本文整理汇总了Python中holoviews.Dataset.aggregate方法的典型用法代码示例。如果您正苦于以下问题：Python Dataset.aggregate方法的具体用法？Python Dataset.aggregate怎么用？Python Dataset.aggregate使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类holoviews.Dataset的用法示例。
在下文中一共展示了Dataset.aggregate方法的10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_dataset_2D_aggregate_partial_hm
# 需要导入模块: from holoviews import Dataset [as 别名]
# 或者: from holoviews.Dataset import aggregate [as 别名]
def test_dataset_2D_aggregate_partial_hm(self):
array = np.random.rand(11, 11)
dataset = Dataset({'x':self.xs, 'y':self.y_ints, 'z': array},
kdims=['x', 'y'], vdims=['z'])
self.assertEqual(dataset.aggregate(['x'], np.mean),
Dataset({'x':self.xs, 'z': np.mean(array, axis=0)},
kdims=['x'], vdims=['z']))示例2: test_dataset_2D_aggregate_spread_fn_with_duplicates
# 需要导入模块: from holoviews import Dataset [as 别名]
# 或者: from holoviews.Dataset import aggregate [as 别名]
def test_dataset_2D_aggregate_spread_fn_with_duplicates(self):
dataset = Dataset({'x': np.array([0, 0, 1, 1]), 'y': np.array([0, 1, 2, 3]),
'z': np.array([1, 2, 3, 4])},
kdims=['x', 'y'], vdims=['z'])
agg = dataset.aggregate('x', function=np.mean, spreadfn=np.var)
self.assertEqual(agg, Dataset({'x': np.array([0, 1]), 'z': np.array([1.5, 3.5]),
'z_var': np.array([0.25, 0.25])},
kdims=['x'], vdims=['z', 'z_var']))示例3: test_dataset_empty_aggregate_with_spreadfn
# 需要导入模块: from holoviews import Dataset [as 别名]
# 或者: from holoviews.Dataset import aggregate [as 别名]
def test_dataset_empty_aggregate_with_spreadfn(self):
dataset = Dataset([], kdims=self.kdims, vdims=self.vdims)
aggregated = Dataset([], kdims=self.kdims[:1], vdims=[d for vd in self.vdims for d in [vd, vd+'_std']])
self.compare_dataset(dataset.aggregate(['Gender'], np.mean, np.std), aggregated)示例4: test_dataset_empty_aggregate
# 需要导入模块: from holoviews import Dataset [as 别名]
# 或者: from holoviews.Dataset import aggregate [as 别名]
def test_dataset_empty_aggregate(self):
dataset = Dataset([], kdims=self.kdims, vdims=self.vdims)
aggregated = Dataset([], kdims=self.kdims[:1], vdims=self.vdims)
self.compare_dataset(dataset.aggregate(['Gender'], np.mean), aggregated)示例5: test_dataset_2D_aggregate_partial_ht
# 需要导入模块: from holoviews import Dataset [as 别名]
# 或者: from holoviews.Dataset import aggregate [as 别名]
def test_dataset_2D_aggregate_partial_ht(self):
dataset = Dataset({'x':self.xs, 'y':self.ys, 'z':self.zs},
kdims=['x', 'y'], vdims=['z'])
reduced = Dataset({'x':self.xs, 'z':self.zs},
kdims=['x'], vdims=['z'])
self.assertEqual(dataset.aggregate(['x'], np.mean), reduced)示例6: test_dataset_aggregate_string_types_size
# 需要导入模块: from holoviews import Dataset [as 别名]
# 或者: from holoviews.Dataset import aggregate [as 别名]
def test_dataset_aggregate_string_types_size(self):
ds = Dataset({'Gender':['M', 'M'], 'Weight':[20, 10], 'Name':['Peter', 'Matt']},
kdims='Gender', vdims=['Weight', 'Name'])
aggregated = Dataset({'Gender': ['M'], 'Weight': [2], 'Name': [2]},
kdims='Gender', vdims=['Weight', 'Name'])
self.compare_dataset(ds.aggregate(['Gender'], np.size), aggregated)示例7: HeterogeneousColumnTests
# 需要导入模块: from holoviews import Dataset [as 别名]
# 或者: from holoviews.Dataset import aggregate [as 别名]
#.........这里部分代码省略.........
kdims=['x'], vdims=['y'])
self.assertEqual(dataset.sort('y'), dataset_sorted)
def test_dataset_sort_string_ht(self):
dataset_sorted = Dataset({'Gender':['F', 'M', 'M'], 'Age':[12, 10, 16],
'Weight':[10,15,18], 'Height':[0.8,0.8,0.6]},
kdims=self.kdims, vdims=self.vdims)
self.assertEqual(self.table.sort(), dataset_sorted)
def test_dataset_sample_ht(self):
samples = self.dataset_ht.sample([0, 5, 10]).dimension_values('y')
self.assertEqual(samples, np.array([0, 0.5, 1]))
def test_dataset_reduce_ht(self):
reduced = Dataset({'Age':self.age, 'Weight':self.weight, 'Height':self.height},
kdims=self.kdims[1:], vdims=self.vdims)
self.assertEqual(self.table.reduce(['Gender'], np.mean), reduced)
def test_dataset_1D_reduce_ht(self):
self.assertEqual(self.dataset_ht.reduce('x', np.mean), np.float64(0.5))
def test_dataset_2D_reduce_ht(self):
reduced = Dataset({'Weight':[14.333333333333334], 'Height':[0.73333333333333339]},
kdims=[], vdims=self.vdims)
self.assertEqual(self.table.reduce(function=np.mean), reduced)
def test_dataset_2D_partial_reduce_ht(self):
dataset = Dataset({'x':self.xs, 'y':self.ys, 'z':self.zs},
kdims=['x', 'y'], vdims=['z'])
reduced = Dataset({'x':self.xs, 'z':self.zs},
kdims=['x'], vdims=['z'])
self.assertEqual(dataset.reduce(['y'], np.mean), reduced)
def test_dataset_2D_aggregate_spread_fn_with_duplicates(self):
dataset = Dataset({'x': np.array([0, 0, 1, 1]), 'y': np.array([0, 1, 2, 3]),
'z': np.array([1, 2, 3, 4])},
kdims=['x', 'y'], vdims=['z'])
agg = dataset.aggregate('x', function=np.mean, spreadfn=np.var)
self.assertEqual(agg, Dataset({'x': np.array([0, 1]), 'z': np.array([1.5, 3.5]),
'z_var': np.array([0.25, 0.25])},
kdims=['x'], vdims=['z', 'z_var']))
def test_dataset_aggregate_ht(self):
aggregated = Dataset({'Gender':['M', 'F'], 'Weight':[16.5, 10], 'Height':[0.7, 0.8]},
kdims=self.kdims[:1], vdims=self.vdims)
self.compare_dataset(self.table.aggregate(['Gender'], np.mean), aggregated)
def test_dataset_aggregate_string_types(self):
ds = Dataset({'Gender':['M', 'M'], 'Weight':[20, 10], 'Name':['Peter', 'Matt']},
kdims='Gender', vdims=['Weight', 'Name'])
aggregated = Dataset({'Gender': ['M'], 'Weight': [15]},
kdims='Gender', vdims=['Weight'])
self.compare_dataset(ds.aggregate(['Gender'], np.mean), aggregated)
def test_dataset_aggregate_string_types_size(self):
ds = Dataset({'Gender':['M', 'M'], 'Weight':[20, 10], 'Name':['Peter', 'Matt']},
kdims='Gender', vdims=['Weight', 'Name'])
aggregated = Dataset({'Gender': ['M'], 'Weight': [2], 'Name': [2]},
kdims='Gender', vdims=['Weight', 'Name'])
self.compare_dataset(ds.aggregate(['Gender'], np.size), aggregated)
def test_dataset_aggregate_ht_alias(self):
aggregated = Dataset({'gender':['M', 'F'], 'weight':[16.5, 10], 'height':[0.7, 0.8]},
kdims=self.alias_kdims[:1], vdims=self.alias_vdims)
self.compare_dataset(self.alias_table.aggregate('Gender', np.mean), aggregated)
示例8: test_dataset_2D_aggregate_partial_hm
# 需要导入模块: from holoviews import Dataset [as 别名]
# 或者: from holoviews.Dataset import aggregate [as 别名]
def test_dataset_2D_aggregate_partial_hm(self):
z_ints = [el**2 for el in self.y_ints]
dataset = Dataset({'x':self.xs, 'y':self.y_ints, 'z':z_ints},
kdims=['x', 'y'], vdims=['z'])
self.assertEqual(dataset.aggregate(['x'], np.mean),
Dataset({'x':self.xs, 'z':z_ints}, kdims=['x'], vdims=['z']))示例9: test_aggregate_2d_with_spreadfn
# 需要导入模块: from holoviews import Dataset [as 别名]
# 或者: from holoviews.Dataset import aggregate [as 别名]
def test_aggregate_2d_with_spreadfn(self):
array = np.random.rand(10, 5)
ds = Dataset((range(5), range(10), array), ['x', 'y'], 'z')
agg = ds.aggregate('x', np.mean, np.std)
example = Dataset((range(5), array.mean(axis=0), array.std(axis=0)), 'x', ['z', 'z_std'])
self.assertEqual(agg, example)示例10: HeterogeneousColumnTypes
# 需要导入模块: from holoviews import Dataset [as 别名]
# 或者: from holoviews.Dataset import aggregate [as 别名]
#.........这里部分代码省略.........
kdims=['x'], vdims=['y'])
self.assertEqual(dataset.sort('y'), dataset_sorted)
def test_dataset_sort_string_ht(self):
dataset_sorted = Dataset({'Gender':['F', 'M', 'M'], 'Age':[12, 10, 16],
'Weight':[10,15,18], 'Height':[0.8,0.8,0.6]},
kdims=self.kdims, vdims=self.vdims)
self.assertEqual(self.table.sort(), dataset_sorted)
def test_dataset_sample_ht(self):
samples = self.dataset_ht.sample([0, 5, 10]).dimension_values('y')
self.assertEqual(samples, np.array([0, 0.5, 1]))
def test_dataset_reduce_ht(self):
reduced = Dataset({'Age':self.age, 'Weight':self.weight, 'Height':self.height},
kdims=self.kdims[1:], vdims=self.vdims)
self.assertEqual(self.table.reduce(['Gender'], np.mean), reduced)
def test_dataset_1D_reduce_ht(self):
self.assertEqual(self.dataset_ht.reduce('x', np.mean), np.float64(0.5))
def test_dataset_2D_reduce_ht(self):
reduced = Dataset({'Weight':[14.333333333333334], 'Height':[0.73333333333333339]},
kdims=[], vdims=self.vdims)
self.assertEqual(self.table.reduce(function=np.mean), reduced)
def test_dataset_2D_partial_reduce_ht(self):
dataset = Dataset({'x':self.xs, 'y':self.ys, 'z':self.zs},
kdims=['x', 'y'], vdims=['z'])
reduced = Dataset({'x':self.xs, 'z':self.zs},
kdims=['x'], vdims=['z'])
self.assertEqual(dataset.reduce(['y'], np.mean), reduced)
def test_column_aggregate_ht(self):
aggregated = Dataset({'Gender':['M', 'F'], 'Weight':[16.5, 10], 'Height':[0.7, 0.8]},
kdims=self.kdims[:1], vdims=self.vdims)
self.compare_dataset(self.table.aggregate(['Gender'], np.mean), aggregated)
def test_dataset_2D_aggregate_partial_ht(self):
dataset = Dataset({'x':self.xs, 'y':self.ys, 'z':self.zs},
kdims=['x', 'y'], vdims=['z'])
reduced = Dataset({'x':self.xs, 'z':self.zs},
kdims=['x'], vdims=['z'])
self.assertEqual(dataset.aggregate(['x'], np.mean), reduced)
def test_dataset_groupby(self):
group1 = {'Age':[10,16], 'Weight':[15,18], 'Height':[0.8,0.6]}
group2 = {'Age':[12], 'Weight':[10], 'Height':[0.8]}
grouped = HoloMap([('M', Dataset(group1, kdims=['Age'], vdims=self.vdims)),
('F', Dataset(group2, kdims=['Age'], vdims=self.vdims))],
kdims=['Gender'])
self.assertEqual(self.table.groupby(['Gender']), grouped)
def test_dataset_add_dimensions_value_ht(self):
table = self.dataset_ht.add_dimension('z', 1, 0)
self.assertEqual(table.kdims[1], 'z')
self.compare_arrays(table.dimension_values('z'), np.zeros(len(table)))
def test_dataset_add_dimensions_values_ht(self):
table = self.dataset_ht.add_dimension('z', 1, range(1,12))
self.assertEqual(table.kdims[1], 'z')
self.compare_arrays(table.dimension_values('z'), np.array(list(range(1,12))))
# Indexing