# CLI Reference

Complete command-line reference for the DataTools bundle.

DataTools ships two CLI modules so each script can be invoked independently:

| Module | Command | Purpose |
|---|---|---|
| `src.cli` | `python -m src.cli INPUT_FILE [OPTIONS]` | Deduplicator (script 01) |
| `src.cli_text_clean` | `python -m src.cli_text_clean INPUT_FILE [OPTIONS]` | Text cleaner (script 02) |

The deduplicator section is below; the text cleaner reference is in [Section: Text Cleaner CLI](#text-cleaner-cli).

## Deduplicator

```
python -m src.cli INPUT_FILE [OPTIONS]
```

## Arguments

| Argument | Required | Description |
|----------|----------|-------------|
| `INPUT_FILE` | Yes | Path to the CSV, delimited text, or Excel file to deduplicate |

## Options

### Core

| Flag | Short | Default | Description |
|------|-------|---------|-------------|
| `--apply` | | `false` | Write output files. Without this flag, only a preview is shown. |
| `--output` | `-o` | `{input}_deduplicated.csv` | Output file path. |

### Column Selection

| Flag | Short | Default | Description |
|------|-------|---------|-------------|
| `--subset` | `-s` | auto-detect | Comma-separated columns to match on. When omitted, columns are auto-detected by name pattern (email, phone, name, address). |
| `--key` | `-k` | none | Comma-separated strong-key columns. Each becomes an independent exact-match strategy. Use for identifiers like `fb_id`, `ein`, `sku`. |

### Fuzzy Matching

| Flag | Short | Default | Description |
|------|-------|---------|-------------|
| `--fuzzy` | | none | Comma-separated columns to fuzzy-match. Other columns in the strategy use exact matching. |
| `--algorithm` | `-a` | `jaro_winkler` | Fuzzy algorithm: `levenshtein`, `jaro_winkler`, or `token_set_ratio`. |
| `--threshold` | `-t` | `85` | Similarity threshold 0-100. Lower values find more matches but increase false positives. |

### Normalization

| Flag | Short | Default | Description |
|------|-------|---------|-------------|
| `--normalize` | | auto-detect | Column normalizers as `col:type` pairs, comma-separated. Types: `email`, `phone`, `name`, `address`, `string`. |

**Normalizer details:**

| Type | What it does | Example |
|------|-------------|---------|
| `email` | Lowercase, strip Gmail dots, strip `+tag` suffixes | `John.Doe+tag@gmail.com` → `johndoe@gmail.com` |
| `phone` | Parse to E.164 format; fallback: digits only | `(555) 123-4567` → `+15551234567` |
| `name` | Strip titles (Dr., Mr.) and suffixes (Jr., PhD), case-fold | `Dr. John Smith Jr.` → `john smith` |
| `address` | USPS abbreviations (Street→St, Avenue→Ave), case-fold | `123 Main Street, Suite 4` → `123 main st ste 4` |
| `string` | Trim, collapse whitespace, case-fold | `  HELLO   WORLD  ` → `hello world` |

### Survivor Selection

| Flag | Short | Default | Description |
|------|-------|---------|-------------|
| `--survivor` | | `first` | Which row to keep per duplicate group. |
| `--date-column` | | none | Date column for the `most-recent` rule. |
| `--merge` | | `false` | Fill missing fields in the surviving row from removed duplicates. |

**Survivor rules:**

| Rule | Behavior |
|------|----------|
| `first` | Keep the first row encountered (lowest row number) |
| `last` | Keep the last row encountered (highest row number) |
| `most-complete` | Keep the row with the fewest blank/empty cells |
| `most-recent` | Keep the row with the latest date (requires `--date-column`) |

### Interactive Review

| Flag | Short | Default | Description |
|------|-------|---------|-------------|
| `--review` | | `false` | Interactively review each match group. For each group, choose: merge (y), keep both (n), or skip remaining (s). |

### Configuration

| Flag | Short | Default | Description |
|------|-------|---------|-------------|
| `--config` | | none | Load all settings from a saved JSON config file. |
| `--save-config` | | none | Save current settings to a JSON config file for reuse. |

### File Handling

| Flag | Short | Default | Description |
|------|-------|---------|-------------|
| `--sheet` | | first sheet | Excel sheet name or 0-based index. Ignored for CSV files. |
| `--encoding` | | auto-detect | Override auto-detected file encoding (e.g., `utf-8`, `windows-1252`). |
| `--header-row` | | auto-detect | 0-based row index for the header row. |

---

## Recipes

### 1. Basic Dedup (Auto-Detect)

Let the engine detect email, phone, name, and address columns automatically.

```bash
# Preview
python -m src.cli customers.csv

# Apply
python -m src.cli customers.csv --apply
```

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.

### 2. Fuzzy Name Matching

Match rows where names are similar but not identical — catches typos, nickname variations, and formatting differences.

```bash
# Fuzzy-match on the "name" column at 80% similarity
python -m src.cli customers.csv --fuzzy name --threshold 80 --apply

# Fuzzy-match on multiple columns
python -m src.cli customers.csv --fuzzy name,address --threshold 85 --apply

# Use Levenshtein distance instead of Jaro-Winkler
python -m src.cli customers.csv --fuzzy name --algorithm levenshtein --threshold 80 --apply
```

**Algorithm comparison:**
- `jaro_winkler` (default) — best for short strings like names; weights early characters more heavily
- `levenshtein` — edit-distance ratio; works well for typos and transpositions
- `token_set_ratio` — best for addresses and long strings; ignores word order

### 3. Custom Strong Keys

Use specific identifier columns to find exact duplicates.

```bash
# Deduplicate by Facebook ID
python -m src.cli donors.csv --key fb_id --apply

# Multiple strong keys (each is independent — matched with OR)
python -m src.cli donors.csv --key fb_id,ein --apply
```

Strong keys are OR'd: a match on `fb_id` alone OR `ein` alone marks rows as duplicates.

### 4. Merge Mode

Keep the most complete row and fill any remaining blanks from the duplicates.

```bash
# Most complete row + merge missing fields
python -m src.cli contacts.csv --survivor most-complete --merge --apply

# Keep most recent row and merge
python -m src.cli contacts.csv --survivor most-recent --date-column updated_at --merge --apply
```

**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.

### 5. Multi-Column Subset

Match on a specific combination of columns rather than auto-detecting.

```bash
# Exact match on email + phone only
python -m src.cli customers.csv --subset email,phone --apply

# Mix exact and fuzzy within a subset
python -m src.cli customers.csv --subset email,name --fuzzy name --threshold 85 --apply
```

When using `--subset`, all listed columns must match (AND logic) for a pair to be considered duplicates.

### 6. Save and Load Config Profiles

Save your settings for repeatable runs on similar files.

```bash
# Save settings to a file
python -m src.cli customers.csv --fuzzy name --threshold 80 --merge \
    --survivor most-complete --save-config customer_dedup.json

# Load saved settings
python -m src.cli new_customers.csv --config customer_dedup.json --apply
```

Config files are JSON. Example:

```json
{
  "strategies": [],
  "survivor_rule": "most_complete",
  "merge": true,
  "default_algorithm": "jaro_winkler",
  "default_threshold": 80.0,
  "fuzzy_columns": ["name"]
}
```

### 7. Interactive Review

Step through each match group and decide whether to merge.

```bash
python -m src.cli customers.csv --review --apply
```

For each group, the CLI displays both rows side-by-side and prompts:

```
============================================================
Match Group 1 — Confidence: 92.3%
Matched on: name, phone
============================================================

  Row 1:
    name: John Smith
    email: john@example.com
    phone: (555) 123-4567

  Row 2:
    name: Jon Smith
    email:
    phone: 555-123-4567

  [y] Merge  [n] Keep both  [s] Skip remaining:
```

- **y** — accept the match; merge/remove duplicate
- **n** — reject the match; keep both rows
- **s** — skip all remaining groups (keep both for all)

### 8. Excel Files and Multi-Sheet

Work with Excel files directly — no CSV conversion needed.

```bash
# Deduplicate first sheet (default)
python -m src.cli data.xlsx --apply

# Specify sheet by name
python -m src.cli data.xlsx --sheet "Sales Data" --apply

# Specify sheet by index (0-based)
python -m src.cli data.xlsx --sheet 1 --apply
```

Output is always CSV by default. To write Excel output, use `-o`:

```bash
python -m src.cli data.xlsx -o cleaned.xlsx --apply
```

---

## Auto-Detection Details

When no `--subset` or `--fuzzy` flags are provided, the engine scans column names and builds strategies:

| Column pattern | Detection regex | Algorithm | Threshold | Normalizer | Key type |
|---------------|----------------|-----------|-----------|------------|----------|
| Email | `e[-_]?mail` | exact | 100% | email | strong |
| Phone | `phone\|telephone\|mobile\|cell` | exact | 100% | phone | strong |
| Name | `^(name\|full_name\|customer_name\|...)$` | jaro_winkler | 85% | name | weak |
| Address | `address\|street\|addr` | token_set_ratio | 80% | address | weak |

**Strategy building rules:**
- Strong keys → standalone OR strategies (email match alone is enough)
- Weak keys → paired with each strong key via AND (name match requires email or phone match too)
- No strong keys found → weak keys promoted to standalone
- No patterns matched → exact match on all columns (equivalent to `drop_duplicates`)

## Output Files

When `--apply` is set, three files are written:

| File | Description |
|------|-------------|
| `{stem}_deduplicated.csv` | Cleaned DataFrame with duplicates removed |
| `{stem}_removed.csv` | Rows that were removed |
| `{stem}_match_groups.csv` | Audit trail with `_group_id`, `_is_survivor`, `_confidence`, `_matched_on`, `_original_row`, plus all original columns |

## Logging

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.

---

# Text Cleaner CLI

Character-level hygiene for CSV / Excel files: whitespace trim and collapse, smart-character folding, Unicode normalization, BOM strip, control-char strip, line-ending normalization, optional case conversion. See TECHNICAL.md Section 10.2 for the full functional spec.

```
python -m src.cli_text_clean INPUT_FILE [OPTIONS]
```

## Arguments

| Argument | Required | Description |
|----------|----------|-------------|
| `INPUT_FILE` | Yes | Path to the CSV, TSV, or Excel file to clean |

## Options

### Core

| Flag | Short | Default | Description |
|------|-------|---------|-------------|
| `--apply` | | `false` | Write output files. Without this flag, only a preview is shown. |
| `--output` | `-o` | `{input}_cleaned.csv` | Output file path. |
| `--preset` | | `excel-hygiene` | Preset bundle of safe defaults. See [Presets](#presets). |

### Scope

| Flag | Default | Description |
|------|---------|-------------|
| `--columns` | all string columns | Comma-separated columns to clean. |
| `--skip` | none | Comma-separated columns to skip even if they look like text. Useful for free-text notes columns you don't want touched. |

### Per-operation toggles

These override the active preset.

| Flag | Effect |
|------|--------|
| `--no-trim` | Disable leading/trailing whitespace strip |
| `--no-collapse` | Disable internal whitespace collapse |
| `--no-nfc` | Disable Unicode NFC normalization |
| `--nfkc` | Enable NFKC compatibility fold (lossy: `①` → `1`, `fi` → `fi`) |
| `--no-smart-chars` | Disable smart-character folding (curly quotes, em/en-dash, NBSP, ellipsis) |
| `--no-zero-width` | Disable zero-width / invisible character strip |
| `--no-bom` | Disable leading BOM strip |
| `--no-control` | Disable control-character strip |
| `--no-line-endings` | Disable line-ending normalization |

### Case conversion

| Flag | Forms | Description |
|------|-------|-------------|
| `--case` | `upper`, `lower`, `title`, `sentence` | Apply this case to every selected column |
| `--case` | `mode:col[,mode:col]` | Per-column case (e.g., `--case title:name,upper:code`) |

Title case preserves all-caps tokens (`USA` stays `USA`) and lowercases mid-string particles (`of`, `and`, `the`, etc.).

### Audit and config

| Flag | Default | Description |
|------|---------|-------------|
| `--full-changelog` | `false` | Write every cell change to the audit CSV (default caps to first 1000). |
| `--config` | none | Load options from a saved JSON config file. |
| `--save-config` | none | Save the current options to a JSON config file. |

### File format / encoding

| Flag | Default | Description |
|------|---------|-------------|
| `--sheet` | `0` | Excel sheet name or 0-based index. |
| `--encoding` | auto-detect | Override auto-detected file encoding. |
| `--header-row` | auto-detect | 0-based row index for the header. |

## Presets

| Preset | What it does |
|---|---|
| `minimal` | Trim + collapse whitespace only. Nothing else. |
| `excel-hygiene` (default) | Trim, collapse, NFC, smart-character fold, zero-width strip, BOM strip, control strip, line-ending normalize. NFKC off. |
| `paranoid` | All of `excel-hygiene` plus NFKC compatibility fold (lossy). |

## Output Files

When `--apply` is set:

| File | Description |
|------|-------------|
| `{stem}_cleaned.csv` | Cleaned DataFrame |
| `{stem}_changes.csv` | Per-cell audit: `row`, `column`, `old`, `new`, `ops_applied` (capped to 1000 rows by default; use `--full-changelog` for all) |

A timestamped log is always written to `logs/text_clean_YYYYMMDD_HHMMSS.log`.

## Recipes

```bash
# Preview what would change with the safe defaults
python -m src.cli_text_clean messy.csv

# Apply the safe defaults
python -m src.cli_text_clean messy.csv --apply

# Just the basics — only trim and collapse, leave Unicode/quotes alone
python -m src.cli_text_clean messy.csv --preset minimal --apply

# Title-case the name column, upper-case the SKU column, leave others alone for case
python -m src.cli_text_clean people.csv --case title:name,upper:sku --apply

# Clean only specific columns
python -m src.cli_text_clean orders.csv --columns vendor,product --apply

# Skip a free-text notes column from cleaning
python -m src.cli_text_clean tickets.csv --skip notes --apply

# Save the current settings as a profile and reload it later
python -m src.cli_text_clean messy.csv --preset minimal --case upper --save-config my.json
python -m src.cli_text_clean other.csv --config my.json --apply
```

---

## Analyzer (upload-time scan)

```
python -m src.cli_analyze INPUT_FILE [OPTIONS]

  --sample-rows N       Cap on rows scanned (default 1000)
  --json                Print findings as a JSON array on stdout
  --strict              Exit non-zero on any warn/error finding
```

JSON output schema (one object per finding):

```json
{
  "id": "smart_punctuation_in_data",
  "severity": "warn",
  "confidence": "high",
  "fix_action": "fold_smart_punctuation",
  "pre_applied": false,
  "tool": "02_text_cleaner",
  "count": 17,
  "description": "17 cell(s) contain curly quotes…",
  "column": null,
  "samples": [{"row": 3, "column": "name", "value": "“Alice”"}]
}
```

- `severity` — `info` / `warn` / `error`. Only `error` blocks the GUI normalization gate.
- `confidence` — `high` (round-trip-safe, eligible for one-click auto-fix), `medium` (preview before applying), `low` (heuristic, opt-in only).
- `fix_action` — stable id naming the algorithm in `src/core/fixes.py` that resolves the finding. Empty string for informational-only findings.
- `pre_applied` — `true` for fixes already applied during the byte-level read pass (BOM strip, NUL strip, line-ending normalize, byte-level smart-quote fold, transcode-to-UTF-8 from UTF-16/32). The GUI gate treats these as already-resolved; the CLI emits them so callers can audit what changed during read.

The detector set covers smart punctuation, NBSP / Unicode whitespace, zero-width characters, dirty headers, whitespace padding, null-like sentinels, mojibake fingerprints (UTF-8-as-cp1252), mixed-case email columns, near-duplicate rows (case-and-padding stripped), leading-zero IDs (Excel hazard), mixed line endings, encoding decode failure (`encoding_decode_failed`), and U+FFFD presence in the loaded text (`encoding_uncertain`). New detectors plug in by appending one entry to `analyze.py` and one matching fix in `fixes.py`.