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If n is greater than the length of the string then most implementations return the whole string (exceptions exist – see code examples). Note that for variable-length encodings such as UTF-8 , UTF-16 or Shift-JIS , it can be necessary to remove string positions at the end, in order to avoid invalid strings.
A basic example of string searching is when the pattern and the searched text are arrays of elements of an alphabet Σ. Σ may be a human language alphabet, for example, the letters A through Z and other applications may use a binary alphabet (Σ = {0,1}) or a DNA alphabet (Σ = {A,C,G,T}) in bioinformatics.
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
Introduced in Python 2.2 as an optional feature and finalized in version 2.3, generators are Python's mechanism for lazy evaluation of a function that would otherwise return a space-prohibitive or computationally intensive list. This is an example to lazily generate the prime numbers:
CLASS words ideally would be a very short list of data types relevant to a particular application. Common CLASS words might be: NO (number), ID (identifier), TXT (text), AMT (amount), QTY (quantity), FL (flag), CD (code), W (work) and so forth. In practice, the available CLASS words would be a list of less than two dozen terms.
This creational pattern [1] is frequently used for numbers and strings in different programming languages. In many object-oriented languages such as Python, even primitive types such as integer numbers are objects. To avoid the overhead of constructing a large number of integer objects, these objects get reused through interning. [2]
String interning also reduces memory usage if there are many instances of the same string value; for instance, it is read from a network or from storage. Such strings may include magic numbers or network protocol information. For example, XML parsers may intern names of tags and attributes to save memory.
The longest common substrings of a set of strings can be found by building a generalized suffix tree for the strings, and then finding the deepest internal nodes which have leaf nodes from all the strings in the subtree below it. The figure on the right is the suffix tree for the strings "ABAB", "BABA" and "ABBA", padded with unique string ...