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Some recent embedded systems also use proprietary character sets, usually extensions to ISO 8859 character sets, which include box-drawing characters or other special symbols. Other types of box-drawing characters are block elements , shade characters, and terminal graphic characters; these can be used for filling regions of the screen and ...
In situations where the number of unique values of a column is far less than the number of rows in the table, column-oriented storage allow significant savings in space through data compression. Columnar storage also allows fast execution of range queries (e.g., show all records where a particular column is between X and Y, or less than X.)
Python sets are very much like mathematical sets, and support operations like set intersection and union. Python also features a frozenset class for immutable sets, see Collection types. Dictionaries (class dict) are mutable mappings tying keys and corresponding values. Python has special syntax to create dictionaries ({key: value})
In SGML, HTML and XML documents, the logical constructs known as character data and attribute values consist of sequences of characters, in which each character can manifest directly (representing itself), or can be represented by a series of characters called a character reference, of which there are two types: a numeric character reference and a character entity reference.
Unique constraint. A unique constraint can be defined on columns that allow nulls, in which case rows that include nulls may not actually be unique across the set of columns defined by the constraint. Each table can have multiple unique constraints. On some RDBMS a unique constraint generates a nonclustered index by default.
In statistics, a categorical variable (also called qualitative variable) is a variable that can take on one of a limited, and usually fixed, number of possible values, assigning each individual or other unit of observation to a particular group or nominal category on the basis of some qualitative property. [1]
Principal component analysis (PCA) is a linear dimensionality reduction technique with applications in exploratory data analysis, visualization and data preprocessing.. The data is linearly transformed onto a new coordinate system such that the directions (principal components) capturing the largest variation in the data can be easily identified.
print length([2+1, 3*2, 1/0, 5-4]) fails under strict evaluation because of the division by zero in the third element of the list. Under lazy evaluation, the length function returns the value 4 (i.e., the number of items in the list), since evaluating it does not attempt to evaluate the terms making up the list.