def test_infer_types():
data = dict(col1=[1.0, 2.0, 3.0],
col2=[0.1, 0.2, 0.3],
col3=['A', 'B', 'C'],
col4=[True, False, True],
col5=[0.1, 0.2, 0.3],
col6=pd.date_range('2012', periods=3, freq='A'),
col7=np.arange(3),
col8=np.arange(3),
col9=np.arange(3))
kwargs = dict(x=('col1', 'Q'),
y=('col2', 'Q'),
row=('col3', 'N'),
col=('col4', 'N'),
size=('col5', 'Q'),
color=('col6', 'T'),
shape=('col7', 'Q'),
text=('col8', 'Q'),
detail=('col9', 'Q'))
spec = api.Viz(data).encode(**{key: val[0] for key, val in kwargs.items()})
for key, val in kwargs.items():
name, typ = val
assert getattr(spec.encoding, key).name == name
assert getattr(spec.encoding, key).type == typ
def test_encode_aggregates():
data = dict(col1=[1.0, 2.0, 3.0],
col2=[0.1, 0.2, 0.3],
col3=['A', 'B', 'C'],
col4=[True, False, True],
col5=[0.1, 0.2, 0.3],
col6=pd.date_range('2012', periods=3, freq='A'),
col7=np.arange(3),
col8=np.arange(3),
col9=np.arange(3))
kwargs = dict(x=('count', 'col1'),
y=('count', 'col2'),
row=('count', 'col3'),
col=('count', 'col4'),
size=('avg', 'col5'),
color=('max', 'col6'),
shape=('count', 'col7'),
text=('avg', 'col8'),
detail=('count', 'col9'))
spec = api.Viz(data).encode(
**{key: "{0}({1})".format(*val)
for key, val in kwargs.items()})
for key, val in kwargs.items():
agg, name = val
assert getattr(spec.encoding, key).name == name
assert getattr(spec.encoding, key).aggregate == agg
def test_select_x():
def _check(data, expected):
v = api.Viz(data)
v.select_x()
assert v.encoding.x.name == expected
data = dict(
col1=[1.0, 2.0, 3.0], # Q
col2=['A', 'B', 'C'], # N
col3=pd.date_range('2012', periods=3, freq='A'), # T
col4=pd.date_range('2012', periods=3, freq='A')) # T
_check(data, 'col3')
data = dict(
col1=[1.0, 2.0, 3.0], # Q
col2=['A', 'B', 'C'], # N
col3=['A', 'B', 'C']) # N
_check(data, 'col2')
data = dict(
col1=[1.0, 2.0, 3.0], # Q
col2=np.arange(3)) # Q
_check(data, 'col1')
# No data
with pytest.raises(AssertionError):
v = api.Viz(None)
v.select_x()
# Empty data
with pytest.raises(AssertionError):
v = api.Viz(dict())
v.select_x()
# Custom order
data = dict(
col1=[1.0, 2.0, 3.0], # Q
col2=['A', 'B', 'C'], # N
col3=pd.date_range('2012', periods=3, freq='A'), # T
col4=pd.date_range('2012', periods=3, freq='A')) # T
v = api.Viz(data)
v.select_x(['N', 'T', 'Q', 'O'])
assert v.encoding.x.name == 'col2'
def _check(data, expected, aggregator=None):
v = api.Viz(data)
v.encode()
v.encoding.x = 'col1'
if aggregator is None:
v.select_y()
assert v.encoding.y.name == expected
else:
v.select_y(None, aggregator)
assert v.encoding.y.name == expected
assert v.encoding.y.aggregate == aggregator
def test_encode():
data = dict(col1=[1.0, 2.0, 3.0],
col2=[0.1, 0.2, 0.3],
col3=['A', 'B', 'C'],
col4=[True, False, True],
col5=[0.1, 0.2, 0.3],
col6=pd.date_range('2012', periods=3, freq='A'),
col7=np.arange(3))
kwargs = dict(x='col1',
y='col2',
row='col3',
col='col4',
size='col5',
color='col6',
shape='col7')
spec = api.Viz(data).encode(**kwargs)
for key, name in kwargs.items():
assert getattr(spec.encoding, key).name == name
def test_to_dict():
data = pd.DataFrame({'x': [1, 2, 3], 'y': [4, 5, 6]})
spec = api.Viz(data).encode(x='x', y='y')
D = spec.to_dict()
assert D == {
'config': {
'gridColor': 'black',
'gridOpacity': 0.08,
'height': 400,
'width': 600
},
'data': {
'formatType': 'json',
'values': [{
'x': 1,
'y': 4
}, {
'x': 2,
'y': 5
}, {
'x': 3,
'y': 6
}]
},
'encoding': {
'x': {
'bin': False,
'name': 'x',
'type': 'Q'
},
'y': {
'bin': False,
'name': 'y',
'type': 'Q'
}
},
'marktype': 'point'
}
def test_dataframe_data():
spec, data = build_simple_spec()
data = data = pd.DataFrame(data)
spec = api.Viz(data)
assert np.all(spec.data == data)
def test_hist():
data = dict(x=[1, 2, 3], y=[4, 5, 6])
viz1 = api.Viz(data).hist(x='x')
assert viz1.encoding.x.name == "x"
assert viz1.encoding.x.bin.maxbins == 10
assert viz1.encoding.y.name == "x"
assert viz1.encoding.y.type == "Q"
assert viz1.encoding.y.aggregate == "count"
viz2 = api.Viz(data).hist(x="x", bins=30)
assert viz2.encoding.x.bin.maxbins == 30
expected = {
'config': {
'gridColor': 'black',
'gridOpacity': 0.08,
'height': 400,
'width': 600
},
'data': {
'formatType': 'json',
'values': [{
'x': 1,
'y': 4
}, {
'x': 2,
'y': 5
}, {
'x': 3,
'y': 6
}]
},
'encoding': {
'x': {
'bin': {
'maxbins': 30
},
'name': 'x',
'type': 'Q'
},
'y': {
'aggregate': 'count',
'bin': False,
'name': 'x',
'type': 'Q'
}
},
'marktype': 'bar'
}
assert viz2.to_dict() == expected
viz3 = api.Viz(data).hist(x="x:O",
color=api.Color(shorthand="bar", type="N"))
assert viz3.encoding.x.name == "x"
assert viz3.encoding.x.type == "O"
expected = {
'config': {
'gridColor': 'black',
'gridOpacity': 0.08,
'height': 400,
'width': 600
},
'data': {
'formatType': 'json',
'values': [{
'x': 1,
'y': 4
}, {
'x': 2,
'y': 5
}, {
'x': 3,
'y': 6
}]
},
'encoding': {
'x': {
'bin': {
'maxbins': 10
},
'name': 'x',
'type': 'O'
},
'y': {
'aggregate': 'count',
'bin': False,
'name': 'x',
'type': 'Q'
},
'color': {
'bin': False,
'name': 'bar',
'opacity': 1.0,
'type': 'N',
'value': '#4682b4'
}
},
'marktype': 'bar'
}
assert viz3.to_dict() == expected
viz4 = api.Viz(data).hist(x=api.X(shorthand="x", bin=api.Bin(maxbins=40)))
assert viz4.encoding.x.name == "x"
assert viz4.encoding.x.bin.maxbins == 40
def build_simple_spec():
data = dict(x=[1, 2, 3], y=[4, 5, 6])
return api.Viz(data), data
def test_select_y():
def _check(data, expected, aggregator=None):
v = api.Viz(data)
v.encode()
v.encoding.x = 'col1'
if aggregator is None:
v.select_y()
assert v.encoding.y.name == expected
else:
v.select_y(None, aggregator)
assert v.encoding.y.name == expected
assert v.encoding.y.aggregate == aggregator
data = dict(
col1=[1.0, 2.0, 3.0], # Chosen X
col2=['A', 'B', 'C'], # N
col3=pd.date_range('2012', periods=3, freq='A'), # T
col4=pd.date_range('2012', periods=3, freq='A'), # T
col5=[1.0, 2.0, 3.0], # Q
col6=[1.0, 2.0, 3.0]) # Q
_check(data, 'col5')
_check(data, 'col5', 'avg')
data = dict(
col1=[1.0, 2.0, 3.0], # Chosen X
col2=['A', 'B', 'C'], # N
col3=pd.date_range('2012', periods=3, freq='A'), # T
col4=['A', 'B', 'C']) # N
_check(data, 'col2')
_check(data, 'col2', 'sum')
data = dict(
col1=[1.0, 2.0, 3.0], # Chosen X
col2=pd.date_range('2012', periods=3, freq='A'), # T
col3=pd.date_range('2012', periods=3, freq='A')) # T
_check(data, 'col2')
_check(data, 'col2', 'sum')
# No data
with pytest.raises(AssertionError):
v = api.Viz(None)
v.select_y()
# Just one column
with pytest.raises(AssertionError):
v = api.Viz(dict(col1=[1.0, 2.0]))
v.select_y()
# No encoding
with pytest.raises(AssertionError):
v = api.Viz(dict(col1=[1.0, 2.0], col2=[1.0, 2.0]))
v.select_y()
# No X
with pytest.raises(AssertionError):
v = api.Viz(dict(col1=[1.0, 2.0], col2=[1.0, 2.0]))
v.encode()
v.select_y()
# Custom order
data = dict(
col1=[1.0, 2.0, 3.0], # Chosen X
col2=['A', 'B', 'C'], # N
col3=pd.date_range('2012', periods=3, freq='A'), # T
col4=pd.date_range('2012', periods=3, freq='A'), # T
col5=[1.0, 2.0, 3.0], # Q
col6=[1.0, 2.0, 3.0]) # Q
v = api.Viz(data)
v.encode()
v.encoding.x = 'col1'
v.select_y(['N', 'T', 'Q', 'O'])
assert v.encoding.y.name == 'col2'
def _check(data, expected):
v = api.Viz(data)
v.select_x()
assert v.encoding.x.name == expected
