Python sqlalchemy.func.bernoulli() Examples

The following are 5 code examples of sqlalchemy.func.bernoulli(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module sqlalchemy.func , or try the search function .
Example #1
Source File: selectable.py    From planespotter with MIT License 4 votes vote down vote up 
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)
Example #2
Source File: selectable.py    From pyRevit with GNU General Public License v3.0 4 votes vote down vote up 
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)
Example #3
Source File: selectable.py    From sqlalchemy with MIT License 4 votes vote down vote up 
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
        )
Example #4
Source File: selectable.py    From jarvis with GNU General Public License v2.0 4 votes vote down vote up 
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)
Example #5
Source File: selectable.py    From android_universal with MIT License 4 votes vote down vote up 
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)