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How to calculate percentages from multiple columns

I want to create a table that looks like this:
So far I have a table I created to get the value counts but I need help with creating a table that calculates the total value of row 0 and 1. I'm using this dataset: https://github.com/fivethirtyeight/data/tree/master/bob-ross
Code:
ross = bobross[['Apple frame', 'Aurora borealis', 'Barn', 'Beach', 'Boat',
'Bridge', 'Building', 'Bushes', 'Cabin', 'Cactus',
'Circle frame', 'Cirrus clouds', 'Cliff', 'Clouds',
'Coniferous tree', 'Cumulus clouds', 'Decidious tree',
'Diane andre', 'Dock', 'Double oval frame', 'Farm',
'Fence', 'Fire', 'Florida frame', 'Flowers', 'Fog',
'Framed', 'Grass', 'Guest', 'Half circle frame',
'Half oval frame', 'Hills', 'Lake', 'Lakes', 'Lighthouse',
'Mill', 'Moon', 'At least one mountain', 'At least two mountains',
'Nighttime', 'Ocean', 'Oval frame', 'Palm trees', 'Path',
'Person', 'Portrait', 'Rectangle 3d frame', 'Rectangular frame',
'River or stream', 'Rocks', 'Seashell frame', 'Snow',
'Snow-covered mountain', 'Split frame', 'Steve ross',
'Man-made structure', 'Sun', 'Tomb frame', 'At least one tree',
'At least two trees', 'Triple frame', 'Waterfall', 'Waves',
'Windmill', 'Window frame', 'Winter setting', 'Wood framed']].apply(pd.Series.value_counts)
ross

IIUC,
import pandas as pd
import numpy as np
df = pd.read_csv('https://raw.githubusercontent.com/fivethirtyeight/data/master/bob-ross/elements-by-episode.csv')
dfi = df.set_index(['EPISODE', 'TITLE'])
(dfi.sum()/np.sum(dfi.to_numpy()))
Output:
APPLE_FRAME 0.000310
AURORA_BOREALIS 0.000621
BARN 0.005278
BEACH 0.008382
BOAT 0.000621
...
WAVES 0.010556
WINDMILL 0.000310
WINDOW_FRAME 0.000310
WINTER 0.021422
WOOD_FRAMED 0.000310
Length: 67, dtype: float64

find words that can be made from a string in 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'}

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()

Move item from list to another one for poker card game Python

I'm trying to make poker game in Python. In the while fuction I want to move the used cards in a separate(used cards) list. The problem is sometimes when I print the hand I can get duplicates. Something is wrong with my sorting strategy and I don't know what. Can you help me?
import random
deck = ['AS', 'KS', 'QS', 'JS', '10S', '9S', '8S', '7S', '6S', '5S', '4S', '3S', '2S',\
'AD', 'KD', 'QD', 'JD', '10D', '9D', '8D', '7D', '6D', '5D', '4D', '3D', '2D',\
'AC', 'KC', 'QC', 'JC', '10C', '9C', '8C', '7C', '6C', '5C', '4C', '3C', '2C',\
'AH', 'KH', 'QH', 'JH', '10H', '9H', '8H', '7H', '6H', '5H', '4H', '3H', '2H']
used = []
p1 = []
p2 = []
a = 0
while (a < 2):
drawn_card = random.choice(deck)
deck.append(drawn_card)
deck = [f for f in deck if f not in used]
p1.append(drawn_card)
a+=1

Well the random choice is not guaranteed to be unique, thus when you do:
drawn_card = random.choice(deck)
..
p1.append(drawn_card)
you may end up having duplicates (that explains that you some time see duplicates and some not).
Check if drawn_card is in the list first and if not, then append. That way you won't have duplicates. In code you could it like this:
if drawn_card not in p1:
p1.append(drawn_card)
Or, as Rory Daulton said:
If you are allowed, you could shuffle the entire deck, then remove consecutive items from that list.

you need to compare the random card with "p1" not with "deck":
import random
deck = ['AS', 'KS', 'QS', 'JS', '10S', '9S', '8S', '7S', '6S', '5S', '4S', '3S', '2S',\
'AD', 'KD', 'QD', 'JD', '10D', '9D', '8D', '7D', '6D', '5D', '4D', '3D', '2D',\
'AC', 'KC', 'QC', 'JC', '10C', '9C', '8C', '7C', '6C', '5C', '4C', '3C', '2C',\
'AH', 'KH', 'QH', 'JH', '10H', '9H', '8H', '7H', '6H', '5H', '4H', '3H', '2H']
used = []
p1 = []
a = 0
while (a < 2):
drawn_card = random.choice(deck)
print(drawn_card)
if drawn_card not in p1:
p1.append(drawn_card)
a += 1
continue
print (p1)

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