py-data-engine 0.3.12

GUI orchestrator for python-based dataframe transform pipelines.

Data Engine

Data Engine is a GUI orchestrator for Python dataframe workflows. It lets you author flows as plain Python modules, run them manually or automatically, and inspect parquet-first outputs from the desktop app.

The runtime is built around:

• workspace-based flow discovery
• manual, poll, and schedule execution modes
• Polars and DuckDB-friendly flow steps
• mirrored output paths for source-driven runs
• saved run, log, and dataframe inspection state
• desktop and terminal operator surfaces

Install

Use the installer for your environment:

• macOS: INSTALL/INSTALL MAC.command
• Windows: INSTALL/INSTALL WINDOWS.bat
• Windows VM / CPU-safe Polars path: INSTALL/INSTALL WINDOWS_VM.bat

For local development:

python -m pip install --constraint requirements/constraints.txt -e ".[dev]"

For a published package install:

python -m pip install py-data-engine

Data Engine requires Python >=3.14.

Dependency pinning, constrained installs, and hash-locked runtime installs are documented in SECURITY.md.

Start

Desktop GUI:

data-engine start gui

Terminal UI:

data-engine start tui

Headless commands:

data-engine list
data-engine show example_summary
data-engine run --once example_summary
data-engine run

Minimal Flow

from data_engine import Flow
import polars as pl


def read_docs(context):
    return pl.read_excel(context.source.path)


def keep_open(context):
    return context.current.filter(pl.col("status") == "OPEN")


def write_parquet(context):
    output = context.mirror.with_suffix(".parquet")
    context.current.write_parquet(output)
    return output


def build():
    return (
        Flow(group="Docs")
        .watch(
            mode="poll",
            source="../../../example_data/Input/docs_flat",
            interval="5s",
            extensions=[".xlsx", ".xls", ".xlsm"],
            settle=1,
        )
        .mirror(root="../../../example_data/Output/example_mirror")
        .step(read_docs, save_as="raw_df")
        .step(keep_open, use="raw_df", save_as="filtered_df")
        .step(write_parquet, use="filtered_df")
    )

Each authored flow module exports build() -> Flow. The module filename is the flow identity.

Workspaces

Data Engine discovers workspaces from a collection root. Each workspace keeps authored flows under:

workspaces/<workspace_id>/flow_modules/

Shared workspace state lives under:

workspaces/<workspace_id>/.workspace_state/

Machine-local runtime artifacts are stored outside the authored workspace.

Useful APIs

from data_engine import Flow, FlowContext, discover_flows, load_flow, run

Common Flow methods:

• .watch(...)
• .mirror(...)
• .date_range_input(...)
• .step(...)
• .collect(...)
• .map(...)
• .step_each(...)
• .preview(...)
• .run_once()
• .run()

Common FlowContext values:

• context.source
• context.mirror
• context.current
• context.objects
• context.metadata
• context.database("analytics.duckdb")
• context.template("reports/base.xlsx")
• context.debug

The full authoring guide and helper reference live in src/data_engine/docs/sphinx_source/guides/.

Testing

python -m pytest -q
python -m build
python -m twine check dist/*

Status

This project is pre-alpha. Internal architecture is still moving quickly, and backwards compatibility is not a current goal.