 'C', 'is_visible': True}],
[{'class': 'index_name level0', 'type': 'th',
'value': 'A'},
{'class': 'blank', 'type': 'th', 'value': ''},
{'class': 'blank', 'type': 'th', 'value': ''}]]
assert result['head'] == expected
def test_multiindex_name(self):
# https://github.com/pandas-dev/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': '',
'display_value': '', 'is_visible': True},
{'class': 'blank level0', 'type': 'th', 'value': '',
'display_value': '', 'is_visible': True},
{'class': 'col_heading level0 col0', 'type': 'th',
'value': 'C', 'display_value': 'C', 'is_visible': True}],
[{'class': 'index_name level0', 'type': 'th',
'value': 'A'},
{'class': 'index_name level1', 'type': 'th',
'value': 'B'},
{'class': 'blank', 'type': 'th', 'value': ''}]]
assert result['head'] == expected
def test_numeric_columns(self):
# https://github.com/pandas-dev/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: {max}'.format(max=x.max()) for v in x]
result = df.style.apply(f, axis=1)
assert len(result._todo) == 1
assert len(result.ctx) == 0
result._compute()
expected = {(0, 0): ['val: 1'], (0, 1): ['val: 1'],
(1, 0): ['val: 1'], (1, 1): ['val: 1']}
assert 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()
assert result.ctx == expected
result = df.style.apply(f) # default
result._compute()
assert 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)
assert 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)
assert result == expected
def test_where_with_one_style(self):
# GH 17474
def f(x):
return x > 0.5
style1 = 'foo: bar'
result = self.df.style.where(f, style1)._compute().ctx
expected = dict(((r, c),
[style1 if f(self.df.loc[row, col]) else ''])
for r, row in enumerate(self.df.index)
for c, col in enumerate(self.df.columns))
assert result == expected
def test_where_subset(self):
# GH 17474
def f(x):
return x > 0.5
style1 = 'foo: bar'
style2 = 'baz: foo'
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.where(f, style1, style2,
subset=slice_)._compute().ctx
expected = dict(((r, c),
[style1 if f(self.df.loc[row, col]) else style2])
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)
assert result == expected
def test_where_subset_compare_with_applymap(self):
# GH 17474
def f(x):
return x > 0.5
style1 = 'foo: bar'
style2 = 'baz: foo'
def g(x):
return style1 if f(x) else style2
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.where(f, style1, style2,
subset=slice_)._compute().ctx
expected = self.df.style.applymap(g, subset=slice_)._compute().ctx
assert 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'}]
assert result == expected
def test_bar_align_left(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%)']
}
assert 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%)']
}
assert result == expected
df['C'] = ['a'] * len(df)
result = df.style.bar(color='red', width=50)._compute().ctx
assert result == expected
df['C'] = df['C'].astype('category')
result = df.style.bar(color='red', width=50)._compute().ctx
assert result == expected
def test_bar_align_left_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%)']}
assert 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%)']}
assert result == expected
def test_bar_align_mid_pos_and_neg(self):
df = pd.DataFrame({'A': [-10, 0, 20, 90]})
result = df.style.bar(align='mid', color=[
'#d65f5f', '#5fba7d'])._compute().ctx
expected = {(0, 0): ['width: 10em', ' height: 80%',
'background: linear-gradient(90deg, '
'transparent 0%, transparent 0.0%, #d65f5f 0.0%, '
'#d65f5f 10.0%, transparent 10.0%)'],
(1, 0): ['width: 10em', ' height: 80%',
'background: linear-gradient(90deg, '
'transparent 0%, transparent 10.0%, '
'#d65f5f 10.0%, #d65f5f 10.0%, '
'transparent 10.0%)'],
(2, 0): ['width: 10em', ' height: 80%',
'background: linear-gradient(90deg, '
'transparent 0%, transparent 10.0%, #5fba7d 10.0%'
', #5fba7d 30.0%, transparent 30.0%)'],
(3, 0): ['width: 10em', ' height: 80%',
'background: linear-gradient(90deg, '
'transparent 0%, transparent 10.0%, '
'#5fba7d 10.0%, #5fba7d 100.0%, '
'transparent 100.0%)']}
assert result == expected
def test_bar_align_mid_all_pos(self):
df = pd.DataFrame({'A': [10, 20, 50, 100]})
result = df.style.bar(align='mid', color=[
'#d65f5f', '#5fba7d'])._compute().ctx
expected = {(0, 0): ['width: 10em', ' height: 80%',
'background: linear-gradient(90deg, '
'transparent 0%, transparent 0.0%, #5fba7d 0.0%, '
'#5fba7d 10.0%, transparent 10.0%)'],
(1, 0): ['width: 10em', ' height: 80%',
'background: linear-gradient(90deg, '
'transparent 0%, transparent 0.0%, #5fba7d 0.0%, '
'#5fba7d 20.0%, transparent 20.0%)'],
(2, 0): ['width: 10em', ' height: 80%',
'background: linear-gradient(90deg, '
'transparent 0%, transparent 0.0%, #5fba7d 0.0%, '
'#5fba7d 50.0%, transparent 50.0%)'],
(3, 0): ['width: 10em', ' height: 80%',
'background: linear-gradient(90deg, '
'transparent 0%, transparent 0.0%, #5fba7d 0.0%, '
'#5fba7d 100.0%, transparent 100.0%)']}
assert result == expected
def test_bar_align_mid_all_neg(self):
df = pd.DataFrame({'A': [-100, -60, -30, -20]})
result = df.style.bar(align='mid', color=[
'#d65f5f', '#5fba7d'])._compute().ctx
expected = {(0, 0): ['width: 10em', ' height: 80%',
'background: linear-gradient(90deg, '
'transparent 0%, transparent 0.0%, '
'#d65f5f 0.0%, #d65f5f 100.0%, '
'transparent 100.0%)'],
(1, 0): ['width: 10em', ' height: 80%',
'background: linear-gradient(90deg, '
'transparent 0%, transparent 40.0%, '
'#d65f5f 40.0%, #d65f5f 100.0%, '
'transparent 100.0%)'],
(2, 0): ['width: 10em', ' height: 80%',
'background: linear-gradient(90deg, '
'transparent 0%, transparent 70.0%, '
'#d65f5f 70.0%, #d65f5f 100.0%, '
'transparent 100.0%)'],
(3, 0): ['width: 10em', ' height: 80%',
'background: linear-gradient(90deg, '
'transparent 0%, transparent 80.0%, '
'#d65f5f 80.0%, #d65f5f 100.0%, '
'transparent 100.0%)']}
assert result == expected
def test_bar_align_zero_pos_and_neg(self):
# See https://github.com/pandas-dev/pandas/pull/14757
df = pd.DataFrame({'A': [-10, 0, 20, 90]})
result = df.style.bar(align='zero', color=[
'#d65f5f', '#5fba7d'], width=90)._compute().ctx
expected = {(0, 0): ['width: 10em', ' height: 80%',
'background: linear-gradient(90deg, '
'transparent 0%, transparent 45.0%, '
'#d65f5f 45.0%, #d65f5f 50%, '
'transparent 50%)'],
(1, 0): ['width: 10em', ' height: 80%',
'background: linear-gradient(90deg, '
'transparent 0%, transparent 50%, '
'#5fba7d 50%, #5fba7d 50.0%, '
'transparent 50.0%)'],
(2, 0): ['width: 10em', ' height: 80%',
'background: linear-gradient(90deg, '
'transparent 0%, transparent 50%, #5fba7d 50%, '
'#5fba7d 60.0%, transparent 60.0%)'],
(3, 0): ['width: 10em', ' height: 80%',
'background: linear-gradient(90deg, '
'transparent 0%, transparent 50%, #5fba7d 50%, '
'#5fba7d 95.0%, transparent 95.0%)']}
assert result == expected
def test_bar_bad_align_raises(self):
df = pd.DataFrame({'A': [-100, -60, -30, -20]})
with pytest.raises(ValueError):
df.style.bar(align='poorly', color=['#d65f5f', '#5fba7d'])
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']}
assert result == expected
def test_nonunique_raises(self):
df = pd.DataFrame([[1, 2]], columns=['A', 'A'])
with pytest.raises(ValueError):
df.style
with pytest.raises(ValueError):
Styler(df)
def test_caption(self):
styler = Styler(self.df, caption='foo')
result = styler.render()
assert all(['caption' in result, 'foo' in result])
styler = self.df.style
result = styler.set_caption('baz')
assert styler is result
assert styler.caption == 'baz'
def test_uuid(self):
styler = Styler(self.df, uuid='abc123')
result = styler.render()
assert 'abc123' in result
styler = self.df.style
result = styler.set_uuid('aaa')
assert result is styler
assert result.uuid == 'aaa'
def test_unique_id(self):
# See https://github.com/pandas-dev/pandas/issues/16780
df = pd.DataFrame({'a': [1, 3, 5, 6], 'b': [2, 4, 12, 21]})
result = df.style.render(uuid='test')
assert 'test' in result
ids = re.findall('id="(.*?)"', result)
assert np.unique(ids).size == len(ids)
def test_table_styles(self):
style = [{'selector': 'th', 'props': [('foo', 'bar')]}]
styler = Styler(self.df, table_styles=style)
result = ' '.join(styler.render().split())
assert 'th { foo: bar; }' in result
styler = self.df.style
result = styler.set_table_styles(style)
assert styler is result
assert styler.table_styles == style
def test_table_attributes(self):
attributes = 'class="foo" data-bar'
styler = Styler(self.df, table_attributes=attributes)
result = styler.render()
assert 'class="foo" data-bar' in result
result = self.df.style.set_table_attributes(attributes).render()
assert 'class="foo" data-bar' in result
def test_precision(self):
with pd.option_context('display.precision', 10):
s = Styler(self.df)
assert s.precision == 10
s = Styler(self.df, precision=2)
assert s.precision == 2
s2 = s.set_precision(4)
assert s is s2
assert 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)
assert result[(1, 1)] == ['color: red']
def test_trim(self):
result = self.df.style.render() # trim=True
assert result.count('#') == 0
result = self.df.style.highlight_max().render()
assert 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
assert result[(1, 1)] == ['background-color: yellow']
result = getattr(df.style, attr)(color='green')._compute().ctx
assert result[(1, 1)] == ['background-color: green']
result = getattr(df.style, attr)(subset='A')._compute().ctx
assert 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): ['']}
assert 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): ['']}
assert 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: {z}'.format(z=z) \
if x > 0 else 'color: {z}'.format(z=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)
assert 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()
assert all(['display_value' in c for c in row]
for row in ctx['body'])
assert all([len(c['display_value']) <= 3 for c in row[1:]]
for row in ctx['body'])
assert len(ctx['body'][0][1]['display_value'].lstrip('-')) <= 3
def test_display_format_raises(self):
df = pd.DataFrame(np.random.randn(2, 2))
with pytest.raises(TypeError):
df.style.format(5)
with pytest.raises(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'
assert ctx['body'][0][1]['display_value'] == expected
assert ctx['body'][1][1]['display_value'] == '1.1234'
assert ctx['body'][0][2]['display_value'] == '12.34%'
raw_11 = '1.1234'
ctx = df.style.format("{:0.1f}",
subset=pd.IndexSlice[0, :])._translate()
assert ctx['body'][0][1]['display_value'] == expected
assert ctx['body'][1][1]['display_value'] == raw_11
ctx = df.style.format("{:0.1f}",
subset=pd.IndexSlice[0, :])._translate()
assert ctx['body'][0][1]['display_value'] == expected
assert ctx['body'][1][1]['display_value'] == raw_11
ctx = df.style.format("{:0.1f}",
subset=pd.IndexSlice['a'])._translate()
assert ctx['body'][0][1]['display_value'] == expected
assert ctx['body'][0][2]['display_value'] == '0.1234'
ctx = df.style.format("{:0.1f}",
subset=pd.IndexSlice[0, 'a'])._translate()
assert ctx['body'][0][1]['display_value'] == expected
assert ctx['body'][1][1]['display_value'] == raw_11
ctx = df.style.format("{:0.1f}",
subset=pd.IndexSlice[[0, 1], ['a']])._translate()
assert ctx['body'][0][1]['display_value'] == expected
assert ctx['body'][1][1]['display_value'] == '1.1'
assert ctx['body'][0][2]['display_value'] == '0.1234'
assert 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()
assert ctx['body'][0][1]['display_value'] == '0.1'
assert ctx['body'][0][2]['display_value'] == '12.34%'
df['c'] = ['aaa', 'bbb']
ctx = df.style.format({"a": "{:0.1f}", "c": str.upper})._translate()
assert ctx['body'][0][1]['display_value'] == '0.1'
assert ctx['body'][0][3]['display_value'] == 'AAA'
def test_bad_apply_shape(self):
df = pd.DataFrame([[1, 2], [3, 4]])
with pytest.raises(ValueError):
df.style._apply(lambda x: 'x', subset=pd.IndexSlice[[0, 1], :])
with pytest.raises(ValueError):
df.style._apply(lambda x: [''], subset=pd.IndexSlice[[0, 1], :])
with pytest.raises(ValueError):
df.style._apply(lambda x: ['', '', '', ''])
with pytest.raises(ValueError):
df.style._apply(lambda x: ['', '', ''], subset=1)
with pytest.raises(ValueError):
df.style._apply(lambda x: ['', '', ''], axis=1)
def test_apply_bad_return(self):
def f(x):
return ''
df = pd.DataFrame([[1, 2], [3, 4]])
with pytest.raises(TypeError):
df.style._apply(f, axis=None)
def test_apply_bad_labels(self):
def f(x):
return pd.DataFrame(index=[1, 2], columns=['a', 'b'])
df = pd.DataFrame([[1, 2], [3, 4]])
with pytest.raises(ValueError):
df.style._apply(f, axis=None)
def test_get_level_lengths(self):
index = pd.MultiIndex.from_product([['a', 'b'], [0, 1, 2]])
expected = {(0, 0): 3, (0, 3): 3, (1, 0): 1, (1, 1): 1, (1, 2): 1,
(1, 3): 1, (1, 4): 1, (1, 5): 1}
result = _get_level_lengths(index)
tm.assert_dict_equal(result, expected)
def test_get_level_lengths_un_sorted(self):
index = pd.MultiIndex.from_arrays([
[1, 1, 2, 1],
['a', 'b', 'b', 'd']
])
expected = {(0, 0): 2, (0, 2): 1, (0, 3): 1,
(1, 0): 1, (1, 1): 1, (1, 2): 1, (1, 3): 1}
result = _get_level_lengths(index)
tm.assert_dict_equal(result, expected)
def test_mi_sparse(self):
df = pd.DataFrame({'A': [1, 2]},
index=pd.MultiIndex.from_arrays([['a', 'a'],
[0, 1]]))
result = df.style._translate()
body_0 = result['body'][0][0]
expected_0 = {
"value": "a", "display_value": "a", "is_visible": True,
"type": "th", "attributes": ["rowspan=2"],
"class": "row_heading level0 row0", "id": "level0_row0"
}
tm.assert_dict_equal(body_0, expected_0)
body_1 = result['body'][0][1]
expected_1 = {
"value": 0, "display_value": 0, "is_visible": True,
"type": "th", "class": "row_heading level1 row0",
"id": "level1_row0"
}
tm.assert_dict_equal(body_1, expected_1)
body_10 = result['body'][1][0]
expected_10 = {
"value": 'a', "display_value": 'a', "is_visible": False,
"type": "th", "class": "row_heading level0 row1",
"id": "level0_row1"
}
tm.assert_dict_equal(body_10, expected_10)
head = result['head'][0]
expected = [
{'type': 'th', 'class': 'blank', 'value': '',
'is_visible': True, "display_value": ''},
{'type': 'th', 'class': 'blank level0', 'value': '',
'is_visible': True, 'display_value': ''},
{'type': 'th', 'class': 'col_heading level0 col0', 'value': 'A',
'is_visible': True, 'display_value': 'A'}]
assert head == expected
def test_mi_sparse_disabled(self):
with pd.option_context('display.multi_sparse', False):
df = pd.DataFrame({'A': [1, 2]},
index=pd.MultiIndex.from_arrays([['a', 'a'],
[0, 1]]))
result = df.style._translate()
body = result['body']
for row in body:
assert 'attributes' not in row[0]
def test_mi_sparse_index_names(self):
df = pd.DataFrame({'A': [1, 2]}, index=pd.MultiIndex.from_arrays(
[['a', 'a'], [0, 1]],
names=['idx_level_0', 'idx_level_1'])
)
result = df.style._translate()
head = result['head'][1]
expected = [{
'class': 'index_name level0', 'value': 'idx_level_0',
'type': 'th'},
{'class': 'index_name level1', 'value': 'idx_level_1',
'type': 'th'},
{'class': 'blank', 'value': '', 'type': 'th'}]
assert head == expected
def test_mi_sparse_column_names(self):
df = pd.DataFrame(
np.arange(16).reshape(4, 4),
index=pd.MultiIndex.from_arrays(
[['a', 'a', 'b', 'a'], [0, 1, 1, 2]],
names=['idx_level_0', 'idx_level_1']),
columns=pd.MultiIndex.from_arrays(
[['C1', 'C1', 'C2', 'C2'], [1, 0, 1, 0]],
names=['col_0', 'col_1']
)
)
result = df.style._translate()
head = result['head'][1]
expected = [
{'class': 'blank', 'value': '', 'display_value': '',
'type': 'th', 'is_visible': True},
{'class': 'index_name level1', 'value': 'col_1',
'display_value': 'col_1', 'is_visible': True, 'type': 'th'},
{'class': 'col_heading level1 col0',
'display_value': 1,
'is_visible': True,
'type': 'th',
'value': 1},
{'class': 'col_heading level1 col1',
'display_value': 0,
'is_visible': True,
'type': 'th',
'value': 0},
{'class': 'col_heading level1 col2',
'display_value': 1,
'is_visible': True,
'type': 'th',
'value': 1},
{'class': 'col_heading level1 col3',
'display_value': 0,
'is_visible': True,
'type': 'th',
'value': 0},
]
assert head == expected
def test_hide_single_index(self):
# GH 14194
# single unnamed index
ctx = self.df.style._translate()
assert ctx['body'][0][0]['is_visible']
assert ctx['head'][0][0]['is_visible']
ctx2 = self.df.style.hide_index()._translate()
assert not ctx2['body'][0][0]['is_visible']
assert not ctx2['head'][0][0]['is_visible']
# single named index
ctx3 = self.df.set_index('A').style._translate()
assert ctx3['body'][0][0]['is_visible']
assert len(ctx3['head']) == 2 # 2 header levels
assert ctx3['head'][0][0]['is_visible']
ctx4 = self.df.set_index('A').style.hide_index()._translate()
assert not ctx4['body'][0][0]['is_visible']
assert len(ctx4['head']) == 1 # only 1 header levels
assert not ctx4['head'][0][0]['is_visible']
def test_hide_multiindex(self):
# GH 14194
df = pd.DataFrame({'A': [1, 2]}, index=pd.MultiIndex.from_arrays(
[['a', 'a'], [0, 1]],
names=['idx_level_0', 'idx_level_1'])
)
ctx1 = df.style._translate()
# tests for 'a' and '0'
assert ctx1['body'][0][0]['is_visible']
assert ctx1['body'][0][1]['is_visible']
# check for blank header rows
assert ctx1['head'][0][0]['is_visible']
assert ctx1['head'][0][1]['is_visible']
ctx2 = df.style.hide_index()._translate()
# tests for 'a' and '0'
assert not ctx2['body'][0][0]['is_visible']
assert not ctx2['body'][0][1]['is_visible']
# check for blank header rows
assert not ctx2['head'][0][0]['is_visible']
assert not ctx2['head'][0][1]['is_visible']
def test_hide_columns_single_level(self):
# GH 14194
# test hiding single column
ctx = self.df.style._translate()
assert ctx['head'][0][1]['is_visible']
assert ctx['head'][0][1]['display_value'] == 'A'
assert ctx['head'][0][2]['is_visible']
assert ctx['head'][0][2]['display_value'] == 'B'
assert ctx['body'][0][1]['is_visible'] # col A, row 1
assert ctx['body'][1][2]['is_visible'] # col B, row 1
ctx = self.df.style.hide_columns('A')._translate()
assert not ctx['head'][0][1]['is_visible']
assert not ctx['body'][0][1]['is_visible'] # col A, row 1
assert ctx['body'][1][2]['is_visible'] # col B, row 1
# test hiding mulitiple columns
ctx = self.df.style.hide_columns(['A', 'B'])._translate()
assert not ctx['head'][0][1]['is_visible']
assert not ctx['head'][0][2]['is_visible']
assert not ctx['body'][0][1]['is_visible'] # col A, row 1
assert not ctx['body'][1][2]['is_visible'] # col B, row 1
def test_hide_columns_mult_levels(self):
# GH 14194
# setup dataframe with multiple column levels and indices
i1 = pd.MultiIndex.from_arrays([['a', 'a'], [0, 1]],
names=['idx_level_0',
'idx_level_1'])
i2 = pd.MultiIndex.from_arrays([['b', 'b'], [0, 1]],
names=['col_level_0',
'col_level_1'])
df = pd.DataFrame([[1, 2], [3, 4]], index=i1, columns=i2)
ctx = df.style._translate()
# column headers
assert ctx['head'][0][2]['is_visible']
assert ctx['head'][1][2]['is_visible']
assert ctx['head'][1][3]['display_value'] == 1
# indices
assert ctx['body'][0][0]['is_visible']
# data
assert ctx['body'][1][2]['is_visible']
assert ctx['body'][1][2]['display_value'] == 3
assert ctx['body'][1][3]['is_visible']
assert ctx['body'][1][3]['display_value'] == 4
# hide top column level, which hides both columns
ctx = df.style.hide_columns('b')._translate()
assert not ctx['head'][0][2]['is_visible'] # b
assert not ctx['head'][1][2]['is_visible'] # 0
assert not ctx['body'][1][2]['is_visible'] # 3
assert ctx['body'][0][0]['is_visible'] # index
# hide first column only
ctx = df.style.hide_columns([('b', 0)])._translate()
assert ctx['head'][0][2]['is_visible'] # b
assert not ctx['head'][1][2]['is_visible'] # 0
assert not ctx['body'][1][2]['is_visible'] # 3
assert ctx['body'][1][3]['is_visible']
assert ctx['body'][1][3]['display_value'] == 4
# hide second column and index
ctx = df.style.hide_columns([('b', 1)]).hide_index()._translate()
assert not ctx['body'][0][0]['is_visible'] # index
assert ctx['head'][0][2]['is_visible'] # b
assert ctx['head'][1][2]['is_visible'] # 0
assert not ctx['head'][1][3]['is_visible'] # 1
assert not ctx['body'][1][3]['is_visible'] # 4
assert ctx['body'][1][2]['is_visible']
assert ctx['body'][1][2]['display_value'] == 3
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_init_non_pandas(self):
with tm.assertRaises(TypeError):
Styler([1, 2, 3])
def test_init_series(self):
result = Styler(pd.Series([1, 2]))
self.assertEqual(result.data.ndim, 2)
def test_repr_html_ok(self):
self.styler._repr_html_()
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)
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_empty_index_name_doesnt_display(self):
# https://github.com/pandas-dev/pandas/pull/12090#issuecomment-180695902
df = pd.DataFrame({'A': [1, 2], 'B': [3, 4], 'C': [5, 6]})
result = df.style._translate()
expected = [[{'class': 'blank level0', 'type': 'th', 'value': '',
'is_visible': True, 'display_value': ''},
{'class': 'col_heading level0 col0',
'display_value': 'A',
'type': 'th',
'value': 'A',
'is_visible': True,
},
{'class': 'col_heading level0 col1',
'display_value': 'B',
'type': 'th',
'value': 'B',
'is_visible': True,
},
{'class': 'col_heading level0 col2',
'display_value': 'C',
'type': 'th',
'value': 'C',
'is_visible': True,
}]]
self.assertEqual(result['head'], expected)
def test_index_name(self):
# https://github.com/pandas-dev/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 level0', 'type': 'th', 'value': '',
'display_value': '', 'is_visible': True},
{'class': 'col_heading level0 col0', 'type': 'th',
'value': 'B', 'display_value': 'B', 'is_visible': True},
{'class': 'col_heading level0 col1', 'type': 'th',
'value': 'C', 'display_value': 'C', 'is_visible': True}],
[{'class': 'index_name level0', '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/pandas-dev/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': '',
'display_value': '', 'is_visible': True},
{'class': 'blank level0', 'type': 'th', 'value': '',
'display_value': '', 'is_visible': True},
{'class': 'col_heading level0 col0', 'type': 'th',
'value': 'C', 'display_value': 'C', 'is_visible': True}],
[{'class': 'index_name level0', 'type': 'th',
'value': 'A'},
{'class': 'index_name level1', 'type': 'th',
'value': 'B'},
{'class': 'blank', 'type': 'th', 'value': ''}]]
self.assertEqual(result['head'], expected)
def test_numeric_columns(self):
# https://github.com/pandas-dev/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_bad_apply_shape(self):
df = pd.DataFrame([[1, 2], [3, 4]])
with tm.assertRaises(ValueError):
df.style._apply(lambda x: 'x', subset=pd.IndexSlice[[0, 1], :])
with tm.assertRaises(ValueError):
df.style._apply(lambda x: [''], subset=pd.IndexSlice[[0, 1], :])
with tm.assertRaises(ValueError):
df.style._apply(lambda x: ['', '', '', ''])
with tm.assertRaises(ValueError):
df.style._apply(lambda x: ['', '', ''], subset=1)
with tm.assertRaises(ValueError):
df.style._apply(lambda x: ['', '', ''], axis=1)
def test_apply_bad_return(self):
def f(x):
return ''
df = pd.DataFrame([[1, 2], [3, 4]])
with tm.assertRaises(TypeError):
df.style._apply(f, axis=None)
def test_apply_bad_labels(self):
def f(x):
return pd.DataFrame(index=[1, 2], columns=['a', 'b'])
df = pd.DataFrame([[1, 2], [3, 4]])
with tm.assertRaises(ValueError):
df.style._apply(f, axis=None)
def test_get_level_lengths(self):
index = pd.MultiIndex.from_product([['a', 'b'], [0, 1, 2]])
expected = {(0, 0): 3, (0, 3): 3, (1, 0): 1, (1, 1): 1, (1, 2): 1,
(1, 3): 1, (1, 4): 1, (1, 5): 1}
result = _get_level_lengths(index)
tm.assert_dict_equal(result, expected)
def test_get_level_lengths_un_sorted(self):
index = pd.MultiIndex.from_arrays([
[1, 1, 2, 1],
['a', 'b', 'b', 'd']
])
expected = {(0, 0): 2, (0, 2): 1, (0, 3): 1,
(1, 0): 1, (1, 1): 1, (1, 2): 1, (1, 3): 1}
result = _get_level_lengths(index)
tm.assert_dict_equal(result, expected)
def test_mi_sparse(self):
df = pd.DataFrame({'A': [1, 2]},
index=pd.MultiIndex.from_arrays([['a', 'a'],
[0, 1]]))
result = df.style._translate()
body_0 = result['body'][0][0]
expected_0 = {
"value": "a", "display_value": "a", "is_visible": True,
"type": "th", "attributes": ["rowspan=2"],
"class": "row_heading level0 row0",
}
tm.assert_dict_equal(body_0, expected_0)
body_1 = result['body'][0][1]
expected_1 = {
"value": 0, "display_value": 0, "is_visible": True,
"type": "th", "class": "row_heading level1 row0",
}
tm.assert_dict_equal(body_1, expected_1)
body_10 = result['body'][1][0]
expected_10 = {
"value": 'a', "display_value": 'a', "is_visible": False,
"type": "th", "class": "row_heading level0 row1",
}
tm.assert_dict_equal(body_10, expected_10)
head = result['head'][0]
expected = [
{'type': 'th', 'class': 'blank', 'value': '',
'is_visible': True, "display_value": ''},
{'type': 'th', 'class': 'blank level0', 'value': '',
'is_visible': True, 'display_value': ''},
{'type': 'th', 'class': 'col_heading level0 col0', 'value': 'A',
'is_visible': True, 'display_value': 'A'}]
self.assertEqual(head, expected)
def test_mi_sparse_disabled(self):
with pd.option_context('display.multi_sparse', False):
df = pd.DataFrame({'A': [1, 2]},
index=pd.MultiIndex.from_arrays([['a', 'a'],
[0, 1]]))
result = df.style._translate()
body = result['body']
for row in body:
assert 'attributes' not in row[0]
def test_mi_sparse_index_names(self):
df = pd.DataFrame({'A': [1, 2]}, index=pd.MultiIndex.from_arrays(
[['a', 'a'], [0, 1]],
names=['idx_level_0', 'idx_level_1'])
)
result = df.style._translate()
head = result['head'][1]
expected = [{
'class': 'index_name level0', 'value': 'idx_level_0',
'type': 'th'},
{'class': 'index_name level1', 'value': 'idx_level_1',
'type': 'th'},
{'class': 'blank', 'value': '', 'type': 'th'}]
self.assertEqual(head, expected)
def test_mi_sparse_column_names(self):
df = pd.DataFrame(
np.arange(16).reshape(4, 4),
index=pd.MultiIndex.from_arrays(
[['a', 'a', 'b', 'a'], [0, 1, 1, 2]],
names=['idx_level_0', 'idx_level_1']),
columns=pd.MultiIndex.from_arrays(
[['C1', 'C1', 'C2', 'C2'], [1, 0, 1, 0]],
names=['col_0', 'col_1']
)
)
result = df.style._translate()
head = result['head'][1]
expected = [
{'class': 'blank', 'value': '', 'display_value': '',
'type': 'th', 'is_visible': True},
{'class': 'index_name level1', 'value': 'col_1',
'display_value': 'col_1', 'is_visible': True, 'type': 'th'},
{'class': 'col_heading level1 col0',
'display_value': 1,
'is_visible': True,
'type': 'th',
'value': 1},
{'class': 'col_heading level1 col1',
'display_value': 0,
'is_visible': True,
'type': 'th',
'value': 0},
{'class': 'col_heading level1 col2',
'display_value': 1,
'is_visible': True,
'type': 'th',
'value': 1},
{'class': 'col_heading level1 col3',
'display_value': 0,
'is_visible': True,
'type': 'th',
'value': 0},
]
self.assertEqual(head, expected)
class TestStyler:
def setup_method(self, method):
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(f"color: {foo}", index=x.index, name=x.name)
self.h = h
self.styler = Styler(self.df)
self.attrs = DataFrame({"A": ["color: red", "color: blue"]})
self.dataframes = [
self.df,
DataFrame({
"f": [1.0, 2.0],
"o": ["a", "b"],
"c": pd.Categorical(["a", "b"])
}),
]
self.blank_value = " "
def test_init_non_pandas(self):
msg = "``data`` must be a Series or DataFrame"
with pytest.raises(TypeError, match=msg):
Styler([1, 2, 3])
def test_init_series(self):
result = Styler(pd.Series([1, 2]))
assert result.data.ndim == 2
def test_repr_html_ok(self):
self.styler._repr_html_()
def test_repr_html_mathjax(self):
# gh-19824
assert "tex2jax_ignore" not in self.styler._repr_html_()
with pd.option_context("display.html.use_mathjax", False):
assert "tex2jax_ignore" in self.styler._repr_html_()
def test_update_ctx(self):
self.styler._update_ctx(self.attrs)
expected = {(0, 0): [("color", "red")], (1, 0): [("color", "blue")]}
assert self.styler.ctx == expected
def test_update_ctx_flatten_multi_and_trailing_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")],
}
assert self.styler.ctx == expected
@pytest.mark.parametrize("do_changes", [True, False])
@pytest.mark.parametrize("do_render", [True, False])
def test_copy(self, do_changes, do_render):
# Updated in GH39708
# Change some defaults (to check later if the new values are copied)
if do_changes:
self.styler.set_table_styles([{
"selector": "th",
"props": [("foo", "bar")]
}])
self.styler.set_table_attributes('class="foo" data-bar')
self.styler.hidden_index = not self.styler.hidden_index
self.styler.hide_columns("A")
classes = DataFrame(
[["favorite-val red", ""], [None, "blue my-val"]],
index=self.df.index,
columns=self.df.columns,
)
self.styler.set_td_classes(classes)
ttips = DataFrame(
data=[["Favorite", ""], [np.nan, "my"]],
columns=self.df.columns,
index=self.df.index,
)
self.styler.set_tooltips(ttips)
self.styler.cell_ids = not self.styler.cell_ids
if do_render:
self.styler.render()
s_copy = copy.copy(self.styler)
s_deepcopy = copy.deepcopy(self.styler)
assert self.styler is not s_copy
assert self.styler is not s_deepcopy
# Check for identity
assert self.styler.ctx is s_copy.ctx
assert self.styler._todo is s_copy._todo
assert self.styler.table_styles is s_copy.table_styles
assert self.styler.hidden_columns is s_copy.hidden_columns
assert self.styler.cell_context is s_copy.cell_context
assert self.styler.tooltips is s_copy.tooltips
if do_changes: # self.styler.tooltips is not None
assert self.styler.tooltips.tt_data is s_copy.tooltips.tt_data
assert (self.styler.tooltips.class_properties is
s_copy.tooltips.class_properties)
assert self.styler.tooltips.table_styles is s_copy.tooltips.table_styles
# Check for non-identity
assert self.styler.ctx is not s_deepcopy.ctx
assert self.styler._todo is not s_deepcopy._todo
assert self.styler.hidden_columns is not s_deepcopy.hidden_columns
assert self.styler.cell_context is not s_deepcopy.cell_context
if do_changes: # self.styler.table_style is not None
assert self.styler.table_styles is not s_deepcopy.table_styles
if do_changes: # self.styler.tooltips is not None
assert self.styler.tooltips is not s_deepcopy.tooltips
assert self.styler.tooltips.tt_data is not s_deepcopy.tooltips.tt_data
assert (self.styler.tooltips.class_properties
is not s_deepcopy.tooltips.class_properties)
assert (self.styler.tooltips.table_styles
is not s_deepcopy.tooltips.table_styles)
self.styler._update_ctx(self.attrs)
self.styler.highlight_max()
assert self.styler.ctx == s_copy.ctx
assert self.styler.ctx != s_deepcopy.ctx
assert self.styler._todo == s_copy._todo
assert self.styler._todo != s_deepcopy._todo
assert s_deepcopy._todo == []
equal_attributes = [
"table_styles",
"table_attributes",
"cell_ids",
"hidden_index",
"hidden_columns",
"cell_context",
]
for s2 in [s_copy, s_deepcopy]:
for att in equal_attributes:
assert self.styler.__dict__[att] == s2.__dict__[att]
if do_changes: # self.styler.tooltips is not None
tm.assert_frame_equal(self.styler.tooltips.tt_data,
s2.tooltips.tt_data)
assert (self.styler.tooltips.class_properties ==
s2.tooltips.class_properties)
assert self.styler.tooltips.table_styles == s2.tooltips.table_styles
def test_clear(self):
# updated in GH 39396
tt = DataFrame({"A": [None, "tt"]})
css = DataFrame({"A": [None, "cls-a"]})
s = self.df.style.highlight_max().set_tooltips(tt).set_td_classes(css)
s = s.hide_index().hide_columns("A")
# _todo, tooltips and cell_context items added to..
assert len(s._todo) > 0
assert s.tooltips
assert len(s.cell_context) > 0
assert s.hidden_index is True
assert len(s.hidden_columns) > 0
s = s._compute()
# ctx item affected when a render takes place. _todo is maintained
assert len(s.ctx) > 0
assert len(s._todo) > 0
s.clear()
# ctx, _todo, tooltips and cell_context items all revert to null state.
assert len(s.ctx) == 0
assert len(s._todo) == 0
assert not s.tooltips
assert len(s.cell_context) == 0
assert s.hidden_index is False
assert len(s.hidden_columns) == 0
def test_render(self):
df = DataFrame({"A": [0, 1]})
style = lambda x: pd.Series(["color: red", "color: blue"], name=x.name)
s = Styler(df, uuid="AB").apply(style)
s.render()
# it worked?
def test_multiple_render(self):
# GH 39396
s = Styler(self.df, uuid_len=0).applymap(lambda x: "color: red;",
subset=["A"])
s.render() # do 2 renders to ensure css styles not duplicated
assert ('<style type="text/css">\n#T__row0_col0, #T__row1_col0 {\n'
" color: red;\n}\n</style>" in s.render())
def test_render_empty_dfs(self):
empty_df = DataFrame()
es = Styler(empty_df)
es.render()
# An index but no columns
DataFrame(columns=["a"]).style.render()
# A column but no index
DataFrame(index=["a"]).style.render()
# No IndexError raised?
def test_render_double(self):
df = 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 = 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}
assert result.keys() == expected.keys()
for v1, v2 in zip(result.values(), expected.values()):
assert sorted(v1) == sorted(v2)
def test_set_properties_subset(self):
df = DataFrame({"A": [0, 1]})
result = (df.style.set_properties(subset=pd.IndexSlice[0, "A"],
color="white")._compute().ctx)
expected = {(0, 0): [("color", "white")]}
assert result == expected
def test_empty_index_name_doesnt_display(self):
# https://github.com/pandas-dev/pandas/pull/12090#issuecomment-180695902
df = DataFrame({"A": [1, 2], "B": [3, 4], "C": [5, 6]})
result = df.style._translate()
expected = [[
{
"class": "blank level0",
"type": "th",
"value": self.blank_value,
"is_visible": True,
"display_value": self.blank_value,
},
{
"class": "col_heading level0 col0",
"display_value": "A",
"type": "th",
"value": "A",
"is_visible": True,
},
{
"class": "col_heading level0 col1",
"display_value": "B",
"type": "th",
"value": "B",
"is_visible": True,
},
{
"class": "col_heading level0 col2",
"display_value": "C",
"type": "th",
"value": "C",
"is_visible": True,
},
]]
assert result["head"] == expected
def test_index_name(self):
# https://github.com/pandas-dev/pandas/issues/11655
df = DataFrame({"A": [1, 2], "B": [3, 4], "C": [5, 6]})
result = df.set_index("A").style._translate()
expected = [
[
{
"class": "blank level0",
"type": "th",
"value": self.blank_value,
"display_value": self.blank_value,
"is_visible": True,
},
{
"class": "col_heading level0 col0",
"type": "th",
"value": "B",
"display_value": "B",
"is_visible": True,
},
{
"class": "col_heading level0 col1",
"type": "th",
"value": "C",
"display_value": "C",
"is_visible": True,
},
],
[
{
"class": "index_name level0",
"type": "th",
"value": "A"
},
{
"class": "blank col0",
"type": "th",
"value": self.blank_value
},
{
"class": "blank col1",
"type": "th",
"value": self.blank_value
},
],
]
assert result["head"] == expected
def test_multiindex_name(self):
# https://github.com/pandas-dev/pandas/issues/11655
df = DataFrame({"A": [1, 2], "B": [3, 4], "C": [5, 6]})
result = df.set_index(["A", "B"]).style._translate()
expected = [
[
{
"class": "blank",
"type": "th",
"value": self.blank_value,
"display_value": self.blank_value,
"is_visible": True,
},
{
"class": "blank level0",
"type": "th",
"value": self.blank_value,
"display_value": self.blank_value,
"is_visible": True,
},
{
"class": "col_heading level0 col0",
"type": "th",
"value": "C",
"display_value": "C",
"is_visible": True,
},
],
[
{
"class": "index_name level0",
"type": "th",
"value": "A"
},
{
"class": "index_name level1",
"type": "th",
"value": "B"
},
{
"class": "blank col0",
"type": "th",
"value": self.blank_value
},
],
]
assert result["head"] == expected
def test_numeric_columns(self):
# https://github.com/pandas-dev/pandas/issues/12125
# smoke test for _translate
df = DataFrame({0: [1, 2, 3]})
df.style._translate()
def test_apply_axis(self):
df = DataFrame({"A": [0, 0], "B": [1, 1]})
f = lambda x: [f"val: {x.max()}" for v in x]
result = df.style.apply(f, axis=1)
assert len(result._todo) == 1
assert len(result.ctx) == 0
result._compute()
expected = {
(0, 0): [("val", "1")],
(0, 1): [("val", "1")],
(1, 0): [("val", "1")],
(1, 1): [("val", "1")],
}
assert 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()
assert result.ctx == expected
result = df.style.apply(f) # default
result._compute()
assert result.ctx == expected
@pytest.mark.parametrize(
"slice_",
[
pd.IndexSlice[:],
pd.IndexSlice[:, ["A"]],
pd.IndexSlice[[1], :],
pd.IndexSlice[[1], ["A"]],
pd.IndexSlice[:2, ["A", "B"]],
],
)
@pytest.mark.parametrize("axis", [0, 1])
def test_apply_subset(self, slice_, axis):
result = (self.df.style.apply(self.h,
axis=axis,
subset=slice_,
foo="baz")._compute().ctx)
expected = {(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}
assert result == expected
@pytest.mark.parametrize(
"slice_",
[
pd.IndexSlice[:],
pd.IndexSlice[:, ["A"]],
pd.IndexSlice[[1], :],
pd.IndexSlice[[1], ["A"]],
pd.IndexSlice[:2, ["A", "B"]],
],
)
def test_applymap_subset(self, slice_):
result = (self.df.style.applymap(lambda x: "color:baz;",
subset=slice_)._compute().ctx)
expected = {(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}
assert result == expected
@pytest.mark.parametrize(
"slice_",
[
pd.IndexSlice[:, pd.IndexSlice["x", "A"]],
pd.IndexSlice[:, pd.IndexSlice[:, "A"]],
pd.IndexSlice[:, pd.IndexSlice[:,
["A", "C"]]], # missing col element
pd.IndexSlice[pd.IndexSlice["a", 1], :],
pd.IndexSlice[pd.IndexSlice[:, 1], :],
pd.IndexSlice[pd.IndexSlice[:, [1, 3]], :], # missing row element
pd.IndexSlice[:, ("x", "A")],
pd.IndexSlice[("a", 1), :],
],
)
def test_applymap_subset_multiindex(self, slice_):
# GH 19861
# edited for GH 33562
idx = pd.MultiIndex.from_product([["a", "b"], [1, 2]])
col = pd.MultiIndex.from_product([["x", "y"], ["A", "B"]])
df = DataFrame(np.random.rand(4, 4), columns=col, index=idx)
df.style.applymap(lambda x: "color: red;", subset=slice_).render()
def test_applymap_subset_multiindex_code(self):
# https://github.com/pandas-dev/pandas/issues/25858
# Checks styler.applymap works with multindex when codes are provided
codes = np.array([[0, 0, 1, 1], [0, 1, 0, 1]])
columns = pd.MultiIndex(levels=[["a", "b"], ["%", "#"]],
codes=codes,
names=["", ""])
df = DataFrame([[1, -1, 1, 1], [-1, 1, 1, 1]],
index=["hello", "world"],
columns=columns)
pct_subset = pd.IndexSlice[:, pd.IndexSlice[:, "%":"%"]]
def color_negative_red(val):
color = "red" if val < 0 else "black"
return f"color: {color}"
df.loc[pct_subset]
df.style.applymap(color_negative_red, subset=pct_subset)
def test_where_with_one_style(self):
# GH 17474
def f(x):
return x > 0.5
style1 = "foo: bar"
result = self.df.style.where(f, style1)._compute().ctx
expected = {(r, c): [("foo", "bar")]
for r, row in enumerate(self.df.index)
for c, col in enumerate(self.df.columns)
if f(self.df.loc[row, col])}
assert result == expected
@pytest.mark.parametrize(
"slice_",
[
pd.IndexSlice[:],
pd.IndexSlice[:, ["A"]],
pd.IndexSlice[[1], :],
pd.IndexSlice[[1], ["A"]],
pd.IndexSlice[:2, ["A", "B"]],
],
)
def test_where_subset(self, slice_):
# GH 17474
def f(x):
return x > 0.5
style1 = "foo: bar"
style2 = "baz: foo"
result = self.df.style.where(f, style1, style2,
subset=slice_)._compute().ctx
expected = {(r, c): [("foo", "bar") if f(self.df.loc[row, col]) else
("baz", "foo")]
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}
assert result == expected
def test_where_subset_compare_with_applymap(self):
# GH 17474
def f(x):
return x > 0.5
style1 = "foo: bar"
style2 = "baz: foo"
def g(x):
return style1 if f(x) else style2
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.where(f, style1, style2,
subset=slice_)._compute().ctx)
expected = self.df.style.applymap(g, subset=slice_)._compute().ctx
assert result == expected
def test_empty(self):
df = DataFrame({"A": [1, 0]})
s = df.style
s.ctx = {(0, 0): [("color", "red")], (1, 0): [("", "")]}
result = s._translate()["cellstyle"]
expected = [
{
"props": [("color", "red")],
"selectors": ["row0_col0"]
},
{
"props": [("", "")],
"selectors": ["row1_col0"]
},
]
assert result == expected
def test_duplicate(self):
df = DataFrame({"A": [1, 0]})
s = df.style
s.ctx = {(0, 0): [("color", "red")], (1, 0): [("color", "red")]}
result = s._translate()["cellstyle"]
expected = [{
"props": [("color", "red")],
"selectors": ["row0_col0", "row1_col0"]
}]
assert result == expected
def test_init_with_na_rep(self):
# GH 21527 28358
df = DataFrame([[None, None], [1.1, 1.2]], columns=["A", "B"])
ctx = Styler(df, na_rep="NA")._translate()
assert ctx["body"][0][1]["display_value"] == "NA"
assert ctx["body"][0][2]["display_value"] == "NA"
def test_set_na_rep(self):
# GH 21527 28358
df = DataFrame([[None, None], [1.1, 1.2]], columns=["A", "B"])
with tm.assert_produces_warning(FutureWarning):
ctx = df.style.set_na_rep("NA")._translate()
assert ctx["body"][0][1]["display_value"] == "NA"
assert ctx["body"][0][2]["display_value"] == "NA"
with tm.assert_produces_warning(FutureWarning):
ctx = (df.style.set_na_rep("NA").format(None,
na_rep="-",
subset=["B"])._translate())
assert ctx["body"][0][1]["display_value"] == "NA"
assert ctx["body"][0][2]["display_value"] == "-"
def test_nonunique_raises(self):
df = DataFrame([[1, 2]], columns=["A", "A"])
msg = "style is not supported for non-unique indices."
with pytest.raises(ValueError, match=msg):
df.style
with pytest.raises(ValueError, match=msg):
Styler(df)
def test_caption(self):
styler = Styler(self.df, caption="foo")
result = styler.render()
assert all(["caption" in result, "foo" in result])
styler = self.df.style
result = styler.set_caption("baz")
assert styler is result
assert styler.caption == "baz"
def test_uuid(self):
styler = Styler(self.df, uuid="abc123")
result = styler.render()
assert "abc123" in result
styler = self.df.style
result = styler.set_uuid("aaa")
assert result is styler
assert result.uuid == "aaa"
def test_unique_id(self):
# See https://github.com/pandas-dev/pandas/issues/16780
df = DataFrame({"a": [1, 3, 5, 6], "b": [2, 4, 12, 21]})
result = df.style.render(uuid="test")
assert "test" in result
ids = re.findall('id="(.*?)"', result)
assert np.unique(ids).size == len(ids)
def test_table_styles(self):
style = [{
"selector": "th",
"props": [("foo", "bar")]
}] # default format
styler = Styler(self.df, table_styles=style)
result = " ".join(styler.render().split())
assert "th { foo: bar; }" in result
styler = self.df.style
result = styler.set_table_styles(style)
assert styler is result
assert styler.table_styles == style
# GH 39563
style = [{"selector": "th", "props": "foo:bar;"}] # css string format
styler = self.df.style.set_table_styles(style)
result = " ".join(styler.render().split())
assert "th { foo: bar; }" in result
def test_table_styles_multiple(self):
ctx = self.df.style.set_table_styles([
{
"selector": "th,td",
"props": "color:red;"
},
{
"selector": "tr",
"props": "color:green;"
},
])._translate()["table_styles"]
assert ctx == [
{
"selector": "th",
"props": [("color", "red")]
},
{
"selector": "td",
"props": [("color", "red")]
},
{
"selector": "tr",
"props": [("color", "green")]
},
]
def test_maybe_convert_css_to_tuples(self):
expected = [("a", "b"), ("c", "d e")]
assert _maybe_convert_css_to_tuples("a:b;c:d e;") == expected
assert _maybe_convert_css_to_tuples("a: b ;c: d e ") == expected
expected = []
assert _maybe_convert_css_to_tuples("") == expected
def test_maybe_convert_css_to_tuples_err(self):
msg = "Styles supplied as string must follow CSS rule formats"
with pytest.raises(ValueError, match=msg):
_maybe_convert_css_to_tuples("err")
def test_table_attributes(self):
attributes = 'class="foo" data-bar'
styler = Styler(self.df, table_attributes=attributes)
result = styler.render()
assert 'class="foo" data-bar' in result
result = self.df.style.set_table_attributes(attributes).render()
assert 'class="foo" data-bar' in result
def test_precision(self):
s = Styler(self.df, precision=2)
assert s.precision == 2
with tm.assert_produces_warning(FutureWarning):
s2 = s.set_precision(4)
assert s is s2
assert s.precision == 4
def test_apply_none(self):
def f(x):
return DataFrame(
np.where(x == x.max(), "color: red", ""),
index=x.index,
columns=x.columns,
)
result = DataFrame([[1, 2],
[3, 4]]).style.apply(f, axis=None)._compute().ctx
assert result[(1, 1)] == [("color", "red")]
def test_trim(self):
result = self.df.style.render() # trim=True
assert result.count("#") == 0
result = self.df.style.highlight_max().render()
assert result.count("#") == len(self.df.columns)
def test_export(self):
f = lambda x: "color: red" if x > 0 else "color: blue"
g = lambda x, z: f"color: {z}" if x > 0 else f"color: {z}"
style1 = self.styler
style1.applymap(f).applymap(
g, z="b").highlight_max()._compute() # = render
result = style1.export()
style2 = self.df.style
style2.use(result)
assert style1._todo == style2._todo
style2.render()
def test_bad_apply_shape(self):
df = DataFrame([[1, 2], [3, 4]])
msg = "returned the wrong shape"
with pytest.raises(ValueError, match=msg):
df.style._apply(lambda x: "x", subset=pd.IndexSlice[[0, 1], :])
with pytest.raises(ValueError, match=msg):
df.style._apply(lambda x: [""], subset=pd.IndexSlice[[0, 1], :])
with pytest.raises(ValueError, match=msg):
df.style._apply(lambda x: ["", "", "", ""])
with pytest.raises(ValueError, match=msg):
df.style._apply(lambda x: ["", "", ""], subset=1)
msg = "Length mismatch: Expected axis has 3 elements"
with pytest.raises(ValueError, match=msg):
df.style._apply(lambda x: ["", "", ""], axis=1)
msg = "returned ndarray with wrong shape"
with pytest.raises(ValueError, match=msg):
df.style._apply(lambda x: np.array([[""], [""]]), axis=None)
def test_apply_bad_return(self):
def f(x):
return ""
df = DataFrame([[1, 2], [3, 4]])
msg = (
"must return a DataFrame or ndarray when passed to `Styler.apply` "
"with axis=None")
with pytest.raises(TypeError, match=msg):
df.style._apply(f, axis=None)
def test_apply_bad_labels(self):
def f(x):
return DataFrame(index=[1, 2], columns=["a", "b"])
df = DataFrame([[1, 2], [3, 4]])
msg = "must have identical index and columns as the input"
with pytest.raises(ValueError, match=msg):
df.style._apply(f, axis=None)
def test_get_level_lengths(self):
index = pd.MultiIndex.from_product([["a", "b"], [0, 1, 2]])
expected = {
(0, 0): 3,
(0, 3): 3,
(1, 0): 1,
(1, 1): 1,
(1, 2): 1,
(1, 3): 1,
(1, 4): 1,
(1, 5): 1,
}
result = _get_level_lengths(index)
tm.assert_dict_equal(result, expected)
def test_get_level_lengths_un_sorted(self):
index = pd.MultiIndex.from_arrays([[1, 1, 2, 1], ["a", "b", "b", "d"]])
expected = {
(0, 0): 2,
(0, 2): 1,
(0, 3): 1,
(1, 0): 1,
(1, 1): 1,
(1, 2): 1,
(1, 3): 1,
}
result = _get_level_lengths(index)
tm.assert_dict_equal(result, expected)
def test_mi_sparse(self):
df = DataFrame({"A": [1, 2]},
index=pd.MultiIndex.from_arrays([["a", "a"], [0, 1]]))
result = df.style._translate()
body_0 = result["body"][0][0]
expected_0 = {
"value": "a",
"display_value": "a",
"is_visible": True,
"type": "th",
"attributes": 'rowspan="2"',
"class": "row_heading level0 row0",
"id": "level0_row0",
}
tm.assert_dict_equal(body_0, expected_0)
body_1 = result["body"][0][1]
expected_1 = {
"value": 0,
"display_value": 0,
"is_visible": True,
"type": "th",
"class": "row_heading level1 row0",
"id": "level1_row0",
}
tm.assert_dict_equal(body_1, expected_1)
body_10 = result["body"][1][0]
expected_10 = {
"value": "a",
"display_value": "a",
"is_visible": False,
"type": "th",
"class": "row_heading level0 row1",
"id": "level0_row1",
}
tm.assert_dict_equal(body_10, expected_10)
head = result["head"][0]
expected = [
{
"type": "th",
"class": "blank",
"value": self.blank_value,
"is_visible": True,
"display_value": self.blank_value,
},
{
"type": "th",
"class": "blank level0",
"value": self.blank_value,
"is_visible": True,
"display_value": self.blank_value,
},
{
"type": "th",
"class": "col_heading level0 col0",
"value": "A",
"is_visible": True,
"display_value": "A",
},
]
assert head == expected
def test_mi_sparse_disabled(self):
with pd.option_context("display.multi_sparse", False):
df = DataFrame({"A": [1, 2]},
index=pd.MultiIndex.from_arrays([["a", "a"], [0,
1]]))
result = df.style._translate()
body = result["body"]
for row in body:
assert "attributes" not in row[0]
def test_mi_sparse_index_names(self):
df = DataFrame(
{"A": [1, 2]},
index=pd.MultiIndex.from_arrays(
[["a", "a"], [0, 1]], names=["idx_level_0", "idx_level_1"]),
)
result = df.style._translate()
head = result["head"][1]
expected = [
{
"class": "index_name level0",
"value": "idx_level_0",
"type": "th"
},
{
"class": "index_name level1",
"value": "idx_level_1",
"type": "th"
},
{
"class": "blank col0",
"value": self.blank_value,
"type": "th"
},
]
assert head == expected
def test_mi_sparse_column_names(self):
df = DataFrame(
np.arange(16).reshape(4, 4),
index=pd.MultiIndex.from_arrays(
[["a", "a", "b", "a"], [0, 1, 1, 2]],
names=["idx_level_0", "idx_level_1"],
),
columns=pd.MultiIndex.from_arrays(
[["C1", "C1", "C2", "C2"], [1, 0, 1, 0]],
names=["col_0", "col_1"]),
)
result = df.style._translate()
head = result["head"][1]
expected = [
{
"class": "blank",
"value": self.blank_value,
"display_value": self.blank_value,
"type": "th",
"is_visible": True,
},
{
"class": "index_name level1",
"value": "col_1",
"display_value": "col_1",
"is_visible": True,
"type": "th",
},
{
"class": "col_heading level1 col0",
"display_value": 1,
"is_visible": True,
"type": "th",
"value": 1,
},
{
"class": "col_heading level1 col1",
"display_value": 0,
"is_visible": True,
"type": "th",
"value": 0,
},
{
"class": "col_heading level1 col2",
"display_value": 1,
"is_visible": True,
"type": "th",
"value": 1,
},
{
"class": "col_heading level1 col3",
"display_value": 0,
"is_visible": True,
"type": "th",
"value": 0,
},
]
assert head == expected
def test_hide_single_index(self):
# GH 14194
# single unnamed index
ctx = self.df.style._translate()
assert ctx["body"][0][0]["is_visible"]
assert ctx["head"][0][0]["is_visible"]
ctx2 = self.df.style.hide_index()._translate()
assert not ctx2["body"][0][0]["is_visible"]
assert not ctx2["head"][0][0]["is_visible"]
# single named index
ctx3 = self.df.set_index("A").style._translate()
assert ctx3["body"][0][0]["is_visible"]
assert len(ctx3["head"]) == 2 # 2 header levels
assert ctx3["head"][0][0]["is_visible"]
ctx4 = self.df.set_index("A").style.hide_index()._translate()
assert not ctx4["body"][0][0]["is_visible"]
assert len(ctx4["head"]) == 1 # only 1 header levels
assert not ctx4["head"][0][0]["is_visible"]
def test_hide_multiindex(self):
# GH 14194
df = DataFrame(
{"A": [1, 2]},
index=pd.MultiIndex.from_arrays(
[["a", "a"], [0, 1]], names=["idx_level_0", "idx_level_1"]),
)
ctx1 = df.style._translate()
# tests for 'a' and '0'
assert ctx1["body"][0][0]["is_visible"]
assert ctx1["body"][0][1]["is_visible"]
# check for blank header rows
assert ctx1["head"][0][0]["is_visible"]
assert ctx1["head"][0][1]["is_visible"]
ctx2 = df.style.hide_index()._translate()
# tests for 'a' and '0'
assert not ctx2["body"][0][0]["is_visible"]
assert not ctx2["body"][0][1]["is_visible"]
# check for blank header rows
assert not ctx2["head"][0][0]["is_visible"]
assert not ctx2["head"][0][1]["is_visible"]
def test_hide_columns_single_level(self):
# GH 14194
# test hiding single column
ctx = self.df.style._translate()
assert ctx["head"][0][1]["is_visible"]
assert ctx["head"][0][1]["display_value"] == "A"
assert ctx["head"][0][2]["is_visible"]
assert ctx["head"][0][2]["display_value"] == "B"
assert ctx["body"][0][1]["is_visible"] # col A, row 1
assert ctx["body"][1][2]["is_visible"] # col B, row 1
ctx = self.df.style.hide_columns("A")._translate()
assert not ctx["head"][0][1]["is_visible"]
assert not ctx["body"][0][1]["is_visible"] # col A, row 1
assert ctx["body"][1][2]["is_visible"] # col B, row 1
# test hiding mulitiple columns
ctx = self.df.style.hide_columns(["A", "B"])._translate()
assert not ctx["head"][0][1]["is_visible"]
assert not ctx["head"][0][2]["is_visible"]
assert not ctx["body"][0][1]["is_visible"] # col A, row 1
assert not ctx["body"][1][2]["is_visible"] # col B, row 1
def test_hide_columns_mult_levels(self):
# GH 14194
# setup dataframe with multiple column levels and indices
i1 = pd.MultiIndex.from_arrays([["a", "a"], [0, 1]],
names=["idx_level_0", "idx_level_1"])
i2 = pd.MultiIndex.from_arrays([["b", "b"], [0, 1]],
names=["col_level_0", "col_level_1"])
df = DataFrame([[1, 2], [3, 4]], index=i1, columns=i2)
ctx = df.style._translate()
# column headers
assert ctx["head"][0][2]["is_visible"]
assert ctx["head"][1][2]["is_visible"]
assert ctx["head"][1][3]["display_value"] == 1
# indices
assert ctx["body"][0][0]["is_visible"]
# data
assert ctx["body"][1][2]["is_visible"]
assert ctx["body"][1][2]["display_value"] == 3
assert ctx["body"][1][3]["is_visible"]
assert ctx["body"][1][3]["display_value"] == 4
# hide top column level, which hides both columns
ctx = df.style.hide_columns("b")._translate()
assert not ctx["head"][0][2]["is_visible"] # b
assert not ctx["head"][1][2]["is_visible"] # 0
assert not ctx["body"][1][2]["is_visible"] # 3
assert ctx["body"][0][0]["is_visible"] # index
# hide first column only
ctx = df.style.hide_columns([("b", 0)])._translate()
assert ctx["head"][0][2]["is_visible"] # b
assert not ctx["head"][1][2]["is_visible"] # 0
assert not ctx["body"][1][2]["is_visible"] # 3
assert ctx["body"][1][3]["is_visible"]
assert ctx["body"][1][3]["display_value"] == 4
# hide second column and index
ctx = df.style.hide_columns([("b", 1)]).hide_index()._translate()
assert not ctx["body"][0][0]["is_visible"] # index
assert ctx["head"][0][2]["is_visible"] # b
assert ctx["head"][1][2]["is_visible"] # 0
assert not ctx["head"][1][3]["is_visible"] # 1
assert not ctx["body"][1][3]["is_visible"] # 4
assert ctx["body"][1][2]["is_visible"]
assert ctx["body"][1][2]["display_value"] == 3
def test_pipe(self):
def set_caption_from_template(styler, a, b):
return styler.set_caption(f"Dataframe with a = {a} and b = {b}")
styler = self.df.style.pipe(set_caption_from_template, "A", b="B")
assert "Dataframe with a = A and b = B" in styler.render()
# Test with an argument that is a (callable, keyword_name) pair.
def f(a, b, styler):
return (a, b, styler)
styler = self.df.style
result = styler.pipe((f, "styler"), a=1, b=2)
assert result == (1, 2, styler)
def test_no_cell_ids(self):
# GH 35588
# GH 35663
df = DataFrame(data=[[0]])
styler = Styler(df, uuid="_", cell_ids=False)
styler.render()
s = styler.render() # render twice to ensure ctx is not updated
assert s.find('<td class="data row0 col0" >') != -1
@pytest.mark.parametrize(
"classes",
[
DataFrame(
data=[["", "test-class"], [np.nan, None]],
columns=["A", "B"],
index=["a", "b"],
),
DataFrame(data=[["test-class"]], columns=["B"], index=["a"]),
DataFrame(data=[["test-class", "unused"]],
columns=["B", "C"],
index=["a"]),
],
)
def test_set_data_classes(self, classes):
# GH 36159
df = DataFrame(data=[[0, 1], [2, 3]],
columns=["A", "B"],
index=["a", "b"])
s = Styler(df, uuid_len=0,
cell_ids=False).set_td_classes(classes).render()
assert '<td class="data row0 col0" >0</td>' in s
assert '<td class="data row0 col1 test-class" >1</td>' in s
assert '<td class="data row1 col0" >2</td>' in s
assert '<td class="data row1 col1" >3</td>' in s
# GH 39317
s = Styler(df, uuid_len=0,
cell_ids=True).set_td_classes(classes).render()
assert '<td id="T__row0_col0" class="data row0 col0" >0</td>' in s
assert '<td id="T__row0_col1" class="data row0 col1 test-class" >1</td>' in s
assert '<td id="T__row1_col0" class="data row1 col0" >2</td>' in s
assert '<td id="T__row1_col1" class="data row1 col1" >3</td>' in s
def test_set_data_classes_reindex(self):
# GH 39317
df = DataFrame(data=[[0, 1, 2], [3, 4, 5], [6, 7, 8]],
columns=[0, 1, 2],
index=[0, 1, 2])
classes = DataFrame(
data=[["mi", "ma"], ["mu", "mo"]],
columns=[0, 2],
index=[0, 2],
)
s = Styler(df, uuid_len=0).set_td_classes(classes).render()
assert '<td id="T__row0_col0" class="data row0 col0 mi" >0</td>' in s
assert '<td id="T__row0_col2" class="data row0 col2 ma" >2</td>' in s
assert '<td id="T__row1_col1" class="data row1 col1" >4</td>' in s
assert '<td id="T__row2_col0" class="data row2 col0 mu" >6</td>' in s
assert '<td id="T__row2_col2" class="data row2 col2 mo" >8</td>' in s
def test_chaining_table_styles(self):
# GH 35607
df = DataFrame(data=[[0, 1], [1, 2]], columns=["A", "B"])
styler = df.style.set_table_styles([{
"selector":
"",
"props": [("background-color", "yellow")]
}]).set_table_styles(
[{
"selector": ".col0",
"props": [("background-color", "blue")]
}],
overwrite=False,
)
assert len(styler.table_styles) == 2
def test_column_and_row_styling(self):
# GH 35607
df = DataFrame(data=[[0, 1], [1, 2]], columns=["A", "B"])
s = Styler(df, uuid_len=0)
s = s.set_table_styles(
{"A": [{
"selector": "",
"props": [("color", "blue")]
}]})
assert "#T__ .col0 {\n color: blue;\n}" in s.render()
s = s.set_table_styles(
{0: [{
"selector": "",
"props": [("color", "blue")]
}]}, axis=1)
assert "#T__ .row0 {\n color: blue;\n}" in s.render()
def test_colspan_w3(self):
# GH 36223
df = DataFrame(data=[[1, 2]], columns=[["l0", "l0"], ["l1a", "l1b"]])
s = Styler(df, uuid="_", cell_ids=False)
assert '<th class="col_heading level0 col0" colspan="2">l0</th>' in s.render(
)
def test_rowspan_w3(self):
# GH 38533
df = DataFrame(data=[[1, 2]], index=[["l0", "l0"], ["l1a", "l1b"]])
s = Styler(df, uuid="_", cell_ids=False)
assert ('<th id="T___level0_row0" class="row_heading '
'level0 row0" rowspan="2">l0</th>' in s.render())
@pytest.mark.parametrize("len_", [1, 5, 32, 33, 100])
def test_uuid_len(self, len_):
# GH 36345
df = DataFrame(data=[["A"]])
s = Styler(df, uuid_len=len_, cell_ids=False).render()
strt = s.find('id="T_')
end = s[strt + 6:].find('"')
if len_ > 32:
assert end == 32 + 1
else:
assert end == len_ + 1
@pytest.mark.parametrize("len_", [-2, "bad", None])
def test_uuid_len_raises(self, len_):
# GH 36345
df = DataFrame(data=[["A"]])
msg = "``uuid_len`` must be an integer in range \\[0, 32\\]."
with pytest.raises(TypeError, match=msg):
Styler(df, uuid_len=len_, cell_ids=False).render()
def test_w3_html_format(self):
s = (Styler(
DataFrame([[2.61], [2.69]], index=["a", "b"], columns=["A"]),
uuid_len=0,
).set_table_styles([{
"selector": "th",
"props": "att2:v2;"
}]).applymap(lambda x: "att1:v1;").set_table_attributes(
'class="my-cls1" style="attr3:v3;"').set_td_classes(
DataFrame(["my-cls2"], index=["a"], columns=[
"A"
])).format("{:.1f}").set_caption("A comprehensive test"))
expected = """<style type="text/css">
#T__ th {
att2: v2;
}
#T__row0_col0, #T__row1_col0 {
att1: v1;
}
</style>
<table id="T__" class="my-cls1" style="attr3:v3;">
<caption>A comprehensive test</caption>
<thead>
<tr>
<th class="blank level0" > </th>
<th class="col_heading level0 col0" >A</th>
</tr>
</thead>
<tbody>
<tr>
<th id="T__level0_row0" class="row_heading level0 row0" >a</th>
<td id="T__row0_col0" class="data row0 col0 my-cls2" >2.6</td>
</tr>
<tr>
<th id="T__level0_row1" class="row_heading level0 row1" >b</th>
<td id="T__row1_col0" class="data row1 col0" >2.7</td>
</tr>
</tbody>
</table>
"""
assert expected == s.render()
@pytest.mark.parametrize(
"slc",
[
pd.IndexSlice[:, :],
pd.IndexSlice[:, 1],
pd.IndexSlice[1, :],
pd.IndexSlice[[1], [1]],
pd.IndexSlice[1, [1]],
pd.IndexSlice[[1], 1],
pd.IndexSlice[1],
pd.IndexSlice[1, 1],
slice(None, None, None),
[0, 1],
np.array([0, 1]),
pd.Series([0, 1]),
],
)
def test_non_reducing_slice(self, slc):
df = DataFrame([[0, 1], [2, 3]])
tslice_ = _non_reducing_slice(slc)
assert isinstance(df.loc[tslice_], DataFrame)
@pytest.mark.parametrize("box", [list, pd.Series, np.array])
def test_list_slice(self, box):
# like dataframe getitem
subset = box(["A"])
df = DataFrame({"A": [1, 2], "B": [3, 4]}, index=["A", "B"])
expected = pd.IndexSlice[:, ["A"]]
result = _non_reducing_slice(subset)
tm.assert_frame_equal(df.loc[result], df.loc[expected])
def test_non_reducing_slice_on_multiindex(self):
# GH 19861
dic = {
("a", "d"): [1, 4],
("a", "c"): [2, 3],
("b", "c"): [3, 2],
("b", "d"): [4, 1],
}
df = DataFrame(dic, index=[0, 1])
idx = pd.IndexSlice
slice_ = idx[:, idx["b", "d"]]
tslice_ = _non_reducing_slice(slice_)
result = df.loc[tslice_]
expected = DataFrame({("b", "d"): [4, 1]})
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize(
"slice_",
[
pd.IndexSlice[:, :],
# check cols
pd.IndexSlice[:,
pd.IndexSlice[["a"]]], # inferred deeper need list
pd.IndexSlice[:,
pd.IndexSlice[["a"],
["c"]]], # inferred deeper need list
pd.IndexSlice[:, pd.IndexSlice["a", "c", :]],
pd.IndexSlice[:, pd.IndexSlice["a", :, "e"]],
pd.IndexSlice[:, pd.IndexSlice[:, "c", "e"]],
pd.IndexSlice[:, pd.IndexSlice["a", ["c", "d"], :]], # check list
pd.IndexSlice[:,
pd.IndexSlice["a",
["c", "d", "-"], :]], # allow missing
pd.IndexSlice[:, pd.IndexSlice["a", ["c", "d", "-"],
"e"]], # no slice
# check rows
pd.IndexSlice[
pd.IndexSlice[["U"]], :], # inferred deeper need list
pd.IndexSlice[pd.IndexSlice[
["U"], ["W"]], :], # inferred deeper need list
pd.IndexSlice[pd.IndexSlice["U", "W", :], :],
pd.IndexSlice[pd.IndexSlice["U", :, "Y"], :],
pd.IndexSlice[pd.IndexSlice[:, "W", "Y"], :],
pd.IndexSlice[pd.IndexSlice[:, "W", ["Y", "Z"]], :], # check list
pd.IndexSlice[pd.IndexSlice[:, "W",
["Y", "Z", "-"]], :], # allow missing
pd.IndexSlice[pd.IndexSlice["U", "W",
["Y", "Z", "-"]], :], # no slice
# check simultaneous
pd.IndexSlice[pd.IndexSlice[:, "W", "Y"], pd.IndexSlice["a",
"c", :]],
],
)
def test_non_reducing_multi_slice_on_multiindex(self, slice_):
# GH 33562
cols = pd.MultiIndex.from_product([["a", "b"], ["c", "d"], ["e", "f"]])
idxs = pd.MultiIndex.from_product([["U", "V"], ["W", "X"], ["Y", "Z"]])
df = DataFrame(np.arange(64).reshape(8, 8), columns=cols, index=idxs)
expected = df.loc[slice_]
result = df.loc[_non_reducing_slice(slice_)]
tm.assert_frame_equal(result, expected)
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_init_non_pandas(self):
with pytest.raises(TypeError):
Styler([1, 2, 3])
def test_init_series(self):
result = Styler(pd.Series([1, 2]))
self.assertEqual(result.data.ndim, 2)
def test_repr_html_ok(self):
self.styler._repr_html_()
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)
assert self.styler is not s2
assert self.styler.ctx is s2.ctx # shallow
assert 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)
assert self.styler is not s2
assert self.styler.ctx is not s2.ctx
assert 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()
assert len(s.ctx) > 0
assert len(s._todo) > 0
s.clear()
assert len(s.ctx) == 0
assert 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)
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_empty_index_name_doesnt_display(self):
# https://github.com/pandas-dev/pandas/pull/12090#issuecomment-180695902
df = pd.DataFrame({'A': [1, 2], 'B': [3, 4], 'C': [5, 6]})
result = df.style._translate()
expected = [[{
'class': 'blank level0',
'type': 'th',
'value': '',
'is_visible': True,
'display_value': ''
}, {
'class': 'col_heading level0 col0',
'display_value': 'A',
'type': 'th',
'value': 'A',
'is_visible': True,
}, {
'class': 'col_heading level0 col1',
'display_value': 'B',
'type': 'th',
'value': 'B',
'is_visible': True,
}, {
'class': 'col_heading level0 col2',
'display_value': 'C',
'type': 'th',
'value': 'C',
'is_visible': True,
}]]
self.assertEqual(result['head'], expected)
def test_index_name(self):
# https://github.com/pandas-dev/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 level0',
'type': 'th',
'value': '',
'display_value': '',
'is_visible': True
}, {
'class': 'col_heading level0 col0',
'type': 'th',
'value': 'B',
'display_value': 'B',
'is_visible': True
}, {
'class': 'col_heading level0 col1',
'type': 'th',
'value': 'C',
'display_value': 'C',
'is_visible': True
}],
[{
'class': 'index_name level0',
'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/pandas-dev/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': '',
'display_value': '',
'is_visible': True
}, {
'class': 'blank level0',
'type': 'th',
'value': '',
'display_value': '',
'is_visible': True
}, {
'class': 'col_heading level0 col0',
'type': 'th',
'value': 'C',
'display_value': 'C',
'is_visible': True
}],
[{
'class': 'index_name level0',
'type': 'th',
'value': 'A'
}, {
'class': 'index_name level1',
'type': 'th',
'value': 'B'
}, {
'class': 'blank',
'type': 'th',
'value': ''
}]]
self.assertEqual(result['head'], expected)
def test_numeric_columns(self):
# https://github.com/pandas-dev/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 pytest.raises(ValueError):
df.style
with pytest.raises(ValueError):
Styler(df)
def test_caption(self):
styler = Styler(self.df, caption='foo')
result = styler.render()
assert all(['caption' in result, 'foo' in result])
styler = self.df.style
result = styler.set_caption('baz')
assert styler is result
self.assertEqual(styler.caption, 'baz')
def test_uuid(self):
styler = Styler(self.df, uuid='abc123')
result = styler.render()
assert 'abc123' in result
styler = self.df.style
result = styler.set_uuid('aaa')
assert 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())
assert 'th { foo: bar; }' in result
styler = self.df.style
result = styler.set_table_styles(style)
assert 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()
assert 'class="foo" data-bar' in result
result = self.df.style.set_table_attributes(attributes).render()
assert '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)
assert 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()
assert all(['display_value' in c for c in row] for row in ctx['body'])
assert (all([len(c['display_value']) <= 3 for c in row[1:]]
for row in ctx['body']))
assert len(ctx['body'][0][1]['display_value'].lstrip('-')) <= 3
def test_display_format_raises(self):
df = pd.DataFrame(np.random.randn(2, 2))
with pytest.raises(TypeError):
df.style.format(5)
with pytest.raises(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_bad_apply_shape(self):
df = pd.DataFrame([[1, 2], [3, 4]])
with pytest.raises(ValueError):
df.style._apply(lambda x: 'x', subset=pd.IndexSlice[[0, 1], :])
with pytest.raises(ValueError):
df.style._apply(lambda x: [''], subset=pd.IndexSlice[[0, 1], :])
with pytest.raises(ValueError):
df.style._apply(lambda x: ['', '', '', ''])
with pytest.raises(ValueError):
df.style._apply(lambda x: ['', '', ''], subset=1)
with pytest.raises(ValueError):
df.style._apply(lambda x: ['', '', ''], axis=1)
def test_apply_bad_return(self):
def f(x):
return ''
df = pd.DataFrame([[1, 2], [3, 4]])
with pytest.raises(TypeError):
df.style._apply(f, axis=None)
def test_apply_bad_labels(self):
def f(x):
return pd.DataFrame(index=[1, 2], columns=['a', 'b'])
df = pd.DataFrame([[1, 2], [3, 4]])
with pytest.raises(ValueError):
df.style._apply(f, axis=None)
def test_get_level_lengths(self):
index = pd.MultiIndex.from_product([['a', 'b'], [0, 1, 2]])
expected = {
(0, 0): 3,
(0, 3): 3,
(1, 0): 1,
(1, 1): 1,
(1, 2): 1,
(1, 3): 1,
(1, 4): 1,
(1, 5): 1
}
result = _get_level_lengths(index)
tm.assert_dict_equal(result, expected)
def test_get_level_lengths_un_sorted(self):
index = pd.MultiIndex.from_arrays([[1, 1, 2, 1], ['a', 'b', 'b', 'd']])
expected = {
(0, 0): 2,
(0, 2): 1,
(0, 3): 1,
(1, 0): 1,
(1, 1): 1,
(1, 2): 1,
(1, 3): 1
}
result = _get_level_lengths(index)
tm.assert_dict_equal(result, expected)
def test_mi_sparse(self):
df = pd.DataFrame({'A': [1, 2]},
index=pd.MultiIndex.from_arrays([['a', 'a'], [0,
1]]))
result = df.style._translate()
body_0 = result['body'][0][0]
expected_0 = {
"value": "a",
"display_value": "a",
"is_visible": True,
"type": "th",
"attributes": ["rowspan=2"],
"class": "row_heading level0 row0",
}
tm.assert_dict_equal(body_0, expected_0)
body_1 = result['body'][0][1]
expected_1 = {
"value": 0,
"display_value": 0,
"is_visible": True,
"type": "th",
"class": "row_heading level1 row0",
}
tm.assert_dict_equal(body_1, expected_1)
body_10 = result['body'][1][0]
expected_10 = {
"value": 'a',
"display_value": 'a',
"is_visible": False,
"type": "th",
"class": "row_heading level0 row1",
}
tm.assert_dict_equal(body_10, expected_10)
head = result['head'][0]
expected = [{
'type': 'th',
'class': 'blank',
'value': '',
'is_visible': True,
"display_value": ''
}, {
'type': 'th',
'class': 'blank level0',
'value': '',
'is_visible': True,
'display_value': ''
}, {
'type': 'th',
'class': 'col_heading level0 col0',
'value': 'A',
'is_visible': True,
'display_value': 'A'
}]
self.assertEqual(head, expected)
def test_mi_sparse_disabled(self):
with pd.option_context('display.multi_sparse', False):
df = pd.DataFrame({'A': [1, 2]},
index=pd.MultiIndex.from_arrays([['a', 'a'],
[0, 1]]))
result = df.style._translate()
body = result['body']
for row in body:
assert 'attributes' not in row[0]
def test_mi_sparse_index_names(self):
df = pd.DataFrame({'A': [1, 2]},
index=pd.MultiIndex.from_arrays(
[['a', 'a'], [0, 1]],
names=['idx_level_0', 'idx_level_1']))
result = df.style._translate()
head = result['head'][1]
expected = [{
'class': 'index_name level0',
'value': 'idx_level_0',
'type': 'th'
}, {
'class': 'index_name level1',
'value': 'idx_level_1',
'type': 'th'
}, {
'class': 'blank',
'value': '',
'type': 'th'
}]
self.assertEqual(head, expected)
def test_mi_sparse_column_names(self):
df = pd.DataFrame(np.arange(16).reshape(4, 4),
index=pd.MultiIndex.from_arrays(
[['a', 'a', 'b', 'a'], [0, 1, 1, 2]],
names=['idx_level_0', 'idx_level_1']),
columns=pd.MultiIndex.from_arrays(
[['C1', 'C1', 'C2', 'C2'], [1, 0, 1, 0]],
names=['col_0', 'col_1']))
result = df.style._translate()
head = result['head'][1]
expected = [
{
'class': 'blank',
'value': '',
'display_value': '',
'type': 'th',
'is_visible': True
},
{
'class': 'index_name level1',
'value': 'col_1',
'display_value': 'col_1',
'is_visible': True,
'type': 'th'
},
{
'class': 'col_heading level1 col0',
'display_value': 1,
'is_visible': True,
'type': 'th',
'value': 1
},
{
'class': 'col_heading level1 col1',
'display_value': 0,
'is_visible': True,
'type': 'th',
'value': 0
},
{
'class': 'col_heading level1 col2',
'display_value': 1,
'is_visible': True,
'type': 'th',
'value': 1
},
{
'class': 'col_heading level1 col3',
'display_value': 0,
'is_visible': True,
'type': 'th',
'value': 0
},
]
self.assertEqual(head, expected)