本文整理汇总了Python中holoviews.Columns类的典型用法代码示例。如果您正苦于以下问题：Python Columns类的具体用法？Python Columns怎么用？Python Columns使用的例子？那么恭喜您, 这里精选的类代码示例或许可以为您提供帮助。
在下文中一共展示了Columns类的15个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: test_columns_sort_vdim_hm
def test_columns_sort_vdim_hm(self):
xs_2 = np.array(self.xs_2)
columns = Columns(np.column_stack([self.xs, -xs_2]),
kdims=['x'], vdims=['y'])
columns_sorted = Columns(np.column_stack([self.xs[::-1], -xs_2[::-1]]),
kdims=['x'], vdims=['y'])
self.assertEqual(columns.sort('y'), columns_sorted)示例2: test_columns_heterogeneous_aggregate
def test_columns_heterogeneous_aggregate(self):
columns = Columns(self.column_data, kdims=self.kdims,
vdims=self.vdims)
aggregated = Columns(pd.DataFrame([('F', 10., 0.8), ('M', 16.5, 0.7)],
columns=['Gender']+self.vdims),
kdims=self.kdims[:1], vdims=self.vdims)
self.compare_columns(columns.aggregate(['Gender'], np.mean), aggregated)示例3: test_columns_2D_aggregate_partial_hm
def test_columns_2D_aggregate_partial_hm(self):
array = np.random.rand(11, 11)
columns = Columns({'x':self.xs, 'y':self.y_ints, 'z': array},
kdims=['x', 'y'], vdims=['z'])
self.assertEqual(columns.aggregate(['x'], np.mean),
Columns({'x':self.xs, 'z': np.mean(array, axis=1)},
kdims=['x'], vdims=['z']))示例4: test_columns_sort_heterogeneous_string
def test_columns_sort_heterogeneous_string(self):
columns = Columns(zip(self.keys1, self.values1),
kdims=self.kdims, vdims=self.vdims)
keys = [('F',12), ('M',10), ('M',16)]
values = [(10, 0.8), (15, 0.8), (18, 0.6)]
columns_sorted = Columns(zip(keys, values),
kdims=self.kdims, vdims=self.vdims)
self.assertEqual(columns.sort(), columns_sorted)示例5: test_columns_heterogeneous_reduce
def test_columns_heterogeneous_reduce(self):
columns = Columns(self.column_data, kdims=self.kdims,
vdims=self.vdims)
reduced_data = pd.DataFrame([(10, 15, 0.8), (12, 10, 0.8), (16, 18, 0.6)],
columns=columns.dimensions(label=True)[1:])
reduced = Columns(reduced_data, kdims=self.kdims[1:],
vdims=self.vdims)
self.assertEqual(columns.reduce(['Gender'], np.mean), reduced)示例6: test_columns_heterogeneous_reduce2d
def test_columns_heterogeneous_reduce2d(self):
columns = Columns(self.column_data, kdims=self.kdims,
vdims=self.vdims)
reduced_data = pd.DataFrame([d[1:] for d in self.column_data],
columns=columns.dimensions(label=True)[1:])
reduced = Columns(pd.DataFrame([(14.333333333333334, 0.73333333333333339)], columns=self.vdims),
kdims=[], vdims=self.vdims)
self.assertEqual(columns.reduce(function=np.mean), reduced)示例7: test_columns_groupby
def test_columns_groupby(self):
columns = Columns(self.column_data, kdims=self.kdims,
vdims=self.vdims)
cols = self.kdims + self.vdims
group1 = pd.DataFrame(self.column_data[:2], columns=cols)
group2 = pd.DataFrame(self.column_data[2:], columns=cols)
grouped = HoloMap({'M': Columns(group1, kdims=['Age'], vdims=self.vdims),
'F': Columns(group2, kdims=['Age'], vdims=self.vdims)},
kdims=['Gender'])
self.assertEqual(columns.groupby(['Gender']), grouped)示例8: init_data
def init_data(self):
self.kdims = ['Gender', 'Age']
self.vdims = ['Weight', 'Height']
self.gender, self.age = ['M','M','F'], [10,16,12]
self.weight, self.height = [15,18,10], [0.8,0.6,0.8]
self.table = Columns({'Gender':self.gender, 'Age':self.age,
'Weight':self.weight, 'Height':self.height},
kdims=self.kdims, vdims=self.vdims)
super(HeterogeneousColumnTypes, self).init_data()
self.ys = np.linspace(0, 1, 11)
self.zs = np.sin(self.xs)
self.columns_ht = Columns({'x':self.xs, 'y':self.ys},
kdims=['x'], vdims=['y'])示例9: setUp
def setUp(self):
self.datatype = Columns.datatype
Columns.datatype = ['dictionary', 'array']
self.xs = range(11)
self.ys = np.linspace(0, 1, 11)
self.zs = np.sin(self.xs)
self.keys1 = [('M',10), ('M',16), ('F',12)]
self.values1 = [(15, 0.8), (18, 0.6), (10, 0.8)]
self.kdims = ['Gender', 'Age']
self.vdims = ['Weight', 'Height']
self.columns = Columns(dict(zip(self.xs, self.ys)),
kdims=['x'], vdims=['y'])示例10: HoloMapTest
class HoloMapTest(ComparisonTestCase):
def setUp(self):
self.xs = range(11)
self.y_ints = [i*2 for i in range(11)]
self.ys = np.linspace(0, 1, 11)
self.columns = Columns(np.column_stack([self.xs, self.y_ints]),
kdims=['x'], vdims=['y'])
def test_columns_collapse_heterogeneous(self):
collapsed = HoloMap({i: Columns({'x':self.xs, 'y':self.ys*i},
kdims=['x'], vdims=['y'])
for i in range(10)}, kdims=['z']).collapse('z', np.mean)
expected = Columns({'x':self.xs, 'y':self.ys*4.5}, kdims=['x'], vdims=['y'])
self.compare_columns(collapsed, expected)
def test_columns_sample_homogeneous(self):
samples = self.columns.sample([0, 5, 10]).dimension_values('y')
self.assertEqual(samples, np.array([0, 10, 20]))示例11: test_columns_2d_partial_reduce
def test_columns_2d_partial_reduce(self):
columns = Columns((self.xs, self.ys, self.zs), kdims=['x', 'y'], vdims=['z'])
self.assertEqual(columns.reduce(['y'], np.mean),
Columns((self.xs, self.zs), kdims=['x'], vdims=['z']))示例12: test_columns_2d_reduce
def test_columns_2d_reduce(self):
columns = Columns(pd.DataFrame({'x': self.xs, 'y': self.ys, 'z': self.zs}),
kdims=['x', 'y'], vdims=['z'])
self.assertEqual(np.array(columns.reduce(['x', 'y'], np.mean)),
np.array(0.12828985192891))示例13: ColumnsNdElementTest
class ColumnsNdElementTest(ComparisonTestCase):
"""
Test for the Chart baseclass methods.
"""
def setUp(self):
self.datatype = Columns.datatype
Columns.datatype = ['dictionary', 'array']
self.xs = range(11)
self.ys = np.linspace(0, 1, 11)
self.zs = np.sin(self.xs)
self.keys1 = [('M',10), ('M',16), ('F',12)]
self.values1 = [(15, 0.8), (18, 0.6), (10, 0.8)]
self.kdims = ['Gender', 'Age']
self.vdims = ['Weight', 'Height']
self.columns = Columns(dict(zip(self.xs, self.ys)),
kdims=['x'], vdims=['y'])
def tearDown(self):
Columns.datatype = self.datatype
def test_columns_sort_vdim(self):
columns = Columns(OrderedDict(zip(self.xs, -self.ys)),
kdims=['x'], vdims=['y'])
columns_sorted = Columns(OrderedDict(zip(self.xs[::-1], -self.ys[::-1])),
kdims=['x'], vdims=['y'])
self.assertEqual(columns.sort('y'), columns_sorted)
def test_columns_sort_heterogeneous_string(self):
columns = Columns(zip(self.keys1, self.values1),
kdims=self.kdims, vdims=self.vdims)
keys = [('F',12), ('M',10), ('M',16)]
values = [(10, 0.8), (15, 0.8), (18, 0.6)]
columns_sorted = Columns(zip(keys, values),
kdims=self.kdims, vdims=self.vdims)
self.assertEqual(columns.sort(), columns_sorted)
def test_columns_shape(self):
self.assertEqual(self.columns.shape, (11, 2))
def test_columns_range(self):
self.assertEqual(self.columns.range('y'), (0., 1.))
def test_columns_odict_construct(self):
columns = Columns(OrderedDict(zip(self.xs, self.ys)), kdims=['A'], vdims=['B'])
self.assertTrue(isinstance(columns.data, NdElement))
def test_columns_closest(self):
closest = self.columns.closest([0.51, 1, 9.9])
self.assertEqual(closest, [1., 1., 10.])
def test_columns_dict_construct(self):
self.assertTrue(isinstance(self.columns.data, NdElement))
def test_columns_ndelement_construct(self):
columns = Columns(NdElement(zip(self.xs, self.ys)))
self.assertTrue(isinstance(columns.data, NdElement))
def test_columns_items_construct(self):
columns = Columns(zip(self.keys1, self.values1),
kdims=self.kdims, vdims=self.vdims)
self.assertTrue(isinstance(columns.data, NdElement))
def test_columns_sample(self):
samples = self.columns.sample([0, 5, 10]).dimension_values('y')
self.assertEqual(samples, np.array([0, 0.5, 1]))
def test_columns_index_row_gender(self):
table = Columns(zip(self.keys1, self.values1),
kdims=self.kdims, vdims=self.vdims)
indexed = Columns(OrderedDict([(('F', 12), (10, 0.8))]),
kdims=self.kdims, vdims=self.vdims)
row = table['F',:]
self.assertEquals(row, indexed)
def test_columns_index_rows_gender(self):
table = Columns(zip(self.keys1, self.values1),
kdims=self.kdims, vdims=self.vdims)
row = table['M',:]
indexed = Columns(OrderedDict([(('M', 10), (15, 0.8)),
(('M', 16), (18, 0.6))]),
kdims=self.kdims, vdims=self.vdims)
self.assertEquals(row, indexed)
def test_columns_index_row_age(self):
table = Columns(zip(self.keys1, self.values1),
kdims=self.kdims, vdims=self.vdims)
indexed = Columns(OrderedDict([(('F', 12), (10, 0.8))]),
kdims=self.kdims, vdims=self.vdims)
self.assertEquals(table[:, 12], indexed)
def test_columns_index_item_table(self):
table = Columns(zip(self.keys1, self.values1),
kdims=self.kdims, vdims=self.vdims)
indexed = Columns(OrderedDict([(('F', 12), (10, 0.8))]),
kdims=self.kdims, vdims=self.vdims)
self.assertEquals(table['F', 12], indexed)
def test_columns_index_value1(self):
#.........这里部分代码省略.........
示例14: test_columns_sort_vdim
def test_columns_sort_vdim(self):
columns = Columns(pd.DataFrame({'x': self.xs, 'y': -self.ys}),
kdims=['x'], vdims=['y'])
columns_sorted = Columns(pd.DataFrame({'x': self.xs[::-1], 'y': -self.ys[::-1]}),
kdims=['x'], vdims=['y'])
self.assertEqual(columns.sort('y'), columns_sorted)示例15: ColumnsDFrameTest
class ColumnsDFrameTest(ComparisonTestCase):
def setUp(self):
self.datatype = Columns.datatype
Columns.datatype = ['dataframe']
self.column_data = [('M',10, 15, 0.8), ('M',16, 18, 0.6),
('F',12, 10, 0.8)]
self.kdims = ['Gender', 'Age']
self.vdims = ['Weight', 'Height']
self.xs = range(11)
self.ys = np.linspace(0, 1, 11)
self.zs = np.sin(self.xs)
self.columns = Columns(pd.DataFrame({'x': self.xs, 'y': self.ys}),
kdims=['x'], vdims=['y'])
def tearDown(self):
Columns.datatype = self.datatype
def test_columns_range(self):
self.assertEqual(self.columns.range('y'), (0., 1.))
def test_columns_shape(self):
self.assertEqual(self.columns.shape, (11, 2))
def test_columns_closest(self):
closest = self.columns.closest([0.51, 1, 9.9])
self.assertEqual(closest, [1., 1., 10.])
def test_columns_sample(self):
samples = self.columns.sample([0, 5, 10]).dimension_values('y')
self.assertEqual(samples, np.array([0, 0.5, 1]))
def test_columns_df_construct(self):
self.assertTrue(isinstance(self.columns.data, pd.DataFrame))
def test_columns_tuple_list_construct(self):
columns = Columns(self.column_data, kdims=self.kdims,
vdims=self.vdims)
self.assertTrue(isinstance(self.columns.data, pd.DataFrame))
def test_columns_slice(self):
data = [('x', range(5, 9)), ('y', np.linspace(0.5, 0.8, 4))]
columns_slice = Columns(pd.DataFrame.from_items(data),
kdims=['x'], vdims=['y'])
self.assertEqual(self.columns[5:9], columns_slice)
def test_columns_index_row_gender(self):
columns = Columns(self.column_data, kdims=self.kdims,
vdims=self.vdims)
row = columns['F',:]
self.assertEquals(type(row), Columns)
self.compare_columns(row, Columns(self.column_data[2:],
kdims=self.kdims,
vdims=self.vdims))
def test_columns_index_rows_gender(self):
columns = Columns(self.column_data, kdims=self.kdims,
vdims=self.vdims)
row = columns['M',:]
self.assertEquals(type(row), Columns)
self.compare_columns(row, Columns(self.column_data[:2],
kdims=self.kdims,
vdims=self.vdims))
def test_columns_index_row_age(self):
columns = Columns(self.column_data, kdims=self.kdims,
vdims=self.vdims)
row = columns[:, 12]
self.assertEquals(type(row), Columns)
self.compare_columns(row, Columns(self.column_data[2:],
kdims=self.kdims,
vdims=self.vdims))
def test_columns_index_single_row(self):
columns = Columns(self.column_data, kdims=self.kdims,
vdims=self.vdims)
row = columns['F', 12]
self.assertEquals(type(row), Columns)
self.compare_columns(row, Columns(self.column_data[2:],
kdims=self.kdims,
vdims=self.vdims))
def test_columns_index_value1(self):
columns = Columns(self.column_data, kdims=self.kdims,
vdims=self.vdims)
self.assertEquals(columns['F', 12, 'Weight'], 10)
def test_columns_index_value2(self):
columns = Columns(self.column_data, kdims=self.kdims,
vdims=self.vdims)
self.assertEquals(columns['F', 12, 'Height'], 0.8)
def test_columns_sort_vdim(self):
columns = Columns(pd.DataFrame({'x': self.xs, 'y': -self.ys}),
kdims=['x'], vdims=['y'])
columns_sorted = Columns(pd.DataFrame({'x': self.xs[::-1], 'y': -self.ys[::-1]}),
kdims=['x'], vdims=['y'])
self.assertEqual(columns.sort('y'), columns_sorted)
def test_columns_sort_heterogeneous_string(self):
#.........这里部分代码省略.........