Michael
ac94208d8f
- Drop unused 'from src.i18n import t' from pages 1-9 (the swap to render_tool_header(tool_id) means no page calls t() directly anymore). Pages 10, 11 and the underscore-prefixed pages were already clean or legitimately use t(). - Rewrite PDF Extractor help_md (en + es). The original prose described features the tool does NOT have — template drawing, per-source saved templates, automatic reuse. The actual tool is a heuristic batch scanner (per its own docstring: "No templates, no per-bank configuration"). New copy: scan → uncheck → pick date format → enable OCR if needed → download. Spanish version tagged with '<!-- TODO: review Spanish -->' since the prose is best-effort. - Document why both stSidebarNavSectionHeader (legacy, streamlit~=1.35) and stNavSectionHeader (current, 1.57) testids appear in the chrome CSS — requirements floor is streamlit>=1.35,<2 so dropping the legacy selector would silently break the lower bound. - Pin the t()-returns-key-on-miss contract that render_tool_header's fallback path depends on, with a comment at the call site. - Pin the demo's intentional skip of hide_streamlit_chrome (so the +/- sidebar swap JS doesn't ever try to load there) with a load- bearing comment in app_demo.py. - Confirmed i18n parity: every tool id has page_title / page_caption / description / name / help_md in BOTH packs; help.button_label and help.missing_body in both. 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 bundles — no Python install, no admin rights, no internet at runtime. Each release ships two flavors per OS: an installer that wires up Desktop + Start Menu / Launchpad shortcuts, and a portable .zip you unzip and double-click. Pick whichever your IT policy allows.
| Platform | Installer (recommended) | Portable (no install) |
|---|---|---|
| macOS | DataTools-X.Y.Z-mac.dmg — open, drag DataTools.app into /Applications, launch from Launchpad. |
DataTools-X.Y.Z-mac-portable.zip — unzip anywhere, double-click DataTools.app. |
| Windows | DataTools-X.Y.Z-win-setup.exe — run installer (per-user, no admin). Desktop shortcut + Start Menu entry created. |
DataTools-X.Y.Z-win-portable.zip — unzip anywhere, double-click DataTools.exe. |
| Linux | DataTools-X.Y.Z-linux-x86_64.AppImage — chmod +x, double-click. |
The AppImage is already portable. |
Latest release: see GitHub Releases (or the Gumroad listing). Each bundle is ~200 MB unpacked; on first launch the app starts a local server at http://127.0.0.1:8501 and opens your default browser. Nothing leaves your machine — installers and portables are byte-identical inside.
First-launch warnings (one-time):
- macOS unsigned builds: right-click → Open → confirm. (Signed builds skip this.)
- Windows SmartScreen: click More info → Run anyway.
Detailed install + troubleshooting walkthrough: User Guide §1.
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.