本文整理汇总了Python中xray.Dataset.groupby方法的典型用法代码示例。如果您正苦于以下问题：Python Dataset.groupby方法的具体用法？Python Dataset.groupby怎么用？Python Dataset.groupby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xray.Dataset的用法示例。
在下文中一共展示了Dataset.groupby方法的7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_groupby
# 需要导入模块: from xray import Dataset [as 别名]
# 或者: from xray.Dataset import groupby [as 别名]
def test_groupby(self):
data = Dataset({'x': ('x', list('abc')),
'c': ('x', [0, 1, 0]),
'z': (['x', 'y'], np.random.randn(3, 5))})
groupby = data.groupby('x')
self.assertEqual(len(groupby), 3)
expected_groups = {'a': 0, 'b': 1, 'c': 2}
self.assertEqual(groupby.groups, expected_groups)
expected_items = [('a', data.isel(x=0)),
('b', data.isel(x=1)),
('c', data.isel(x=2))]
self.assertEqual(list(groupby), expected_items)
identity = lambda x: x
for k in ['x', 'c', 'y']:
actual = data.groupby(k, squeeze=False).apply(identity)
self.assertEqual(data, actual)示例2: test_groupby
# 需要导入模块: from xray import Dataset [as 别名]
# 或者: from xray.Dataset import groupby [as 别名]
def test_groupby(self):
data = Dataset({'x': ('x', list('abc')),
'c': ('x', [0, 1, 0]),
'z': (['x', 'y'], np.random.randn(3, 5))})
groupby = data.groupby('x')
self.assertEqual(len(groupby), 3)
expected_groups = {'a': 0, 'b': 1, 'c': 2}
self.assertEqual(groupby.groups, expected_groups)
expected_items = [('a', data.indexed(x=0)),
('b', data.indexed(x=1)),
('c', data.indexed(x=2))]
self.assertEqual(list(groupby), expected_items)
identity = lambda x: x
for k in ['x', 'c', 'y']:
actual = data.groupby(k, squeeze=False).apply(identity)
self.assertEqual(data, actual)
data = create_test_data()
for n, (t, sub) in enumerate(list(data.groupby('dim1'))[:3]):
self.assertEqual(data['dim1'][n], t)
self.assertVariableEqual(data['var1'][n], sub['var1'])
self.assertVariableEqual(data['var2'][n], sub['var2'])
self.assertVariableEqual(data['var3'][:, n], sub['var3'])
# TODO: test the other edge cases
with self.assertRaisesRegexp(ValueError, 'must be 1 dimensional'):
data.groupby('var1')
with self.assertRaisesRegexp(ValueError, 'length does not match'):
data.groupby(data['dim1'][:3])示例3: test_groupby_reduce
# 需要导入模块: from xray import Dataset [as 别名]
# 或者: from xray.Dataset import groupby [as 别名]
def test_groupby_reduce(self):
data = Dataset({'xy': (['x', 'y'], np.random.randn(3, 4)),
'xonly': ('x', np.random.randn(3)),
'yonly': ('y', np.random.randn(4)),
'letters': ('y', ['a', 'a', 'b', 'b'])})
expected = data.mean('y')
actual = data.groupby('x').mean()
self.assertDatasetAllClose(expected, actual)
actual = data.groupby('x').mean('y')
self.assertDatasetAllClose(expected, actual)
expected = Dataset({'xy': data['xy'].groupby('letters').mean(),
'xonly': data['xonly'].mean(),
'yonly': data['yonly'].groupby('letters').mean()})
actual = data.groupby('letters').mean()
self.assertDatasetAllClose(expected, actual)示例4: test_groupby_returns_new_type
# 需要导入模块: from xray import Dataset [as 别名]
# 或者: from xray.Dataset import groupby [as 别名]
def test_groupby_returns_new_type(self):
data = Dataset({'z': (['x', 'y'], np.random.randn(3, 5))})
actual = data.groupby('x').apply(lambda ds: ds['z'])
expected = data['z']
self.assertDataArrayIdentical(expected, actual)
actual = data['z'].groupby('x').apply(lambda x: x.to_dataset())
expected = data
self.assertDatasetIdentical(expected, actual)示例5: TestDataArray
# 需要导入模块: from xray import Dataset [as 别名]
# 或者: from xray.Dataset import groupby [as 别名]
#.........这里部分代码省略.........
actual = orig.mean(['x', 'y'])
self.assertDataArrayIdentical(expected, actual)
actual = orig.mean('x')
expected = DataArray([-2, 0, 2], {'y': coords['y'], 'c': -999}, 'y')
self.assertDataArrayIdentical(expected, actual)
actual = orig.mean(['x'])
self.assertDataArrayIdentical(expected, actual)
actual = orig.mean('y')
expected = DataArray([0, 0], {'x': coords['x'], 'c': -999}, 'x')
self.assertDataArrayIdentical(expected, actual)
self.assertVariableEqual(self.dv.reduce(np.mean, 'x'),
self.v.reduce(np.mean, 'x'))
orig = DataArray([[1, 0, np.nan], [3, 0, 3]], coords, dims=['x', 'y'])
actual = orig.count()
expected = DataArray(5, {'c': -999})
self.assertDataArrayIdentical(expected, actual)
def test_reduce_keep_attrs(self):
# Test dropped attrs
vm = self.va.mean()
self.assertEqual(len(vm.attrs), 0)
self.assertEqual(vm.attrs, OrderedDict())
# Test kept attrs
vm = self.va.mean(keep_attrs=True)
self.assertEqual(len(vm.attrs), len(self.attrs))
self.assertEqual(vm.attrs, self.attrs)
def test_groupby_iter(self):
for ((act_x, act_dv), (exp_x, exp_ds)) in \
zip(self.dv.groupby('y'), self.ds.groupby('y')):
self.assertEqual(exp_x, act_x)
self.assertDataArrayIdentical(exp_ds['foo'], act_dv)
for ((_, exp_dv), act_dv) in zip(self.dv.groupby('x'), self.dv):
self.assertDataArrayIdentical(exp_dv, act_dv)
def make_groupby_example_array(self):
da = self.dv.copy()
da.coords['abc'] = ('y', np.array(['a'] * 9 + ['c'] + ['b'] * 10))
da.coords['y'] = 20 + 100 * da['y']
return da
def test_groupby_properties(self):
grouped = self.make_groupby_example_array().groupby('abc')
expected_unique = Variable('abc', ['a', 'b', 'c'])
self.assertVariableEqual(expected_unique, grouped.unique_coord)
self.assertEqual(3, len(grouped))
def test_groupby_apply_identity(self):
expected = self.make_groupby_example_array()
idx = expected.coords['y']
identity = lambda x: x
for g in ['x', 'y', 'abc', idx]:
for shortcut in [False, True]:
for squeeze in [False, True]:
grouped = expected.groupby(g, squeeze=squeeze)
actual = grouped.apply(identity, shortcut=shortcut)
self.assertDataArrayIdentical(expected, actual)
def test_groupby_sum(self):
array = self.make_groupby_example_array()示例6: TestDataArray
# 需要导入模块: from xray import Dataset [as 别名]
# 或者: from xray.Dataset import groupby [as 别名]
#.........这里部分代码省略.........
actual = orig.mean()
expected = DataArray(0, {'c': -999})
self.assertDataArrayIdentical(expected, actual)
actual = orig.mean(['x', 'y'])
self.assertDataArrayIdentical(expected, actual)
actual = orig.mean('x')
expected = DataArray([-2, 0, 2], {'y': coords['y'], 'c': -999}, 'y')
self.assertDataArrayIdentical(expected, actual)
actual = orig.mean(['x'])
self.assertDataArrayIdentical(expected, actual)
actual = orig.mean('y')
expected = DataArray([0, 0], {'x': coords['x'], 'c': -999}, 'x')
self.assertDataArrayIdentical(expected, actual)
self.assertVariableEqual(self.dv.reduce(np.mean, 'x'),
self.v.reduce(np.mean, 'x'))
def test_reduce_keep_attrs(self):
# Test dropped attrs
vm = self.va.mean()
self.assertEqual(len(vm.attrs), 0)
self.assertEqual(vm.attrs, OrderedDict())
# Test kept attrs
vm = self.va.mean(keep_attrs=True)
self.assertEqual(len(vm.attrs), len(self.attrs))
self.assertEqual(vm.attrs, self.attrs)
def test_groupby_iter(self):
for ((act_x, act_dv), (exp_x, exp_ds)) in \
zip(self.dv.groupby('y'), self.ds.groupby('y')):
self.assertEqual(exp_x, act_x)
self.assertDataArrayIdentical(exp_ds['foo'], act_dv)
for ((_, exp_dv), act_dv) in zip(self.dv.groupby('x'), self.dv):
self.assertDataArrayIdentical(exp_dv, act_dv)
def make_groupby_example_array(self):
da = self.dv.copy()
da.coords['abc'] = ('y', np.array(['a'] * 9 + ['c'] + ['b'] * 10))
da.coords['y'] = 20 + 100 * da['y']
return da
def test_groupby_properties(self):
grouped = self.make_groupby_example_array().groupby('abc')
expected_unique = Variable('abc', ['a', 'b', 'c'])
self.assertVariableEqual(expected_unique, grouped.unique_coord)
self.assertEqual(3, len(grouped))
def test_groupby_apply_identity(self):
expected = self.make_groupby_example_array()
idx = expected.coords['y']
identity = lambda x: x
for g in ['x', 'y', 'abc', idx]:
for shortcut in [False, True]:
for squeeze in [False, True]:
grouped = expected.groupby(g, squeeze=squeeze)
actual = grouped.apply(identity, shortcut=shortcut)
self.assertDataArrayIdentical(expected, actual)
def test_groupby_sum(self):
array = self.make_groupby_example_array()示例7: TestDataArray
# 需要导入模块: from xray import Dataset [as 别名]
# 或者: from xray.Dataset import groupby [as 别名]
#.........这里部分代码省略.........
self.assertIs(source_ndarray(b.values), x)
self.assertDatasetIdentical(b.dataset, self.ds)
def test_transpose(self):
self.assertVariableEqual(self.dv.variable.transpose(),
self.dv.transpose())
def test_squeeze(self):
self.assertVariableEqual(self.dv.variable.squeeze(), self.dv.squeeze())
def test_reduce(self):
self.assertVariableEqual(self.dv.reduce(np.mean, 'x'),
self.v.reduce(np.mean, 'x'))
# needs more...
# should check which extra dimensions are dropped
def test_reduce_keep_attrs(self):
# Test dropped attrs
vm = self.va.mean()
self.assertEqual(len(vm.attrs), 0)
self.assertEqual(vm.attrs, OrderedDict())
# Test kept attrs
vm = self.va.mean(keep_attrs=True)
self.assertEqual(len(vm.attrs), len(self.attrs))
self.assertEqual(vm.attrs, self.attrs)
def test_unselect(self):
with self.assertRaisesRegexp(ValueError, 'cannot unselect the name'):
self.dv.unselect('foo')
with self.assertRaisesRegexp(ValueError, 'must be a variable in'):
self.dv.unselect('y')
def test_groupby_iter(self):
for ((act_x, act_dv), (exp_x, exp_ds)) in \
zip(self.dv.groupby('y'), self.ds.groupby('y')):
self.assertEqual(exp_x, act_x)
self.assertDataArrayIdentical(exp_ds['foo'], act_dv)
for ((_, exp_dv), act_dv) in zip(self.dv.groupby('x'), self.dv):
self.assertDataArrayIdentical(exp_dv, act_dv)
def test_groupby(self):
agg_var = Variable(['y'], np.array(['a'] * 9 + ['c'] + ['b'] * 10))
self.dv['abc'] = agg_var
self.dv['y'] = 20 + 100 * self.ds['y'].variable
identity = lambda x: x
for g in ['x', 'y', 'abc']:
for shortcut in [False, True]:
for squeeze in [False, True]:
expected = self.dv
grouped = self.dv.groupby(g, squeeze=squeeze)
actual = grouped.apply(identity, shortcut=shortcut)
self.assertDataArrayIdentical(expected, actual)
grouped = self.dv.groupby('abc', squeeze=True)
expected_sum_all = Dataset(
{'foo': Variable(['abc'], np.array([self.x[:, :9].sum(),
self.x[:, 10:].sum(),
self.x[:, 9:10].sum()]).T),
'abc': Variable(['abc'], np.array(['a', 'b', 'c']))})['foo']
self.assertDataArrayAllClose(
expected_sum_all, grouped.reduce(np.sum))
self.assertDataArrayAllClose(
expected_sum_all, grouped.sum())
self.assertDataArrayAllClose(