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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.
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.
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 ...
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 ...
Concatenated JSON works with pretty-printed JSON but requires more effort and complexity to parse. It doesn't work well with traditional line-oriented tools. Concatenated JSON streaming is a superset of line-delimited JSON streaming. Length-prefixed JSON works with pretty-printed JSON.
Support for JSON and plain-text transformation was added in later updates to the XSLT 1.0 specification. As of August 2022, the most recent stable version of the language is XSLT 3.0, which achieved Recommendation status in June 2017. XSLT 3.0 implementations support Java, .NET, C/C++, Python, PHP and NodeJS.
Java, SQL, Python, C++, R: Massive parallel processing (MPP) database incorporating patented engines supporting native SQL, MapReduce, and graph data storage and manipulation; provides a set of analytic function libraries and data visualization [43] TerminusDB: 11.0.6: 2023-05-03 [44] Apache 2: Prolog, Rust, Python, JSON-LD
By default, a Pandas index is a series of integers ascending from 0, similar to the indices of Python arrays. However, indices can use any NumPy data type, including floating point, timestamps, or strings. [4]: 112 Pandas' syntax for mapping index values to relevant data is the same syntax Python uses to map dictionary keys to values.