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Comma-separated values (CSV) is a text file format that uses commas to separate values, and newlines to separate records. A CSV file stores tabular data (numbers and text) in plain text, where each line of the file typically represents one data record. Each record consists of the same number of fields, and these are separated by commas in the ...
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 ...
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.
XML separates "data" from "metadata" (via the use of elements and attributes), while JSON does not have such a concept. Another key difference is the addressing of values. JSON has objects with a simple "key" to "value" mapping, whereas in XML addressing happens on "nodes", which all receive a unique ID via the XML processor.
In computing, Jackson is a high-performance JSON processor for Java. Its developers extol the combination of fast, correct, lightweight, and ergonomic attributes of the library. Its developers extol the combination of fast, correct, lightweight, and ergonomic attributes of the library.
Wes McKinney is an American software developer and businessman. He is the creator and "Benevolent Dictator for Life" (BDFL) of the open-source pandas package for data analysis in the Python programming language, and has also authored three versions of the reference book Python for Data Analysis.
Some academic papers use this word to mean the act of compiling the Java bytecode to C [1] or the timing when optimization pipeline are performed. [2] An academic project [ 3 ] uses this word to mean the act of pre-compiling JavaScript to a machine-dependent optimized IR for V8 (JavaScript engine) [ 4 ] and to a machine independent bytecode for ...
Java bytecode is used at runtime either interpreted by a JVM or compiled to machine code via just-in-time (JIT) compilation and run as a native application. As Java bytecode is designed for a cross-platform compatibility and security, a Java bytecode application tends to run consistently across various hardware and software configurations. [3]