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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.
There are multiple ways to implement the flyweight pattern. One example is mutability: whether the objects storing extrinsic flyweight state can change. Immutable objects are easily shared, but require creating new extrinsic objects whenever a change in state occurs.
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.
A string homomorphism (often referred to simply as a homomorphism in formal language theory) is a string substitution such that each character is replaced by a single string. That is, f ( a ) = s {\displaystyle f(a)=s} , where s {\displaystyle s} is a string, for each character a {\displaystyle a} .
In object-oriented programming, a helper class is used to assist in providing some functionality, which isn't the main goal of the application or class in which it is used.
If the inner relation has an index on the attributes used in the join, then the naive nest loop join can be replaced with an index join. algorithm index_join is for each tuple r in R do for each tuple s in S in the index lookup do yield tuple <r,s>
In object-oriented programming, the decorator pattern is a design pattern that allows behavior to be added to an individual object, dynamically, without affecting the behavior of other instances of the same class. [1]
By default, a Pandas index is a series of integers ascending from 0, similar to the indices of Python arrays. However, indices can use any NumPy data type, including floating point, timestamps, or strings. [4]: 112 Pandas' syntax for mapping index values to relevant data is the same syntax Python uses to map dictionary keys to values.