def test_groupby(test_data):
ds = ChartDataSource(df=test_data.auto_data)
groups = list(ds.groupby(**test_data.single_col_spec))
assert len(groups) == 5
ds = ChartDataSource(df=test_data.auto_data)
groups = list(ds.groupby(**test_data.multi_col_spec))
assert len(groups) == 9
def test_pandas(test_data):
"""Test creating chart data source from existing dataframe."""
ds = ChartDataSource.from_data(test_data.pd_data)
assert len(ds.columns) == 2
assert len(ds.index) == 4
ds = ChartDataSource(test_data.pd_data)
assert len(ds.columns) == 2
assert len(ds.index) == 4
def test_pandas(test_data):
"""Test creating chart data source from existing dataframe."""
pd_data = test_data.pd_data.copy()
ds = ChartDataSource.from_data(pd_data)
assert len(ds.columns) == 2
assert len(ds.index) == 4
ds = ChartDataSource(pd_data)
assert len(ds.columns) == 2
assert len(ds.index) == 4
# Test for issue #3899
# ChartDataSource shouldn't modify the input DataFrame
assert pd_data.equals(test_data.pd_data)
def test_custom_derived_cols_from_df(test_data):
"""Wide dataframe columns assigned to y dimension for the chart.
This tests using the NumericalColumnsAssigner, which is used for charts
that prefer behavior of expecting series-like data. Typically the x axis
is used as an index, then the y axis will be assigned multiple columns.
"""
ds = ChartDataSource.from_data(test_data.pd_data, dims=('x', 'y'),
column_assigner=NumericalColumnsAssigner)
assert ds['y'] == ['col1', 'col2']
def test_groupby(test_data):
ds = ChartDataSource(df=test_data.auto_data)
groups = list(ds.groupby(**test_data.single_col_spec))
assert len(groups) == 5
ds = ChartDataSource(df=test_data.auto_data)
groups = list(ds.groupby(**test_data.multi_col_spec))
assert len(groups) == 9
def _generate_items(self, df, columns):
"""Produce list of unique tuples that identify each item."""
if not self.bin:
super(ColorAttr, self)._generate_items(df, columns)
else:
if len(columns) == 1 and ChartDataSource.is_number(df[columns[0]]):
self.bins = Bins(source=ColumnDataSource(df), column=columns[0],
bin_count=len(self.iterable), aggregate=False)
if self.sort:
self.bins.sort(ascending=self.ascending)
self.items = [bin.label[0] for bin in self.bins]
else:
raise ValueError('Binned colors can only be created for one column of \
numerical data.')
def test_groupby(test_data):
# Note: this works because MarkerAttr and DashAttr are permissive in
# what is accepted for iterable, i.e. List(String)
for cls in (ColorAttr, MarkerAttr, DashAttr):
single_col_spec = {'test': cls(df=test_data.auto_data, columns='cyl',
name='test', iterable=['red', 'blue'])}
multi_col_spec = {'test': cls(df=test_data.auto_data,
columns=('cyl', 'origin'),
name='test', iterable=['red', 'blue'])}
ds = ChartDataSource(df=test_data.auto_data)
groups = list(ds.groupby(**single_col_spec))
assert len(groups) == 5
ds = ChartDataSource(df=test_data.auto_data)
groups = list(ds.groupby(**multi_col_spec))
assert len(groups) == 9
def test_derived_cols_from_lists(test_data):
"""List of lists for dimension results in column derivation."""
ds = ChartDataSource.from_data(test_data.array_data, dims=('y', 'x'))
assert ds['y'] == ['a', 'b']
def test_derived_selections(test_data):
ds = ChartDataSource.from_data(*test_data.array_data)
try:
[[ds[dim] for dim in req_dims] for req_dims in ds._required_dims]
except KeyError:
pytest.fail('Required dimension not correctly set by ChartDataSource.')
def test_records(test_data):
"""Test creating chart data source from array of dicts."""
ds = ChartDataSource.from_data(test_data.records_data)
assert len(ds.columns) == 2
assert len(ds.index) == 4
def test_dict(test_data):
"""Test creating chart data source from dict of arrays."""
ds = ChartDataSource.from_data(test_data.dict_data)
assert len(ds.columns) == 2
assert len(ds.index) == 4
def test_array(test_data):
"""Test creating chart data source from array-like numpy data."""
ds = ChartDataSource.from_data(*test_data.array_data)
assert len(ds.columns) == 2
assert len(ds.index) == 4