Python文本处理的案例有哪些

2024-10-26 技术教程

本篇内容主要讲解“Python文本处理的案例有哪些”，感兴趣的朋友不妨来看看。本文介绍的方法操作简单快捷，实用性强。下面就让小编来带大家学习“Python文本处理的案例有哪些”吧!

1提取 PDF 内容

#pipinstallPyPDF2安装PyPDF2importPyPDF2fromPyPDF2importPdfFileReader#Creatingapdffileobject.pdf=open("test.pdf","rb")#Creatingpdfreaderobject.pdf_reader=PyPDF2.PdfFileReader(pdf)#Checkingtotalnumberofpagesinapdffile.print("TotalnumberofPages:",pdf_reader.numPages)#Creatingapageobject.page=pdf_reader.getPage(200)#Extractdatafromaspecificpagenumber.print(page.extractText())#Closingtheobject.pdf.close()2提取 Word 内容

#pipinstallpython-docx安装python-docximportdocxdefmain():try:doc=docx.Document('test.docx')#Creatingwordreaderobject.data=""fullText=[]forparaindoc.paragraphs:fullText.append(para.text)data='\n'.join(fullText)print(data)exceptIOError:print('Therewasanerroropeningthefile!')returnif__name__=='__main__':main()3提取 Web 网页内容

#pipinstallbs4安装bs4fromurllib.requestimportRequest,urlopenfrombs4importBeautifulSoupreq=Request('http://www.cmegroup.com/trading/products/#sortField=oi&sortAsc=false&venues=3&page=1&cleared=1&group=1',headers={'User-Agent':'Mozilla/5.0'})webpage=urlopen(req).read()#Parsingsoup=BeautifulSoup(webpage,'html.parser')#Formatingtheparsedhtmlfilestrhtm=soup.prettify()#Printfirst500linesprint(strhtm[:500])#Extractmetatagvalueprint(soup.title.string)print(soup.find('meta',attrs={'property':'og:description'}))#Extractanchortagvalueforxinsoup.find_all('a'):print(x.string)#ExtractParagraphtagvalueforxinsoup.find_all('p'):print(x.text)4读取 Json 数据

importrequestsimportjsonr=requests.get("https://support.oneskyapp.com/hc/en-us/article_attachments/202761727/example_2.json")res=r.json()#Extractspecificnodecontent.print(res['quiz']['sport'])#Dumpdataasstringdata=json.dumps(res)print(data)5读取 CSV 数据

importcsvwithopen('test.csv','r')ascsv_file:reader=csv.reader(csv_file)next(reader)#Skipfirstrowforrowinreader:print(row)6删除字符串中的标点符号

importreimportstringdata="Stuningevenforthenon-gamer:Thissoundtrackwasbeautiful!\ItpaintstheseneryinyourmindsowellIwouldrecomend\iteventopeoplewhohatevid.gamemusic!IhaveplayedthegameChrono\CrossbutoutofallofthegamesIhaveeverplayedithasthebestmusic!\Itbacksawayfromcrudekeyboardingandtakesafresherstepwithgrate\guitarsandsoulfulorchestras.\Itwouldimpressanyonewhocarestolisten!"#Methood1:Regex#Removethespecialcharatersfromthereadstring.no_specials_string=re.sub('[!#?,.:";]','',data)print(no_specials_string)#Methood2:translate()#Raketranslatorobjecttranslator=str.maketrans('','',string.punctuation)data=data.translate(translator)print(data)7使用 NLTK 删除停用词

fromnltk.corpusimportstopwordsdata=['Stuningevenforthenon-gamer:Thissoundtrackwasbeautiful!\ItpaintstheseneryinyourmindsowellIwouldrecomend\iteventopeoplewhohatevid.gamemusic!IhaveplayedthegameChrono\CrossbutoutofallofthegamesIhaveeverplayedithasthebestmusic!\Itbacksawayfromcrudekeyboardingandtakesafresherstepwithgrate\guitarsandsoulfulorchestras.\Itwouldimpressanyonewhocarestolisten!']#Removestopwordsstopwords=set(stopwords.words('english'))output=[]forsentenceindata:temp_list=[]forwordinsentence.split():ifword.lower()notinstopwords:temp_list.append(word)output.append(''.join(temp_list))print(output)8使用 TextBlob 更正拼写

fromtextblobimportTextBlobdata="Naturallanguageisacantralpartofourdaytodaylife,andit'ssoantrestingtoworkonanyproblemrelatedtolangages."output=TextBlob(data).correct()print(output)9使用 NLTK 和 TextBlob 的词标记化

importnltkfromtextblobimportTextBlobdata="Naturallanguageisacentralpartofourdaytodaylife,andit'ssointerestingtoworkonanyproblemrelatedtolanguages."nltk_output=nltk.word_tokenize(data)textblob_output=TextBlob(data).wordsprint(nltk_output)print(textblob_output)

Output:

['Natural', 'language', 'is', 'a', 'central', 'part', 'of', 'our', 'day', 'to', 'day', 'life', ',', 'and', 'it', "'s", 'so', 'interesting', 'to', 'work', 'on', 'any', 'problem', 'related', 'to', 'languages', '.']
['Natural', 'language', 'is', 'a', 'central', 'part', 'of', 'our', 'day', 'to', 'day', 'life', 'and', 'it', "'s", 'so', 'interesting', 'to', 'work', 'on', 'any', 'problem', 'related', 'to', 'languages']

10使用 NLTK 提取句子单词或短语的词干列表

fromnltk.stemimportPorterStemmerst=PorterStemmer()text=['Wheredidhelearntodancelikethat?','Hiseyesweredancingwithhumor.','Sheshookherheadanddancedaway','Alexwasanexcellentdancer.']output=[]forsentenceintext:output.append("".join([st.stem(i)foriinsentence.split()]))foriteminoutput:print(item)print("-"*50)print(st.stem('jumping'),st.stem('jumps'),st.stem('jumped'))

Output:

where did he learn to danc like that?
hi eye were danc with humor.
she shook her head and danc away
alex wa an excel dancer.
--------------------------------------------------
jump jump jump

11使用 NLTK 进行句子或短语词形还原

fromnltk.stemimportWordNetLemmatizerwnl=WordNetLemmatizer()text=['Shegrippedthearmrestashepassedtwocarsatatime.','Hercarwasinfullview.','Anumberofcarscarriedoutofstatelicenseplates.']output=[]forsentenceintext:output.append("".join([wnl.lemmatize(i)foriinsentence.split()]))foriteminoutput:print(item)print("*"*10)print(wnl.lemmatize('jumps','n'))print(wnl.lemmatize('jumping','v'))print(wnl.lemmatize('jumped','v'))print("*"*10)print(wnl.lemmatize('saddest','a'))print(wnl.lemmatize('happiest','a'))print(wnl.lemmatize('easiest','a'))

Output:

She gripped the armrest a he passed two car at a time.
Her car wa in full view.
A number of car carried out of state license plates.
**********
jump
jump
jump
**********
sad
happy
easy

12使用 NLTK 从文本文件中查找每个单词的频率

importnltkfromnltk.corpusimportwebtextfromnltk.probabilityimportFreqDistnltk.download('webtext')wt_words=webtext.words('testing.txt')data_analysis=nltk.FreqDist(wt_words)#Let'stakethespecificwordsonlyiftheirfrequencyisgreaterthan3.filter_words=dict([(m,n)form,nindata_analysis.items()iflen(m)>3])forkeyinsorted(filter_words):print("%s:%s"%(key,filter_words[key]))data_analysis=nltk.FreqDist(filter_words)data_analysis.plot(25,cumulative=False)

Output:

[nltk_data] Downloading package webtext to
[nltk_data] C:\Users\amit\AppData\Roaming\nltk_data...
[nltk_data] Unzipping corpora\webtext.zip.
1989: 1
Accessing: 1
Analysis: 1
Anyone: 1
Chapter: 1
Coding: 1
Data: 1
...

13从语料库中创建词云

importnltkfromnltk.corpusimportwebtextfromnltk.probabilityimportFreqDistfromwordcloudimportWordCloudimportmatplotlib.pyplotaspltnltk.download('webtext')wt_words=webtext.words('testing.txt')#Sampledatadata_analysis=nltk.FreqDist(wt_words)filter_words=dict([(m,n)form,nindata_analysis.items()iflen(m)>3])wcloud=WordCloud().generate_from_frequencies(filter_words)#Plottingthewordcloudplt.imshow(wcloud,interpolation="bilinear")plt.axis("off")(-0.5,399.5,199.5,-0.5)plt.show()14NLTK 词法散布图

importnltkfromnltk.corpusimportwebtextfromnltk.probabilityimportFreqDistfromwordcloudimportWordCloudimportmatplotlib.pyplotaspltwords=['data','science','dataset']nltk.download('webtext')wt_words=webtext.words('testing.txt')#Sampledatapoints=[(x,y)forxinrange(len(wt_words))foryinrange(len(words))ifwt_words[x]==words[y]]ifpoints:x,y=zip(*points)else:x=y=()plt.plot(x,y,"rx",scalex=.1)plt.yticks(range(len(words)),words,color="b")plt.ylim(-1,len(words))plt.title("LexicalDispersionPlot")plt.xlabel("WordOffset")plt.show()15使用 countvectorizer 将文本转换为数字

importpandasaspdfromsklearn.feature_extraction.textimportCountVectorizer#Sampledataforanalysisdata1="Javaisalanguageforprogrammingthatdevelopsasoftwareforseveralplatforms.AcompiledcodeorbytecodeonJavaapplicationcanrunonmostoftheoperatingsystemsincludingLinux,Macoperatingsystem,andLinux.MostofthesyntaxofJavaisderivedfromtheC++andClanguages."data2="Pythonsupportsmultipleprogrammingparadigmsandcomesupwithalargestandardlibrary,paradigmsincludedareobject-oriented,imperative,functionalandprocedural."data3="Goistypedstaticallycompiledlanguage.ItwascreatedbyRobertGriesemer,KenThompson,andRobPikein2009.Thislanguageoffersgarbagecollection,concurrencyofCSP-style,memorysafety,andstructuraltyping."df1=pd.DataFrame({'Java':[data1],'Python':[data2],'Go':[data2]})#Initializevectorizer=CountVectorizer()doc_vec=vectorizer.fit_transform(df1.iloc[0])#CreatedataFramedf2=pd.DataFrame(doc_vec.toarray().transpose(),index=vectorizer.get_feature_names())#Changecolumnheadersdf2.columns=df1.columnsprint(df2)

Output:

Go Java Python
and 2 2 2
application 0 1 0
are 1 0 1
bytecode 0 1 0
can 0 1 0
code 0 1 0
comes 1 0 1
compiled 0 1 0
derived 0 1 0
develops 0 1 0
for 0 2 0
from 0 1 0
functional 1 0 1
imperative 1 0 1
...

16使用 TF-IDF 创建文档术语矩阵

importpandasaspdfromsklearn.feature_extraction.textimportTfidfVectorizer#Sampledataforanalysisdata1="Javaisalanguageforprogrammingthatdevelopsasoftwareforseveralplatforms.AcompiledcodeorbytecodeonJavaapplicationcanrunonmostoftheoperatingsystemsincludingLinux,Macoperatingsystem,andLinux.MostofthesyntaxofJavaisderivedfromtheC++andClanguages."data2="Pythonsupportsmultipleprogrammingparadigmsandcomesupwithalargestandardlibrary,paradigmsincludedareobject-oriented,imperative,functionalandprocedural."data3="Goistypedstaticallycompiledlanguage.ItwascreatedbyRobertGriesemer,KenThompson,andRobPikein2009.Thislanguageoffersgarbagecollection,concurrencyofCSP-style,memorysafety,andstructuraltyping."df1=pd.DataFrame({'Java':[data1],'Python':[data2],'Go':[data2]})#Initializevectorizer=TfidfVectorizer()doc_vec=vectorizer.fit_transform(df1.iloc[0])#CreatedataFramedf2=pd.DataFrame(doc_vec.toarray().transpose(),index=vectorizer.get_feature_names())#Changecolumnheadersdf2.columns=df1.columnsprint(df2)

Output:

Go Java Python
and 0.323751 0.137553 0.323751
application 0.000000 0.116449 0.000000
are 0.208444 0.000000 0.208444
bytecode 0.000000 0.116449 0.000000
can 0.000000 0.116449 0.000000
code 0.000000 0.116449 0.000000
comes 0.208444 0.000000 0.208444
compiled 0.000000 0.116449 0.000000
derived 0.000000 0.116449 0.000000
develops 0.000000 0.116449 0.000000
for 0.000000 0.232898 0.000000
...

17为给定句子生成 N-gram

自然语言工具包：NLTK

importnltkfromnltk.utilimportngrams#Functiontogeneraten-gramsfromsentences.defextract_ngrams(data,num):n_grams=ngrams(nltk.word_tokenize(data),num)return[''.join(grams)forgramsinn_grams]data='Aclassisablueprintfortheobject.'print("1-gram:",extract_ngrams(data,1))print("2-gram:",extract_ngrams(data,2))print("3-gram:",extract_ngrams(data,3))print("4-gram:",extract_ngrams(data,4))

文本处理工具：TextBlob

fromtextblobimportTextBlob#Functiontogeneraten-gramsfromsentences.defextract_ngrams(data,num):n_grams=TextBlob(data).ngrams(num)return[''.join(grams)forgramsinn_grams]data='Aclassisablueprintfortheobject.'print("1-gram:",extract_ngrams(data,1))print("2-gram:",extract_ngrams(data,2))print("3-gram:",extract_ngrams(data,3))print("4-gram:",extract_ngrams(data,4))

Output:

1-gram: ['A', 'class', 'is', 'a', 'blueprint', 'for', 'the', 'object']
2-gram: ['A class', 'class is', 'is a', 'a blueprint', 'blueprint for', 'for the', 'the object']
3-gram: ['A class is', 'class is a', 'is a blueprint', 'a blueprint for', 'blueprint for the', 'for the object']
4-gram: ['A class is a', 'class is a blueprint', 'is a blueprint for', 'a blueprint for the', 'blueprint for the object']

18使用带有二元组的 sklearn CountVectorize 词汇规范

importpandasaspdfromsklearn.feature_extraction.textimportCountVectorizer#Sampledataforanalysisdata1="Machinelanguageisalow-levelprogramminglanguage.Itiseasilyunderstoodbycomputersbutdifficulttoreadbypeople.Thisiswhypeopleusehigherlevelprogramminglanguages.Programswritteninhigh-levellanguagesarealsoeithercompiledand/orinterpretedintomachinelanguagesothatcomputerscanexecutethem."data2="Assemblylanguageisarepresentationofmachinelanguage.Inotherwords,eachassemblylanguageinstructiontranslatestoamachinelanguageinstruction.Thoughassemblylanguagestatementsarereadable,thestatementsarestilllow-level.Adisadvantageofassemblylanguageisthatitisnotportable,becauseeachplatformcomeswithaparticularAssemblyLanguage"df1=pd.DataFrame({'Machine':[data1],'Assembly':[data2]})#Initializevectorizer=CountVectorizer(ngram_range=(2,2))doc_vec=vectorizer.fit_transform(df1.iloc[0])#CreatedataFramedf2=pd.DataFrame(doc_vec.toarray().transpose(),index=vectorizer.get_feature_names())#Changecolumnheadersdf2.columns=df1.columnsprint(df2)

Output:

Assembly Machine
also either 0 1
and or 0 1
are also 0 1
are readable 1 0
are still 1 0
assembly language 5 0
because each 1 0
but difficult 0 1
by computers 0 1
by people 0 1
can execute 0 1
...

19使用 TextBlob 提取名词短语

fromtextblobimportTextBlob#Extractnounblob=TextBlob("CanadaisacountryinthenorthernpartofNorthAmerica.")fornounsinblob.noun_phrases:print(nouns)

Output:

canada
northern part
america

20如何计算词-词共现矩阵

importnumpyasnpimportnltkfromnltkimportbigramsimportitertoolsimportpandasaspddefgenerate_co_occurrence_matrix(corpus):vocab=set(corpus)vocab=list(vocab)vocab_index={word:ifori,wordinenumerate(vocab)}#Createbigramsfromallwordsincorpusbi_grams=list(bigrams(corpus))#Frequencydistributionofbigrams((word1,word2),num_occurrences)bigram_freq=nltk.FreqDist(bi_grams).most_common(len(bi_grams))#Initialiseco-occurrencematrix#co_occurrence_matrix[current][previous]co_occurrence_matrix=np.zeros((len(vocab),len(vocab)))#Loopthroughthebigramstakingthecurrentandpreviousword,#andthenumberofoccurrencesofthebigram.forbigraminbigram_freq:current=bigram[0][1]previous=bigram[0][0]count=bigram[1]pos_current=vocab_index[current]pos_previous=vocab_index[previous]co_occurrence_matrix[pos_current][pos_previous]=countco_occurrence_matrix=np.matrix(co_occurrence_matrix)#returnthematrixandtheindexreturnco_occurrence_matrix,vocab_indextext_data=[['Where','Python','is','used'],['What','is','Python''used','in'],['Why','Python','is','best'],['What','companies','use','Python']]#Createonelistusingmanylistsdata=list(itertools.chain.from_iterable(text_data))matrix,vocab_index=generate_co_occurrence_matrix(data)data_matrix=pd.DataFrame(matrix,index=vocab_index,columns=vocab_index)print(data_matrix)

Output:

best use What Where ... in is Python used
best 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 1.0
use 0.0 0.0 0.0 0.0 ... 0.0 1.0 0.0 0.0
What 1.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0
Where 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0
Pythonused 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 1.0
Why 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 1.0
companies 0.0 1.0 0.0 1.0 ... 1.0 0.0 0.0 0.0
in 0.0 0.0 0.0 0.0 ... 0.0 0.0 1.0 0.0
is 0.0 0.0 1.0 0.0 ... 0.0 0.0 0.0 0.0
Python 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0
used 0.0 0.0 1.0 0.0 ... 0.0 0.0 0.0 0.0

[11 rows x 11 columns]

21使用 TextBlob 进行情感分析

fromtextblobimportTextBlobdefsentiment(polarity):ifblob.sentiment.polarity<0:print("Negative")elifblob.sentiment.polarity>0:print("Positive")else:print("Neutral")blob=TextBlob("Themoviewasexcellent!")print(blob.sentiment)sentiment(blob.sentiment.polarity)blob=TextBlob("Themoviewasnotbad.")print(blob.sentiment)sentiment(blob.sentiment.polarity)blob=TextBlob("Themoviewasridiculous.")print(blob.sentiment)sentiment(blob.sentiment.polarity)

Output:

Sentiment(polarity=1.0, subjectivity=1.0)
Positive
Sentiment(polarity=0.3499999999999999, subjectivity=0.6666666666666666)
Positive
Sentiment(polarity=-0.3333333333333333, subjectivity=1.0)
Negative

22使用 Goslate 进行语言翻译

importgoslatetext="Commentvas-tu?"gs=goslate.Goslate()translatedText=gs.translate(text,'en')print(translatedText)translatedText=gs.translate(text,'zh')print(translatedText)translatedText=gs.translate(text,'de')print(translatedText)23使用 TextBlob 进行语言检测和翻译

fromtextblobimportTextBlobblob=TextBlob("Commentvas-tu?")print(blob.detect_language())print(blob.translate(to='es'))print(blob.translate(to='en'))print(blob.translate(to='zh'))

Output:

fr
¿Como estas tu?
How are you?
你好吗？

24使用 TextBlob 获取定义和同义词

fromtextblobimportTextBlobfromtextblobimportWordtext_word=Word('safe')print(text_word.definitions)synonyms=set()forsynsetintext_word.synsets:forlemmainsynset.lemmas():synonyms.add(lemma.name())print(synonyms)

Output:

['strongbox where valuables can be safely kept', 'a ventilated or refrigerated cupboard for securing provisions from pests', 'contraceptive device consisting of a sheath of thin rubber or latex that is worn over the penis during intercourse', 'free from danger or the risk of harm', '(of an undertaking) secure from risk', 'having reached a base without being put out', 'financially sound']
{'secure', 'rubber', 'good', 'safety', 'safe', 'dependable', 'condom', 'prophylactic'}

25使用 TextBlob 获取反义词列表

fromtextblobimportTextBlobfromtextblobimportWordtext_word=Word('safe')antonyms=set()forsynsetintext_word.synsets:forlemmainsynset.lemmas():iflemma.antonyms():antonyms.add(lemma.antonyms()[0].name())print(antonyms)

Output: