本文整理汇总了Python中sqlalchemy.func.bernoulli方法的典型用法代码示例。如果您正苦于以下问题：Python func.bernoulli方法的具体用法？Python func.bernoulli怎么用？Python func.bernoulli使用的例子？那么, 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sqlalchemy.func的用法示例。
在下文中一共展示了func.bernoulli方法的2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: tablesample
# 需要导入模块: from sqlalchemy import func [as 别名]
# 或者: from sqlalchemy.func import bernoulli [as 别名]
def tablesample(selectable, sampling, name=None, seed=None):
"""Return a :class:`.TableSample` object.
:class:`.TableSample` is an :class:`.Alias` subclass that represents
a table with the TABLESAMPLE clause applied to it.
:func:`~.expression.tablesample`
is also available from the :class:`.FromClause` class via the
:meth:`.FromClause.tablesample` method.
The TABLESAMPLE clause allows selecting a randomly selected approximate
percentage of rows from a table. It supports multiple sampling methods,
most commonly BERNOULLI and SYSTEM.
e.g.::
from sqlalchemy import func
selectable = people.tablesample(
func.bernoulli(1),
name='alias',
seed=func.random())
stmt = select([selectable.c.people_id])
Assuming ``people`` with a column ``people_id``, the above
statement would render as::
SELECT alias.people_id FROM
people AS alias TABLESAMPLE bernoulli(:bernoulli_1)
REPEATABLE (random())
.. versionadded:: 1.1
:param sampling: a ``float`` percentage between 0 and 100 or
:class:`.functions.Function`.
:param name: optional alias name
:param seed: any real-valued SQL expression. When specified, the
REPEATABLE sub-clause is also rendered.
"""
return _interpret_as_from(selectable).tablesample(
sampling, name=name, seed=seed) 示例2: _factory
# 需要导入模块: from sqlalchemy import func [as 别名]
# 或者: from sqlalchemy.func import bernoulli [as 别名]
def _factory(cls, selectable, sampling, name=None, seed=None):
"""Return a :class:`_expression.TableSample` object.
:class:`_expression.TableSample` is an :class:`_expression.Alias`
subclass that represents
a table with the TABLESAMPLE clause applied to it.
:func:`_expression.tablesample`
is also available from the :class:`_expression.FromClause`
class via the
:meth:`_expression.FromClause.tablesample` method.
The TABLESAMPLE clause allows selecting a randomly selected approximate
percentage of rows from a table. It supports multiple sampling methods,
most commonly BERNOULLI and SYSTEM.
e.g.::
from sqlalchemy import func
selectable = people.tablesample(
func.bernoulli(1),
name='alias',
seed=func.random())
stmt = select([selectable.c.people_id])
Assuming ``people`` with a column ``people_id``, the above
statement would render as::
SELECT alias.people_id FROM
people AS alias TABLESAMPLE bernoulli(:bernoulli_1)
REPEATABLE (random())
.. versionadded:: 1.1
:param sampling: a ``float`` percentage between 0 and 100 or
:class:`_functions.Function`.
:param name: optional alias name
:param seed: any real-valued SQL expression. When specified, the
REPEATABLE sub-clause is also rendered.
"""
return coercions.expect(roles.FromClauseRole, selectable).tablesample(
sampling, name=name, seed=seed
)