Skip to main content

lightweight, simple, and fast declarative XML and JSON data extraction

Project description

Yankee - Simple Declarative Data Extraction from XML and JSON

This is kind of like Marshmallow, but only does deserialization. What it lacks in reversibility, it makes up for in speed. Schemas are compiled in advance allowing data extraction to occur very quickly.

Motivation

I have another package called patent_client. I also do a lot with legal data, some of which is in XML, and some of which is in JSON. But there's a lot of it. And I mean a lot, so speed matters.

Quick Start

There are two main modules: yankee.json.schema and yankee.xml.schema. Those modules support defining class-style deserializers. Both start by subclassing a Schema class, and then defining attributes from the fields submodule.

JSON Deserializer Example

    from yankee.json import Schema, fields

    class JsonExample(Schema):
        name = fields.String()
        birthday = fields.Date("birthdate")
        deep_data = fields.Int("something.0.many.levels.deep")

    obj = {
        "name": "Johnny Appleseed",
        "birthdate": "2000-01-01",
        "something": [
            {"many": {
                "levels": {
                    "deep": 123
                }
            }}
        ]
    }

    JsonExample().deserialize(obj)
    # Returns
    {
        "name": "Johnny Appleseed",
        "birthday": datetime.date(2000, 1, 1),
        "deep_data": 123
    }

For JSON, the attributes are filled by pulling values off of the JSON object. If no path is provided, then the attribute name is used. Otherwise, a dotted string can be used to pluck an item from the JSON object.

XML Deserializer Example

    import lxml.etree as ET
    from yankee.xml import Schema, fields

    class XmlExample(Schema):
        name = fields.String("./name")
        birthday = fields.Date("./birthdate")
        deep_data = fields.Int("./something/many/levels/deep")

    obj = ET.fromstring(b"""
    <xmlObject>
        <name>Johnny Appleseed</name>
        <birthdate>2000-01-01</birthdate>
        <something>
            <many>
                <levels>
                    <deep>123</deep>
                </levels>
            </many>
        </something>
    </xmlObject>
    """.strip())

    XmlExample().deserialize(obj)
    # Returns
    {
        "name": "Johnny Appleseed",
        "birthday": datetime.date(2000, 1, 1),
        "deep_data": 123
    }

For XML, the attributes are filled using XPath expressions. If no path is provided, then the entire object is passed to the field (no implicit paths). Any valid Xpath expression can be used.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

yankee-0.1.33.tar.gz (18.4 kB view details)

Uploaded Source

Built Distribution

yankee-0.1.33-py3-none-any.whl (24.9 kB view details)

Uploaded Python 3

File details

Details for the file yankee-0.1.33.tar.gz.

File metadata

  • Download URL: yankee-0.1.33.tar.gz
  • Upload date:
  • Size: 18.4 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: poetry/1.2.2 CPython/3.10.9 Windows/10

File hashes

Hashes for yankee-0.1.33.tar.gz
Algorithm Hash digest
SHA256 01b1ff403674d554356b9ddd0300ad07702710e3bbf50f0fc448b39c1c8f5fbf
MD5 da5f38a40bd7238bedd89004775683b8
BLAKE2b-256 af06b453c115d2da86276d1c8a9baa8e2437117fbc1f6337275d6c9e8387b4e0

See more details on using hashes here.

File details

Details for the file yankee-0.1.33-py3-none-any.whl.

File metadata

  • Download URL: yankee-0.1.33-py3-none-any.whl
  • Upload date:
  • Size: 24.9 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: poetry/1.2.2 CPython/3.10.9 Windows/10

File hashes

Hashes for yankee-0.1.33-py3-none-any.whl
Algorithm Hash digest
SHA256 e31956459c385d8a8adfdbb56ce07c1a9614b07509f1f7929bc6c64bc0ba1aed
MD5 ce3e8b844a3cd68c51d404353ac3aba0
BLAKE2b-256 3a1253b85567dff76883d3fd969bfc9c1cd309915ab609d8877ea28030295122

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page