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A spreadsheet's concatenate ("&") function is used to assemble a complex text string—in this example, XML code for an SVG "circle" element. In formal language theory and computer programming, string concatenation is the operation of joining character strings end-to-end. For example, the concatenation of "snow" and "ball" is "snowball".
The enclosed text becomes a string literal, which Python usually ignores (except when it is the first statement in the body of a module, class or function; see docstring). Elixir The above trick used in Python also works in Elixir, but the compiler will throw a warning if it spots this.
Many languages have a syntax specifically intended for strings with multiple lines. In some of these languages, this syntax is a here document or "heredoc": A token representing the string is put in the middle of a line of code, but the code continues after the starting token and the string's content doesn't appear until the next line.
Concatenation theory, also called string theory, character-string theory, or theoretical syntax, studies character strings over finite alphabets of characters, signs, symbols, or marks. String theory is foundational for formal linguistics , computer science, logic, and metamathematics especially proof theory. [ 1 ]
Two types of literal expression are usually offered: one with interpolation enabled, the other without. Non-interpolated strings may also escape sequences, in which case they are termed a raw string, though in other cases this is separate, yielding three classes of raw string, non-interpolated (but escaped) string, interpolated (and escaped) string.
Em dash (— or —, MOS:EMDASH) are even longer and are used solely to set aside a phrase—like this—in a sentence. Em dashes and en dashes, when used to set off parentheticals, are interrupters, along with parentheses (round brackets) and commas; [1] the principal differences are that parentheses are used to offset text of an optional or ...
In information theory, linguistics, and computer science, the Levenshtein distance is a string metric for measuring the difference between two sequences. The Levenshtein distance between two words is the minimum number of single-character edits (insertions, deletions or substitutions) required to change one word into the other.
Another recent idea is the similarity join. When matching database relates to a large scale of data, the O ( mn ) time with the dynamic programming algorithm cannot work within a limited time. So, the idea is to reduce the number of candidate pairs, instead of computing the similarity of all pairs of strings.