去掉停用詞

問(wèn)題描述現(xiàn)有某電視劇彈幕信息，請(qǐng)去掉彈幕信息里面的停用詞，然后以列表的形式輸出彈幕中詞頻最高的10個(gè)詞。contentslikeCounttv_name0二刷的朋友有嗎20111我希望一切能重來(lái)312這段眼神變化的太妙了913良心啊，一小時(shí)18414基本都好201............59995這個(gè)葉爸有點(diǎn)東西2271259996眼鏡掉在案發(fā)現(xiàn)場(chǎng)了901259997俺的眼睛掉在廠里了101259998他不戴假發(fā)你更不習(xí)慣171259999那是什么藥呀3312輸出結(jié)果詞語(yǔ)詞頻孩子2030爬山1913嚴(yán)良1511真的1407一個(gè)1305媽媽939演技902一起865普普846感覺(jué)782問(wèn)題分析問(wèn)題描述問(wèn)題解答怎樣將句子切割成為詞語(yǔ)？

怎樣把彈幕信息表和停用詞表聯(lián)合起來(lái)？怎樣統(tǒng)計(jì)詞頻？cut()merge()value_counts()操作提示利用jieba庫(kù)中的cut()函數(shù)對(duì)彈幕信息進(jìn)行分詞后轉(zhuǎn)換為數(shù)據(jù)框，將之與停用詞數(shù)據(jù)框進(jìn)行合并，篩選出不在停用詞表中的詞語(yǔ)，統(tǒng)計(jì)這些詞出現(xiàn)的詞頻，這樣得到了題目要求的結(jié)果。程序代碼importpandasaspdimportjiebadata=pd.read_csv(r"D:\pydata\項(xiàng)目四\某電視劇彈幕信息.csv")stop_word=open(r"D:\pydata\項(xiàng)目四\停用詞.txt","r",encoding='utf-8')stop_word=stop_word.read().split()stop_word=pd.DataFrame(stop_word,columns=["stopword"])word=pd.DataFrame(jieba.cut("".join(data.iloc[:,1].astype(str))),columns=["word"])word=pd.merge(word,stop_word,left_on=["word"],right_on=["stopword"],how="left")word=word.query("stopword.isnull()andword.str.len()>1",engine='python')["word"]word=word.value_counts()print(word.head(10))程序代碼importpandasaspdimportjiebadata=pd.read_csv(r"D:\pydata\項(xiàng)目四\某電視劇彈幕信息.csv")stop_word=open(r"D:\pydata\項(xiàng)目四\停用詞.txt","r",encoding='utf-8')stop_word=stop_word.read().split()stop_word=pd.DataFrame(stop_word,columns=["stopword"])word=pd.DataFrame(jieba.cut("".join(data.iloc[:,1].astype(str))),columns=["word"])word=pd.merge(word,stop_word,left_on=["word"],right_on=["stopword"],how="left")word=word.query("stopword.isnull()andword.str.len()>1",engine='python')["word"]word=word.value_counts()print(word.head(10))程序代碼pandas提供了大量能使我們快速便捷地處理數(shù)據(jù)的函數(shù)和方法。程序代碼importpandasaspdimportjiebadata=pd.read_csv(r"D:\pydata\項(xiàng)目四\某電視劇彈幕信息.csv")stop_word=open(r"D:\pydata\項(xiàng)目四\停用詞.txt","r",encoding='utf-8')stop_word=stop_word.read().split()stop_word=pd.DataFrame(stop_word,columns=["stopword"])word=pd.DataFrame(jieba.cut("".join(data.iloc[:,1].astype(str))),columns=["word"])word=pd.merge(word,stop_word,left_on=["word"],right_on=["stopword"],how="left")word=word.query("stopword.isnull()andword.str.len()>1",engine='python')["word"]word=word.value_counts()print(word.head(10))程序代碼jieba是python的一個(gè)中文分詞庫(kù)，具有高性能、高準(zhǔn)確率、可擴(kuò)展等特點(diǎn)程序代碼importpandasaspdimportjiebadata=pd.read_csv(r"D:\pydata\項(xiàng)目四\某電視劇彈幕信息.csv")stop_word=open(r"D:\pydata\項(xiàng)目四\停用詞.txt","r",encoding='utf-8')stop_word=stop_word.read().split()stop_word=pd.DataFrame(stop_word,columns=["stopword"])word=pd.DataFrame(jieba.cut("".join(data.iloc[:,1].astype(str))),columns=["word"])word=pd.merge(word,stop_word,left_on=["word"],right_on=["stopword"],how="left")word=word.query("stopword.isnull()andword.str.len()>1",engine='python')["word"]word=word.value_counts()print(word.head(10))程序代碼contentslikeCounttv_name0二刷的朋友有嗎20111我希望一切能重來(lái)312這段眼神變化的太妙了913良心啊，一小時(shí)18414基本都好201............59995這個(gè)葉爸有點(diǎn)東西2271259996眼鏡掉在案發(fā)現(xiàn)場(chǎng)了901259997俺的眼睛掉在廠里了101259998他不戴假發(fā)你更不習(xí)慣171259999那是什么藥呀3312data=pd.read_csv(r"D:\pydata\項(xiàng)目四\某電視劇彈幕信息.csv")程序代碼importpandasaspdimportjiebadata=pd.read_csv(r"D:\pydata\項(xiàng)目四\某電視劇彈幕信息.csv")stop_word=open(r"D:\pydata\項(xiàng)目四\停用詞.txt","r",encoding='utf-8')stop_word=stop_word.read().split()stop_word=pd.DataFrame(stop_word,columns=["stopword"])word=pd.DataFrame(jieba.cut("".join(data.iloc[:,1].astype(str))),columns=["word"])word=pd.merge(word,stop_word,left_on=["word"],right_on=["stopword"],how="left")word=word.query("stopword.isnull()andword.str.len()>1",engine='python')["word"]word=word.value_counts()print(word.head(10))程序代碼importpandasaspdimportjiebadata=pd.read_csv(r"D:\pydata\項(xiàng)目四\某電視劇彈幕信息.csv")stop_word=open(r"D:\pydata\項(xiàng)目四\停用詞.txt","r",encoding='utf-8')stop_word=stop_word.read().split()stop_word=pd.DataFrame(stop_word,columns=["stopword"])word=pd.DataFrame(jieba.cut("".join(data.iloc[:,1].astype(str))),columns=["word"])word=pd.merge(word,stop_word,left_on=["word"],right_on=["stopword"],how="left")word=word.query("stopword.isnull()andword.str.len()>1",engine='python')["word"]word=word.value_counts()print(word.head(10))程序代碼切割后的列表為：['$','0','1','2','3','4','5','6','7','8','9......'非獨(dú)','靠','順','順著','首先','！','，','：','；','？']程序代碼importpandasaspdimportjiebadata=pd.read_csv(r"D:\pydata\項(xiàng)目四\某電視劇彈幕信息.csv")stop_word=open(r"D:\pydata\項(xiàng)目四\停用詞.txt","r",encoding='utf-8')stop_word=stop_word.read().split()stop_word=pd.DataFrame(stop_word,columns=["stopword"])word=pd.DataFrame(jieba.cut("".join(data.iloc[:,1].astype(str))),columns=["word"])word=pd.merge(word,stop_word,left_on=["word"],right_on=["stopword"],how="left")word=word.query("stopword.isnull()andword.str.len()>1",engine='python')["word"]word=word.value_counts()print(word.head(10))程序代碼

stopword0$10213243.....741！742，743：744；745？生成的停用詞表stop_word=pd.DataFrame(stop_word,columns=["stopword"])程序代碼importpandasaspdimportjiebadata=pd.read_csv(r"D:\pydata\項(xiàng)目四\某電視劇彈幕信息.csv")stop_word=open(r"D:\pydata\項(xiàng)目四\停用詞.txt","r",encoding='utf-8')stop_word=stop_word.read().split()stop_word=pd.DataFrame(stop_word,columns=["stopword"])word=pd.DataFrame(jieba.cut("".join(data.iloc[:,1].astype(str))),columns=["word"])word=pd.merge(word,stop_word,left_on=["word"],right_on=["stopword"],how="left")word=word.query("stopword.isnull()andword.str.len()>1",engine='python')["word"]word=word.value_counts()print(word.head(10))程序代碼word0二刷1的2朋友3有4嗎......339467那339468是339469什么339470藥339471呀word=pd.DataFrame(jieba.cut("".join(data.iloc[:,1].astype(str))),columns=["word"])程序代碼word=pd.DataFrame(jieba.cut("".join(data.iloc[:,1].astype(str))),columns=["word"])contentslikeCounttv_name0二刷的朋友有嗎20111我希望一切能重來(lái)312這段眼神變化的太妙了913良心啊，一小時(shí)18414基本都好201............59995這個(gè)葉爸有點(diǎn)東西2271259996眼鏡掉在案發(fā)現(xiàn)場(chǎng)了901259997俺的眼睛掉在廠里了101259998他不戴假發(fā)你更不習(xí)慣171259999那是什么藥呀3312“二刷的朋友有嗎我希望一切能重來(lái)這段眼神變化的太妙了良心啊，一小時(shí)……好了警官你是下一個(gè)好一個(gè)不戴眼鏡的斯文敗類(lèi)兒子你啥時(shí)候?qū)W習(xí)啊居然還不說(shuō)實(shí)話？我不戴假發(fā)更厲害演完這部電影，伊能靜開(kāi)始怕了你看我還有機(jī)會(huì)嗎這個(gè)葉爸有點(diǎn)東西眼鏡掉在案發(fā)現(xiàn)場(chǎng)了俺的眼睛掉在廠里了他不戴假發(fā)你更不習(xí)慣那是什么藥呀”程序代碼word=pd.DataFrame(jieba.cut("".join(data.iloc[:,1].astype(str))),columns=["word"])“二刷的朋友有嗎我希望一切能重來(lái)這段眼神變化的太妙了良心啊，一小時(shí)……好了警官你是下一個(gè)好一個(gè)不戴眼鏡的斯文敗類(lèi)兒子你啥時(shí)候?qū)W習(xí)啊居然還不說(shuō)實(shí)話？我不戴假發(fā)更厲害演完這部電影，伊能靜開(kāi)始怕了你看我還有機(jī)會(huì)嗎這個(gè)葉爸有點(diǎn)東西眼鏡掉在案發(fā)現(xiàn)場(chǎng)了俺的眼睛掉在廠里了他不戴假發(fā)你更不習(xí)慣那是什么藥呀”[‘二刷’,‘的’,‘朋友’,‘有’,‘嗎’,‘我’,‘希望’,……‘他’，’不戴’，‘假發(fā)’，你，‘更不’，‘習(xí)慣’，‘那是’，‘什么’，‘藥’，‘呀’]程序代碼word=pd.DataFrame(jieba.cut("".join(data.iloc[:,1].astype(str))),columns=["word"])[‘二刷’,‘的’,‘朋友’,‘有’,‘嗎’,‘我’,‘希望’,……‘他’，’不戴’，‘假發(fā)’，你，‘更不’，‘習(xí)慣’，‘那是’，‘什么’，‘藥’，‘呀’]word0二刷1的2朋友3有4嗎......339467那339468是339469什么339470藥339471呀程序代碼importpandasaspdimportjiebadata=pd.read_csv(r"D:\pydata\項(xiàng)目四\某電視劇彈幕信息.csv")stop_word=open(r"D:\pydata\項(xiàng)目四\停用詞.txt","r",encoding='utf-8')stop_word=stop_word.read().split()stop_word=pd.DataFrame(stop_word,columns=["stopword"])word=pd.DataFrame(jieba.cut("".join(data.iloc[:,1].astype(str))),columns=["word"])word=pd.merge(word,stop_word,left_on=["word"],right_on=["stopword"],how="left")word=word.query("stopword.isnull()andword.str.len()>1",engine='python')["word"]word=word.value_counts()print(word.head(10))程序代碼word=pd.merge(word,stop_word,left_on=["word"],right_on=["stopword"],how="left")wordstopword0二刷NaN1的的2朋友NaN3有有4嗎嗎.........339467那那339468是是339469什么什么339470藥NaN339471呀呀程序代碼importpandasaspdimportjiebadata=pd.read_csv(r"D:\pydata\項(xiàng)目四\某電視劇彈幕信息.csv")stop_word=open(r"D:\pydata\項(xiàng)目四\停用詞.txt","r",encoding='utf-8')stop_word=stop_word.read().split()stop_word=pd.DataFrame(stop_word,columns=["stopword"])word=pd.DataFrame(jieba.cut("".join(data.iloc[:,1].astype(str))),columns=["word"])word=pd.merge(word,stop_word,left_on=["word"],right_on=["stopword"],how="left")word=word.query("stopword.isnull()andword.str.len()>1",engine='python')["word"]word=word.value_counts()print(word.head(10))程序代碼wordstopword0二刷NaN2朋友NaN6希望NaN9重來(lái)NaN10這段NaN.........339451案發(fā)現(xiàn)場(chǎng)NaN339455眼睛NaN339458廠里NaN339462戴假發(fā)NaN339466習(xí)慣NaNword=word.query("stopword.isnull()andword.str.len()>1",engine='python')["word"]程序代碼importpandasaspdimportjiebadata=pd.read_csv(r"D:\pydata\項(xiàng)目四\某電視劇彈幕信息.csv")stop_word=open(r"D:\pydata\項(xiàng)目四\停用詞.txt","r",encoding='utf-8')stop_word=stop_word.read().split()stop_word=pd.DataFrame(stop_word,columns=["stopword"])word=pd.DataFrame(jieba.cut("".join(data.iloc[:,1].astype(str))),columns=["word"])word=pd.merge(word,stop_word,left_on=["word"],right_on=["stopword"],how="left")word=word.query("stopword.isnull()andword.str.len()>1",engine='python')["word"]word=word.value_counts()print(word.head(10))程序代碼詞語(yǔ)數(shù)量孩子2023爬山1911嚴(yán)良1511真的1407一個(gè)1305

...

...卡爾1胡成1親熱1碰過(guò)1案發(fā)現(xiàn)場(chǎng)1word=word.value_counts()程序代碼importpandasaspdimportjiebadata=pd.read_csv(r"D:\pydata\項(xiàng)目四\某電視劇彈幕信息.csv")stop_word=open(r"D:\pydata\項(xiàng)目四\停用詞.txt","r",encoding='utf-8')stop_word=stop_word.read().split()stop_word=pd.DataFrame(stop_word,columns=["stopword"])word=pd.DataFrame(jieba.cut("".join(data.iloc[:,1].astype(str))),columns=["word"])word=pd.merge(word,stop_word,left_on=["word"],right_on=["stopword"],how="left")word=word.query("stopword.isnull()andword.str.len()>1",engine='python')["word"]word=word.value_counts()print(word.head(10))任務(wù)小結(jié)merge()函數(shù)通過(guò)列或索引將兩個(gè)數(shù)據(jù)框相關(guān)的數(shù)據(jù)行合并成一行，構(gòu)成一個(gè)新的數(shù)據(jù)框。為了提供更為靈活的操作來(lái)滿足實(shí)際工作的需要。一展身手現(xiàn)有某電視劇彈幕信息，請(qǐng)去掉彈幕信息里面的停用詞，然后以列表的形式輸出第一集的彈幕中詞頻最高的10個(gè)詞。結(jié)果為如下列表：['爬山','真實(shí)','一起','電影','豐田','不錯(cuò)','感覺(jué)','欺負(fù)','秦昊','真的']制作團(tuán)隊(duì)制作：劉學(xué)重慶市九龍坡職業(yè)教育中心選取男士最喜歡的電影主講人：劉學(xué)重慶市九龍坡職業(yè)教育中心問(wèn)題描述現(xiàn)有三張表，“users”（用戶(hù)信息）表，“ratings”（評(píng)分）表，“movies”（電影信息）表，三個(gè)表的字段如圖所示，請(qǐng)統(tǒng)計(jì)出男士最喜歡的10部電影的信息。UserID用戶(hù)idGender性別Age年齡Occupation職業(yè)Zip-code郵編MovieID電影idTitle電影名Genres類(lèi)型UserID用戶(hù)idMovieID電影idRating評(píng)分Timestamp時(shí)間戳users

ratingsmovies輸出結(jié)果MovieIDTitleGenresRating787GateofHeavenlyPeace,The(1995)Documentary5.0985SmallWonders(1996)Documentary5.03233SmashingTime(1967)Comedy5.03280Baby,The(1973)Horror5.03172Ulysses(Ulisse)(1954)Adventure5.0439DangerousGame(1993)Drama5.0130Angela(1995)Drama5.03656Lured(1947)Crime5.01830FollowtheBitch(1998)Comedy5.0989SchlafesBruder(BrotherofSleep)(1995)Drama5.0問(wèn)題分析問(wèn)題描述問(wèn)題解答最終輸出的信息從那幾個(gè)表中獲??？

怎樣對(duì)表進(jìn)行合并？怎樣得出男性評(píng)分最高的電影？3張表都需要merge()先合并表再統(tǒng)計(jì)操作提示首先是合并評(píng)分表和用戶(hù)信息表，得出男性評(píng)分最高的電影的ID和評(píng)分，然后把得到的新表和電影信息表進(jìn)行合并，最后對(duì)評(píng)分進(jìn)行降序排序就得出了男性最喜歡的電影信息。程序代碼importpandasaspdmovies=pd.read_table(r"D:\pydata\項(xiàng)目四\movies.dat",sep='::',header=None,names=['MovieID','Title','Genres'],engine='python',encoding='iso-8859-15')ratings=pd.read_table(r"D:\pydata\項(xiàng)目四\ratings.dat",sep='::',header=None,names=['UserID','MovieID','Rating','Timestamp'],engine='python',encoding='iso-8859-15')users=pd.read_table(r"D:\pydata\項(xiàng)目四\users.dat",sep='::',header=None,names=['UserID','Gender','Age','Occupation','Zip-code'],engine='python',encoding='iso-8859-15')info=pd.merge(ratings,users,on="UserID",how="inner")①info=info[info["Gender"]=="M"]②info=info.groupby("MovieID")["Rating"].mean()③res=pd.merge(movies,info,on="MovieID")④res=res.sort_values(by="Rating",ascending=False)⑤res=res.round({"Rating":2})res.head(10)程序代碼importpandasaspdmovies=pd.read_table(r"D:\pydata\項(xiàng)目四\movies.dat",sep='::',header=None,names=['MovieID','Title','Genres'],engine='python',encoding='iso-8859-15')ratings=pd.read_table(r"D:\pydata\項(xiàng)目四\ratings.dat",sep='::',header=None,names=['UserID','MovieID','Rating','Timestamp'],engine='python',encoding='iso-8859-15')users=pd.read_table(r"D:\pydata\項(xiàng)目四\users.dat",sep='::',header=None,names=['UserID','Gender','Age','Occupation','Zip-code'],engine='python',encoding='iso-8859-15')info=pd.merge(ratings,users,on="UserID",how="inner")①info=info[info["Gender"]=="M"]②info=info.groupby("MovieID")["Rating"].mean()③res=pd.merge(movies,info,on="MovieID")④res=res.sort_values(by="Rating",ascending=False)⑤res=res.round({"Rating":2})res.head(10)程序代碼pandas提供了大量能使我們快速便捷地處理數(shù)據(jù)的函數(shù)和方法。程序代碼importpandasaspdmovies=pd.read_table(r"D:\pydata\項(xiàng)目四\movies.dat",sep='::',header=None,names=['MovieID','Title','Genres'],engine='python',encoding='iso-8859-15')ratings=pd.read_table(r"D:\pydata\項(xiàng)目四\ratings.dat",sep='::',header=None,names=['UserID','MovieID','Rating','Timestamp'],engine='python',encoding='iso-8859-15')users=pd.read_table(r"D:\pydata\項(xiàng)目四\users.dat",sep='::',header=None,names=['UserID','Gender','Age','Occupation','Zip-code'],engine='python',encoding='iso-8859-15')info=pd.merge(ratings,users,on="UserID",how="inner")①info=info[info["Gender"]=="M"]②info=info.groupby("MovieID")["Rating"].mean()③res=pd.merge(movies,info,on="MovieID")④res=res.sort_values(by="Rating",ascending=False)⑤res=res.round({"Rating":2})res.head(10)程序代碼

MovieIDTitleGenres01ToyStory(1995)Animation|Children's|Comedy12Jumanji(1995)Adventure|Children's|Fantasy23GrumpierOldMen(1995)Comedy|Romance34WaitingtoExhale(1995)Comedy|Drama45FatheroftheBridePartII(1995)Comedy............38783948MeettheParents(2000)Comedy38793949RequiemforaDream(2000)Drama38803950Tigerland(2000)Drama38813951TwoFamilyHouse(2000)Drama38823952Contender,The(2000)Drama|ThrillerMovies表中的數(shù)據(jù)程序代碼importpandasaspdmovies=pd.read_table(r"D:\pydata\項(xiàng)目四\movies.dat",sep='::',header=None,names=['MovieID','Title','Genres'],engine='python',encoding='iso-8859-15')ratings=pd.read_table(r"D:\pydata\項(xiàng)目四\ratings.dat",sep='::',header=None,names=['UserID','MovieID','Rating','Timestamp'],engine='python',encoding='iso-8859-15')users=pd.read_table(r"D:\pydata\項(xiàng)目四\users.dat",sep='::',header=None,names=['UserID','Gender','Age','Occupation','Zip-code'],engine='python',encoding='iso-8859-15')info=pd.merge(ratings,users,on="UserID",how="inner")info=info[info["Gender"]=="M"]info=info.groupby("MovieID")["Rating"].mean()res=pd.merge(movies,info,on="MovieID")res=res.sort_values(by="Rating",ascending=False)res=res.round({"Rating":2})res.head(10)程序代碼UserIDMovieIDRatingTimestamp011193597830076011661397830210921914397830196831340849783002754123555978824291...............1000204604010911956716541100020560401094595670488710002066040562595670474610002076040109649567156481000208604010974956715569ratings表中的數(shù)據(jù)程序代碼importpandasaspdmovies=pd.read_table(r"D:\pydata\項(xiàng)目四\movies.dat",sep='::',header=None,names=['MovieID','Title','Genres'],engine='python',encoding='iso-8859-15')ratings=pd.read_table(r"D:\pydata\項(xiàng)目四\ratings.dat",sep='::',header=None,names=['UserID','MovieID','Rating','Timestamp'],engine='python',encoding='iso-8859-15')users=pd.read_table(r"D:\pydata\項(xiàng)目四\users.dat",sep='::',header=None,names=['UserID','Gender','Age','Occupation','Zip-code'],engine='python',encoding='iso-8859-15')info=pd.merge(ratings,users,on="UserID",how="inner")info=info[info["Gender"]=="M"]info=info.groupby("MovieID")["Rating"].mean()res=pd.merge(movies,info,on="MovieID")res=res.sort_values(by="Rating",ascending=False)res=res.round({"Rating":2})res.head(10)程序代碼UserIDGenderAgeOccupationZip-code01F1104806712M56167007223M25155511734M4570246045M252055455..................60356036F25153260360366037F4517600660376038F5611470660386039F4500106060396040M25611106

users表中的數(shù)據(jù)程序代碼importpandasaspdmovies=pd.read_table(r"D:\pydata\項(xiàng)目四\movies.dat",sep='::',header=None,names=['MovieID','Title','Genres'],engine='python',encoding='iso-8859-15')ratings=pd.read_table(r"D:\pydata\項(xiàng)目四\ratings.dat",sep='::',header=None,names=['UserID','MovieID','Rating','Timestamp'],engine='python',encoding='iso-8859-15')users=pd.read_table(r"D:\pydata\項(xiàng)目四\users.dat",sep='::',header=None,names=['UserID','Gender','Age','Occupation','Zip-code'],engine='python',encoding='iso-8859-15')info=pd.merge(ratings,users,on="UserID",how="inner")info=info[info["Gender"]=="M"]info=info.groupby("MovieID")["Rating"].mean()res=pd.merge(movies,info,on="MovieID")res=res.sort_values(by="Rating",ascending=False)res=res.round({"Rating":2})res.head(10)程序代碼UserIDMovieIDRatingTimestampGenderAgeOccupationZip-code111935978300760F1104806716613978302109F1104806719143978301968F11048067134084978300275F11048067123555978824291F11048067........................604010911956716541M25611106604010945956704887M2561110660405625956704746M25611106604010964956715648M25611106604010974956715569M25611106info=pd.merge(ratings,users,on="UserID",how="inner")評(píng)分表和用戶(hù)表合并后的數(shù)據(jù)框程序代碼importpandasaspdmovies=pd.read_table(r"D:\pydata\項(xiàng)目四\movies.dat",sep='::',header=None,names=['MovieID','Title','Genres'],engine='python',encoding='iso-8859-15')ratings=pd.read_table(r"D:\pydata\項(xiàng)目四\ratings.dat",sep='::',header=None,names=['UserID','MovieID','Rating','Timestamp'],engine='python',encoding='iso-8859-15')users=pd.read_table(r"D:\pydata\項(xiàng)目四\users.dat",sep='::',header=None,names=['UserID','Gender','Age','Occupation','Zip-code'],engine='python',encoding='iso-8859-15')info=pd.merge(ratings,users,on="UserID",how="inner")info=info[info["Gender"]=="M"]info=info.groupby("MovieID")["Rating"].mean()res=pd.merge(movies,info,on="MovieID")res=res.sort_values(by="Rating",ascending=False)res=res.round({"Rating":2})res.head(10)程序代碼UserIDMovieIDRatingTimestampGenderAgeOccupationZip-code213575978298709M561670072230684978299000M561670072215374978299620M56167007226473978299351M561670072221944978299297M561670072........................604010911956716541M25611106604010945956704887M2561110660405625956704746M25611106604010964956715648M25611106604010974956715569M25611106info=info[info["Gender"]=="M"]篩選出所有男性用戶(hù)后的表程序代碼importpandasaspdmovies=pd.read_table(r"D:\pydata\項(xiàng)目四\movies.dat",sep='::',header=None,names=['MovieID','Title','Genres'],engine='python',encoding='iso-8859-15')ratings=pd.read_table(r"D:\pydata\項(xiàng)目四\ratings.dat",sep='::',header=None,names=['UserID','MovieID','Rating','Timestamp'],engine='python',encoding='iso-8859-15')users=pd.read_table(r"D:\pydata\項(xiàng)目四\users.dat",sep='::',header=None,names=['UserID','Gender','Age','Occupation','Zip-code'],engine='python',encoding='iso-8859-15')info=pd.merge(ratings,users,on="UserID",how="inner")info=info[info["Gender"]=="M"]info=info.groupby("MovieID")["Rating"].mean()res=pd.merge(movies,info,on="MovieID")res=res.sort_values(by="Rating",ascending=False)res=res.round({"Rating":2})res.head(10)程序代碼MovieID

14.13055223.17523832.99415242.48235352.888298...…39483.6418383949468181839514.04347839523.787986男性用戶(hù)對(duì)各個(gè)電影的評(píng)分平均值info=info.groupby("MovieID")["Rating"].mean()程序代碼importpandasaspdmovies=pd.read_table(r"D:\pydata\項(xiàng)目四\movies.dat",sep='::',header=None,names=['MovieID','Title','Genres'],engine='python',encoding='iso-8859-15')ratings=pd.read_table(r"D:\pydata\項(xiàng)目四\ratings.dat",sep='::',header=None,names=['UserID','MovieID','Rating','Timestamp'],engine='python',encoding='iso-8859-15')users=pd.read_table(r"D:\pydata\項(xiàng)目四\users.dat",sep='::',header=None,names=['UserID','Gender','Age','Occupation','Zip-code'],engine='python',encoding='iso-8859-15')info=pd.merge(ratings,users,on="UserID",how="inner")info=info[info["Gender"]=="M"]info=info.groupby("MovieID")["Rating"].mean()res=pd.merge(movies,info,on="MovieID")res=res.sort_values(by="Rating",ascending=False)res=res.round({"Rating":2})res.head(10)程序代碼MovieIDTitleGenresRating1ToyStory(1995)Animation|Children's|Comedy4.1305522Jumanji(1995)Adventure|Children's|Fantasy3.1752383GrumpierOldMen(1995)Comedy|Romance2.9941524WaitingtoExhale(1995)Comedy|Drama2.4823535FatheroftheBridePartII(1995)Comedy2.888298............3948MeettheParents(2000)Comedy3.6418383949RequiemforaDream(2000)Drama4.1741073950Tigerland(2000)Drama3.6818183951TwoFamilyHouse(2000)Drama4.0434783952Contender,The(2000)Drama|Thriller3.787986各個(gè)電影的男性用戶(hù)評(píng)分均值res=pd.merge(movies,info,on="MovieID")程序代碼importpandasaspdmovies=pd.read_table(r"D:\pydata\項(xiàng)目四\movies.dat",sep='::',header=None,names=['MovieID','Title','Genres'],engine='python',encoding='iso-8859-15')ratings=pd.read_table(r"D:\pydata\項(xiàng)目四\ratings.dat",sep='::',header=None,names=['UserID','MovieID','Rating','Timestamp'],engine='python',encoding='iso-8859-15')users=pd.read_table(r"D:\pydata\項(xiàng)目四\users.dat",sep='::',header=None,names=['UserID','Gender','Age','Occupation','Zip-code'],engine='python',encoding='iso-8859-15')info=pd.merge(ratings,users,on="UserID",how="inner")info=info[info["Gender"]=="M"]info=info.groupby("MovieID")["Rating"].mean()res=pd.merge(movies,info,on="MovieID")res=res.sort_values(by="Rating",ascending=False)res=res.round({"Rating":2})res.head(10)程序代碼MovieIDTitleGenresRating787GateofHeavenlyPeace,The(1995)Documentary5.0985SmallWonders(1996)Documentary5.03233SmashingTime(1967)Comedy5.03280Baby,The(1973)Horror5.03172Ulysses(Ulisse)(1954)Adventure5.0............3460HillbillysinaHauntedHouse(1967)Comedy1.0834PhatBeach(1996)Comedy1.03136JamesDeanStory,The(1957)Documentary1.03904UninvitedGuest,An(2000)Drama1.0684Windows(1980)Drama1.0排序后的數(shù)據(jù)表res=res.sort_values(by="Rating",ascending=False)程序代碼importpandasaspdmovies=pd.read_table(r"D:\pydata\項(xiàng)目四\movies.dat",sep='::',header=None,names=['MovieID','Title','Genres'],engine='python',encoding='iso-8859-15')ratings=pd.read_table(r"D:\pydata\項(xiàng)目四\ratings.dat",sep='::',header=None,names=['UserID','MovieID','Rating','Timestamp'],engine='python',encoding='iso-8859-15')users=pd.read_table(r"D:\pydata\項(xiàng)目四\users.dat",sep='::',header=None,names=['UserID','Gender','Age','Occupation','Zip-code'],engine='python',encoding='iso-8859-15')info=pd.merge(ratings,users,on="UserID",how="inner")info=info[info["Gender"]=="M"]info=info.groupby("MovieID")["Rating"].mean()res=pd.merge(movies,info,on="MovieID")res=res.sort_values(by="Rating",ascending=False)res=res.round({"Rating":2})res.head(10)程序代碼MovieIDTitleGenresRating787GateofHeavenlyPeace,The(1995)Documentary5.00985SmallWonders(1996)Documentary5.003233SmashingTime(1967)Comedy5.003280Baby,The(1973)Horror5.003172Ulysses(Ulisse)(1954)Adventure5.00439DangerousGame(1993)Drama5.00130Angela(1995)Drama5.003656Lured(1947)Crime5.001830FollowtheBitch(1998)Comedy5.00989SchlafesBruder(BrotherofSleep)(1995)Drama5.003517Bells,The(1926)Crime|Drama5.002931TimeoftheGypsies(Domzavesanje)(1989)Drama4.833245IAmCuba(SoyCuba/YaKuba)(1964)Drama4.75598WindowtoParis(1994)Comedy4.6753Lamerica(1994)Drama4.67res=res.round({"Rating":2})程序代碼importpandasaspdmovies=pd.read_table(r"D:\pydata\項(xiàng)目四\movies.dat",sep='::',header=None,names=['MovieID','Title','Genres'],engine='python',encoding='iso-8859-15')ratings=pd.read_table(r"D:\pydata\項(xiàng)目四\ratings.dat",sep='::',header=None,names=['UserID','MovieID','Rating','Timestamp'],engine='python',encoding='iso-8859-15')users=pd.read_table(r"D:\pydata\項(xiàng)目四\users.dat",sep='::',header=None,names=['UserID','Gender','Age','Occupation','Zip-code'],engine='python',encoding='iso-8859-15')info=pd.merge(ratings,users,on="UserID",how="inner")info=info[info["Gender"]=="M"]info=info.groupby("MovieID")["Rating"].mean()res=pd.merge(movies,info,on="MovieID")res=res.sort_values(by="Rating",ascending=False)res=res.round({"Rating":2})res.head(10)程序代碼MovieIDTitleGenresRating787GateofHeavenlyPeace,The(1995)Documentary5.0985SmallWonders(1996)Documentary5.03233SmashingTime(1967)Comedy5.03280Baby,The(1973)Horror5.03172Ulysses(Ulisse)(1954)Adventure5.0439DangerousGame(1993)Drama5.0130Angela(1995)Drama5.03656Lured(1947)Crime5.01830FollowtheBitch(1998)Comedy5.0989SchlafesBruder(BrotherofSleep)(1995)Drama5.0res.head(10)任務(wù)小結(jié)merge()函數(shù)通過(guò)列或索引將兩個(gè)數(shù)據(jù)框相關(guān)的數(shù)據(jù)行合并成一行，構(gòu)成一個(gè)新的數(shù)據(jù)框。為了提供更為靈活的操作來(lái)滿足實(shí)際工作的需要。一展身手請(qǐng)根據(jù)“movies”（電影信息）表，“users”（用戶(hù)信息）表，“ratings”（評(píng)分）表，求出女性最不喜歡的10部電影。結(jié)果如圖所示。MovieIDTitleGenresRating3695ToxicAvengerPartIII:TheLastTemptationof...Comedy|Horror1.075BigBully(1996)Comedy|Drama1.01439MeetWallySparks(1997)Comedy1.02207JamaicaInn(1939)Drama1.02256Parasite(1982)Horror|Sci-Fi1.03899Circus(2000)Comedy1.03027Slaughterhouse2(1988)Horror1.03592TimeMasters(LesMa?tresduTemps)(1982)Animation|Sci-Fi1.03574Carnosaur3:PrimalSpecies(1996)Horror|Sci-Fi1.02039Cheetah(1989)Adventure|Children's1.0制作團(tuán)隊(duì)制作：劉學(xué)重慶市九龍坡職業(yè)教育中心統(tǒng)計(jì)各競(jìng)賽項(xiàng)目的人數(shù)主講人：劉學(xué)重慶市九龍坡職業(yè)教育中心問(wèn)題描述學(xué)校技能大賽啟動(dòng)后，老師收到了各個(gè)班級(jí)的技能大賽報(bào)名表，怎樣快速地統(tǒng)計(jì)出各個(gè)項(xiàng)目的參數(shù)人數(shù)呢？老師收到的報(bào)名表文件如圖所示。輸出結(jié)果

比賽項(xiàng)目人數(shù)2019C程序設(shè)計(jì)522019VF數(shù)據(jù)庫(kù)482020C程序設(shè)計(jì)442020VF數(shù)據(jù)庫(kù)41三維動(dòng)畫(huà)3二維動(dòng)畫(huà)制作18二維動(dòng)畫(huà)制作（2021級(jí)）129圖像處理（2021級(jí)）168圖文混排147幻燈片制作129表格處理113視頻剪輯（2021級(jí)）129問(wèn)題分析問(wèn)題描述問(wèn)題解答怎樣將多個(gè)文件的數(shù)據(jù)讀入到一個(gè)數(shù)據(jù)框？

數(shù)據(jù)要以什么為依據(jù)來(lái)分組然后計(jì)算出各個(gè)項(xiàng)目的參賽人數(shù)？依次追加“比賽項(xiàng)目”操作提示首先是讀取班級(jí)表文件夾下面的第一個(gè)數(shù)據(jù)表，然后把其他的表追加到它的后面，最后通過(guò)gro