excel-backend 0.1.0

Turn any Excel or CSV file into a backend API with one command

excel-backend

Turn any Excel or CSV file into a REST API with one command.

pip install excel-backend
excel-backend run students.xlsx

✓ Loaded students.xlsx
  students: 50 rows, 5 columns
  courses: 12 rows, 3 columns
  3 images extracted

✓ API running at http://127.0.0.1:8000
  Docs at http://127.0.0.1:8000/docs

Your data is now a full CRUD API:

GET    /students        → list all students
GET    /students/1      → get student by id
POST   /students        → create a student
PUT    /students/1      → update a student
DELETE /students/1      → delete a student
GET    /images/file.png → serve extracted images

Auto-generated Swagger docs at /docs.

Install

pip install excel-backend

CLI Usage

Start API server

excel-backend run data.xlsx
excel-backend run data.csv
excel-backend run data.tsv

Options:

excel-backend run data.xlsx --port 3000
excel-backend run data.xlsx --host 0.0.0.0
excel-backend run data.xlsx --db data.sqlite  # persist to file

Inspect schema

excel-backend schema data.xlsx

table: students
columns:
  - id INTEGER (primary key)
  - name TEXT
  - age INTEGER
  - photo IMAGE

Python API

from excel_backend import ExcelBackend

eb = ExcelBackend("students.xlsx")

# List tables
print(eb.tables)  # ["students", "courses"]

# Read data
for row in eb["students"]:
    print(row["name"])

# CRUD
eb.create("students", {"name": "Alice", "age": 22})
eb.update("students", 1, {"age": 23})
eb.delete("students", 1)

# Start API server
eb.serve(port=8000)

How It Works

Excel/CSV → Schema Model → SQLite → FastAPI

1. Loader parses your file (.xlsx, .csv, .tsv)
2. Schema engine infers column types (INTEGER, FLOAT, TEXT, BOOLEAN, DATE, IMAGE)
3. Storage adapter creates tables and inserts data (SQLite)
4. API generator creates CRUD endpoints from the schema

Each sheet in an Excel file becomes a separate table with its own endpoints.

Image Extraction

Images embedded in .xlsx files are automatically:

• Extracted from the zip archive
• Mapped to their row/column position
• Served at /images/{filename}
• Referenced in API responses

Architecture

excel_backend/
├── schema/model.py      # Schema, Column, type inference
├── loader/
│   ├── base.py          # BaseLoader interface
│   ├── excel.py         # .xlsx parser + image extraction
│   └── csv.py           # .csv/.tsv parser
├── storage/
│   ├── base.py          # BaseStorage interface
│   └── sqlite.py        # SQLite adapter
├── api/generator.py     # Schema → FastAPI routes
└── cli/main.py          # CLI entry point

The Schema model is the core abstraction. Everything flows through it. Storage and API layers are pluggable — they only depend on the Schema, not on each other.

Supported Types

Excel Data	Detected Type
1, 42, -3	INTEGER
3.14, 0.5	FLOAT
"hello"	TEXT
TRUE/FALSE	BOOLEAN
2024-01-15	DATE
embedded image	IMAGE

License

MIT