rolodexter 2.6.4

The universal contact field mapper — route messy, inconsistent contact data to a clean canonical schema.

The universal contact field mapper.

Route messy, inconsistent contact data from any source to a clean, canonical schema.

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The Problem

Every CRM, email platform, and CSV export uses different field names for the same data:

Service	First Name	Phone	Company
HubSpot	firstname	mobilephone	company
Salesforce	FirstName	MobilePhone	Company
Mailchimp	FNAME	PHONE	COMPANY
Google CSV	Given Name	Phone 1 - Value	Organization 1 - Name
Random CSV	Column A	Column B	Column C

The Solution

from rolodexter import ContactMapper

mapper = ContactMapper()

result = mapper.map_payload({
    "fname": "jane",
    "surname": "doe",
    "mobile": "[phone]",
    "employer": "Tech Corp",
    "Column 1": "[e-mail]",  # auto-detected by value shape
})

print(result.normalized)
# {
#     "first_name": "Jane",
#     "last_name": "Doe",
#     "phone": "[phone]",
#     "company": "Tech Corp",
#     "email": "[e-mail]"
# }

Installation

# Core (phonenumbers + nameparser)
pip install rolodexter

# With fuzzy matching for typo recovery
pip install rolodexter[fuzzy]

# With on-demand i18n translation (40 languages)
pip install rolodexter[i18n]

# Everything
pip install rolodexter[all]

# Development
pip install rolodexter[dev]

Features

🎯 Four-Layer Matching Pipeline

Every field runs through the strategy chain in priority order:

1. Exact Match — O(1) lookup against 615+ known aliases across 62 canonical fields
2. Normalized Match — handles CamelCase, dot.path, space → underscore, and similar variations
3. Fuzzy Match — rapidfuzz catches typos like "phne_nmbr" → phone
4. Heuristic Match — regex detects emails, phones, URLs, postal codes by data shape

📊 Confidence Scoring

Every match comes with a confidence score (0.0–1.0):

match = mapper.identify("fname")
# FieldMatch(original='fname', canonical='first_name', confidence=1.0, strategy='exact')

match = mapper.identify("phne")
# FieldMatch(original='phne', canonical='phone', confidence=0.85, strategy='fuzzy')

match = mapper.identify("Column X", value="[e-mail]")
# FieldMatch(original='Column X', canonical='email', confidence=0.6, strategy='heuristic')

� Per-Caller Field Overrides

For vendor-specific or account-level field names that won't be in the standard alias table:

mapper = ContactMapper(
    overrides={
        "MMERGE6": "company",   # Mailchimp custom merge field
        "cf_lead_score": "tags",
    }
)

📱 Phone Extraction

# Extract phones embedded in arbitrary string values
result = mapper.map_payload(
    {"notes": "call me at [phone] or [phone]"},
    extract_embedded_phones=True,
)
print(result.get_all_phones())
# ['[phone]', '[phone]']

🗂️ Tags / List Fields

Fields like tags are automatically list-normalised — comma-separated strings, JSON arrays, and Python lists all collapse to a clean list:

result = mapper.map_payload({"tags": "vip, newsletter, beta"})
print(result.normalized["tags"])
# ['vip', 'newsletter', 'beta']

🌍 On-Demand i18n (40 Languages)

English ships by default. Request any of 40 supported languages and aliases are generated on the fly via Google Translate, then cached so translation only happens once:

from rolodexter import ContactMapper

# Load Spanish aliases on demand
mapper = ContactMapper(languages=["es"])
result = mapper.map_payload({"correo_electronico": "[e-mail]"})
print(result.normalized["email"])  # [e-mail]

# CLI: generate and cache all 40 languages
python -m rolodexter.i18n

# Or specific languages
python -m rolodexter.i18n --languages es,fr,de

# List supported languages
python -m rolodexter.i18n --list

Supported: Spanish, French, German, Portuguese, Italian, Dutch, Polish, Romanian, Turkish, Russian, Japanese, Chinese (Simplified), Korean, Arabic, Hindi, Swedish, Danish, Norwegian, Finnish, Czech, Ukrainian, Greek, Hungarian, Thai, Vietnamese, Indonesian, Malay, Hebrew, Bulgarian, Croatian, Slovak, Slovenian, Serbian, Lithuanian, Latvian, Estonian, Catalan, Filipino, Swahili, Afrikaans.

🧹 Value Normalization

Automatic cleanup on matched fields:

• Phone → E.164 format via libphonenumber ([phone])
• Email → lowercase, trimmed
• Names → title case with particle awareness ("jane van der berg" → "Jane van der Berg")
• Addresses → excess whitespace collapsed, title-cased
• Tags → normalized to list[str]

📦 Batch Processing

results = mapper.map_batch([contact1, contact2, contact3, ...])

📈 Rich Diagnostics

result = mapper.map_payload(data)

print(result.match_rate)        # 0.857
print(result.matched_count)     # 6
print(result.unmatched_count)   # 1
print(result.get_all_phones())  # ['[phone]']
print(result.to_dict())         # Full JSON-serializable report

🔢 Nested Payload Support

# Flatten one level of nesting with depth=2
result = mapper.map_payload(
    {"contact": {"fname": "Jane", "lname": "Doe"}},
    depth=2,
)
# Accesses "contact.fname" and "contact.lname"

API Reference

ContactMapper

ContactMapper(
    *,
    patterns=None,        # Custom pattern dict (overrides built-in)
    patterns_path=None,   # Path to a custom patterns.json file
    normalize=True,       # Apply value normalization after mapping
    strategies=None,      # Override the default strategy pipeline
    languages=None,       # None=English only | "es" | ["es","fr"] | "all"
    overrides=None,       # Extra alias→canonical mappings {"MMERGE6": "company"}
)

Methods:

Method	Description
identify(header, *, value)	Resolve a single header to a FieldMatch
map_payload(payload, *, depth, extract_embedded_phones)	Normalize an entire dict
map_batch(payloads, *, depth)	Process a list of payloads
registry	Access the underlying PatternRegistry

FieldMatch

FieldMatch(
    original='fname',
    canonical='first_name',
    confidence=1.0,
    strategy='exact',      # 'exact' | 'normalized' | 'fuzzy' | 'heuristic' | 'none'
    is_matched=True,
)

MappingResult

Attribute / Method	Type	Description
normalized	dict	Canonical key → cleaned value
unmapped	dict	Fields that couldn't be resolved
field_matches	tuple[FieldMatch, ...]	Full match detail for every input field
match_rate	float	Fraction of fields successfully matched
matched_count	int	Count of matched fields
unmatched_count	int	Count of unmatched fields
get_match(header)	FieldMatch | None	Look up the match for a specific input header
get_all_phones()	list[str]	All phone values across all phone-adjacent fields
to_dict()	dict	Full JSON-serializable report

CanonicalField

Enum of all 62 canonical fields. Inherits from str for JSON compatibility:

from rolodexter import CanonicalField

assert CanonicalField.EMAIL == "email"
assert CanonicalField.PHONE.value == "phone"

All 62 canonical fields

first_name · last_name · full_name · email · phone · home_phone · work_phone · fax · whatsapp · address · address_line_2 · city · state · postal_code · country · company · job_title · department · website · linkedin · twitter · instagram · facebook · birthday · gender · language · timezone · currency · tags · notes · source · source_id · source_service · subscribed · verified · created_at · updated_at · ip_address · user_agent · referrer · utm_source · utm_medium · utm_campaign · utm_term · utm_content · revenue · lifetime_value · company_size · industry · message · subject · salutation · suffix · nickname · middle_name · maiden_name · preferred_name · pronouns · age · annual_income · score · unknown

Custom Patterns

custom = {
    "fields": {
        "first_name": ["fname", "given", "nombre"],
        "loyalty_tier": ["tier", "vip_level", "membership"],
    }
}

mapper = ContactMapper(patterns=custom)

Architecture

rolodexter/
├── __init__.py      # Public API
├── core.py          # ContactMapper, PatternRegistry, strategies, normalizers
├── _phone.py        # E.164 phone parser (wraps libphonenumber)
├── i18n.py          # On-demand i18n generator (40 languages, cached)
├── patterns.json    # Master alias table (615+ aliases, 62 canonical fields)
└── i18n/            # Cached language files (generated on demand)

Contributing

git clone https://github.com/LunarWerx/rolodexter.git
cd rolodexter
pip install -e ".[dev]"
pytest

License

MIT — see LICENSE.