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In functional and list-based languages a string is represented as a list (of character codes), therefore all list-manipulation procedures could be considered string functions. However such languages may implement a subset of explicit string-specific functions as well.
Substitution of a single symbol x for a symbol y ≠ x changes u x v to u y v (x → y). In Levenshtein's original definition, each of these operations has unit cost (except that substitution of a character by itself has zero cost), so the Levenshtein distance is equal to the minimum number of operations required to transform a to b.
In SQL, wildcard characters can be used in LIKE expressions; the percent sign % matches zero or more characters, and underscore _ a single character. Transact-SQL also supports square brackets ([and ]) to list sets and ranges of characters to match, a leading caret ^ negates the set and matches only a character not within the list.
The closeness of a match is measured in terms of the number of primitive operations necessary to convert the string into an exact match. This number is called the edit distance between the string and the pattern. The usual primitive operations are: [1] insertion: cot → coat; deletion: coat → cot; substitution: coat → cost
The text editor could replace this byte with the replacement character to produce a valid string of Unicode code points for display, so the user sees "f r". A poorly implemented text editor might write out the replacement character when the user saves the file; the data in the file will then become 0x66 0xEF 0xBF 0xBD 0x72 .
Python supports a wide variety of string operations. Strings in Python are immutable, so a string operation such as a substitution of characters, that in other programming languages might alter the string in place, returns a new string in Python. Performance considerations sometimes push for using special techniques in programs that modify ...
Usually such patterns are used by string-searching algorithms for "find" or "find and replace" operations on strings, or for input validation. Regular expression techniques are developed in theoretical computer science and formal language theory.
Replace and expand placeholders: creating a new string from the original one, by find–replace operations. Find variable reference (placeholder), replace it by its variable value. This algorithm offers no cache strategy. Split and join string: splitting the string into an array, merging it with the corresponding array of values, then joining ...