feat: add documentation, Streamlit GUI, and full source tree
- Rewrite README.md with project overview, quick-start, and CLI summary - Add docs/CLI-REFERENCE.md with full flag reference and 8 recipe sections - Add docs/DEVELOPER.md with architecture, data flow, and extension guides - Rewrite src/core/__init__.py with public API exports and module docstring - Add Streamlit GUI (src/gui/) with file upload, advanced options, interactive match group review with side-by-side diff, and download buttons - Add .gitignore, requirements.txt, all source code, tests, and sample data - Add streamlit to requirements.txt Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
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# CLI Reference
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Complete command-line reference for the DataTools Deduplicator.
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```
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python -m src.cli INPUT_FILE [OPTIONS]
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```
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## Arguments
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| Argument | Required | Description |
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|----------|----------|-------------|
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| `INPUT_FILE` | Yes | Path to the CSV or Excel file to deduplicate |
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## Options
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### Core
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| Flag | Short | Default | Description |
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|------|-------|---------|-------------|
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| `--apply` | | `false` | Write output files. Without this flag, only a preview is shown. |
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| `--output` | `-o` | `{input}_deduplicated.csv` | Output file path. |
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### Column Selection
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| Flag | Short | Default | Description |
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|------|-------|---------|-------------|
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| `--subset` | `-s` | auto-detect | Comma-separated columns to match on. When omitted, columns are auto-detected by name pattern (email, phone, name, address). |
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| `--key` | `-k` | none | Comma-separated strong-key columns. Each becomes an independent exact-match strategy. Use for identifiers like `fb_id`, `ein`, `sku`. |
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### Fuzzy Matching
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| Flag | Short | Default | Description |
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|------|-------|---------|-------------|
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| `--fuzzy` | | none | Comma-separated columns to fuzzy-match. Other columns in the strategy use exact matching. |
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| `--algorithm` | `-a` | `jaro_winkler` | Fuzzy algorithm: `levenshtein`, `jaro_winkler`, or `token_set_ratio`. |
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| `--threshold` | `-t` | `85` | Similarity threshold 0-100. Lower values find more matches but increase false positives. |
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### Normalization
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| Flag | Short | Default | Description |
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|------|-------|---------|-------------|
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| `--normalize` | | auto-detect | Column normalizers as `col:type` pairs, comma-separated. Types: `email`, `phone`, `name`, `address`, `string`. |
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**Normalizer details:**
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| Type | What it does | Example |
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|------|-------------|---------|
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| `email` | Lowercase, strip Gmail dots, strip `+tag` suffixes | `John.Doe+tag@gmail.com` → `johndoe@gmail.com` |
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| `phone` | Parse to E.164 format; fallback: digits only | `(555) 123-4567` → `+15551234567` |
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| `name` | Strip titles (Dr., Mr.) and suffixes (Jr., PhD), case-fold | `Dr. John Smith Jr.` → `john smith` |
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| `address` | USPS abbreviations (Street→St, Avenue→Ave), case-fold | `123 Main Street, Suite 4` → `123 main st ste 4` |
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| `string` | Trim, collapse whitespace, case-fold | ` HELLO WORLD ` → `hello world` |
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### Survivor Selection
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| Flag | Short | Default | Description |
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|------|-------|---------|-------------|
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| `--survivor` | | `first` | Which row to keep per duplicate group. |
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| `--date-column` | | none | Date column for the `most-recent` rule. |
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| `--merge` | | `false` | Fill missing fields in the surviving row from removed duplicates. |
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**Survivor rules:**
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| Rule | Behavior |
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|------|----------|
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| `first` | Keep the first row encountered (lowest row number) |
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| `last` | Keep the last row encountered (highest row number) |
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| `most-complete` | Keep the row with the fewest blank/empty cells |
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| `most-recent` | Keep the row with the latest date (requires `--date-column`) |
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### Interactive Review
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| Flag | Short | Default | Description |
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|------|-------|---------|-------------|
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| `--review` | | `false` | Interactively review each match group. For each group, choose: merge (y), keep both (n), or skip remaining (s). |
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### Configuration
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| Flag | Short | Default | Description |
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|------|-------|---------|-------------|
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| `--config` | | none | Load all settings from a saved JSON config file. |
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| `--save-config` | | none | Save current settings to a JSON config file for reuse. |
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### File Handling
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| Flag | Short | Default | Description |
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|------|-------|---------|-------------|
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| `--sheet` | | first sheet | Excel sheet name or 0-based index. Ignored for CSV files. |
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| `--encoding` | | auto-detect | Override auto-detected file encoding (e.g., `utf-8`, `windows-1252`). |
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| `--header-row` | | auto-detect | 0-based row index for the header row. |
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---
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## Recipes
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### 1. Basic Dedup (Auto-Detect)
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Let the engine detect email, phone, name, and address columns automatically.
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```bash
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# Preview
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python -m src.cli customers.csv
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# Apply
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python -m src.cli customers.csv --apply
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```
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The engine scans column names for patterns like `email`, `phone`, `name`, `address` and builds strategies automatically. Strong keys (email, phone) become standalone strategies; weak keys (name, address) are paired with strong keys.
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### 2. Fuzzy Name Matching
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Match rows where names are similar but not identical — catches typos, nickname variations, and formatting differences.
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```bash
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# Fuzzy-match on the "name" column at 80% similarity
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python -m src.cli customers.csv --fuzzy name --threshold 80 --apply
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# Fuzzy-match on multiple columns
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python -m src.cli customers.csv --fuzzy name,address --threshold 85 --apply
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# Use Levenshtein distance instead of Jaro-Winkler
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python -m src.cli customers.csv --fuzzy name --algorithm levenshtein --threshold 80 --apply
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```
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**Algorithm comparison:**
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- `jaro_winkler` (default) — best for short strings like names; weights early characters more heavily
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- `levenshtein` — edit-distance ratio; works well for typos and transpositions
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- `token_set_ratio` — best for addresses and long strings; ignores word order
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### 3. Custom Strong Keys
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Use specific identifier columns to find exact duplicates.
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```bash
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# Deduplicate by Facebook ID
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python -m src.cli donors.csv --key fb_id --apply
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# Multiple strong keys (each is independent — matched with OR)
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python -m src.cli donors.csv --key fb_id,ein --apply
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```
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Strong keys are OR'd: a match on `fb_id` alone OR `ein` alone marks rows as duplicates.
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### 4. Merge Mode
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Keep the most complete row and fill any remaining blanks from the duplicates.
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```bash
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# Most complete row + merge missing fields
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python -m src.cli contacts.csv --survivor most-complete --merge --apply
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# Keep most recent row and merge
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python -m src.cli contacts.csv --survivor most-recent --date-column updated_at --merge --apply
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```
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**How merge works:** The survivor row keeps all its non-empty fields. For any blank/null fields, the engine fills from the removed rows (in row order). The result is a single row with maximum data retention.
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### 5. Multi-Column Subset
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Match on a specific combination of columns rather than auto-detecting.
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```bash
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# Exact match on email + phone only
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python -m src.cli customers.csv --subset email,phone --apply
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# Mix exact and fuzzy within a subset
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python -m src.cli customers.csv --subset email,name --fuzzy name --threshold 85 --apply
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```
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When using `--subset`, all listed columns must match (AND logic) for a pair to be considered duplicates.
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### 6. Save and Load Config Profiles
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Save your settings for repeatable runs on similar files.
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```bash
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# Save settings to a file
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python -m src.cli customers.csv --fuzzy name --threshold 80 --merge \
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--survivor most-complete --save-config customer_dedup.json
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# Load saved settings
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python -m src.cli new_customers.csv --config customer_dedup.json --apply
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```
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Config files are JSON. Example:
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```json
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{
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"strategies": [],
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"survivor_rule": "most_complete",
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"merge": true,
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"default_algorithm": "jaro_winkler",
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"default_threshold": 80.0,
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"fuzzy_columns": ["name"]
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}
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```
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### 7. Interactive Review
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Step through each match group and decide whether to merge.
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```bash
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python -m src.cli customers.csv --review --apply
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```
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For each group, the CLI displays both rows side-by-side and prompts:
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```
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============================================================
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Match Group 1 — Confidence: 92.3%
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Matched on: name, phone
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============================================================
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Row 1:
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name: John Smith
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email: john@example.com
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phone: (555) 123-4567
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Row 2:
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name: Jon Smith
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email:
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phone: 555-123-4567
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[y] Merge [n] Keep both [s] Skip remaining:
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```
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- **y** — accept the match; merge/remove duplicate
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- **n** — reject the match; keep both rows
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- **s** — skip all remaining groups (keep both for all)
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### 8. Excel Files and Multi-Sheet
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Work with Excel files directly — no CSV conversion needed.
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```bash
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# Deduplicate first sheet (default)
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python -m src.cli data.xlsx --apply
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# Specify sheet by name
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python -m src.cli data.xlsx --sheet "Sales Data" --apply
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# Specify sheet by index (0-based)
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python -m src.cli data.xlsx --sheet 1 --apply
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```
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Output is always CSV by default. To write Excel output, use `-o`:
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```bash
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python -m src.cli data.xlsx -o cleaned.xlsx --apply
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```
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---
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## Auto-Detection Details
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When no `--subset` or `--fuzzy` flags are provided, the engine scans column names and builds strategies:
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| Column pattern | Detection regex | Algorithm | Threshold | Normalizer | Key type |
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|---------------|----------------|-----------|-----------|------------|----------|
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| Email | `e[-_]?mail` | exact | 100% | email | strong |
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| Phone | `phone\|telephone\|mobile\|cell` | exact | 100% | phone | strong |
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| Name | `^(name\|full_name\|customer_name\|...)$` | jaro_winkler | 85% | name | weak |
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| Address | `address\|street\|addr` | token_set_ratio | 80% | address | weak |
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**Strategy building rules:**
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- Strong keys → standalone OR strategies (email match alone is enough)
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- Weak keys → paired with each strong key via AND (name match requires email or phone match too)
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- No strong keys found → weak keys promoted to standalone
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- No patterns matched → exact match on all columns (equivalent to `drop_duplicates`)
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## Output Files
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When `--apply` is set, three files are written:
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| File | Description |
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|------|-------------|
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| `{stem}_deduplicated.csv` | Cleaned DataFrame with duplicates removed |
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| `{stem}_removed.csv` | Rows that were removed |
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| `{stem}_match_groups.csv` | Audit trail with `_group_id`, `_is_survivor`, `_confidence`, `_matched_on`, `_original_row`, plus all original columns |
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## Logging
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Every run writes a timestamped log to `logs/dedup_YYYYMMDD_HHMMSS.log` with full debug-level details: strategies used, pair comparisons, survivor decisions, and merge actions.
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