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-0.1.31.tar.gz (17.7 kB view details)

Uploaded Source

Built Distribution

yankee-0.1.31-py3-none-any.whl (23.7 kB view details)

Uploaded Python 3

File details

Details for the file yankee-0.1.31.tar.gz.

File metadata

  • Download URL: yankee-0.1.31.tar.gz
  • Upload date:
  • Size: 17.7 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: poetry/1.1.6 CPython/3.10.6 Darwin/21.6.0

File hashes

Hashes for yankee-0.1.31.tar.gz
Algorithm Hash digest
SHA256 fea3a929e65f26eecf70426342cc66ebd142d173ae923922db802c4f5616bcb8
MD5 85c8cf38c50b6e6e1d1b894bf3478e2a
BLAKE2b-256 ad0ac539c1729135b268a7580aa155f6dff82d0bd426bbf24e39bc0351ec7867

See more details on using hashes here.

File details

Details for the file yankee-0.1.31-py3-none-any.whl.

File metadata

  • Download URL: yankee-0.1.31-py3-none-any.whl
  • Upload date:
  • Size: 23.7 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: poetry/1.1.6 CPython/3.10.6 Darwin/21.6.0

File hashes

Hashes for yankee-0.1.31-py3-none-any.whl
Algorithm Hash digest
SHA256 fe03ee1aefb62c28c972c15b935f405d51820e509775e59a0352eca4b26c4224
MD5 23c6a5a980c9a9176c390b369dbad3eb
BLAKE2b-256 6866ac973e290d3e3843bfef5cfcbd3fb3f449a2c44d6189c1a19b2b26eb878f

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