Catch CSV row-width drift before it breaks an import

SUAD CUMA — Fri, 30 Oct 2026 18:20:02 +0000

Catch CSV row-width drift before it breaks an import

A spreadsheet export can look ordinary but break a bulk import when a line has fewer or more columns than the header. This is a structural problem, separate from missing values or duplicate business identifiers.

Consider this entirely synthetic sample:

order_id,amount,currency
1001,29.50,USD
1002,19.00
1003,"39,00",EUR,EXTRA
1004,10.00,USD

Line 3 has only two fields. Line 4 has four. The comma inside the quoted amount 39,00 belongs to a single field, and the extra field is a separate problem.

Reproducible validation with standard-library Python

import csv
from io import StringIO

sample = """order_id,amount,currency
1001,29.50,USD
1002,19.00
1003,"39,00",EUR,EXTRA
1004,10.00,USD
"""

reader = csv.reader(StringIO(sample), strict=True)
header = next(reader)
width_errors = [
    (line_number, len(fields))
    for line_number, fields in enumerate(reader, start=2)
    if len(fields) != len(header)
]

print("Expected columns:", len(header))
print("Width errors (line, columns):", width_errors)

Expected output:

Expected columns: 3
Width errors (line, columns): [(3, 2), (4, 4)]

What the test does—and what it cannot do

The check counts parsed CSV fields, not literal comma characters. Python's CSV parser respects quoted commas, so do not split a CSV record manually on commas. Row-width validation is useful before trying to map exports to database columns.

Rows with correct field counts may still contain invalid amounts, duplicate business keys, empty required values, or unsupported currency codes. Those are separate data-quality controls. When testing a real import, preserve the original file, confirm encoding and delimiter settings, and check the target importer's mapping before writing production data.

This example uses fabricated data and accesses no client account. You can also explore the free browser-local CSV audit on a small test file.

Disclosure: This technical tutorial was prepared with AI assistance. The Python example was run with synthetic input, and its output was checked before approval for publication.

Duplicate CSV rows are not the same as duplicate product SKUs

SUAD CUMA — Fri, 23 Oct 2026 17:16:28 +0000

Duplicate CSV rows are not the same as duplicate product SKUs

Suppose a product export contains these rows:

Type,SKU,Name,Regular price
simple,TSHIRT-S,Shirt S,19.00
simple,TSHIRT-S,Shirt S,19.00
simple,MUG-1,Ceramic Mug,12.00
simple,MUG-1,Ceramic Mug updated,12.00

There are two different problems:

Rows 1 and 2 are identical after CSV parsing.
Rows 3 and 4 are different rows but share the same non-empty SKU.

A generic row deduplication tool may catch the first issue while missing the second. Before a bulk import, it helps to check both.

A small standard-library Python check

import csv
from collections import Counter
from io import StringIO

source = """Type,SKU,Name,Regular price
simple,TSHIRT-S,Shirt S,19.00
simple,TSHIRT-S,Shirt S,19.00
simple,MUG-1,Ceramic Mug,12.00
simple,MUG-1,Ceramic Mug updated,12.00
"""

reader = csv.DictReader(StringIO(source))
rows = list(reader)
header = reader.fieldnames or []
exact_keys = [tuple(row.get(name) for name in header) for row in rows]
sku_keys = [row["SKU"].strip() for row in rows if row.get("SKU", "").strip()]

print("Duplicate row occurrences:", sum(v - 1 for v in Counter(exact_keys).values() if v > 1))
print("Duplicate SKU values:", sorted(k for k, v in Counter(sku_keys).items() if v > 1))

Expected output:

Duplicate row occurrences: 1
Duplicate SKU values: ['MUG-1', 'TSHIRT-S']

The first number counts additional repeated rows beyond the first instance. The SKU list represents duplicate identifiers, not the number of duplicates.

Import hygiene matters more than the counter

Before applying an automatic fix, consider whether identifiers are intended to be unique in your target workflow. For variations, check the expected parent relationships and the WooCommerce import schema separately. Preserve a copy of the source and review the import preview before writing to production.

This example deliberately uses fabricated data and does not access anyone's store.

A free, browser-local CSV audit is available for experimenting with a sample before importing. It does not replace testing business-specific SKU rules.

Optional next step: a reproducible local calculator

The Python snippet above is free and sufficient for reproducing the example. For repeatable checks on your own files, USA-1 also offers a separate Duplicate Row Rate Checker ZIP for 49 Telegram Stars as an optional one-time digital download. The ZIP is a local Python calculator, not a WooCommerce plugin and not a replacement for validating SKU uniqueness, parent/variation mapping, or a staging import. The product page describes the files; Telegram shows the charge before any payment. No purchase is required to use the free example or browser-local audit.

Disclosure: This tutorial was prepared with AI assistance. The Python example was run against the synthetic data shown above, and its output was verified before publication.

DEV Community: SUAD CUMA

Catch CSV row-width drift before it breaks an import

Catch CSV row-width drift before it breaks an import

Reproducible validation with standard-library Python

What the test does—and what it cannot do

Duplicate CSV rows are not the same as duplicate product SKUs

Duplicate CSV rows are not the same as duplicate product SKUs

A small standard-library Python check

Import hygiene matters more than the counter

Optional next step: a reproducible local calculator