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The SQL From clause is the source of a rowset to be operated upon in a Data Manipulation Language (DML) statement. From clauses are very common, and will provide the rowset to be exposed through a Select statement, the source of values in an Update statement, and the target rows to be deleted in a Delete statement.
In SQL procedures, a cursor makes it possible to define a result set (a set of data rows) and perform complex logic on a row by row basis. By using the same mechanics, a SQL procedure can also define a result set and return it directly to the caller of the SQL procedure or to a client application.
The purpose of DQL commands is to get the schema relation based on the query passed to it. Although often considered part of DML, the SQL SELECT statement is strictly speaking an example of DQL. When adding FROM or WHERE data manipulators to the SELECT statement the statement is then considered part of the DML.
In SQL, a window function or analytic function [1] is a function which uses values from one or multiple rows to return a value for each row. (This contrasts with an aggregate function, which returns a single value for multiple rows.) Window functions have an OVER clause; any function without an OVER clause is not a window function, but rather ...
First Page: select only the first {rows} rows, depending on the type of database; Next Page: select only the first {rows} rows, depending on the type of database, where the {unique_key} is greater than {last_val} (the value of the {unique_key} of the last row in the current page)
In SQL, the data manipulation language comprises the SQL-data change statements, [3] which modify stored data but not the schema or database objects. Manipulation of persistent database objects, e.g., tables or stored procedures, via the SQL schema statements, [3] rather than the data stored within them, is considered to be part of a separate data definition language (DDL).
SQL includes operators and functions for calculating values on stored values. SQL allows the use of expressions in the select list to project data, as in the following example, which returns a list of books that cost more than 100.00 with an additional sales_tax column containing a sales tax figure calculated at 6% of the price.
[4]: 114 A DataFrame is a 2-dimensional data structure of rows and columns, similar to a spreadsheet, and analogous to a Python dictionary mapping column names (keys) to Series (values), with each Series sharing an index. [4]: 115 DataFrames can be concatenated together or "merged" on columns or indices in a manner similar to joins in SQL.