Michael
35d46a0c1a
Until now every test ran against core or the CLI; the Streamlit GUI was verified by hand. This commit adds tests/gui/ — 139 AppTest- driven tests behind a 'gui' marker so the quick loop (``pytest -m 'not gui'``) stays at 1777 tests / ~10s while ``pytest`` runs everything (1916 / ~14s). Coverage: - test_smoke.py (59): every page renders in EN and ES, expected substring present, sidebar selector mounted. - test_chrome.py (18): language selector flips session state and re-renders; quit button + farewell strings localize; tool-card names use the active language. - test_gate.py (9): require_normalization_gate no-op / warning / short-circuit / hash-mismatch invariants; warning + button localized. - test_workflows.py (14): happy path per Ready tool — stash upload, render, find primary action, verify result lands in session state. - test_dedup_review.py (8): Accept All / Reject All / Clear Decisions wire through to review_decisions; apply_review_decisions semantics (keep-all, merge, column override). - test_advanced_panels.py (15): config_panel widget defaults and options (algorithm, threshold, survivor rule, merge, multiselects, config save/load). - test_errors.py (4): garbage / empty / single-column uploads don't crash; duplicate-target mapping raises InputValidationError. - test_findings_panel.py (12): driven via a small standalone harness page so we test the component without faking a file_uploader. EN + ES strings, per-tool grouping, open-tool button label, untargeted expander, severity summary. Shared infrastructure in tests/gui/conftest.py: - ``stash_upload`` / ``stash_upload_without_gate`` — populate session_state to pre-pass or block the gate. - ``with_language`` — set ``ui_lang`` before run(). - ``collected_text`` — flatten title/caption/markdown/etc. into one string for substring assertions. - Auto-marking: every test in tests/gui/ gets ``@pytest.mark.gui`` via ``pytest_collection_modifyitems``, so the marker isn't per-test boilerplate. 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 | Deduplicator — exact + fuzzy match, 5 normalizers, survivor rules, audit | Ready |
| 02 | Text Cleaner — whitespace, smart chars, BOM, line endings, case ops | Ready |
| 03 | Format Standardizer — dates, phones, emails, addresses, names, currencies, booleans | Ready |
| 04 | Missing Value Handler — disguised-null detection, profile, mean/median/mode/ffill/bfill/interpolate, drop strategies | Ready |
| 05 | Column Mapper — fuzzy auto-rename, target schema with type coercion, required fields with defaults, drop/reorder | Ready |
| 06 | Outlier Detector | Coming Soon |
| 07 | Multi-File Merger | Coming Soon |
| 08 | Validator & Reporter | Coming Soon |
| 09 | Pipeline Runner — 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.