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  2. JData - Wikipedia

    en.wikipedia.org/wiki/JData

    It can convert a wide range of complex data structures, including dict, array, numpy ndarray, into JData representations and export the data as JSON or UBJSON files. The BJData Python module, pybj, [4] enabling reading/writing BJData/UBJSON files, is also available on PyPI, Debian/Ubuntu and GitHub.

  3. Comparison of data-serialization formats - Wikipedia

    en.wikipedia.org/wiki/Comparison_of_data...

    JSON: No Smile Format Specification: Yes No Yes Partial (JSON Schema Proposal, other JSON schemas/IDLs) Partial (via JSON APIs implemented with Smile backend, on Jackson, Python) — SOAP: W3C: XML: Yes W3C Recommendations: SOAP/1.1 SOAP/1.2: Partial (Efficient XML Interchange, Binary XML, Fast Infoset, MTOM, XSD base64 data) Yes Built-in id ...

  4. Serialization - Wikipedia

    en.wikipedia.org/wiki/Serialization

    Flow diagram. In computing, serialization (or serialisation, also referred to as pickling in Python) is the process of translating a data structure or object state into a format that can be stored (e.g. files in secondary storage devices, data buffers in primary storage devices) or transmitted (e.g. data streams over computer networks) and reconstructed later (possibly in a different computer ...

  5. JSON - Wikipedia

    en.wikipedia.org/wiki/JSON

    JSON is promoted as a low-overhead alternative to XML as both of these formats have widespread support for creation, reading, and decoding in the real-world situations where they are commonly used. [39] Apart from XML, examples could include CSV and supersets of JSON.

  6. YAML - Wikipedia

    en.wikipedia.org/wiki/YAML

    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.).

  7. JSONPath - Wikipedia

    en.wikipedia.org/wiki/JSONPath

    JSONiq [11] is a query and transformation language for JSON. XPath 3.1 [12] is an expression language that allows the processing of values conforming to the XDM [13] data model. The version 3.1 of XPath supports JSON as well as XML. jq is like sed for JSON data – it can be used to slice and filter and map and transform structured data.

  8. Apache Avro - Wikipedia

    en.wikipedia.org/wiki/Apache_Avro

    File metadata, including the schema definition. The 16-byte, randomly-generated sync marker for this file. For data blocks Avro specifies two serialization encodings: [6] binary and JSON. Most applications will use the binary encoding, as it is smaller and faster. For debugging and web-based applications, the JSON encoding may sometimes be ...

  9. Project Jupyter - Wikipedia

    en.wikipedia.org/wiki/Project_Jupyter

    IPython continues to exist as a Python shell and a kernel for Jupyter, while the notebook and other language-agnostic parts of IPython moved under the Jupyter name. [ 4 ] [ 5 ] Jupyter supports execution environments (called "kernels") in several dozen languages, including Julia, R, Haskell , Ruby , and Python (via the IPython kernel).