Michael
00d3f28865
Replaces the raw-JSON summary column in the Results table with the mockup's plain-English phrasing: "312 duplicates removed across 147 groups (18,442 → 18,130 rows)", "1,204 cells cleaned in name & city", etc. (correct singular/plural via a small _n helper). Adds step_phrase() and step_status() to pipeline_modules.py. step_status derives the status pill (✓ ok / ⚠ ok · N skipped / ✗ error / ⏭ skipped) and, for warn/error steps (e.g. format_standardize unparseable cells, column_map coercion failures / missing required targets), an inline detail callout rendered directly below the results table — surfacing non-fatal issues in context without a dedicated always-empty column. Extends tests/gui/test_pipeline_builder.py with phrasing + status assertions. Co-Authored-By: Claude Opus 4.8 (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 |
Every tool page has an in-tool Help popover (right of the title) with a compact When-to-use / Steps / Examples / Tip card. Copy lives in the language packs (tools.<id>.help_md).
Download (non-technical users)
Pre-built bundles — no Python install, no admin rights, no internet at runtime. Each release ships an installer per OS that wires up Desktop + Start Menu / Launchpad shortcuts.
| Platform | Installer |
|---|---|
| macOS | DataTools-X.Y.Z-mac.dmg — open, drag DataTools.app into /Applications, launch from Launchpad. |
| Windows | DataTools-X.Y.Z-win-setup.exe — run installer (per-user, no admin). Desktop shortcut + Start Menu entry created. |
| 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 ~300 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.
Tesseract OCR is bundled. Scanned-PDF support in the PDF Extractor works out of the box on all three platforms — no separate Tesseract install required. License attribution: see LICENSE_TESSERACT.txt.
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.