电影数据可视化项目--数据清理

Posted 2021-04-28 天天学点数据分析

tags:

篇首语：本文由小常识网(cha138.com)小编为大家整理，主要介绍了电影数据可视化项目--数据清理相关的知识，希望对你有一定的参考价值。

电影数据可视化项目--数据清理

提出问题：

1：电影类型是如何随着时间的推移发生变化的？

2.Universal Pictures 和 ParamountPictures 之间的对比情况如何？

3.改编电影和原创电影的对比情况如何？(通过keywords变量中的based on novel字段来判断)

导入需要的库

importpandas as pd
import numpy as np
import re

将csv数据加载到pandas数据框

movies =pd.read_csv("movies.csv")
movies.head()

	id	imdb_id	popularity	budget	revenue	original_title	cast	homepage	director	tagline	...	overview	runtime	genres	production_companies	release_date	vote_count	vote_average	release_year	budget_adj	revenue_adj
0	135397	tt0369610	32.985763	150000000	1513528810	Jurassic World	Chris Pratt\|Bryce Dallas Howard\|Irrfan Khan\|Vi...	http://www.jurassicworld.com/	Colin Trevorrow	The park is open.	...	Twenty-two years after the events of Jurassic ...	124	Action\|Adventure\|Science Fiction\|Thriller	Universal Studios\|Amblin Entertainment\|Legenda...	2015-06-09	5562	6.5	2015	1.379999e+08	1.392446e+09
1	76341	tt1392190	28.419936	150000000	378436354	Mad Max: Fury Road	Tom Hardy\|Charlize Theron\|Hugh Keays-Byrne\|Nic...	http://www.madmaxmovie.com/	George Miller	What a Lovely Day.	...	An apocalyptic story set in the furthest reach...	120	Action\|Adventure\|Science Fiction\|Thriller	Village Roadshow Pictures\|Kennedy Miller Produ...	2015-05-13	6185	7.1	2015	1.379999e+08	3.481613e+08
2	262500	tt2908446	13.112507	110000000	295238201	Insurgent	Shailene Woodley\|Theo James\|Kate Winslet\|Ansel...	http://www.thedivergentseries.movie/#insurgent	Robert Schwentke	One Choice Can Destroy You	...	Beatrice Prior must confront her inner demons ...	119	Adventure\|Science Fiction\|Thriller	Summit Entertainment\|Mandeville Films\|Red Wago...	2015-03-18	2480	6.3	2015	1.012000e+08	2.716190e+08
3	140607	tt2488496	11.173104	200000000	2068178225	Star Wars: The Force Awakens	Harrison Ford\|Mark Hamill\|Carrie Fisher\|Adam D...	http://www.starwars.com/films/star-wars-episod...	J.J. Abrams	Every generation has a story.	...	Thirty years after defeating the Galactic Empi...	136	Action\|Adventure\|Science Fiction\|Fantasy	Lucasfilm\|Truenorth Productions\|Bad Robot	2015-12-15	5292	7.5	2015	1.839999e+08	1.902723e+09
4	168259	tt2820852	9.335014	190000000	1506249360	Furious 7	Vin Diesel\|Paul Walker\|Jason Statham\|Michelle ...	http://www.furious7.com/	James Wan	Vengeance Hits Home	...	Deckard Shaw seeks revenge against Dominic Tor...	137	Action\|Crime\|Thriller	Universal Pictures\|Original Film\|Media Rights ...	2015-04-01	2947	7.3	2015	1.747999e+08	1.385749e+09

5 rows × 21 columns

数据清理

问题 1：电影类型是如何随着时间的推移发生变化的？

Question 1: How havemovie genres changed over time?

构建一个数据框子集movies_genres

movies_genres= movies[['id','original_title','genres']].reset_index(drop =True)
movies_genres.head()

	id	original_title	genres
0	135397	Jurassic World	Action\|Adventure\|Science Fiction\|Thriller
1	76341	Mad Max: Fury Road	Action\|Adventure\|Science Fiction\|Thriller
2	262500	Insurgent	Adventure\|Science Fiction\|Thriller
3	140607	Star Wars: The Force Awakens	Action\|Adventure\|Science Fiction\|Fantasy
4	168259	Furious 7	Action\|Crime\|Thriller

使用split函数对genres分列处理

movies_genres[['genres1','genres2','genres3','genres4','genres5']]= movies_genres['genres'].str.split('|',expand = True)
del movies_genres['genres']
movies_genres.head()

	id	original_title	genres1	genres2	genres3	genres4	genres5
0	135397	Jurassic World	Action	Adventure	Science Fiction	Thriller	None
1	76341	Mad Max: Fury Road	Action	Adventure	Science Fiction	Thriller	None
2	262500	Insurgent	Adventure	Science Fiction	Thriller	None	None
3	140607	Star Wars: The Force Awakens	Action	Adventure	Science Fiction	Fantasy	None
4	168259	Furious 7	Action	Crime	Thriller	None	None

对新分出来的5列genres列进行逆透视处理

movies_genres= pd.melt(movies_genres,id_vars = ['id','original_title'],value_name ='genres',var_name = 'genre_n')
movies_genres.dropna(axis = 0,subset = ['genres'],inplace =True)
movies_genres.head()

	id	original_title	genre_n	genres
0	135397	Jurassic World	genres1	Action
1	76341	Mad Max: Fury Road	genres1	Action
2	262500	Insurgent	genres1	Adventure
3	140607	Star Wars: The Force Awakens	genres1	Action
4	168259	Furious 7	genres1	Action

删除movies中的原始genres列,然后合并数据框

delmovies['genres']

movies_cleaned_genres = pd.merge(movies,movies_genres,how = 'right',left_on =['id','original_title'],right_on =['id','original_title'])
movies_cleaned_genres.head()

	id	imdb_id	popularity	budget	revenue	original_title	cast	homepage	director	tagline	...	runtime	production_companies	release_date	vote_count	vote_average	release_year	budget_adj	revenue_adj	genre_n	genres
0	135397	tt0369610	32.985763	150000000	1513528810	Jurassic World	Chris Pratt\|Bryce Dallas Howard\|Irrfan Khan\|Vi...	http://www.jurassicworld.com/	Colin Trevorrow	The park is open.	...	124	Universal Studios\|Amblin Entertainment\|Legenda...	2015-06-09	5562	6.5	2015	1.379999e+08	1.392446e+09	genres1	Action
1	135397	tt0369610	32.985763	150000000	1513528810	Jurassic World	Chris Pratt\|Bryce Dallas Howard\|Irrfan Khan\|Vi...	http://www.jurassicworld.com/	Colin Trevorrow	The park is open.	...	124	Universal Studios\|Amblin Entertainment\|Legenda...	2015-06-09	5562	6.5	2015	1.379999e+08	1.392446e+09	genres2	Adventure
2	135397	tt0369610	32.985763	150000000	1513528810	Jurassic World	Chris Pratt\|Bryce Dallas Howard\|Irrfan Khan\|Vi...	http://www.jurassicworld.com/	Colin Trevorrow	The park is open.	...	124	Universal Studios\|Amblin Entertainment\|Legenda...	2015-06-09	5562	6.5	2015	1.379999e+08	1.392446e+09	genres3	Science Fiction
3	135397	tt0369610	32.985763	150000000	1513528810	Jurassic World	Chris Pratt\|Bryce Dallas Howard\|Irrfan Khan\|Vi...	http://www.jurassicworld.com/	Colin Trevorrow	The park is open.	...	124	Universal Studios\|Amblin Entertainment\|Legenda...	2015-06-09	5562	6.5	2015	1.379999e+08	1.392446e+09	genres4	Thriller
4	76341	tt1392190	28.419936	150000000	378436354	Mad Max: Fury Road	Tom Hardy\|Charlize Theron\|Hugh Keays-Byrne\|Nic...	http://www.madmaxmovie.com/	George Miller	What a Lovely Day.	...	120	Village Roadshow Pictures\|Kennedy Miller Produ...	2015-05-13	6185	7.1	2015	1.379999e+08	3.481613e+08	genres1	Action

5 rows × 22 columns

保存对genres处理后的文件

movies_cleaned_genres.to_csv('movies_cleaned_genres.csv',index= False)

问题 2： Universal Pictures 和 Paramount Pictures 之间的对比情况如何？

Question 2: How dothe attributes differ between Universal Pictures and Paramount Pictures?

构建数据框子集movies_production_companies

movies_production_companies= movies[['id','original_title','production_companies']].reset_index(drop =True)
movies_production_companies.head()

	id	original_title	production_companies
0	135397	Jurassic World	Universal Studios\|Amblin Entertainment\|Legenda...
1	76341	Mad Max: Fury Road	Village Roadshow Pictures\|Kennedy Miller Produ...
2	262500	Insurgent	Summit Entertainment\|Mandeville Films\|Red Wago...
3	140607	Star Wars: The Force Awakens	Lucasfilm\|Truenorth Productions\|Bad Robot
4	168259	Furious 7	Universal Pictures\|Original Film\|Media Rights ...

采用正则表达式匹配Universal和Paramount

deffind_Universal(production_company):
    try:
        match =re.search("(\|{0,1}[\w\s]*Universal[\w\s]*\|{0,1})",production_company)
        if match:
            returnmatch.group(0)
        else:
            return None
    except TypeError:
        return None

filtering_Universal = set()
for text in movies_production_companies['production_companies'].tolist():
    results =find_Universal(text)
   filtering_Universal.add(results)
print(filtering_Universal)

{'UniversalPictures International ', '|Universal Music', '|Universal Studios HomeEntertainment|', '|Universal|', 'Universal Studios', 'Universal Studios HomeEntertainment', 'Universal Cartoon Studios|', 'Universal Productions France S','|Universal Home Video', '|Universal Family and Home Entertainment','|Universal City Studios|', None, 'Universal Pictures', '|Universal PicturesInternational ', '|Universal City Studios', 'Universal', 'NBC UniversalTelevision', '|Universal CGI|', 'Universal Studios Home Entertainment FamilyProductions|', 'Universal Home Entertainment', 'Universal PicturesCorporation', 'Universal Pictures Germany GmbH', '|Universal Television', '|NBCUniversal Global Networks|', 'Universal TV|', '|Universal InternationalPictures ', 'Universal Studios|', '|Universal Pictures', 'Universal TV','Universal Cable Productions|', '|Universal Network Television|', '|UniversalPictures|', '|Universal Studios Home Entertainment', 'Universal Pictures|','|Universal Studios Sound Facilities', '|Universal Home Entertainment','Universal Cartoon Studios', '|Universal 1440 Entertainment|', 'NBC UniversalTelevision|', '|Universal 1440 Entertainment', 'Universal 1440 Entertainment','|Universal Cartoon Studios|'}

deffind_Paramount(production_company):
    try:
        match =re.search("(\|{0,1}[\w\s]*Paramount[\w\s]*\|{0,1})",production_company)
        if match:
            returnmatch.group(0)
        else:
            return None
    except TypeError:
        return None

filtering_Paramount = set()
for text in movies_production_companies['production_companies'].tolist():
    results =find_Paramount(text)
   filtering_Paramount.add(results)
print(filtering_Paramount)

{'|ParamountClassics|', '|Paramount Pictures', 'Paramount Pictures|', None, 'ParamountPictures', '|Paramount Pictures Digital Entertainment|', '|ParamountTelevision|', '|Paramount Classics', 'Paramount|', 'Paramount FamousProductions', '|Paramount Home Entertainment', 'Paramount Home Entertainment','Paramount Vantage', '|Paramount Vantage|', 'Paramount Vantage|', 'ParamountPictures Digital Entertainment', '|Paramount Vantage', '|Paramount Animation','Paramount Classics'}

转化production_company列的内容

def modified_as_Universal_or_Paramount(production_company):
    try:
        Universal =re.search("(\|{0,1}[\w\s]*Universal[\w\s]*\|{0,1})",production_company)
        Paramount =re.search("(\|{0,1}[\w\s]*Paramount[\w\s]*\|{0,1})",production_company)
        if Universal:
            return 'Universal'
        elif Paramount:
            return 'Paramount'
        else:
            return None
    except TypeError:
        return None
#直接通过数据框调用函数

movies['production_companies'] =movies['production_companies'].apply(modified_as_Universal_or_Paramount)
movies.head()

	id	imdb_id	popularity	budget	revenue	original_title	cast	homepage	director	tagline	keywords	overview	runtime	production_companies	release_date	vote_count	vote_average	release_year	budget_adj	revenue_adj
0	135397	tt0369610	32.985763	150000000	1513528810	Jurassic World	Chris Pratt\|Bryce Dallas Howard\|Irrfan Khan\|Vi...	http://www.jurassicworld.com/	Colin Trevorrow	The park is open.	monster\|dna\|tyrannosaurus rex\|velociraptor\|island	Twenty-two years after the events of Jurassic ...	124	Universal	2015-06-09	5562	6.5	2015	1.379999e+08	1.392446e+09
1	76341	tt1392190	28.419936	150000000	378436354	Mad Max: Fury Road	Tom Hardy\|Charlize Theron\|Hugh Keays-Byrne\|Nic...	http://www.madmaxmovie.com/	George Miller	What a Lovely Day.	future\|chase\|post-apocalyptic\|dystopia\|australia	An apocalyptic story set in the furthest reach...	120	None	2015-05-13	6185	7.1	2015	1.379999e+08	3.481613e+08
2	262500	tt2908446	13.112507	110000000	295238201	Insurgent	Shailene Woodley\|Theo James\|Kate Winslet\|Ansel...	http://www.thedivergentseries.movie/#insurgent	Robert Schwentke	One Choice Can Destroy You	based on novel\|revolution\|dystopia\|sequel\|dyst...	Beatrice Prior must confront her inner demons ...	119	None	2015-03-18	2480	6.3	2015	1.012000e+08	2.716190e+08
3	140607	tt2488496	11.173104	200000000	2068178225	Star Wars: The Force Awakens	Harrison Ford\|Mark Hamill\|Carrie Fisher\|Adam D...	http://www.starwars.com/films/star-wars-episod...	J.J. Abrams	Every generation has a story.	android\|spaceship\|jedi\|space opera\|3d	Thirty years after defeating the Galactic Empi...	136	None	2015-12-15	5292	7.5	2015	1.839999e+08	1.902723e+09
4	168259	tt2820852	9.335014	190000000	1506249360	Furious 7	Vin Diesel\|Paul Walker\|Jason Statham\|Michelle ...	http://www.furious7.com/	James Wan	Vengeance Hits Home	car race\|speed\|revenge\|suspense\|car	Deckard Shaw seeks revenge against Dominic Tor...	137	Universal	2015-04-01	2947	7.3	2015	1.747999e+08	1.385749e+09

添加计算列profit，profit_rate,profit_adj

movies['profit']= movies['revenue'] - movies['budget']
movies['profit_rate'] =(movies['revenue']-movies['budget'])*100/movies['budget']
movies['profit_adj'] = movies['revenue_adj'] -movies['budget_adj']
movies.head()

	id	imdb_id	popularity	budget	revenue	original_title	cast	homepage	director	tagline	...	production_companies	release_date	vote_count	vote_average	release_year	budget_adj	revenue_adj	profit	profit_rate	profit_adj
0	135397	tt0369610	32.985763	150000000	1513528810	Jurassic World	Chris Pratt\|Bryce Dallas Howard\|Irrfan Khan\|Vi...	http://www.jurassicworld.com/	Colin Trevorrow	The park is open.	...	Universal	2015-06-09	5562	6.5	2015	1.379999e+08	1.392446e+09	1363528810	909.019207	1.254446e+09
1	76341	tt1392190	28.419936	150000000	378436354	Mad Max: Fury Road	Tom Hardy\|Charlize Theron\|Hugh Keays-Byrne\|Nic...	http://www.madmaxmovie.com/	George Miller	What a Lovely Day.	...	None	2015-05-13	6185	7.1	2015	1.379999e+08	3.481613e+08	228436354	152.290903	2.101614e+08
2	262500	tt2908446	13.112507	110000000	295238201	Insurgent	Shailene Woodley\|Theo James\|Kate Winslet\|Ansel...	http://www.thedivergentseries.movie/#insurgent	Robert Schwentke	One Choice Can Destroy You	...	None	2015-03-18	2480	6.3	2015	1.012000e+08	2.716190e+08	185238201	168.398365	1.704191e+08
3	140607	tt2488496	11.173104	200000000	2068178225	Star Wars: The Force Awakens	Harrison Ford\|Mark Hamill\|Carrie Fisher\|Adam D...	http://www.starwars.com/films/star-wars-episod...	J.J. Abrams	Every generation has a story.	...	None	2015-12-15	5292	7.5	2015	1.839999e+08	1.902723e+09	1868178225	934.089113	1.718723e+09
4	168259	tt2820852	9.335014	190000000	1506249360	Furious 7	Vin Diesel\|Paul Walker\|Jason Statham\|Michelle ...	http://www.furious7.com/	James Wan	Vengeance Hits Home	...	Universal	2015-04-01	2947	7.3	2015	1.747999e+08	1.385749e+09	1316249360	692.762821	1.210949e+09

5 rows × 23 columns

#movies.to_csv('movies_production_company.csv')

问题 3：改编电影和原创电影的对比情况如何？(通过keywords变量中的based on novel字段来判断)

Question 3: How havemovies based on novels performed relative to movies not based on novels?

构建一个子数据框movies_novels

movies_novels= movies[['id','original_title','keywords','tagline']].reset_index(drop =True)
movies_novels.head()

	id	original_title	keywords	tagline
0	135397	Jurassic World	monster\|dna\|tyrannosaurus rex\|velociraptor\|island	The park is open.
1	76341	Mad Max: Fury Road	future\|chase\|post-apocalyptic\|dystopia\|australia	What a Lovely Day.
2	262500	Insurgent	based on novel\|revolution\|dystopia\|sequel\|dyst...	One Choice Can Destroy You
3	140607	Star Wars: The Force Awakens	android\|spaceship\|jedi\|space opera\|3d	Every generation has a story.
4	168259	Furious 7	car race\|speed\|revenge\|suspense\|car	Vengeance Hits Home

通过正则表达式分别在keywords和tagline两列中匹配含有novel字符的记录

deffind_novel_from_keywords(keywords):
    try:
        match =re.search('(\|{0,1}[\w\s]*novel[\w\s]*\|{0,1})',keywords)
        if match:
            returnmatch.group(0)
        else:
            return None
    except TypeError:
        return None

words_with_novel = set()
for text in movies_novels['keywords'].tolist():
    results =find_novel_from_keywords(text)
    words_with_novel.add(results)

print(words_with_novel)
#用来处理错误

{'|stolennovel|', 'based on novel|', '|tell all novel|', '|based on graphic novel','|novelist', None, '|novelist|', '|based on graphic novel|', 'based on novel','based on graphic novel|', '|inspired by novel', '|based on novel|', '|based onnovel'}

deffind_novel_based_movie(keywords):
    try:
        match1 =re.search('(\|{0,1}based[\w\s]*novel\|{0,1})',keywords)
        match2 =re.search('(\|{0,1}inspired[\w\s]*novel\|{0,1})',keywords)
        if match1 or match2:
            return True
        else:
            return False
    except TypeError:
        returnNone
movies_novels['based_on_novel_0'] =movies_novels['keywords'].apply(find_novel_based_movie)
movies_novels.head()

	id	original_title	keywords	tagline	based_on_novel_0
0	135397	Jurassic World	monster\|dna\|tyrannosaurus rex\|velociraptor\|island	The park is open.	False
1	76341	Mad Max: Fury Road	future\|chase\|post-apocalyptic\|dystopia\|australia	What a Lovely Day.	False
2	262500	Insurgent	based on novel\|revolution\|dystopia\|sequel\|dyst...	One Choice Can Destroy You	True
3	140607	Star Wars: The Force Awakens	android\|spaceship\|jedi\|space opera\|3d	Every generation has a story.	False
4	168259	Furious 7	car race\|speed\|revenge\|suspense\|car	Vengeance Hits Home	False

deffind_novel_from_tagline(tagline):
    try:
        match =re.search('(.*novel.*)',tagline)
        if match:
            returnmatch.group(0)
        else:
            return None
    except TypeError:
        return None

words_with_novel_tagline = set()
for text in movies_novels['tagline'].tolist():
    results =find_novel_from_tagline(text)
   words_with_novel_tagline.add(results)

print(words_with_novel_tagline)

{'Basedon the novel by Henry James', 'Based on the novel of Chico Xavier', None, 'The#1 novel of the year - now a motion picture!', 'Based on the best-sellingnovel'}

deffind_novel_based_movie_1(tagline):
    try:
        match =re.search('(.*novel.*)',tagline)

        if match:
            return True
        else:
            return False
    except TypeError:
        returnNone
movies_novels['based_on_novel_1'] =movies_novels['tagline'].apply(find_novel_based_movie_1)
movies_novels.head()

	id	original_title	keywords	tagline	based_on_novel_0	based_on_novel_1
0	135397	Jurassic World	monster\|dna\|tyrannosaurus rex\|velociraptor\|island	The park is open.	False	False
1	76341	Mad Max: Fury Road	future\|chase\|post-apocalyptic\|dystopia\|australia	What a Lovely Day.	False	False
2	262500	Insurgent	based on novel\|revolution\|dystopia\|sequel\|dyst...	One Choice Can Destroy You	True	False
3	140607	Star Wars: The Force Awakens	android\|spaceship\|jedi\|space opera\|3d	Every generation has a story.	False	False
4	168259	Furious 7	car race\|speed\|revenge\|suspense\|car	Vengeance Hits Home	False	False

通过逻辑值的运算，合并tagline和keywords两列包含novel的结果

movies_novels['based_on_novel']= movies_novels['based_on_novel_0'] +movies_novels['based_on_novel_1']
movies_novels.drop(['keywords','tagline','based_on_novel_0','based_on_novel_1'],axis= 1, inplace = True)
movies_novels.head()

	id	original_title	based_on_novel
0	135397	Jurassic World	0
1	76341	Mad Max: Fury Road	0
2	262500	Insurgent	1
3	140607	Star Wars: The Force Awakens	0
4	168259	Furious 7	0

print(movies_novels['based_on_novel'].unique())

[0 1 nan2]

movies_novels[movies_novels['based_on_novel']==2]

	id	original_title	based_on_novel
10660	10671	Airport	2

movies.loc[movies["original_title"]=="Airport",["original_title","keywords","tagline"]]

	original_title	keywords	tagline
10660	Airport	bomb\|based on novel\|airport\|desperation\|snow s...	The #1 novel of the year - now a motion picture!

转换成逻辑值

defconvert_to_bool(value):
    if value==1 or value==2:
        return True
    else:
        returnFalse
movies_novels['based_on_novel'] =movies_novels['based_on_novel'].apply(convert_to_bool)
movies_novels.head()

	id	original_title	based_on_novel
0	135397	Jurassic World	False
1	76341	Mad Max: Fury Road	False
2	262500	Insurgent	True
3	140607	Star Wars: The Force Awakens	False
4	168259	Furious 7	False

合并movies和movies_novels，合并之前删除keywords和tagline列

movies.drop(['keywords','tagline'],axis= 1, inplace = True)

movies = pd.merge(movies,movies_novels,how = 'left',left_on =['id','original_title'],right_on = ['id','original_title'])
movies.head()

	id	imdb_id	popularity	budget	revenue	original_title	cast	homepage	director	overview	...	release_date	vote_count	vote_average	release_year	budget_adj	revenue_adj	profit	profit_rate	profit_adj	based_on_novel
0	135397	tt0369610	32.985763	150000000	1513528810	Jurassic World	Chris Pratt\|Bryce Dallas Howard\|Irrfan Khan\|Vi...	http://www.jurassicworld.com/	Colin Trevorrow	Twenty-two years after the events of Jurassic ...	...	2015-06-09	5562	6.5	2015	1.379999e+08	1.392446e+09	1363528810	909.019207	1.254446e+09	False
1	76341	tt1392190	28.419936	150000000	378436354	Mad Max: Fury Road	Tom Hardy\|Charlize Theron\|Hugh Keays-Byrne\|Nic...	http://www.madmaxmovie.com/	George Miller	An apocalyptic story set in the furthest reach...	...	2015-05-13	6185	7.1	2015	1.379999e+08	3.481613e+08	228436354	152.290903	2.101614e+08	False
2	262500	tt2908446	13.112507	110000000	295238201	Insurgent	Shailene Woodley\|Theo James\|Kate Winslet\|Ansel...	http://www.thedivergentseries.movie/#insurgent	Robert Schwentke	Beatrice Prior must confront her inner demons ...	...	2015-03-18	2480	6.3	2015	1.012000e+08	2.716190e+08	185238201	168.398365	1.704191e+08	True
3	140607	tt2488496	11.173104	200000000	2068178225	Star Wars: The Force Awakens	Harrison Ford\|Mark Hamill\|Carrie Fisher\|Adam D...	http://www.starwars.com/films/star-wars-episod...	J.J. Abrams	Thirty years after defeating the Galactic Empi...	...	2015-12-15	5292	7.5	2015	1.839999e+08	1.902723e+09	1868178225	934.089113	1.718723e+09	False
4	168259	tt2820852	9.335014	190000000	1506249360	Furious 7	Vin Diesel\|Paul Walker\|Jason Statham\|Michelle ...	http://www.furious7.com/	James Wan	Deckard Shaw seeks revenge against Dominic Tor...	...	2015-04-01	2947	7.3	2015	1.747999e+08	1.385749e+09	1316249360	692.762821	1.210949e+09	False

5 rows × 22 columns

movies.to_csv('movies_cleaned.csv',index= False)

参考文献

1.Tidydata in Python http://www.jeannicholashould.com/tidy-data-in-python.html

2.Udacity

3.https://www.youtube.com/watch?v=2CwzOjYbi-w&list=PLXbU-2B80FvCKj0aqdpudCqpif2vNuING&index=1

4.原始数据链接 https://d17h27t6h515a5.cloudfront.net/topher/2017/January/587e7057_movies/movies.csv

以上是关于电影数据可视化项目--数据清理的主要内容，如果未能解决你的问题，请参考以下文章

毕业设计之 - 题目：基于大数据的电影数据分析可视化系统

python实现的带GUI界面电影票房数据可视化程序

keggle项目之 TMDb电影最赚钱电影有那特征

毕业设计-基于大数据的电影爬取与可视化分析系统-python

记录一次真实的接单记录：猫眼电影数据可视化，三个小时完成收入1000

python数据分析实战电影票房数据分析数据采集