excel-orm 0.1.2

A lightweight Excel ORM for generating templates and parsing typed row models.

Excel ORM

A lightweight, typed “Excel ORM” for generating Excel templates and parsing Excel workbooks into Python objects using column descriptors.

This project is designed for the common enterprise pattern where you:

1. generate a structured .xlsx template for users,
2. let users fill it in,
3. load the workbook back into Python, producing typed objects grouped by model.

It uses openpyxl for reading/writing Excel files and supports multiple model “tables” on the same worksheet.

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Features

• Typed column descriptors (text_column, int_column, bool_column, date_column)
• Template generation with:
  • merged table title cells (pluralized model name)
  • bold headers
  • sensible column widths
  • multiple tables laid out horizontally on the same sheet with a configurable gap
• Workbook parsing into model-specific repositories:
  • excel_file.cars.all() → list[Car]
  • excel_file.manufacturing_plants.all() → list[ManufacturingPlant]
• Validation hooks
  • column-level not_null
  • optional row exclusion rules via excludes
  • optional model-level validate() method

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Installation

From PyPI (once published)

pip install excel-orm

From source (uv)

git clone <your-repo-url>
cd excel-orm
uv sync

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Quick Start

1) Define models using Column[...] descriptors

from excel_orm.column import Column, text_column, int_column
from excel_orm.orm import ExcelFile, SheetSpec

class Car:
    make: Column[str] = text_column(header="Make", not_null=True)
    model: Column[str] = text_column(header="Model", not_null=True)
    year: Column[int] = int_column(header="Year", not_null=True)

class ManufacturingPlant:
    name: Column[str] = text_column(header="Factory Name", not_null=True)
    location: Column[str] = text_column(header="Location")

2) Declare a sheet containing multiple models

Each model becomes its own table on the same worksheet.

sheet = SheetSpec(
    name="Cars",
    models=[Car, ManufacturingPlant],

    # Layout rows
    title_row=1,
    header_row=2,
    data_start_row=3,

    # Horizontal spacing between model tables
    template_table_gap=2,
)

3) Create an ExcelFile, generate a template, then load data

excel_file = ExcelFile(sheets=[sheet])

# Generate a blank template workbook
excel_file.generate_template("car_inventory_template.xlsx")

# Users fill in data in Excel...

# Load the filled workbook into repositories
excel_file.load_data("car_inventory_data.xlsx")

cars = excel_file.cars.all()
plants = excel_file.manufacturing_plants.all()

print(cars[0].make, cars[0].year)
print(plants[0].name, plants[0].location)

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How It Works

Repositories

For each model you register, ExcelFile creates a repository attribute on the instance using a snake_case pluralized name:

• Car → excel_file.cars
• ManufacturingPlant → excel_file.manufacturing_plants

Repositories are simple list-like containers with an all() helper:

cars = excel_file.cars.all()  # list[Car]

Multi-table Sheets

A single worksheet can host multiple model tables. During template generation:

• A merged title cell is written above each table (pluralized class name in title case).
• Headers appear under the title.
• Data rows begin at data_start_row.
• Tables are placed horizontally with template_table_gap blank columns between them.

During parsing:

• The library locates each model table by matching the expected header sequence.
• It reads contiguous rows until a blank row is encountered.

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Column Types

Text

from excel_orm.column import Column, text_column

class Example:
    name: Column[str] = text_column(header="Name", not_null=True, strip=True)

• None parses to "" (empty string).
• strip=True trims whitespace.

Integer

from excel_orm.column import Column, int_column

class Example:
    qty: Column[int] = int_column(header="Qty", not_null=True)

• None or "" parses to 0.

Boolean

from excel_orm.column import Column, bool_column

class Example:
    active: Column[bool] = bool_column(header="Active")

Accepted values include:

• True: true, t, yes, y, 1 (case-insensitive)
• False: false, f, no, n, 0
• None / empty parses to False

Invalid values raise ValueError.

Date

from excel_orm.column import Column, date_column

class Example:
    start_date: Column[date] = date_column(header="Start Date")

The date parser supports:

• Excel-native datetime/date values from openpyxl
• ISO strings like 2025-06-01 and 2025-06-01T13:45:00
• Common business formats including 01-JUN-2025

Invalid/empty values raise ValueError.

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Validation

Column-level: not_null

class Car:
    make: Column[str] = text_column(header="Make", not_null=True)

If a not_null=True column parses to None or "", a ValueError is raised.

Row exclusion: excludes

If you set excludes, rows matching those raw values in that column will be skipped.

status: Column[str] = text_column(header="Status")
status.spec.excludes = {"IGNORE", "SKIP"}  # example pattern

(If you want a nicer API for excludes, consider adding it directly to the column factory signature.)

Model-level: validate()

If your model defines a validate(self) method, it is called after a row is parsed.

class Car:
    make: Column[str] = text_column(header="Make", not_null=True)
    year: Column[int] = int_column(header="Year", not_null=True)

    def validate(self) -> None:
        if self.year < 1886:
            raise ValueError("Invalid car year")

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Development

Run tests

uv run pytest

Lint/format (example)

If you use Ruff:

uv run ruff check .
uv run ruff format .