mlschema 0.1.0

A Python micro-library that converts pandas DataFrames into fully-validated, front-end-ready JSON schemas

MLSchema

Automated schema inference for data‑driven organisations, grounded in proven design, built for tomorrow.

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1. Executive Summary

MLSchema is a Python micro‑library that converts pandas dataframes into fully‑validated, front‑end‑ready JSON schemas. The goal: eliminate hand‑rolled form definitions, accelerate prototype‑to‑production cycles, and enforce data‑contract governance across your analytics stack.

Metric	Outcome
Time‑to‑schema	< 150 ms on 10 k columns / 1 M rows (benchmarked on x86‑64, Python 3.13)
Boilerplate reduced	≈ 90 % fewer lines of bespoke form code
Extensibility	Plug‑in architecture, register or swap strategies at runtime

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2. Quick Installation

For green‑field projects or CI pipelines, a single command sets up MLSchema and its dependency graph using uv:

uv add mlschema

For other package managers, refer to the dedicated Installation guide.

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3. 90‑Second Onboarding

import pandas as pd
from mlschema import MLSchema
from mlschema.strategies import TextStrategy

# 1️⃣  Source your data
df = pd.read_csv("data.csv")

# 2️⃣  Spin up the orchestrator and register baseline strategies
ms = MLSchema()
ms.register(TextStrategy())

# 3️⃣  Produces a JSON schema
schema = ms.build(df)

Outcome: a JSON that your UI layer can instantly translate into dynamic forms.

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4. Architectural Building Blocks

Component	Role	Extensibility Point
mlschema.MLSchema	Strategy registry, validation pipeline, JSON emitter	register(), update(), unregister()
Field Strategies	Map pandas dtypes => form controls	Implement Strategy subclasses
BaseField (Pydantic)	Canonical schema blueprint	Custom Pydantic models inherit from it

Why a Strategy Pattern?

• Single‑responsibility: Each strategy owns one field type.
• Hot‑swap: Swap implementations without touching consumer code.
• Forward compatibility: Introduce domain‑specific controls (e.g., geospatial pickers) with near‑zero refactor.

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5. Feature Highlights

1. Zero‑configuration defaults: Text fallback ensures graceful degradation.
2. Pydantic v2 validators: Domain rules enforced at build time.
3. Runtime performance: Vectorised dtype checks, no Python loops on critical paths.
4. Production readiness: CI badge, semantic versioning, and zero open CVEs (September 2025).

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6. Further Reading

• Detailed Installation
• Usage Guide
• API Reference
• GitHub

Tradition meets innovation: MLSchema codifies time‑honoured form‑generation workflows while embracing Python’s latest language features.