yankee 0.1.45

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

Summary

Simple declarative data extraction and loading in Python, featuring:

• 🍰 Ease of use: Data extraction is performed in a simple, declarative types.
• ⚙ XML / HTML / JSON Extraction: Extraction can be performed across a wide array of structured data
• 🐼 Pandas Integration: Results are easily castable to Pandas Dataframes and Series.
• 😀 Custom Output Classes: Results can be automatically loaded into autogenerated dataclasses, or custom model types.
• 🚀 Performance: XML loading is supported by the excellent and fast lxml library, JSON is supported by UltraJSON for fast parsing, and jsonpath_ng for flexible data extraction.

Quick Start

To extract data from XML, use this import statement, and see the example below:

from yankee.xml.schema import Schema, fields as f, CSSSelector

To extract data from JSON, use this import statement, and see the example below:

from yankee.xml.schema import Schema, fields as f, JSONPath

To extract data from HTML, use this import statement:

from yankee.html.schema import Schema, fields as f, CSSSelector

To extract data from Python objects (either objects or dictionaries), use this import statement:

from yankee.base.schema import Schema, fields as f

Documentation

Complete documentation is available on Read The Docs

Examples

Extract data from XML

Data extraction from XML. By default, data keys are XPath expressions, but can also be CSS selectors.

Take this:

    <xmlObject>
        <name>Johnny Appleseed</name>
        <birthdate>2000-01-01</birthdate>
        <something>
            <many>
                <levels>
                    <deep>123</deep>
                </levels>
            </many>
        </something>
    </xmlObject>

Do this:

from yankee.xml.schema import Schema, fields as f, CSSSelector

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

XmlExample().load(xml_doc)

Get this:

{
    "name": "Johnny Appleseed",
    "birthday": datetime.date(2000, 1, 1),
    "deep_data": 123
}

Extract data from JSON

Data extraction from JSON. By default, data keys are implied from the field names, but can also be JSONPath expressions

Take this:

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

Do this:

from yankee.json.schema import Schema, fields as f

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

Get this:

{
    "name": "Johnny Appleseed",
    "birthday": datetime.date(2000, 1, 1),
    "deep_data": 123
}