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It was developed for, and is used extensively by, the Python project for documentation. [9] Since its introduction in 2008, Sphinx has been adopted by many other important Python projects, including Bazaar, SQLAlchemy, MayaVi, SageMath, SciPy, Django and Pylons. It is also used for the Blender user manual [10] and Python API documentation. [11]
Supported data types include scalar value types such as boolean, string, number, integer, and floating-point numbers. It also supports temporal types like datetime, localdatetime, date, time, localtime, and duration. Container data types for maps and lists are available, along with graph types for node, relationship, path, and a void type. [5]
Some file archivers and some version control software, when they copy a file from some remote computer to the local computer, adjust the timestamps of the local file to show the date/time in the past when that file was created or modified on that remote computer, rather than the date/time when that file was copied to the local computer.
reStructuredText (RST, ReST, or reST) is a file format for textual data used primarily in the Python programming language community for technical documentation.. It is part of the Docutils project of the Python Doc-SIG (Documentation Special Interest Group), aimed at creating a set of tools for Python similar to Javadoc for Java or Plain Old Documentation (POD) for Perl.
Python supports a wide variety of string operations. Strings in Python are immutable, so a string operation such as a substitution of characters, that in other programming languages might alter the string in place, returns a new string in Python. Performance considerations sometimes push for using special techniques in programs that modify ...
The user can search for elements in an associative array, and delete elements from the array. The following shows how multi-dimensional associative arrays can be simulated in standard AWK using concatenation and the built-in string-separator variable SUBSEP:
doc2vec, generates distributed representations of variable-length pieces of texts, such as sentences, paragraphs, or entire documents. [14] [15] doc2vec has been implemented in the C, Python and Java/Scala tools (see below), with the Java and Python versions also supporting inference of document embeddings on new, unseen documents.
Integer addition, for example, can be performed as a single machine instruction, and some offer specific instructions to process sequences of characters with a single instruction. [7] But the choice of primitive data type may affect performance, for example it is faster using SIMD operations and data types to operate on an array of floats.