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Columns of any conceivable data type (from string types and numeric types to array types and table types) are then acceptable in a 1NF table—although perhaps not always desirable; for example, it may be more desirable to separate a Customer Name column into two separate columns as First Name, Surname.
Data cleansing may also involve harmonization (or normalization) of data, which is the process of bringing together data of "varying file formats, naming conventions, and columns", [2] and transforming it into one cohesive data set; a simple example is the expansion of abbreviations ("st, rd, etc." to "street, road, etcetera").
Comma-separated values (CSV) is a text file format that uses commas to separate values, and newlines to separate records. A CSV file stores tabular data (numbers and text) in plain text, where each line of the file typically represents one data record.
Even though the row is indicated by the first index and the column by the second index, no grouping order between the dimensions is implied by this. The choice of how to group and order the indices, either by row-major or column-major methods, is thus a matter of convention. The same terminology can be applied to even higher dimensional arrays.
In the context of storage such as in a database or spreadsheet a record is often called a row and each field is called a column. [2] [3] [4] [5]In object-oriented programming, an object is a record that contains state and method fields.
At the very least, the attribute definitions table would contain the following columns: an attribute ID, attribute name, description, data type, units of measurement, and columns assisting input validation, e.g., maximum string length and regular expression, maximum and minimum permissible values, set of permissible values, etc.
A column family is a database object that contains columns of related data. It is a tuple (pair) that consists of a key–value pair , where the key is mapped to a value that is a set of columns. In analogy with relational databases, a column family is as a "table", each key-value pair being a "row".
PostgreSQL has a distinct BOOLEAN type as in the standard, [21] which allows predicates to be stored directly into a BOOLEAN column, and allows using a BOOLEAN column directly as a predicate in a WHERE clause. In MySQL, BOOLEAN is treated as an alias of TINYINT (1); [22] TRUE is the same as integer 1 and FALSE is the same is integer 0. [23]