Normalize CSV headers (no upload)

What this problem looks like

CSV failures are usually delimiter/quote/header mismatches that shift columns and break row shape.

Treat errors as signals. Fix the input format first, validate locally, then convert and export.

• Validate first: run a local validator for strict JSON/XML/CSV.
• Sample: test 20–50 rows/items before processing a full export.
• Spot-check: verify a few keys/rows before downloading.

Practical checklist (fast)

Use a fast checklist: confirm strict syntax, confirm expected shape, and only then convert. This reduces “random fixing” and prevents exporting broken output.

If you handle sensitive data, local processing avoids accidental uploads while debugging.

• Confirm your delimiter/quotes for CSV or your tag structure for XML.
• Confirm JSON is strict (double quotes, no trailing commas, no comments).
• Export only after the sample output looks correct.

Local conversion workflow

For CSV and JSON exports, keep transformations simple: parse → normalize → serialize. When issues appear, fix the input rather than adding hidden “auto-fixes”.

Use the built-in download/copy actions to move data to the next step without sending it to a server.

Header normalization rules (practical)

Header keys become your JSON field names. Normalizing them early makes downstream work easier: consistent casing, no accidental duplicates, and predictable exports.

Keep normalization minimal and reversible. The goal is stable keys, not rewriting business meaning.

• Trim leading/trailing whitespace in header cells.
• Collapse repeated spaces (e.g. “First Name” → “First Name”).
• Choose a consistent casing (e.g. snake_case or camelCase) and stick to it.

Trust note: All processing happens locally in your browser. Files are never uploaded.

FAQ

Why does it work in one tool but not in another? Different tools accept different non-standard inputs. Stick to strict JSON/XML/CSV for reliability.

Is it safe to convert private files online? If privacy matters, prefer local no-upload tools and verify via the Network tab.

import csv
from io import StringIO

sample = text[:50000]  # keep first chunk for fast local triage
rows = list(csv.reader(StringIO(sample)))
print('rows:', len(rows), 'columns(first row):', len(rows[0]) if rows else 0)