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clean_data.py
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from __future__ import annotations
# fmt: off
import sys # isort: skip
from pathlib import Path # isort: skip
ROOT = Path(__file__).resolve().parent # isort: skip
sys.path.append(str(ROOT)) # isort: skip
# fmt: on
import re
import sys
from pathlib import Path
import numpy as np
import pandas as pd
from df_analyze.analysis.metrics import cramer_v
from geopy.distance import geodesic
from joblib import Memory, Parallel, delayed
from numpy import ndarray
from pandas import DataFrame, Series
from scipy.stats.contingency import association
from tqdm import tqdm
SOURCE = ROOT / "traffic_violations_complete.csv"
"""
https://data.montgomerycountymd.gov/Public-Safety/Traffic-Violations-API/y8ms-hri9/about_data
https://data.montgomerycountymd.gov/Public-Safety/Traffic-Violations/4mse-ku6q/about_data
"""
DATA_OUT = ROOT / "traffic_data"
DATA_OUT.mkdir(exist_ok=True)
MIN_CLEAN = DATA_OUT / "min_cleaned.parquet"
FINAL_OUT = DATA_OUT / "traffic_data_processed.parquet"
MEMOIZER = Memory(location=ROOT / "__JOBLIB_CACHE__")
DESC_OUT = DATA_OUT / "unique_descriptions.txt"
YN_BINS = [
"belts", # [No, Yes]
"pers_injury", # [No, Yes]
"prop_dmg", # [No, Yes]
"fatal", # [No, Yes]
"comm_license", # [No, Yes]
"hazmat", # [No, Yes]
"comm_vehicle", # [No, Yes]
"accident", # [No, Yes]
"alcohol", # [No, Yes]
"work_zone", # [No, Yes]
]
TF_BINS = [
"acc_blame", # False, True
]
DROPS = [
"time_of_stop",
"date_of_stop",
"agency",
# could be lemma-tized and clustered, but also this gives away the
# charge, so maybe should just not be used fully
"description",
# Most common location is only 2341 times, so this is too undersampled
"location",
# Duplicates lat/long features
"geolocation",
]
NaN = float("nan")
STATE_COORDS = { # obtained through geopy via Nominatim, using query "{XX}, USA"
"AK": (64.4459613, -149.680909), # AK: Alaska
"KS": (38.27312, -98.5821872), # KS: Kansas
"VT": (44.5990718, -72.5002608), # VT: Vermont
"OR": (43.9792797, -120.737257), # OR: Oregon
"IA": (41.9216734, -93.3122705), # IA: Iowa
"UT": (39.4225192, -111.714358), # UT: Utah
"NV": (39.5158825, -116.853722), # NV: Nevada
"NH": (43.4849133, -71.6553992), # NH: New Hampshire
"NM": (34.5802074, -105.996047), # NM: New Mexico
"AR": (35.2048883, -92.4479108), # AR: Arkansas
"RI": (41.7962409, -71.5992372), # RI: Rhode Island
"MN": (45.9896587, -94.6113288), # MN: Minnesota
"ME": (45.709097, -68.8590201), # ME: Maine
"WI": (44.4308975, -89.6884637), # WI: Wisconsin
"LA": (34.0536909, -118.242766), # LA: Los Angeles
"MS": (32.9715285, -89.7348497), # MS: Mississippi
"CO": (38.7251776, -105.607716), # CO: Colorado
"WA": (47.2868352, -120.212613), # WA: Washington
"MO": (38.7604815, -92.5617875), # MO: Missouri
"OK": (34.9550817, -97.2684063), # OK: Oklahoma
"KY": (37.5726028, -85.1551411), # KY: Kentucky
# "ND": (NaN, NaN), # ND: Unknown
"AL": (33.2588817, -86.8295337), # AL: Alabama
"CT": (41.6500201, -72.7342163), # CT: Connecticut
"IN": (40.3270127, -86.1746933), # IN: Indiana
"AZ": (34.395342, -111.763275), # AZ: Arizona
"MI": (43.6211955, -84.6824346), # MI: Michigan
"TN": (35.7730076, -86.2820081), # TN: Tennessee
"SC": (33.6874388, -80.4363743), # SC: South Carolina
"IL": (40.0796606, -89.4337288), # IL: Illinois
"MA": (42.3788774, -72.032366), # MA: Massachusetts
"OH": (40.2253569, -82.6881395), # OH: Ohio
"CA": (36.7014631, -118.755997), # CA: California
"DE": (38.6920451, -75.4013315), # DE: Delaware
"GA": (32.3293809, -83.1137366), # GA: Georgia
"NJ": (40.0757384, -74.4041622), # NJ: New Jersey
"NY": (43.1561681, -75.8449946), # NY: New York
"NC": (35.6729639, -79.0392919), # NC: North Carolina
"WV": (38.4758406, -80.8408415), # WV: West Virginia
"FL": (27.7567667, -81.4639835), # FL: Florida
"TX": (31.2638905, -98.5456116), # TX: Texas
"PA": (40.9699889, -77.7278831), # PA: Pennsylvania
# "XX": (NaN, NaN), # XX: Unknown
"DC": (39.7940527, -100.472769), # DC: Decatur County, Kansas
"VA": (37.1232245, -78.4927721), # VA: Virginia
"MD": (39.5162401, -76.9382069), # MD: Maryland
}
def sorted_counts(s: Series) -> tuple[ndarray, ndarray, int]:
unqs, cnts = np.unique(s.apply(str), return_counts=True)
idx = np.argsort(-cnts)
unqs, cnts = unqs[idx], cnts[idx]
coverages = np.cumsum(cnts) / len(s)
n = (coverages < 0.95).sum()
return unqs, cnts, n
def renamer(s: str) -> str:
s = str(s).lower().replace(" ", "_")
remaps = {
"personal_injury": "pers_injury",
"property_damage": "prop_dmg",
"commercial_license": "comm_license",
"commercial_vehicle": "comm_vehicle",
"contributed_to_accident": "acc_blame",
"year": "vehicle_year",
"make": "vehicle_make",
"model": "vehicle_model",
"color": "vehicle_color",
"gender": "sex",
"state": "reg_state",
"vehicletype": "vehicle_type",
"arrest_type": "patrol_entity", # completely wrong name by them
}
if s in remaps:
return remaps[s]
return s
def get_coords(state: str) -> tuple[float, float]:
if state not in STATE_COORDS or state == "XX":
return (NaN, NaN)
state_point = STATE_COORDS[state]
return state_point
def compute_distance(state: str, lat: float, long: float) -> float:
if state not in STATE_COORDS or state == "XX":
return NaN
state_point = STATE_COORDS[state]
md_point = (lat, long)
return geodesic(md_point, state_point).km
# @MEMOIZER.cache()
def states_to_distances(df: DataFrame) -> DataFrame:
df = df.copy()
# (0, 0) is used to represent unknown, XX is unknown state, so we need to axe these
idx_nan = (df.latitude.abs() < 0.1) | (df.longitude.abs() < 0.1)
df.loc[idx_nan, "latitude"] = NaN
df.loc[idx_nan, "longitude"] = NaN
df.loc[idx_nan, "reg_state"] = "XX"
states, lats, longs = df["reg_state"], df["latitude"], df["longitude"]
reg_lats, reg_longs = [], []
for state in states:
lat, long = get_coords(state)
reg_lats.append(lat)
reg_longs.append(long)
reg_kms = Parallel(n_jobs=-1)(
delayed(compute_distance)(state, lat, long)
for (state, lat, long) in tqdm(
zip(states, lats, longs),
total=len(states),
desc="Computing vehicle regisration state geodesics",
)
)
states = df["driver_state"]
home_lats, home_longs = [], []
for state in states:
lat, long = get_coords(state)
home_lats.append(lat)
home_longs.append(long)
# it turns out these are all zero or Nan!
home_kms = Parallel(n_jobs=-1)(
delayed(compute_distance)(state, lat, long)
for (state, lat, long) in tqdm(
zip(states, home_lats, home_longs),
total=len(states),
desc="Computing home state geodesics",
)
)
states = df["dl_state"]
license_lats, license_longs = [], []
for state in states:
lat, long = get_coords(state)
license_lats.append(lat)
license_longs.append(long)
# it turns out these are all zero or Nan!
license_kms = Parallel(n_jobs=-1)(
delayed(compute_distance)(state, lat, long)
for (state, lat, long) in tqdm(
zip(states, license_lats, license_longs),
total=len(states),
desc="Computing license state geodesics",
)
)
df["reg_lat"] = reg_lats
df["reg_long"] = reg_longs
df["reg_km"] = reg_kms
df["home_lat"] = home_lats
df["home_long"] = home_longs
df["home_km"] = home_kms
df["license_lat"] = license_lats
df["license_long"] = license_longs
df["license_km"] = license_kms
df = df.drop(columns=["reg_state", "driver_state", "driver_city", "dl_state"])
return df
def rename_makes_1(s: str) -> str:
# These are the common makes, and their most common miss-spellings
# fmt: off
spellings = {
"acura": ("acur",),
"buick": ("buic",),
"cadillac": ("cad", "cadi", "cadilac"),
"chevy": ("chev", "cheverolet", "chevorlet", "chevrolet"),
"chrysler": ("chry", "chrys", "chrystler", "crysler", "cry"),
"dodge": ("dodg", "ram"),
"freightliner": ("frht",),
"honda": ("hinda", "hino", "hond"),
"hummer": ("humm"),
"infinity": ("inf", "infi", "infin", "infiniti"),
"international": ("intl",),
"isuzu": ("isu", "isuz"),
"jaguar": ("jag", "jagu"),
"kawasaki": ("kawk",),
"kenworth": ("kw",),
"landrover": ("land rover", "lndr"),
"lexus": ("lex", "lexs", "lexu"),
"lincoln": ("linc",),
"mazda": ("maz", "mazada", "mazd"),
"mercury": ("merc",),
"mercedes": ("mercedes benz", "mercedes-benz", "mercedez", "merz", "merz benz", "benz",),
"mini": ("mini cooper", "mnni"),
"mitsubishi": ("mits", "mitsu", "mitz"),
"nissan": ("niss", "nissa", "nissian"),
"none": ("unknown",),
"oldsmobile": ("olds",),
"peterbilt": ("pete", "peterbuilt", "ptrb"),
"plymouth": ("plym",),
"pontiac": ("pont",),
"porsche": ("pors",),
"range rover": ("rang",),
"saab": ("saa",),
"saturn": ("satr", "satu", "strn"),
"scion": ("scio",),
"subaru": ("sub", "suba", "subu", "suburu"),
"suzuki": ("suzi", "suzu"),
"taotao": ("tao tao",),
"tesla": ("tesl",),
"toyota": ("toty", "toy", "toyo", "toyot", "toyt", "toyta", "toytoa"),
"volkswagen": ("volk", "volks", "volkswagon", "vw"),
"volvo": ("volv",),
"yamaha": ("yama",),
}
# fmt: on
for correct, misspellings in spellings.items():
if s in misspellings:
return correct
return s
def rename_makes_2(s: str) -> str:
# These are the common makes after the first renaming, and their most common miss-spellings
# fmt: off
spellings = {
"acura": ("accura", "acrua"),
"alfa": ("alfa romeo"),
"bmw": ("bwm",),
"cadillac": ("caddilac", ),
"chevy": ("cehvy", "cheve", "cheverlot", "chevey", ),
"chrysler": ("chrylser", "chyrsler", "chysler", "crys", "crystler",),
"ducati": ("duca",),
"ford": ("for", ),
"freightliner": ("frei", "freight", ),
"gillig": ("gill", ),
"harley": ("harley davidson", "hd",),
"honda": ("hnda", "hoda", "hon", "hona", ),
"hyundai": ("hundai", "huyn", "huyndai", "hyandai", "hyn", "hynd", "hyndai", "hyu", "hyudai", "hyunday",),
"infinity": ("infini", "infinit", "infinite", "infinti", "inifiniti",),
"international": ("int", "inte", ),
"izuzu": ("isuzu", ),
"kenworth": ("kenw", ),
"landrover": ("land", ),
"mazda": ("madza",),
"maserati": ("mase", "mast",),
"mercury": ("mer", ),
"mercedes": ("merzedes", "mb", "merc benz", "mercades", "mercedez benz",),
"mitsubishi": ("mistubishi", "mit", "mitsibishi", "mitsubushi",),
"nissan": ("nisan", ),
"porsche": ("porche", "porshe",),
"rangerover": ("range", "range rover", "rov",),
"saturn": ("sat", ),
"smart": ("smrt",),
"subaru": ("subura",),
"suzuki": ("suz", ),
"toyota": ("totota", "toyoa", "toyoya", "toyoyta", "toytota", "tyota",),
"unknown": ("unk", ),
"volkswagen": ("volks wagon", "volkswaggon", "volkwagen", "volkwagon", "volswagon", "voltswagon",),
}
# fmt: on
for correct, misspellings in spellings.items():
if s in misspellings:
return correct
return s
def rename_makes_3(s: str) -> str:
# fmt: off
spellings = {
"4s": ["4d", "4dr", "4x4"],
"acura": ["a ura", "aacura", "accur", "acira", "aciura", "acora", "acra", "acru", "acruaa",
"acrura", "acu", "acua", "acuar", "acuara", "acuea", "acufra", "acuira", "acur g",
"acura", "acuras", "acurax", "acurra", "acurs", "acuru", "acurua", "acurva", "acurval",
"acuta", "acutra", "acuura", "acyr", "acyra", "acyua", "aqcura", "arcura", "aruca",
"aucra", "aur", "aura", "aurca", "aurra", "avcura", "avora", "avur", "avura", "axura",
],
"alfa": ["alpha"],
"audi": ["adui", "aidi", "au", "aud", "aud1", "auddi", "audi", "audie", "audii", "audio",
"audiq", "audo", "audu", "audui", "audy", "aufi", "augi", "aui", "auid", "auidi", "ausi",
],
"bentley": ["bart", "bartley", "bear", "beck", "belm", "bend", "bent", "bentley", "bently",
"bently", "bett", "bigt", "bnw", "bnz", "bolt", "bona", "bwn",
],
"bmw": ["b,w", "b0w", "big", "bm", "bm,w", "bma", "bmc", "bmd", "bme", "bmew", "bmi", "bmq",
"bms", "bmv", "bmw", "bmw2s", "bmw4", "bmw`", "bmw`1", "bmws", "bmwv", "bmwx1", "bmx",
"bnmw", "bnw", "bomw", "box", "bri", "brma", "brmr", "brp", "bu", "bui", "bus", "buw",
"bw", "bwi", "bwn", "bws", "bww",
],
"buick": ["bbuick", "beck", "big", "biick", "biuck", "biuick", "bruik", "bu", "bucik",
"buck", "bui ck", "bui", "buic2", "buicj", "buick \\", "buick`", "buicl", "buiick",
"buiik", "buik", "buix", "buuick",
],
"cadillac": ["caadillac", "cadailac", "cadaillic", "cadalac", "cadallac", "cadalliac",
"cadallic", "caddiliac", "caddillac", "cadi;ac", "cadiac", "cadiallac", "cadiallic",
"cadiilac", "cadiillac", "cadikkac", "cadilaac", "cadilack", "cadiliac", "cadilic",
"cadilkac", "cadilla", "cadillac", "cadillace", "cadillacq", "cadillad", "cadillaic",
"cadillas", "cadillav", "cadilliac", "cadillic", "cadilllac", "cadllac", "cadullac",
"cafillac", "caidilac", "caidllac", "caillac", "cdillac",
],
"chevy": [
"c hev", "cady", "camy", "cary", "cchevrolet", "cchevy", "cehev", "cehevrolet", "cehevy",
"cehv", "cehvrolet", "cev", "ceverolet", "cevrolet", "cevy", "cgev", "cgevorlet",
"cgevy", "ch", "chan", "chavy", "chchevy", "chdvy", "che v", "che vrolet", "che vy",
"che", "cheb", "chebrolet", "cheby", "chec", "check", "checorolet", "checrolet", "checvy",
"checy", "chedvy", "cheevrolet", "cheevy", "cheey", "chek", "chen", "cheny", "cher",
"cherolet", "cherovet", "cherovlet", "chervolet", "chervrolet", "chery", "chev i",
"chev.", "chev1", "chev2d", "chevc", "cheverlet", "cheverloet", "chevf", "chevl",
"chevolet", "chevolete", "chevollet", "chevolret", "chevorelet", "chevorlete", "chevorlett",
"chevorley", "chevorolet", "chevq", "chevr", "chevrelet", "chevreloet", "chevrelot",
"chevrilet", "chevriolet", "chevrlet", "chevrlete", "chevrlette", "chevrlo", "chevrloet",
"chevrlot", "chevrlote", "chevro;et", "chevro", "chevroelet", "chevroelt", "chevroet",
"chevrole", "chevrole6t", "chevroleet", "chevrolegt", "chevroler", "chevrolert",
"chevrolet`", "chevrolete", "chevrolette", "chevrolety", "chevroley", "chevroliet",
"chevrollet", "chevrollete", "chevroloet", "chevrolret", "chevrolrt", "chevrolt",
"chevrolte", "chevroltet", "chevroltt", "chevrolwt", "chevrooet", "chevroolet",
"chevrotet", "chevrp;et", "chevrplet", "chevry", "chevt", "chevtolet", "chevu",
"chevvrolet", "chevvy", "chevy c", "chevy i", "chevy p", "chevy", "chevy0", "chevyc",
"chevyq", "chevyrolet", "chevyt", "chevyy", "chey", "cheyv", "cheyy", "chez",
"chhevrolet", "chhevy", "chivy", "chmm", "chon", "chr", "chrevrolet", "chrey", "chrl",
"chrs", "chrsy", "chrv", "chrverolet", "chrvrolet", "chrvy", "chry.", "chrya", "chryl",
"chty", "chtys", "chv", "chve", "chveolet", "chverolet", "chvey", "chvorlet",
"chvrlolet", "chvrolet", "chvy", "chvye", "chwvy", "chy", "chyl", "chyr", "chys", "civc",
"cjev", "cjevrolet", "cjevy", "cjhevy", "cjry", "cnev", "cnry", "cuevy", "cvehrolet",
"cvev", "cvevrolet",
],
"chrysler": [
"chr", "chrl", "chrs", "chry.", "chrya", "chryl", "chy", "chyl", "chyr", "chyrl", "chyrs",
"chys", "chyst", "crhy", "cry", "cryh", "cryl", "cryn", "cgrysler", "cheysler", "chhrysler",
"chreysler", "chrisl", "chrisler", "chrisler", "christler", "chrrysler", "chrsler",
"chrslyer", "chrsyler", "chrsysler", "chrtsler", "chrusler", "chrustler", "chryler",
"chryseler", "chryselr", "chryser", "chrysker", "chrysl", "chryslar", "chrysleer",
"chrysler", "chrysler\\", "chryslerq", "chryslert", "chryslet", "chryslewr", "chrysley",
"chrysller", "chryslr", "chryslter", "chryslyer", "chryster", "chrystlar", "chrystle",
"chrystlet", "chrysyler", "chryxler", "chryysler", "chtysler", "chyrler", "chyrstler",
"chyrysler", "chyslyer", "chystler", "crhrsler", "crhsyler", "crhysler", "crrysler",
"cryser", "cryslr", "cyrsler", "cysler",
],
"cooper": ["coop", "cooper", "copper"],
"daewoo": ["daew", "daewo", "daewoo", "daewood"],
"dodge": [
"d0dge", "dadg", "dadge", "dddge", "ddge", "ddodge", "ddoge", "didg", "didge", "diodge",
"dod", "dodb", "dodde", "doddge", "dode", "dodeg", "dodege", "dodfe", "dodga", "dodgd",
"dodge", "dodgec", "dodgee", "dodgeg", "dodgei", "dodgeq", "dodger", "dodgge", "dodgr",
"dodgw", "dodhe", "dodke", "dododge", "dodoge", "doege", "dofge", "dog", "dogd", "dogde",
"dogdge", "doge", "dogg", "dogge", "donde", "donf", "dong", "doodge", "dord", "dors",
"dosg", "dosge", "dudge",
],
"ducati": ["ducadi", "ducat", "ducati", "ducato", "ducatti", "ducoti", "dukati"],
"eagle": ["eagl", "eagle"],
"ferrari": ["ferarri", "ferrari", "ferreri"],
"fiat": ["fia", "fiar", "fiat", "fiiatt", "flat"],
"ford": [
"f0rd", "fd", "fford", "fiord", "fird", "flrd", "fod", "fodd", "fode", "fodr", "foed",
"foedq", "foird", "food", "foord", "forc", "ford .", "ford t", "ford", "ford`", "ford1",
"ford5", "fordcn", "fordd", "forde", "fordf", "fordm", "fordq", "fore", "fored", "forf",
"forg", "forj", "fork", "form", "forn", "forod", "fors", "forsd", "fotd", "fprd", "frd",
"frg", "frh", "frod", "frord", "frrw", "frt", "ftr", "ftwd",
],
"freightliner": [
"feightliner", "fraight liner", "freighliner", "freight liner", "freightiner",
"freightlier", "freightline", "freightlineer", "freightliner", "freightlinrt",
"freightlinter", "freightlnr", "freigtliner", "freithliner", "frghtliner",
"frieghtliner", "friehtliner", "frightliner", ],
"genesis": [
"genesis", "genisis", "gensis", "gen", "gena", "gene", "gens", "genu",
],
"geo": [
"geel", "geio", "geo", "gi", "gil", "gio", "goe",
],
"gillig": [
"giilig", "gilg", "gili", "gilig", "gillag", "gilleg", "gillian", "gillig", "gillis",
],
"gmc": [
"gc", "gm", "gma", "gmc", "gmc s", "gmc?", "gmcc", "gmcv", "gmd", "gmec", "gmf", "gmg",
"gms", "gmt", "gmv", "gmx", "gmz", "gnc", "gnmc",
],
"harley": [
"haeley", "hand", "hari", "harl", "harl", "harley d", "harley", "harley", "haul",
"hawk", "hcry", "heil",
],
"honda": [
"h d", "h-d", "h0nd", "h0nda", "h6nda", "hand", "handa", "hando", "hhond", "hhonda",
"hind", "hindo", "hiond", "hionda", "hiunda", "hiundai", "hiunday", "hiyunda", "hlms",
"hmc", "hmd", "hmde", "hmw", "hnd", "hnhonda", "hnoda", "ho nda", "ho0nda",
"hoada", "hobda", "hobnda", "hocnda", "hod", "hodd", "hodda", "hodna", "hoha",
"hohd", "hohda", "hoidna", "hoind", "hoinda", "holda", "holm", "holnda", "homd",
"homda", "home", "homnda", "homs", "hon da", "honad", "honada", "honca", "hond`",
"hond4d", "hond4s", "honda b", "honda", "honda]", "honda`", "honda1", "honda2d", "honda4",
"honda4d", "hondaa", "hondac", "hondad", "hondai", "hondaq", "hondas", "honday", "hondd",
"hondda", "hondfa", "hondm", "hondq", "honds", "hondsa", "hondtk", "hondva", "hondval",
"hondy", "honf", "honfa", "hong", "honga", "honhda", "honida", "honnd", "honnda",
"honoda", "hons", "honsa", "hontd", "hoond", "hoonda", "hooonda", "hpnda", "hum",
"hund", "hunda", "hunday", "hundi", "hundy", "hunyda", "huyna", "huynda", "hyd",
"hyinda", "hynda", "hynday", "hyndi", "hyndia", "hyud", "hyuda", "hyuna",
],
"hyundai": [
"h0nda", "h6nda", "handa", "hayundai", "hayundi", "hhyunda", "hhyundai", "hionda",
"hiudai", "hiuday", "hiunda", "hiundai", "hiunday", "hiyunda", "honda", "honda]",
"honda`", "honda1", "honda4", "hondaa", "hondac", "hondad", "hondai", "hondaq",
"hondas", "honday", "hondy", "hoyundai", "hpnda", "htudai", "htundai", "huand",
"huandai", "hudayi", "huinday", "hund", "hunda", "hunday", "hundayi", "hundi",
"hundy", "hundyai", "hundyi", "huni", "huny", "hunyda", "huudai", "huundai",
"huyandai", "huyandi", "huydai", "huynda", "huynday", "huyndi", "huyunda", "huyundai",
"huyundi", "hyandau", "hyandi", "hyandia", "hyanduai", "hyandui", "hyaundai", "hyaundi",
"hydai", "hydunai", "hydundai", "hyhundai", "hyidai", "hyinda", "hyindai", "hyiundai",
"hynda", "hyndaui", "hynday", "hyndi", "hynduai", "hyni", "hynndai", "hynudai",
"hynundai", "hyu ndai", "hyu ndia", "hyua", "hyuadai", "hyuan", "hyuand1", "hyuanda",
"hyuandai", "hyuandi", "hyub=ndai", "hyubdai", "hyubndai", "hyuda", "hyudao", "hyuday",
"hyuddai", "hyudnai", "hyuindai", "hyumdai", "hyun dai", "hyuna", "hyunadai", "hyunadi",
"hyunai", "hyunandi", "hyund.", "hyund1", "hyunda5", "hyundah", "hyundai", "hyundai/",
"hyundai`", "hyundaia", "hyundaie", "hyundaii", "hyundain", "hyundaiq", "hyundair", "hyundaiw",
"hyundao", "hyundaoi", "hyundap", "hyundaqi", "hyundau", "hyundaui", "hyunddai", "hyunddia",
"hyunde", "hyundhi", "hyundoia", "hyundri", "hyundui", "hyundy", "hyunfai", "hyuni",
"hyunndai", "hyunsai", "hyunudai", "hyunundai", "hyunva", "hyusai", "hyuundai",
"hyyndai", "hyyunda", "hyyundai",
],
"hudson": ["hdsn", "huds", "hudson", "husdon"],
"hummer": ["humer", "hummer"],
"infinity": [
"i finiti", "ifini", "ifiniti", "ifinity", "iinfiniti", "indi", "inff",
"infi nti", "infii", "infiiniti", "infiinti", "infiit", "infiiti", "infiity",
"infim", "infimiti", "infinidi", "infiniete", "infinifty", "infinii", "infiniiti",
"infininti", "infininty", "infinit1", "infinita", "infinitie", "infinitif", "infinitii",
"infinitit", "infinitt", "infinitti", "infinitu", "infinity", "infinityi", "infinityq",
"infinityy", "infiniy", "infiniyi", "infinnity", "infinte", "infintit", "infinty",
"infit", "infiti", "infititi", "infitnite", "infity", "infn", "infni",
"infniity", "infniti", "infnity", "infnti", "infoniti", "infonity", "inft",
"inginiti", "inginity", "ini", "inif", "inifi", "inifinit", "inifinity",
"inifinti", "inifinty", "inifiti", "inifity", "inifnity", "ininiti", "ininity",
"init", "inti", "intiniti", "inviniti",
],
"international": [
"int'l", "intel", "intenational", "inter", "intern", "interna", "internati0nal",
"internatinal", "internatioal", "internation", "internationa", "international d",
"international", "internationl", "internl", "interntional", "intertational", "inti",
"intn", "intnl", "into", "intr",
],
"izuzu": [
"isizu", "issuzu", "isszu", "isuku", "isusu", "isuza", "isuze", "isuzi", "isuzo",
"isuzue", "isuzui", "iszu", "iszuu", "iszuzu", "iusuzu", "iuzu", "izu", "izus",
"izusu", "izuz", "izuzi", "izuzu",
],
"jaguar": [
"jaduar", "jagjuar", "jagr", "jagua", "jaguar", "jaguuar", "jahuar", "jajuar",
"janguar", "januar", "jaquar", "jargua", "jauar", "jauguar", "jugar", "juguar",
],
"jeep": [
"jee", "jeed", "jeedp", "jeeep", "jeef", "jeeg", "jeek", "jeem", "jeeo", "jeep",
"jeep l", "jeep1", "jeepd", "jeepp", "jeepq", "jeer", "jees", "jeesp", "jeet", "jeff",
"jep", "jepp", "jevw", "jjeep", "joop", "jp", "jrrp", "jwwp",
],
"kawasaki": [
"kaasaki", "kawasacki", "kawasake", "kawasaki", "kawasawki", "kawaski", "kawkasaki",
"kawkaski", "kawsaki", "kowasaki", "kwasaki",
],
"kenworth": [
"ken worth", "kenalworth", "kenilworth", "keniwerth", "keniworth", "kenoworth",
"kentworth", "kenworh", "kenwork", "kenwort", "kenworth", "kenworthy", "kenwoth",
"keworth", "kwenworth",
],
"kia": [
"ka", "kai", "kais", "kait", "kaka", "kara", "ken", "ki", "kia", "kia4", "kiav", "kida",
"kiia", "kinc", "kino", "kio", "kis", "kisa", "kiw",
],
"kymco": ["kmco", "kyco", "kymc", "kymco", "kymoco"],
"landrover": [
"la nd rover", "labd rover", "lad rover", "lamd rover", "lan rover", "land rover",
"land eover", "land over", "land raover", "land roaver", "land roer", "land rove",
"land rovr", "land rovwr", "land rver", "land-rover", "landdrover", "landover",
"landr rover", "landroer", "landrov", "landrove", "landrover", "landrvr", "lanrover",
"lmand rover", "lnd rover", "lndrover",
],
"lexis": [
"lecxus", "ledxus", "leexus", "leik", "lemus", "les", "lesus", "lesux", "lesxus",
"leus", "leux", "levu", "levus", "lexas", "lexcus", "lexes", "lexi", "lexis", "lexiu",
"lexius", "lexo", "lexstk", "lexsus", "lexua", "lexuas", "lexuc", "lexucs", "lexud",
"lexue", "lexues", "lexus", "lexus`", "lexus4d", "lexusq", "lexuss", "lexustr",
"lexuus", "lexux", "lexuxs", "lexuxus", "lexuz", "lexuzs", "lexxs", "lexxus", "lexy",
"lexys", "lexz", "lezus", "llexus", "lotus", "lrxus", "luxu", "luxus", "lxs", "lxus",
],
"lincoln": [
"licn", "licolin", "licoln", "liincoln", "linciln", "lincl", "lincln", "linclon",
"linco", "lincokn", "lincol", "lincolb", "lincolcn", "lincolin", "lincolm", "lincoln",
"lincolnn", "lincon", "linconl", "linconln", "linlcon", "linncoln", "linvoln", "loncoln",
],
"mack": [
"mac", "macda", "mach", "mack", "macl",
],
"maserati": [
"masarati", "masaratti", "maseati", "maseradi", "maseraii", "maserat", "maserati",
"maseratti", "maseriti", "maseritti", "masersti", "maserti", "masserati", "mazeradi",
"mazeratti",
],
"mazda": [
"maazda", "mac", "macda", "mach", "mack", "macl", "mad", "mada", "madaza", "madd",
"madz", "madzda", "mail", "make", "marc", "marm", "marz", "mas", "masd", "masda",
"masdia", "masr", "masz", "maszda", "matl", "matr", "mavda", "mawo", "maxd", "maxda",
"mayb", "maz4s", "maza", "mazad", "mazc", "mazd3", "mazda 3", "mazda 6", "mazda", "mazda`",
"mazda3", "mazda4s", "mazda5", "mazda6", "mazdad", "mazdai", "mazddda", "mazds",
"mazdva", "mazdz", "mazfa", "mazra", "mazs", "mazsa", "mazzda", "mcla",
"mera", "mez", "mezda", "miazda", "misa", "mita", "mizz", "monda",
"mozda", "mrz", "mrzd", "mtz", "mz", "mzad", "mzada", "mzd", "mzda",
],
"mercury": [
"mecrury", "mecurt", "mecury", "mer ury", "merccury", "mercedy", "mercery",
"merchury", "mercruy", "mercry", "mercu", "mercucy", "mercur", "mercuray",
"mercurey", "mercuri", "mercurt", "mercury", "mercuty", "mercuy", "mercy",
"mercyrt", "mercyry", "merury", "mrcury", "mrecury", "murcary", "murcry",
"murcury",
],
"mercedes": [
"marcedes", "marcedez", "mecedes", "mecedez", "mecerdes", "meercedes", "merc edes",
"merc edez", "mercadez", "mercdes", "mercdez", "merceades", "merced", "mercede",
"mercede z", "mercedea", "mercedec", "merceded", "mercededs", "mercedees", "mercedes",
"mercedese", "mercedex", "mercedezq", "mercedies", "mercedis", "merceds", "mercedses",
"mercedy", "mercedz", "merceedes", "mercees", "mercendes", "mercerdes", "merces",
"mercesdes", "mercesed", "mercidez", "mercsdez", "merczdez", "merdedes", "merecdes",
"merecedes", "meredes", "mersede", "mersedes", "mersedez", "mervedes", "mervedez",
"merzades", "merzcedes", "merzede", "merzeds", "mmercedes", "mrcedes", "mrcedez",
"mrecedes", "mrercedes",
],
"mifu": ["mi/f", "mi/fu", "mifu" ],
"mini": [
"mimi", "min", "minc", "mini c", "mini", "mini2d", "minia", "minii", "minin", "minji",
"minni", "minnian", "mino", "misti", "miti", "mitsi", "mni",
],
"mitsubishi": [
"miits", "miitsubishi", "mis", "misbshi", "mishibishi", "miss", "missibishi",
"missubishi", "mist", "misth", "misti", "mists", "mistsubishi", "mistu", "mistubuishi",
"mistusubishi", "misubishi", "mita", "mitbubishi", "mitch", "miti", "mitibishi",
"mitibshi", "mitis", "mitisbishi", "mitishbshi", "mitishibishi", "mitisibishi",
"mitisubishi", "mitisubshi", "mitisubushi", "mits.", "mitsabushi", "mitsb", "mitsbishi",
"mitsbishii", "mitsbuishi", "mitsbushi", "mitsh", "mitshbishi", "mitshibishi",
"mitshibshi", "mitshubishi", "mitshubitshi", "mitsi", "mitsibish", "mitsibushi",
"mitsiubishi", "mitso", "mitssubishi", "mitsubashi", "mitsubeshi", "mitsubhi",
"mitsubi", "mitsubichi", "mitsubighi", "mitsubihi", "mitsubihshi", "mitsubiishi",
"mitsubis", "mitsubisgu", "mitsubish", "mitsubishi", "mitsubishi`", "mitsubishie",
"mitsubishii", "mitsubishit", "mitsubisho", "mitsubishu", "mitsubishui", "mitsubisi",
"mitsubisih", "mitsubisihi", "mitsubisiu", "mitsubisshi", "mitsubisui", "mitsubitshi",
"mitsubitu", "mitsuboshi", "mitsubshi", "mitsubuishi", "mitsubushu", "mitsuhishi",
"mitsuibishi", "mitsuishi", "mitsunshi", "mitsushi", "mitsuubishi", "mitt", "mitts",
"mittsubishi", "mitu", "mitubish", "mitubishi", "mitubishi`", "mitusbishi", "mitusbuishi",
"mitushishi", "mitxs", "mitxubishi", "mitzibishi", "mitzibushi", "mitzl", "mitzu",
"mitzubishi", "mizubishi", "motsubishi", "mtis", "mtsbshi", "mtsubishi",
],
"mustang": ["mustang"],
"ndmc": ["nc", "ndmc"],
"new flyer": ["new fluer", "new flyer", "new glyer", "newflyer"],
"nissan": [
"n5ssan", "n9ssan", "niaan", "niaasan", "niassan", "niddan", "nidssan",
"nii", "niis", "niisan", "niiss", "niissan", "niissian", "nimr",
"ninsan", "ninssan", "niro", "nis", "nisaan", "nisano", "nisasan",
"nisd", "nisian", "nisn", "niss.", "niss4", "nissa n", "nissaan",
"nissab", "nissabn", "nissain", "nissak", "nissal", "nissam", "nissan",
"nissan`", "nissana", "nissane", "nissang", "nissanm", "nissann", "nissano",
"nissans", "nissas", "nissasn", "nissasnm", "nissav", "nissen", "nissi",
"nissia", "nissiam", "nissin", "nissina", "nission", "nissn", "nissna",
"nisson", "nisss", "nisssan", "nisssn", "nissvan", "nisu", "nits",
"niu", "nixx", "nizzan", "nnisan", "nniss", "nnissan", "nnt",
"noissan", "nosan", "nossan", "nss", "nssan", "nssian", "nsssan",
"nuissan", "nus", "nussan",
],
"oldsmobile": [
"old mobile", "oldesmobile", "oldmobile", "olds mobile", "oldsmobil", "oldsmobile",
"oldsmobile`", "oldsmoble", "oldsmoblie", "oldsmobole", "oldsmoile", "olsmobile",
],
"orion": ["ori", "orino", "orio", "orion"],
"peterbilt": [
"peerbilt", "pererbuilt", "peter built", "peterbelt", "peterbilt", "peterblt",
"peterbu;lt", "peterbulit", "peterbult", "piterbuilt", "pterbilt",
],
"plymouth": [
"plmonth", "plmouth", "plumouth", "plymith", "plymonth", "plymoth", "plymounth",
"plymouth", "plymouyh", "plymuth", "pylmouth", "pymouth",
],
"pontiac": [
"p0ntiac", "pntiac", "pobtiac", "pointiac", "pomtiac", "ponatiac", "poniac",
"ponic", "ponitac", "ponitiac", "pontac", "pontaic", "ponti", "pontia",
"pontiac", "pontiac`", "pontiacc", "pontiace", "pontiacq", "pontian", "pontiav",
"pontic", "pontica", "ponticas", "pontoac", "ponyiac", "poontiac", "popntiac",
"potiac",
],
"porsche": [
"poesche", "porch", "porcha", "porchse", "porcsche", "porcse", "porcshe",
"poreche", "porsc", "porsce", "porscge", "porsch", "porscha", "porsche",
"porse", "porseche", "porsh", "porsh1", "porshce", "posch", "prosche",
"prrsche", "prsche", "pursche",
],
"prem": ["perm", "prei", "prem", "prim"],
"rangerover": [
"rage rover", "rainge rover", "rand rover", "randge rover", "ranfe rover", "rang rover",
"range rover", "range over", "range roger", "range rovery", "range rovr", "range rver",
"ranger rover", "rangerover", "rangervr", "rangevrover", "rangr rover", "rangrover",
"rng rover",
],
"saab": [
"saab", "saabb", "sab", "sabb", "sabu", "sag", "sarn", "satn", "saub", "sba", "smar",
"spal", "ssa", "ssab", "sssb", "star", "sua", "surb",
],
"saturn": [
"sarn", "sarurn", "satarn", "satern", "satn", "satrn", "satrun", "satun", "satur",
"saturan", "saturb", "saturen", "saturm", "saturn", "saturn`", "saturne", "saturni",
"saturnq", "saturrn", "satutn", "saurn", "sautn", "sayurn", "staturn", "staurn",
"sturn", "suturn",
],
"scion": [
"sarn", "satn", "sci", "sciaon", "scid", "scin", "scion", "scione", "scionia",
"scionq", "scoin", "scon", "scwinn", "shor", "si", "sican", "sicion", "sico",
"sicon", "sien", "sion", "sizi", "slin", "sol", "spor", "sun", "sxion", "syion",
"sion",
],
"scooter": ["scooter"],
"smart": ["sarn", "sbaru", "smar", "smart", "smartc", "star", "suaru"],
"sterling": [
"steerling", "ster", "sterl", "sterlig", "sterling", "stirling", "stlg", "str",
"strg", "sts",
],
"subaru": [
"sabaru", "saburu", "sba", "sbaru", "sibaru", "su baru", "sua", "suaru", "sub aru",
"sub4dr", "subaa", "subaaru", "subabu", "subaca", "subaeu", "subai", "subar", "subara",
"subarau", "subarbu", "subarh", "subari", "subariu", "subaro", "subarru",
"subaru", "subarua", "subarue", "subarui", "subarus", "subarusw", "subaruu",
"subary", "subaryu", "subau", "subaur", "subauru", "subbaru", "subburu",
"subero", "suberu", "subi", "subie", "subr", "subra", "subraru",
"subrau", "subru", "subrur", "subs", "subsru", "subuaru", "subur",
"suburau", "suburi", "suda", "sudaru", "suna", "sunaru", "supr",
"suraru", "surbaru", "susbaru", "susubaru",
],
"susuki": [
"sazuki", "sizuki", "suburi", "suki", "susk", "suski", "susu", "susuki",
"suzik", "suziki", "suziuki", "suzk", "suzki", "suzuiki", "suzuk", "suzuki",
"suzuki`", "suzukia", "suzukii", "suzukki", "suzuku", "suzuky", "suzuzi",
],
"taizhou": ["taizhou", "taizou"],
"taotao": [
"taoato", "taot", "taota", "taotan", "taotao", "taotao50", "taotaro", "taoto", "tautau",
"toota", "toto",
],
"tesla": [
"telsa", "temsa", "tes", "tesal", "tesca", "tesla", "tesla4", "teslda",
"teslla", "tess", "tessla", "test", "testla", "texa", "tresla", "tsla", "tusla",
],
"thomas": ["thms", "thoas", "thom", "thoma", "thomas", "thomos", "toma", "tomas"],
"toyota": [
"t0y0ta", "t0yot", "t0yota", "t6oyota", "ti=oyota", "tiyita", "tiyot", "tiyota",
"tloyota", "toat", "toatoa", "toiyota", "toota", "tooyota", "tooyt", "tooyta",
"tora", "torota", "tot", "toto", "totoa", "totora", "totot", "totoya",
"totoyot", "totoyta", "tott", "totya", "totyoa", "totyota", "totyt", "totyta",
"touota", "tout", "toy0a", "toy0ta", "toya", "toyata", "toyato", "toyiooa",
"toyiota", "toyita", "toyo scion", "toyo0ta", "toyo4d", "toyoat", "toyoata", "toyoato",
"toyoota", "toyopta", "toyora", "toyorta", "toyot a", "toyota (scion)",
"toyota / scion", "toyota c", "toyota p", "toyota s", "toyota scion", "toyota-scion",
"toyota", "toyota/scion", "toyota`", "toyota``", "toyota2", "toyota4s", "toyotaa",
"toyotal", "toyotao", "toyotaq", "toyotas", "toyotat", "toyoto", "toyotoa", "toyotq",
"toyotra", "toyots", "toyotsa", "toyotta", "toyoty", "toyotya", "toyoua", "toyouta",
"toyova", "toyoval", "toyovan", "toyoy", "toyoyt", "toypta", "toyr", "toyt scion",
"toyt scion", "toyt]ota", "toyt`", "toyt=ota", "toyto", "toytq", "toyts", "toytta",
"toyttk", "toyuota", "toyut", "toyuta", "toyyota", "tpoyota", "tpyota", "tpypta",
"tpyta", "ttoy", "ttoyota", "tyot", "tyotao", "tyoyta", "tyta",
],
"triumph": ["trimuph", "trium", "triump", "triumph"],
"unknown": ["unknown", "unkown"],
"vespa": ["versa", "vesp", "vespa"],
"volkswagen": [
"v0lkswagen", "valks", "vikswagen", "vilks", "vilkswagon", "vilkw", "violks",
"vlks", "vlkswa", "vlkswagon", "vo;ks", "voiks", "voilks", "voks wagen",
"voks", "vokswagen", "vokswagon", "voljs", "volk sw", "volk swagon", "volk w",
"volk wagan", "volk wagon", "volk.", "volka", "volkawagen", "volkawagon", "volkcswagon",
"volkd", "volke", "volkes", "volkeswagen", "volkeswagon", "volkks", "volkkswagon",
"volks w", "volks wagan", "volks wagaon", "volks wagen", "volksagen", "volksagon", "volksewagon",
"volksswagon", "volksvagen", "volksvagon", "volksw", "volkswa", "volkswaegn", "volkswaen",
"volkswag", "volkswagan", "volkswagaon", "volkswage", "volkswageb", "volkswageg", "volkswagem",
"volkswagen", "volkswagewn", "volkswaggo", "volkswagin", "volkswagkon", "volkswagn", "volkswago",
"volkswagog", "volkswagom", "volkswagonq", "volkswagoon", "volkswagpn", "volkswagwen", "volkswahen",
"volkswaon", "volkswaton", "volkswg", "volkswgan", "volkswgen", "volkswgn", "volkswgon",
"volkw", "volkwage", "volkwaggen", "volkwasgen", "volkxs", "volkz", "volkzwagen",
"vollkswagen", "vollkswagon", "vollswagon", "vols", "volsk", "volsks", "volskwagen",
"volskwagon", "volsswagon", "volsw", "volswagen", "volswaggen", "volswago", "volts",
"voltswagen", "voltwagon", "volvs", "volvswagen", "volvswagon", "volwagon", "vowlks",
"vvolkswagen", "vvolkswagon", "vwolks",
],
"volvo": [
"v olvo", "v0lv", "vilv", "vilvo", "vllv", "vlovo", "vlv", "vlvo", "vol vo",
"volco", "vollvo", "volo", "volov", "vols", "volv0", "volva", "volve", "volvi",
"volvl", "volvo", "volvo`", "volvoe", "volvoo", "volvot", "volvs", "volz", "vov",
"vovl", "vovlo", "vovlv", "vovlvo", "vovo", "vovol", "vvolvo",
],
"west": ["weha", "well", "wesr", "west", "whit", "wort", "wstr"],
"white": ["whi", "whit", "white"],
"xx": ["x", "x5", "xb", "xx", "xxx", "xxxx"],
"yamaha": ["yahaha", "yahama", "yahmaha", "yamah", "yamaha", "yamaya"],
}
# fmt: on
for correct, misspellings in spellings.items():
if s in misspellings:
return correct
return s
def rename_makes_4(s: str) -> str:
# fmt: off
spellings = {
'accord': ['acco', 'accord'],
'alfa': ['adva', 'aga', 'alfa'],
'apollo': ['apol', 'apollo', 'appolo'],
'aston martin': ['asto martin',
'aston martin',
'astro martin',
'austin martin'],
'buick': ['black', 'bruick', 'buick'],
'cadillac': ['cadillac', 'cadal', 'cadalic', 'cadli', 'catalac'],
'camry': ['caddy', 'cam', 'cama', 'camaro', 'camery', 'camry', 'car',
'carg', 'carm', 'carr', 'carry', 'catr', 'crhry', 'crry'],
'civic': ['cidi', 'cimc', 'citi', 'civic'],
'dodge': ['dodge ram', 'dodge van'],
'dongfang': ['dong fang', 'dongfang'],
'ferrari': [ 'ferr', 'ferrari'],
'ford': ['ford', 'fort', 'forte'],
'freightliner': ['freightliner' 'fhrt', 'fht', 'fhtl', 'fre', 'fret',
'frf', 'frgh', 'frgt', 'frhy', 'frie', 'frli', 'frnt', 'fron',
'frth', 'frtl', 'ftl' 'ftl', 'ftlr', 'ftlr'],
'honda': ['ond', 'onda', 'ondah'],
'kawasaki': ['kaw', 'kawa', 'kawa', 'kawak', 'kawas', 'kawc', 'kawi',
'kazda', 'kwak'],
'landrover': ['lanr', 'landr', 'landro', 'lanr', 'lanro', 'laro', 'lnd',
'lnrr', 'lnrv'],
'mercedes': ['mercedes', 'm benz', 'm-benz', 'm/benz', 'marcz', 'mbenz',
'mebe', 'mebz', 'mec', 'mece', 'merb', 'merbenz', 'merc.',
'mercb', 'mercben', 'mercbz', 'mercd', 'mercdz', 'merce',
'merce', 'mercez', 'mercq', 'mercs', 'mercz', 'merd', 'merdz',
'merec', 'merece', 'merez', 'merk', 'merq', 'merrz', 'mers',
'mertz', 'merv', 'merx', 'merxz', 'merzb', 'merzd', 'merzds',
'merze', 'merzs', 'merzz', 'merzzz', 'metz', 'mmerz', 'modz',
'mrc', 'mrcb', 'mrcd', 'mrcy', 'mrez', 'mrrz', 'mrrz'],
'mitsubishi': ['mitsubishi', 'mitsub', 'mitsuh.'],
'moped': ['mobed', 'moped'],
'nissan': ['nissan', 'missan'],
'none': ['unknown'],
'rav4': ['rav', 'rav 4', 'rav4', 'rave'],
'shanghai': ['shanghai', 'shanhaie'],
'sunny': ['sunl', 'sunny', 'surly'],
'taotao': ['taotao', 'tayotayo' 'tao', 'taoi', 'tatt', 'toay'],
'toyota': ['yoyota', 'yotoa', 'yotota', 'youota', 'yoyo', 'yoyota',
'yoyt', 'ytoyota'],
'volkswagen': ['vlkwg', 'volk wag', 'volks wag', 'volkswagen',
'volkval', 'volkwag', 'volkwgn', 'volwag', 'wilkswagon',
'wolks wagon', 'wolkswagen', 'wolkswagon', 'wolkwagen',
'wolskwagen', 'wolswagon', 'wvolkswagen'],
'volvo': ['vol', 'volvo'],
'western star': ['western sta', 'western star', 'west'],
'yongfu': ['yonfu', 'yong', 'yongfu', 'youngfu'],
'zhejiang': ['zhe jiang', 'zhejiang'],
'zhejiang jiajue': ['zhejiang jiajue', 'zhej', 'zheng', 'zhil', 'zhng']
}
# fmt: on
for correct, misspellings in spellings.items():
if s in misspellings:
return correct
return s
def fix_makes(df: DataFrame) -> DataFrame:
"""Use Levenshtein distance for now to heuristically reduce the makes"""
df = df.copy()
makes = df["vehicle_make"].str.lower().str.strip().apply(str)
print("Cleaning vehicle makes round 1")
makes = makes.apply(rename_makes_1)
print("Cleaning vehicle makes round 2")
makes = makes.apply(rename_makes_2)
print("Cleaning vehicle makes round 3")
makes = makes.apply(rename_makes_3)
print("Cleaning vehicle makes round 4")
makes = makes.apply(rename_makes_4).apply(str).convert_dtypes()
unqs, cnts = np.unique(makes.str.lower(), return_counts=True)
idx = np.argsort(-cnts)
unqs, cnts = unqs[idx], cnts[idx]
"""
After all this, a few common categories cover most of the data
>>> for n in [5, 10, 20, 25, 30, 40, 50]: print(n, np.sum(cnts[:n])/N)
5 0.569058997726716
10 0.7255442541459739
20 0.8987631899619767
25 0.9390410392512049
30 0.9642817207891328
40 0.9840417647673746
50 0.9915798517658647
>>> print(unqs[:40])
['toyota' 'honda' 'ford' 'nissan' 'chevy' 'hummer' 'dodge' 'acura' 'bmw'
'mercedes' 'volkswagen' 'jeep' 'lexis' 'mazda' 'subaru' 'chrysler' 'kia'
'gmc' 'infinity' 'mitsubishi' 'audi' 'cadillac' 'volvo' 'buick' 'mercury'
'pontiac' 'none' 'lincoln' 'saturn' 'scion' 'izuzu' 'susuki' 'landrover'
'mini' 'jaguar' 'porsche' 'saab' 'tesla' 'oldsmobile' 'international'
'hyundai' 'freightliner' 'rangerover' 'plymouth' 'yamaha' 'mack'
'kawasaki' 'fiat' 'peterbilt' 'kenworth']
"""
common_makes = unqs.tolist()[:30]
idx = makes.isin(common_makes)
makes[~idx] = "unknown"
# all_dists = cdist(common_makes, unqs, scorer=lev_dist)
# all_spellings = {}
# for m, common_make in enumerate(common_makes):
# dists = all_dists[m] # len == len(unqs)
# idx = dists <= 2
# matches = sorted(unqs[idx].tolist())
# matches = [mtch for mtch in matches if mtch[0] == common_make[0]]
# all_spellings[common_make] = matches
# for make, spellings in all_spellings.items():
# ...
# commons = sorted(all_spellings.keys())
# for some reason we are left with "none" despite above code
ix = makes == "none"
makes[ix] = "unknown"
assert (makes == "none").sum() == 0
df["vehicle_make"] = makes
return df
def clean_vehicle_info(df: DataFrame) -> DataFrame:
RARE_COLORS = {
"BEIGE": "TAN",
"None": "OTHER",
"CAMOUFLAGE": "OTHER",
"CHROME": "OTHER",
"PINK": "OTHER",
"COPPER": "OTHER",
"CREAM": "OTHER",
"MULTICOLOR": "OTHER",
"PURPLE": "OTHER",
"BRONZE": "OTHER",
}
df = df.copy()
# the year column is full of total garbage and nonsense, number from 0 to 10000
# just assume anything in 1960 to 2025 (range based on actual numbers present)
df["vehicle_year"] = df["vehicle_year"].astype(float)
idx_invalid = (df["vehicle_year"] < 1960) | (df["vehicle_year"] > 2025)
df.loc[idx_invalid, "vehicle_year"] = NaN
# We could manually limit the color classes because df-analyze will not
# so limit them, which may cause a cost explosion in feature selection
# There could also be the argument for dropping this feature if it has
# real association with anything.
df["vehicle_color"] = df["vehicle_color"].apply(
lambda c: RARE_COLORS[c] if c in RARE_COLORS else c
)
# there are over 22 000 unique vehicle models recorded, the the most
# common "45" (???) and "TK" (???), followed be "ACCORD", "CIVIC", ...,
# "SUV".
#
# Models should probably be properly cleaned to "sedan", "truck", "suv",
# "sport" or otherwise the column should be broken into price and class,
# or other things This is a lot of work, so for now we drop to prevent
# exploding compute costs.
df.drop(columns="vehicle_model", inplace=True)
df = fix_makes(df)
"""
02 - Automobile
05 - Light Duty Truck
06 - Heavy Duty Truck
20 - Commercial Rig
28 - Other
03 - Station Wagon
04 - Limousine
01 - Motorcycle
19 - Moped
29 - Unknown
08 - Recreational Vehicle
25 - Utility Trailer
26 - Boat Trailer
21 - Tandem Trailer
07 - Truck/Road Tractor
27 - Farm Equipment
09 - Farm Vehicle
10 - Transit Bus
12 - School Bus
11 - Cross Country Bus
23 - Travel/Home Trailer
22 - Mobile Home
24 - Camper
18 - Police(Non-Emerg)
14 - Ambulance(Non-Emerg)
13 - Ambulance(Emerg)
15 - Fire(Emerg)
17 - Police(Emerg)
16 - Fire(Non-Emerg)
18 - Police Vehicle
15 - Fire Vehicle
14 - Ambulance
13 - Ambulance
[1700901 102419 34380 27701 17237 15777 10970 5345 1946
1903 1746 1043 949 638 399 135 108 101
86 65 35 27 26 21 20 14 13
5 4 4 3 2 1]
First two cover 95%
"""
# fmt: off
emergency = ["18 - Police(Non-Emerg)", "14 - Ambulance(Non-Emerg)", "13 - Ambulance(Emerg)",
"15 - Fire(Emerg)", "17 - Police(Emerg)", "16 - Fire(Non-Emerg)", "18 - Police Vehicle",
"15 - Fire Vehicle", "14 - Ambulance", "13 - Ambulance",
]
trucks = ["05 - Light Duty Truck", "06 - Heavy Duty Truck", "20 - Commercial Rig"]
other = ["28 - Other", "03 - Station Wagon", "04 - Limousine", "29 - Unknown"]
cycle = ["01 - Motorcycle", "19 - Moped"]
rec = ["08 - Recreational Vehicle"]
trailer = ["25 - Utility Trailer", "26 - Boat Trailer", "21 - Tandem Trailer"]
farm = ["07 - Truck/Road Tractor", "27 - Farm Equipment", "09 - Farm Vehicle"]
bus = ["10 - Transit Bus", "12 - School Bus", "11 - Cross Country Bus"]
camping = ["23 - Travel/Home Trailer", "22 - Mobile Home", "24 - Camper"]