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As such, a DataFrame can be thought of as having two indices: one column-based and one row-based. Because column names are stored as an index, these are not required to be unique. [9]: 103–105 If data is a Series, then data['a'] returns all values with the index value of a. However, if data is a DataFrame, then data['a'] returns all values in ...
In relational algebra, a rename is a unary operation written as / where: . R is a relation; a and b are attribute names; b is an attribute of R; The result is identical to R except that the b attribute in all tuples is renamed to a. [1]
A data element name is a name given to a data element in, for example, a data dictionary or metadata registry.In a formal data dictionary, there is often a requirement that no two data elements may have the same name, to allow the data element name to become an identifier, though some data dictionaries may provide ways to qualify the name in some way, for example by the application system or ...
The terms data dictionary and data repository indicate a more general software utility than a catalogue. A catalogue is closely coupled with the DBMS software. It provides the information stored in it to the user and the DBA, but it is mainly accessed by the various software modules of the DBMS itself, such as DDL and DML compilers, the query optimiser, the transaction processor, report ...
This is the renaming style used in the MIPS R10000, the Alpha 21264, and in the FP section of the AMD Athlon. In the renaming stage, every architectural register referenced (for read or write) is looked up in an architecturally-indexed remap file. This file returns a tag and a ready bit.
Sparse dictionary learning (also known as sparse coding or SDL) is a representation learning method which aims to find a sparse representation of the input data in the form of a linear combination of basic elements as well as those basic elements themselves. These elements are called atoms, and they compose a dictionary.
In computational linguistics, lemmatization is the algorithmic process of determining the lemma of a word based on its intended meaning. Unlike stemming , lemmatization depends on correctly identifying the intended part of speech and meaning of a word in a sentence, as well as within the larger context surrounding that sentence, such as ...
This method of Dictionary-Based Machine translation explores a different paradigm from systems such as LMT. An example-based machine translation system is supplied with only a "sentence-aligned bilingual corpus". [3] Using this data the translating program generates a "word-for-word bilingual dictionary" [3] which is used for further translation.