pyde-toolkit 1.2.1

A growing toolkit of data-engineering helper functions and CLI commands — starting with schema inference (column standardisation, type inference, schema + DDL generation for Pandas/ANSI SQL or PySpark/Spark SQL).

pyde-toolkit

A growing toolkit of data-engineering helper functions and CLI commands. The first tool is schema inference: standardise column names, infer data types from sample data, and emit ready-to-use schema definitions and CREATE TABLE DDL — either Pandas/ANSI SQL or PySpark/Spark SQL (with bronze/silver/gold layer support for Databricks / Unity Catalog workflows).

Install

pip install pyde-toolkit

# with Excel support (.xlsx, .xls, .xlsm, .xlsb, .ods)
pip install "pyde-toolkit[excel]"

# with the pre-flight memory check for large full-file reads
pip install "pyde-toolkit[memcheck]"

# everything
pip install "pyde-toolkit[all]"

Already have it installed and want the latest release?

pip install --upgrade pyde-toolkit

Quick start — Python

from pyde_toolkit import infer_file          # top-level convenience re-export
# or, namespaced (recommended as the toolkit grows):
from pyde_toolkit.schema_inferencer import infer_file

result = infer_file(
    "sales.csv",
    pyspark=True,
    casing="pascal",
    table_name="sales_fact",
    header_row=0,        # skip junk title rows if needed, e.g. header_row=4
    type_threshold=0.95, # tolerate a few dirty values before falling back to string
)

print(result["schema"])         # PySpark StructType or pandas dtype dict
print(result["create_table"])   # SQL DDL
print(result["rename_code"])    # copy-paste column rename snippet
print(result["report"])         # full formatted text report

Quick start — CLI

pyde-toolkit schema-infer sales.csv
pyde-toolkit schema-infer sales.csv --pyspark true --case pascal --layer bronze --catalog prod
pyde-toolkit schema-infer sales.xlsx --sheet Sheet2 --layer silver
pyde-toolkit schema-infer messy.csv --header-row 4 --type-threshold 0.80
pyde-toolkit --version

Run pyde-toolkit schema-infer --help for the full flag reference, or see the module docstring in pyde_toolkit/schema_inferencer/core.py.

Features

• Column standardisation — camel, pascal, snake, screaming, kebab, or skip casing, with symbol expansion (/ → or, % → pct, etc.)
• Type inference — bool, int32/int64, float, date, datetime, string, with a configurable conformance threshold (--type-threshold) to tolerate dirty data
• Header offset — --header-row to skip junk/title rows above the real header, for both CSV and Excel
• Dual output modes — Pandas dtypes + ANSI SQL, or PySpark StructType + Spark SQL
• Layered outputs — bronze, parquet_bronze, silver, gold, gold_vw (view), or all five at once
• Table types — managed Delta, external, or external Delta tables
• Flexible input — CSV/TSV (delimiter auto-detected), Excel (.xlsx .xls .xlsm .xlsb .ods), or a pandas DataFrame directly

Project structure (for contributors)

src/pyde_toolkit/
├── __init__.py            # top-level re-exports + __version__
├── cli.py                 # top-level CLI dispatcher (registers subcommands)
└── schema_inferencer/     # one subpackage per feature
    ├── __init__.py        # public API for this feature
    ├── core.py            # logic only, no argparse
    └── cli.py             # add_arguments(parser) + run(args) for this feature

Adding a new feature later: create pyde_toolkit/<your_feature>/ with the same three-file shape, then register it with one line in pyde_toolkit/cli.py's build_parser(). No other files need to change.

Releasing a new version

Version lives in one place (pyproject.toml); the installed package's __version__ is read live from package metadata, so there's nothing else to keep in sync.

python scripts/bump_version.py patch   # or minor / major / an exact X.Y.Z
python -m build
twine upload dist/*

Anyone with it already installed just runs pip install --upgrade pyde-toolkit — no need to uninstall first.

License

MIT — see LICENSE.