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YAML (/ ˈ j æ m əl /, rhymes with camel [4]) was first proposed by Clark Evans in 2001, [15] who designed it together with Ingy döt Net [16] and Oren Ben-Kiki. [16]Originally YAML was said to mean Yet Another Markup Language, [17] because it was released in an era that saw a proliferation of markup languages for presentation and connectivity (HTML, XML, SGML, etc.).
Tom's Obvious, Minimal Language (TOML, originally Tom's Own Markup Language [2]) is a file format for configuration files. [3] It is intended to be easy to read and write due to obvious semantics which aim to be "minimal", and it is designed to map unambiguously to a dictionary.
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:
RAML was first proposed in 2013. The initial RAML specification was authored by Uri Sarid, Emiliano Lesende, Santiago Vacas and Damian Martinez, and garnered support from technology leaders like MuleSoft, AngularJS, Intuit, Box, PayPal, Programmable Web and API Web Science, Kin Lane, SOA Software, and Cisco. [4]
YAML version 1.2 is a superset of JSON; prior versions were not strictly compatible. For example, escaping a slash / with a backslash \ is valid in JSON, but was not valid in YAML. [46] YAML supports comments, while JSON does not. [46] [44] [21]
YAML (Yet Another Multicolumn Layout) is a cross-browser CSS framework. [2] [3] It allows web designers to create a low-barrier website with comparatively little effort. Integrations of the YAML layouts have been created for various content management systems. These include WordPress, LifeType, TYPO3, Joomla, xt: Commerce and Drupal. [4]
Project Jupyter's name is a reference to the three core programming languages supported by Jupyter, which are Julia, Python and R. Its name and logo are an homage to Galileo's discovery of the moons of Jupiter, as documented in notebooks attributed to Galileo. Jupyter is financially sponsored by NumFOCUS. [1]
Python supports normal floating point numbers, which are created when a dot is used in a literal (e.g. 1.1), when an integer and a floating point number are used in an expression, or as a result of some mathematical operations ("true division" via the / operator, or exponentiation with a negative exponent).