Michael
f0885aeb1e
Two reported issues addressed together because they're the same UX flow (home findings panel → jump to relevant tool). (1) Format-Standardizer recommendations weren't firing. Reported: uploading a file from the format-cleaner test corpus (``24_format_dates.csv``, ``25_format_phones.csv``, ``29_format_currencies.csv``, ``30_format_integration.csv``) showed zero "Standardize Formats" recommendations even though the columns clearly mixed multiple date / phone / currency formats. Two underlying causes: - ``_detect_inconsistent_date_format`` required two MATCHES per distinct format. A test column with N rows each in a different format had ≤1 match per format and was silently passed over. Loosened to "≥1 match per format" — the inconsistency signal is the presence of ≥2 distinct formats, not their volume. - Only date inconsistency was detected. Phones, currency, and booleans (the other format-standardizer fix categories) had no detector at all. Added three new detectors: - ``_detect_inconsistent_phone_format``: nine phone-format regexes (plain-10, US paren / dash / dot / space, +country, extension, intl plus). Fires when a column is ≥35% phone-shaped AND mixes ≥2 formats. - ``_detect_inconsistent_currency_format``: thirteen currency regexes covering US ($1,234.56 / $1234.56), EU (€1.234,56), India lakh notation, Swiss apostrophe, trailing-symbol, parens-negative, prefix-currency-code, suffix-currency-code, and negative variants. Same fire criteria as phone. - ``_detect_inconsistent_boolean_format``: column is ≥80% boolean tokens (yes/no/y/n/true/false/1/0) AND uses ≥3 distinct surface forms (e.g. yes / Y / true / 1 mixed together). Verified on every file in ``test-cases/format-cleaner-corpus/``: 24_format_dates, 25_format_phones, 29_format_currencies all now produce a format-standardizer Finding. The integration test file flags all three. The threshold loosening (from 50% to 35% of values format-shaped) is still strict enough to avoid false-positives on free-text comment columns where a few cells happen to look phone- or date-shaped. (2) The "Open <Tool>" jump links blended into the page. Reported: the per-tool jump links inside the home findings panel were too subtle to notice. Replaced ``st.page_link`` with ``st.button(type="primary")`` so the buttons render in Streamlit's primary-action red colour, matching the "Clean Text" / "Find Duplicates" / etc. run buttons. Click handler delegates to ``st.switch_page(page_slug)`` so it's still a soft in-app navigation (no full reload). 2220 tests pass. 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.