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Many statistical and data processing systems have functions to convert between these two presentations, for instance the R programming language has several packages such as the tidyr package. The pandas package in Python implements this operation as "melt" function which converts a wide table to a narrow one. The process of converting a narrow ...
A table has a specified number of columns, but can have any number of rows. [2] Each row is identified by one or more values appearing in a particular column subset. A specific choice of columns which uniquely identify rows is called the primary key. "Table" is another term for "relation"; although there is the difference in that a table is ...
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.
Set-Membership constraints: The values for a column come from a set of discrete values or codes. For example, a person's sex may be Female, Male, or Non-Binary. Foreign-key constraints: This is the more general case of set membership. The set of values in a column is defined in a column of another table that contains unique values.
Indexes are useful for many applications but come with some limitations. Consider the following SQL statement: SELECT first_name FROM people WHERE last_name = 'Smith';. To process this statement without an index the database software must look at the last_name column on every row in the table (this is known as a full table scan).
The rationale is that passing an (x,y,z) record to a function that expects an (x,y) record as argument should work, since that function will find all the fields it requires within the record. Many ways of practically implementing records in programming languages would have trouble with allowing such variability, but the matter is a central ...
Columns with an atomic data type (e.g., numeric, varchar or datetime columns) can be designated as sparse simply by including the word SPARSE in the column definition of the CREATE TABLE statement. Sparse columns optimize the storage of NULL values (which now take up no space at all) and are useful when the majority records in a table will have ...
SELECT list is the list of columns or SQL expressions to be returned by the query. This is approximately the relational algebra projection operation. AS optionally provides an alias for each column or expression in the SELECT list. This is the relational algebra rename operation. FROM specifies from which table to get the data. [3]