Appending the values list in dictionary - python

I have dictionary, which contains different list.
I want toc combine data in different dictionary appending values of similar keys in it.
{'mental_issue': ['stress', 'live in fear', 'stomach curl', 'wracked by stress', 'chest tighten']}
{'sexual_abuse': ['stalking'], 'mental_issue': ['abuse', 'pica']}
{}
{'verbal_abuse': ['in the face'], 'mental_issue': ['abuse']}
{}
{'mental_issue': ['abuse']}
{'mental_issue': ['abuse']}
{'mental_abuse': ['name call'], 'mental_issue': ['abuse']}
{}
{'mental_issue': ['broken jaw', 'abuse', 'heart broken']}
{'mental_issue': ['live in fear']}
{'mental_issue': ['abuse', 'trauma']}
{'mental_issue': ['nightmares', 'abuse', 'panic attack']}
{'mental_issue': ['stress']}
{'mental_issue': ['severe']}
{'mental_issue': ['nightmares', 'bad emotional state']}
{'sexual_abuse': ['rape'], 'mental_issue': ['stress', 'paranoia']}
{'mental_issue': ['fracturing both my', 'both my knees']}
{'mental_issue': ['abuse']}
{'sexual_abuse': ['rape'], 'verbal_abuse': ['kicking'], 'mental_issue': ['nightmares']}
I want to combine values of all issues like mental_abuse should have all mental aboses from all dictionary.
I wrote this logic -
sexual_abuse_list = []
d = {'sexual_abuse': ['rape'], 'verbal_abuse': ['kicking'], 'mental_issue': ['nightmares']}
if 'sexual_abuse' in d:
for item in d['sexual_abuse']:
sexual_abuse_list.append(item)
Is this correct logic? or any better logic can be written?


Using collections.defaultdict
Ex:
from collections import defaultdict
lst = [{'mental_issue': ['stress', 'live in fear', 'stomach curl', 'wracked by stress', 'chest tighten']},
{'sexual_abuse': ['stalking'], 'mental_issue': ['abuse', 'pica']},
{},
{'verbal_abuse': ['in the face'], 'mental_issue': ['abuse']},
{},
{'mental_issue': ['abuse']},
{'mental_issue': ['abuse']},
{'mental_abuse': ['name call'], 'mental_issue': ['abuse']},
{},
{'mental_issue': ['broken jaw', 'abuse', 'heart broken']},
{'mental_issue': ['live in fear']},
{'mental_issue': ['abuse', 'trauma']},
{'mental_issue': ['nightmares', 'abuse', 'panic attack']},
{'mental_issue': ['stress']},
{'mental_issue': ['severe']},
{'mental_issue': ['nightmares', 'bad emotional state']},
{'sexual_abuse': ['rape'], 'mental_issue': ['stress', 'paranoia']},
{'mental_issue': ['fracturing both my', 'both my knees']},
{'mental_issue': ['abuse']},
{'sexual_abuse': ['rape'], 'verbal_abuse': ['kicking'], 'mental_issue': ['nightmares']}]
d = defaultdict(list)
for i in lst:
for k, v in i.items():
d[k].extend(v)
print(d)
Output:
defaultdict(<type 'list'>, {'sexual_abuse': ['stalking', 'rape', 'rape'], 'verbal_abuse': ['in the face', 'kicking'], 'mental_abuse': ['name call'], 'mental_issue': ['stress', 'live in fear', 'stomach curl', 'wracked by stress', 'chest tighten', 'abuse', 'pica', 'abuse', 'abuse', 'abuse', 'abuse', 'broken jaw', 'abuse', 'heart broken', 'live in fear', 'abuse', 'trauma', 'nightmares', 'abuse', 'panic attack', 'stress', 'severe', 'nightmares', 'bad emotional state', 'stress', 'paranoia', 'fracturing both my', 'both my knees', 'abuse', 'nightmares']})

Related

Looking for pair in a list of playing cards

So I am creating a card game in python. For the part of the game I an currently working on, I need to check an array of playing cards to see if there are any pairs. A pair is considered two cards with the same value and color. For example, king of hearts and king of diamonds are a pair, but king of hearts and king of clubs aren't.
I need to return a new list with all of the pairs removed.
Suppose we have
list = ['9♠', '5♠', 'K♢', 'A♣', 'K♣', 'K♡', '2♠', 'Q♠', 'K♠', 'Q♢', 'J♠', 'A♡', '4♣', '5♣', '7♡', 'A♠', '10♣', 'Q♡', '8♡', '9♢', '10♢', 'J♡', '10♡', 'J♣', '3♡']
The result should be:
list without pairs = ['10♣', '2♠', '3♡', '4♣', '7♡', '8♡', '9♠', '9♢', 'A♣', 'A♡', 'A♠', 'J♠', 'J♡', 'J♣', 'K♢', 'K♣', 'K♡', 'K♠', 'Q♠']
I currently have this code:
import random
result=[]
for i in range(len(l)):
if '♣' in l[i]:
pass
elif '♠' in l[i]:
pass
elif '♡' in l[i]:
pass
elif '♢' in l[i]:
pass
random.shuffle(result)
return result

cards_list = [
"9♠",
"5♠",
"K♢",
"A♣",
"K♣",
"K♡",
"2♠",
"Q♠",
"K♠",
"Q♢",
"J♠",
"A♡",
"4♣",
"5♣",
"7♡",
"A♠",
"10♣",
"Q♡",
"8♡",
"9♢",
"10♢",
"J♡",
"10♡",
"J♣",
"3♡",
]
SAME_COLORS = {"♢": "♡", "♡": "♢", "♠": "♣", "♣": "♠"}
list_without_pairs = []
for card in cards_list:
if card[:-1] + SAME_COLORS[card[-1]] in cards_list:
continue
else:
list_without_pairs.append(card)
print(list_without_pairs)
Output:
['9♠', '2♠', 'Q♠', 'A♡', '4♣', '7♡', '10♣', '8♡', '9♢', 'J♡', '3♡']
This work exactly the same but could be a little bit more confuse:
list_without_pairs = [card for card in cards_list if card[:-1] + SAME_COLORS[card[-1]] not in cards_list]

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...

How to find pairs of a card pack list using python

I have the following list in python.
list = ['10♠', '10♣', '2♡', '4♠', '4♣', '5♡', '5♣', '6♡', '6♣', '7♠', '7♡', '7♢', '7♣', '8♡', '8♢', '8♣', '9♡', '9♢', '9♣', 'A♠', 'A♢', 'A♣', 'J♢', 'K♠', 'K♢', 'Q♡']
how can I remove pairs from this? As an example, if a number appears an odd number of times, the last appearance of it should be kept. All others should be removed. Including all the ones that appear an even number of times.
ex: From '9♡', '9♢', '9♣', only the '9♣' should be kept.
Can someone help me with this?
I tried to use the below code to identify indices first. But still no luck.
i = 0
while i < len(deck):
count = 0
k = len(deck[i])
pivot = i
j = i
while j < len(deck):
if deck[i][:k-1] == deck[j][:k-1]:
print(deck[i]+','+deck[j])
count+= 1
pivot = j
j+=1
if (count %2 != 0):
print('pivot:'+str(pivot))
i = pivot +1
i +=1
No need to consider symbols. Just want to remove pairs from the list.
Please provide your suggestions.

Is this what you are looking for?
from collections import defaultdict
deck = ['10♠', '10♣', '2♡', '4♠', '4♣', '5♡', '5♣', '6♡', '6♣', '7♠', '7♡', '7♢', '7♣', '8♡', '8♢', '8♣', '9♡', '9♢', '9♣', 'A♠', 'A♢', 'A♣', 'J♢', 'K♠', 'K♢', 'Q♡']
# Create a dictionary and group all the cards with same number
groups = defaultdict(list)
for card in deck:
key = card[:-1]
groups[key].append(card)
new_deck = []
for subgroup in groups.values():
# iterate through the dictionary
# If you have odd number of cards in a subgroup
# consider the last card in that subgroup
if len(subgroup)%2 == 1:
new_deck.append(subgroup[-1])
for card in new_deck:
print(card)
Output
2♡ 8♣ 9♣ A♣ J♢ Q♡
Edit: A minor simplification to the second iteration with groups.values, thanks to RoadRunner.

Group the card pairs into a collections.defaultdict, then only return the last card from uneven pairs in a new list using a list comprehension:
from collections import defaultdict
lst = ['10♠', '10♣', '2♡', '4♠', '4♣', '5♡', '5♣', '6♡', '6♣', '7♠', '7♡', '7♢', '7♣', '8♡', '8♢', '8♣', '9♡', '9♢', '9♣', 'A♠', 'A♢', 'A♣', 'J♢', 'K♠', 'K♢', 'Q♡']
cards = defaultdict(list)
for card in lst:
cards[card[:-1]].append(card)
result = [pairs[-1] for pairs in cards.values() if len(pairs) % 2]
print(result)
Output:
['2♡', '8♣', '9♣', 'A♣', 'J♢', 'Q♡']

Keeping the same order, you can use:
import re
l = ['10♠', '10♣', '2♡', '4♠', '4♣', '5♡', '5♣', '6♡', '6♣', '7♠', '7♡', '7♢', '7♣', '8♡', '8♢', '8♣', '9♡', '9♢', '9♣', 'A♠', 'A♢', 'A♣', 'J♢', 'K♠', 'K♢', 'Q♡']
nc, nl = [], [0]
for x in l:
clean = re.sub(r"[^A-Z\d]", "", x)
if clean != nl[-1]:
nl.append(clean)
nc.append(x)
else:
del nl[-1]
del nc[-1]
print(nc)
# ['2♡', '8♣', '9♣', 'A♣', 'J♢', 'Q♡']
Demo

First of all, list is a reserved keyword, you should never name your variables after reserved keywords, use lst instead of list
Now, Here is the minimal solution:
lst = ['10♠', '10♣', '2♡', '4♠', '4♣', '5♡', '5♣', '6♡', '6♣', '7♠', '7♡', '7♢', '7♣', '8♡', '8♢', '8♣', '9♡', '9♢', '9♣', 'A♠', 'A♢', 'A♣', 'J♢', 'K♠', 'K♢', 'Q♡']
dictionary = dict.fromkeys(list('A23456789JQK')+['10'])
for item in lst:
dictionary[item[:-1]] = item if dictionary[item[:-1]] is None else None
print(list(filter(None.__ne__, dictionary.values())))
output:
['A♣', '2♡', '8♣', '9♣', 'J♢', 'Q♡']

How do i replace a specific combination of integer and string?

I'm trying to replace a python list with a deck of cards with symbols.
I have tried using Pythons replace function, but i assume the best solution for this is probably based on regular expression replacement.
The desired result would be this:
"Ah" => "A♥"
"5h" => "5♥"
etc.
Currently the list features items like this:
[Player name], [Player wallet], [1st player card], [2nd player card]
This could be i.e.:
["Don Johnson", 100, "Ks", "5d"]
["Davey Jones", 100, "4c", "3h"]
Any help for this would be greatly appreciated. Thanks.
(Edited for clarification on request - Thanks for all the input so far!)

Here, we can simply use four simple expressions and make that replacement that we wish:
([AKJQ0-9]{1,2})h
([AKJQ0-9]{1,2})d
and similarly the other two.
Demo
# coding=utf8
# the above tag defines encoding for this document and is for Python 2.x compatibility
import re
regex = r"([AKJQ0-9]{1,2})h"
test_str = ("Ah\n"
"10h")
subst = "\\1♥"
# You can manually specify the number of replacements by changing the 4th argument
result = re.sub(regex, subst, test_str, 0, re.MULTILINE)
if result:
print (result)
# Note: for Python 2.7 compatibility, use ur"" to prefix the regex and u"" to prefix the test string and substitution.

If you had just a list of cards then it would probably look something like this:
cards = ['2h', '2s', '2c', '2d', '3h', '3s', '3c', '3d', '4h', '4s', '4c', '4d', '5h', '5s', '5c', '5d', '6h', '6s', '6c', '6d', '7h', '7s', '7c', '7d', '8h', '8s', '8c', '8d', '9h', '9s', '9c', '9d', '10h', '10s', '10c', '10d', 'Ah', 'As', 'Ac', 'Ad', 'Kh', 'Ks', 'Kc', 'Kd', 'Jh', 'Js', 'Jc', 'Jd', 'Qh', 'Qs', 'Qc', 'Qd']
If so then just use a dict and a comprehension:
suits = {'h': '♥', 's': '♠', 'c': '♣', 'd': '♦'}
new_cards = [''.join(rank)+suits[suit] for *rank, suit in cards]
Output for this is:
['2♥', '2♠', '2♣', '2♦', '3♥', '3♠', '3♣', '3♦', '4♥', '4♠', '4♣', '4♦', '5♥', '5♠', '5♣', '5♦', '6♥', '6♠', '6♣', '6♦', '7♥', '7♠', '7♣', '7♦', '8♥', '8♠', '8♣', '8♦', '9♥', '9♠', '9♣', '9♦', '10♥', '10♠', '10♣', '10♦', 'A♥', 'A♠', 'A♣', 'A♦', 'K♥', 'K♠', 'K♣', 'K♦', 'J♥', 'J♠', 'J♣', 'J♦', 'Q♥', 'Q♠', 'Q♣', 'Q♦']
For your solution you could define a function that corrects the card:
def fix_card(card):
suits = {'h': '♥', 's': '♠', 'c': '♣', 'd': '♦'}
*rank, suit = card
return ''.join(rank)+suits[suit]
Then just use it like this:
player = ["Don Johnson", 100, "Ks", "5d"]
player[2] = fix_card(player[2])
player[3] = fix_card(player[3])
print(player)
#["Don Johnson", 100, "K♣", "5♦"]

No, regex is not needed for a simple replacement like this. Just use str.replace:
>>> cards = ['Ah', '5h']
>>> [s.replace('h', '♥') for s in cards]
['A♥', '5♥']

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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