A free, local Python tool that transforms messy supplier inventory CSV files into clean, standardized datasets in minutes. It maps 40+ column header variations, strips currency symbols and commas, expands pack quantities (e.g. "Pack of 3" or "Dozen") into true unit stock, and separates corrupted rows into an exceptions file—all on your own computer without touching live store data.
Any raw vendor CSV or spreadsheet export with messy headers (Item #, QOH), currency signs ($ or ₹), or packaging notes.
Local Python script automatically maps columns, strips symbols/commas, expands pack counts, and validates every row.
normalized_inventory.csv (clean ready-to-import data) and normalization_exceptions.csv (quarantined bad rows).
Setup scope covers installation on customer-controlled infrastructure, configuration of authorised integrations, functional testing and handover.
Hosting, third-party charges and ongoing support are excluded unless separately agreed. Free workflows must never run on Sanocea-hosted infrastructure.
Supplier inventory spreadsheets arrive in completely inconsistent formats: random column names (Item #, PartNo, QOH), currency symbols inside price fields ($24.50, ₹499), and pack sizes (Box of 12, Pack of 6) that cause automated store imports to crash or dangerously under-count your sellable stock.
Manually cleaning vendor spreadsheets in Excel takes hours every morning and leads to human typing mistakes. If your store importer crashes or reads a "Pack of 6" as a single unit, you either lose sales by advertising depleted stock or oversell items you cannot fulfill.
Reads your raw supplier CSV file, detects column headers using an internal 40+ alias dictionary, cleans numeric pricing, converts pack multipliers into individual piece counts, and writes a pristine canonical CSV plus an audit file of any corrupt rows.
Your operations team stops editing spreadsheets by hand. You run one command, and within seconds you have a clean, validated CSV ready for Shopify, WooCommerce, or your warehouse management system.
Below is an exact demonstration of how the CSV Inventory Normalizer processes real vendor data. The input on the left contains irregular column headers, currency symbols, and pack quantities. The output on the right is the exact CSV generated by the script.
Item #,Description,Qty on Hand,Wholesale Price,MSRP,Packaging
sku-linen-shirt-wht-m,Classic Linen Shirt - White / M, 24 , $24.50 , $49.99 ,PCS
sku-cotton-tee-blk-3pk,Organic Cotton Crewneck Tee - Black, 10 , $15.00 , $35.00 ,Pack of 3
sku-wool-socks-gry-os,Merino Wool Boot Socks - Charcoal, 4 , $6.00 , $16.00 ,Dozen
,Missing SKU Product Line Item,15,$10.00,$20.00,PCS
sku-bad-stock,Sample Item,not-a-number,$12.00,$30.00,PCSNotice: lowercase SKUs with spaces, currency symbols in prices, pack sizes ("Pack of 3", "Dozen"), and corrupted rows at the bottom.
sku,title,available_stock,raw_stock_input,pack_multiplier,unit_of_measure,cost_price,retail_price,category,barcode
SKU-LINEN-SHIRT-WHT-M,Classic Linen Shirt - White / M,24,24,1,PCS,24.50,49.99,,
SKU-COTTON-TEE-BLK-3PK,Organic Cotton Crewneck Tee - Black,30,10,3,PCS,15.00,35.00,,
SKU-WOOL-SOCKS-GRY-OS,Merino Wool Boot Socks - Charcoal,48,4,12,PCS,6.00,16.00,,Clean numbers, uppercase SKUs, pack multipliers expanded (10×3=30 units, 4×12=48 units). Missing SKU and non-numeric rows were safely quarantined into normalization_exceptions.csv!
Technical Setup Guidance: Offline local utility. Reads local files only; makes zero network calls and does not require API keys or cloud credentials.
Takes 2 minutes. Unzip the package, drop in your supplier CSV, and run python3 inventory_normalizer.py.
Every deliverable is clean, self-contained, and ready for immediate deployment. You receive the complete files, configuration templates, setup guides, and automated tests.
| File Name | Deliverable Description |
|---|---|
inventory_normalizer.py | Core CSV inventory normalizer and supplier feed standardizer |
run.sh | Execution wrapper script |
requirements.txt | Test execution requirements (standard library only for runtime) |
README.md | Operational usage guide & schema specifications |
SETUP.md | Step‑by‑step local setup instructions & mapping reference |
COSTS.md | Zero recurring cost declaration |
LICENSING.md | Sanocea Commercial License |
tests/test_inventory_normalizer.py | Pytest suite covering alias mapping, currency cleaning, pack expansions, and exceptions |
data/messy_supplier_feed.csv | Realistic sample supplier feed with currency signs and pack sizes |
For technical teams or developers assisting your business, below is the visual execution architecture and automated verification breakdown:
The engine detects 40+ fuzzy header aliases across supplier variations, strips currency formatting and commas, expands pack sizes into piece counts, and quarantines invalid rows into an exceptions manifest.
Automatically aligns arbitrary column names (Item #, PartNo, QOH, Wholesale Price) to canonical inventory fields using a 40+ alias dictionary.
Strips currency symbols ($, €, £, ₹, Rs., INR, USD) and commas, outputting clean standard floating-point prices and integer quantities.
Detects packaging descriptors like Pack of 3 or Dozen and computes true piece-level inventory counts (e.g. 10 packs of 3 = 30 retail units).
Safely moves rows with missing SKUs or non-numeric stock values into normalization_exceptions.csv without crashing or aborting the run.
Generates an instant terminal summary table and normalization_summary.json recording row counts, unit totals, and detected mappings.
We believe in complete transparency. Review the documented technical boundaries and external service fee breakdown below prior to obtaining the package:
| Component | Estimated Cost | Details & Free Tier Allowances |
|---|---|---|
| CSV Inventory Normalizer Toolkit | ₹0 (Free to self-host) | Download full source code and self-host |
| Optional Assisted Setup | ₹500 one-time | Assisted installation, environment setup, and handover |
| Hosting & Third-Party Fees | ₹0 recurring | Standard Python local execution, no third-party APIs needed |
Receive the complete distributable archive containing inventory_normalizer.py, step-by-step setup guides, mock fixtures, and automated test suites.
Setup scope covers installation on customer-controlled infrastructure, configuration of authorised integrations, functional testing and handover.
Hosting, third-party charges and ongoing support are excluded unless separately agreed. Free workflows must never run on Sanocea-hosted infrastructure.
The normalizer includes a built-in dictionary of over 40 fuzzy aliases. Whether your supplier labels their column 'Item #', 'PartNo', 'Item_Code', or 'Vendor SKU', the tool automatically maps it to the product SKU. The same applies to stock ('Qty on Hand', 'QOH', 'Units'), prices ('Wholesale Price', 'MSRP'), and barcodes ('UPC', 'EAN').
By default, the normalizer extracts the packaging multiplier (e.g. 'Pack of 3' -> 3x, 'Dozen' -> 12x) and multiplies the raw quantity by that number (e.g. 10 packs of 3 = 30 available retail units). This prevents under-counting your stock when listing items individually in your store. If you sell the entire pack as a single unit, simply pass the --no-expand-packs flag.
It automatically strips currency symbols ($, €, £, ₹, Rs., INR, USD), commas, and spaces. For example, a wholesale cost formatted as ' $24.50 ' or 'Rs. 1,299' is cleaned into a standard decimal number like '24.50' or '1299.00' that your store or ERP import tool can ingest without errors.
No. The CSV Inventory Normalizer is a local-first offline utility. It cleans and standardizes your supplier spreadsheets on your computer and outputs a clean CSV file. You can review the output and upload it to Shopify, WooCommerce, or your inventory management system using their standard CSV import features. It never makes external web requests or touches your live store data.
Any row missing an essential product SKU or containing text inside a numeric quantity field (like 'not-a-number') is safely moved to normalization_exceptions.csv with its row number and reason. The valid rows continue processing, so a single bad row will never crash your entire catalog update.
How inconsistent vendor spreadsheets crash inventory syncs, why pack multipliers distort unit counts, and how automated normalizers clean supplier data.
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