Member Avatar for flebber

Hi

What would be a good recommendation for a practical resources on using xml, xpath and xquery?

By practical I mean.

Accessing values, storing files, creating set queries on xml to create a feed to push data to other applications (eg ipython, rstudio, the Web) maintaining data integrity, and transformng data types.

Note I have completed the W3c tutorials.
Thank you for taking time to help me.
Sayth

Edited by flebber
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  • Latest Post Latest Post by Mike Askew

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— practical, hands‑on next steps (building on ’s point that the host language matters).

Start by picking one runtime (Python, Java or R are the easiest for your targets) and treat XML/XPath/XQuery as two separate jobs: (a) querying/transforming (XPath/XQuery/XSLT), and (b) integration/storage (language libraries, DBs or files). Learn how to validate and normalize early — validated, typed input makes downstream queries and casts far safer.

Recommended, practical toolkit and workflow

  • For quick scripting and pushing data into IPython/Jupyter: Python + lxml (fast XPath, XSLT) and pandas for tabular exports.
  • For production XQuery/XPath/XSLT processing and HTTP feeds: a processor/DB like BaseX, eXist‑db or Saxon (lets you run XQuery and produce Atom/RSS).
  • For RStudio: use xml2 or XML to parse/XPath, then convert nodes to data.frames (tibble).
  • For integrity: author an XSD/RelaxNG and run schema validation before anything else; add Schematron for business rules.

Concrete, small examples

  • XPath with namespaces (Python/lxml):

    ns = {"a":"http://example.org/ns"}
doc.xpath("//a:record/a:field", namespaces=ns)

  • Validate with lxml:

    schema = etree.XMLSchema(etree.parse("schema.xsd"))
schema.assertValid(etree.parse("data.xml"))

  • XQuery to emit an Atom feed:

    xquery version "3.0";
<feed xmlns="http://www.w3.org/2005/Atom">{
for $b in doc("books.xml")//book
return <entry><title>{data($b/title)}</title></entry>
}</feed>

Troubleshooting tips

  • Watch namespaces: default namespaces break plain //tag XPath.
  • For large files use streaming APIs (iterparse, StAX) to avoid memory spikes.
  • Validate early, cast explicitly (xs:date, xs:decimal) when your queries depend on numeric/date comparisons.

This approach gives repeatable pipelines: validate → query/transform (XQuery/XSLT) → export (JSON/CSV) → ingest (IPython/RStudio).

Member Avatar for Mike Askew

This would mainly depend on which language you intend to use.

A programming language will be the main driver for most of the above tasks and therefore each language will have its own ways of doing them.

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