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find the value (if any) that is bound to a given key. The argument to this operation is the key, and the value is returned from the operation. If no value is found, some lookup functions raise an exception, while others return a default value (such as zero, null, or a specific value passed to the constructor).
RDFLib is a Python library for working with RDF, [2] a simple yet powerful language for representing information. This library contains parsers/serializers for almost all of the known RDF serializations, such as RDF/XML, Turtle, N-Triples, & JSON-LD, many of which are now supported in their updated form (e.g. Turtle 1.1).
base-10 real values are represented as character strings in ISO 6093 format; binary real values are represented in a binary format that includes the mantissa, the base (2, 8, or 16), and the exponent; the special values NaN, -INF, +INF, and negative zero are also supported
A small phone book as a hash table. In computer science, a hash table is a data structure that implements an associative array, also called a dictionary or simply map; an associative array is an abstract data type that maps keys to values. [3]
For example, one could define a dictionary having a string "toast" mapped to the integer 42 or vice versa. The keys in a dictionary must be of an immutable Python type, such as an integer or a string, because under the hood they are implemented via a hash function. This makes for much faster lookup times, but requires keys not change.
Strings are represented in C literal style: "This is a plist string\n"; simpler, unquoted strings are allowed as long as they consist of alphanumericals and one of _$+/:.-. Binary data are represented as: < [hexadecimal codes in ASCII] >. Spaces and comments between paired hex-codes are ignored. Arrays are represented as: ( "1", "2", "3 ...
The word with embeddings most similar to the topic vector might be assigned as the topic's title, whereas far away word embeddings may be considered unrelated. As opposed to other topic models such as LDA , top2vec provides canonical ‘distance’ metrics between two topics, or between a topic and another embeddings (word, document, or otherwise).
Single-value containers store each object independently. Objects may be accessed directly, by a language loop construct (e.g. for loop) or with an iterator. An associative container uses an associative array, map, or dictionary, composed of key-value pairs, such that each key appears at most once in the container. The key is used to find the ...