def test_row(self):
dxp.scatter(x='neighborhood',
y='price',
data=airbnb,
aggfunc='median',
split='superhost',
row='property_type')
def test_stacked(self):
dxp.scatter(x='neighborhood',
y='price',
data=airbnb,
aggfunc='median',
split='superhost',
split_order=['Yes', 'No'])
def test_row_order(self):
dxp.scatter(x='neighborhood',
y='price',
data=airbnb,
aggfunc='median',
split='superhost',
row='property_type',
row_order=['House', 'Condominium'])
def test_col_wrap(self):
dxp.scatter(x='neighborhood',
y='price',
data=airbnb,
aggfunc='median',
split='superhost',
col='property_type',
wrap=2)
def test_function(self):
dxp.scatter(x='neighborhood',
y='price',
data=airbnb,
aggfunc=np.median)
dxp.scatter(x='neighborhood', y='price', data=airbnb, aggfunc=np.mean)
dxp.scatter(x='neighborhood', y='price', data=airbnb, aggfunc=np.min)
dxp.scatter(x='neighborhood', y='price', data=airbnb, aggfunc=np.max)
dxp.scatter(x='neighborhood', y='price', data=airbnb, aggfunc=np.size)
def test_row_col(self):
dxp.scatter(x='neighborhood',
y='price',
data=airbnb,
aggfunc='median',
split='superhost',
col='property_type',
col_order=['House', 'Condominium', 'Apartment'],
row='bedrooms',
row_order=[0, 1, 2, 3])
def test_x_order(self):
dxp.scatter(x='neighborhood',
y='price',
data=airbnb,
aggfunc='median',
x_order=['Dupont Circle', 'Edgewood', 'Union Station'])
with pytest.raises(ValueError):
dxp.scatter(
x='neighborhood',
y='price',
data=airbnb,
aggfunc='median',
x_order=['Dupont Circle', 'Edgewood', 'DOES NOT EXIST'])
def test_lex_asc(self):
fig = dxp.scatter(x='neighborhood',
y='price',
data=airbnb,
aggfunc='median')
ticklabels = [t.get_text() for t in fig.axes[0].get_xticklabels()]
correct = sorted(ticklabels)
assert ticklabels == correct
def test_lex_desc(self):
fig = dxp.scatter(x='neighborhood',
y='price',
data=airbnb,
aggfunc='median',
sort_values='lex_desc')
ticklabels = [t.get_text() for t in fig.axes[0].get_xticklabels()]
correct = sorted(ticklabels, reverse=True)
assert ticklabels == correct
def test_asc_values(self):
fig = dxp.scatter(x='neighborhood',
y='price',
data=airbnb,
aggfunc='median',
sort='asc')
ticklabels = [t.get_text() for t in fig.axes[0].get_xticklabels()]
ticklabels = [label.replace('\n', ' ') for label in ticklabels]
values = [p.get_height() for p in fig.axes[0].patches]
s = airbnb.groupby('neighborhood')['price'].median().sort_values()
correct_labels = s.index.tolist()
correct_values = s.values.tolist()
assert ticklabels == correct_labels
def test_desc_values(self):
fig = dxp.scatter(x='neighborhood',
y='price',
data=airbnb,
aggfunc='median',
sort='desc')
ticklabels = [t.get_text() for t in fig.axes[0].get_xticklabels()]
ticklabels = [label.replace('\n', ' ') for label in ticklabels]
values = [p.get_height() for p in fig.axes[0].patches]
df = airbnb.groupby('neighborhood').agg({'price': 'median'}).reset_index() \
.sort_values(['price', 'neighborhood'], ascending=[False, True])
s = df.set_index('neighborhood').squeeze()
correct_labels = s.index.tolist()
correct_values = s.values.tolist()
assert ticklabels == correct_labels
def test_horiz(self):
dxp.scatter(x='price',
y='neighborhood',
data=airbnb,
aggfunc='median',
orientation='h')
def test_string_name(self):
dxp.scatter(x='neighborhood', y='price', data=airbnb, aggfunc='median')
dxp.scatter(x='neighborhood', y='price', data=airbnb, aggfunc='mean')
dxp.scatter(x='neighborhood', y='price', data=airbnb, aggfunc='min')
dxp.scatter(x='neighborhood', y='price', data=airbnb, aggfunc='max')
dxp.scatter(x='neighborhood', y='price', data=airbnb, aggfunc='size')
def test_split(self):
dxp.scatter(x='neighborhood',
y='price',
data=airbnb,
aggfunc='median',
split='superhost')
