fishlib 0.6.4

A Python library for parsing, standardizing, and comparing seafood product descriptions in foodservice

fishlib 🐟

A Python library for parsing, standardizing, and comparing seafood product descriptions in foodservice.

The Problem: Seafood product descriptions are messy. The same product can be described a hundred different ways. Comparing prices across distributors, suppliers, or market data requires deep domain knowledge to know if two items are actually comparable.

The Solution: fishlib parses item descriptions into structured attributes, standardizes them to common codes, and enables apples-to-apples comparisons — so you don't need to be a fish expert to work with seafood data.

What's New in 0.6.4

• Fixed: Bare WALLEYE no longer false-matches Alaska Pollock (new dedicated walleye and pike categories)
• Fixed: Bare-letter trim codes (A, B, C, D, E) now recognized with word-boundary safety
• Fixed: Squid TUBES no longer misclassified as rings — new TUBE form code added
• Added 16 new categories: trout, sea_bass, branzino, snapper, grouper, monkfish, mackerel, walleye, pike, perch, barramundi, hamachi, rockfish, roe, crawfish, conch
• Expanded oyster: added Kumamoto, European Flat (Belon), and Olympia species
• Cleaner schema: removed duplicate codes (SKOFF folded into SKLS, MRNTD kept only in value_added, meat-grade CLAW renamed to CLAW_MEAT to disambiguate from form)
• Zero dependencies: pandas removed from install requirements (never actually imported in library code)

Coverage: 38 categories, 109 species/subspecies, 411 aliases.

Installation

pip install fishlib

Quick Start

import fishlib

# Parse any item description
item = fishlib.parse("SALMON FIL ATL SKON D 6OZ IVP")

print(item['category'])       # 'salmon'
print(item['subspecies'])     # 'atlantic'
print(item['form'])           # 'FIL'
print(item['skin'])           # 'SKON'
print(item['trim'])           # 'D'
print(item['size'])           # '6OZ'
print(item['pack'])           # 'IVP'

# Get a comparison key for matching
key = fishlib.comparison_key(item)
# "SALMON|ATLANTIC|FIL|SKON|D|6OZ"

# Check if two items are comparable
result = fishlib.match(
    "SALMON PRTN ATL BNLS SKLS 6 OZ CENTER CUT",
    "Portico Salmon Fillet 6 oz Boneless / Skinless",
)
print(result['is_comparable'])        # True
print(result['confidence'])           # e.g., 0.85
print(result['different_attributes']) # ['form']
print(result['recommendation'])       # human-readable summary

Features

Parse Item Descriptions

Turn messy text into structured data:

fishlib.parse("POLLOCK FIL WILD ALASKA PROCESSED CHINA 6OZ IVP")
# {
#   'category': 'pollock', 'subspecies': 'alaska',
#   'form': 'FIL', 'size': '6OZ', 'pack': 'IVP',
#   'origin_harvest': 'USA', 'origin_processed': 'CHN',
#   'freeze_cycle': 'TWICE',   # finfish processed in Asia ⇒ twice frozen
#   ...
# }

Standardized Codes

Attribute	Codes
Form	FIL (Fillet), PRTN (Portion), LOIN, WHL (Whole), STEAK, TAIL, TUBE, RING, CLUSTER, LEG, CLAW, MEAT, H&G, PD, PUD, HLSO, EZPL, ...
Skin	SKON (Skin On), SKLS (Skinless)
Bone	BNLS (Boneless), BIN (Bone In), PBO (Pin Bone Out)
Trim	A, B, C, D, E, FTRIM — bare letter or TRIM-D / DTRM variants
Pack	IVP, IQF, CVP, BULK, SHL, TRAY
Storage	FRZ (Frozen), FRSH (Fresh), RFRSH (Refreshed)
Harvest	WILD, FARM
Preparation	RAW, CKD, SMKD, CURED
Value-added	BRDD, STFD, MRNTD, SSNDD, POF

Origin & Freeze Cycle

# origin split into harvest (where caught/farmed) and processed (where cut/portioned)
item['origin_harvest']     # 'USA'  — caught in Alaska
item['origin_processed']   # 'CHN'  — portioned in China
item['freeze_cycle']       # 'TWICE' — inferred from Asian processing of finfish

# origin is also populated for legacy single-field use
item['origin']             # 'USA'

Species Coverage

38 categories across parent groups:

• Finfish: salmon, tuna, mahi, swordfish, sea_bass, branzino, snapper, grouper, monkfish, mackerel, barramundi, hamachi, rockfish
• Groundfish: cod (incl. black cod/sablefish), haddock, pollock
• Flatfish: halibut, flounder, sole
• Freshwater: tilapia, swai (pangasius/basa/tra), catfish, trout, walleye, pike, perch
• Crustacean: crab, lobster, shrimp, crawfish
• Mollusk: scallop, clam, oyster, mussel, calamari, octopus, conch
• Roe: ikura, tobiko, masago, caviar, uni

Trim Guide (Salmon)

Trim	Description	Skin
A	Backbone off, bellybone off	ON
B	A + backfin off, collarbone off, belly fat/fins off	ON
C	B + pin bone out	ON
D	C + back trimmed, tailpiece off, belly membrane off, nape trimmed	ON
E	D + skin removed	OFF

Foodservice standard: Trim D (skin on) and Trim E (skin off).

Cut Styles (Portions)

Style	Description	Value
Center Cut	From center of fish only, no tails/nape	Premium
Bias	Cut at angle for better presentation	Premium
Block	Straight cuts end-to-end, includes tails	Mid
Random	Mixed pieces, various shapes	Value

Match & Compare

fishlib.is_comparable(item1, item2)              # True / False
fishlib.match(item1, item2)                      # full match dict
fishlib.find_matches(target, candidates, threshold=0.8)
fishlib.explain_difference(item1, item2)         # human-readable explanation

Reference Data

from fishlib import reference

reference.trim_levels('salmon')       # Trim A–E definitions
reference.is_trim_skin_on('D')        # True  (trim D is skin-on)
reference.cut_style('CENTER')         # {'description': ..., 'premium': True}
reference.price_tier('salmon', 'king')  # {'tier': 'ultra-premium'}

Why This Exists

In foodservice distribution, comparing prices requires knowing if products are truly comparable. A "6oz salmon fillet" from two different sources might be:

• Center-cut bias portion at $12/lb (premium)
• Block-cut with tail pieces at $8/lb (commodity)

Without the right attributes, price comparisons are meaningless. fishlib encodes the domain knowledge needed to make accurate comparisons — so you don't need 20 years of fish experience to work with seafood data.

Contributing

Contributions welcome. Areas of interest:

• Additional species and regional variants
• International market terminology
• Packaging and processing codes
• Price reference data

Author

Karen Morton — seafood industry professional with 20+ years of experience in category management and procurement. Built from years of experience managing seafood categories and the realization that this knowledge should be accessible to everyone, not trapped in experts' heads.

Acknowledgments

Developed with assistance from Claude (Anthropic) for code scaffolding, refactoring, test harness construction, and documentation. All seafood domain knowledge — species classification, trim logic, cut styles, freeze-cycle inference rules, alias curation, and design decisions — comes from the author's 20+ years in foodservice category management.

License

MIT License — use it, modify it, share it. Just make seafood data better for everyone.