Release: 1.0.14 | Release Date: July 6, 2016

# SQLAlchemy 1.0 Documentation

# Column Elements and Expressions

The most fundamental part of the SQL expression API are the “column elements”, which allow for basic SQL expression support. The core of all SQL expression constructs is the `ClauseElement`, which is the base for several sub-branches. The `ColumnElement` class is the fundamental unit used to construct any kind of typed SQL expression.

`sqlalchemy.sql.expression.``and_`(*clauses)

  Produce a conjunction of expressions joined by `AND`.

  E.g.:

      from sqlalchemy import and_

      stmt = select([users_table]).where(
                      and_(
                          users_table.c.name == 'wendy',
                          users_table.c.enrolled == True
                      )
                  )

  The `and_()` conjunction is also available using the Python `&` operator (though note that compound expressions need to be parenthesized in order to function with Python operator precedence behavior):

      stmt = select([users_table]).where(
                      (users_table.c.name == 'wendy') &
                      (users_table.c.enrolled == True)
                  )

  The `and_()` operation is also implicit in some cases; the `Select.where()` method for example can be invoked multiple times against a statement, which will have the effect of each clause being combined using `and_()`:

      stmt = select([users_table]).\
                  where(users_table.c.name == 'wendy').\
                  where(users_table.c.enrolled == True)

  See also

  `or_()`

`sqlalchemy.sql.expression.``asc`(column)

  Produce an ascending `ORDER BY` clause element.

  e.g.:

      from sqlalchemy import asc
      stmt = select([users_table]).order_by(asc(users_table.c.name))

  will produce SQL as:

      SELECT id, name FROM user ORDER BY name ASC

  The `asc()` function is a standalone version of the `ColumnElement.asc()` method available on all SQL expressions, e.g.:

      stmt = select([users_table]).order_by(users_table.c.name.asc())

  Parameters: | column – A `ColumnElement` (e.g. scalar SQL expression) with which to apply the `asc()` operation.

  See also

  `desc()`

  `nullsfirst()`

  `nullslast()`

  `Select.order_by()`

`sqlalchemy.sql.expression.``between`(expr, lower_bound, upper_bound, symmetric=False)

  Produce a `BETWEEN` predicate clause.

  E.g.:

      from sqlalchemy import between
      stmt = select([users_table]).where(between(users_table.c.id, 5, 7))

  Would produce SQL resembling:

      SELECT id, name FROM user WHERE id BETWEEN :id_1 AND :id_2

  The `between()` function is a standalone version of the `ColumnElement.between()` method available on all SQL expressions, as in:

      stmt = select([users_table]).where(users_table.c.id.between(5, 7))

  All arguments passed to `between()`, including the left side column expression, are coerced from Python scalar values if a the value is not a `ColumnElement` subclass. For example, three fixed values can be compared as in:

      print(between(5, 3, 7))

  Which would produce:

      :param_1 BETWEEN :param_2 AND :param_3

  Parameters:

  - expr – a column expression, typically a `ColumnElement` instance or alternatively a Python scalar expression to be coerced into a column expression, serving as the left side of the `BETWEEN` expression.
  - lower_bound – a column or Python scalar expression serving as the lower bound of the right side of the `BETWEEN` expression.
  - upper_bound – a column or Python scalar expression serving as the upper bound of the right side of the `BETWEEN` expression.
  - symmetric –

    if True, will render ” BETWEEN SYMMETRIC ”. Note that not all databases support this syntax.

    New in version 0.9.5.

  See also

  `ColumnElement.between()`

`sqlalchemy.sql.expression.``bindparam`(key, value=symbol('NO_ARG'), type_=None, unique=False, required=symbol('NO_ARG'), quote=None, callable_=None, isoutparam=False, _compared_to_operator=None, _compared_to_type=None)

  Produce a “bound expression”.

  The return value is an instance of `BindParameter`; this is a `ColumnElement` subclass which represents a so-called “placeholder” value in a SQL expression, the value of which is supplied at the point at which the statement in executed against a database connection.

  In SQLAlchemy, the `bindparam()` construct has the ability to carry along the actual value that will be ultimately used at expression time. In this way, it serves not just as a “placeholder” for eventual population, but also as a means of representing so-called “unsafe” values which should not be rendered directly in a SQL statement, but rather should be passed along to the DBAPI as values which need to be correctly escaped and potentially handled for type-safety.

  When using `bindparam()` explicitly, the use case is typically one of traditional deferment of parameters; the `bindparam()` construct accepts a name which can then be referred to at execution time:

      from sqlalchemy import bindparam

      stmt = select([users_table]).\
                  where(users_table.c.name == bindparam('username'))

  The above statement, when rendered, will produce SQL similar to:

      SELECT id, name FROM user WHERE name = :username

  In order to populate the value of `:username` above, the value would typically be applied at execution time to a method like `Connection.execute()`:

      result = connection.execute(stmt, username='wendy')

  Explicit use of `bindparam()` is also common when producing UPDATE or DELETE statements that are to be invoked multiple times, where the WHERE criterion of the statement is to change on each invocation, such as:

      stmt = (users_table.update().
              where(user_table.c.name == bindparam('username')).
              values(fullname=bindparam('fullname'))
              )

      connection.execute(
          stmt, [{"username": "wendy", "fullname": "Wendy Smith"},
                 {"username": "jack", "fullname": "Jack Jones"},
                 ]
      )

  SQLAlchemy’s Core expression system makes wide use of `bindparam()` in an implicit sense. It is typical that Python literal values passed to virtually all SQL expression functions are coerced into fixed `bindparam()` constructs. For example, given a comparison operation such as:

      expr = users_table.c.name == 'Wendy'

  The above expression will produce a `BinaryExpression` construct, where the left side is the `Column` object representing the `name` column, and the right side is a `BindParameter` representing the literal value:

      print(repr(expr.right))
      BindParameter('%(4327771088 name)s', 'Wendy', type_=String())

  The expression above will render SQL such as:

      user.name = :name_1

  Where the `:name_1` parameter name is an anonymous name. The actual string `Wendy` is not in the rendered string, but is carried along where it is later used within statement execution. If we invoke a statement like the following:

      stmt = select([users_table]).where(users_table.c.name == 'Wendy')
      result = connection.execute(stmt)

  We would see SQL logging output as:

      SELECT "user".id, "user".name
      FROM "user"
      WHERE "user".name = %(name_1)s
      {'name_1': 'Wendy'}

  Above, we see that `Wendy` is passed as a parameter to the database, while the placeholder `:name_1` is rendered in the appropriate form for the target database, in this case the Postgresql database.

  Similarly, `bindparam()` is invoked automatically when working with CRUD statements as far as the “VALUES” portion is concerned. The `insert()` construct produces an `INSERT` expression which will, at statement execution time, generate bound placeholders based on the arguments passed, as in:

      stmt = users_table.insert()
      result = connection.execute(stmt, name='Wendy')

  The above will produce SQL output as:

      INSERT INTO "user" (name) VALUES (%(name)s)
      {'name': 'Wendy'}

  The `Insert` construct, at compilation/execution time, rendered a single `bindparam()` mirroring the column name `name` as a result of the single `name` parameter we passed to the `Connection.execute()` method.

  Parameters:

  - key – the key (e.g. the name) for this bind param. Will be used in the generated SQL statement for dialects that use named parameters. This value may be modified when part of a compilation operation, if other `BindParameter` objects exist with the same key, or if its length is too long and truncation is required.
  - value – Initial value for this bind param. Will be used at statement execution time as the value for this parameter passed to the DBAPI, if no other value is indicated to the statement execution method for this particular parameter name. Defaults to `None`.
  - callable_ – A callable function that takes the place of “value”. The function will be called at statement execution time to determine the ultimate value. Used for scenarios where the actual bind value cannot be determined at the point at which the clause construct is created, but embedded bind values are still desirable.
  - type_ –

    A `TypeEngine` class or instance representing an optional datatype for this `bindparam()`. If not passed, a type may be determined automatically for the bind, based on the given value; for example, trivial Python types such as `str`, `int`, `bool` may result in the `String`, `Integer` or `Boolean` types being autoamtically selected.

    The type of a `bindparam()` is significant especially in that the type will apply pre-processing to the value before it is passed to the database. For example, a `bindparam()` which refers to a datetime value, and is specified as holding the `DateTime` type, may apply conversion needed to the value (such as stringification on SQLite) before passing the value to the database.

  - unique – if True, the key name of this `BindParameter` will be modified if another `BindParameter` of the same name already has been located within the containing expression. This flag is used generally by the internals when producing so-called “anonymous” bound expressions, it isn’t generally applicable to explicitly-named `bindparam()` constructs.
  - required –

    If `True`, a value is required at execution time. If not passed, it defaults to `True` if neither `bindparam.value` or `bindparam.callable` were passed. If either of these parameters are present, then `bindparam.required` defaults to `False`.

    Changed in version 0.8: If the `required` flag is not specified, it will be set automatically to `True` or `False` depending on whether or not the `value` or `callable` parameters were specified.

  - quote – True if this parameter name requires quoting and is not currently known as a SQLAlchemy reserved word; this currently only applies to the Oracle backend, where bound names must sometimes be quoted.
  - isoutparam – if True, the parameter should be treated like a stored procedure “OUT” parameter. This applies to backends such as Oracle which support OUT parameters.

  See also

  Bind Parameter Objects

  Insert Expressions

  `outparam()`

`sqlalchemy.sql.expression.``case`(whens, value=None, else_=None)

  Produce a `CASE` expression.

  The `CASE` construct in SQL is a conditional object that acts somewhat analogously to an “if/then” construct in other languages. It returns an instance of `Case`.

  `case()` in its usual form is passed a list of “when” constructs, that is, a list of conditions and results as tuples:

      from sqlalchemy import case

      stmt = select([users_table]).\
                  where(
                      case(
                          [
                              (users_table.c.name == 'wendy', 'W'),
                              (users_table.c.name == 'jack', 'J')
                          ],
                          else_='E'
                      )
                  )

  The above statement will produce SQL resembling:

      SELECT id, name FROM user
      WHERE CASE
          WHEN (name = :name_1) THEN :param_1
          WHEN (name = :name_2) THEN :param_2
          ELSE :param_3
      END

  When simple equality expressions of several values against a single parent column are needed, `case()` also has a “shorthand” format used via the `case.value` parameter, which is passed a column expression to be compared. In this form, the `case.whens` parameter is passed as a dictionary containing expressions to be compared against keyed to result expressions. The statement below is equivalent to the preceding statement:

      stmt = select([users_table]).\
                  where(
                      case(
                          {"wendy": "W", "jack": "J"},
                          value=users_table.c.name,
                          else_='E'
                      )
                  )

  The values which are accepted as result values in `case.whens` as well as with `case.else_` are coerced from Python literals into `bindparam()` constructs. SQL expressions, e.g. `ColumnElement` constructs, are accepted as well. To coerce a literal string expression into a constant expression rendered inline, use the `literal_column()` construct, as in:

      from sqlalchemy import case, literal_column

      case(
          [
              (
                  orderline.c.qty > 100,
                  literal_column("'greaterthan100'")
              ),
              (
                  orderline.c.qty > 10,
                  literal_column("'greaterthan10'")
              )
          ],
          else_=literal_column("'lessthan10'")
      )

  The above will render the given constants without using bound parameters for the result values (but still for the comparison values), as in:

      CASE
          WHEN (orderline.qty > :qty_1) THEN 'greaterthan100'
          WHEN (orderline.qty > :qty_2) THEN 'greaterthan10'
          ELSE 'lessthan10'
      END

  Parameters:

  - whens –

    The criteria to be compared against, `case.whens` accepts two different forms, based on whether or not `case.value` is used.

    In the first form, it accepts a list of 2-tuples; each 2-tuple consists of `(<sql expression>, <value>)`, where the SQL expression is a boolean expression and “value” is a resulting value, e.g.:

        case([
            (users_table.c.name == 'wendy', 'W'),
            (users_table.c.name == 'jack', 'J')
        ])

    In the second form, it accepts a Python dictionary of comparison values mapped to a resulting value; this form requires `case.value` to be present, and values will be compared using the `==` operator, e.g.:

        case(
            {"wendy": "W", "jack": "J"},
            value=users_table.c.name
        )

  - value – An optional SQL expression which will be used as a fixed “comparison point” for candidate values within a dictionary passed to `case.whens`.
  - else_ – An optional SQL expression which will be the evaluated result of the `CASE` construct if all expressions within `case.whens` evaluate to false. When omitted, most databases will produce a result of NULL if none of the “when” expressions evaluate to true.

`sqlalchemy.sql.expression.``cast`(expression, type_)

  Produce a `CAST` expression.

  `cast()` returns an instance of `Cast`.

  E.g.:

      from sqlalchemy import cast, Numeric

      stmt = select([
                  cast(product_table.c.unit_price, Numeric(10, 4))
              ])

  The above statement will produce SQL resembling:

      SELECT CAST(unit_price AS NUMERIC(10, 4)) FROM product

  The `cast()` function performs two distinct functions when used. The first is that it renders the `CAST` expression within the resulting SQL string. The second is that it associates the given type (e.g. `TypeEngine` class or instance) with the column expression on the Python side, which means the expression will take on the expression operator behavior associated with that type, as well as the bound-value handling and result-row-handling behavior of the type.

  Changed in version 0.9.0: `cast()` now applies the given type to the expression such that it takes effect on the bound-value, e.g. the Python-to-database direction, in addition to the result handling, e.g. database-to-Python, direction.

  An alternative to `cast()` is the `type_coerce()` function. This function performs the second task of associating an expression with a specific type, but does not render the `CAST` expression in SQL.

  Parameters:

  - expression – A SQL expression, such as a `ColumnElement` expression or a Python string which will be coerced into a bound literal value.
  - type_ – A `TypeEngine` class or instance indicating the type to which the `CAST` should apply.

  See also

  `type_coerce()` - Python-side type coercion without emitting CAST.

`sqlalchemy.sql.expression.``column`(text, type_=None, is_literal=False, _selectable=None)

  Produce a `ColumnClause` object.

  The `ColumnClause` is a lightweight analogue to the `Column` class. The `column()` function can be invoked with just a name alone, as in:

      from sqlalchemy import column

      id, name = column("id"), column("name")
      stmt = select([id, name]).select_from("user")

  The above statement would produce SQL like:

      SELECT id, name FROM user

  Once constructed, `column()` may be used like any other SQL expression element such as within `select()` constructs:

      from sqlalchemy.sql import column

      id, name = column("id"), column("name")
      stmt = select([id, name]).select_from("user")

  The text handled by `column()` is assumed to be handled like the name of a database column; if the string contains mixed case, special characters, or matches a known reserved word on the target backend, the column expression will render using the quoting behavior determined by the backend. To produce a textual SQL expression that is rendered exactly without any quoting, use `literal_column()` instead, or pass `True` as the value of `column.is_literal`. Additionally, full SQL statements are best handled using the `text()` construct.

  `column()` can be used in a table-like fashion by combining it with the `table()` function (which is the lightweight analogue to `Table`) to produce a working table construct with minimal boilerplate:

      from sqlalchemy import table, column, select

      user = table("user",
              column("id"),
              column("name"),
              column("description"),
      )

      stmt = select([user.c.description]).where(user.c.name == 'wendy')

  A `column()` / `table()` construct like that illustrated above can be created in an ad-hoc fashion and is not associated with any `schema.MetaData`, DDL, or events, unlike its `Table` counterpart.

  Changed in version 1.0.0: `expression.column()` can now be imported from the plain `sqlalchemy` namespace like any other SQL element.

  Parameters:

  - text – the text of the element.
  - type – `types.TypeEngine` object which can associate this `ColumnClause` with a type.
  - is_literal – if True, the `ColumnClause` is assumed to be an exact expression that will be delivered to the output with no quoting rules applied regardless of case sensitive settings. the `literal_column()` function essentially invokes `column()` while passing `is_literal=True`.

  See also

  `Column`

  `literal_column()`

  `table()`

  `text()`

  Using More Specific Text with table(), literal_column(), and column()

`sqlalchemy.sql.expression.``collate`(expression, collation)

  Return the clause `expression COLLATE collation`.

  e.g.:

      collate(mycolumn, 'utf8_bin')

  produces:

      mycolumn COLLATE utf8_bin

`sqlalchemy.sql.expression.``desc`(column)

  Produce a descending `ORDER BY` clause element.

  e.g.:

      from sqlalchemy import desc

      stmt = select([users_table]).order_by(desc(users_table.c.name))

  will produce SQL as:

      SELECT id, name FROM user ORDER BY name DESC

  The `desc()` function is a standalone version of the `ColumnElement.desc()` method available on all SQL expressions, e.g.:

      stmt = select([users_table]).order_by(users_table.c.name.desc())

  Parameters: | column – A `ColumnElement` (e.g. scalar SQL expression) with which to apply the `desc()` operation.

  See also

  `asc()`

  `nullsfirst()`

  `nullslast()`

  `Select.order_by()`

`sqlalchemy.sql.expression.``distinct`(expr)

  Produce an column-expression-level unary `DISTINCT` clause.

  This applies the `DISTINCT` keyword to an individual column expression, and is typically contained within an aggregate function, as in:

      from sqlalchemy import distinct, func
      stmt = select([func.count(distinct(users_table.c.name))])

  The above would produce an expression resembling:

      SELECT COUNT(DISTINCT name) FROM user

  The `distinct()` function is also available as a column-level method, e.g. `ColumnElement.distinct()`, as in:

      stmt = select([func.count(users_table.c.name.distinct())])

  The `distinct()` operator is different from the `Select.distinct()` method of `Select`, which produces a `SELECT` statement with `DISTINCT` applied to the result set as a whole, e.g. a `SELECT DISTINCT` expression. See that method for further information.

  See also

  `ColumnElement.distinct()`

  `Select.distinct()`

  `func`

`sqlalchemy.sql.expression.``extract`(field, expr, **kwargs)

  Return a `Extract` construct.

  This is typically available as `extract()` as well as `func.extract` from the `func` namespace.

`sqlalchemy.sql.expression.``false`()

  Return a `False_` construct.

  E.g.:

      >>> from sqlalchemy import false
      >>> print select([t.c.x]).where(false())
      SELECT x FROM t WHERE false

  A backend which does not support true/false constants will render as an expression against 1 or 0:

      >>> print select([t.c.x]).where(false())
      SELECT x FROM t WHERE 0 = 1

  The `true()` and `false()` constants also feature “short circuit” operation within an `and_()` or `or_()` conjunction:

      >>> print select([t.c.x]).where(or_(t.c.x > 5, true()))
      SELECT x FROM t WHERE true

      >>> print select([t.c.x]).where(and_(t.c.x > 5, false()))
      SELECT x FROM t WHERE false

  Changed in version 0.9: `true()` and `false()` feature better integrated behavior within conjunctions and on dialects that don’t support true/false constants.

  See also

  `true()`

`sqlalchemy.sql.expression.``func` = <sqlalchemy.sql.functions._FunctionGenerator object>

  Generate SQL function expressions.

  `func` is a special object instance which generates SQL functions based on name-based attributes, e.g.:

      >>> print(func.count(1))
      count(:param_1)

  The element is a column-oriented SQL element like any other, and is used in that way:

      >>> print(select([func.count(table.c.id)]))
      SELECT count(sometable.id) FROM sometable

  Any name can be given to `func`. If the function name is unknown to SQLAlchemy, it will be rendered exactly as is. For common SQL functions which SQLAlchemy is aware of, the name may be interpreted as a generic function which will be compiled appropriately to the target database:

      >>> print(func.current_timestamp())
      CURRENT_TIMESTAMP

  To call functions which are present in dot-separated packages, specify them in the same manner:

      >>> print(func.stats.yield_curve(5, 10))
      stats.yield_curve(:yield_curve_1, :yield_curve_2)

  SQLAlchemy can be made aware of the return type of functions to enable type-specific lexical and result-based behavior. For example, to ensure that a string-based function returns a Unicode value and is similarly treated as a string in expressions, specify `Unicode` as the type:

      >>> print(func.my_string(u'hi', type_=Unicode) + ' ' +
      ...       func.my_string(u'there', type_=Unicode))
      my_string(:my_string_1) || :my_string_2 || my_string(:my_string_3)

  The object returned by a `func` call is usually an instance of `Function`. This object meets the “column” interface, including comparison and labeling functions. The object can also be passed the `execute()` method of a `Connection` or `Engine`, where it will be wrapped inside of a SELECT statement first:

      print(connection.execute(func.current_timestamp()).scalar())

  In a few exception cases, the `func` accessor will redirect a name to a built-in expression such as `cast()` or `extract()`, as these names have well-known meaning but are not exactly the same as “functions” from a SQLAlchemy perspective.

  New in version 0.8: `func` can return non-function expression constructs for common quasi-functional names like `cast()` and `extract()`.

  Functions which are interpreted as “generic” functions know how to calculate their return type automatically. For a listing of known generic functions, see SQL and Generic Functions.

  Note

  The `func` construct has only limited support for calling standalone “stored procedures”, especially those with special parameterization concerns.

  See the section Calling Stored Procedures for details on how to use the DBAPI-level `callproc()` method for fully traditional stored procedures.

`sqlalchemy.sql.expression.``funcfilter`(func, *criterion)

  Produce a `FunctionFilter` object against a function.

  Used against aggregate and window functions, for database backends that support the “FILTER” clause.

  E.g.:

      from sqlalchemy import funcfilter
      funcfilter(func.count(1), MyClass.name == 'some name')

  Would produce “COUNT(1) FILTER (WHERE myclass.name = ‘some name’)”.

  This function is also available from the `func` construct itself via the `FunctionElement.filter()` method.

  New in version 1.0.0.

  See also

  `FunctionElement.filter()`

`sqlalchemy.sql.expression.``label`(name, element, type_=None)

  Return a `Label` object for the given `ColumnElement`.

  A label changes the name of an element in the columns clause of a `SELECT` statement, typically via the `AS` SQL keyword.

  This functionality is more conveniently available via the `ColumnElement.label()` method on `ColumnElement`.

  Parameters:

  - name – label name
  - obj – a `ColumnElement`.

`sqlalchemy.sql.expression.``literal`(value, type_=None)

  Return a literal clause, bound to a bind parameter.

  Literal clauses are created automatically when non- `ClauseElement` objects (such as strings, ints, dates, etc.) are used in a comparison operation with a `ColumnElement` subclass, such as a `Column` object. Use this function to force the generation of a literal clause, which will be created as a `BindParameter` with a bound value.

  Parameters:

  - value – the value to be bound. Can be any Python object supported by the underlying DB-API, or is translatable via the given type argument.
  - type_ – an optional `TypeEngine` which will provide bind-parameter translation for this literal.

`sqlalchemy.sql.expression.``literal_column`(text, type_=None)

  Produce a `ColumnClause` object that has the `column.is_literal` flag set to True.

  `literal_column()` is similar to `column()`, except that it is more often used as a “standalone” column expression that renders exactly as stated; while `column()` stores a string name that will be assumed to be part of a table and may be quoted as such, `literal_column()` can be that, or any other arbitrary column-oriented expression.

  Parameters:

  - text – the text of the expression; can be any SQL expression. Quoting rules will not be applied. To specify a column-name expression which should be subject to quoting rules, use the `column()` function.
  - type_ – an optional `TypeEngine` object which will provide result-set translation and additional expression semantics for this column. If left as None the type will be NullType.

  See also

  `column()`

  `text()`

  Using More Specific Text with table(), literal_column(), and column()

`sqlalchemy.sql.expression.``not_`(clause)

  Return a negation of the given clause, i.e. `NOT(clause)`.

  The `~` operator is also overloaded on all `ColumnElement` subclasses to produce the same result.

`sqlalchemy.sql.expression.``null`()

  Return a constant `Null` construct.

`sqlalchemy.sql.expression.``nullsfirst`(column)

  Produce the `NULLS FIRST` modifier for an `ORDER BY` expression.

  `nullsfirst()` is intended to modify the expression produced by `asc()` or `desc()`, and indicates how NULL values should be handled when they are encountered during ordering:

      from sqlalchemy import desc, nullsfirst

      stmt = select([users_table]).\
                  order_by(nullsfirst(desc(users_table.c.name)))

  The SQL expression from the above would resemble:

      SELECT id, name FROM user ORDER BY name DESC NULLS FIRST

  Like `asc()` and `desc()`, `nullsfirst()` is typically invoked from the column expression itself using `ColumnElement.nullsfirst()`, rather than as its standalone function version, as in:

      stmt = (select([users_table]).
              order_by(users_table.c.name.desc().nullsfirst())
              )

  See also

  `asc()`

  `desc()`

  `nullslast()`

  `Select.order_by()`

`sqlalchemy.sql.expression.``nullslast`(column)

  Produce the `NULLS LAST` modifier for an `ORDER BY` expression.

  `nullslast()` is intended to modify the expression produced by `asc()` or `desc()`, and indicates how NULL values should be handled when they are encountered during ordering:

      from sqlalchemy import desc, nullslast

      stmt = select([users_table]).\
                  order_by(nullslast(desc(users_table.c.name)))

  The SQL expression from the above would resemble:

      SELECT id, name FROM user ORDER BY name DESC NULLS LAST

  Like `asc()` and `desc()`, `nullslast()` is typically invoked from the column expression itself using `ColumnElement.nullslast()`, rather than as its standalone function version, as in:

      stmt = select([users_table]).\
                  order_by(users_table.c.name.desc().nullslast())

  See also

  `asc()`

  `desc()`

  `nullsfirst()`

  `Select.order_by()`

`sqlalchemy.sql.expression.``or_`(*clauses)

  Produce a conjunction of expressions joined by `OR`.

  E.g.:

      from sqlalchemy import or_

      stmt = select([users_table]).where(
                      or_(
                          users_table.c.name == 'wendy',
                          users_table.c.name == 'jack'
                      )
                  )

  The `or_()` conjunction is also available using the Python `|` operator (though note that compound expressions need to be parenthesized in order to function with Python operator precedence behavior):

      stmt = select([users_table]).where(
                      (users_table.c.name == 'wendy') |
                      (users_table.c.name == 'jack')
                  )

  See also

  `and_()`

`sqlalchemy.sql.expression.``outparam`(key, type_=None)

  Create an ‘OUT’ parameter for usage in functions (stored procedures), for databases which support them.

  The `outparam` can be used like a regular function parameter. The “output” value will be available from the `ResultProxy` object via its `out_parameters` attribute, which returns a dictionary containing the values.

`sqlalchemy.sql.expression.``over`(func, partition_by=None, order_by=None)

  Produce an `Over` object against a function.

  Used against aggregate or so-called “window” functions, for database backends that support window functions.

  E.g.:

      from sqlalchemy import over
      over(func.row_number(), order_by='x')

  Would produce “ROW_NUMBER() OVER(ORDER BY x)”.

  Parameters:

  - func – a `FunctionElement` construct, typically generated by `func`.
  - partition_by – a column element or string, or a list of such, that will be used as the PARTITION BY clause of the OVER construct.
  - order_by – a column element or string, or a list of such, that will be used as the ORDER BY clause of the OVER construct.

  This function is also available from the `func` construct itself via the `FunctionElement.over()` method.

  New in version 0.7.

`sqlalchemy.sql.expression.``text`(text, bind=None, bindparams=None, typemap=None, autocommit=None)

  Construct a new `TextClause` clause, representing a textual SQL string directly.

  E.g.:

      from sqlalchemy import text

      t = text("SELECT * FROM users")
      result = connection.execute(t)

  The advantages `text()` provides over a plain string are backend-neutral support for bind parameters, per-statement execution options, as well as bind parameter and result-column typing behavior, allowing SQLAlchemy type constructs to play a role when executing a statement that is specified literally. The construct can also be provided with a `.c` collection of column elements, allowing it to be embedded in other SQL expression constructs as a subquery.

  Bind parameters are specified by name, using the format `:name`. E.g.:

      t = text("SELECT * FROM users WHERE id=:user_id")
      result = connection.execute(t, user_id=12)

  For SQL statements where a colon is required verbatim, as within an inline string, use a backslash to escape:

      t = text("SELECT * FROM users WHERE name='\:username'")

  The `TextClause` construct includes methods which can provide information about the bound parameters as well as the column values which would be returned from the textual statement, assuming it’s an executable SELECT type of statement. The `TextClause.bindparams()` method is used to provide bound parameter detail, and `TextClause.columns()` method allows specification of return columns including names and types:

      t = text("SELECT * FROM users WHERE id=:user_id").\
              bindparams(user_id=7).\
              columns(id=Integer, name=String)

      for id, name in connection.execute(t):
          print(id, name)

  The `text()` construct is used in cases when a literal string SQL fragment is specified as part of a larger query, such as for the WHERE clause of a SELECT statement:

      s = select([users.c.id, users.c.name]).where(text("id=:user_id"))
      result = connection.execute(s, user_id=12)

  `text()` is also used for the construction of a full, standalone statement using plain text. As such, SQLAlchemy refers to it as an `Executable` object, and it supports the `Executable.execution_options()` method. For example, a `text()` construct that should be subject to “autocommit” can be set explicitly so using the `Connection.execution_options.autocommit` option:

      t = text("EXEC my_procedural_thing()").\
              execution_options(autocommit=True)

  Note that SQLAlchemy’s usual “autocommit” behavior applies to `text()` constructs implicitly - that is, statements which begin with a phrase such as `INSERT`, `UPDATE`, `DELETE`, or a variety of other phrases specific to certain backends, will be eligible for autocommit if no transaction is in progress.

  Parameters:

  - text – the text of the SQL statement to be created. use `:<param>` to specify bind parameters; they will be compiled to their engine-specific format.
  - autocommit – Deprecated. Use .execution_options(autocommit=<True|False>) to set the autocommit option.
  - bind – an optional connection or engine to be used for this text query.
  - bindparams –

    Deprecated. A list of `bindparam()` instances used to provide information about parameters embedded in the statement. This argument now invokes the `TextClause.bindparams()` method on the construct before returning it. E.g.:

        stmt = text("SELECT * FROM table WHERE id=:id",
                  bindparams=[bindparam('id', value=5, type_=Integer)])

    Is equivalent to:

        stmt = text("SELECT * FROM table WHERE id=:id").\
                  bindparams(bindparam('id', value=5, type_=Integer))

    Deprecated since version 0.9.0: the `TextClause.bindparams()` method supersedes the `bindparams` argument to `text()`.

  - typemap –

    Deprecated. A dictionary mapping the names of columns represented in the columns clause of a `SELECT` statement to type objects, which will be used to perform post-processing on columns within the result set. This parameter now invokes the `TextClause.columns()` method, which returns a `TextAsFrom` construct that gains a `.c` collection and can be embedded in other expressions. E.g.:

        stmt = text("SELECT * FROM table",
                      typemap={'id': Integer, 'name': String},
                  )

    Is equivalent to:

        stmt = text("SELECT * FROM table").columns(id=Integer,
                                                   name=String)

    Or alternatively:

        from sqlalchemy.sql import column
        stmt = text("SELECT * FROM table").columns(
                              column('id', Integer),
                              column('name', String)
                          )

    Deprecated since version 0.9.0: the `TextClause.columns()` method supersedes the `typemap` argument to `text()`.

`sqlalchemy.sql.expression.``true`()

  Return a constant `True_` construct.

  E.g.:

      >>> from sqlalchemy import true
      >>> print select([t.c.x]).where(true())
      SELECT x FROM t WHERE true

  A backend which does not support true/false constants will render as an expression against 1 or 0:

      >>> print select([t.c.x]).where(true())
      SELECT x FROM t WHERE 1 = 1

  The `true()` and `false()` constants also feature “short circuit” operation within an `and_()` or `or_()` conjunction:

      >>> print select([t.c.x]).where(or_(t.c.x > 5, true()))
      SELECT x FROM t WHERE true

      >>> print select([t.c.x]).where(and_(t.c.x > 5, false()))
      SELECT x FROM t WHERE false

  Changed in version 0.9: `true()` and `false()` feature better integrated behavior within conjunctions and on dialects that don’t support true/false constants.

  See also

  `false()`

`sqlalchemy.sql.expression.``tuple_`(*clauses, **kw)

  Return a `Tuple`.

  Main usage is to produce a composite IN construct:

      from sqlalchemy import tuple_

      tuple_(table.c.col1, table.c.col2).in_(
          [(1, 2), (5, 12), (10, 19)]
      )

  Warning

  The composite IN construct is not supported by all backends, and is currently known to work on Postgresql and MySQL, but not SQLite. Unsupported backends will raise a subclass of `DBAPIError` when such an expression is invoked.

`sqlalchemy.sql.expression.``type_coerce`(expression, type_)

  Associate a SQL expression with a particular type, without rendering `CAST`.

  E.g.:

      from sqlalchemy import type_coerce

      stmt = select([type_coerce(log_table.date_string, StringDateTime())])

  The above construct will produce SQL that is usually otherwise unaffected by the `type_coerce()` call:

      SELECT date_string FROM log

  However, when result rows are fetched, the `StringDateTime` type will be applied to result rows on behalf of the `date_string` column.

  A type that features bound-value handling will also have that behavior take effect when literal values or `bindparam()` constructs are passed to `type_coerce()` as targets. For example, if a type implements the `TypeEngine.bind_expression()` method or `TypeEngine.bind_processor()` method or equivalent, these functions will take effect at statement compilation/execution time when a literal value is passed, as in:

      # bound-value handling of MyStringType will be applied to the
      # literal value "some string"
      stmt = select([type_coerce("some string", MyStringType)])

  `type_coerce()` is similar to the `cast()` function, except that it does not render the `CAST` expression in the resulting statement.

  Parameters:

  - expression – A SQL expression, such as a `ColumnElement` expression or a Python string which will be coerced into a bound literal value.
  - type_ – A `TypeEngine` class or instance indicating the type to which the expression is coerced.

  See also

  `cast()`

class `sqlalchemy.sql.expression.``BinaryExpression`(left, right, operator, type_=None, negate=None, modifiers=None)

  Bases: `sqlalchemy.sql.expression.ColumnElement`

  Represent an expression that is `LEFT <operator> RIGHT`.

  A `BinaryExpression` is generated automatically whenever two column expressions are used in a Python binary expression:

      >>> from sqlalchemy.sql import column
      >>> column('a') + column('b')
      <sqlalchemy.sql.expression.BinaryExpression object at 0x101029dd0>
      >>> print column('a') + column('b')
      a + b

  `compare`(other, **kw)

    Compare this `BinaryExpression` against the given `BinaryExpression`.

class `sqlalchemy.sql.expression.``BindParameter`(key, value=symbol('NO_ARG'), type_=None, unique=False, required=symbol('NO_ARG'), quote=None, callable_=None, isoutparam=False, _compared_to_operator=None, _compared_to_type=None)

  Bases: `sqlalchemy.sql.expression.ColumnElement`

  Represent a “bound expression”.

  `BindParameter` is invoked explicitly using the `bindparam()` function, as in:

      from sqlalchemy import bindparam

      stmt = select([users_table]).\
                  where(users_table.c.name == bindparam('username'))

  Detailed discussion of how `BindParameter` is used is at `bindparam()`.

  See also

  `bindparam()`

  `__init__`(key, value=symbol('NO_ARG'), type_=None, unique=False, required=symbol('NO_ARG'), quote=None, callable_=None, isoutparam=False, _compared_to_operator=None, _compared_to_type=None)

    Construct a new `BindParameter` object.

    This constructor is mirrored as a public API function; see `bindparam()` for a full usage and argument description.

  `compare`(other, **kw)

    Compare this `BindParameter` to the given clause.

  `effective_value`

    Return the value of this bound parameter, taking into account if the `callable` parameter was set.

    The `callable` value will be evaluated and returned if present, else `value`.

class `sqlalchemy.sql.expression.``Case`(whens, value=None, else_=None)

  Bases: `sqlalchemy.sql.expression.ColumnElement`

  Represent a `CASE` expression.

  `Case` is produced using the `case()` factory function, as in:

      from sqlalchemy import case

      stmt = select([users_table]).\
                  where(
                      case(
                          [
                              (users_table.c.name == 'wendy', 'W'),
                              (users_table.c.name == 'jack', 'J')
                          ],
                          else_='E'
                      )
                  )

  Details on `Case` usage is at `case()`.

  See also

  `case()`

  `__init__`(whens, value=None, else_=None)

    Construct a new `Case` object.

    This constructor is mirrored as a public API function; see `case()` for a full usage and argument description.

class `sqlalchemy.sql.expression.``Cast`(expression, type_)

  Bases: `sqlalchemy.sql.expression.ColumnElement`

  Represent a `CAST` expression.

  `Cast` is produced using the `cast()` factory function, as in:

      from sqlalchemy import cast, Numeric

      stmt = select([
                  cast(product_table.c.unit_price, Numeric(10, 4))
              ])

  Details on `Cast` usage is at `cast()`.

  See also

  `cast()`

  `__init__`(expression, type_)

    Construct a new `Cast` object.

    This constructor is mirrored as a public API function; see `cast()` for a full usage and argument description.

class `sqlalchemy.sql.expression.``ClauseElement`

  Bases: `sqlalchemy.sql.visitors.Visitable`

  Base class for elements of a programmatically constructed SQL expression.

  `compare`(other, **kw)

    Compare this ClauseElement to the given ClauseElement.

    Subclasses should override the default behavior, which is a straight identity comparison.

    **kw are arguments consumed by subclass compare() methods and may be used to modify the criteria for comparison. (see `ColumnElement`)

  `compile`(bind=None, dialect=None, **kw)

    Compile this SQL expression.

    The return value is a `Compiled` object. Calling `str()` or `unicode()` on the returned value will yield a string representation of the result. The `Compiled` object also can return a dictionary of bind parameter names and values using the `params` accessor.

    Parameters:

    - bind – An `Engine` or `Connection` from which a `Compiled` will be acquired. This argument takes precedence over this `ClauseElement`‘s bound engine, if any.
    - column_keys – Used for INSERT and UPDATE statements, a list of column names which should be present in the VALUES clause of the compiled statement. If `None`, all columns from the target table object are rendered.
    - dialect – A `Dialect` instance from which a `Compiled` will be acquired. This argument takes precedence over the bind argument as well as this `ClauseElement`‘s bound engine, if any.
    - inline – Used for INSERT statements, for a dialect which does not support inline retrieval of newly generated primary key columns, will force the expression used to create the new primary key value to be rendered inline within the INSERT statement’s VALUES clause. This typically refers to Sequence execution but may also refer to any server-side default generation function associated with a primary key Column.
    - compile_kwargs –

      optional dictionary of additional parameters that will be passed through to the compiler within all “visit” methods. This allows any custom flag to be passed through to a custom compilation construct, for example. It is also used for the case of passing the `literal_binds` flag through:

          from sqlalchemy.sql import table, column, select

          t = table('t', column('x'))

          s = select([t]).where(t.c.x == 5)

          print s.compile(compile_kwargs={"literal_binds": True})

      New in version 0.9.0.

    See also

    How do I render SQL expressions as strings, possibly with bound parameters inlined?

  `get_children`(**kwargs)

    Return immediate child elements of this `ClauseElement`.

    This is used for visit traversal.

    **kwargs may contain flags that change the collection that is returned, for example to return a subset of items in order to cut down on larger traversals, or to return child items from a different context (such as schema-level collections instead of clause-level).

  `params`(*optionaldict, **kwargs)

    Return a copy with `bindparam()` elements replaced.

    Returns a copy of this ClauseElement with `bindparam()` elements replaced with values taken from the given dictionary:

        >>> clause = column('x') + bindparam('foo')
        >>> print clause.compile().params
        {'foo':None}
        >>> print clause.params({'foo':7}).compile().params
        {'foo':7}

  `self_group`(against=None)

    Apply a ‘grouping’ to this `ClauseElement`.

    This method is overridden by subclasses to return a “grouping” construct, i.e. parenthesis. In particular it’s used by “binary” expressions to provide a grouping around themselves when placed into a larger expression, as well as by `select()` constructs when placed into the FROM clause of another `select()`. (Note that subqueries should be normally created using the `Select.alias()` method, as many platforms require nested SELECT statements to be named).

    As expressions are composed together, the application of `self_group()` is automatic - end-user code should never need to use this method directly. Note that SQLAlchemy’s clause constructs take operator precedence into account - so parenthesis might not be needed, for example, in an expression like `x OR (y AND z)` - AND takes precedence over OR.

    The base `self_group()` method of `ClauseElement` just returns self.

  `unique_params`(*optionaldict, **kwargs)

    Return a copy with `bindparam()` elements replaced.

    Same functionality as `params()`, except adds unique=True to affected bind parameters so that multiple statements can be used.

class `sqlalchemy.sql.expression.``ClauseList`(*clauses, **kwargs)

  Bases: `sqlalchemy.sql.expression.ClauseElement`

  Describe a list of clauses, separated by an operator.

  By default, is comma-separated, such as a column listing.

  `compare`(other, **kw)

    Compare this `ClauseList` to the given `ClauseList`, including a comparison of all the clause items.

class `sqlalchemy.sql.expression.``ColumnClause`(text, type_=None, is_literal=False, _selectable=None)

  Bases: `sqlalchemy.sql.expression.Immutable`, `sqlalchemy.sql.expression.ColumnElement`

  Represents a column expression from any textual string.

  The `ColumnClause`, a lightweight analogue to the `Column` class, is typically invoked using the `column()` function, as in:

      from sqlalchemy import column

      id, name = column("id"), column("name")
      stmt = select([id, name]).select_from("user")

  The above statement would produce SQL like:

      SELECT id, name FROM user

  `ColumnClause` is the immediate superclass of the schema-specific `Column` object. While the `Column` class has all the same capabilities as `ColumnClause`, the `ColumnClause` class is usable by itself in those cases where behavioral requirements are limited to simple SQL expression generation. The object has none of the associations with schema-level metadata or with execution-time behavior that `Column` does, so in that sense is a “lightweight” version of `Column`.

  Full details on `ColumnClause` usage is at `column()`.

  See also

  `column()`

  `Column`

  `__init__`(text, type_=None, is_literal=False, _selectable=None)

    Construct a new `ColumnClause` object.

    This constructor is mirrored as a public API function; see `column()` for a full usage and argument description.

class `sqlalchemy.sql.expression.``ColumnCollection`(*columns)

  Bases: `sqlalchemy.util._collections.OrderedProperties`

  An ordered dictionary that stores a list of ColumnElement instances.

  Overrides the `__eq__()` method to produce SQL clauses between sets of correlated columns.

  `add`(column)

    Add a column to this collection.

    The key attribute of the column will be used as the hash key for this dictionary.

  `replace`(column)

    add the given column to this collection, removing unaliased versions of this column as well as existing columns with the same key.

    > e.g.:

    >     t = Table('sometable', metadata, Column('col1', Integer))
    >     t.columns.replace(Column('col1', Integer, key='columnone'))

    > will remove the original ‘col1’ from the collection, and add the new column under the name ‘columnname’.

    Used by schema.Column to override columns during table reflection.

class `sqlalchemy.sql.expression.``ColumnElement`

  Bases: `sqlalchemy.sql.operators.ColumnOperators`, `sqlalchemy.sql.expression.ClauseElement`

  Represent a column-oriented SQL expression suitable for usage in the “columns” clause, WHERE clause etc. of a statement.

  While the most familiar kind of `ColumnElement` is the `Column` object, `ColumnElement` serves as the basis for any unit that may be present in a SQL expression, including the expressions themselves, SQL functions, bound parameters, literal expressions, keywords such as `NULL`, etc. `ColumnElement` is the ultimate base class for all such elements.

  A wide variety of SQLAlchemy Core functions work at the SQL expression level, and are intended to accept instances of `ColumnElement` as arguments. These functions will typically document that they accept a “SQL expression” as an argument. What this means in terms of SQLAlchemy usually refers to an input which is either already in the form of a `ColumnElement` object, or a value which can be coerced into one. The coercion rules followed by most, but not all, SQLAlchemy Core functions with regards to SQL expressions are as follows:

  > - a literal Python value, such as a string, integer or floating point value, boolean, datetime, `Decimal` object, or virtually any other Python object, will be coerced into a “literal bound value”. This generally means that a `bindparam()` will be produced featuring the given value embedded into the construct; the resulting `BindParameter` object is an instance of `ColumnElement`. The Python value will ultimately be sent to the DBAPI at execution time as a paramterized argument to the `execute()` or `executemany()` methods, after SQLAlchemy type-specific converters (e.g. those provided by any associated `TypeEngine` objects) are applied to the value.
  > - any special object value, typically ORM-level constructs, which feature a method called `__clause_element__()`. The Core expression system looks for this method when an object of otherwise unknown type is passed to a function that is looking to coerce the argument into a `ColumnElement` expression. The `__clause_element__()` method, if present, should return a `ColumnElement` instance. The primary use of `__clause_element__()` within SQLAlchemy is that of class-bound attributes on ORM-mapped classes; a `User` class which contains a mapped attribute named `.name` will have a method `User.name.__clause_element__()` which when invoked returns the `Column` called `name` associated with the mapped table.
  > - The Python `None` value is typically interpreted as `NULL`, which in SQLAlchemy Core produces an instance of `null()`.

  A `ColumnElement` provides the ability to generate new `ColumnElement` objects using Python expressions. This means that Python operators such as `==`, `!=` and `<` are overloaded to mimic SQL operations, and allow the instantiation of further `ColumnElement` instances which are composed from other, more fundamental `ColumnElement` objects. For example, two `ColumnClause` objects can be added together with the addition operator `+` to produce a `BinaryExpression`. Both `ColumnClause` and `BinaryExpression` are subclasses of `ColumnElement`:

      >>> from sqlalchemy.sql import column
      >>> column('a') + column('b')
      <sqlalchemy.sql.expression.BinaryExpression object at 0x101029dd0>
      >>> print column('a') + column('b')
      a + b

  See also

  `Column`

  `expression.column()`

  `__eq__`(other)
    inherited from the `__eq__()` method of `ColumnOperators`

    Implement the `==` operator.

    In a column context, produces the clause `a = b`. If the target is `None`, produces `a IS NULL`.

  `__init__`
    inherited from the `__init__` attribute of `object`

    x.__init__(...) initializes x; see help(type(x)) for signature

  `__le__`(other)
    inherited from the `__le__()` method of `ColumnOperators`

    Implement the `<=` operator.

    In a column context, produces the clause `a <= b`.

  `__lt__`(other)
    inherited from the `__lt__()` method of `ColumnOperators`

    Implement the `<` operator.

    In a column context, produces the clause `a < b`.

  `__ne__`(other)
    inherited from the `__ne__()` method of `ColumnOperators`

    Implement the `!=` operator.

    In a column context, produces the clause `a != b`. If the target is `None`, produces `a IS NOT NULL`.

  `anon_label`

    provides a constant ‘anonymous label’ for this ColumnElement.

    This is a label() expression which will be named at compile time. The same label() is returned each time anon_label is called so that expressions can reference anon_label multiple times, producing the same label name at compile time.

    the compiler uses this function automatically at compile time for expressions that are known to be ‘unnamed’ like binary expressions and function calls.

  `asc`()
    inherited from the `asc()` method of `ColumnOperators`

    Produce a `asc()` clause against the parent object.

  `base_columns`
  `between`(cleft, cright, symmetric=False)
    inherited from the `between()` method of `ColumnOperators`

    Produce a `between()` clause against the parent object, given the lower and upper range.

  `bind` = None
  `cast`(type_)

    Produce a type cast, i.e. `CAST(<expression> AS <type>)`.

    This is a shortcut to the `cast()` function.

    New in version 1.0.7.

  `collate`(collation)
    inherited from the `collate()` method of `ColumnOperators`

    Produce a `collate()` clause against the parent object, given the collation string.

  `comparator`
  `compare`(other, use_proxies=False, equivalents=None, **kw)

    Compare this ColumnElement to another.

    Special arguments understood:

    Parameters:

    - use_proxies – when True, consider two columns that share a common base column as equivalent (i.e. shares_lineage())
    - equivalents – a dictionary of columns as keys mapped to sets of columns. If the given “other” column is present in this dictionary, if any of the columns in the corresponding set() pass the comparison test, the result is True. This is used to expand the comparison to other columns that may be known to be equivalent to this one via foreign key or other criterion.

  `compile`(bind=None, dialect=None, **kw)
    inherited from the `compile()` method of `ClauseElement`

    Compile this SQL expression.

    The return value is a `Compiled` object. Calling `str()` or `unicode()` on the returned value will yield a string representation of the result. The `Compiled` object also can return a dictionary of bind parameter names and values using the `params` accessor.

    Parameters:

    - bind – An `Engine` or `Connection` from which a `Compiled` will be acquired. This argument takes precedence over this `ClauseElement`‘s bound engine, if any.
    - column_keys – Used for INSERT and UPDATE statements, a list of column names which should be present in the VALUES clause of the compiled statement. If `None`, all columns from the target table object are rendered.
    - dialect – A `Dialect` instance from which a `Compiled` will be acquired. This argument takes precedence over the bind argument as well as this `ClauseElement`‘s bound engine, if any.
    - inline – Used for INSERT statements, for a dialect which does not support inline retrieval of newly generated primary key columns, will force the expression used to create the new primary key value to be rendered inline within the INSERT statement’s VALUES clause. This typically refers to Sequence execution but may also refer to any server-side default generation function associated with a primary key Column.
    - compile_kwargs –

      optional dictionary of additional parameters that will be passed through to the compiler within all “visit” methods. This allows any custom flag to be passed through to a custom compilation construct, for example. It is also used for the case of passing the `literal_binds` flag through:

          from sqlalchemy.sql import table, column, select

          t = table('t', column('x'))

          s = select([t]).where(t.c.x == 5)

          print s.compile(compile_kwargs={"literal_binds": True})

      New in version 0.9.0.

    See also

    How do I render SQL expressions as strings, possibly with bound parameters inlined?

  `concat`(other)
    inherited from the `concat()` method of `ColumnOperators`

    Implement the ‘concat’ operator.

    In a column context, produces the clause `a || b`, or uses the `concat()` operator on MySQL.

  `contains`(other, **kwargs)
    inherited from the `contains()` method of `ColumnOperators`

    Implement the ‘contains’ operator.

    In a column context, produces the clause `LIKE '%<other>%'`

  `desc`()
    inherited from the `desc()` method of `ColumnOperators`

    Produce a `desc()` clause against the parent object.

  `description` = None
  `distinct`()
    inherited from the `distinct()` method of `ColumnOperators`

    Produce a `distinct()` clause against the parent object.

  `endswith`(other, **kwargs)
    inherited from the `endswith()` method of `ColumnOperators`

    Implement the ‘endswith’ operator.

    In a column context, produces the clause `LIKE '%<other>'`

  `expression`

    Return a column expression.

    Part of the inspection interface; returns self.

  `foreign_keys` = []
  `get_children`(**kwargs)
    inherited from the `get_children()` method of `ClauseElement`

    Return immediate child elements of this `ClauseElement`.

    This is used for visit traversal.

    **kwargs may contain flags that change the collection that is returned, for example to return a subset of items in order to cut down on larger traversals, or to return child items from a different context (such as schema-level collections instead of clause-level).

  `ilike`(other, escape=None)
    inherited from the `ilike()` method of `ColumnOperators`

    Implement the `ilike` operator.

    In a column context, produces the clause `a ILIKE other`.

    E.g.:

        select([sometable]).where(sometable.c.column.ilike("%foobar%"))

    Parameters:

    - other – expression to be compared
    - escape –

      optional escape character, renders the `ESCAPE` keyword, e.g.:

          somecolumn.ilike("foo/%bar", escape="/")

    See also

    `ColumnOperators.like()`

  `in_`(other)
    inherited from the `in_()` method of `ColumnOperators`

    Implement the `in` operator.

    In a column context, produces the clause `a IN other`. “other” may be a tuple/list of column expressions, or a `select()` construct.

  `is_`(other)
    inherited from the `is_()` method of `ColumnOperators`

    Implement the `IS` operator.

    Normally, `IS` is generated automatically when comparing to a value of `None`, which resolves to `NULL`. However, explicit usage of `IS` may be desirable if comparing to boolean values on certain platforms.

    New in version 0.7.9.

    See also

    `ColumnOperators.isnot()`

  `is_clause_element` = True
  `is_selectable` = False
  `isnot`(other)
    inherited from the `isnot()` method of `ColumnOperators`

    Implement the `IS NOT` operator.

    Normally, `IS NOT` is generated automatically when comparing to a value of `None`, which resolves to `NULL`. However, explicit usage of `IS NOT` may be desirable if comparing to boolean values on certain platforms.

    New in version 0.7.9.

    See also

    `ColumnOperators.is_()`

  `key` = None

    the ‘key’ that in some circumstances refers to this object in a Python namespace.

    This typically refers to the “key” of the column as present in the `.c` collection of a selectable, e.g. sometable.c[“somekey”] would return a Column with a .key of “somekey”.

  `label`(name)

    Produce a column label, i.e. `<columnname> AS <name>`.

    This is a shortcut to the `label()` function.

    if ‘name’ is None, an anonymous label name will be generated.

  `like`(other, escape=None)
    inherited from the `like()` method of `ColumnOperators`

    Implement the `like` operator.

    In a column context, produces the clause `a LIKE other`.

    E.g.:

        select([sometable]).where(sometable.c.column.like("%foobar%"))

    Parameters:

    - other – expression to be compared
    - escape –

      optional escape character, renders the `ESCAPE` keyword, e.g.:

          somecolumn.like("foo/%bar", escape="/")

    See also

    `ColumnOperators.ilike()`

  `match`(other, **kwargs)
    inherited from the `match()` method of `ColumnOperators`

    Implements a database-specific ‘match’ operator.

    `match()` attempts to resolve to a MATCH-like function or operator provided by the backend. Examples include:

    - Postgresql - renders `x @@ to_tsquery(y)`
    - MySQL - renders `MATCH (x) AGAINST (y IN BOOLEAN MODE)`
    - Oracle - renders `CONTAINS(x, y)`
    - other backends may provide special implementations.
    - Backends without any special implementation will emit the operator as “MATCH”. This is compatible with SQlite, for example.
  `notilike`(other, escape=None)
    inherited from the `notilike()` method of `ColumnOperators`

    implement the `NOT ILIKE` operator.

    This is equivalent to using negation with `ColumnOperators.ilike()`, i.e. `~x.ilike(y)`.

    New in version 0.8.

    See also

    `ColumnOperators.ilike()`

  `notin_`(other)
    inherited from the `notin_()` method of `ColumnOperators`

    implement the `NOT IN` operator.

    This is equivalent to using negation with `ColumnOperators.in_()`, i.e. `~x.in_(y)`.

    New in version 0.8.

    See also

    `ColumnOperators.in_()`

  `notlike`(other, escape=None)
    inherited from the `notlike()` method of `ColumnOperators`

    implement the `NOT LIKE` operator.

    This is equivalent to using negation with `ColumnOperators.like()`, i.e. `~x.like(y)`.

    New in version 0.8.

    See also

    `ColumnOperators.like()`

  `nullsfirst`()
    inherited from the `nullsfirst()` method of `ColumnOperators`

    Produce a `nullsfirst()` clause against the parent object.

  `nullslast`()
    inherited from the `nullslast()` method of `ColumnOperators`

    Produce a `nullslast()` clause against the parent object.

  `op`(opstring, precedence=0, is_comparison=False)
    inherited from the `op()` method of `Operators`

    produce a generic operator function.

    e.g.:

        somecolumn.op("*")(5)

    produces:

        somecolumn * 5

    This function can also be used to make bitwise operators explicit. For example:

        somecolumn.op('&')(0xff)

    is a bitwise AND of the value in `somecolumn`.

    Parameters:

    - operator – a string which will be output as the infix operator between this element and the expression passed to the generated function.
    - precedence –

      precedence to apply to the operator, when parenthesizing expressions. A lower number will cause the expression to be parenthesized when applied against another operator with higher precedence. The default value of `0` is lower than all operators except for the comma (`,`) and `AS` operators. A value of 100 will be higher or equal to all operators, and -100 will be lower than or equal to all operators.

      New in version 0.8: - added the ‘precedence’ argument.

    - is_comparison –

      if True, the operator will be considered as a “comparison” operator, that is which evaluates to a boolean true/false value, like `==`, `>`, etc. This flag should be set so that ORM relationships can establish that the operator is a comparison operator when used in a custom join condition.

      New in version 0.9.2: - added the `Operators.op.is_comparison` flag.

    See also

    Redefining and Creating New Operators

    Using custom operators in join conditions

  `operate`(op, *other, **kwargs)
  `params`(*optionaldict, **kwargs)
    inherited from the `params()` method of `ClauseElement`

    Return a copy with `bindparam()` elements replaced.

    Returns a copy of this ClauseElement with `bindparam()` elements replaced with values taken from the given dictionary:

        >>> clause = column('x') + bindparam('foo')
        >>> print clause.compile().params
        {'foo':None}
        >>> print clause.params({'foo':7}).compile().params
        {'foo':7}

  `primary_key` = False
  `proxy_set`
  `reverse_operate`(op, other, **kwargs)
  `self_group`(against=None)
  `shares_lineage`(othercolumn)

    Return True if the given `ColumnElement` has a common ancestor to this `ColumnElement`.

  `startswith`(other, **kwargs)
    inherited from the `startswith()` method of `ColumnOperators`

    Implement the `startwith` operator.

    In a column context, produces the clause `LIKE '<other>%'`

  `supports_execution` = False
  `timetuple` = None
  `type`
  `unique_params`(*optionaldict, **kwargs)
    inherited from the `unique_params()` method of `ClauseElement`

    Return a copy with `bindparam()` elements replaced.

    Same functionality as `params()`, except adds unique=True to affected bind parameters so that multiple statements can be used.

class `sqlalchemy.sql.operators.``ColumnOperators`

  Bases: `sqlalchemy.sql.operators.Operators`

  Defines boolean, comparison, and other operators for `ColumnElement` expressions.

  By default, all methods call down to `operate()` or `reverse_operate()`, passing in the appropriate operator function from the Python builtin `operator` module or a SQLAlchemy-specific operator function from `sqlalchemy.expression.operators`. For example the `__eq__` function:

      def __eq__(self, other):
          return self.operate(operators.eq, other)

  Where `operators.eq` is essentially:

      def eq(a, b):
          return a == b

  The core column expression unit `ColumnElement` overrides `Operators.operate()` and others to return further `ColumnElement` constructs, so that the `==` operation above is replaced by a clause construct.

  See also:

  Redefining and Creating New Operators

  `TypeEngine.comparator_factory`

  `ColumnOperators`

  `PropComparator`

  `__add__`(other)

    Implement the `+` operator.

    In a column context, produces the clause `a + b` if the parent object has non-string affinity. If the parent object has a string affinity, produces the concatenation operator, `a || b` - see `ColumnOperators.concat()`.

  `__and__`(other)
    inherited from the `__and__()` method of `Operators`

    Implement the `&` operator.

    When used with SQL expressions, results in an AND operation, equivalent to `and_()`, that is:

        a & b

    is equivalent to:

        from sqlalchemy import and_
        and_(a, b)

    Care should be taken when using `&` regarding operator precedence; the `&` operator has the highest precedence. The operands should be enclosed in parenthesis if they contain further sub expressions:

        (a == 2) & (b == 4)

  `__delattr__`
    inherited from the `__delattr__` attribute of `object`

    x.__delattr__(‘name’) <==> del x.name

  `__div__`(other)

    Implement the `/` operator.

    In a column context, produces the clause `a / b`.

  `__eq__`(other)

    Implement the `==` operator.

    In a column context, produces the clause `a = b`. If the target is `None`, produces `a IS NULL`.

  `__format__`()
    inherited from the `__format__()` method of `object`

    default object formatter

  `__ge__`(other)

    Implement the `>=` operator.

    In a column context, produces the clause `a >= b`.

  `__getattribute__`
    inherited from the `__getattribute__` attribute of `object`

    x.__getattribute__(‘name’) <==> x.name

  `__getitem__`(index)

    Implement the [] operator.

    This can be used by some database-specific types such as Postgresql ARRAY and HSTORE.

  `__gt__`(other)

    Implement the `>` operator.

    In a column context, produces the clause `a > b`.

  `__hash__`
  `__init__`
    inherited from the `__init__` attribute of `object`

    x.__init__(...) initializes x; see help(type(x)) for signature

  `__invert__`()
    inherited from the `__invert__()` method of `Operators`

    Implement the `~` operator.

    When used with SQL expressions, results in a NOT operation, equivalent to `not_()`, that is:

        ~a

    is equivalent to:

        from sqlalchemy import not_
        not_(a)

  `__le__`(other)

    Implement the `<=` operator.

    In a column context, produces the clause `a <= b`.

  `__lshift__`(other)

    implement the << operator.

    Not used by SQLAlchemy core, this is provided for custom operator systems which want to use << as an extension point.

  `__lt__`(other)

    Implement the `<` operator.

    In a column context, produces the clause `a < b`.

  `__mod__`(other)

    Implement the `%` operator.

    In a column context, produces the clause `a % b`.

  `__mul__`(other)

    Implement the `*` operator.

    In a column context, produces the clause `a * b`.

  `__ne__`(other)

    Implement the `!=` operator.

    In a column context, produces the clause `a != b`. If the target is `None`, produces `a IS NOT NULL`.

  `__neg__`()

    Implement the `-` operator.

    In a column context, produces the clause `-a`.

  `__new__`(S, ...) → a new object with type S, a subtype of T
    inherited from the `__new__()` method of `object`
  `__or__`(other)
    inherited from the `__or__()` method of `Operators`

    Implement the `|` operator.

    When used with SQL expressions, results in an OR operation, equivalent to `or_()`, that is:

        a | b

    is equivalent to:

        from sqlalchemy import or_
        or_(a, b)

    Care should be taken when using `|` regarding operator precedence; the `|` operator has the highest precedence. The operands should be enclosed in parenthesis if they contain further sub expressions:

        (a == 2) | (b == 4)

  `__radd__`(other)

    Implement the `+` operator in reverse.

    See `ColumnOperators.__add__()`.

  `__rdiv__`(other)

    Implement the `/` operator in reverse.

    See `ColumnOperators.__div__()`.

  `__reduce__`()
    inherited from the `__reduce__()` method of `object`

    helper for pickle

  `__reduce_ex__`()
    inherited from the `__reduce_ex__()` method of `object`

    helper for pickle

  `__repr__`
    inherited from the `__repr__` attribute of `object`
  `__rmod__`(other)

    Implement the `%` operator in reverse.

    See `ColumnOperators.__mod__()`.

  `__rmul__`(other)

    Implement the `*` operator in reverse.

    See `ColumnOperators.__mul__()`.

  `__rshift__`(other)

    implement the >> operator.

    Not used by SQLAlchemy core, this is provided for custom operator systems which want to use >> as an extension point.

  `__rsub__`(other)

    Implement the `-` operator in reverse.

    See `ColumnOperators.__sub__()`.

  `__rtruediv__`(other)

    Implement the `//` operator in reverse.

    See `ColumnOperators.__truediv__()`.

  `__setattr__`
    inherited from the `__setattr__` attribute of `object`

    x.__setattr__(‘name’, value) <==> x.name = value

  `__sizeof__`() → int
    inherited from the `__sizeof__()` method of `object`

    size of object in memory, in bytes

  `__str__`
    inherited from the `__str__` attribute of `object`
  `__sub__`(other)

    Implement the `-` operator.

    In a column context, produces the clause `a - b`.

  `__subclasshook__`()
    inherited from the `__subclasshook__()` method of `object`

    Abstract classes can override this to customize issubclass().

    This is invoked early on by abc.ABCMeta.__subclasscheck__(). It should return True, False or NotImplemented. If it returns NotImplemented, the normal algorithm is used. Otherwise, it overrides the normal algorithm (and the outcome is cached).

  `__truediv__`(other)

    Implement the `//` operator.

    In a column context, produces the clause `a / b`.

  `asc`()

    Produce a `asc()` clause against the parent object.

  `between`(cleft, cright, symmetric=False)

    Produce a `between()` clause against the parent object, given the lower and upper range.

  `collate`(collation)

    Produce a `collate()` clause against the parent object, given the collation string.

  `concat`(other)

    Implement the ‘concat’ operator.

    In a column context, produces the clause `a || b`, or uses the `concat()` operator on MySQL.

  `contains`(other, **kwargs)

    Implement the ‘contains’ operator.

    In a column context, produces the clause `LIKE '%<other>%'`

  `desc`()

    Produce a `desc()` clause against the parent object.

  `distinct`()

    Produce a `distinct()` clause against the parent object.

  `endswith`(other, **kwargs)

    Implement the ‘endswith’ operator.

    In a column context, produces the clause `LIKE '%<other>'`

  `ilike`(other, escape=None)

    Implement the `ilike` operator.

    In a column context, produces the clause `a ILIKE other`.

    E.g.:

        select([sometable]).where(sometable.c.column.ilike("%foobar%"))

    Parameters:

    - other – expression to be compared
    - escape –

      optional escape character, renders the `ESCAPE` keyword, e.g.:

          somecolumn.ilike("foo/%bar", escape="/")

    See also

    `ColumnOperators.like()`

  `in_`(other)

    Implement the `in` operator.

    In a column context, produces the clause `a IN other`. “other” may be a tuple/list of column expressions, or a `select()` construct.

  `is_`(other)

    Implement the `IS` operator.

    Normally, `IS` is generated automatically when comparing to a value of `None`, which resolves to `NULL`. However, explicit usage of `IS` may be desirable if comparing to boolean values on certain platforms.

    New in version 0.7.9.

    See also

    `ColumnOperators.isnot()`

  `isnot`(other)

    Implement the `IS NOT` operator.

    Normally, `IS NOT` is generated automatically when comparing to a value of `None`, which resolves to `NULL`. However, explicit usage of `IS NOT` may be desirable if comparing to boolean values on certain platforms.

    New in version 0.7.9.

    See also

    `ColumnOperators.is_()`

  `like`(other, escape=None)

    Implement the `like` operator.

    In a column context, produces the clause `a LIKE other`.

    E.g.:

        select([sometable]).where(sometable.c.column.like("%foobar%"))

    Parameters:

    - other – expression to be compared
    - escape –

      optional escape character, renders the `ESCAPE` keyword, e.g.:

          somecolumn.like("foo/%bar", escape="/")

    See also

    `ColumnOperators.ilike()`

  `match`(other, **kwargs)

    Implements a database-specific ‘match’ operator.

    `match()` attempts to resolve to a MATCH-like function or operator provided by the backend. Examples include:

    - Postgresql - renders `x @@ to_tsquery(y)`
    - MySQL - renders `MATCH (x) AGAINST (y IN BOOLEAN MODE)`
    - Oracle - renders `CONTAINS(x, y)`
    - other backends may provide special implementations.
    - Backends without any special implementation will emit the operator as “MATCH”. This is compatible with SQlite, for example.
  `notilike`(other, escape=None)

    implement the `NOT ILIKE` operator.

    This is equivalent to using negation with `ColumnOperators.ilike()`, i.e. `~x.ilike(y)`.

    New in version 0.8.

    See also

    `ColumnOperators.ilike()`

  `notin_`(other)

    implement the `NOT IN` operator.

    This is equivalent to using negation with `ColumnOperators.in_()`, i.e. `~x.in_(y)`.

    New in version 0.8.

    See also

    `ColumnOperators.in_()`

  `notlike`(other, escape=None)

    implement the `NOT LIKE` operator.

    This is equivalent to using negation with `ColumnOperators.like()`, i.e. `~x.like(y)`.

    New in version 0.8.

    See also

    `ColumnOperators.like()`

  `nullsfirst`()

    Produce a `nullsfirst()` clause against the parent object.

  `nullslast`()

    Produce a `nullslast()` clause against the parent object.

  `op`(opstring, precedence=0, is_comparison=False)
    inherited from the `op()` method of `Operators`

    produce a generic operator function.

    e.g.:

        somecolumn.op("*")(5)

    produces:

        somecolumn * 5

    This function can also be used to make bitwise operators explicit. For example:

        somecolumn.op('&')(0xff)

    is a bitwise AND of the value in `somecolumn`.

    Parameters:

    - operator – a string which will be output as the infix operator between this element and the expression passed to the generated function.
    - precedence –

      precedence to apply to the operator, when parenthesizing expressions. A lower number will cause the expression to be parenthesized when applied against another operator with higher precedence. The default value of `0` is lower than all operators except for the comma (`,`) and `AS` operators. A value of 100 will be higher or equal to all operators, and -100 will be lower than or equal to all operators.

      New in version 0.8: - added the ‘precedence’ argument.

    - is_comparison –

      if True, the operator will be considered as a “comparison” operator, that is which evaluates to a boolean true/false value, like `==`, `>`, etc. This flag should be set so that ORM relationships can establish that the operator is a comparison operator when used in a custom join condition.

      New in version 0.9.2: - added the `Operators.op.is_comparison` flag.

    See also

    Redefining and Creating New Operators

    Using custom operators in join conditions

  `operate`(op, *other, **kwargs)
    inherited from the `operate()` method of `Operators`

    Operate on an argument.

    This is the lowest level of operation, raises `NotImplementedError` by default.

    Overriding this on a subclass can allow common behavior to be applied to all operations. For example, overriding `ColumnOperators` to apply `func.lower()` to the left and right side:

        class MyComparator(ColumnOperators):
            def operate(self, op, other):
                return op(func.lower(self), func.lower(other))

    Parameters:

    - op – Operator callable.
    - *other – the ‘other’ side of the operation. Will be a single scalar for most operations.
    - **kwargs – modifiers. These may be passed by special operators such as `ColumnOperators.contains()`.

  `reverse_operate`(op, other, **kwargs)
    inherited from the `reverse_operate()` method of `Operators`

    Reverse operate on an argument.

    Usage is the same as `operate()`.

  `startswith`(other, **kwargs)

    Implement the `startwith` operator.

    In a column context, produces the clause `LIKE '<other>%'`

  `timetuple` = None

    Hack, allows datetime objects to be compared on the LHS.

class `sqlalchemy.sql.base.``DialectKWArgs`

  Establish the ability for a class to have dialect-specific arguments with defaults and constructor validation.

  The `DialectKWArgs` interacts with the `DefaultDialect.construct_arguments` present on a dialect.

  See also

  `DefaultDialect.construct_arguments`

  classmethod `argument_for`(dialect_name, argument_name, default)

    Add a new kind of dialect-specific keyword argument for this class.

    E.g.:

        Index.argument_for("mydialect", "length", None)

        some_index = Index('a', 'b', mydialect_length=5)

    The `DialectKWArgs.argument_for()` method is a per-argument way adding extra arguments to the `DefaultDialect.construct_arguments` dictionary. This dictionary provides a list of argument names accepted by various schema-level constructs on behalf of a dialect.

    New dialects should typically specify this dictionary all at once as a data member of the dialect class. The use case for ad-hoc addition of argument names is typically for end-user code that is also using a custom compilation scheme which consumes the additional arguments.

    Parameters:

    - dialect_name – name of a dialect. The dialect must be locatable, else a `NoSuchModuleError` is raised. The dialect must also include an existing `DefaultDialect.construct_arguments` collection, indicating that it participates in the keyword-argument validation and default system, else `ArgumentError` is raised. If the dialect does not include this collection, then any keyword argument can be specified on behalf of this dialect already. All dialects packaged within SQLAlchemy include this collection, however for third party dialects, support may vary.
    - argument_name – name of the parameter.
    - default – default value of the parameter.

    New in version 0.9.4.

  `dialect_kwargs`

    A collection of keyword arguments specified as dialect-specific options to this construct.

    The arguments are present here in their original `<dialect>_<kwarg>` format. Only arguments that were actually passed are included; unlike the `DialectKWArgs.dialect_options` collection, which contains all options known by this dialect including defaults.

    The collection is also writable; keys are accepted of the form `<dialect>_<kwarg>` where the value will be assembled into the list of options.

    New in version 0.9.2.

    Changed in version 0.9.4: The `DialectKWArgs.dialect_kwargs` collection is now writable.

    See also

    `DialectKWArgs.dialect_options` - nested dictionary form

  `dialect_options`

    A collection of keyword arguments specified as dialect-specific options to this construct.

    This is a two-level nested registry, keyed to `<dialect_name>` and `<argument_name>`. For example, the `postgresql_where` argument would be locatable as:

        arg = my_object.dialect_options['postgresql']['where']

    New in version 0.9.2.

    See also

    `DialectKWArgs.dialect_kwargs` - flat dictionary form

  `kwargs`

    A synonym for `DialectKWArgs.dialect_kwargs`.

class `sqlalchemy.sql.expression.``Extract`(field, expr, **kwargs)

  Bases: `sqlalchemy.sql.expression.ColumnElement`

  Represent a SQL EXTRACT clause, `extract(field FROM expr)`.

  `__init__`(field, expr, **kwargs)

    Construct a new `Extract` object.

    This constructor is mirrored as a public API function; see `extract()` for a full usage and argument description.

class `sqlalchemy.sql.elements.``False_`

  Bases: `sqlalchemy.sql.expression.ColumnElement`

  Represent the `false` keyword, or equivalent, in a SQL statement.

  `False_` is accessed as a constant via the `false()` function.

class `sqlalchemy.sql.expression.``FunctionFilter`(func, *criterion)

  Bases: `sqlalchemy.sql.expression.ColumnElement`

  Represent a function FILTER clause.

  This is a special operator against aggregate and window functions, which controls which rows are passed to it. It’s supported only by certain database backends.

  Invocation of `FunctionFilter` is via `FunctionElement.filter()`:

      func.count(1).filter(True)

  New in version 1.0.0.

  See also

  `FunctionElement.filter()`

  `__init__`(func, *criterion)

    Construct a new `FunctionFilter` object.

    This constructor is mirrored as a public API function; see `funcfilter()` for a full usage and argument description.

  `filter`(*criterion)

    Produce an additional FILTER against the function.

    This method adds additional criteria to the initial criteria set up by `FunctionElement.filter()`.

    Multiple criteria are joined together at SQL render time via `AND`.

  `over`(partition_by=None, order_by=None)

    Produce an OVER clause against this filtered function.

    Used against aggregate or so-called “window” functions, for database backends that support window functions.

    The expression:

        func.rank().filter(MyClass.y > 5).over(order_by='x')

    is shorthand for:

        from sqlalchemy import over, funcfilter
        over(funcfilter(func.rank(), MyClass.y > 5), order_by='x')

    See `over()` for a full description.

class `sqlalchemy.sql.expression.``Label`(name, element, type_=None)

  Bases: `sqlalchemy.sql.expression.ColumnElement`

  Represents a column label (AS).

  Represent a label, as typically applied to any column-level element using the `AS` sql keyword.

  `__init__`(name, element, type_=None)

    Construct a new `Label` object.

    This constructor is mirrored as a public API function; see `label()` for a full usage and argument description.

class `sqlalchemy.sql.elements.``Null`

  Bases: `sqlalchemy.sql.expression.ColumnElement`

  Represent the NULL keyword in a SQL statement.

  `Null` is accessed as a constant via the `null()` function.

class `sqlalchemy.sql.expression.``Over`(func, partition_by=None, order_by=None)

  Bases: `sqlalchemy.sql.expression.ColumnElement`

  Represent an OVER clause.

  This is a special operator against a so-called “window” function, as well as any aggregate function, which produces results relative to the result set itself. It’s supported only by certain database backends.

  `__init__`(func, partition_by=None, order_by=None)

    Construct a new `Over` object.

    This constructor is mirrored as a public API function; see `over()` for a full usage and argument description.

class `sqlalchemy.sql.expression.``TextClause`(text, bind=None)

  Bases: `sqlalchemy.sql.expression.Executable`, `sqlalchemy.sql.expression.ClauseElement`

  Represent a literal SQL text fragment.

  E.g.:

      from sqlalchemy import text

      t = text("SELECT * FROM users")
      result = connection.execute(t)

  The `Text` construct is produced using the `text()` function; see that function for full documentation.

  See also

  `text()`

  `bindparams`(*binds, **names_to_values)

    Establish the values and/or types of bound parameters within this `TextClause` construct.

    Given a text construct such as:

        from sqlalchemy import text
        stmt = text("SELECT id, name FROM user WHERE name=:name "
                    "AND timestamp=:timestamp")

    the `TextClause.bindparams()` method can be used to establish the initial value of `:name` and `:timestamp`, using simple keyword arguments:

        stmt = stmt.bindparams(name='jack',
                    timestamp=datetime.datetime(2012, 10, 8, 15, 12, 5))

    Where above, new `BindParameter` objects will be generated with the names `name` and `timestamp`, and values of `jack` and `datetime.datetime(2012, 10, 8, 15, 12, 5)`, respectively. The types will be inferred from the values given, in this case `String` and `DateTime`.

    When specific typing behavior is needed, the positional `*binds` argument can be used in which to specify `bindparam()` constructs directly. These constructs must include at least the `key` argument, then an optional value and type:

        from sqlalchemy import bindparam
        stmt = stmt.bindparams(
                        bindparam('name', value='jack', type_=String),
                        bindparam('timestamp', type_=DateTime)
                    )

    Above, we specified the type of `DateTime` for the `timestamp` bind, and the type of `String` for the `name` bind. In the case of `name` we also set the default value of `"jack"`.

    Additional bound parameters can be supplied at statement execution time, e.g.:

        result = connection.execute(stmt,
                    timestamp=datetime.datetime(2012, 10, 8, 15, 12, 5))

    The `TextClause.bindparams()` method can be called repeatedly, where it will re-use existing `BindParameter` objects to add new information. For example, we can call `TextClause.bindparams()` first with typing information, and a second time with value information, and it will be combined:

        stmt = text("SELECT id, name FROM user WHERE name=:name "
                    "AND timestamp=:timestamp")
        stmt = stmt.bindparams(
            bindparam('name', type_=String),
            bindparam('timestamp', type_=DateTime)
        )
        stmt = stmt.bindparams(
            name='jack',
            timestamp=datetime.datetime(2012, 10, 8, 15, 12, 5)
        )

    New in version 0.9.0: The `TextClause.bindparams()` method supersedes the argument `bindparams` passed to `text()`.

  `columns`(*cols, **types)

    Turn this `TextClause` object into a `TextAsFrom` object that can be embedded into another statement.

    This function essentially bridges the gap between an entirely textual SELECT statement and the SQL expression language concept of a “selectable”:

        from sqlalchemy.sql import column, text

        stmt = text("SELECT id, name FROM some_table")
        stmt = stmt.columns(column('id'), column('name')).alias('st')

        stmt = select([mytable]).\
                select_from(
                    mytable.join(stmt, mytable.c.name == stmt.c.name)
                ).where(stmt.c.id > 5)

    Above, we used untyped `column()` elements. These can also have types specified, which will impact how the column behaves in expressions as well as determining result set behavior:

        stmt = text("SELECT id, name, timestamp FROM some_table")
        stmt = stmt.columns(
                    column('id', Integer),
                    column('name', Unicode),
                    column('timestamp', DateTime)
                )

        for id, name, timestamp in connection.execute(stmt):
            print(id, name, timestamp)

    Keyword arguments allow just the names and types of columns to be specified, where the `column()` elements will be generated automatically:

        stmt = text("SELECT id, name, timestamp FROM some_table")
        stmt = stmt.columns(
                    id=Integer,
                    name=Unicode,
                    timestamp=DateTime
                )

        for id, name, timestamp in connection.execute(stmt):
            print(id, name, timestamp)

    The `TextClause.columns()` method provides a direct route to calling `FromClause.alias()` as well as `SelectBase.cte()` against a textual SELECT statement:

        stmt = stmt.columns(id=Integer, name=String).cte('st')

        stmt = select([sometable]).where(sometable.c.id == stmt.c.id)

    New in version 0.9.0: `text()` can now be converted into a fully featured “selectable” construct using the `TextClause.columns()` method. This method supersedes the `typemap` argument to `text()`.

class `sqlalchemy.sql.expression.``Tuple`(*clauses, **kw)

  Bases: `sqlalchemy.sql.expression.ClauseList`, `sqlalchemy.sql.expression.ColumnElement`

  Represent a SQL tuple.

  `__init__`(*clauses, **kw)

    Construct a new `Tuple` object.

    This constructor is mirrored as a public API function; see `tuple_()` for a full usage and argument description.

class `sqlalchemy.sql.elements.``True_`

  Bases: `sqlalchemy.sql.expression.ColumnElement`

  Represent the `true` keyword, or equivalent, in a SQL statement.

  `True_` is accessed as a constant via the `true()` function.

class `sqlalchemy.sql.operators.``custom_op`(opstring, precedence=0, is_comparison=False)

  Represent a ‘custom’ operator.

  `custom_op` is normally instantitated when the `ColumnOperators.op()` method is used to create a custom operator callable. The class can also be used directly when programmatically constructing expressions. E.g. to represent the “factorial” operation:

      from sqlalchemy.sql import UnaryExpression
      from sqlalchemy.sql import operators
      from sqlalchemy import Numeric

      unary = UnaryExpression(table.c.somecolumn,
              modifier=operators.custom_op("!"),
              type_=Numeric)

class `sqlalchemy.sql.operators.``Operators`

  Base of comparison and logical operators.

  Implements base methods `operate()` and `reverse_operate()`, as well as `__and__()`, `__or__()`, `__invert__()`.

  Usually is used via its most common subclass `ColumnOperators`.

  `__and__`(other)

    Implement the `&` operator.

    When used with SQL expressions, results in an AND operation, equivalent to `and_()`, that is:

        a & b

    is equivalent to:

        from sqlalchemy import and_
        and_(a, b)

    Care should be taken when using `&` regarding operator precedence; the `&` operator has the highest precedence. The operands should be enclosed in parenthesis if they contain further sub expressions:

        (a == 2) & (b == 4)

  `__invert__`()

    Implement the `~` operator.

    When used with SQL expressions, results in a NOT operation, equivalent to `not_()`, that is:

        ~a

    is equivalent to:

        from sqlalchemy import not_
        not_(a)

  `__or__`(other)

    Implement the `|` operator.

    When used with SQL expressions, results in an OR operation, equivalent to `or_()`, that is:

        a | b

    is equivalent to:

        from sqlalchemy import or_
        or_(a, b)

    Care should be taken when using `|` regarding operator precedence; the `|` operator has the highest precedence. The operands should be enclosed in parenthesis if they contain further sub expressions:

        (a == 2) | (b == 4)

  `op`(opstring, precedence=0, is_comparison=False)

    produce a generic operator function.

    e.g.:

        somecolumn.op("*")(5)

    produces:

        somecolumn * 5

    This function can also be used to make bitwise operators explicit. For example:

        somecolumn.op('&')(0xff)

    is a bitwise AND of the value in `somecolumn`.

    Parameters:

    - operator – a string which will be output as the infix operator between this element and the expression passed to the generated function.
    - precedence –

      precedence to apply to the operator, when parenthesizing expressions. A lower number will cause the expression to be parenthesized when applied against another operator with higher precedence. The default value of `0` is lower than all operators except for the comma (`,`) and `AS` operators. A value of 100 will be higher or equal to all operators, and -100 will be lower than or equal to all operators.

      New in version 0.8: - added the ‘precedence’ argument.

    - is_comparison –

      if True, the operator will be considered as a “comparison” operator, that is which evaluates to a boolean true/false value, like `==`, `>`, etc. This flag should be set so that ORM relationships can establish that the operator is a comparison operator when used in a custom join condition.

      New in version 0.9.2: - added the `Operators.op.is_comparison` flag.

    See also

    Redefining and Creating New Operators

    Using custom operators in join conditions

  `operate`(op, *other, **kwargs)

    Operate on an argument.

    This is the lowest level of operation, raises `NotImplementedError` by default.

    Overriding this on a subclass can allow common behavior to be applied to all operations. For example, overriding `ColumnOperators` to apply `func.lower()` to the left and right side:

        class MyComparator(ColumnOperators):
            def operate(self, op, other):
                return op(func.lower(self), func.lower(other))

    Parameters:

    - op – Operator callable.
    - *other – the ‘other’ side of the operation. Will be a single scalar for most operations.
    - **kwargs – modifiers. These may be passed by special operators such as `ColumnOperators.contains()`.

  `reverse_operate`(op, other, **kwargs)

    Reverse operate on an argument.

    Usage is the same as `operate()`.

class `sqlalchemy.sql.elements.``quoted_name`

  Bases: `sqlalchemy.util.langhelpers.MemoizedSlots`, `__builtin__.unicode`

  Represent a SQL identifier combined with quoting preferences.

  `quoted_name` is a Python unicode/str subclass which represents a particular identifier name along with a `quote` flag. This `quote` flag, when set to `True` or `False`, overrides automatic quoting behavior for this identifier in order to either unconditionally quote or to not quote the name. If left at its default of `None`, quoting behavior is applied to the identifier on a per-backend basis based on an examination of the token itself.

  A `quoted_name` object with `quote=True` is also prevented from being modified in the case of a so-called “name normalize” option. Certain database backends, such as Oracle, Firebird, and DB2 “normalize” case-insensitive names as uppercase. The SQLAlchemy dialects for these backends convert from SQLAlchemy’s lower-case-means-insensitive convention to the upper-case-means-insensitive conventions of those backends. The `quote=True` flag here will prevent this conversion from occurring to support an identifier that’s quoted as all lower case against such a backend.

  The `quoted_name` object is normally created automatically when specifying the name for key schema constructs such as `Table`, `Column`, and others. The class can also be passed explicitly as the name to any function that receives a name which can be quoted. Such as to use the `Engine.has_table()` method with an unconditionally quoted name:

      from sqlaclchemy import create_engine
      from sqlalchemy.sql.elements import quoted_name

      engine = create_engine("oracle+cx_oracle://some_dsn")
      engine.has_table(quoted_name("some_table", True))

  The above logic will run the “has table” logic against the Oracle backend, passing the name exactly as `"some_table"` without converting to upper case.

  New in version 0.9.0.

class `sqlalchemy.sql.expression.``UnaryExpression`(element, operator=None, modifier=None, type_=None, negate=None, wraps_column_expression=False)

  Bases: `sqlalchemy.sql.expression.ColumnElement`

  Define a ‘unary’ expression.

  A unary expression has a single column expression and an operator. The operator can be placed on the left (where it is called the ‘operator’) or right (where it is called the ‘modifier’) of the column expression.

  `UnaryExpression` is the basis for several unary operators including those used by `desc()`, `asc()`, `distinct()`, `nullsfirst()` and `nullslast()`.

  `compare`(other, **kw)

    Compare this `UnaryExpression` against the given `ClauseElement`.