Michael
64452dd783
Three follow-on wins from the audit, each with shape-pinning tests.
1. Dedup blocking
- Exact-only strategies (every column EXACT @ 100 — covers strong-
key dedup like email/phone, the drop-duplicates fallback, and
explicit "match on this exact column" calls) now route through
an O(n) groupby fast path. Lossless; no API change required.
Measured: 10k-row email-exact dedup → 73 ms (was ~30 minutes
via the O(n²) pair compare).
- Fuzzy strategies still pair-compare, with opt-in prefix blocking
via deduplicate(..., blocking_columns=[...], blocking_prefix_len=1).
Measured: 5k-row fuzzy-name → 25.6s with blocking vs 179s
without (7x). Trade-off: cross-block matches missed.
2. Column-parallel standardize
- StandardizeOptions.parallel_columns (default 1) lands a
ThreadPoolExecutor over the column loop. Output order and
audit-record order are preserved deterministically via a merge
step keyed off column_types order. Honest doc: under CPython
3.12's GIL the win is roughly neutral (phonenumbers/dateutil
hold the GIL); the API is ready for free-threaded Python 3.13+.
3. Lazy-copy in missing / column_mapper
- _standardize_sentinels now builds per-column changes in a dict
and only materialises the output frame when at least one column
actually changed. On a clean 1 GB file this skips a 1 GB
allocation.
- handle_missing carries an out_is_owned flag, copying on demand
before any mutating step. No-op runs return the input frame.
- map_columns drops the unconditional upfront df.copy(); rename
and drop both return fresh frames already, and schema-add /
coerce trigger _ensure_owned() lazily.
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.