Michael
ee0b1f6f6b
New ``docs/FUTURE-TOOLS.md`` captures post-launch tool ideas with a consistent shape — What / Why / Can we ship now / Approach / GUI sketch / Effort / Risks / Ship criteria. Resting place for things the new-tool freeze in ``PLAN.md`` §2.1 refuses to build but that keep coming up. First entry: **#10 PDF → CSV extractor** (bank statements et al.). Key facts captured: - **Current state**: no PDF infrastructure exists. Zero PDF dependencies in requirements.txt; zero PDF-touching code under ``src/``. The only "PDF" string in the codebase is the planned- output copy for the Quality Check tool, unrelated to extraction. - **Library picks**: pdfplumber as the extraction core (BSD-3, no native compiler, gives coordinate-aware text), Tesseract via pytesseract as the OCR fallback for scanned PDFs, streamlit-drawable-canvas as the region-picker component. - **GUI sketch**: user draws a header strip + a row template on a rendered page; the tool applies that template across N pages, saves the template by layout fingerprint for next month's statement, emits CSV. - **Effort phased A–E**: 3–4 weeks for a text-only MVP; 6–10 weeks for a polished version with multi-page template recall; +2–3 weeks if scanned-PDF OCR is required. - **Difficulty**: medium-hard. The pieces are well-trodden; the combination (region selection that persists across pages and across documents with similar layouts) is where the engineering goes. - **Ship criteria**: ≥1 paying customer + ≥3 paid or ≥5 demo emails asking for PDF extraction + the bookkeeper niche converting at least one customer first. None have fired. Cross-references added: - ``docs/REQUIREMENTS.md`` §11: pointer to FUTURE-TOOLS.md for parked tool ideas, with a one-paragraph summary of #10. - ``docs/PLAN.md`` §2.1: notes that the freeze parks future tools in FUTURE-TOOLS.md and explicitly names #10 as the current highest-pressure entry. - ``docs/NEXT-STEPS.md`` Phase 5 "what NOT to build" table: a new row for the PDF tool tied to the same ship-trigger language. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
🌐 Language: English · Español
DataTools
Local CSV / Excel cleaning. CLI + browser GUI, no cloud, no install ceremony. GUI ships with English and Spanish language packs.
Tools
| # | Tool | Status |
|---|---|---|
| 01 | Find Duplicates — exact + fuzzy match, 5 normalizers, survivor rules, audit | Ready |
| 02 | Clean Text — whitespace, smart chars, BOM, line endings, case ops | Ready |
| 03 | Standardize Formats — dates, phones, emails, addresses, names, currencies, booleans | Ready |
| 04 | Fix Missing Values — disguised-null detection, profile, mean/median/mode/ffill/bfill/interpolate, drop strategies | Ready |
| 05 | Map Columns — fuzzy auto-rename, target schema with type coercion, required fields with defaults, drop/reorder | Ready |
| 06 | Find Unusual Values | Coming Soon |
| 07 | Combine Files | Coming Soon |
| 08 | Quality Check | Coming Soon |
| 09 | Automated Workflows — chain tools with recommended (not forced) order, save/load JSON, automate weekly cleanups | Ready |
Download (non-technical users)
Pre-built installers — no Python required:
| Platform | Download | First-launch note |
|---|---|---|
| macOS | DataTools-X.Y.Z-mac.dmg |
Drag DataTools.app into /Applications, then double-click. |
| Windows | DataTools-X.Y.Z-win-setup.exe |
Run the installer; launches from Start Menu. |
| Linux | DataTools-X.Y.Z-linux-x86_64.AppImage |
chmod +x the file, then double-click. |
Latest release: see GitHub Releases (or the Gumroad listing). The installers are ~150–200 MB; the launcher boots a local server at http://127.0.0.1:8501 and opens your browser. Nothing is sent to the cloud.
Install from source (developers)
pip install -r requirements.txt
Python 3.10+ required.
Run
GUI (recommended):
streamlit run src/gui/app.py
CLI — seven entry points:
python -m src.cli customers.csv [--apply] # dedup
python -m src.cli_text_clean messy.csv [--apply] # text clean
python -m src.cli_format intl.csv [--apply] # format standardize (auto-streams >100 MB)
python -m src.cli_missing holes.csv [--apply] # missing values
python -m src.cli_column_map vendor.csv [--apply] # column mapper
python -m src.cli_pipeline any_file.csv [--apply] # chain tools end-to-end
python -m src.cli_analyze any_file.csv [--json] # scan only
Every CLI runs preview-only by default; add --apply to write output.
Language
The GUI sidebar has a language picker. Packs ship for English and Español (src/i18n/packs/); the choice persists for the session. Adding a language: drop a <code>.json next to en.json mirroring its key tree, then list it in LANGUAGES. See Developer Guide §i18n.
Review & Normalize gate
Every uploaded file passes through a CSV-normalization gate before any tool sees it. The analyzer flags ~15 issue types (whitespace, NBSP / zero-width chars, BOM, encoding, smart punct, dirty headers, null sentinels, mojibake, …) tagged by confidence (high / medium / low) and fix action. The GUI shows each finding with Auto-fix / Skip / Customize, a live before/after preview, and an encoding-override picker. Tool pages refuse to load until the gate passes.
Output
Every run writes:
{input}_<tool>.csv— the cleaned data{input}_changes.csv(text cleaner) or{input}_match_groups.csv(dedup) — audit traillogs/<tool>_YYYYMMDD_HHMMSS.log— debug-level run log
Original input file is never modified.
Docs
- User Guide — install, GUI workflow, gate
- CLI Reference — every flag with recipes
- Requirements — file sizes, encodings, detectors, perf targets
- Technical — architecture, gate internals, fix registry
- Developer Guide — adding fixes / detectors / standardizers
Dependencies
pandas, openpyxl, rapidfuzz, phonenumbers, typer, loguru, charset-normalizer, streamlit. Optional: ftfy for mojibake repair.
License
Proprietary.