Michael
da7d86f457
Two pieces of the mockup 2 layout that hadn't landed yet: 1. Sidebar nav icons — emoji glyphs (🧹 ✂️ 🔍 …) swapped for Streamlit's ``:material/<name>:`` syntax, picking the outline Material Symbol that best matches each mockup SVG: Home → :material/home: Fix Missing Values → :material/help_outline: Find Unusual Vals → :material/insights: Clean Text → :material/text_format: Standardize Fmts → :material/format_list_bulleted: Find Duplicates → :material/search: Quality Check → :material/check_circle: Map Columns → :material/view_column: Combine Files → :material/account_tree: Auto Workflows → :material/auto_awesome: Activate → :material/key: Close → :material/close: Streamlit injects the icon name as a literal ligature inside a first-child ``<span>`` of the nav anchor, expected to render through the Material Symbols font. theme.py's base rule was forcing Geist on every span under ``stSidebarNav``, turning the ligatures back into plain text labels — added a structural exception that targets ``[data-testid="stSidebarNavLink"] > span:first-child`` (and any descendant), restoring the Material font family, neutralizing the inherited ``ss01/cv01/cv11`` feature settings, and sizing to 18px. Also stripped the leading emojis from every page title in the en/es i18n packs (``home.title``, ``close_page.title``, ``activation.title``, ``tools.*.page_title``) — the icons live in the sidebar now, the page H1 no longer needs to carry one. 2. Stats overview on home — new ``_render_stats_overview`` in _home.py emits a 4-card grid above the per-file findings panels: Files analyzed, Total findings, Warnings (severity ``warn`` ∪ ``error``), Info (severity ``info``). Card layout follows the mockup §stats verbatim — Geist 28px / 600 / -0.03em for the numeric value (the "Display number" row in spec §4), tiny uppercase tracked label, paper-surface card with the standard warm border + faint shadow. The Warnings / Info cards tint the number with ``--warn`` / ``--info`` when the count is non-zero. CSS for ``.dt-stats / .dt-stat / .dt-stat-label / .dt-stat-value / .dt-stat-unit`` added to ``_DESIGN_TOKENS_CSS``; falls to a 2-column grid below 900px viewport, matching the mockup's media query. 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.