aptoro 0.2.0

A minimal, functional Python ETL library for reading, validating, and transforming data using YAML schemas

Aptoro

Aptoro is a Xavante word for "preparing the arrows for hunting".

A minimal, functional Python ETL library for reading, validating, and transforming data using YAML schemas.

Installation

pip install aptoro

Quick Start

from aptoro import load, load_schema, read, validate, to_json

# All-in-one: read + validate
entries = load(source="data.csv", schema="schema.yaml")

# Or step by step:
schema = load_schema("schema.yaml")
data = read("data.csv")
entries = validate(data, schema)

# Export
json_str = to_json(entries)

# Export with embedded metadata (for self-contained files)
json_meta = to_json(entries, schema=schema, include_meta=True)

# Load back with metadata
from aptoro import load_meta
loaded_schema, loaded_data = load_meta("output.json")

Schema Language

Define your data schema in YAML:

name: lexicon_entry
description: Dictionary entries

fields:
  id: str
  lemma: str
  pos: str[noun|verb|adj|adv]    # Constrained values
  definition: str
  translation: str?               # Optional field
  examples: list[str]?            # Optional list
  frequency: int = 0              # Default value

Type Syntax

• Basic types: str, int, float, bool
• Optional: str?
• Default value: str = "default", int = 0
• Constrained: str[a|b|c]
• Lists: list[str], list[int]

Schema Inheritance

# child.yaml
name: child_entry
extends: base.yaml

fields:
  name: str
  # inherits fields from base.yaml

Supported Formats

• CSV
• JSON
• YAML
• TOML

License

GNU General Public License v3 (GPLv3)