Michael
e6ee2e3481
User tried ``brew install tesseract`` in PowerShell after seeing all three OSes listed inline in the OCR banner — easy mistake when the install commands are crammed on one line with ``·`` separators. Two changes pre-empt this: **OS-aware OCR banner.** The expander now detects the user's platform via ``platform.system()`` and shows only the relevant install instructions: - **Windows**: UB-Mannheim installer link, numbered steps, explicit "keep the Add to PATH checkbox on" callout, plus a fallback paragraph telling the user how to set ``DATATOOLS_TESSERACT_PATH`` if they already installed without PATH and don't want to reinstall. - **macOS**: ``brew install tesseract`` with a Homebrew link. - **Linux**: ``apt install tesseract-ocr`` with a "or your distro's equivalent" hedge. **Robust binary discovery in ``ocr_available()``.** Three-stage: 1. Honor ``DATATOOLS_TESSERACT_PATH`` env var if set — explicit override for portable installs or non-default locations. 2. Try ``pytesseract``'s default PATH-based lookup. 3. If PATH lookup fails, probe known Windows install paths (``C:\Program Files\Tesseract-OCR\tesseract.exe``, the x86 variant, and ``%LOCALAPPDATA%\Programs\Tesseract-OCR\``) via the new ``_autodetect_tesseract_path``. On hit, set ``pytesseract.pytesseract.tesseract_cmd`` so all subsequent ``image_to_data`` calls use the same binary without re-discovering. This means a user who runs the UB-Mannheim installer with default options but forgets the PATH checkbox will still get OCR working after a launcher restart, without env-var gymnastics. Tests (4 new, 85 total in the suite): - Auto-detect returns None on non-Windows (no false positives on dev laptops). - Auto-detect finds the binary at a mocked ``C:\Program Files\Tesseract-OCR\tesseract.exe``. - Auto-detect returns None when no candidate exists. - ``DATATOOLS_TESSERACT_PATH`` env var beats both PATH lookup and auto-detect (sets ``tesseract_cmd`` even when the path doesn't resolve, so a real binary at a custom location works). 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.