第1章 语言处理与Python
2024-05-01 02:24:49
1、尝试使用Python解释器作为一个计算器，输入表达式，如12/(4+1)。
>>> 12/(4+1)
22、26个字母可以组成26的10次方或者26**10个10字母长的字符串。也就是141167095653376L（结尾处的L只表示这是Python长数字格式）。100个字母长的度的字符串可能有多少个？
>>> 26**100
3142930641582938830174357788501626427282669988762475256374173175398995908420104023465432599069702289330964075081611719197835869803511992549376L3、Python乘法运算可应用于链表。当你输入[‘Monty’,’Python’]*20或者3*sent1会发生什么？
（1）
>>> ['Monty','Python']*20
['Monty', 'Python', 'Monty', 'Python', 'Monty', 'Python', 'Monty', 'Python', 'Monty', 'Python', 'Monty', 'Python', 'Monty', 'Python', 'Monty', 'Python', 'Monty', 'Python', 'Monty', 'Python', 'Monty', 'Python', 'Monty', 'Python', 'Monty', 'Python', 'Monty', 'Python', 'Monty', 'Python', 'Monty', 'Python', 'Monty', 'Python', 'Monty', 'Python', 'Monty', 'Python', 'Monty', 'Python']（2）
>>> from nltk.book import *
*** Introductory Examples for the NLTK Book ***
Loading text1, ..., text9 and sent1, ..., sent9
Type the name of the text or sentence to view it.
Type: 'texts()' or 'sents()' to list the materials.
text1: Moby Dick by Herman Melville 1851
text2: Sense and Sensibility by Jane Austen 1811
text3: The Book of Genesis
text4: Inaugural Address Corpus
text5: Chat Corpus
text6: Monty Python and the Holy Grail
text7: Wall Street Journal
text8: Personals Corpus
text9: The Man Who Was Thursday by G . K . Chesterton 1908
>>> 3*sent1
['Call', 'me', 'Ishmael', '.', 'Call', 'me', 'Ishmael', '.', 'Call', 'me', 'Ishmael', '.']4、复习1.1节关于语言计算的内容。在text2中有多少个词？有多少个不同的词？
>>> len(text2)
141576
>>> len(set(text2))
68335、比较表格1-1中幽默和言情小说的词汇多样性得分，哪一个文体中词汇更丰富？
幽默。
6、制作《理智与情感》中四个主角：Elinor,Marianne,Edward和Willoughby的分布图。
在这部小说中关于男性和女性所扮演的不同角色，你能观察到什么？你能找出一对夫妻
吗？
>>> text2
<Text: Sense and Sensibility by Jane Austen 1811>
>>> text2.dispersion_plot(["Elinor","Marianne","Edward","Willoughby"])
Elinor与Edward是夫妻。
7、查找text5中的搭配。
>>> text5.collocations()
wanna chat; PART JOIN; MODE #14-19teens; JOIN PART; PART PART;
cute.-ass MP3; MP3 player; JOIN JOIN; times .. .; ACTION watches; guys
wanna; song lasts; last night; ACTION sits; -...)...- S.M.R.; Lime
Player; Player 12%; dont know; lez gurls; long time8、思考下面的Python表达式：len(set(text4))。说明这个表达式的用途。描述在执行
此计算中涉及的两个步骤。
第1步：由text4生成词典。
第2步：计算词典中的单词量。
9、复习1.2节关于链表和字符串的内容。
a. 定义一个字符串，并且将它分配给一个变量，如my_string = ‘My String’（在
字符串中放一些更有趣的东西）。两种方法输出这个变量的内容，一种是通过简
单地输入变量的名称，然后按回车；另一种是通过使用print语句。
>>> my_string='My String'
>>> my_string
'My String'
>>> print my_string
My Stringb. 尝试使用my_string+my_string或者用它乘以一个字符串添加到它自身，
例如：my_string*3。请注意，连接在一起的字符串之间没有空格。怎样能解决
这个问题？
>>> my_string+my_string
'My StringMy String'
>>> (my_string+' ')*3
'My String My String My String '10、使用的语法my_sent=[“My”,”sent”]，定义一个词链表变量my_sent（用你
自己的词或喜欢的话）。
a. 使用’ ‘.join(my_sent)将其转换成一个字符串。
>>> ' '.join(my_sent)
'My sent'b. 使用split()在你指定的地方将字符串分割回链表。
>>> 'My sent'.split()
['My', 'sent']11、定义几个包含词链表的变量，例如phrase1，phrase2等。将它们连接在一起组
成不同的组合（使用加法运算符），最终形成完整的句子。len(phrase1+phrase2)
与len(phrase1)+len(phrase2)之间的关系是什么？
>>> phrase1 = ['I','Love','dragon']
>>> phrase2 = ['I','Love','NLP and Python']
>>> len(phrase1+phrase2)
6
>>> len(phrase1)+len(phrase2)
6相等。
12、考虑下面两个具有相同值的表达式。哪一个在NLP中更常用？为什么？
a. “Monty Python”[6:12]
b. [“Monty”,”Python”][1]
b.更常用。自然语言处理中都已单词为单位处理。
13、我们已经看到如何用词链表表示一个句子，其中每个词是一个字符序列。sent1[2][2]
代表什么意思？为什么？请用其他的索引值做实验。
>>> sent1
['Call', 'me', 'Ishmael', '.']
>>> sent1[2][2]
'h'代表在句子中第3个单词的第3个字母。
14、在变量sent3中保存的是text3的第一句话。在sent3中the的索引值是1，因为
sent3[1]的值是”the”。sent3中”the”的其他出现的索引值是多少？
>>> sent3
['In', 'the', 'beginning', 'God', 'created', 'the', 'heaven', 'and', 'the', 'earth', '.']
>>> for i in range(len(sent3)):
... if sent3[i] == "the":
... print i
...
1
5
815、复习1.4节讨论的条件语句。在聊天语料库（text5）中查找所有以字母b开头的词，按字母顺序显示出来。
>>> [w for w in set(text5) if w.startswith('b')]
[u'brought', u'brings', u'blade', u'babycakeses', u'bomb', u'busy', u'bust', u'bliss', u'blew', u'best', u'bachelorette', u'bigest', u'babies', u'bandito', u'boost', u'bloody', u'by', u'brrrrrrr', u'blowup', u'banjoes', u'bagels', u'besides', u'bitdh', u'bite', u'boyfriend', u'bright', u'beatles', u'breath', u'blah', u'being', u'buying', u'bedford', u'beautiful', u'bummer', u'blues', u'bluer', u'bumped', u'burried', u'brooklyn', u'barrel', u'beams', u'banned', u'barely', u'bloe', u'boed', u'b4', u'behave', u'be', u'bf', u'bc', u'bi', u'bj', u'baby', u'bird', u'babe', u'babi', u'balls', u'benz', u'bend', u'bothering', u'borderline', u'bother', u'babes', u'bred', u'bring', u'bedroom', u'buttons', u'btw', u'ball', u'become', u'betta', u'blowing', u'barfights', u'bike', u'bro', u'brb', u'bra', u'blueberry', u'blocking', u'beach', u'bares', u'break', u'band', u'ballin', u'beleive', u'babay', u'bied', u'brat', u'brad', u'bisexual', u'b-day', u'bunny', u'burito', u'board', u'book', u'born', u'bumber', u'bound', u'born-again', u'brothers', u'belive', u'brown', u'bikes', u'barbie', u'brain', u'blind', u'beachhhh', u'begin', u'between', u'boning', u'backup', u'bbiam', u'boing', u'bouncers', u'beckley', u'brunswick', u'broke', u'beeehave', u'bugs', u'bless', u'blank', u'base', u'birfday', u'boredom', u'both', u'battery', u'bruises', u'byeeeeeeeeeeeee', u'bonus', u'bahahahaa', u'beeeeehave', u'brakes', u'bossy', u'b/c', u'button', u'booty', u'boots', u'belongings', u'bitch', u'bears', u'blankie', u'ben', u'beg', u'bed', u'bare', u'bet', u'border', u'bases', u'bucks', u'baord', u'beats', u'bikini', u'barks', u'biatch', u'boooooooooooglyyyyyy', u'belong', u'before', u'better', u'bootay', u'bong', u'bone', u'bandsaw', u'bar', u'bay', u'bag', u'bad', u'ban', u'bak', u'balance', u'belly', u'butter', u'boinked', u'beattles', u'burp', u'builds', u'black', u'box', u'boy', u'bot', u'bow', u'boi', u'boo', u'bob', u'bites', u'basket', u'blooded', u'bein', u'butt', u'booted', u'burryed', u'behind', u'bottle', u'bread', u'buffalo', u'burger', u'bible', u'buses', u'blood', u'brady', u'bosom', u'brbbb', u'bulls', u'believe', u'b', u'boght', u'build', u'byeee', u'burned', u'bio', u'big', u'blowjob', u'biz', u'bit', u'blech', u'bacl', u'back', u'beans', u'bored', u'boys', u'blinks', u'body', u'boned', u'bones', u'bunch', u'balck', u'beuty', u'beat', u'bear', u'beam', u'busted', u'bull', u'babiess', u'brightened', u'buddyyyyyy', u'blessings', u'been', u'biggest', u'buy', u'bus', u'but', u'buh', u'bum', u'bug', u'backfrontsidewaysandallaroundtheworld', u'balad', u'booboo', u'baked', u'blue', u'beaten', u'breathe', u'bleach', u'bishes', u'bouts', u'biebsa', u'boss', u'bes', u'beer', u'beside', u'breaks', u'burns', u'beanbag', u'birthday', u'babblein', u'burps', u'boobs', u'blunt', u'betrayal', u'byes', u'bowl', u'bodies', u'bbbbbyyyyyyyeeeeeeeee', u'blow', u'byeeee', u'brass', u'basically', u'bumper', u'bout', u'brother', u'burpin', u'buff', u'babble', u'barn', u'blazed', u'biyatch', u'bwahahahahahahahahahaha', u'built', u'bouncer', u'bounced', u'butterscotch', u'bell', u'backatchya', u'bye', u'byb', u'bois', u'boring', u'brwn', u'bagel', u'bought', u'biiiatch', u'bbl', u'bbs', u'backroom', u'because', u'breeding', u'bitches', u'byeeeeeeee', u'boot', u'boom']16、在Python解释器提示符下输入表达式range(10)。再尝试range(10,20)，
range(10,20,2)和range(20,10,-2)。在后续章节中我们将看到这个内置函数的多用途。
>>> range(10)
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
>>> range(20,10,-2)
[20, 18, 16, 14, 12]
>>> range(10,20,2)
[10, 12, 14, 16, 18]17、 使用text9.index()查找词sunset的索引值。你需要将这个词作为一个参数插入到圆
括号之间。通过尝试和出错的过程中，找到完整的句子中包含这个词的切片。
>>> text9.index('sunset')
629
>>> text9[629:630]
[u'sunset']18、使用链表加法、set和sorted操作，计算句子sent1…sent8的词汇表。
>>> sent = sent1+sent2+sent3+sent4+sent5+sent6+sent7+sent8
>>> word_set = set(sent)
>>> word_li = sorted([w for w in word_set])
>>> word_li
['!', ',', '-', '.', '1', '25', '29', '61', ':', 'ARTHUR', 'Call', 'Citizens', 'Dashwood', 'Fellow', 'God', 'House', 'I', 'In', 'Ishmael', 'JOIN', 'KING', 'MALE', 'Nov.', 'PMing', 'Pierre', 'Representatives', 'SCENE', 'SEXY', 'Senate', 'Sussex', 'The', 'Vinken', 'Whoa', '[', ']', 'a', 'and', 'as', 'attrac', 'been', 'beginning', 'board', 'clop', 'created', 'director', 'discreet', 'earth', 'encounters', 'family', 'for', 'had', 'have', 'heaven', 'in', 'join', 'lady', 'lol', 'long', 'me', 'nonexecutive', 'of', 'old', 'older', 'people', 'problem', 'seeks', 'settled', 'single', 'the', 'there', 'to', 'will', 'wind', 'with', 'years']19、下面两行之间的差异是什么？哪一个的值比较大？其他文本也是同样情况吗？
>>> len(sorted(set([w.lower() for w in text1])))
17231
>>> len(sorted([w.lower() for w in set(text1)]))
19317第2种构建词典的方法可能存在重复单词，因此第1种单词数<=第2种单词数。
20、w.isupper()和not w.islower()这两个测试之间的差异是什么？
>>> 'Hello'.isupper()
False
>>> not 'Hello'.islower()
True21、写一个切片表达式提取text2中最后两个词。
>>> text2[-2::]
[u'THE', u'END']22、找出聊天语料库（text5）中所有四个字母的词。使用频率分布函数（FreqDist），
以频率从高到低显示这些词。
p22.py
#coding=gbk
from nltk import FreqDist
from nltk.book import text5
word_li = [w for w in text5 if len(w)==4]
fdist = FreqDist(word_li)
sorted_word_li = sorted(fdist.keys(),key=lambda x:fdist[x],reverse=True)
for w in sorted_word_li:print "%s\t%d; "%(w,fdist[w]),*** Introductory Examples for the NLTK Book ***
Loading text1, ..., text9 and sent1, ..., sent9
Type the name of the text or sentence to view it.
Type: 'texts()' or 'sents()' to list the materials.
text1: Moby Dick by Herman Melville 1851
text2: Sense and Sensibility by Jane Austen 1811
text3: The Book of Genesis
text4: Inaugural Address Corpus
text5: Chat Corpus
text6: Monty Python and the Holy Grail
text7: Wall Street Journal
text8: Personals Corpus
text9: The Man Who Was Thursday by G . K . Chesterton 1908
JOIN 1021; PART 1016; that 274; what 183; here 181; .... 170; have 164; like 156; with 152; chat 142; your 137; good 130; just 125; lmao 107; know 103; room 98; from 92; this 86; well 81; hiya 78; back 78; they 77; yeah 75; dont 75; want 71; love 60; guys 58; some 58; been 57; talk 56; nice 52; time 50; when 48; haha 44; make 44; girl 43; need 43; U122 42; MODE 41; then 40; much 40; will 40; over 39; work 38; were 38; take 37; song 36; U115 36; U121 36; even 35; seen 35; U105 35; U156 35; does 35; more 34; damn 34; come 33; only 33; hell 29; them 28; long 28; tell 27; name 27; call 26; baby 26; sure 26; away 26; look 26; play 25; U114 25; U110 25; cool 24; NICK 24; down 24; hate 23; sexy 23; said 23; many 23; ever 22; last 22; hear 21; life 21; live 20; very 19; must 19; give 19; mean 19; feel 19; stop 19; same 19; LMAO 19; hugs 18; What 18; find 18; !!!! 18; cant 18; nite 17; busy 17; left 17; ???? 17; lost 17; hair 17; shit 17; U104 17; fine 16; real 16; game 16; fuck 15; eyes 15; heya 15; sits 15; kill 15; lets 15; goes 14; wait 14; shut 14; keep 14; true 14; read 14; U168 13; pick 13; free 13; nope 13; else 13; near 13; told 12; male 12; cold 12; bout 12; hehe 12; This 12; than 12; U102 12; hope 12; awww 12; gets 12; used 12; head 12; stay 12; yall 11; kids 11; perv 11; babe 11; wont 11; year 11; doin 11; face 11; U107 11; U119 11; home 11; into 11; .. . 11; U132 10; help 10; Liam 10; hard 10; U101 10; show 10; mind 10; week 10; Well 10; Yeah 10; once 10; hmmm 9; aint 9; full 9; pics 9; crap 9; type 9; hour 9; such 9; neck 9; soon 9; rock 9; care 9; days 9; dang 9; mine 9; runs 9; ; .. 9; best 9; kiss 9; dead 9; nick 9; book 9; sick 9; sang 8; says 8; word 8; wana 8; U139 8; suck 8; went 8; blue 8; U144 8; case 8; heyy 8; hows 8; lady 8; made 8; wife 8; U169 7; dude 7; ahhh 7; okay 7; fast 7; took 7; U108 7; Hiya 7; That 7; alot 7; wear 7; hand 7; kick 7; dear 7; rule 7; send 6; Song 6; U165 6; list 6; <--- 6; next 6; thru 6; ride 6; pink 6; U520 6; main 6; ball 6; sock 6; done 6; part 6; seem 6; They 6; most 6; U103 6; )))) 6; comp 6; sing 6; U142 6; blah 6; food 6; oops 6; U116 6; knew 6; Last 6; U197 6; whos 6; U129 6; U120 6; gone 6; poor 6; goin 6; meds 5; fall 5; When 5; cali 5; warm 5; soul 5; meet 5; till 5; late 5; heck 5; feet 5; miss 5; legs 5; lick 5; also 5; came 5; kool 5; boss 5; both 5; Lime 5; wall 5; beer 5; fire 5; fool 5; hang 5; #### 5; Have 5; easy 5; ohhh 5; joke 5; caps 5; xbox 5; nose 5; lose 5; yoko 5; luck 5; idea 5; boys 5; wish 5; U128 5; roll 5; felt 5; land 5; ouch 4; lord 4; kent 4; jerk 4; sigh 4; pass 4; ummm 4; holy 4; ,,,, 4; glad 4; none 4; high 4; lame 4; U133 4; U130 4; U988 4; U989 4; huge 4; fart 4; date 4; cute 4; hook 4; U820 4; team 4; evil 4; turn 4; ways 4; mmmm 4; self 4; pain 4; U219 4; ones 4; pfft 4; ROOM 4; U146 4; U154 4; U819 4; quit 4; ugly 4; open 4; puff 4; woot 4; rest 4; U117 4; shes 4; U196 4; grrr 4; each 4; beat 4; line 4; U126 4; U123 4; door 4; shot 4; Like 4; skin 3; imma 3; hump 3; hola 3; Elev 3; elle 3; U163 3; slow 3; jump 3; Only 3; roof 3; hick 3; nana 3; hail 3; army 3; deop 3; hurt 3; town 3; Your 3; bend 3; U136 3; guyz 3; road 3; wine 3; AKDT 3; move 3; Same 3; isnt 3; band 3; half 3; DING 3; hank 3; hawt 3; (((( 3; wazz 3; wash 3; CHAT 3; vote 3; ring 3; butt 3; rain 3; orgy 3; bare 3; piff 3; slap 3; snow 3; note 3; U109 3; U106 3; gold 3; yawn 3; gawd 3; toes 3; yada 3; amen 3; U148 3; U141 3; swim 3; walk 3; rubs 3; THAT 3; ello 3; itch 3; tune 3; Wind 3; ahem 3; soft 3; clap 3; deal 3; lead 3; wack 3; U153 3; died 3; U145 3; hiii 3; mary 3; toss 3; 2006 3; hint 2; luvs 2; fits 2; zone 2; ciao 2; humm 2; Just 2; 1996 2; sand 2; U190 2; Here 2; porn 2; cost 2; cast 2; cell 2; haze 2; >:-> 2; limp 2; Nice 2; john 2; typo 2; sort 2; flaw 2; club 2; sore 2; hold 2; Down 2; Lies 2; root 2; chip 2; YOUR 2; hmph 2; spot 2; wOOt 2; eats 2; meat 2; Tisk 2; Stop 2; sooo 2; WITH 2; U138 2; hall 2; drop 2; From 2; Live 2; yeas 2; whip 2; U170 2; U175 2; Cool 2; cars 2; argh 2; Okay 2; opps 2; yard 2; Ummm 2; city 2; hott 2; bite 2; mama 2; kewl 2; park 2; past 2; kind 2; Love 2; rent 2; mins 2; sell 2; tyvm 2; John 2; trip 2; NONE 2; plan 2; wats 2; lawl 2; phil 2; High 2; aunt 2; U100 2; shop 2; golf 2; ltns 2; Poor 2; ages 2; rich 2; wooo 2; Days 2; bear 2; rofl 2; ohio 2; Gosh 2; ears 2; blew 2; HAVE 2; dumb 2; !!!. 2; n9ne 2; Lmao 2; flow 2; gays 2; drew 2; Dang 2; U111 2; newp 2; hits 2; <<<< 2; twin 2; Drew 2; Sure 2; whoa 2; mike 2; ??!! 2; spin 2; cash 2; adds 2; Tell 2; gimp 2; uses 2; howz 2; foot 2; ewww 2; U112 2; O.k. 2; five 2; tick 2; pies 2; DOES 2; tisk 2; <333 2; doll 2; deaf 2; born 2; Ahhh 2; any1 2; moon 2; corn 2; ex's 2; burp 2; Heyy 2; grrl 2; ?!?! 2; KoOL 2; side 2; tock 2; STOP 2; lies 2; DONT 2; area 2; U155 2; Ohio 2; Come 2; babi 2; heal 2; FROM 2; temp 2; cmon 2; deep 2; Lets 2; eric 2; mass 2; U172 2; clue 2; pool 2; whud 2; fawk 1; NAME 1; 1200 1; Nooo 1; four 1; disc 1; Take 1; bomb 1; vega 1; 9:10 1; pope 1; COME 1; :o * 1; U181 1; laid 1; tail 1; bike 1; sent 1; WHOA 1; cyas 1; 7:45 1; WHEN 1; Teck 1; 45.5 1; jack 1; eeek 1; Rang 1; LAst 1; NTMN 1; WILL 1; Does 1; prep 1; oooh 1; anal 1; pork 1; pasa 1; None 1; crop 1; sign 1; sayn 1; haaa 1; kmph 1; hide 1; ssid 1; wide 1; feat 1; dirt 1; 9.53 1; addy 1; ltnc 1; daft 1; Boyz 1; tips 1; bird 1; junk 1; Rush 1; coem 1; toke 1; ELSE 1; scum 1; mkay 1; sexs 1; sext 1; sink 1; nawp 1; dork 1; News 1; Ctrl 1; tlak 1; heee 1; Back 1; herE 1; orta 1; Kold 1; MRIs 1; Home 1; king 1; 64.8 1; smax 1; ROFL 1; offa 1; hogs 1; giva 1; Evil 1; VVil 1; gosh 1; 1900 1; plow 1; Oops 1; PM's 1; ques 1; 2DAY 1; hurr 1; Rule 1; Chop 1; hgey 1; Time 1; pmsl 1; z-ro 1; sori 1; QUIT 1; givs 1; Tiff 1; worl 1; pour 1; fock 1; Yoko 1; 6:53 1; Male 1; 6:51 1; slip 1; YALL 1; benz 1; chit 1; lol. 1; chik 1; Kiss 1; Lord 1; scar 1; Maps 1; rape 1; HUGE 1; Dude 1; pine 1; nuff 1; U137 1; U134 1; syck 1; mess 1; soda 1; hong 1; Will 1; pigs 1; numb 1; Joey 1; hawT 1; beam 1; coat 1; Deep 1; CALI 1; jail 1; tall 1; AWAY 1; dojn 1; kong 1; cook 1; 1.98 1; 1.99 1; Hero 1; 18ST 1; allo 1; frst 1; thnx 1; LONG 1; cepn 1; tory 1; Away 1; ally 1; poop 1; pure 1; gooo 1; docs 1; bein 1; GrlZ 1; nads 1; mahn 1; lois 1; GUYS 1; halo 1; term 1; Came 1; tere 1; Rofl 1; boed 1; hazy 1; mode 1; bong 1; whou 1; bone 1; GIRL 1; OOPS 1; fish 1; SOME 1; bois 1; ussy 1; hooo 1; Save 1; cums 1; Room 1; yes. 1; waaa 1; yout 1; Haha 1; thot 1; 39.3 1; Dood 1; hill 1; okey 1; Hold 1; akon 1; U147 1; shup 1; Wyte 1; dman 1; Judy 1; base 1; icky 1; lisa 1; weed 1; Meep 1; card 1; raed 1; yesh 1; 100% 1; Chat 1; TIME 1; loud 1; ooer 1; Even 1; rang 1; thje 1; LoVe 1; crib 1; xmas 1; dint 1; Hugs 1; Prof 1; size 1; hots 1; dump 1; mami 1; !... 1; mame 1; dogs 1; soup 1; t he 1; U164 1; gear 1; tooo 1; 2:55 1; HERE 1; cops 1; febe 1; Long 1; toop 1; thah 1; Road 1; gret 1; kina 1; ebay 1; serg 1; 2Pac 1; 10th 1; Rick 1; este 1; gals 1; seth 1; brwn 1; yeee 1; bugs 1; Jane 1; seee 1; slam 1; U158 1; Then 1; able 1; sexi 1; tenn 1; barn 1; noth 1; buff 1; surf 1; "... 1; Drop 1; HAHA 1; paid 1; aime 1; wire 1; mofo 1; pair 1; knee 1; Bone 1; GOOD 1; cams 1; wore 1; salt 1; Nova 1; wind 1; Slip 1; teck 1; Matt 1; Need 1; Hill 1; Kent 1; TEXT 1; fear 1; dick 1; bust 1; woof 1; LIVE 1; wood 1; tape 1; York 1; mena 1; geez 1; lyin 1; gees 1; Turn 1; sum1 1; SExy 1; gray 1; Help 1; pimp 1; Over 1; lala 1; guns 1; hiom 1; CAPS 1; body 1; Hott 1; fair 1; Very 1; Reub 1; seat 1; sean 1; sips 1; Kewl 1; ladz 1; jush 1; Iowa 1; gags 1; lots 1; Nope 1; ohwa 1; Rock 1; tend 1; caan 1; wean 1; bied 1; mono 1; grea 1; grin 1; blow 1; lazy 1; U542 1; City 1; otay 1; Werd 1; Were 1; herd 1; duet 1; HALO 1; pull 1; wuts 1; U113 1; brat 1; Hard 1; o.k. 1; nawt 1; drug 1; pray 1; asss 1; brad 1; Dawn 1; wubs 1; vent 1; guts 1; bell 1; <3's 1; 6:38 1; lapd 1; anti 1; THEY 1; poll 1; ok'd 1; puts 1; it's 1; bloe 1; mark 1; VBox 1; SSRI 1; Sexy 1; byes 1; TALK 1; tjhe 1; spit 1; King 1; Phil 1; bull 1; dotn 1; firs 1; Cute 1; lool 1; wins 1; 3:45 1; woah 1; TYPR 1; ahah 1; abou 1; wild 1; mauh 1; cock 1; scuk 1; MORE 1; Fade 1; Kick 1; goof 1; Good 1; cuss 1; U143 1; lust 1; Kids 1; Hail 1; SEEN 1; Eyes 1; samn 1; Born 1; Uhhh 1; bowl 1; Seee 1; nerd 1; inch 1; nada 1; MUAH 1; urls 1; keys 1; mang 1; Pour 1; puke 1; dust 1; ruff 1; moms 1; safe 1; HOTT 1; kept 1; tthe 1; Mine 1; Tide 1; Food 1; acid 1; sets 1; owww 1; Girl 1; LOUD 1; howl 1; lube 1; pm's 1; boot 1; wrek 1; jude 1; vamp 1; pm'n 1; waht 1; U118 1; Paul 1; PMSL 1; ghet 1; rose 1; Eggs 1; jeff 1; lake 1; ther 1; twit 1; 1299 1; Talk 1; !??? 1; boom 1; tits 1; Mono 1; 98.6 1; dark 1; JUST 1; loss 1; Show 1; nude 1; clay 1; saME 1; LATE 1; Troy 1; tart 1; page 1; KNOW 1; yoll 1; West 1; bacl 1; fake 1; Holy 1; kold 1; Damn 1; Tina 1; <~~~ 1; FINE 1; Mary 1; EVEN 1; quiz 1; Life 1; outs 1; bred 1; outa 1; Awww 1; exit 1; prob 1; U149 1; enuf 1; peek 1; Look 1; peel 1; poem 1; Heya 1; 1930 1; Ruth 1; post 1; mite 1; SIZE 1; choc 1; asks 1; jeep 1; ribs 1; Elle 1; http 1; perk 1; Lion 1; plus 1; west 1; out. 1; rats 1; eeww 1; tiff 1; arms 1; lung 1; yw's 1; wrap 1; RN's 1; east 1; 1985 1; span 1; 1980 1; U150 1; gift 1; Hand 1; 4:03 1; uyes 1; whoo 1; DAMN 1; grew 1; spat 1; calm 1; 6:41 1; form 1; .op. 1; heat 1; Been 1; AKST 1; rush 1; Sat. 1; whys 1; dawg 1; site 1; Care 1; cure 1; dyed 1; Ohhh 1; FACE 1; Swim 1; Heys 1; Type 1; Fort 1; menu 1; wher 1; 98.5 1; whew 1; ogan 1; test 1; draw 1; star 1; poot 1; pwns 1; dies 1; 1cos 1; evah 1; poof 1; nods 1; 4.20 1; yess 1; idnt 1; Jess 1; push 1; caca 1; yell 1; 23、复习1.4节中条件循环的讨论。使用for和if语句组合循环遍历《巨蟒和圣杯》（text6）的电影
剧本中的词，输出所有的大写词，每行输出一个。
>>> for word in [w for w in text6 if w.isupper()]:
... print "%s;"%word,
...
SCENE; KING; ARTHUR; SOLDIER; ARTHUR; I; SOLDIER; ARTHUR; I; I; SOLDIER; ARTHUR; SOLDIER; ARTHUR; SOLDIER; ARTHUR; SOLDIER; ARTHUR; SOLDIER; ARTHUR; SOLDIER; ARTHUR; SOLDIER; ARTHUR; SOLDIER; A; ARTHUR; SOLDIER; A; ARTHUR; SOLDIER; ARTHUR; SOLDIER; I; ARTHUR; I; SOLDIER; SOLDIER; SOLDIER; I; ARTHUR; SOLDIER; SOLDIER; SOLDIER; SOLDIER; SOLDIER; SOLDIER; SOLDIER; SOLDIER; SCENE; CART; MASTER; CUSTOMER; CART; MASTER; DEAD; PERSON; I; CART; MASTER; CUSTOMER; DEAD; PERSON; I; CART; MASTER; CUSTOMER; DEAD; PERSON; I; CART; MASTER; CUSTOMER; DEAD; PERSON; I; CUSTOMER; CART; MASTER; I; DEAD; PERSON; I; CUSTOMER; CART; MASTER; I; DEAD; PERSON; I; CUSTOMER; CART; MASTER; I; CUSTOMER; CART; MASTER; I; CUSTOMER; CART; MASTER; DEAD; PERSON; I; I; CUSTOMER; DEAD; PERSON; I; I; CUSTOMER; CART; MASTER; CUSTOMER; CART; MASTER; I; CUSTOMER; CART; MASTER; SCENE; ARTHUR; DENNIS; ARTHUR; DENNIS; I; ARTHUR; I; DENNIS; I; I; ARTHUR; I; DENNIS; ARTHUR; I; DENNIS; ARTHUR; I; DENNIS; I; ARTHUR; I; DENNIS; WOMAN; ARTHUR; I; WOMAN; ARTHUR; WOMAN; ARTHUR; I; WOMAN; I; I; DENNIS; A; WOMAN; DENNIS; ARTHUR; I; WOMAN; ARTHUR; WOMAN; ARTHUR; DENNIS; I; ARTHUR; DENNIS; ARTHUR; I; DENNIS; ARTHUR; DENNIS; ARTHUR; I; WOMAN; ARTHUR; I; WOMAN; I; ARTHUR; WOMAN; ARTHUR; I; I; DENNIS; ARTHUR; DENNIS; ARTHUR; DENNIS; I; I; I; ARTHUR; DENNIS; ARTHUR; DENNIS; I; ARTHUR; DENNIS; I; SCENE; BLACK; KNIGHT; BLACK; KNIGHT; GREEN; KNIGHT; BLACK; KNIGHT; GREEN; KNIGHT; BLACK; KNIGHT; BLACK; KNIGHT; GREEN; KNIGHT; GREEN; KNIGHT; BLACK; KNIGHT; GREEN; KNIGHT; BLACK; KNIGHT; ARTHUR; I; I; BLACK; KNIGHT; ARTHUR; BLACK; KNIGHT; ARTHUR; I; I; BLACK; KNIGHT; ARTHUR; I; BLACK; KNIGHT; I; ARTHUR; ARTHUR; BLACK; KNIGHT; ARTHUR; BLACK; KNIGHT; ARTHUR; BLACK; KNIGHT; ARTHUR; A; BLACK; KNIGHT; ARTHUR; BLACK; KNIGHT; I; ARTHUR; BLACK; KNIGHT; ARTHUR; BLACK; KNIGHT; ARTHUR; BLACK; KNIGHT; ARTHUR; BLACK; KNIGHT; ARTHUR; BLACK; KNIGHT; ARTHUR; BLACK; KNIGHT; I; ARTHUR; BLACK; KNIGHT; ARTHUR; BLACK; KNIGHT; ARTHUR; I; ARTHUR; BLACK; KNIGHT; BLACK; KNIGHT; I; ARTHUR; BLACK; KNIGHT; ARTHUR; BLACK; KNIGHT; I; ARTHUR; BLACK; KNIGHT; ARTHUR; BLACK; KNIGHT; BLACK; KNIGHT; ARTHUR; BLACK; KNIGHT; I; I; SCENE; MONKS; CROWD; A; A; A; A; MONKS; CROWD; A; A; A; A; A; A; A; A; A; A; A; A; A; VILLAGER; CROWD; BEDEVERE; VILLAGER; CROWD; BEDEVERE; WITCH; I; I; BEDEVERE; WITCH; CROWD; WITCH; BEDEVERE; VILLAGER; BEDEVERE; VILLAGER; VILLAGER; CROWD; BEDEVERE; VILLAGER; VILLAGER; VILLAGER; VILLAGER; VILLAGERS; VILLAGER; VILLAGER; VILLAGER; VILLAGER; A; VILLAGERS; A; VILLAGER; A; VILLAGER; RANDOM; BEDEVERE; VILLAGER; BEDEVERE; A; VILLAGER; I; VILLAGER; VILLAGER; CROWD; BEDEVERE; VILLAGER; VILLAGER; VILLAGER; CROWD; BEDEVERE; VILLAGER; VILLAGER; CROWD; BEDEVERE; VILLAGER; VILLAGER; VILLAGER; BEDEVERE; VILLAGER; B; BEDEVERE; CROWD; BEDEVERE; VILLAGER; BEDEVERE; VILLAGER; RANDOM; BEDEVERE; VILLAGER; VILLAGER; VILLAGER; CROWD; BEDEVERE; VILLAGER; VILLAGER; VILLAGER; VILLAGER; VILLAGER; VILLAGER; VILLAGER; VILLAGER; VILLAGER; ARTHUR; A; CROWD; BEDEVERE; VILLAGER; BEDEVERE; VILLAGER; A; VILLAGER; A; CROWD; A; A; VILLAGER; BEDEVERE; CROWD; BEDEVERE; CROWD; A; A; A; WITCH; VILLAGER; CROWD; BEDEVERE; ARTHUR; I; BEDEVERE; ARTHUR; BEDEVERE; I; ARTHUR; BEDEVERE; ARTHUR; I; NARRATOR; SCENE; SIR; BEDEVERE; ARTHUR; BEDEVERE; SIR; LAUNCELOT; ARTHUR; SIR; GALAHAD; LAUNCELOT; PATSY; ARTHUR; I; KNIGHTS; PRISONER; KNIGHTS; MAN; I; ARTHUR; KNIGHTS; SCENE; GOD; I; ARTHUR; GOD; I; I; ARTHUR; I; O; GOD; ARTHUR; GOD; ARTHUR; O; GOD; LAUNCELOT; A; A; GALAHAD; SCENE; ARTHUR; FRENCH; GUARD; ARTHUR; FRENCH; GUARD; ARTHUR; FRENCH; GUARD; I; I; ARTHUR; GALAHAD; ARTHUR; FRENCH; GUARD; I; ARTHUR; FRENCH; GUARD; ARTHUR; FRENCH; GUARD; I; I; GALAHAD; FRENCH; GUARD; ARTHUR; FRENCH; GUARD; I; GALAHAD; ARTHUR; FRENCH; GUARD; I; I; GALAHAD; FRENCH; GUARD; I; ARTHUR; I; FRENCH; GUARD; OTHER; FRENCH; GUARD; FRENCH; GUARD; ARTHUR; I; KNIGHTS; ARTHUR; KNIGHTS; FRENCH; GUARD; FRENCH; GUARD; ARTHUR; KNIGHTS; FRENCH; GUARD; FRENCH; GUARDS; LAUNCELOT; I; ARTHUR; BEDEVERE; I; FRENCH; GUARDS; C; A; ARTHUR; BEDEVERE; I; ARTHUR; BEDEVERE; U; I; ARTHUR; BEDEVERE; ARTHUR; KNIGHTS; CRASH; FRENCH; GUARDS; SCENE; VOICE; DIRECTOR; HISTORIAN; KNIGHT; KNIGHT; HISTORIAN; HISTORIAN; S; WIFE; SCENE; NARRATOR; MINSTREL; O; SIR; ROBIN; DENNIS; WOMAN; ALL; HEADS; MINSTREL; ROBIN; I; ALL; HEADS; MINSTREL; ROBIN; I; ALL; HEADS; I; ROBIN; W; I; I; ALL; HEADS; ROBIN; I; LEFT; HEAD; I; MIDDLE; HEAD; I; RIGHT; HEAD; I; MIDDLE; HEAD; I; LEFT; HEAD; I; RIGHT; HEAD; LEFT; HEAD; ROBIN; I; LEFT; HEAD; I; RIGHT; HEAD; MIDDLE; HEAD; LEFT; HEAD; RIGHT; HEAD; MIDDLE; HEAD; LEFT; HEAD; MIDDLE; HEAD; LEFT; HEAD; I; MIDDLE; HEAD; RIGHT; HEAD; LEFT; HEAD; MIDDLE; HEAD; RIGHT; HEAD; LEFT; HEAD; ALL; HEADS; MIDDLE; HEAD; RIGHT; HEAD; MINSTREL; ROBIN; MINSTREL; ROBIN; I; MINSTREL; ROBIN; MINSTREL; ROBIN; I; MINSTREL; ROBIN; I; MINSTREL; ROBIN; MINSTREL; ROBIN; I; CARTOON; MONKS; CARTOON; CHARACTER; CARTOON; MONKS; CARTOON; CHARACTERS; CARTOON; MONKS; CARTOON; CHARACTER; VOICE; CARTOON; CHARACTER; SCENE; NARRATOR; GALAHAD; GIRLS; ZOOT; GALAHAD; ZOOT; GALAHAD; ZOOT; GALAHAD; ZOOT; MIDGET; CRAPPER; O; ZOOT; MIDGET; CRAPPER; ZOOT; GALAHAD; I; I; ZOOT; GALAHAD; ZOOT; GALAHAD; ZOOT; GALAHAD; I; ZOOT; GALAHAD; I; I; ZOOT; I; GALAHAD; ZOOT; PIGLET; GALAHAD; ZOOT; GALAHAD; B; ZOOT; WINSTON; GALAHAD; PIGLET; GALAHAD; PIGLET; GALAHAD; I; PIGLET; GALAHAD; I; PIGLET; GALAHAD; I; I; I; GIRLS; GALAHAD; GIRLS; GALAHAD; DINGO; I; GALAHAD; I; DINGO; GALAHAD; I; I; DINGO; GALAHAD; DINGO; I; GALAHAD; DINGO; I; LEFT; HEAD; DENNIS; OLD; MAN; TIM; THE; ENCHANTER; ARMY; OF; KNIGHTS; DINGO; I; GOD; DINGO; GIRLS; A; A; DINGO; AMAZING; STUNNER; LOVELY; DINGO; GIRLS; A; A; DINGO; GIRLS; GALAHAD; I; LAUNCELOT; GALAHAD; LAUNCELOT; GALAHAD; LAUNCELOT; GALAHAD; LAUNCELOT; DINGO; LAUNCELOT; GALAHAD; LAUNCELOT; GALAHAD; I; LAUNCELOT; GIRLS; GALAHAD; I; DINGO; GIRLS; LAUNCELOT; GALAHAD; I; I; DINGO; GIRLS; LAUNCELOT; GALAHAD; I; DINGO; GIRLS; DINGO; LAUNCELOT; GALAHAD; I; I; LAUNCELOT; GALAHAD; LAUNCELOT; GALAHAD; I; LAUNCELOT; GALAHAD; LAUNCELOT; GALAHAD; I; LAUNCELOT; I; NARRATOR; I; I; CROWD; NARRATOR; I; SCENE; OLD; MAN; ARTHUR; OLD; MAN; ARTHUR; OLD; MAN; ARTHUR; OLD; MAN; ARTHUR; OLD; MAN; ARTHUR; OLD; MAN; ARTHUR; OLD; MAN; SCENE; HEAD; KNIGHT; OF; NI; KNIGHTS; OF; NI; ARTHUR; HEAD; KNIGHT; RANDOM; ARTHUR; HEAD; KNIGHT; BEDEVERE; HEAD; KNIGHT; RANDOM; ARTHUR; HEAD; KNIGHT; ARTHUR; HEAD; KNIGHT; KNIGHTS; OF; NI; ARTHUR; HEAD; KNIGHT; ARTHUR; HEAD; KNIGHT; ARTHUR; A; KNIGHTS; OF; NI; ARTHUR; PARTY; ARTHUR; HEAD; KNIGHT; ARTHUR; O; HEAD; KNIGHT; ARTHUR; HEAD; KNIGHT; ARTHUR; HEAD; KNIGHT; CARTOON; CHARACTER; SUN; CARTOON; CHARACTER; SUN; CARTOON; CHARACTER; SUN; CARTOON; CHARACTER; SCENE; NARRATOR; FATHER; PRINCE; HERBERT; FATHER; HERBERT; FATHER; HERBERT; B; I; FATHER; I; I; I; I; I; I; HERBERT; I; I; FATHER; HERBERT; I; FATHER; I; HERBERT; B; I; FATHER; HERBERT; FATHER; HERBERT; I; FATHER; HERBERT; I; I; I; FATHER; I; GUARD; GUARD; FATHER; I; GUARD; FATHER; GUARD; GUARD; FATHER; GUARD; FATHER; GUARD; FATHER; GUARD; GUARD; FATHER; GUARD; FATHER; GUARD; FATHER; GUARD; FATHER; GUARD; FATHER; GUARD; I; FATHER; N; GUARD; FATHER; GUARD; FATHER; GUARD; GUARD; FATHER; GUARD; FATHER; GUARD; GUARD; FATHER; GUARD; FATHER; GUARD; FATHER; GUARD; GUARD; GUARD; I; FATHER; GUARD; GUARD; FATHER; GUARD; FATHER; I; GUARD; I; HERBERT; FATHER; GUARD; FATHER; SCENE; LAUNCELOT; CONCORDE; LAUNCELOT; CONCORDE; LAUNCELOT; I; I; A; A; CONCORDE; I; I; LAUNCELOT; CONCORDE; I; I; I; I; I; LAUNCELOT; I; CONCORDE; I; I; LAUNCELOT; I; I; CONCORDE; LAUNCELOT; CONCORDE; I; LAUNCELOT; CONCORDE; I; I; I; SCENE; PRINCESS; LUCKY; GIRLS; GUEST; SENTRY; SENTRY; SENTRY; LAUNCELOT; SENTRY; LAUNCELOT; PRINCESS; LUCKY; GIRLS; LAUNCELOT; GUESTS; LAUNCELOT; GUARD; LAUNCELOT; O; I; I; HERBERT; LAUNCELOT; I; I; HERBERT; LAUNCELOT; I; HERBERT; I; I; LAUNCELOT; I; HERBERT; FATHER; HERBERT; I; FATHER; LAUNCELOT; I; HERBERT; LAUNCELOT; FATHER; LAUNCELOT; FATHER; LAUNCELOT; I; I; HERBERT; I; FATHER; LAUNCELOT; I; FATHER; I; HERBERT; FATHER; LAUNCELOT; I; FATHER; LAUNCELOT; FATHER; LAUNCELOT; I; I; I; FATHER; HERBERT; LAUNCELOT; I; FATHER; LAUNCELOT; HERBERT; I; FATHER; LAUNCELOT; HERBERT; I; LAUNCELOT; I; HERBERT; LAUNCELOT; I; I; I; FATHER; HERBERT; SCENE; GUESTS; FATHER; GUEST; FATHER; LAUNCELOT; FATHER; LAUNCELOT; I; I; I; GUEST; GUESTS; FATHER; LAUNCELOT; GUEST; GUESTS; FATHER; GUESTS; FATHER; I; I; GUEST; FATHER; GUEST; FATHER; BRIDE; S; FATHER; GUEST; FATHER; I; I; LAUNCELOT; GUEST; GUESTS; CONCORDE; HERBERT; I; FATHER; HERBERT; I; FATHER; HERBERT; I; FATHER; GUESTS; FATHER; GUESTS; FATHER; GUESTS; FATHER; GUESTS; FATHER; GUESTS; CONCORDE; GUESTS; CONCORDE; GUESTS; LAUNCELOT; GUESTS; LAUNCELOT; I; GUESTS; CONCORDE; LAUNCELOT; GUESTS; LAUNCELOT; GUESTS; LAUNCELOT; SCENE; ARTHUR; OLD; CRONE; ARTHUR; CRONE; ARTHUR; I; CRONE; ARTHUR; CRONE; ARTHUR; CRONE; BEDEVERE; ARTHUR; BEDEVERE; ARTHUR; BEDEVERE; ARTHUR; BEDEVERE; ARTHUR; BEDEVERE; ARTHUR; ARTHUR; BEDEVERE; CRONE; BEDEVERE; ARTHUR; CRONE; BEDEVERE; ARTHUR; BEDEVERE; ARTHUR; BEDEVERE; ROGER; THE; SHRUBBER; ARTHUR; ROGER; ARTHUR; ROGER; I; I; BEDEVERE; ARTHUR; SCENE; ARTHUR; O; HEAD; KNIGHT; I; ARTHUR; HEAD; KNIGHT; KNIGHTS; OF; NI; HEAD; KNIGHT; RANDOM; HEAD; KNIGHT; ARTHUR; O; HEAD; KNIGHT; ARTHUR; RANDOM; HEAD; KNIGHT; KNIGHTS; OF; NI; A; A; A; HEAD; KNIGHT; ARTHUR; HEAD; KNIGHT; ARTHUR; KNIGHTS; OF; NI; HEAD; KNIGHT; ARTHUR; HEAD; KNIGHT; I; ARTHUR; KNIGHTS; OF; NI; HEAD; KNIGHT; ARTHUR; KNIGHTS; OF; NI; HEAD; KNIGHT; KNIGHTS; OF; NI; BEDEVERE; MINSTREL; ARTHUR; ROBIN; HEAD; KNIGHT; ARTHUR; MINSTREL; ROBIN; HEAD; KNIGHT; KNIGHTS; OF; NI; ROBIN; I; KNIGHTS; OF; NI; ROBIN; ARTHUR; KNIGHTS; OF; NI; HEAD; KNIGHT; ARTHUR; KNIGHTS; OF; NI; HEAD; KNIGHT; ARTHUR; HEAD; KNIGHT; I; I; I; KNIGHTS; OF; NI; NARRATOR; KNIGHTS; NARRATOR; MINSTREL; NARRATOR; KNIGHTS; NARRATOR; A; CARTOON; CHARACTER; NARRATOR; CARTOON; CHARACTER; NARRATOR; CARTOON; CHARACTER; NARRATOR; CARTOON; CHARACTER; NARRATOR; CARTOON; CHARACTER; NARRATOR; SCENE; KNIGHTS; ARTHUR; TIM; THE; ENCHANTER; I; ARTHUR; TIM; ARTHUR; TIM; ARTHUR; TIM; I; ARTHUR; O; TIM; ROBIN; ARTHUR; KNIGHTS; ARTHUR; BEDEVERE; GALAHAD; ROBIN; BEDEVERE; ROBIN; BEDEVERE; ARTHUR; GALAHAD; ARTHUR; I; I; TIM; A; ARTHUR; A; TIM; A; ARTHUR; I; ROBIN; Y; ARTHUR; GALAHAD; KNIGHTS; TIM; ROBIN; ARTHUR; ROBIN; GALAHAD; ARTHUR; ROBIN; KNIGHTS; ARTHUR; TIM; I; KNIGHTS; TIM; ARTHUR; O; TIM; ARTHUR; SCENE; GALAHAD; ARTHUR; TIM; ARTHUR; GALAHAD; ARTHUR; W; TIM; ARTHUR; TIM; ARTHUR; TIM; ARTHUR; TIM; ARTHUR; TIM; ARTHUR; TIM; ARTHUR; TIM; ROBIN; I; I; TIM; GALAHAD; TIM; GALAHAD; ROBIN; TIM; I; ROBIN; TIM; ARTHUR; BORS; TIM; BORS; ARTHUR; TIM; I; ROBIN; I; TIM; I; I; ARTHUR; TIM; ARTHUR; TIM; KNIGHTS; KNIGHTS; ARTHUR; KNIGHTS; TIM; ARTHUR; LAUNCELOT; GALAHAD; ARTHUR; GALAHAD; ARTHUR; ROBIN; ARTHUR; GALAHAD; ARTHUR; GALAHAD; LAUNCELOT; ARTHUR; LAUNCELOT; ARTHUR; MONKS; ARTHUR; LAUNCELOT; I; ARTHUR; BROTHER; MAYNARD; SECOND; BROTHER; O; MAYNARD; SECOND; BROTHER; MAYNARD; KNIGHTS; ARTHUR; GALAHAD; ARTHUR; SCENE; ARTHUR; LAUNCELOT; GALAHAD; ARTHUR; MAYNARD; GALAHAD; LAUNCELOT; ARTHUR; MAYNARD; ARTHUR; MAYNARD; BEDEVERE; MAYNARD; LAUNCELOT; MAYNARD; ARTHUR; MAYNARD; GALAHAD; ARTHUR; MAYNARD; LAUNCELOT; ARTHUR; BEDEVERE; GALAHAD; BEDEVERE; I; LAUNCELOT; ARTHUR; LAUNCELOT; KNIGHTS; BEDEVERE; LAUNCELOT; BEDEVERE; N; LAUNCELOT; BEDEVERE; I; ARTHUR; GALAHAD; MAYNARD; BROTHER; MAYNARD; BEDEVERE; ARTHUR; KNIGHTS; BEDEVERE; KNIGHTS; NARRATOR; ANIMATOR; NARRATOR; SCENE; GALAHAD; ARTHUR; ROBIN; ARTHUR; BEDEVERE; ARTHUR; GALAHAD; ARTHUR; GALAHAD; ARTHUR; ROBIN; ARTHUR; ROBIN; I; GALAHAD; ARTHUR; ROBIN; ARTHUR; ROBIN; I; LAUNCELOT; I; I; ARTHUR; GALAHAD; ARTHUR; LAUNCELOT; I; ARTHUR; BRIDGEKEEPER; LAUNCELOT; I; BRIDGEKEEPER; LAUNCELOT; BRIDGEKEEPER; LAUNCELOT; BRIDGEKEEPER; LAUNCELOT; BRIDGEKEEPER; LAUNCELOT; ROBIN; BRIDGEKEEPER; ROBIN; I; BRIDGEKEEPER; ROBIN; BRIDGEKEEPER; ROBIN; BRIDGEKEEPER; ROBIN; I; BRIDGEKEEPER; GALAHAD; BRIDGEKEEPER; GALAHAD; I; BRIDGEKEEPER; GALAHAD; BRIDGEKEEPER; ARTHUR; BRIDGEKEEPER; ARTHUR; BRIDGEKEEPER; ARTHUR; BRIDGEKEEPER; I; I; BEDEVERE; ARTHUR; SCENE; ARTHUR; BEDEVERE; ARTHUR; BEDEVERE; ARTHUR; FRENCH; GUARD; ARTHUR; I; FRENCH; GUARD; I; I; ARTHUR; FRENCH; GUARD; I; ARTHUR; FRENCH; GUARDS; ARTHUR; FRENCH; GUARD; ARTHUR; FRENCH; GUARD; FRENCH; GUARDS; ARTHUR; BEDEVERE; ARTHUR; FRENCH; GUARDS; ARTHUR; FRENCH; GUARDS; ARTHUR; FRENCH; GUARDS; ARTHUR; ARMY; OF; KNIGHTS; HISTORIAN; S; WIFE; I; INSPECTOR; OFFICER; HISTORIAN; S; WIFE; OFFICER; INSPECTOR; OFFICER; BEDEVERE; INSPECTOR; OFFICER; INSPECTOR; OFFICER; OFFICER; RANDOM; RANDOM; OFFICER; OFFICER; OFFICER; OFFICER; INSPECTOR; OFFICER; CAMERAMAN;24、写表达式找出text6中所有符合下列条件的词。结果应该是词链表的形式：[‘word1’,’word2’,…]。
a. 以ize结尾
>>> [w for w in text6 if w.endswith('ize')]
[]b. 包含字母z
>>> [w for w in text6 if 'z' in w]
[u'zone', u'amazes', u'Fetchez', u'Fetchez', u'zoop', u'zoo', u'zhiv', u'frozen', u'zoosh']c. 包含字母序列pt
>>> [w for w in text6 if 'pt' in w]
[u'empty', u'aptly', u'Thpppppt', u'Thppt', u'Thppt', u'empty', u'Thppppt', u'temptress', u'temptation', u'ptoo', u'Chapter', u'excepting', u'Thpppt']d. 除了首字母外是全部小写字母的词（即titlecase）
>>> list(set([w for w in text6 if w.istitle()]))
[u'Welcome', u'Winter', u'Lead', u'Uugh', u'Does', u'Saint', u'Until', u'Today', u'Thou', u'Burn', u'Lucky', u'Uhh', u'Not', u'Now', u'Twenty', u'Where', u'Just', u'Course', u'Go', u'Erbert', u'Uther', u'Actually', u'Cherries', u'Thpppt', u'Bloody', u'Aramaic', u'Mmm', u'Put', u'Haw', u'True', u'Pull', u'Fiends', u'Agh', u'Yup', u'We', u'Arthur', u'Zoot', u'English', u'Alright', u'My', u'Silence', u'Clark', u'Bedevere', u'Bors', u'Back', u'Maynard', u'Fetchez', u'Seek', u'Exactly', u'Doctor', u'Rather', u'When', u'Three', u'Providence', u'Book', u'Therefore', u'Huh', u'Stay', u'Umhm', u'Aaaaaaaah', u'Huy', u'Those', u'Dingo', u'Cider', u'Chop', u'Aauuugh', u'So', u'Found', u'Guy', u'Oui', u'Anarcho', u'Torment', u'Our', u'Your', u'Lie', u'Almighty', u'Galahad', u'Britons', u'Lord', u'Who', u'Beast', u'Loimbard', u'Why', u'A', u'Don', u'Guards', u'Oooh', u'All', u'Aaauugh', u'Assyria', u'Yeaaah', u'One', u'Farewell', u'Greetings', u'Beyond', u'Blue', u'What', u'Ayy', u'His', u'Recently', u'Here', u'Hic', u'Away', u'Wait', u'Concorde', u'Herbert', u'Ere', u'Bad', u'She', u'Mother', u'Shh', u'Erm', u'Tower', u'Robin', u'Summer', u'Chaste', u'Enchanter', u'Skip', u'Four', u'Say', u'Anthrax', u'Mud', u'Armaments', u'Build', u'Which', u'Nador', u'Hiyaah', u'Woa', u'More', u'Picture', u'Holy', u'Very', u'Practice', u'Packing', u'Uuh', u'Hold', u'Huyah', u'Throw', u'Must', u'None', u'This', u'Leaving', u'Ives', u'Nine', u'Stand', u'W', u'Firstly', u'Brother', u'Oooo', u'Eh', u'Amen', u'Jesus', u'Camaaaaaargue', u'Divine', u'Speak', u'Even', u'Hallo', u'Dappy', u'Yay', u'Iiiives', u'Prepare', u'There', u'Please', u'Black', u'Pure', u'Quoi', u'Excalibur', u'Iesu', u'Hmm', u'Midget', u'Angnor', u'B', u'Splendid', u'Aggh', u'Lancelot', u'Victory', u'See', u'Will', u'Shrubberies', u'Court', u'Aauuuves', u'God', u'Father', u'Patsy', u'It', u'Peng', u'Other', u'Then', u'Halt', u'Thee', u'Ridden', u'Aaaah', u'Knight', u'Antioch', u'They', u'Ask', u'With', u'Gallahad', u'Off', u'Thy', u'Well', u'Didn', u'Anybody', u'Isn', u'Grail', u'Neee', u'The', u'Bridge', u'Thsss', u'Hiyah', u'Yapping', u'Robinson', u'Hah', u'Explain', u'Aauuggghhh', u'Hill', u'Forward', u'Behold', u'European', u'Shut', u'Meanwhile', u'Chickennn', u'French', u'Psalms', u'Auuuuuuuugh', u'Ector', u'Aah', u'Keep', u'Quick', u'Once', u'Right', u'Help', u'Over', u'Anyway', u'Aaaugh', u'For', u'France', u'Umm', u'Walk', u'Dramatically', u'Good', u'Run', u'That', u'Arimathea', u'Forgive', u'Ecky', u'King', u'C', u'Could', u'Quiet', u'Hooray', u'S', u'Himself', u'African', u'Launcelot', u'Gable', u'Bravest', u'Bring', u'Shrubber', u'Aaah', u'Yes', u'Death', u'Christ', u'Would', u'Hey', u'Waa', u'Hee', u'Sorry', u'Heh', u'Get', u'Crapper', u'But', u'Hiyya', u'Aaaaaaaaah', u'Schools', u'Hurry', u'Princess', u'Together', u'N', u'Honestly', u'Caerbannog', u'Action', u'Knights', u'Round', u'And', u'Old', u'How', u'Winston', u'Mercea', u'Battle', u'Follow', u'Aaaaugh', u'Open', u'Ahh', u'Bedwere', u'Hya', u'Tis', u'Til', u'Tim', u'Charge', u'Wood', u'You', u'Nay', u'Tell', u'Stop', u'Aaaaaah', u'Excuse', u'Riiight', u'Supposing', u'Aaauggh', u'Attila', u'Do', u'I', u'Clear', u'Alice', u'Apples', u'Bristol', u'Y', u'Order', u'Try', u'Piglet', u'Tall', u'Spring', u'Is', u'Mind', u'Mine', u'Have', u'In', u'Table', u'Dennis', u'If', u'Wayy', u'Thank', u'Ninepence', u'Said', u'Hyy', u'Churches', u'Be', u'Augh', u'Ewing', u'Far', u'Oooohoohohooo', u'Surely', u'Consult', u'By', u'On', u'Unfortunately', u'Oh', u'Did', u'Of', u'Supreme', u'Morning', u'Tale', u'Ow', u'England', u'Or', u'Dis', u'Brave', u'Ohh', u'Pin', u'Pendragon', u'Are', u'Bones', u'Fine', u'Prince', u'Too', u'Iiiiives', u'Since', u'Pie', u'Idiom', u'Between', u'Whoa', u'Listen', u'Monsieur', u'Oooooooh', u'Frank', u'Quite', u'Let', u'Ho', u'Hm', u'Nothing', u'Ha', u'He', u'Chapter', u'Look', u'O', u'Thppppt', u'Um', u'Un', u'Uh', u'Bon', u'Hello', u'First', u'Ages', u'Autumn', u'Looks', u'Olfin', u'Message', u'Really', u'Ni', u'Use', u'Cut', u'No', u'Make', u'Aauuuuugh', u'Two', u'Quickly', u'Everything', u'Thpppppt', u'Nu', u'Rheged', u'Most', u'Hang', u'Ooh', u'Hand', u'Gawain', u'Every', u'Aaagh', u'Come', u'Bread', u'Peril', u'Steady', u'Thppt', u'Ulk', u'Silly', u'Defeat', u'Eee', u'Castle', u'Grenade', u'Camelot', u'Aagh', u'Britain', u'Joseph', u'Badon', u'Sir', u'Hoa', u'Perhaps', u'Hoo', u'Saxons', u'Lake', u'Thursday', u'To', u'Shall', u'May', u'Never', u'Eternal', u'As', u'Cornwall', u'Running', u'Five', u'Gorge', u'Lady', u'Man', u'Great', u'Like', u'Yeaah', u'Remove', u'Swamp', u'U', u'Heee', u'Dragon', u'Ah', u'Am', u'Yeah', u'An', u'Bravely', u'Allo', u'At', u'Ay', u'Roger', u'Chicken']25、定义sent为词链表[‘she’,’sells’,’sea’,’shells’,’by’,’the’,’sea’,’shore’]。
编写代码执行以下任务：
a. 输出所有sh开头的单词
>>> sent = ['she','sells','sea','shells','by','the','sea','shore']
>>> print [w for w in sent if w.startswith('sh')]
['she', 'shells', 'shore']b. 输出所有长度超过4个字符的词
>>> print [w for w in sent if len(w)>4]
['sells', 'shells', 'shore']26、下面的Python代码是做什么的？sum([len(w) for w in text1])，你可以用它来算出
一个文本的平均字长吗？
平均字长计算公式
>>> sum([len(w) for w in text1])*1.0/len(text1)
3.83041112802364927、定义一个名为vocab_size(text)的函数，以文本作为唯一的参数，返回文本的词汇量。
def vocab_size(text):return len(set(text))28、定义一个函数percent(word,text)，计算一个给定的词在文本中出现的频率，结果以百分比显示。
def percent(word,text):freq = len([w for w in text if w == word])*1.0/len(text)print "%.2f"%freq29、我们一直在使用集合存储词汇表。试试下面的Python表达式：set(sent3)
>>> set(text3)<set(text1)
False
>>> s1 = ['I','Love']
>>> s2 = ['I','Love','dragon']
>>> set(s1)<set(s2)
True表达式 set1 < set2 用来判断set1是否为set2的子集。
第1章 语言处理与Python相关推荐
- 《Python自然语言处理（第二版）-Steven Bird等》学习笔记：第01章 语言处理与Python
第01章 语言处理与Python 1.1 语言计算:文本和单词 Python入门 NLTK 入门 搜索文本 计数词汇 1.2 近观Python:将文本当做词链表 链表(list,也叫列表) 索引列表 ...
- 《Python自然语言处理》——第1章 语言处理与Python 1.1 语言计算：文本和词汇...
本节书摘来自异步社区<Python自然语言处理>一书中的第1章,第1.1节,作者[美]Steven Bird,Ewan Klein,Edward Loper, 陈涛,张旭,崔杨,刘海平 译 ...
- 《用Python进行自然语言处理》第 1 章 语言处理与 Python
1. 将简单的程序与大量的文本结合起来,我们能实现什么? 2. 我们如何能自动提取概括文本风格和内容的关键词和短语? 3. Python 编程语言为上述工作提供了哪些工具和技术? 4. 自然语言处理中 ...
- 2的10次方-1的python表达式_第1章 语言处理与Python
1.尝试使用Python解释器作为一个计算器,输入表达式,如12/(4+1). >>>12/(4+1) 2 2.26个字母可以组成26的10次方或者26**10个10字母长的字符串. ...
- Python自然语言处理第一章 - 语言处理与Python
我的运行环境 python 3.5.2 nltk 3.2.1 nltk-data numpy:科学计算库 matplotlib:数据可视化的2D会图库 因为python.nltk版本不同,所以一些内容 ...
- 《基于ArcGIS的Python编程秘笈（第2版）》——第1章 面向ArcGIS的Python语言基础
本节书摘来自异步社区<基于ArcGIS的Python编程秘笈(第2版)>一书中的第1章,第1.1节,作者: [美]Eric Pimpler(派普勒) 更多章节内容可以访问云栖社区" ...
- 《Python自然语言处理（第二版）-Steven Bird等》学习笔记：第11章 语言数据管理
第11章 语言数据管理 11.1 语料库结构:一个案例研究 主要设计特点 基本数据类型 11.2 语料库生命周期 语料库创建的三种方案 质量控制 维护与演变 11.3 数据采集 从网上获取数据 从字处 ...
- 【Python自然语言处理】读书笔记：第一章：语言处理与Python
原书:<Python自然语言处理>:https://usyiyi.github.io/nlp-py-2e-zh/ 语言处理与Python 原文:https://usyiyi.github. ...
- c语言和python哪个自学好-有c语言基础 自学python 应该选什么书来看？
然后,目前你还处在新手村阶段.还是先关注下入门资料.Python 新手入门课_Python - 实验楼www.shiyanlou.com 这是实验楼为 Python 零基础的同学定制的课程,仅需要一 ...
最新文章
- OpenCV+python：图像金字塔
- 拉格朗日乘数法的原理，我用10幅图把它讲清楚了
- numpy之argmax()函数
- 最新机器学习库Scikit-learn库使用总结.pptx
- Java那些事之多线程
- Redis:redis cluster的实现细节
- 软件测试术语中英文对照(部分)
- 【mathematical statistics】5 distributional testing
- 四川中级职称计算机考试考b级,四川省职称计算机B级考试1卷
- python中的threading_python中的threading模块使用说明
- 中科大快手提出多模态交叉注意力模型：MMCA，促进图像-文本多模态匹配！
- 03 回归算法 - 线性回归求解 θ（最小二乘求解）
- 2018年AI智商将达到多少？未来智能实验室启动第三次世界AI智商评测
- 慧荣SM2258XT+B17颗粒，固态硬盘无法格式化已开卡成功，经验分享+量产工具，SM2259XT2类似
- 基于 SpringBoot + MyBatis 的在线五子棋对战
- 显卡（N卡）内存被占用如何处理？
- MVX-Net: Multimodal VoxelNet for 3D Object Detection
- 什么是局域计算机网络,什么是局域网_局域网概述
- 商汤内推：2023届（AI先锋/校园招聘）
- nginx resolver失败