find words that can be made from a string in python

find words that can be made from a string in python - python

im fairly new to python and im not sure how to tackle my problem
im trying to make a program that can take a string of 15 characters from a .txt file and find words that you can make from those characters with a dictionary file, than output those words to another text file.
this is what i have tried:
attempting to find words that don't contain the characters and removing them from the list
various anagram solver type programs of git hub
i tried this sudo pip3 install anagram-solverbut it has been 3 hours on 15 characters and it is still running
im new so please tell me if im forgetting something

If you're looking for "perfect" anagrams, i.e. those that contain exactly the same number of characters, not a subset, it's pretty easy:
take your word-to-find, sort it by its letters
take your dictionary, sort each word by its letters
if the sorted versions match, they're anagrams
def find_anagrams(seek_word):
sorted_seek_word = sorted(seek_word.lower())
for word in open("/usr/share/dict/words"):
word = word.strip() # remove trailing newline
sorted_word = sorted(word.lower())
if sorted_word == sorted_seek_word and word != seek_word:
print(seek_word, word)
if __name__ == "__main__":
find_anagrams("begin")
find_anagrams("nicer")
find_anagrams("decor")
prints (on my macOS machine – Windows machines won't have /usr/share/dict/words by default, and some Linux distributions need it installed separately)
begin being
begin binge
nicer cerin
nicer crine
decor coder
decor cored
decor Credo
EDIT
A second variation that finds all words that are assemblable from the letters in the original word, using collections.Counter:
import collections
def find_all_anagrams(seek_word):
seek_word_counter = collections.Counter(seek_word.lower())
for word in open("/usr/share/dict/words"):
word = word.strip() # remove trailing newline
word_counter = collections.Counter(word.strip())
if word != seek_word and all(
n <= seek_word_counter[l] for l, n in word_counter.items()
):
yield word
if __name__ == "__main__":
print("decoration", set(find_all_anagrams("decoration")))
Outputs e.g.
decoration {'carte', 'drona', 'roit', 'oat', 'cantred', 'rond', 'rid', 'centroid', 'trine', 't', 'tenai', 'cond', 'toroid', 'recon', 'contra', 'dain', 'cootie', 'iao', 'arctoid', 'oner', 'indart', 'tine', 'nace', 'rident', 'cerotin', 'cran', 'eta', 'eoan', 'cardoon', 'tone', 'trend', 'trinode', 'coaid', 'ranid', 'rein', 'end', 'actine', 'ide', 'cero', 'iodate', 'corn', 'oer', 'retia', 'nidor', 'diter', 'drat', 'tec', 'tic', 'creat', 'arent', 'coon', 'doater', 'ornoite', 'terna', 'docent', 'tined', 'edit', 'octroi', 'eric', 'read', 'toned', 'c', 'tera', 'can', 'rocta', 'cortina', 'adonite', 'iced', 'no', 'natr', 'net', 'oe', 'rodeo', 'actor', 'otarine', 'on', 'cretin', 'ericad', 'dance', 'tornade', 'tinea', 'coontie', 'anerotic', 'acrite', 'ra', 'danio', 'inroad', 'inde', 'tied', 'tar', 'coronae', 'tid', 'rad', 'doc', 'derat', 'tea', 'acerin', 'ronde', 'recti', 'areito', 'drain', 'odontic', 'octoad', 'rio', 'actin', 'tread', 'rect', 'ariot', 'road', 'doctrine', 'enactor', 'indoor', 'toco', 'ton', 'trice', 'norite', 'nea', 'coda', 'noria', 'rot', 'trona', 'rice', 'arite', 'eria', 'orad', 'rate', 'toed', 'enact', 'crinet', 'cento', 'arid', 'coot', 'nat', 'nar', 'cain', 'at', 'antired', 'ear', 'triode', 'doter', 'cedarn', 'orna', 'rand', 'tari', 'crea', 'tiar', 'retan', 'tire', 'cora', 'aroid', 'iron', 'tenio', 'enroot', 'd', 'oaric', 'acetin', 'tain', 'neat', 'noter', 'tien', 'aortic', 'tode', 'dicer', 'irate', 'tie', 'canid', 'ado', 'noticer', 'arn', 'nacre', 'ceration', 'ratine', 'denaro', 'cotoin', 'aint', 'canto', 'cinter', 'decani', 'roon', 'donor', 'acnode', 'aide', 'doer', 'tacnode', 'oread', 'acetoin', 'rine', 'acton', 'conoid', 'a', 'otocrane', 'norate', 'care', 'ticer', 'io', 'detain', 'cedar', 'ta', 'toadier', 'atone', 'cornet', 'dacoit', 'toric', 'orate', 'arni', 'adroit', 'rend', 'tanier', 'rooted', 'doit', 'dier', 'odorate', 'trica', 'rated', 'cotonier', 'dine', 'roid', 'cairned', 'cat', 'i', 'coin', 'octine', 'trod', 'orc', 'cardo', 'eniac', 'arenoid', 'erd', 'creant', 'oda', 'ratio', 'ceria', 'ad', 'acorn', 'dorn', 'deric', 'credit', 'door', 'cinder', 'cantor', 'er', 'doon', 'coner', 'donate', 'roe', 'tora', 'antic', 'racoon', 'ooid', 'noa', 'tae', 'coroa', 'earn', 'retain', 'canted', 'norie', 'rota', 'tao', 'redan', 'rondo', 'entia', 'ctenoid', 'cent', 'daroo', 'inrooted', 'roed', 'adore', 'coat', 'e', 'rat', 'deair', 'arend', 'coir', 'acid', 'coronate', 'rodent', 'acider', 'iota', 'codo', 'redaction', 'cot', 'aeric', 'tonic', 'candier', 'decart', 'dicta', 'dot', 'recoat', 'caroon', 'rone', 'tarie', 'tarin', 'teca', 'oar', 'ocrea', 'ante', 'creation', 'tore', 'conto', 'tairn', 'roc', 'conter', 'coeditor', 'certain', 'roncet', 'decator', 'not', 'coatie', 'toran', 'caid', 'redia', 'root', 'cad', 'cartoon', 'n', 'coed', 'cand', 'neo', 'coronadite', 'dare', 'dartoic', 'acoin', 'detar', 'dite', 'trade', 'train', 'ordinate', 'racon', 'citron', 'dan', 'doat', 'nito', 'tercia', 'rote', 'cooer', 'acone', 'rita', 'caret', 'dern', 'enatic', 'too', 'cried', 'tade', 'dit', 'orient', 'ria', 'torn', 'coati', 'cnida', 'note', 'tried', 'acrid', 'nitro', 'acron', 'tern', 'one', 'it', 'naio', 'dor', 'ea', 'ca', 'ire', 'inert', 'orcanet', 'cine', 'coe', 'nardoo', 'deota', 'den', 'toi', 'adion', 'to', 'rite', 'nectar', 'rane', 'riant', 'cod', 'de', 'adit', 'airt', 'ie', 'retin', 'toon', 'cane', 'aeon', 'are', 'cointer', 'actioner', 'crin', 'detrain', 'art', 'cant', 'ort', 'tored', 'antoeci', 'tier', 'cite', 'onto', 'coater', 'tranced', 'atonic', 'roi', 'in', 'roan', 'decoat', 'rain', 'cronet', 'ronco', 'dont', 'citer', 'redact', 'cider', 'nor', 'octan', 'ration', 'doina', 'rie', 'aero', 'noted', 'crate', 'crain', 'cadet', 'condite', 'ran', 'odeon', 'date', 'eat', 'intoed', 'cation', 'carone', 'ratoon', 'retina', 'tiao', 'nice', 'nodi', 'codon', 'coo', 'torc', 'dent', 'entad', 'ne', 'toe', 'dae', 'decant', 'redcoat', 'coiner', 'irade', 'air', 'oint', 'coronet', 'radon', 'ce', 'octonare', 'oaten', 'citrean', 'dice', 'dancer', 'carotid', 'cretion', 'don', 'cion', 'nei', 'tead', 'nori', 'nacrite', 'ootid', 'rancid', 'dornic', 'orenda', 'cairn', 'aroon', 'coardent', 'aider', 'notice', 'cored', 'adorn', 'tad', 'carid', 'otic', 'dian', 'od', 'dint', 'tercio', 'die', 'conred', 'tice', 'rant', 'candor', 'anti', 'dar', 'antre', 'cornea', 'ordain', 'corona', 'recta', 'redo', 'tare', 'coranto', 'action', 'caird', 'creta', 'naid', 'tri', 'acre', 'crane', 'coated', 'citronade', 'anoetic', 'tenor', 'anode', 'triad', 'ceratoid', 'rod', 'idea', 'carton', 'cortin', 'endaortic', 'dicot', 'tend', 'da', 'tod', 'erotica', 'cord', 'coreid', 'toader', 'dace', 'tan', 'editor', 'rection', 'toner', 'cone', 'ni', 'tide', 'coder', 'din', 'ocote', 'ore', 'daer', 'octane', 'darn', 'do', 'reit', 'na', 'catenoid', 'tron', 'condor', 'crinated', 'cordon', 'crone', 'toad', 'noir', 'into', 'tirade', 'nadir', 'ant', 'ade', 'droit', 'icon', 'drone', 'ared', 'cardin', 'nid', 'dire', 'orcin', 'donator', 'rani', 'tane', 'ace', 'iodo', 'doria', 'ride', 'eon', 'ornate', 'cedrat', 'aire', 'carotin', 'dation', 'tear', 'onca', 'cote', 'taroc', 'con', 'nod', 'dinero', 'ecad', 'recant', 'ae', 'octad', 'cor', 'doctor', 'acridone', 'neti', 'cordite', 'crotin', 'aneroid', 'diota', 'coorie', 'dita', 'aconite', 'nard', 'cadent', 'ectad', 'rance', 'rea', 'tai', 'denat', 'rood', 'acne', 'decan', 'ani', 'rit', 'cit', 'cetin', 'odor', 'acorned', 'iceroot', 'inro', 'crood', 'daric', 'dacite', 'trone', 'acier', 'reina', 'oncia', 'drant', 'acrodont', 'nacred', 'cotrine', 'dinar', 'tean', 'atoner', 'toorie', 'nadorite', 'cardon', 'taen', 'tin', 'conte', 'acoine', 'dater', 'diact', 'aid', 'anodic', 'coronated', 'direct', 're', 'era', 'anticor', 'triace', 'octoid', 'dao', 'corta', 'edict', 'trode', 'ode', 'orant', 'niter', 'centrad', 'cater', 'tronc', 'coronad', 'r', 'toro', 'ar', 'once', 'ora', 'trace', 'creodont', 'erotic', 'ai', 'troca', 'ion', 'tecon', 'tra', 'acor', 'radio', 'acred', 'croon', 'tricae', 'recto', 'riden', 'andorite', 'taro', 'red', 'dear', 'ate', 'tinder', 'trin', 'deacon', 'ardent', 'aer', 'arc', 'crine', 'dart', 'diet', 'riot', 'tanrec', 'tor', 'noetic', 'ret', 'trance', 'ona', 'rind', 'coto', 'daoine', 'teind', 'toa', 'inter', 'code', 'cart', 'aion', 'detin', 'core', 'oont', 'rent', 'cedrin', 'card', 'trained', 'o', 'recoin', 'cro', 'and', 'diner', 'id', 'cordant', 'cedron', 'ditone', 'odic', 'cadi', 'cerin', 'nit', 'ecoid', 'nide', 'ean', 'andric', 'tind', 'raid', 'crena', 'oroide', 'roadite', 'canter', 'idant', 'cade', 'race', 'ten', 'caner', 'tarn', 'cooter', 'etna', 'tornadic', 'irone', 'ice', 'en', 'oord', 'oared', 'draine', 'cordate', 'react', 'reaction', 'tornado', 'troco', 'niota', 'carotenoid', 'an', 'cader', 'naric', 'car', 'centiar', 'ti', 'cearin', 'aroint', 'crined', 'iter', 'di', 'or', 'trio', 'dari', 'oration', 'orcein', 'coned', 'odorant', 'dean', 'coadore', 'cate', 'drate', 'dirten', 'ted', 'done', 'cadre', 'ocean', 'tired', 'adet', 'dirt', 'te', 'nae', 'ceti', 'cern', 'rotan', 'doe', 'roto', 'dote', 'node', 'ait', 'act', 'canoe', 'rode'}

Related

Running 1000 functions gracefully using python multi-processing

I'm trying to receive stock data for about 1000 stocks, to speed up the process I'm using multiprocessing, unfortunately due to the large amount of stock data I'm trying to receive python as a whole just crashes.
Is there a way to use multiprocessing without python crashing, I understand it would still take some time to do all of the 1000 stocks, but all I need is to do this process as fast as possible.
import threading
import yfinance as yf
from multiprocessing import Process
database = {}
mylock = threading.RLock()
stocks = ['AAU', 'ABEO', 'ABEV', 'ABIO', 'ABUS', 'ACCO', 'ACER', 'ACIU', 'ACOR', 'ACRX', 'ACST', 'ACTG', 'ADAP', 'ADIL', 'ADMA', 'ADMP', 'ADT', 'ADTX', 'ADXS', 'AEG', 'AEHL', 'AEHR', 'AEMD', 'AESE', 'AEY', 'AEZS', 'AFIN', 'AFMD', 'AGEN', 'AGI', 'AGRO', 'AGRX', 'AGS', 'AGTC', 'AHPI', 'AHT', 'AIHS', 'AIKI', 'AIM', 'AINC', 'AIRI', 'AIV', 'AKBA', 'AKER', 'AKTX', 'ALNA', 'ALRN', 'ALSK', 'AM', 'AMBO', 'AMC', 'AMPE', 'AMPY', 'AMRN', 'AMRS', 'AMRX', 'AMTX', 'ANCN', 'ANH', 'ANIX', 'ANPC', 'ANTE', 'ANY', 'APDN', 'APM', 'APRE', 'APRN', 'APTO', 'APTS', 'APTX', 'APWC', 'AQMS', 'AQST', 'AR', 'ARAY', 'ARC', 'ARCO', 'ARDX', 'AREC', 'ARKO', 'ARLO', 'ARLP', 'AROC', 'ARPO', 'ARTL', 'ASC', 'ASLN', 'ASM', 'ASMB', 'ASRT', 'ASTC', 'ASX', 'ATAX', 'ATHE', 'ATHX', 'ATIF', 'ATNF', 'ATNM', 'ATOS', 'ATRS', 'ATXI', 'AUMN', 'AUTO', 'AUVI', 'AUY', 'AVCO', 'AVDL', 'AVEO', 'AVGR', 'AVXL', 'AWH', 'AWX', 'AXAS', 'AXL', 'AXU', 'AYRO', 'AYTU', 'AZRX', 'BBAR', 'BBD', 'BBGI', 'BBI', 'BBIG', 'BBVA', 'BBW', 'BCDA', 'BCLI', 'BCRX', 'BCS', 'BDR', 'BDSI', 'BEST', 'BGCP', 'BGI', 'BHAT', 'BHR', 'BHTG', 'BIMI', 'BIOC', 'BIOL', 'BKCC', 'BKD', 'BKEP', 'BKYI', 'BLCM', 'BLCT', 'BLIN', 'BLRX', 'BLU', 'BMRA', 'BNED', 'BNTC', 'BORR', 'BOXL', 'BPT', 'BPTH', 'BQ', 'BREZR', 'BRFS', 'BRN', 'BRPAR', 'BRQS', 'BRY', 'BSBR', 'BSGM', 'BSM', 'BSMX', 'BTG', 'BTU', 'BVXV', 'BW', 'BWEN', 'BXRX', 'BYFC', 'CAAP', 'CAAS', 'CALA', 'CAN', 'CANF', 'CAPR', 'CARV', 'CASA', 'CASI', 'CATB', 'CBAT', 'CBAY', 'CBIO', 'CBLI', 'CCO', 'CCRC', 'CCRN', 'CDE', 'CDEV', 'CDTX', 'CDXC', 'CEI', 'CEIX', 'CEMI', 'CERC', 'CERS', 'CETX', 'CFMS', 'CGIX', 'CHEK', 'CHMA', 'CHNR', 'CHRA', 'CHS', 'CHU', 'CIDM', 'CIG', 'CIO', 'CJJD', 'CKPT', 'CLBS', 'CLIR', 'CLNC', 'CLNY', 'CLPS', 'CLRB', 'CLS', 'CLSD', 'CLSN', 'CLVR', 'CLVS', 'CLXT', 'CMCM', 'CMO', 'CMRE', 'CMRX', 'CNDT', 'CNET', 'CNFR', 'CNSL', 'CNSP', 'CNTY', 'COCP', 'COGT', 'COMS', 'CORR', 'CPG', 'CPHI', 'CPRX', 'CPSH', 'CRBP', 'CREG', 'CREX', 'CRIS', 'CRK', 'CRKN', 'CRMD', 'CRNT', 'CSCW', 'CSLT', 'CSPR', 'CTEK', 'CTIB', 'CTIC', 'CTK', 'CTMX', 'CTRM', 'CTSO', 'CTXR', 'CVE', 'CVGI', 'CWBR', 'CX', 'CXW', 'CYCC', 'CYCN', 'CYRN', 'CYTH', 'DARE', 'DBVT', 'DFFN', 'DGLY', 'DHC', 'DHT', 'DLPN', 'DNK', 'DNN', 'DNOW', 'DOGZ', 'DPW', 'DRH', 'DRRX', 'DRTT', 'DS', 'DSKE', 'DSS', 'DSSI', 'DSX', 'DTEA', 'DTSS', 'DUO', 'DVAX', 'DXF', 'DXLG', 'DYNT', 'EARS', 'EBON', 'EBR', 'ECOR', 'EDSA', 'EGY', 'EIGR', 'ELVT', 'ELYS', 'EMAN', 'EMKR', 'EMX', 'ENBL', 'ENDP', 'ENG', 'ENIA', 'ENIC', 'ENLC', 'ENSV', 'ENTX', 'ENVB', 'ENZ', 'EOLS', 'EQ', 'EQX', 'ERF', 'ERJ', 'ESGC', 'ESTE', 'ET', 'ETM', 'ETRN', 'ETTX', 'EURN', 'EVC', 'EVFM', 'EVGN', 'EVK', 'EVOK', 'EXK', 'EXPR', 'EXTR', 'EYEG', 'EYES', 'EZGO', 'EZPW', 'FAMI', 'FBIO', 'FBP', 'FENG', 'FI', 'FINV', 'FLDM', 'FLMN', 'FLNT', 'FLY', 'FORD', 'FPAY', 'FRBK', 'FRO', 'FRSX', 'FSM', 'FSP', 'FTEK', 'FTFT', 'FTK', 'FURY', 'GAU', 'GBS', 'GCI', 'GEL', 'GEN', 'GENE', 'GEO', 'GERN', 'GFI', 'GGAL', 'GGB', 'GHSI', 'GLBS', 'GLDG', 'GLG', 'GLOG', 'GLOP', 'GLUU', 'GLYC', 'GMBL', 'GMDA', 'GMLP', 'GNCA', 'GNK', 'GNLN', 'GNPX', 'GNUS', 'GNW', 'GOGL', 'GOL', 'GORO', 'GOSS', 'GOVX', 'GPL', 'GPMT', 'GPRO', 'GRIL', 'GRNQ', 'GSAT', 'GSKY', 'GSM', 'GSS', 'GSV', 'GTE', 'GTEC', 'GTT', 'GV', 'GVP', 'HAPP', 'HBM', 'HCDI', 'HCHC', 'HDSN', 'HEPA', 'HEXO', 'HGSH', 'HIL', 'HIMX', 'HJLI', 'HL', 'HLIT', 'HLX', 'HMHC', 'HMY', 'HNRG', 'HOFV', 'HOTH', 'HSTO', 'HT', 'HTBX', 'HUGE', 'HUSA', 'HUSN', 'HX', 'HYRE', 'IAG', 'IBIO', 'ICD', 'ICON', 'ID', 'IDEX', 'IDRA', 'IFMK', 'IFRX', 'IGC', 'IHT', 'IKT', 'IMAC', 'IMGN', 'IMMP', 'IMTE', 'IMV', 'INDO', 'INFI', 'ING', 'INN', 'INOD', 'INPX', 'INUV', 'IO', 'IPDN', 'IRIX', 'ISEE', 'ISIG', 'ISR', 'ITP', 'ITRM', 'ITUB', 'IVR', 'IZEA', 'JAGX', 'JE', 'JFIN', 'JFU', 'JG', 'JIH.W', 'JILL', 'JOB', 'JUPW', 'KALA', 'KBNT', 'KBSF', 'KDMN', 'KERN', 'KGC', 'KIN', 'KIQ', 'KMPH', 'KNDI', 'KODK', 'KOPN', 'KOS', 'KRKR', 'KRMD', 'KTRA', 'KUKE', 'KXIN', 'KZIA', 'LCI', 'LCTX', 'LEAF', 'LEE', 'LGHL', 'LIFE', 'LITB', 'LIVX', 'LIZI', 'LJPC', 'LKCO', 'LLIT', 'LLNW', 'LMFA', 'LMNL', 'LODE', 'LOMA', 'LPCN', 'LPTH', 'LPTX', 'LQDA', 'LSEA', 'LTBR', 'LTRPA', 'LX', 'LXRX', 'LYG', 'MACK', 'MARK', 'MBI', 'MBII', 'MBIO', 'MBRX', 'MBT', 'MCEP', 'MCF', 'MDGS', 'MDXG', 'MEIP', 'MESA', 'MESO', 'METX', 'MFA', 'MFG', 'MFGP', 'MFH', 'MGI', 'MGY', 'MHLD', 'MICT', 'MIN', 'MIND', 'MITO', 'MITT', 'MKD', 'MKGI', 'MLND', 'MLSS', 'MNKD', 'MOGO', 'MOGU', 'MOHO', 'MOSY', 'MOTS', 'MOXC', 'MPLN', 'MRC', 'MREO', 'MRIN', 'MRKR', 'MRO', 'MSN', 'MTA', 'MTC', 'MTL', 'MTNB', 'MTP', 'MTSL', 'MUFG', 'MUX', 'MVIS', 'MYSZ', 'MYT', 'NAK', 'NAKD', 'NAOV', 'NAT', 'NAVB', 'NBEV', 'NBRV', 'NBSE', 'NBY', 'NCMI', 'NCNA', 'NDRA', 'NEOS', 'NEPT', 'NERV', 'NES', 'NEW', 'NEX', 'NG', 'NGD', 'NGL', 'NH', 'NLY', 'NMCI', 'NMRK', 'NMTR', 'NNVC', 'NOK', 'NOVN', 'NR', 'NRZ', 'NSCO', 'NSPR', 'NTEC', 'NTN', 'NURO', 'NVCN', 'NVIV', 'NWG', 'NXE', 'NXTD', 'NYMT', 'OBLG', 'OBLN', 'OBSV', 'OCG', 'OCGN', 'OCSL', 'OCX', 'OEG', 'OGEN', 'OGI', 'OIBR.C', 'OII', 'OIIM', 'OIS', 'ONCT', 'ONCY', 'ONTX', 'OPGN', 'OPK', 'OPTN', 'OPTT', 'ORBC', 'ORC', 'ORMP', 'ORN', 'ORTX', 'OSMT', 'OSW', 'OTIC', 'OTLK', 'OVID', 'OXBR', 'OXLC', 'PAA', 'PAE', 'PAGP', 'PAVM', 'PAYS', 'PBF', 'PBI', 'PDSB', 'PED', 'PEI', 'PEIX', 'PFMT', 'PGEN', 'PGRE', 'PHAS', 'PHIO', 'PHUN', 'PIRS', 'PIXY', 'PLAG', 'PLG', 'PLIN', 'PLM', 'PLYA', 'PNNT', 'POAI', 'POWW', 'PPBT', 'PPR', 'PPSI', 'PPT', 'PROG', 'PRPO', 'PRQR', 'PRTK', 'PRTY', 'PSEC', 'PSTI', 'PSTV', 'PT', 'PTE', 'PTEN', 'PTMN', 'PTN', 'PULM', 'PUMP', 'PVL', 'PXLW', 'PXS', 'QD', 'QEP', 'QIWI', 'QLGN', 'QLI', 'QTNT', 'QTT', 'QUAD', 'QUOT', 'RAIL', 'RAVE', 'RBBN', 'RCON', 'RDHL', 'REED', 'REFR', 'REI', 'REPH', 'RES', 'RESN', 'RETO', 'RFP', 'RGLS', 'RGS', 'RHE', 'RIBT', 'RIG', 'RIGL', 'RKDA', 'RLH', 'RMED', 'RMTI', 'RNWK', 'RPAI', 'RPT', 'RRC', 'RRD', 'RTLR', 'RUHN', 'RWLK', 'RWT', 'RYAM', 'SALM', 'SAN', 'SAND', 'SB', 'SBBP', 'SBS', 'SCKT', 'SCOR', 'SCYX', 'SD', 'SDPI', 'SEAC', 'SEEL', 'SELB', 'SENS', 'SESN', 'SFET', 'SFL', 'SFT', 'SGBX', 'SGLB', 'SGOC', 'SHIP', 'SID', 'SIEN', 'SIF', 'SIFY', 'SILV', 'SINO', 'SINT', 'SIOX', 'SIRI', 'SLCA', 'SLDB', 'SLGG', 'SLRX', 'SLS', 'SM', 'SMFG', 'SMSI', 'SMTS', 'SMTX', 'SNCA', 'SNCR', 'SND', 'SNDE', 'SNDL', 'SNES', 'SNGX', 'SNMP', 'SNOA', 'SNR', 'SNSS', 'SOI', 'SOLO', 'SONM', 'SONN', 'SOS', 'SPCB', 'SPPI', 'SQFT', 'SQNS', 'SREV', 'SRGA', 'SSL', 'STAF', 'STCN', 'STON', 'STSA', 'SUP', 'SUPV', 'SVM', 'SVRA', 'SWN', 'SXC', 'SXTC', 'SYBX', 'SYN', 'SYPR', 'TACO', 'TALO', 'TANH', 'TAOP', 'TAST', 'TAT', 'TATT', 'TBLT', 'TCCO', 'TCDA', 'TCON', 'TEDU', 'TEF', 'TELL', 'TENX', 'TEO', 'TGA', 'TGB', 'TGC', 'THM', 'THMO', 'TK', 'TKAT', 'TKC', 'TLGT', 'TLMD', 'TLSA', 'TLYS', 'TMBR', 'TMDI', 'TMQ', 'TMST', 'TNAV', 'TNXP', 'TOPS', 'TOUR', 'TPRE', 'TRCH', 'TRIB', 'TRIT', 'TRST', 'TRUE', 'TRVG', 'TRVN', 'TRX', 'TRXC', 'TTI', 'TTNP', 'TTOO', 'TUSK', 'TV', 'TWI', 'TWO', 'TXMD', 'TYME', 'UAMY', 'UBX', 'UEC', 'UEPS', 'UGP', 'UMC', 'UONE', 'UONEK', 'URG', 'USAS', 'USAT', 'USEG', 'USIO', 'USWS', 'USX', 'UTSI', 'UUUU', 'UWMC', 'UXIN', 'VBIV', 'VBLT', 'VCNX', 'VEDL', 'VEON', 'VERB', 'VERO', 'VERU', 'VET', 'VGZ', 'VHC', 'VIOT', 'VIRI', 'VISL', 'VIV', 'VIVE', 'VKTX', 'VNTR', 'VRA', 'VRAY', 'VSTM', 'VTGN', 'VTNR', 'VTVT', 'VVOS', 'VXRT', 'VYGR', 'VYNE', 'WATT', 'WEI', 'WETF', 'WIMI', 'WISA', 'WIT', 'WKEY', 'WMC', 'WORX', 'WPRT', 'WPX', 'WRAP', 'WRN', 'WSR', 'WTER', 'WTI', 'WTRH', 'WTTR', 'WVE', 'WWR', 'XAIR', 'XAN', 'XBIO', 'XCUR', 'XELA', 'XELB', 'XERS', 'XNET', 'XPL', 'XSPA', 'XXII', 'YCBD', 'YGYI', 'YJ', 'YPF', 'YRCW', 'YTRA', 'YVR', 'ZAGG', 'ZIOP', 'ZIXI', 'ZKIN', 'ZNGA', 'ZOM', 'ZSAN', 'ZVO', 'ZYNE', '']
class info():
def __init__(self, name):
self.name = name
self.goo()
def goo(self):
self.x = {'ticker':self.name, 'name': str(yf.Ticker(self.name).info['longName'])}
print(self.x, )
with mylock:
database[self.name] = self.x
def run_in_p():
proc = []
for name_s in stocks:
p = Process(target=info, args=(name_s, ))
p.start()
proc.append(p)
for p in proc:
p.join()
print(database)
if __name__ == "__main__":
run_in_p()
Edit:
Here's the error window that pops up
Along with this error window I get another error in the python console
raise HTTPError(req.full_url, code, msg, hdrs, fp)
urllib.error.HTTPError: HTTP Error 404: Not Found

I'd like to offer a solution using a package called yahooquery. Disclaimer: I am the author of the package. You can get that same data in a few seconds with the following:
from yahooquery import Ticker
stocks = ['AAU', 'ABEO', 'ABEV', 'ABIO', 'ABUS', 'ACCO', 'ACER', 'ACIU', 'ACOR', 'ACRX', 'ACST', 'ACTG', 'ADAP', 'ADIL', 'ADMA', 'ADMP', 'ADT', 'ADTX', 'ADXS', 'AEG', 'AEHL', 'AEHR', 'AEMD', 'AESE', 'AEY', 'AEZS', 'AFIN', 'AFMD', 'AGEN', 'AGI', 'AGRO', 'AGRX', 'AGS', 'AGTC', 'AHPI', 'AHT', 'AIHS', 'AIKI', 'AIM', 'AINC', 'AIRI', 'AIV', 'AKBA', 'AKER', 'AKTX', 'ALNA', 'ALRN', 'ALSK', 'AM', 'AMBO', 'AMC', 'AMPE', 'AMPY', 'AMRN', 'AMRS', 'AMRX', 'AMTX', 'ANCN', 'ANH', 'ANIX', 'ANPC', 'ANTE', 'ANY', 'APDN', 'APM', 'APRE', 'APRN', 'APTO', 'APTS', 'APTX', 'APWC', 'AQMS', 'AQST', 'AR', 'ARAY', 'ARC', 'ARCO', 'ARDX', 'AREC', 'ARKO', 'ARLO', 'ARLP', 'AROC', 'ARPO', 'ARTL', 'ASC', 'ASLN', 'ASM', 'ASMB', 'ASRT', 'ASTC', 'ASX', 'ATAX', 'ATHE', 'ATHX', 'ATIF', 'ATNF', 'ATNM', 'ATOS', 'ATRS', 'ATXI', 'AUMN', 'AUTO', 'AUVI', 'AUY', 'AVCO', 'AVDL', 'AVEO', 'AVGR', 'AVXL', 'AWH', 'AWX', 'AXAS', 'AXL', 'AXU', 'AYRO', 'AYTU', 'AZRX', 'BBAR', 'BBD', 'BBGI', 'BBI', 'BBIG', 'BBVA', 'BBW', 'BCDA', 'BCLI', 'BCRX', 'BCS', 'BDR', 'BDSI', 'BEST', 'BGCP', 'BGI', 'BHAT', 'BHR', 'BHTG', 'BIMI', 'BIOC', 'BIOL', 'BKCC', 'BKD', 'BKEP', 'BKYI', 'BLCM', 'BLCT', 'BLIN', 'BLRX', 'BLU', 'BMRA', 'BNED', 'BNTC', 'BORR', 'BOXL', 'BPT', 'BPTH', 'BQ', 'BREZR', 'BRFS', 'BRN', 'BRPAR', 'BRQS', 'BRY', 'BSBR', 'BSGM', 'BSM', 'BSMX', 'BTG', 'BTU', 'BVXV', 'BW', 'BWEN', 'BXRX', 'BYFC', 'CAAP', 'CAAS', 'CALA', 'CAN', 'CANF', 'CAPR', 'CARV', 'CASA', 'CASI', 'CATB', 'CBAT', 'CBAY', 'CBIO', 'CBLI', 'CCO', 'CCRC', 'CCRN', 'CDE', 'CDEV', 'CDTX', 'CDXC', 'CEI', 'CEIX', 'CEMI', 'CERC', 'CERS', 'CETX', 'CFMS', 'CGIX', 'CHEK', 'CHMA', 'CHNR', 'CHRA', 'CHS', 'CHU', 'CIDM', 'CIG', 'CIO', 'CJJD', 'CKPT', 'CLBS', 'CLIR', 'CLNC', 'CLNY', 'CLPS', 'CLRB', 'CLS', 'CLSD', 'CLSN', 'CLVR', 'CLVS', 'CLXT', 'CMCM', 'CMO', 'CMRE', 'CMRX', 'CNDT', 'CNET', 'CNFR', 'CNSL', 'CNSP', 'CNTY', 'COCP', 'COGT', 'COMS', 'CORR', 'CPG', 'CPHI', 'CPRX', 'CPSH', 'CRBP', 'CREG', 'CREX', 'CRIS', 'CRK', 'CRKN', 'CRMD', 'CRNT', 'CSCW', 'CSLT', 'CSPR', 'CTEK', 'CTIB', 'CTIC', 'CTK', 'CTMX', 'CTRM', 'CTSO', 'CTXR', 'CVE', 'CVGI', 'CWBR', 'CX', 'CXW', 'CYCC', 'CYCN', 'CYRN', 'CYTH', 'DARE', 'DBVT', 'DFFN', 'DGLY', 'DHC', 'DHT', 'DLPN', 'DNK', 'DNN', 'DNOW', 'DOGZ', 'DPW', 'DRH', 'DRRX', 'DRTT', 'DS', 'DSKE', 'DSS', 'DSSI', 'DSX', 'DTEA', 'DTSS', 'DUO', 'DVAX', 'DXF', 'DXLG', 'DYNT', 'EARS', 'EBON', 'EBR', 'ECOR', 'EDSA', 'EGY', 'EIGR', 'ELVT', 'ELYS', 'EMAN', 'EMKR', 'EMX', 'ENBL', 'ENDP', 'ENG', 'ENIA', 'ENIC', 'ENLC', 'ENSV', 'ENTX', 'ENVB', 'ENZ', 'EOLS', 'EQ', 'EQX', 'ERF', 'ERJ', 'ESGC', 'ESTE', 'ET', 'ETM', 'ETRN', 'ETTX', 'EURN', 'EVC', 'EVFM', 'EVGN', 'EVK', 'EVOK', 'EXK', 'EXPR', 'EXTR', 'EYEG', 'EYES', 'EZGO', 'EZPW', 'FAMI', 'FBIO', 'FBP', 'FENG', 'FI', 'FINV', 'FLDM', 'FLMN', 'FLNT', 'FLY', 'FORD', 'FPAY', 'FRBK', 'FRO', 'FRSX', 'FSM', 'FSP', 'FTEK', 'FTFT', 'FTK', 'FURY', 'GAU', 'GBS', 'GCI', 'GEL', 'GEN', 'GENE', 'GEO', 'GERN', 'GFI', 'GGAL', 'GGB', 'GHSI', 'GLBS', 'GLDG', 'GLG', 'GLOG', 'GLOP', 'GLUU', 'GLYC', 'GMBL', 'GMDA', 'GMLP', 'GNCA', 'GNK', 'GNLN', 'GNPX', 'GNUS', 'GNW', 'GOGL', 'GOL', 'GORO', 'GOSS', 'GOVX', 'GPL', 'GPMT', 'GPRO', 'GRIL', 'GRNQ', 'GSAT', 'GSKY', 'GSM', 'GSS', 'GSV', 'GTE', 'GTEC', 'GTT', 'GV', 'GVP', 'HAPP', 'HBM', 'HCDI', 'HCHC', 'HDSN', 'HEPA', 'HEXO', 'HGSH', 'HIL', 'HIMX', 'HJLI', 'HL', 'HLIT', 'HLX', 'HMHC', 'HMY', 'HNRG', 'HOFV', 'HOTH', 'HSTO', 'HT', 'HTBX', 'HUGE', 'HUSA', 'HUSN', 'HX', 'HYRE', 'IAG', 'IBIO', 'ICD', 'ICON', 'ID', 'IDEX', 'IDRA', 'IFMK', 'IFRX', 'IGC', 'IHT', 'IKT', 'IMAC', 'IMGN', 'IMMP', 'IMTE', 'IMV', 'INDO', 'INFI', 'ING', 'INN', 'INOD', 'INPX', 'INUV', 'IO', 'IPDN', 'IRIX', 'ISEE', 'ISIG', 'ISR', 'ITP', 'ITRM', 'ITUB', 'IVR', 'IZEA', 'JAGX', 'JE', 'JFIN', 'JFU', 'JG', 'JIH.W', 'JILL', 'JOB', 'JUPW', 'KALA', 'KBNT', 'KBSF', 'KDMN', 'KERN', 'KGC', 'KIN', 'KIQ', 'KMPH', 'KNDI', 'KODK', 'KOPN', 'KOS', 'KRKR', 'KRMD', 'KTRA', 'KUKE', 'KXIN', 'KZIA', 'LCI', 'LCTX', 'LEAF', 'LEE', 'LGHL', 'LIFE', 'LITB', 'LIVX', 'LIZI', 'LJPC', 'LKCO', 'LLIT', 'LLNW', 'LMFA', 'LMNL', 'LODE', 'LOMA', 'LPCN', 'LPTH', 'LPTX', 'LQDA', 'LSEA', 'LTBR', 'LTRPA', 'LX', 'LXRX', 'LYG', 'MACK', 'MARK', 'MBI', 'MBII', 'MBIO', 'MBRX', 'MBT', 'MCEP', 'MCF', 'MDGS', 'MDXG', 'MEIP', 'MESA', 'MESO', 'METX', 'MFA', 'MFG', 'MFGP', 'MFH', 'MGI', 'MGY', 'MHLD', 'MICT', 'MIN', 'MIND', 'MITO', 'MITT', 'MKD', 'MKGI', 'MLND', 'MLSS', 'MNKD', 'MOGO', 'MOGU', 'MOHO', 'MOSY', 'MOTS', 'MOXC', 'MPLN', 'MRC', 'MREO', 'MRIN', 'MRKR', 'MRO', 'MSN', 'MTA', 'MTC', 'MTL', 'MTNB', 'MTP', 'MTSL', 'MUFG', 'MUX', 'MVIS', 'MYSZ', 'MYT', 'NAK', 'NAKD', 'NAOV', 'NAT', 'NAVB', 'NBEV', 'NBRV', 'NBSE', 'NBY', 'NCMI', 'NCNA', 'NDRA', 'NEOS', 'NEPT', 'NERV', 'NES', 'NEW', 'NEX', 'NG', 'NGD', 'NGL', 'NH', 'NLY', 'NMCI', 'NMRK', 'NMTR', 'NNVC', 'NOK', 'NOVN', 'NR', 'NRZ', 'NSCO', 'NSPR', 'NTEC', 'NTN', 'NURO', 'NVCN', 'NVIV', 'NWG', 'NXE', 'NXTD', 'NYMT', 'OBLG', 'OBLN', 'OBSV', 'OCG', 'OCGN', 'OCSL', 'OCX', 'OEG', 'OGEN', 'OGI', 'OIBR.C', 'OII', 'OIIM', 'OIS', 'ONCT', 'ONCY', 'ONTX', 'OPGN', 'OPK', 'OPTN', 'OPTT', 'ORBC', 'ORC', 'ORMP', 'ORN', 'ORTX', 'OSMT', 'OSW', 'OTIC', 'OTLK', 'OVID', 'OXBR', 'OXLC', 'PAA', 'PAE', 'PAGP', 'PAVM', 'PAYS', 'PBF', 'PBI', 'PDSB', 'PED', 'PEI', 'PEIX', 'PFMT', 'PGEN', 'PGRE', 'PHAS', 'PHIO', 'PHUN', 'PIRS', 'PIXY', 'PLAG', 'PLG', 'PLIN', 'PLM', 'PLYA', 'PNNT', 'POAI', 'POWW', 'PPBT', 'PPR', 'PPSI', 'PPT', 'PROG', 'PRPO', 'PRQR', 'PRTK', 'PRTY', 'PSEC', 'PSTI', 'PSTV', 'PT', 'PTE', 'PTEN', 'PTMN', 'PTN', 'PULM', 'PUMP', 'PVL', 'PXLW', 'PXS', 'QD', 'QEP', 'QIWI', 'QLGN', 'QLI', 'QTNT', 'QTT', 'QUAD', 'QUOT', 'RAIL', 'RAVE', 'RBBN', 'RCON', 'RDHL', 'REED', 'REFR', 'REI', 'REPH', 'RES', 'RESN', 'RETO', 'RFP', 'RGLS', 'RGS', 'RHE', 'RIBT', 'RIG', 'RIGL', 'RKDA', 'RLH', 'RMED', 'RMTI', 'RNWK', 'RPAI', 'RPT', 'RRC', 'RRD', 'RTLR', 'RUHN', 'RWLK', 'RWT', 'RYAM', 'SALM', 'SAN', 'SAND', 'SB', 'SBBP', 'SBS', 'SCKT', 'SCOR', 'SCYX', 'SD', 'SDPI', 'SEAC', 'SEEL', 'SELB', 'SENS', 'SESN', 'SFET', 'SFL', 'SFT', 'SGBX', 'SGLB', 'SGOC', 'SHIP', 'SID', 'SIEN', 'SIF', 'SIFY', 'SILV', 'SINO', 'SINT', 'SIOX', 'SIRI', 'SLCA', 'SLDB', 'SLGG', 'SLRX', 'SLS', 'SM', 'SMFG', 'SMSI', 'SMTS', 'SMTX', 'SNCA', 'SNCR', 'SND', 'SNDE', 'SNDL', 'SNES', 'SNGX', 'SNMP', 'SNOA', 'SNR', 'SNSS', 'SOI', 'SOLO', 'SONM', 'SONN', 'SOS', 'SPCB', 'SPPI', 'SQFT', 'SQNS', 'SREV', 'SRGA', 'SSL', 'STAF', 'STCN', 'STON', 'STSA', 'SUP', 'SUPV', 'SVM', 'SVRA', 'SWN', 'SXC', 'SXTC', 'SYBX', 'SYN', 'SYPR', 'TACO', 'TALO', 'TANH', 'TAOP', 'TAST', 'TAT', 'TATT', 'TBLT', 'TCCO', 'TCDA', 'TCON', 'TEDU', 'TEF', 'TELL', 'TENX', 'TEO', 'TGA', 'TGB', 'TGC', 'THM', 'THMO', 'TK', 'TKAT', 'TKC', 'TLGT', 'TLMD', 'TLSA', 'TLYS', 'TMBR', 'TMDI', 'TMQ', 'TMST', 'TNAV', 'TNXP', 'TOPS', 'TOUR', 'TPRE', 'TRCH', 'TRIB', 'TRIT', 'TRST', 'TRUE', 'TRVG', 'TRVN', 'TRX', 'TRXC', 'TTI', 'TTNP', 'TTOO', 'TUSK', 'TV', 'TWI', 'TWO', 'TXMD', 'TYME', 'UAMY', 'UBX', 'UEC', 'UEPS', 'UGP', 'UMC', 'UONE', 'UONEK', 'URG', 'USAS', 'USAT', 'USEG', 'USIO', 'USWS', 'USX', 'UTSI', 'UUUU', 'UWMC', 'UXIN', 'VBIV', 'VBLT', 'VCNX', 'VEDL', 'VEON', 'VERB', 'VERO', 'VERU', 'VET', 'VGZ', 'VHC', 'VIOT', 'VIRI', 'VISL', 'VIV', 'VIVE', 'VKTX', 'VNTR', 'VRA', 'VRAY', 'VSTM', 'VTGN', 'VTNR', 'VTVT', 'VVOS', 'VXRT', 'VYGR', 'VYNE', 'WATT', 'WEI', 'WETF', 'WIMI', 'WISA', 'WIT', 'WKEY', 'WMC', 'WORX', 'WPRT', 'WPX', 'WRAP', 'WRN', 'WSR', 'WTER', 'WTI', 'WTRH', 'WTTR', 'WVE', 'WWR', 'XAIR', 'XAN', 'XBIO', 'XCUR', 'XELA', 'XELB', 'XERS', 'XNET', 'XPL', 'XSPA', 'XXII', 'YCBD', 'YGYI', 'YJ', 'YPF', 'YRCW', 'YTRA', 'YVR', 'ZAGG', 'ZIOP', 'ZIXI', 'ZKIN', 'ZNGA', 'ZOM', 'ZSAN', 'ZVO', 'ZYNE', '']
# validate is optional but will go through your list and keep only valid symbols
t = Ticker(symbols, validate=True)
data = t.quotes
d = {k: v['longName'] for k, v in data.items()}

Ok,
here is one way to obtain what you want in about 2min.
Some tickers are bad, that's why it crashes.
Here's the code. I use joblib for threading or multiprocess since it doesn't work in my env. But, that's the spirit.
%%time
import joblib
from joblib import Parallel,delayed
WRONG_TICKERS = []
database = {}
stocks = ['AAU', 'ABEO', 'ABEV', 'ABIO', 'ABUS', 'ACCO', 'ACER', 'ACIU', 'ACOR', 'ACRX', 'ACST', 'ACTG', 'ADAP', 'ADIL', 'ADMA', 'ADMP', 'ADT', 'ADTX', 'ADXS', 'AEG', 'AEHL', 'AEHR', 'AEMD', 'AESE', 'AEY', 'AEZS', 'AFIN', 'AFMD', 'AGEN', 'AGI', 'AGRO', 'AGRX', 'AGS', 'AGTC', 'AHPI', 'AHT', 'AIHS', 'AIKI', 'AIM', 'AINC', 'AIRI', 'AIV', 'AKBA', 'AKER', 'AKTX', 'ALNA', 'ALRN', 'ALSK', 'AM', 'AMBO', 'AMC', 'AMPE', 'AMPY', 'AMRN', 'AMRS', 'AMRX', 'AMTX', 'ANCN', 'ANH', 'ANIX', 'ANPC', 'ANTE', 'ANY', 'APDN', 'APM', 'APRE', 'APRN', 'APTO', 'APTS', 'APTX', 'APWC', 'AQMS', 'AQST', 'AR', 'ARAY', 'ARC', 'ARCO', 'ARDX', 'AREC', 'ARKO', 'ARLO', 'ARLP', 'AROC', 'ARPO', 'ARTL', 'ASC', 'ASLN', 'ASM', 'ASMB', 'ASRT', 'ASTC', 'ASX', 'ATAX', 'ATHE', 'ATHX', 'ATIF', 'ATNF', 'ATNM', 'ATOS', 'ATRS', 'ATXI', 'AUMN', 'AUTO', 'AUVI', 'AUY', 'AVCO', 'AVDL', 'AVEO', 'AVGR', 'AVXL', 'AWH', 'AWX', 'AXAS', 'AXL', 'AXU', 'AYRO', 'AYTU', 'AZRX', 'BBAR', 'BBD', 'BBGI', 'BBI', 'BBIG', 'BBVA', 'BBW', 'BCDA', 'BCLI', 'BCRX', 'BCS', 'BDR', 'BDSI', 'BEST', 'BGCP', 'BGI', 'BHAT', 'BHR', 'BHTG', 'BIMI', 'BIOC', 'BIOL', 'BKCC', 'BKD', 'BKEP', 'BKYI', 'BLCM', 'BLCT', 'BLIN', 'BLRX', 'BLU', 'BMRA', 'BNED', 'BNTC', 'BORR', 'BOXL', 'BPT', 'BPTH', 'BQ', 'BREZR', 'BRFS', 'BRN', 'BRPAR', 'BRQS', 'BRY', 'BSBR', 'BSGM', 'BSM', 'BSMX', 'BTG', 'BTU', 'BVXV', 'BW', 'BWEN', 'BXRX', 'BYFC', 'CAAP', 'CAAS', 'CALA', 'CAN', 'CANF', 'CAPR', 'CARV', 'CASA', 'CASI', 'CATB', 'CBAT', 'CBAY', 'CBIO', 'CBLI', 'CCO', 'CCRC', 'CCRN', 'CDE', 'CDEV', 'CDTX', 'CDXC', 'CEI', 'CEIX', 'CEMI', 'CERC', 'CERS', 'CETX', 'CFMS', 'CGIX', 'CHEK', 'CHMA', 'CHNR', 'CHRA', 'CHS', 'CHU', 'CIDM', 'CIG', 'CIO', 'CJJD', 'CKPT', 'CLBS', 'CLIR', 'CLNC', 'CLNY', 'CLPS', 'CLRB', 'CLS', 'CLSD', 'CLSN', 'CLVR', 'CLVS', 'CLXT', 'CMCM', 'CMO', 'CMRE', 'CMRX', 'CNDT', 'CNET', 'CNFR', 'CNSL', 'CNSP', 'CNTY', 'COCP', 'COGT', 'COMS', 'CORR', 'CPG', 'CPHI', 'CPRX', 'CPSH', 'CRBP', 'CREG', 'CREX', 'CRIS', 'CRK', 'CRKN', 'CRMD', 'CRNT', 'CSCW', 'CSLT', 'CSPR', 'CTEK', 'CTIB', 'CTIC', 'CTK', 'CTMX', 'CTRM', 'CTSO', 'CTXR', 'CVE', 'CVGI', 'CWBR', 'CX', 'CXW', 'CYCC', 'CYCN', 'CYRN', 'CYTH', 'DARE', 'DBVT', 'DFFN', 'DGLY', 'DHC', 'DHT', 'DLPN', 'DNK', 'DNN', 'DNOW', 'DOGZ', 'DPW', 'DRH', 'DRRX', 'DRTT', 'DS', 'DSKE', 'DSS', 'DSSI', 'DSX', 'DTEA', 'DTSS', 'DUO', 'DVAX', 'DXF', 'DXLG', 'DYNT', 'EARS', 'EBON', 'EBR', 'ECOR', 'EDSA', 'EGY', 'EIGR', 'ELVT', 'ELYS', 'EMAN', 'EMKR', 'EMX', 'ENBL', 'ENDP', 'ENG', 'ENIA', 'ENIC', 'ENLC', 'ENSV', 'ENTX', 'ENVB', 'ENZ', 'EOLS', 'EQ', 'EQX', 'ERF', 'ERJ', 'ESGC', 'ESTE', 'ET', 'ETM', 'ETRN', 'ETTX', 'EURN', 'EVC', 'EVFM', 'EVGN', 'EVK', 'EVOK', 'EXK', 'EXPR', 'EXTR', 'EYEG', 'EYES', 'EZGO', 'EZPW', 'FAMI', 'FBIO', 'FBP', 'FENG', 'FI', 'FINV', 'FLDM', 'FLMN', 'FLNT', 'FLY', 'FORD', 'FPAY', 'FRBK', 'FRO', 'FRSX', 'FSM', 'FSP', 'FTEK', 'FTFT', 'FTK', 'FURY', 'GAU', 'GBS', 'GCI', 'GEL', 'GEN', 'GENE', 'GEO', 'GERN', 'GFI', 'GGAL', 'GGB', 'GHSI', 'GLBS', 'GLDG', 'GLG', 'GLOG', 'GLOP', 'GLUU', 'GLYC', 'GMBL', 'GMDA', 'GMLP', 'GNCA', 'GNK', 'GNLN', 'GNPX', 'GNUS', 'GNW', 'GOGL', 'GOL', 'GORO', 'GOSS', 'GOVX', 'GPL', 'GPMT', 'GPRO', 'GRIL', 'GRNQ', 'GSAT', 'GSKY', 'GSM', 'GSS', 'GSV', 'GTE', 'GTEC', 'GTT', 'GV', 'GVP', 'HAPP', 'HBM', 'HCDI', 'HCHC', 'HDSN', 'HEPA', 'HEXO', 'HGSH', 'HIL', 'HIMX', 'HJLI', 'HL', 'HLIT', 'HLX', 'HMHC', 'HMY', 'HNRG', 'HOFV', 'HOTH', 'HSTO', 'HT', 'HTBX', 'HUGE', 'HUSA', 'HUSN', 'HX', 'HYRE', 'IAG', 'IBIO', 'ICD', 'ICON', 'ID', 'IDEX', 'IDRA', 'IFMK', 'IFRX', 'IGC', 'IHT', 'IKT', 'IMAC', 'IMGN', 'IMMP', 'IMTE', 'IMV', 'INDO', 'INFI', 'ING', 'INN', 'INOD', 'INPX', 'INUV', 'IO', 'IPDN', 'IRIX', 'ISEE', 'ISIG', 'ISR', 'ITP', 'ITRM', 'ITUB', 'IVR', 'IZEA', 'JAGX', 'JE', 'JFIN', 'JFU', 'JG', 'JIH.W', 'JILL', 'JOB', 'JUPW', 'KALA', 'KBNT', 'KBSF', 'KDMN', 'KERN', 'KGC', 'KIN', 'KIQ', 'KMPH', 'KNDI', 'KODK', 'KOPN', 'KOS', 'KRKR', 'KRMD', 'KTRA', 'KUKE', 'KXIN', 'KZIA', 'LCI', 'LCTX', 'LEAF', 'LEE', 'LGHL', 'LIFE', 'LITB', 'LIVX', 'LIZI', 'LJPC', 'LKCO', 'LLIT', 'LLNW', 'LMFA', 'LMNL', 'LODE', 'LOMA', 'LPCN', 'LPTH', 'LPTX', 'LQDA', 'LSEA', 'LTBR', 'LTRPA', 'LX', 'LXRX', 'LYG', 'MACK', 'MARK', 'MBI', 'MBII', 'MBIO', 'MBRX', 'MBT', 'MCEP', 'MCF', 'MDGS', 'MDXG', 'MEIP', 'MESA', 'MESO', 'METX', 'MFA', 'MFG', 'MFGP', 'MFH', 'MGI', 'MGY', 'MHLD', 'MICT', 'MIN', 'MIND', 'MITO', 'MITT', 'MKD', 'MKGI', 'MLND', 'MLSS', 'MNKD', 'MOGO', 'MOGU', 'MOHO', 'MOSY', 'MOTS', 'MOXC', 'MPLN', 'MRC', 'MREO', 'MRIN', 'MRKR', 'MRO', 'MSN', 'MTA', 'MTC', 'MTL', 'MTNB', 'MTP', 'MTSL', 'MUFG', 'MUX', 'MVIS', 'MYSZ', 'MYT', 'NAK', 'NAKD', 'NAOV', 'NAT', 'NAVB', 'NBEV', 'NBRV', 'NBSE', 'NBY', 'NCMI', 'NCNA', 'NDRA', 'NEOS', 'NEPT', 'NERV', 'NES', 'NEW', 'NEX', 'NG', 'NGD', 'NGL', 'NH', 'NLY', 'NMCI', 'NMRK', 'NMTR', 'NNVC', 'NOK', 'NOVN', 'NR', 'NRZ', 'NSCO', 'NSPR', 'NTEC', 'NTN', 'NURO', 'NVCN', 'NVIV', 'NWG', 'NXE', 'NXTD', 'NYMT', 'OBLG', 'OBLN', 'OBSV', 'OCG', 'OCGN', 'OCSL', 'OCX', 'OEG', 'OGEN', 'OGI', 'OIBR.C', 'OII', 'OIIM', 'OIS', 'ONCT', 'ONCY', 'ONTX', 'OPGN', 'OPK', 'OPTN', 'OPTT', 'ORBC', 'ORC', 'ORMP', 'ORN', 'ORTX', 'OSMT', 'OSW', 'OTIC', 'OTLK', 'OVID', 'OXBR', 'OXLC', 'PAA', 'PAE', 'PAGP', 'PAVM', 'PAYS', 'PBF', 'PBI', 'PDSB', 'PED', 'PEI', 'PEIX', 'PFMT', 'PGEN', 'PGRE', 'PHAS', 'PHIO', 'PHUN', 'PIRS', 'PIXY', 'PLAG', 'PLG', 'PLIN', 'PLM', 'PLYA', 'PNNT', 'POAI', 'POWW', 'PPBT', 'PPR', 'PPSI', 'PPT', 'PROG', 'PRPO', 'PRQR', 'PRTK', 'PRTY', 'PSEC', 'PSTI', 'PSTV', 'PT', 'PTE', 'PTEN', 'PTMN', 'PTN', 'PULM', 'PUMP', 'PVL', 'PXLW', 'PXS', 'QD', 'QEP', 'QIWI', 'QLGN', 'QLI', 'QTNT', 'QTT', 'QUAD', 'QUOT', 'RAIL', 'RAVE', 'RBBN', 'RCON', 'RDHL', 'REED', 'REFR', 'REI', 'REPH', 'RES', 'RESN', 'RETO', 'RFP', 'RGLS', 'RGS', 'RHE', 'RIBT', 'RIG', 'RIGL', 'RKDA', 'RLH', 'RMED', 'RMTI', 'RNWK', 'RPAI', 'RPT', 'RRC', 'RRD', 'RTLR', 'RUHN', 'RWLK', 'RWT', 'RYAM', 'SALM', 'SAN', 'SAND', 'SB', 'SBBP', 'SBS', 'SCKT', 'SCOR', 'SCYX', 'SD', 'SDPI', 'SEAC', 'SEEL', 'SELB', 'SENS', 'SESN', 'SFET', 'SFL', 'SFT', 'SGBX', 'SGLB', 'SGOC', 'SHIP', 'SID', 'SIEN', 'SIF', 'SIFY', 'SILV', 'SINO', 'SINT', 'SIOX', 'SIRI', 'SLCA', 'SLDB', 'SLGG', 'SLRX', 'SLS', 'SM', 'SMFG', 'SMSI', 'SMTS', 'SMTX', 'SNCA', 'SNCR', 'SND', 'SNDE', 'SNDL', 'SNES', 'SNGX', 'SNMP', 'SNOA', 'SNR', 'SNSS', 'SOI', 'SOLO', 'SONM', 'SONN', 'SOS', 'SPCB', 'SPPI', 'SQFT', 'SQNS', 'SREV', 'SRGA', 'SSL', 'STAF', 'STCN', 'STON', 'STSA', 'SUP', 'SUPV', 'SVM', 'SVRA', 'SWN', 'SXC', 'SXTC', 'SYBX', 'SYN', 'SYPR', 'TACO', 'TALO', 'TANH', 'TAOP', 'TAST', 'TAT', 'TATT', 'TBLT', 'TCCO', 'TCDA', 'TCON', 'TEDU', 'TEF', 'TELL', 'TENX', 'TEO', 'TGA', 'TGB', 'TGC', 'THM', 'THMO', 'TK', 'TKAT', 'TKC', 'TLGT', 'TLMD', 'TLSA', 'TLYS', 'TMBR', 'TMDI', 'TMQ', 'TMST', 'TNAV', 'TNXP', 'TOPS', 'TOUR', 'TPRE', 'TRCH', 'TRIB', 'TRIT', 'TRST', 'TRUE', 'TRVG', 'TRVN', 'TRX', 'TRXC', 'TTI', 'TTNP', 'TTOO', 'TUSK', 'TV', 'TWI', 'TWO', 'TXMD', 'TYME', 'UAMY', 'UBX', 'UEC', 'UEPS', 'UGP', 'UMC', 'UONE', 'UONEK', 'URG', 'USAS', 'USAT', 'USEG', 'USIO', 'USWS', 'USX', 'UTSI', 'UUUU', 'UWMC', 'UXIN', 'VBIV', 'VBLT', 'VCNX', 'VEDL', 'VEON', 'VERB', 'VERO', 'VERU', 'VET', 'VGZ', 'VHC', 'VIOT', 'VIRI', 'VISL', 'VIV', 'VIVE', 'VKTX', 'VNTR', 'VRA', 'VRAY', 'VSTM', 'VTGN', 'VTNR', 'VTVT', 'VVOS', 'VXRT', 'VYGR', 'VYNE', 'WATT', 'WEI', 'WETF', 'WIMI', 'WISA', 'WIT', 'WKEY', 'WMC', 'WORX', 'WPRT', 'WPX', 'WRAP', 'WRN', 'WSR', 'WTER', 'WTI', 'WTRH', 'WTTR', 'WVE', 'WWR', 'XAIR', 'XAN', 'XBIO', 'XCUR', 'XELA', 'XELB', 'XERS', 'XNET', 'XPL', 'XSPA', 'XXII', 'YCBD', 'YGYI', 'YJ', 'YPF', 'YRCW', 'YTRA', 'YVR', 'ZAGG', 'ZIOP', 'ZIXI', 'ZKIN', 'ZNGA', 'ZOM', 'ZSAN', 'ZVO', 'ZYNE']
def get_name(_ticker):
try:
database[_ticker] = yf.Ticker(_ticker).info['longName']
except:
WRONG_TICKERS.append(_ticker)
pass
return(database)
number_of_cpu = joblib.cpu_count()
delayed_funcs = [delayed(get_name)(_ticker) for _ticker in stocks]
parallel_pool = Parallel(n_jobs=number_of_cpu,prefer="threads") # processes threads
parallel_pool(delayed_funcs)
OUTPUT:
Note that depending on your computer, it could be faster withe 'processes' instead of 'threads'. You have to test to know it.
Edit : When re-running the same code, everything is in 404 ERROR. Bust be a problem with yfinance
Edit 2 : It's okay again...

why does this it say in the console Process finished with exit code 0 instead of printing the 'sen' variable? [duplicate]

This question already has answers here:
How to check if type of a variable is string? [duplicate]
(22 answers)
Closed 2 years ago.
import random
import sys
def v1_debug(v1, subject):
if v1 != str and subject != str:
sys.exit()
else:
if subject == 'He' or 'She' or 'It':
for i in v1:
if i == [len(v1)+1]:
if i == 's' or 'z' or 'x' or 'o':
v1 = v1 + 'es'
elif i == 'y':
v1 = v1 - 'y' + 'ies'
elif v1[len(v1)] == 's' and v1[len(v1)+1] == 'h':
v1 = v1 + 'es'
elif v1[len(v1)] == 'c' and v1[len(v1)+1] == 'h':
v1 = v1 + 'es'
if subject == 'I' or 'You' or 'We' or 'They':
for i in v1:
if i == v1[len(v1)+1]:
v1 = v1 + 'ing'
return ''
def default_positive_form():
try:
sbj = ['He',
'She',
'It',
'I',
'You',
'We',
'They']
v1 = ['be',
'beat',
'become',
'begin',
'bend',
'bet',
'bid',
'bite',
'blow',
'break',
'bring',
'build',
'burn',
'buy',
'catch',
'choose',
'come',
'cost',
'cut',
'dig',
'dive',
'do',
'draw',
'dream',
'drive',
'drink',
'eat',
'fall',
'feel',
'fight',
'find',
'fly',
'forget',
'forgive',
'freeze',
'get',
'give',
'go',
'grow',
'hang',
'have',
'hear',
'hide',
'hit',
'hold',
'hurt',
'keep',
'know',
'lay',
'lead',
'leave',
'lend',
'let',
'lie',
'lose',
'make',
'mean',
'meet',
'pay',
'put',
'read',
'ride',
'ring',
'rise',
'run',
'say',
'see',
'sell',
'send',
'show',
'shut',
'sing',
'sit',
'sleep',
'speak',
'spend',
'stand',
'swim',
'take',
'teach',
'tear',
'tell',
'think',
'throw',
'understand',
'wake',
'wear',
'win',
'write']
sbj = random.choice(sbj)
v1 = random.choice(v1)
verb_debug = v1_debug(v1, sbj)
sen = ''
if sbj == 'I':
sen = sbj + 'am' + verb_debug
elif sbj == 'He' or 'She' or 'It':
sen = sbj + 'is' + verb_debug
elif sbj == 'You' or 'We' or 'They':
sen = sbj + 'are' + verb_debug
print(f'{sen}')
except NameError:
print('this is bullshit')
return
default_positive_form()
this is python 3.8

sen will only consist of an empty string if none of the conditions of your if/elif/elif blocks are met. Change the print line to
print(f"sen is: {sen}")
But that's not the real problem. obj != str does not check if obj is a string, it checks to see if the object is pointing to the type constant str (Thanks Charles Duffy for the comment). Instead, use the builtin function isinstance() like so:
if not isinstance(v1, str) and not isinstance(subject, str):
print("Variables are the wrong type!")
sys.exit()

Finding word context with regular expressions

I have created a function to search for the contexts of a given word(w) in a text, with left and right as parameters for flexibility in the number of words to record.
import re
def get_context (text, w, left, right):
text.insert (0, "*START*")
text.append ("*END*")
all_contexts = []
for i in range(len(text)):
if re.match(w,text[i], 0):
if i < left:
context_left = text[:i]
else:
context_left = text[i-left:i]
if len(text) < (i+right):
context_right = text[i:]
else:
context_right = text[i:(i+right+1)]
context = context_left + context_right
all_contexts.append(context)
return all_contexts
So for example if a have a text in the form of a list like this:
text = ['Python', 'is', 'dynamically', 'typed', 'language', 'Python',
'functions', 'really', 'care', 'about', 'what', 'you', 'pass', 'to',
'them', 'but', 'you', 'got', 'it', 'the', 'wrong', 'way', 'if', 'you',
'want', 'to', 'pass', 'one', 'thousand', 'arguments', 'to', 'your',
'function', 'then', 'you', 'can', 'explicitly', 'define', 'every',
'parameter', 'in', 'your', 'function', 'definition', 'and', 'your',
'function', 'will', 'be', 'automagically', 'able', 'to', 'handle',
'all', 'the', 'arguments', 'you', 'pass', 'to', 'them', 'for', 'you']
The function works fine for example:
get_context(text, "function",2,2)
[['language', 'python', 'functions', 'really', 'care'], ['to', 'your', 'function', 'then', 'you'], ['in', 'your', 'function', 'definition', 'and'], ['and', 'your', 'function', 'will', 'be']]
Now I am trying to build a dictionary with the contexts of every word in the text doing the following:
d = {}
for w in set(text):
d[w] = get_context(text,w,2,2)
But I am getting this error.
Traceback (most recent call last):
File "<pyshell#32>", line 2, in <module>
d[w] = get_context(text,w,2,2)
File "<pyshell#20>", line 9, in get_context
if re.match(w,text[i], 0):
File "/usr/lib/python3.4/re.py", line 160, in match
return _compile(pattern, flags).match(string)
File "/usr/lib/python3.4/re.py", line 294, in _compile
p = sre_compile.compile(pattern, flags)
File "/usr/lib/python3.4/sre_compile.py", line 568, in compile
p = sre_parse.parse(p, flags)
File "/usr/lib/python3.4/sre_parse.py", line 760, in parse
p = _parse_sub(source, pattern, 0)
File "/usr/lib/python3.4/sre_parse.py", line 370, in _parse_sub
itemsappend(_parse(source, state))
File "/usr/lib/python3.4/sre_parse.py", line 579, in _parse
raise error("nothing to repeat")
sre_constants.error: nothing to repeat
I don't understand this error. Can anyone help me with this?

The problem is that "*START*" and "*END*" are being interpreted as regex. Also, note that inserting "*START*" and "*END*" in text in the begging of the function will cause problem. You should do it just once.
Here is a complete version of the working code:
import re
def get_context(text, w, left, right):
all_contexts = []
for i in range(len(text)):
if re.match(w,text[i], 0):
if i < left:
context_left = text[:i]
else:
context_left = text[i-left:i]
if len(text) < (i+right):
context_right = text[i:]
else:
context_right = text[i:(i+right+1)]
context = context_left + context_right
all_contexts.append(context)
return all_contexts
text = ['Python', 'is', 'dynamically', 'typed', 'language',
'Python', 'functions', 'really', 'care', 'about', 'what',
'you', 'pass', 'to', 'them', 'but', 'you', 'got', 'it', 'the',
'wrong', 'way', 'if', 'you', 'want', 'to', 'pass', 'one',
'thousand', 'arguments', 'to', 'your', 'function', 'then',
'you', 'can', 'explicitly', 'define', 'every', 'parameter',
'in', 'your', 'function', 'definition', 'and', 'your',
'function', 'will', 'be', 'automagically', 'able', 'to', 'handle',
'all', 'the', 'arguments', 'you', 'pass', 'to', 'them', 'for', 'you']
text.insert(0, "START")
text.append("END")
d = {}
for w in set(text):
d[w] = get_context(text,w,2,2)
Maybe you can replace re.match(w,text[i], 0) with w == text[i].

The whole thing can be re-written very succinctly follows,
text = 'Python is dynamically typed language Python functions really care about what you pass to them but you got it the wrong way if you want to pass one thousand arguments to your function then you can explicitly define every parameter in your function definition and your function will be automagically able to handle all the arguments you pass to them for you'
Keeping it a str, assuming context = 'function',
pat = re.compile(r'(\w+\s\w+\s)functions?(?=(\s\w+\s\w+))')
pat.findall(text)
[('language Python ', ' really care'),
('to your ', ' then you'),
('in your ', ' definition and'),
('and your ', ' will be')]
Now, minor customization will be needed in the regex to allow for, words like say, functional or functioning not only function or functions. But the important idea is to do away with indexing and go more functional.
Please comment if this doesn't work out for you, when you apply it in bulk.

At least one of the elements in text contains characters that are special in a regular expression. If you're just trying to find whether the word is in the string, just use str.startswith, i.e.
if text[i].startswith(w): # instead of re.match(w,text[i], 0):
But I don't understand why you are checking for that anyways, and not for equality.

Python #define equivalent

I'm developing a Hebrew python library for my toddler, who doesn't speak English yet. So far I've managed to make it work (function names and variables work fine). The problem is with 'if', 'while', 'for', etc. statements. if this were C++, for ex., I'd use
#define if אם
are there are any alternatives for #define in Python?
****EDIT*****
For now a quick and dirty solution works for me; instead of running the program I run this code:
def RunReady(Path):
source = open(Path, 'rb')
program = source.read().decode()
output = open('curr.py', 'wb')
program = program.replace('כל_עוד', 'while')
program = program.replace('עבור', 'for')
program = program.replace('אם', 'if')
program = program.replace(' ב ', ' in ')
program = program.replace('הגדר', 'def')
program = program.replace('אחרת', 'else')
program = program.replace('או', 'or')
program = program.replace('וגם', 'and')
output.write(program.encode('utf-8'))
output.close()
source.close()
import curr
current_file = 'Sapir_1.py'
RunReady(current_file)

Python 3 has 33 keywords of which only a few are used by beginners:
['False', 'None', 'True', 'and', 'as', 'assert', 'break', 'case', 'class', 'continue', 'def', 'default', 'del', 'elif', 'else', 'except', 'finally', 'for', 'from', 'global', 'if', 'import', 'in', 'is', 'lambda', 'match', 'nonlocal', 'not', 'or', 'pass', 'raise', 'return', 'try', 'while', 'with', 'yield']
Given that Python doesn't support renaming keywords it's probably easier to teach a few of these keywords along with teaching programming.

How about if you add the #define stuff then run the c preprocessor (but not the compiler) which will give you a python source.

Find a list of things (e.g. a list of rivers) using NLTK

I would like to get lists of things, for example a list of names of rivers or a list of types of animals.
NLTK looks like it might be the thing for this, but I'm not sure how to do what I want. I'd like to have a function like:
get_list_of("river")
that would return something like
["amazon", "mississippi", "thames", ...]

I would suggest looking at NLTK wordnet API, see http://www.nltk.org/howto/wordnet.html.
But after doing some digging seems like Proper Nouns (i.e. names of river are not easy to track down in wordnet)
>>> from nltk.corpus import wordnet as wn
>>> wn.synsets('river')
[Synset('river.n.01')]
>>> wn.synset('river.n.01')
Synset('river.n.01')
>>> wn.synset('river.n.01').lemma_names
['river']
>>> wn.synsets('amazon')
[Synset('amazon.n.01'), Synset('amazon.n.02'), Synset('amazon.n.03'), Synset('amazon.n.04')]
>>> wn.synset('amazon.n.01').definition
'a large strong and aggressive woman'
>>> wn.synset('amazon.n.02').definition
'(Greek mythology) one of a nation of women warriors of Scythia (who burned off the right breast in order to use a bow and arrow more effectively)'
>>> wn.synset('amazon.n.03').definition
"a major South American river; arises in the Andes and flows eastward into the South Atlantic; the world's 2nd longest river (4000 miles)"
>>> wn.synset('amazon.n.04').definition
'mainly green tropical American parrots'
As a brute force way, look for "river" in a synsets' definitions, as such:
from itertools import chain
list(chain(*[i.lemma_names for i in wn.all_synsets() if "river" in i.definition]))
[out]:
['anaclinal', 'cataclinal', 'Acheronian', 'Acherontic', 'Stygian', 'hit-and-run', 'fluvial', 'riparian', 'Lao', 'debouch', 'rejuvenate', 'drive', 'ford', 'ascend', 'plant', 'drive', 'ford', 'fording', 'drive', 'driving', 'flood_control', 'conservancy', 'road_rage', 'Aegospotami', 'Aegospotamos', 'Yalu_River', 'three-spined_stickleback', 'Gasterosteus_aculeatus', 'ten-spined_stickleback', 'Gasterosteus_pungitius', 'placoderm', 'hellbender', 'mud_puppy', 'Cryptobranchus_alleganiensis', 'plains_spadefoot', 'Scaphiopus_bombifrons', 'mud_turtle', 'cooter', 'river_cooter', 'Pseudemys_concinna', 'spiny_softshell', 'Trionyx_spiniferus', 'smooth_softshell', 'Trionyx_muticus', 'teal', 'pintail', 'pin-tailed_duck', 'Anas_acuta', 'Ancylus', 'genus_Ancylus', 'freshwater_mussel', 'freshwater_clam', 'long-clawed_prawn', 'river_prawn', 'Palaemon_australis', 'Platanistidae', 'family_Platanistidae', 'hippopotamus', 'hippo', 'river_horse', 'Hippopotamus_amphibius', 'waterbuck', 'Australian_lungfish', 'Queensland_lungfish', 'Neoceratodus_forsteri', 'alewife', 'Alosa_pseudoharengus', 'Pomolobus_pseudoharengus', 'sockeye', 'sockeye_salmon', 'red_salmon', 'blueback_salmon', 'Oncorhynchus_nerka', 'brown_trout', 'salmon_trout', 'Salmo_trutta', 'Australian_arowana', 'Dawson_River_salmon', 'saratoga', 'spotted_barramundi', 'spotted_bonytongue', 'Scleropages_leichardti', 'Australian_bonytongue', 'northern_barramundi', 'Scleropages_jardinii', 'crappie', 'striped_bass', 'striper', 'Roccus_saxatilis', 'rockfish', 'bolti', 'Tilapia_nilotica', 'Chinese_paddlefish', 'Psephurus_gladis', 'air_bag', 'Augean_stables', 'barouche', 'bend', 'curve', 'boathouse', 'box', 'box_seat', 'brassie', 'bridge', 'span', 'bridle', 'brougham', 'buggy_whip', 'cab', 'car_mirror', 'coach', 'four-in-hand', 'coach-and-four', 'cockpit', 'death_seat', 'dredge', 'dredging_bucket', 'elbow', 'flat_tip_screwdriver', 'hansom', 'hansom_cab', 'keelboat', 'Lake_Volta', 'levee', 'levee', 'L-plate', 'machine_screw', 'outfall', 'Phillips_screwdriver', 'pull-in', 'pull-up', 'river_boat', 'showboat', 'skidpan', 'spiral_ratchet_screwdriver', 'ratchet_screwdriver', 'towpath', 'towing_path', 'truck_stop', 'willowware', 'willow-pattern', 'woodscrew', 'Copehan', 'Volgaic', 'horn', 'rip', 'riptide', 'tide_rip', 'crosscurrent', 'countercurrent', 'crappie', 'red_salmon', 'sockeye', 'sockeye_salmon', 'logjam', 'Teamsters_Union', 'car_pool', 'conservancy', 'headwater', 'river_basin', 'basin', 'watershed', 'drainage_basin', 'catchment_area', 'catchment_basin', 'drainage_area', 'confluence', 'meeting', 'Mammoth_Cave_National_Park', 'Zion_National_Park', 'watershed', 'water_parting', 'divide', 'Yangon', 'Rangoon', "N'Djamena", 'Ndjamena', 'Fort-Lamy', 'capital_of_Chad', 'Kinshasa', 'Leopoldville', 'Saxony', 'Sachsen', 'Saxe', 'Cologne', 'Koln', 'Mannheim', 'Rhineland', 'Rheinland', 'Ruhr', 'Ruhr_Valley', 'West_Bank', 'Pennines', 'Pennine_Chain', 'Ottawa', 'Canadian_capital', 'capital_of_Canada', 'Antwerpen', 'Antwerp', 'Anvers', 'Orleans', 'Rhone-Alpes', 'Friesland', 'Timbuktu', 'Bydgoszcz', 'Bromberg', 'Novosibirsk', 'Tbilisi', 'Tiflis', 'capital_of_Georgia', 'Toledo', 'Selma', 'Denver', 'Mile-High_City', 'capital_of_Colorado', 'Hartford', 'capital_of_Connecticut', 'Savannah', 'Topeka', 'capital_of_Kansas', 'Louisville', 'New_Orleans', 'Detroit', 'Motor_City', 'Motown', 'Minneapolis', 'Saint_Paul', 'St._Paul', 'capital_of_Minnesota', 'Jefferson_City', 'capital_of_Missouri', 'Saint_Louis', 'St._Louis', 'Gateway_to_the_West', 'Billings', 'Great_Falls', 'Omaha', 'Concord', 'capital_of_New_Hampshire', 'Manchester', 'Trenton', 'capital_of_New_Jersey', 'Albuquerque', 'New_Netherland', 'Albany', 'capital_of_New_York', 'Erie_Canal', 'New_York', 'New_York_City', 'Greater_New_York', 'West_Point', 'Niagara_Falls', 'Schenectady', 'Bismarck', 'capital_of_North_Dakota', 'Fargo', 'Cincinnati', 'Tulsa', 'Chester', 'Philadelphia', 'City_of_Brotherly_Love', 'Pierre', 'capital_of_South_Dakota', 'Mount_Vernon', 'Charleston', 'capital_of_West_Virginia', 'Huntington', 'Morgantown', 'Parkersburg', 'Wheeling', 'Casper', 'Ciudad_Bolivar', 'Aare', 'Aar', 'Aare_River', 'Acheron', 'River_Acheron', 'Adige', 'River_Adige', 'Aire', 'River_Aire', 'Aire_River', 'Alabama', 'Alabama_River', 'Allegheny', 'Allegheny_River', 'Amazon', 'Amazon_River', 'Amur', 'Amur_River', 'Heilong_Jiang', 'Heilong', 'Angara', 'Angara_River', 'Tunguska', 'Upper_Tunguska', 'Apalachicola', 'Apalachicola_River', 'Araguaia', 'Araguaia_River', 'Araguaya', 'Araguaya_River', 'Aras', 'Araxes', 'Arauca', 'Argun', 'Argun_River', 'Ergun_He', 'Arkansas', 'Arkansas_River', 'Arno', 'Arno_River', 'River_Arno', 'Avon', 'River_Avon', 'Upper_Avon', 'Upper_Avon_River', 'Avon', 'River_Avon', 'bar', 'Bighorn', 'Bighorn_River', 'Big_Sioux_River', 'billabong', 'bluff', 'body_of_water', 'water', 'bottomland', 'bottom', 'Brahmaputra', 'Brahmaputra_River', 'branch', 'Brazos', 'Brazos_River', 'brook', 'creek', 'Caloosahatchee', 'Caloosahatchee_River', 'Cam', 'River_Cam', 'Cam_River', 'Canadian', 'Canadian_River', 'canyon', 'canon', 'Cape_Fear_River', 'channel', 'Chao_Phraya', 'Charles', 'Charles_River', 'Chattahoochee', 'Chattahoochee_River', 'Cimarron', 'Cimarron_River', 'Clinch_River', 'Clyde', 'Cocytus', 'River_Cocytus', 'Colorado', 'Colorado_River', 'Colorado', 'Colorado_River', 'Columbia', 'Columbia_River', 'Congo', 'Congo_River', 'Zaire_River', 'Connecticut', 'Connecticut_River', 'Coosa', 'Coosa_River', 'Cumberland', 'Cumberland_River', 'dale', 'Danube', 'Danube_River', 'Danau', 'Darling', 'Darling_River', 'Delaware', 'Delaware_River', 'delta', 'Demerara', 'Detroit_River', 'distributary', 'Dnieper', 'Dnieper_River', 'Don', 'Don_River', 'Ebro', 'Ebro_River', 'Elbe', 'Elbe_River', 'Elizabeth_River', 'estuary', 'Euphrates', 'Euphrates_River', 'Flint', 'Flint_River', 'floodplain', 'flood_plain', 'Forth', 'Forth_River', 'Fox_River', 'Ganges', 'Ganges_River', 'Gan_Jiang', 'Kan_River', 'Garonne', 'Garonne_River', 'Gila', 'Gila_River', 'gorge', 'Grand_River', 'Green', 'Green_River', 'headstream', 'Housatonic', 'Housatonic_River', 'Huang_He', 'Hwang_Ho', 'Yellow_River', 'Hudson', 'Hudson_River', 'IJssel', 'IJssel_river', 'Illinois_River', 'Indigirka', 'Indigirka_River', 'Indus', 'Indus_River', 'Irrawaddy', 'Irrawaddy_River', 'Irtish', 'Irtish_River', 'Irtysh', 'Irtysh_River', 'Isere', 'Isere_River', 'James', 'James_River', 'James', 'James_River', 'Jordan', 'Jordan_River', 'Kansas', 'Kansas_River', 'Kaw_River', 'Kasai', 'Kasai_River', 'River_Kasai', 'Kissimmee', 'Kissimmee_River', 'Klamath', 'Klamath_River', 'Kura', 'Kura_River', 'Lake_Chad', 'Chad', 'Lehigh_River', 'Lena', 'Lena_River', 'Lethe', 'River_Lethe', 'liman', 'Limpopo', 'Crocodile_River', 'Little_Bighorn', 'Little_Bighorn_River', 'Little_Horn', 'Little_Missouri', 'Little_Missouri_River', 'Little_Sioux_River', 'Little_Wabash', 'Little_Wabash_River', 'Loire', 'Loire_River', 'Mackenzie', 'Mackenzie_River', 'Madeira', 'Madeira_River', 'Magdalena', 'Magdalena_River', 'meander', 'Mekong', 'Mekong_River', 'Merrimack', 'Merrimack_River', 'Meuse', 'Meuse_River', 'Milk', 'Milk_River', 'Mississippi', 'Mississippi_River', 'Missouri', 'Missouri_River', 'Mobile', 'Mobile_River', 'Mohawk_River', 'Monongahela', 'Monongahela_River', 'Moreau_River', 'Murray', 'Murray_River', 'Murrumbidgee', 'Murrumbidgee_River', 'Namoi', 'Namoi_River', 'Nan', 'Nan_River', 'Neckar', 'Neckar_River', 'Neosho', 'Neosho_River', 'Neva', 'Neva_River', 'New_River', 'Niagara', 'Niagara_River', 'Niger', 'Niger_River', 'Nile', 'Nile_River', 'North_Platte', 'North_Platte_River', 'Ob', 'Ob_River', 'Oder', 'Oder_River', 'Ohio', 'Ohio_River', 'Orange', 'Orange_River', 'Orinoco', 'Orinoco_River', 'Osage', 'Osage_River', 'Outaouais', 'Ottawa', 'Ottawa_river', 'Ouachita', 'Ouachita_River', 'Ouse', 'Ouse_River', 'oxbow', 'oxbow_lake', 'Parana', 'Parana_River', 'Parnaiba', 'Parnahiba', 'Pearl_River', 'Pee_Dee', 'Pee_Dee_River', 'Penobscot', 'Penobscot_River', 'Ping', 'Ping_River', 'Platte', 'Platte_River', 'Po', 'Po_River', 'Potomac', 'Potomac_River', 'Purus', 'Purus_River', 'rapid', 'Rappahannock', 'Rappahannock_River', 'Rhine', 'Rhine_River', 'Rhein', 'Rhone', 'Rhone_River', 'Rio_Grande', 'Rio_Bravo', 'riparian_forest', 'riverbank', 'riverside', 'riverbed', 'river_bottom', 'river_boulder', 'Russian_River', 'Saale', 'Saale_River', 'Sabine', 'Sabine_River', 'Sacramento_River', 'Saint_John', 'Saint_John_River', 'St._John', 'St._John_River', 'Saint_Johns', 'Saint_Johns_River', 'St._Johns', 'St._Johns_River', 'Saint_Lawrence', 'Saint_Lawrence_River', 'St._Lawrence', 'St._Lawrence_River', 'Sambre', 'Sambre_River', 'sandbank', 'San_Joaquin_River', 'Sao_Francisco', 'Saone', 'Saone_River', 'Savannah', 'Savannah_River', 'Scheldt', 'Scheldt_River', 'Seine', 'Seine_River', 'Severn', 'River_Severn', 'Severn_River', 'Severn', 'Severn_River', 'Seyhan', 'Seyhan_River', 'Shari', 'Shari_River', 'Chari', 'Chari_River', 'Shenandoah_River', 'Styx', 'River_Styx', 'Sun_River', 'Suriname_River', 'Surinam_River', 'Susquehanna', 'Susquehanna_River', 'Tagus', 'Tagus_River', 'Tallapoosa', 'Tallapoosa_River', 'Tennessee', 'Tennessee_River', 'Thames', 'River_Thames', 'Thames_River', 'Tiber', 'Tevere', 'Tigris', 'Tigris_River', 'Tocantins', 'Tocantins_River', 'Tombigbee', 'Tombigbee_River', 'Trent', 'River_Trent', 'Trent_River', 'Trinity_River', 'Tunguska', 'Lower_Tunguska', 'Tunguska', 'Stony_Tunguska', 'Tyne', 'River_Tyne', 'Tyne_River', 'Urubupunga', 'Urubupunga_Falls', 'Uruguay_River', 'valley', 'vale', 'Vetluga', 'Vetluga_River', 'Vistula', 'Vistula_River', 'Volga', 'Volga_River', 'Volkhov', 'Volkhov_River', 'Volta', 'waterfall', 'falls', 'water_system', 'Weser', 'Weser_River', 'Willamette', 'Willamette_River', 'Yalu', 'Yalu_River', 'Chang_Jiang', 'Changjiang', 'Chang', 'Yangtze', 'Yangtze_River', 'Yangtze_Kiang', 'Yazoo', 'Yazoo_River', 'Yenisei', 'Yenisei_River', 'Yenisey', 'Yenisey_River', 'Yukon', 'Yukon_River', 'Zambezi', 'Zambezi_River', 'Zhu_Jiang', 'Canton_River', 'Chu_Kiang', 'Pearl_River', 'Charon', 'naiad', 'Achilles', 'finisher', 'Algonkian', 'Algonkin', 'Arikara', 'Aricara', 'Chinook', 'Conoy', 'Halchidhoma', 'Hidatsa', 'Gros_Ventre', 'Kansa', 'Kansas', 'Karok', 'Maidu', 'Maricopa', 'Missouri', 'Mohave', 'Mojave', 'Ofo', 'Omaha', 'Maha', 'Osage', 'Oto', 'Otoe', 'Pamlico', 'Ponca', 'Ponka', 'Quapaw', 'Shahaptian', 'Sahaptin', 'Sahaptino', 'Shawnee', 'Tsimshian', 'Walapai', 'Hualapai', 'Hualpai', 'Yeniseian', 'Yakut', 'charioteer', 'driver', 'honker', 'lasher', 'mahout', 'nondriver', 'road_hog', 'roadhog', 'speeder', 'speed_demon', 'tailgater', 'teamster', 'test_driver', 'wagoner', 'waggoner', 'Cartier', 'Jacques_Cartier', 'Oldfield', 'Barney_Oldfield', 'Berna_Eli_Oldfield', 'debacle', 'bald_cypress', 'swamp_cypress', 'pond_bald_cypress', 'southern_cypress', 'Taxodium_distichum', 'Montezuma_cypress', 'Mexican_swamp_cypress', 'Taxodium_mucronatum', 'pistia', 'water_lettuce', 'water_cabbage', 'Pistia_stratiotes', 'Pistia_stratoites', 'great_yellowcress', 'Rorippa_amphibia', 'Nasturtium_amphibium', 'giant_reed', 'Arundo_donax', 'Phragmites', 'genus_Phragmites', 'black_birch', 'river_birch', 'red_birch', 'Betula_nigra', 'river_red_gum', 'river_gum', 'Eucalyptus_camaldulensis', 'Eucalyptus_rostrata', 'false_indigo', 'bastard_indigo', 'Amorpha_fruticosa', 'thermal_pollution', 'water_pollution', 'alluvial_soil', 'Senegal_gum', 'silt']
But it seems like there are still some noise from the "brute force" method. Let's try to assume that if it is the name of the river it should start with an uppercase, so let's try:
list(chain(*[ [j for j in i.lemma_names if j[0].isupper()] for i in wn.all_synsets() if "river" in i.definition]))
[out]:
['Acheronian', 'Acherontic', 'Stygian', 'Lao', 'Aegospotami', 'Aegospotamos', 'Yalu_River', 'Gasterosteus_aculeatus', 'Gasterosteus_pungitius', 'Cryptobranchus_alleganiensis', 'Scaphiopus_bombifrons', 'Pseudemys_concinna', 'Trionyx_spiniferus', 'Trionyx_muticus', 'Anas_acuta', 'Ancylus', 'Palaemon_australis', 'Platanistidae', 'Hippopotamus_amphibius', 'Australian_lungfish', 'Queensland_lungfish', 'Neoceratodus_forsteri', 'Alosa_pseudoharengus', 'Pomolobus_pseudoharengus', 'Oncorhynchus_nerka', 'Salmo_trutta', 'Australian_arowana', 'Dawson_River_salmon', 'Scleropages_leichardti', 'Australian_bonytongue', 'Scleropages_jardinii', 'Roccus_saxatilis', 'Tilapia_nilotica', 'Chinese_paddlefish', 'Psephurus_gladis', 'Augean_stables', 'Lake_Volta', 'L-plate', 'Phillips_screwdriver', 'Copehan', 'Volgaic', 'Teamsters_Union', 'Mammoth_Cave_National_Park', 'Zion_National_Park', 'Yangon', 'Rangoon', "N'Djamena", 'Ndjamena', 'Fort-Lamy', 'Kinshasa', 'Leopoldville', 'Saxony', 'Sachsen', 'Saxe', 'Cologne', 'Koln', 'Mannheim', 'Rhineland', 'Rheinland', 'Ruhr', 'Ruhr_Valley', 'West_Bank', 'Pennines', 'Pennine_Chain', 'Ottawa', 'Canadian_capital', 'Antwerpen', 'Antwerp', 'Anvers', 'Orleans', 'Rhone-Alpes', 'Friesland', 'Timbuktu', 'Bydgoszcz', 'Bromberg', 'Novosibirsk', 'Tbilisi', 'Tiflis', 'Toledo', 'Selma', 'Denver', 'Mile-High_City', 'Hartford', 'Savannah', 'Topeka', 'Louisville', 'New_Orleans', 'Detroit', 'Motor_City', 'Motown', 'Minneapolis', 'Saint_Paul', 'St._Paul', 'Jefferson_City', 'Saint_Louis', 'St._Louis', 'Gateway_to_the_West', 'Billings', 'Great_Falls', 'Omaha', 'Concord', 'Manchester', 'Trenton', 'Albuquerque', 'New_Netherland', 'Albany', 'Erie_Canal', 'New_York', 'New_York_City', 'Greater_New_York', 'West_Point', 'Niagara_Falls', 'Schenectady', 'Bismarck', 'Fargo', 'Cincinnati', 'Tulsa', 'Chester', 'Philadelphia', 'City_of_Brotherly_Love', 'Pierre', 'Mount_Vernon', 'Charleston', 'Huntington', 'Morgantown', 'Parkersburg', 'Wheeling', 'Casper', 'Ciudad_Bolivar', 'Aare', 'Aar', 'Aare_River', 'Acheron', 'River_Acheron', 'Adige', 'River_Adige', 'Aire', 'River_Aire', 'Aire_River', 'Alabama', 'Alabama_River', 'Allegheny', 'Allegheny_River', 'Amazon', 'Amazon_River', 'Amur', 'Amur_River', 'Heilong_Jiang', 'Heilong', 'Angara', 'Angara_River', 'Tunguska', 'Upper_Tunguska', 'Apalachicola', 'Apalachicola_River', 'Araguaia', 'Araguaia_River', 'Araguaya', 'Araguaya_River', 'Aras', 'Araxes', 'Arauca', 'Argun', 'Argun_River', 'Ergun_He', 'Arkansas', 'Arkansas_River', 'Arno', 'Arno_River', 'River_Arno', 'Avon', 'River_Avon', 'Upper_Avon', 'Upper_Avon_River', 'Avon', 'River_Avon', 'Bighorn', 'Bighorn_River', 'Big_Sioux_River', 'Brahmaputra', 'Brahmaputra_River', 'Brazos', 'Brazos_River', 'Caloosahatchee', 'Caloosahatchee_River', 'Cam', 'River_Cam', 'Cam_River', 'Canadian', 'Canadian_River', 'Cape_Fear_River', 'Chao_Phraya', 'Charles', 'Charles_River', 'Chattahoochee', 'Chattahoochee_River', 'Cimarron', 'Cimarron_River', 'Clinch_River', 'Clyde', 'Cocytus', 'River_Cocytus', 'Colorado', 'Colorado_River', 'Colorado', 'Colorado_River', 'Columbia', 'Columbia_River', 'Congo', 'Congo_River', 'Zaire_River', 'Connecticut', 'Connecticut_River', 'Coosa', 'Coosa_River', 'Cumberland', 'Cumberland_River', 'Danube', 'Danube_River', 'Danau', 'Darling', 'Darling_River', 'Delaware', 'Delaware_River', 'Demerara', 'Detroit_River', 'Dnieper', 'Dnieper_River', 'Don', 'Don_River', 'Ebro', 'Ebro_River', 'Elbe', 'Elbe_River', 'Elizabeth_River', 'Euphrates', 'Euphrates_River', 'Flint', 'Flint_River', 'Forth', 'Forth_River', 'Fox_River', 'Ganges', 'Ganges_River', 'Gan_Jiang', 'Kan_River', 'Garonne', 'Garonne_River', 'Gila', 'Gila_River', 'Grand_River', 'Green', 'Green_River', 'Housatonic', 'Housatonic_River', 'Huang_He', 'Hwang_Ho', 'Yellow_River', 'Hudson', 'Hudson_River', 'IJssel', 'IJssel_river', 'Illinois_River', 'Indigirka', 'Indigirka_River', 'Indus', 'Indus_River', 'Irrawaddy', 'Irrawaddy_River', 'Irtish', 'Irtish_River', 'Irtysh', 'Irtysh_River', 'Isere', 'Isere_River', 'James', 'James_River', 'James', 'James_River', 'Jordan', 'Jordan_River', 'Kansas', 'Kansas_River', 'Kaw_River', 'Kasai', 'Kasai_River', 'River_Kasai', 'Kissimmee', 'Kissimmee_River', 'Klamath', 'Klamath_River', 'Kura', 'Kura_River', 'Lake_Chad', 'Chad', 'Lehigh_River', 'Lena', 'Lena_River', 'Lethe', 'River_Lethe', 'Limpopo', 'Crocodile_River', 'Little_Bighorn', 'Little_Bighorn_River', 'Little_Horn', 'Little_Missouri', 'Little_Missouri_River', 'Little_Sioux_River', 'Little_Wabash', 'Little_Wabash_River', 'Loire', 'Loire_River', 'Mackenzie', 'Mackenzie_River', 'Madeira', 'Madeira_River', 'Magdalena', 'Magdalena_River', 'Mekong', 'Mekong_River', 'Merrimack', 'Merrimack_River', 'Meuse', 'Meuse_River', 'Milk', 'Milk_River', 'Mississippi', 'Mississippi_River', 'Missouri', 'Missouri_River', 'Mobile', 'Mobile_River', 'Mohawk_River', 'Monongahela', 'Monongahela_River', 'Moreau_River', 'Murray', 'Murray_River', 'Murrumbidgee', 'Murrumbidgee_River', 'Namoi', 'Namoi_River', 'Nan', 'Nan_River', 'Neckar', 'Neckar_River', 'Neosho', 'Neosho_River', 'Neva', 'Neva_River', 'New_River', 'Niagara', 'Niagara_River', 'Niger', 'Niger_River', 'Nile', 'Nile_River', 'North_Platte', 'North_Platte_River', 'Ob', 'Ob_River', 'Oder', 'Oder_River', 'Ohio', 'Ohio_River', 'Orange', 'Orange_River', 'Orinoco', 'Orinoco_River', 'Osage', 'Osage_River', 'Outaouais', 'Ottawa', 'Ottawa_river', 'Ouachita', 'Ouachita_River', 'Ouse', 'Ouse_River', 'Parana', 'Parana_River', 'Parnaiba', 'Parnahiba', 'Pearl_River', 'Pee_Dee', 'Pee_Dee_River', 'Penobscot', 'Penobscot_River', 'Ping', 'Ping_River', 'Platte', 'Platte_River', 'Po', 'Po_River', 'Potomac', 'Potomac_River', 'Purus', 'Purus_River', 'Rappahannock', 'Rappahannock_River', 'Rhine', 'Rhine_River', 'Rhein', 'Rhone', 'Rhone_River', 'Rio_Grande', 'Rio_Bravo', 'Russian_River', 'Saale', 'Saale_River', 'Sabine', 'Sabine_River', 'Sacramento_River', 'Saint_John', 'Saint_John_River', 'St._John', 'St._John_River', 'Saint_Johns', 'Saint_Johns_River', 'St._Johns', 'St._Johns_River', 'Saint_Lawrence', 'Saint_Lawrence_River', 'St._Lawrence', 'St._Lawrence_River', 'Sambre', 'Sambre_River', 'San_Joaquin_River', 'Sao_Francisco', 'Saone', 'Saone_River', 'Savannah', 'Savannah_River', 'Scheldt', 'Scheldt_River', 'Seine', 'Seine_River', 'Severn', 'River_Severn', 'Severn_River', 'Severn', 'Severn_River', 'Seyhan', 'Seyhan_River', 'Shari', 'Shari_River', 'Chari', 'Chari_River', 'Shenandoah_River', 'Styx', 'River_Styx', 'Sun_River', 'Suriname_River', 'Surinam_River', 'Susquehanna', 'Susquehanna_River', 'Tagus', 'Tagus_River', 'Tallapoosa', 'Tallapoosa_River', 'Tennessee', 'Tennessee_River', 'Thames', 'River_Thames', 'Thames_River', 'Tiber', 'Tevere', 'Tigris', 'Tigris_River', 'Tocantins', 'Tocantins_River', 'Tombigbee', 'Tombigbee_River', 'Trent', 'River_Trent', 'Trent_River', 'Trinity_River', 'Tunguska', 'Lower_Tunguska', 'Tunguska', 'Stony_Tunguska', 'Tyne', 'River_Tyne', 'Tyne_River', 'Urubupunga', 'Urubupunga_Falls', 'Uruguay_River', 'Vetluga', 'Vetluga_River', 'Vistula', 'Vistula_River', 'Volga', 'Volga_River', 'Volkhov', 'Volkhov_River', 'Volta', 'Weser', 'Weser_River', 'Willamette', 'Willamette_River', 'Yalu', 'Yalu_River', 'Chang_Jiang', 'Changjiang', 'Chang', 'Yangtze', 'Yangtze_River', 'Yangtze_Kiang', 'Yazoo', 'Yazoo_River', 'Yenisei', 'Yenisei_River', 'Yenisey', 'Yenisey_River', 'Yukon', 'Yukon_River', 'Zambezi', 'Zambezi_River', 'Zhu_Jiang', 'Canton_River', 'Chu_Kiang', 'Pearl_River', 'Charon', 'Achilles', 'Algonkian', 'Algonkin', 'Arikara', 'Aricara', 'Chinook', 'Conoy', 'Halchidhoma', 'Hidatsa', 'Gros_Ventre', 'Kansa', 'Kansas', 'Karok', 'Maidu', 'Maricopa', 'Missouri', 'Mohave', 'Mojave', 'Ofo', 'Omaha', 'Maha', 'Osage', 'Oto', 'Otoe', 'Pamlico', 'Ponca', 'Ponka', 'Quapaw', 'Shahaptian', 'Sahaptin', 'Sahaptino', 'Shawnee', 'Tsimshian', 'Walapai', 'Hualapai', 'Hualpai', 'Yeniseian', 'Yakut', 'Cartier', 'Jacques_Cartier', 'Oldfield', 'Barney_Oldfield', 'Berna_Eli_Oldfield', 'Taxodium_distichum', 'Montezuma_cypress', 'Mexican_swamp_cypress', 'Taxodium_mucronatum', 'Pistia_stratiotes', 'Pistia_stratoites', 'Rorippa_amphibia', 'Nasturtium_amphibium', 'Arundo_donax', 'Phragmites', 'Betula_nigra', 'Eucalyptus_camaldulensis', 'Eucalyptus_rostrata', 'Amorpha_fruticosa', 'Senegal_gum']
Let's go crazy and say that only if the word "River" appears in the lemma, it is a river:
>>> list(chain(*[ [j for j in i.lemma_names if j[0].isupper() and "River" in j] for i in wn.all_synsets() if "river" in i.definition]))
[out]:
['Yalu_River', 'Dawson_River_salmon', 'Aare_River', 'River_Acheron', 'River_Adige', 'River_Aire', 'Aire_River', 'Alabama_River', 'Allegheny_River', 'Amazon_River', 'Amur_River', 'Angara_River', 'Apalachicola_River', 'Araguaia_River', 'Araguaya_River', 'Argun_River', 'Arkansas_River', 'Arno_River', 'River_Arno', 'River_Avon', 'Upper_Avon_River', 'River_Avon', 'Bighorn_River', 'Big_Sioux_River', 'Brahmaputra_River', 'Brazos_River', 'Caloosahatchee_River', 'River_Cam', 'Cam_River', 'Canadian_River', 'Cape_Fear_River', 'Charles_River', 'Chattahoochee_River', 'Cimarron_River', 'Clinch_River', 'River_Cocytus', 'Colorado_River', 'Colorado_River', 'Columbia_River', 'Congo_River', 'Zaire_River', 'Connecticut_River', 'Coosa_River', 'Cumberland_River', 'Danube_River', 'Darling_River', 'Delaware_River', 'Detroit_River', 'Dnieper_River', 'Don_River', 'Ebro_River', 'Elbe_River', 'Elizabeth_River', 'Euphrates_River', 'Flint_River', 'Forth_River', 'Fox_River', 'Ganges_River', 'Kan_River', 'Garonne_River', 'Gila_River', 'Grand_River', 'Green_River', 'Housatonic_River', 'Yellow_River', 'Hudson_River', 'Illinois_River', 'Indigirka_River', 'Indus_River', 'Irrawaddy_River', 'Irtish_River', 'Irtysh_River', 'Isere_River', 'James_River', 'James_River', 'Jordan_River', 'Kansas_River', 'Kaw_River', 'Kasai_River', 'River_Kasai', 'Kissimmee_River', 'Klamath_River', 'Kura_River', 'Lehigh_River', 'Lena_River', 'River_Lethe', 'Crocodile_River', 'Little_Bighorn_River', 'Little_Missouri_River', 'Little_Sioux_River', 'Little_Wabash_River', 'Loire_River', 'Mackenzie_River', 'Madeira_River', 'Magdalena_River', 'Mekong_River', 'Merrimack_River', 'Meuse_River', 'Milk_River', 'Mississippi_River', 'Missouri_River', 'Mobile_River', 'Mohawk_River', 'Monongahela_River', 'Moreau_River', 'Murray_River', 'Murrumbidgee_River', 'Namoi_River', 'Nan_River', 'Neckar_River', 'Neosho_River', 'Neva_River', 'New_River', 'Niagara_River', 'Niger_River', 'Nile_River', 'North_Platte_River', 'Ob_River', 'Oder_River', 'Ohio_River', 'Orange_River', 'Orinoco_River', 'Osage_River', 'Ouachita_River', 'Ouse_River', 'Parana_River', 'Pearl_River', 'Pee_Dee_River', 'Penobscot_River', 'Ping_River', 'Platte_River', 'Po_River', 'Potomac_River', 'Purus_River', 'Rappahannock_River', 'Rhine_River', 'Rhone_River', 'Russian_River', 'Saale_River', 'Sabine_River', 'Sacramento_River', 'Saint_John_River', 'St._John_River', 'Saint_Johns_River', 'St._Johns_River', 'Saint_Lawrence_River', 'St._Lawrence_River', 'Sambre_River', 'San_Joaquin_River', 'Saone_River', 'Savannah_River', 'Scheldt_River', 'Seine_River', 'River_Severn', 'Severn_River', 'Severn_River', 'Seyhan_River', 'Shari_River', 'Chari_River', 'Shenandoah_River', 'River_Styx', 'Sun_River', 'Suriname_River', 'Surinam_River', 'Susquehanna_River', 'Tagus_River', 'Tallapoosa_River', 'Tennessee_River', 'River_Thames', 'Thames_River', 'Tigris_River', 'Tocantins_River', 'Tombigbee_River', 'River_Trent', 'Trent_River', 'Trinity_River', 'River_Tyne', 'Tyne_River', 'Uruguay_River', 'Vetluga_River', 'Vistula_River', 'Volga_River', 'Volkhov_River', 'Weser_River', 'Willamette_River', 'Yalu_River', 'Yangtze_River', 'Yazoo_River', 'Yenisei_River', 'Yenisey_River', 'Yukon_River', 'Zambezi_River', 'Canton_River', 'Pearl_River']
Much better but i think you're better off just crawling the names from http://en.wikipedia.org/wiki/Lists_of_rivers . Have fun!

To show that the solution for "river" using NLTK wordnet won't scale other entities, and also answer #tripleee's question.
If you're looking for animals, you can simply recursively get all hyponyms of animals, as such:
list(set([w for s in vehicle.closure(lambda s:s.hyponyms()) for w in s.lemma_names]))

We can use sentence tagging to get proper nouns. Filter them for required output.
Perhaps:
from nltk.tag import pos_tag
sentence = "Amazon is great river. Mississippi is awesome too."
tagged_sent = pos_tag(sentence.split())
tagged_sent will yield similar tagged out where NNP is the proper noun.
[('Amazon', 'NNP'), ('is', 'VBZ'), ('great', 'JJ'), ('river.', 'NNP'), ('Mississippi', 'NNP'), ('is', 'VBZ'), ('awesome', 'VBN'), ('too.', '-NONE-')]
propernouns = [word for word,pos in tagged_sent if pos == 'NNP']
propernouns would return
['Amazon', 'river.', 'Mississippi']
You can set categories for each and use a function to return them.

If you want to get lists of things of a specific type e.g. rivers http://dbPedia.org, http://freebase.com or http://wikidata.org are the better choice.
This dbPedia SPARQL query returns all rivers known to Wikipedia:
SELECT ?name ?description WHERE {
{?river rdf:type dbpedia-owl:River} .
?river foaf:name ?name .
?river rdfs:comment ?description .
}
ORDER BY ?name
http://bit.ly/1jc8Ip6

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