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-0.1.27.tar.gz (17.3 kB view details)

Uploaded Source

Built Distribution

yankee-0.1.27-py3-none-any.whl (23.5 kB view details)

Uploaded Python 3

File details

Details for the file yankee-0.1.27.tar.gz.

File metadata

  • Download URL: yankee-0.1.27.tar.gz
  • Upload date:
  • Size: 17.3 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: poetry/1.1.13 CPython/3.8.10 Windows/10

File hashes

Hashes for yankee-0.1.27.tar.gz
Algorithm Hash digest
SHA256 4fc48c9a664886e9e28583ab942be14838ca691c6f58053ed6d7157cbfd0c526
MD5 a4565feb685166fde886c5751d318165
BLAKE2b-256 911a12b35babdd26323a102eeaaaa5c7882ebfeecb00e43a86d9d4840c071b07

See more details on using hashes here.

File details

Details for the file yankee-0.1.27-py3-none-any.whl.

File metadata

  • Download URL: yankee-0.1.27-py3-none-any.whl
  • Upload date:
  • Size: 23.5 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: poetry/1.1.13 CPython/3.8.10 Windows/10

File hashes

Hashes for yankee-0.1.27-py3-none-any.whl
Algorithm Hash digest
SHA256 68d497f0f07bc8d0543d3cf3873452dcf6c29438591d682efc566d67691f36e6
MD5 99b380ea665f981a4a55183b53e284d3
BLAKE2b-256 95e83a45bfc8c5184e4781fc2acbc5b453a01b69195096c6e573d7e6a4c550ee

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