数据来自kaggle上tmdb5000电影数据集，本次数据分析主要包括电影数据可视化和简单的电影推荐模型，如：
1.电影类型分配及其随时间的变化
2.利润、评分、受欢迎程度直接的关系
3.哪些导演的电影卖座或较好
4.最勤劳的演职人员
5.电影关键字分析
6.电影相似性推荐
数据分析
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
%matplotlib inline
plt.style.use('ggplot')
import json
import warnings
warnings.filterwarnings('ignore')#忽略警告movie = pd.read_csv('tmdb_5000_movies.csv')
credit = pd.read_csv('tmdb_5000_credits.csv')movie.head(1)
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| budget | genres | homepage | id | keywords | original_language | original_title | overview | popularity | production_companies | production_countries | release_date | revenue | runtime | spoken_languages | status | tagline | title | vote_average | vote_count | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 237000000 | [{“id”: 28, “name”: “Action”}, {“id”: 12, “nam… | http://www.avatarmovie.com/ | 19995 | [{“id”: 1463, “name”: “culture clash”}, {“id”:… | en | Avatar | In the 22nd century, a paraplegic Marine is di… | 150.437577 | [{“name”: “Ingenious Film Partners”, “id”: 289… | [{“iso_3166_1”: “US”, “name”: “United States o… | 2009-12-10 | 2787965087 | 162.0 | [{“iso_639_1”: “en”, “name”: “English”}, {“iso… | Released | Enter the World of Pandora. | Avatar | 7.2 | 11800 |
movie.tail(3)
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| budget | genres | homepage | id | keywords | original_language | original_title | overview | popularity | production_companies | production_countries | release_date | revenue | runtime | spoken_languages | status | tagline | title | vote_average | vote_count | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 4800 | 0 | [{“id”: 35, “name”: “Comedy”}, {“id”: 18, “nam… | http://www.hallmarkchannel.com/signedsealeddel… | 231617 | [{“id”: 248, “name”: “date”}, {“id”: 699, “nam… | en | Signed, Sealed, Delivered | “Signed, Sealed, Delivered” introduces a dedic… | 1.444476 | [{“name”: “Front Street Pictures”, “id”: 3958}… | [{“iso_3166_1”: “US”, “name”: “United States o… | 2013-10-13 | 0 | 120.0 | [{“iso_639_1”: “en”, “name”: “English”}] | Released | NaN | Signed, Sealed, Delivered | 7.0 | 6 |
| 4801 | 0 | [] | http://shanghaicalling.com/ | 126186 | [] | en | Shanghai Calling | When ambitious New York attorney Sam is sent t… | 0.857008 | [] | [{“iso_3166_1”: “US”, “name”: “United States o… | 2012-05-03 | 0 | 98.0 | [{“iso_639_1”: “en”, “name”: “English”}] | Released | A New Yorker in Shanghai | Shanghai Calling | 5.7 | 7 |
| 4802 | 0 | [{“id”: 99, “name”: “Documentary”}] | NaN | 25975 | [{“id”: 1523, “name”: “obsession”}, {“id”: 224… | en | My Date with Drew | Ever since the second grade when he first saw … | 1.929883 | [{“name”: “rusty bear entertainment”, “id”: 87… | [{“iso_3166_1”: “US”, “name”: “United States o… | 2005-08-05 | 0 | 90.0 | [{“iso_639_1”: “en”, “name”: “English”}] | Released | NaN | My Date with Drew | 6.3 | 16 |
movie.info()#样本数量为4803，部分特征有缺失值<class 'pandas.core.frame.DataFrame'>
RangeIndex: 4803 entries, 0 to 4802
Data columns (total 20 columns):
budget 4803 non-null int64
genres 4803 non-null object
homepage 1712 non-null object
id 4803 non-null int64
keywords 4803 non-null object
original_language 4803 non-null object
original_title 4803 non-null object
overview 4800 non-null object
popularity 4803 non-null float64
production_companies 4803 non-null object
production_countries 4803 non-null object
release_date 4802 non-null object
revenue 4803 non-null int64
runtime 4801 non-null float64
spoken_languages 4803 non-null object
status 4803 non-null object
tagline 3959 non-null object
title 4803 non-null object
vote_average 4803 non-null float64
vote_count 4803 non-null int64
dtypes: float64(3), int64(4), object(13)
memory usage: 750.5+ KB
样本数为4803，部分特征有缺失值，homepage,tagline缺损较多，但这俩不影响基本分析，release_date和runtime可以填充；仔细观察，部分样本的genres,keywords,production company特征值是[]，需要注意。
credit.info数据清理
数据特征中有很多特征为json格式，即类似于字典的键值对形式，为了方便后续处理，我们需要将其转换成便于python操作的str或者list形式，利于提取有用信息。
#movie genres电影流派，便于归类
movie['genres']=movie['genres'].apply(json.loads)
#apply function to axis in df,对df中某一行、列应用某种操作。movie['genres'].head(1)0 [{'id': 28, 'name': 'Action'}, {'id': 12, 'nam...
Name: genres, dtype: object
list(zip(movie.index,movie['genres']))[:2][(0,
[{'id': 28, 'name': 'Action'},
{'id': 12, 'name': 'Adventure'},
{'id': 14, 'name': 'Fantasy'},
{'id': 878, 'name': 'Science Fiction'}]),
(1,
[{'id': 12, 'name': 'Adventure'},
{'id': 14, 'name': 'Fantasy'},
{'id': 28, 'name': 'Action'}])]
for index,i in zip(movie.index,movie['genres']):
list1=[]
for j in range(len(i)):
list1.append((i[j]['name']))# name:genres,Action...
movie.loc[index,'genres']=str(list1)movie.head(1)
#genres列已经不是json格式，而是将name将的value即电影类型提取出来重新赋值给genres
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| budget | genres | homepage | id | keywords | original_language | original_title | overview | popularity | production_companies | production_countries | release_date | revenue | runtime | spoken_languages | status | tagline | title | vote_average | vote_count | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 237000000 | [‘Action’, ‘Adventure’, ‘Fantasy’, ‘Science Fi… | http://www.avatarmovie.com/ | 19995 | [{“id”: 1463, “name”: “culture clash”}, {“id”:… | en | Avatar | In the 22nd century, a paraplegic Marine is di… | 150.437577 | [{“name”: “Ingenious Film Partners”, “id”: 289… | [{“iso_3166_1”: “US”, “name”: “United States o… | 2009-12-10 | 2787965087 | 162.0 | [{“iso_639_1”: “en”, “name”: “English”}, {“iso… | Released | Enter the World of Pandora. | Avatar | 7.2 | 11800 |
#同样的方法应用到keywords列
movie['keywords'] = movie['keywords'].apply(json.loads)
for index,i in zip(movie.index,movie['keywords']):
list2=[]
for j in range(len(i)):
list2.append(i[j]['name'])
movie.loc[index,'keywords'] = str(list2)#同理production_companies
movie['production_companies'] = movie['production_companies'].apply(json.loads)
for index,i in zip(movie.index,movie['production_companies']):
list3=[]
for j in range(len(i)):
list3.append(i[j]['name'])
movie.loc[index,'production_companies']=str(list3)movie['production_countries'] = movie['production_countries'].apply(json.loads)
for index,i in zip(movie.index,movie['production_countries']):
list3=[]
for j in range(len(i)):
list3.append(i[j]['name'])
movie.loc[index,'production_countries']=str(list3)movie['spoken_languages'] = movie['spoken_languages'].apply(json.loads)
for index,i in zip(movie.index,movie['spoken_languages']):
list3=[]
for j in range(len(i)):
list3.append(i[j]['name'])
movie.loc[index,'spoken_languages']=str(list3)movie.head(1)
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| budget | genres | homepage | id | keywords | original_language | original_title | overview | popularity | production_companies | production_countries | release_date | revenue | runtime | spoken_languages | status | tagline | title | vote_average | vote_count | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 237000000 | [‘Action’, ‘Adventure’, ‘Fantasy’, ‘Science Fi… | http://www.avatarmovie.com/ | 19995 | [‘culture clash’, ‘future’, ‘space war’, ‘spac… | en | Avatar | In the 22nd century, a paraplegic Marine is di… | 150.437577 | [‘Ingenious Film Partners’, ‘Twentieth Century… | [‘United States of America’, ‘United Kingdom’] | 2009-12-10 | 2787965087 | 162.0 | [‘English’, ‘Español’] | Released | Enter the World of Pandora. | Avatar | 7.2 | 11800 |
credit.head(1)
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| movie_id | title | cast | crew | |
|---|---|---|---|---|
| 0 | 19995 | Avatar | [{“cast_id”: 242, “character”: “Jake Sully”, “… | [{“credit_id”: “52fe48009251416c750aca23”, “de… |
credit['cast'] = credit['cast'].apply(json.loads)
for index,i in zip(credit.index,credit['cast']):
list3=[]
for j in range(len(i)):
list3.append(i[j]['name'])
credit.loc[index,'cast']=str(list3)credit['crew'] = credit['crew'].apply(json.loads)
#提取crew中director，增加电影导演一列，用作后续分析
def director(x):
for i in x:
if i['job'] == 'Director':
return i['name']
credit['crew']=credit['crew'].apply(director)
credit.rename(columns={'crew':'director'},inplace=True)credit.head(1)
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| movie_id | title | cast | director | |
|---|---|---|---|---|
| 0 | 19995 | Avatar | [‘Sam Worthington’, ‘Zoe Saldana’, ‘Sigourney … | James Cameron |
观察movie中id和credit中movie_id相同，可以将两个表合并，将所有信息统一在一个表中。
fulldf = pd.merge(movie,credit,left_on='id',right_on='movie_id',how='left')fulldf.head(1)
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| budget | genres | homepage | id | keywords | original_language | original_title | overview | popularity | production_companies | … | spoken_languages | status | tagline | title_x | vote_average | vote_count | movie_id | title_y | cast | director | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 237000000 | [‘Action’, ‘Adventure’, ‘Fantasy’, ‘Science Fi… | http://www.avatarmovie.com/ | 19995 | [‘culture clash’, ‘future’, ‘space war’, ‘spac… | en | Avatar | In the 22nd century, a paraplegic Marine is di… | 150.437577 | [‘Ingenious Film Partners’, ‘Twentieth Century… | … | [‘English’, ‘Español’] | Released | Enter the World of Pandora. | Avatar | 7.2 | 11800 | 19995 | Avatar | [‘Sam Worthington’, ‘Zoe Saldana’, ‘Sigourney … | James Cameron |
1 rows × 24 columns
fulldf.shape#观察到有相同列title，合并后自动命名成title_x,title_y
fulldf.rename(columns={'title_x':'title'},inplace=True)
fulldf.drop('title_y',axis=1,inplace=True)#缺失值
NAs = pd.DataFrame(fulldf.isnull().sum())
NAs[NAs.sum(axis=1)>0].sort_values(by=[0],ascending=False)
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| 0 | |
|---|---|
| homepage | 3091 |
| tagline | 844 |
| director | 30 |
| overview | 3 |
| runtime | 2 |
| release_date | 1 |
#补充release_date
fulldf.loc[fulldf['release_date'].isnull(),'title']#上网查询补充
fulldf['release_date']=fulldf['release_date'].fillna('2014-06-01')#runtime为电影时长，按均值补充
fulldf['runtime'] = fulldf['runtime'].fillna(fulldf['runtime'].mean())#为方便分析，将release_date（object）转为datetime类型，并提取year,month
fulldf['release_year'] = pd.to_datetime(fulldf['release_date'],format='%Y-%m-%d').dt.year
fulldf['release_month'] = pd.to_datetime(fulldf['release_date'],format='%Y-%m-%d').dt.month数据探索
#电影类型genres
#观察其格式，我们需要做str相关处理,先移除两边中括号
#相邻类型间有空格，需要移除
#再移除单引号，并按,分割提取即可
fulldf['genres']=fulldf['genres'].str.strip('[]').str.replace(" ","").str.replace("'","")#每种类型现在以，分割
fulldf['genres']=fulldf['genres'].str.split(',')list1=[]
for i in fulldf['genres']:
list1.extend(i)
gen_list=pd.Series(list1).value_counts()[:10].sort_values(ascending=False)
gen_df = pd.DataFrame(gen_list)
gen_df.rename(columns={0:'Total'},inplace=True)fulldf.ix[4801] budget 0
genres []
homepage http://shanghaicalling.com/
id 126186
keywords []
original_language en
original_title Shanghai Calling
overview When ambitious New York attorney Sam is sent t...
popularity 0.857008
production_companies []
production_countries ['United States of America', 'China']
release_date 2012-05-03
revenue 0
runtime 98
spoken_languages ['English']
status Released
tagline A New Yorker in Shanghai
title Shanghai Calling
vote_average 5.7
vote_count 7
movie_id 126186
cast ['Daniel Henney', 'Eliza Coupe', 'Bill Paxton'...
director Daniel Hsia
release_year 2012
release_month 5
Name: 4801, dtype: object
plt.subplots(figsize=(10,8))
sns.barplot(y=gen_df.index,x='Total',data=gen_df,palette='GnBu_d')
plt.xticks(fontsize=15)#设置刻度字体大小
plt.yticks(fontsize=15)
plt.xlabel('Total',fontsize=15)
plt.ylabel('Genres',fontsize=15)
plt.title('Top 10 Genres',fontsize=20)
plt.show()
数量最多的前10种电影类型，有剧情、喜剧、惊悚、动作等，也是目前影院常见电影类型，那这些电影类型数量较多的背后原因有哪些呢？
我们再看看电影数量和时间的关系。
#对电影类型去重
l=[]
for i in list1:
if i not in l:
l.append(i)
#l.remove("")#有部分电影类型为空
len(l)#l就是去重后的电影类型year_min = fulldf['release_year'].min()
year_max = fulldf['release_year'].max()
year_genr =pd.DataFrame(index=l,columns=range(year_min,year_max+1))#生成类型为index，年份为列的dataframe，用于每种类型在各年份的数量
year_genr.fillna(value=0,inplace=True)#初始值为0
intil_y = np.array(fulldf['release_year'])#用于遍历所有年份
z = 0
for i in fulldf['genres']:
splt_gen = list(i)#每一部电影的所有类型
for j in splt_gen:
year_genr.loc[j,intil_y[z]] = year_genr.loc[j,intil_y[z]]+1#计数该类型电影在某一年份的数量
z+=1
year_genr = year_genr.sort_values(by=2006,ascending=False)
year_genr = year_genr.iloc[0:10,-49:-1]
year_genr
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| 1969 | 1970 | 1971 | 1972 | 1973 | 1974 | 1975 | 1976 | 1977 | 1978 | … | 2007 | 2008 | 2009 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Drama | 7 | 8 | 3 | 6 | 4 | 3 | 2 | 4 | 8 | 5 | … | 97 | 106 | 122 | 115 | 99 | 79 | 110 | 110 | 95 | 37 |
| Comedy | 3 | 4 | 1 | 3 | 3 | 3 | 3 | 3 | 3 | 1 | … | 67 | 82 | 97 | 87 | 82 | 80 | 71 | 62 | 52 | 26 |
| Thriller | 3 | 2 | 3 | 1 | 2 | 1 | 1 | 2 | 4 | 5 | … | 53 | 55 | 59 | 56 | 69 | 58 | 53 | 66 | 67 | 27 |
| Action | 4 | 4 | 4 | 1 | 2 | 1 | 1 | 2 | 6 | 5 | … | 44 | 46 | 51 | 49 | 58 | 43 | 56 | 54 | 46 | 39 |
| Romance | 2 | 1 | 3 | 0 | 1 | 2 | 1 | 2 | 2 | 3 | … | 37 | 38 | 57 | 45 | 30 | 39 | 25 | 24 | 23 | 9 |
| Family | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | … | 20 | 29 | 28 | 29 | 28 | 17 | 22 | 23 | 17 | 9 |
| Crime | 3 | 0 | 2 | 3 | 2 | 2 | 0 | 2 | 0 | 0 | … | 28 | 33 | 32 | 30 | 24 | 27 | 37 | 27 | 26 | 10 |
| Adventure | 2 | 3 | 1 | 2 | 1 | 2 | 2 | 2 | 5 | 4 | … | 25 | 37 | 36 | 30 | 32 | 25 | 36 | 37 | 35 | 23 |
| Fantasy | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 2 | 2 | … | 19 | 20 | 22 | 21 | 15 | 19 | 21 | 16 | 10 | 13 |
| Horror | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 3 | 4 | … | 27 | 21 | 30 | 27 | 24 | 33 | 25 | 21 | 33 | 20 |
10 rows × 48 columns
plt.subplots(figsize=(10,8))
plt.plot(year_genr.T)
plt.title('Genres vs Time',fontsize=20)
plt.xticks(range(1969,2020,5))
plt.legend(year_genr.T)
plt.show()
可以看到，从1994年左右，电影进入繁荣发展时期，各种类型的电影均有大幅增加，而增加最多的又以剧情、喜剧、惊悚、动作等类型电影，可见，这些类型电影数量居多和电影艺术整体繁荣发展有一定关系。
#为了方便分析，构造一个新的dataframe,选取部分特征，分析这些特征和电影类型的关系。
partdf = fulldf[['title','vote_average','vote_count','release_year','popularity','budget','revenue']].reset_index(drop=True)partdf.head(2)
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| title | vote_average | vote_count | release_year | popularity | budget | revenue | |
|---|---|---|---|---|---|---|---|
| 0 | Avatar | 7.2 | 11800 | 2009 | 150.437577 | 237000000 | 2787965087 |
| 1 | Pirates of the Caribbean: At World’s End | 6.9 | 4500 | 2007 | 139.082615 | 300000000 | 961000000 |
因为一部电影可能有多种电影类型，将每种类型加入column中，对每部电影，是某种类型就赋值1，不是则赋值0
for per in l:
partdf[per]=0
z=0
for gen in fulldf['genres']:
if per in list(gen):
partdf.loc[z,per] = 1
else:
partdf.loc[z,per] = 0
z+=1
partdf.head(2)
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| title | vote_average | vote_count | release_year | popularity | budget | revenue | Action | Adventure | Fantasy | … | Romance | Horror | Mystery | History | War | Music | Documentary | Foreign | TVMovie | ||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Avatar | 7.2 | 11800 | 2009 | 150.437577 | 237000000 | 2787965087 | 1 | 1 | 1 | … | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 1 | Pirates of the Caribbean: At World’s End | 6.9 | 4500 | 2007 | 139.082615 | 300000000 | 961000000 | 1 | 1 | 1 | … | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
2 rows × 28 columns
现在我们想了解每种电影类型一些特征的平均值，创建一个新的dataframe，index就是电影类型，列是平均特征，如平分vote，收入revenue，受欢迎程度等。
mean_gen = pd.DataFrame(l)#点评分数取均值
newArray = []*len(l)
for genre in l:
newArray.append(partdf.groupby(genre, as_index=True)['vote_average'].mean())
#现在newArray中是按类型[0]平均值[1]平均值存放，我们只关心[1]的值。
newArray2 = []*len(l)
for i in range(len(l)):
newArray2.append(newArray[i][1])
mean_gen['mean_votes_average']=newArray2
mean_gen.head(2)
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| 0 | mean_votes_average | |
|---|---|---|
| 0 | Action | 5.989515 |
| 1 | Adventure | 6.156962 |
#同理，用到别的特征上
#预算budget
newArray = []*len(l)
for genre in l:
newArray.append(partdf.groupby(genre, as_index=True)['budget'].mean())
newArray2 = []*len(l)
for i in range(len(l)):
newArray2.append(newArray[i][1])
mean_gen['mean_budget']=newArray2#收入revenue
newArray = []*len(l)
for genre in l:
newArray.append(partdf.groupby(genre, as_index=True)['revenue'].mean())
newArray2 = []*len(l)
for i in range(len(l)):
newArray2.append(newArray[i][1])
mean_gen['mean_revenue']=newArray2#popularity:相关页面查看次数
newArray = []*len(l)
for genre in l:
newArray.append(partdf.groupby(genre, as_index=True)['popularity'].mean())
newArray2 = []*len(l)
for i in range(len(l)):
newArray2.append(newArray[i][1])
mean_gen['mean_popular']=newArray2#vote_count:评分次数取count
newArray = []*len(l)
for genre in l:
newArray.append(partdf.groupby(genre, as_index=True)['vote_count'].count())
newArray2 = []*len(l)
for i in range(len(l)):
newArray2.append(newArray[i][1])
mean_gen['vote_count']=newArray2mean_gen.rename(columns={0:'genre'},inplace=True)
mean_gen.replace('','none',inplace=True)
#none代表有些电影类型或其他特征有缺失，可以看到数量很小，我们将其舍得不考虑
mean_gen.drop(20,inplace=True)mean_gen['vote_count'].describe()count 20.000000
mean 608.000000
std 606.931974
min 8.000000
25% 174.750000
50% 468.500000
75% 816.000000
max 2297.000000
Name: vote_count, dtype: float64
mean_gen['mean_votes_average'].describe()count 20.000000
mean 6.173921
std 0.278476
min 5.626590
25% 6.009644
50% 6.180978
75% 6.344325
max 6.719797
Name: mean_votes_average, dtype: float64
#fig = plt.figure(figsize=(10, 8))
f,ax = plt.subplots(figsize=(10,6))
ax1 = f.add_subplot(111)
ax2 = ax1.twinx()
grid1 = sns.factorplot(x='genre', y='mean_votes_average',data=mean_gen,ax=ax1)
ax1.axes.set_ylabel('votes_average')
ax1.axes.set_ylim((4,7))
grid2 = sns.factorplot(x='genre',y='mean_popular',data=mean_gen,ax=ax2,color='blue')
ax2.axes.set_ylabel('popularity')
ax2.axes.set_ylim((0,40))
ax1.set_xticklabels(mean_gen['genre'],rotation=90)
plt.show()
从上图可知，外国电影并不受欢迎，虽然评分不低，但也是因为评分人数太少，动漫电影（Animation）、科幻（Science Fiction）、奇幻电影（Fantasy）、动作片（Action）受欢迎程度较高，评分也不低，数量最多的剧情片评分很高，但受欢迎程度较低，猜测可能大部分剧情片不是商业类型。
mean_gen['profit'] = mean_gen['mean_revenue']-mean_gen['mean_budget']s = mean_gen['profit'].sort_values(ascending=False)[:10]
pdf = mean_gen.ix[s.index]
plt.subplots(figsize=(10,6))
sns.barplot(x='profit',y='genre',data=pdf,palette='BuGn_r')
plt.xticks(fontsize=15)#设置刻度字体大小
plt.yticks(fontsize=15)
plt.xlabel('Profit',fontsize=15)
plt.ylabel('Genres',fontsize=15)
plt.title('Top 10 Profit of Genres',fontsize=20)
plt.show()
可以看出，动画、探险、家庭和科幻是最赚钱的电影类型，适合去电影院观看，同时也是受欢迎的类型，那么我们看看变量的关系。
cordf = partdf.drop(l,axis=1)
cordf.columns#含有我们想了解的特征，适合分析 Index(['title', 'vote_average', 'vote_count', 'release_year', 'popularity',
'budget', 'revenue'],
dtype='object')
corrmat = cordf.corr()
f, ax = plt.subplots(figsize=(10,7))
sns.heatmap(corrmat,cbar=True, annot=True,vmax=.8, cmap='PuBu',square=True)
从上图可以看出，评分次数和受欢迎程度有比较强的关系，证明看的人多参与度也高，预算和票房也关系较强，票房和受欢迎程度、评分次数也有比较强的关系，为电影做好宣传很重要，我们再进一步看一下。
#budget, revenue在数据中都有为0的项，我们去除这些脏数据，
partdf = partdf[partdf['budget']>0]
partdf = partdf[partdf['revenue']>0]
partdf = partdf[partdf['vote_count']>3]
plt.subplots(figsize=(6,5))
plt.xlabel('Budget',fontsize=15)
plt.ylabel('Revenue',fontsize=15)
plt.title('Budget vs Revenue',fontsize=20)
sns.regplot(x='budget',y='revenue',data=partdf,ci=None)
plt.subplots(figsize=(6,5))
plt.xlabel('vote_average',fontsize=15)
plt.ylabel('popularity',fontsize=15)
plt.title('Score vs Popular',fontsize=20)
sns.regplot(x='vote_average',y='popularity',data=partdf)
可以看出，成本和票房、评分高低和受欢迎程度还是呈线性关系的。但成本较低的电影，成本对票房的影响不大，评分高的的电影基本上也很受欢迎，我们再看看究竟是哪几部电影最挣钱、最受欢迎、口碑最好。
print(partdf.loc[partdf['revenue']==partdf['revenue'].max()]['title'])
print(partdf.loc[partdf['popularity']==partdf['popularity'].max()]['title'])
print(partdf.loc[partdf['vote_average']==partdf['vote_average'].max()]['title'])0 Avatar
Name: title, dtype: object
546 Minions
Name: title, dtype: object
1881 The Shawshank Redemption
Name: title, dtype: object
partdf['profit'] = partdf['revenue']-partdf['budget']
print(partdf.loc[partdf['profit']==partdf['profit'].max()]['title'])0 Avatar
Name: title, dtype: object
小黄人电影最受欢迎，阿凡达最赚钱，肖申克的救赎口碑最好。
s1 = cordf.groupby(by='release_year').budget.sum()
s2 = cordf.groupby(by='release_year').revenue.sum()
sdf = pd.concat([s1,s2],axis=1)
sdf = sdf.iloc[-39:-2]
plt.plot(sdf)
plt.xticks(range(1979,2020,5))
plt.legend(sdf)
plt.show()
电影业果然是蓬勃发展啊！现在大制作的电影越来越多，看来是有原因的啊！
对于科幻迷们，也可以看看最受欢迎的科幻电影都有哪些：
#最受欢迎的科幻电影
s = partdf.loc[partdf['ScienceFiction']==1,'popularity'].sort_values(ascending=False)[:10]
sdf = partdf.ix[s.index]
sns.barplot(x='popularity',y='title',data=sdf)
plt.show()
星际穿越最受欢迎，银河护卫队紧随其后，同理，我们也可以了解其他电影类型的情况。现在。让我们再看看电影人对电影市场的影响，一部好电影离不开台前幕后工作人员的贡献，是每一位优秀的电影人为我们带来好看的电影，这里，我们主要分析导演和演员。
#平均票房最高的导演
rev_d = fulldf.groupby('director')['revenue'].mean()
top_rev_d = rev_d.sort_values(ascending=False).head(20)
top_rev_d = pd.DataFrame(top_rev_d)
plt.subplots(figsize=(10,6))
sns.barplot(x='revenue',y=top_rev_d.index,data=top_rev_d,palette='BuGn_r')
plt.xticks(fontsize=15)
plt.yticks(fontsize=15)
plt.xlabel('Average Revenue',fontsize=15)
plt.ylabel('Director',fontsize=15)
plt.title('Top 20 Revenue by Director',fontsize=20)
plt.show()
如图是市场好的导演，那么电影产量最高、或者既叫好又叫座的导演有哪些呢？
list2 = fulldf[fulldf['director']!=''].director.value_counts()[:10].sort_values(ascending=True)
list2 = pd.Series(list2)
list2Oliver Stone 14
Renny Harlin 15
Steven Soderbergh 15
Robert Rodriguez 16
Spike Lee 16
Ridley Scott 16
Martin Scorsese 20
Clint Eastwood 20
Woody Allen 21
Steven Spielberg 27
Name: director, dtype: int64
plt.subplots(figsize=(10,6))
ax = list2.plot.barh(width=0.85,color='y')
for i,v in enumerate(list2.values):
ax.text(.5, i, v,fontsize=12,color='white',weight='bold')
ax.patches[9].set_facecolor('g')
plt.title('Directors with highest movies')
plt.show()
top_vote_d = fulldf[fulldf['vote_average']>=8].sort_values(by='vote_average',ascending=False)
top_vote_d = top_vote_d.dropna()
top_vote_d = top_vote_d.loc[:,['director','vote_average']]tmp = rev_d.sort_values(ascending=False)
vote_rev_d = tmp[tmp.index.isin(list(top_vote_d['director']))]
vote_rev_d = vote_rev_d.sort_values(ascending=False)
vote_rev_d = pd.DataFrame(vote_rev_d)plt.subplots(figsize=(10,6))
sns.barplot(x='revenue',y=vote_rev_d.index,data=vote_rev_d,palette='BuGn_r')
plt.xticks(fontsize=15)
plt.yticks(fontsize=15)
plt.xlabel('Average Revenue',fontsize=15)
plt.ylabel('Director',fontsize=15)
plt.title('Revenue by vote above 8 Director',fontsize=20)
plt.show()
再看看演职人员，cast特征里每一部电影有很多演职人员，幸运的是，cast是按演职人员的重要程度排序的，那么排名靠前的我们可以认为是主要演员。
fulldf['cast']=fulldf['cast'].str.strip('[]').str.replace(' ','').str.replace("'",'').str.replace('"','')
fulldf['cast']=fulldf['cast'].str.split(',')
list1=[]
for i in fulldf['cast']:
list1.extend(i)
list1 = pd.Series(list1)
list1 = list1.value_counts()[:15].sort_values(ascending=True)
plt.subplots(figsize=(10,6))
ax = list1.plot.barh(width=0.9,color='green')
for i,v in enumerate(list1.values):
ax.text(.8, i, v,fontsize=10,color='white',weight='bold')
plt.title('Actors with highest appearance')
ax.patches[14].set_facecolor('b')
plt.show()
fulldf['keywords'][2]“[‘spy’, ‘based on novel’, ‘secret agent’, ‘sequel’, ‘mi6’, ‘british secret service’, ‘united kingdom’]”
from wordcloud import WordCloud, STOPWORDS
import nltk
from nltk.corpus import stopwords
#如果stopwords报错没有安装，可以在anaconda cmd中import nltk;nltk.download()
#在弹出窗口中选择corpa,stopword,刷新并下载
import io
from PIL import Imageplt.subplots(figsize=(12,12))
stop_words=set(stopwords.words('english'))
stop_words.update(',',';','!','?','.','(',')','$','#','+',':','...',' ','')
img1 = Image.open('timg1.jpg')
hcmask1 = np.array(img1)
words=fulldf['keywords'].dropna().apply(nltk.word_tokenize)
word=[]
for i in words:
word.extend(i)
word=pd.Series(word)
word=([i for i in word.str.lower() if i not in stop_words])
wc = WordCloud(background_color="black", max_words=4000, mask=hcmask1,
stopwords=STOPWORDS, max_font_size= 60)
wc.generate(" ".join(word))
plt.imshow(wc,interpolation="bilinear")
plt.axis('off')
plt.figure()
plt.show()
我们可以对关键词有大概了解，女性导演、独立电影占比较大，这也可能是电影的一个发展趋势。
电影推荐模型
现在我们根据上述的分析，可以考虑做一个电影推荐，通常来说，我们在搜索电影时，我们会去找同类的电影、或者同一导演演员的电影、或者评分较高的电影，那么需要的特征有genres,cast,director,score
l[:5][‘Action’, ‘Adventure’, ‘Fantasy’, ‘ScienceFiction’, ‘Crime’]
特征向量化
genre
def binary(genre_list):
binaryList = []
for genre in l:
if genre in genre_list:
binaryList.append(1)
else:
binaryList.append(0)
return binaryListfulldf['genre_vec'] = fulldf['genres'].apply(lambda x: binary(x))fulldf['genre_vec'][0][1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
cast
for i,j in zip(fulldf['cast'],fulldf.index):
list2=[]
list2=i[:4]
list2.sort()
fulldf.loc[j,'cast']=str(list2)
fulldf['cast'][0]fulldf['cast']=fulldf['cast'].str.strip('[]').str.replace(' ','').str.replace("'",'')
fulldf['cast']=fulldf['cast'].str.split(',')
fulldf['cast'][0]castList = []
for index, row in fulldf.iterrows():
cast = row["cast"]
for i in cast:
if i not in castList:
castList.append(i)len(castList)def binary(cast_list):
binaryList = []
for genre in castList:
if genre in cast_list:
binaryList.append(1)
else:
binaryList.append(0)
return binaryListfulldf['cast_vec'] = fulldf['cast'].apply(lambda x:binary(x))
fulldf['cast_vec'].head(2)0 [1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
1 [0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, …
Name: cast_vec, dtype: object
director
fulldf['director'][0]def xstr(s):
if s is None:
return ''
return str(s)
fulldf['director']=fulldf['director'].apply(xstr)directorList=[]
for i in fulldf['director']:
if i not in directorList:
directorList.append(i)def binary(director_list):
binaryList = []
for direct in directorList:
if direct in director_list:
binaryList.append(1)
else:
binaryList.append(0)
return binaryListfulldf['director_vec'] = fulldf['director'].apply(lambda x:binary(x))keywords
fulldf['keywords'][0]“[‘culture clash’, ‘future’, ‘space war’, ‘space colony’, ‘society’, ‘space travel’, ‘futuristic’, ‘romance’, ‘space’, ‘alien’, ‘tribe’, ‘alien planet’, ‘cgi’, ‘marine’, ‘soldier’, ‘battle’, ‘love affair’, ‘anti war’, ‘power relations’, ‘mind and soul’, ‘3d’]”
#change keywords to type list
fulldf['keywords']=fulldf['keywords'].str.strip('[]').str.replace(' ','').str.replace("'",'').str.replace('"','')
fulldf['keywords']=fulldf['keywords'].str.split(',')for i,j in zip(fulldf['keywords'],fulldf.index):
list2=[]
list2 = i
list2.sort()
fulldf.loc[j,'keywords']=str(list2)
fulldf['keywords'][0]“[‘3d’, ‘alien’, ‘alienplanet’, ‘antiwar’, ‘battle’, ‘cgi’, ‘cultureclash’, ‘future’, ‘futuristic’, ‘loveaffair’, ‘marine’, ‘mindandsoul’, ‘powerrelations’, ‘romance’, ‘society’, ‘soldier’, ‘space’, ‘spacecolony’, ‘spacetravel’, ‘spacewar’, ‘tribe’]”
fulldf['keywords']=fulldf['keywords'].str.strip('[]').str.replace(' ','').str.replace("'",'').str.replace('"','')
fulldf['keywords']=fulldf['keywords'].str.split(',')words_list = []
for index, row in fulldf.iterrows():
genres = row["keywords"]
for genre in genres:
if genre not in words_list:
words_list.append(genre)
len(words_list)def binary(words):
binaryList = []
for genre in words_list:
if genre in words:
binaryList.append(1)
else:
binaryList.append(0)
return binaryListfulldf['words_vec'] = fulldf['keywords'].apply(lambda x: binary(x))recommend model
取余弦值作为相似性度量，根据选取的特征向量计算影片间的相似性；计算距离最近的前10部影片作为推荐
fulldf=fulldf[(fulldf['vote_average']!=0)] #removing the fulldf with 0 score and without drector names
fulldf=fulldf[fulldf['director']!='']from scipy import spatial
def Similarity(movieId1, movieId2):
a = fulldf.iloc[movieId1]
b = fulldf.iloc[movieId2]
genresA = a['genre_vec']
genresB = b['genre_vec']
genreDistance = spatial.distance.cosine(genresA, genresB)
castA = a['cast_vec']
castB = b['cast_vec']
castDistance = spatial.distance.cosine(castA, castB)
directA = a['director_vec']
directB = b['director_vec']
directDistance = spatial.distance.cosine(directA, directB)
wordsA = a['words_vec']
wordsB = b['words_vec']
wordsDistance = spatial.distance.cosine(directA, directB)
return genreDistance + directDistance + castDistance + wordsDistanceSimilarity(3,160)columns =['original_title','genres','vote_average','genre_vec','cast_vec','director','director_vec','words_vec']
tmp = fulldf.copy()
tmp =tmp[columns]
tmp['id'] = list(range(0,fulldf.shape[0]))
tmp.head()
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| original_title | genres | vote_average | genre_vec | cast_vec | director | director_vec | words_vec | id | |
|---|---|---|---|---|---|---|---|---|---|
| 0 | Avatar | [Action, Adventure, Fantasy, ScienceFiction] | 7.2 | [1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, … | [1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, … | James Cameron | [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, … | [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, … | 0 |
| 1 | Pirates of the Caribbean: At World’s End | [Adventure, Fantasy, Action] | 6.9 | [1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, … | [0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, … | Gore Verbinski | [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, … | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, … | 1 |
| 2 | Spectre | [Action, Adventure, Crime] | 6.3 | [1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, … | [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, … | Sam Mendes | [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, … | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, … | 2 |
| 3 | The Dark Knight Rises | [Action, Crime, Drama, Thriller] | 7.6 | [1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, … | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, … | Christopher Nolan | [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, … | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, … | 3 |
| 4 | John Carter | [Action, Adventure, ScienceFiction] | 6.1 | [1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, … | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, … | Andrew Stanton | [0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, … | [1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, … | 4 |
tmp.isnull().sum()original_title 0
genres 0
vote_average 0
genre_vec 0
cast_vec 0
director 0
director_vec 0
words_vec 0
id 0
dtype: int64
import operator
def recommend(name):
film=tmp[tmp['original_title'].str.contains(name)].iloc[0].to_frame().T
print('Selected Movie: ',film.original_title.values[0])
def getNeighbors(baseMovie):
distances = []
for index, movie in tmp.iterrows():
if movie['id'] != baseMovie['id'].values[0]:
dist = Similarity(baseMovie['id'].values[0], movie['id'])
distances.append((movie['id'], dist))
distances.sort(key=operator.itemgetter(1))
neighbors = []
for x in range(10):
neighbors.append(distances[x])
return neighbors
neighbors = getNeighbors(film)
print('\nRecommended Movies: \n')
for nei in neighbors:
print( tmp.iloc[nei[0]][0]+" | Genres: "+
str(tmp.iloc[nei[0]][1]).strip('[]').replace(' ','')+" | Rating: "
+str(tmp.iloc[nei[0]][2]))
print('\n')recommend('Godfather')Selected Movie: The Godfather: Part III
Recommended Movies:
The Godfather: Part II | Genres: 'Drama','Crime' | Rating: 8.3
The Godfather | Genres: 'Drama','Crime' | Rating: 8.4
The Rainmaker | Genres: 'Drama','Crime','Thriller' | Rating: 6.7
The Outsiders | Genres: 'Crime','Drama' | Rating: 6.9
The Conversation | Genres: 'Crime','Drama','Mystery' | Rating: 7.5
The Cotton Club | Genres: 'Music','Drama','Crime','Romance' | Rating: 6.6
Apocalypse Now | Genres: 'Drama','War' | Rating: 8.0
Twixt | Genres: 'Horror','Thriller' | Rating: 5.0
New York Stories | Genres: 'Comedy','Drama','Romance' | Rating: 6.2
Peggy Sue Got Married | Genres: 'Comedy','Drama','Fantasy','Romance' | Rating: 5.9
相关函数解释
json格式处理
json是一种数据交换格式，以键值对的形式呈现，支持任何类型
- json.loads用于解码json格式，将其转为dict;
- 其逆操作，即转为json格式，是json.dumps(),若要存储为json文件，需要先dumps转换再写入
- json.dump()用于将dict类型的数据转成str，并写入到json文件中，json.dump(json,file)
- json.load()用于从json文件中读取数据。json.load(file)
exam = {'a':'1111','b':'2222','c':'3333','d':'4444'}
file = 'exam.json'
jsobj = json.dumps(exam)
# solution 1
with open(file,'w') as f:
f.write(jsobj)
f.close()
#solution 2
json.dump(exam,open(file,'w'))zip()操作
- zip()操作：用于将可迭代的对象作为参数，将对象中对应的元素打包成一个个元组，然后返回由这些元组组成的列表。
- 其逆操作为*zip(),举例如下：
a = [1,2,3]
b = [4,5,6]
c = [4,5,6,7,8]
zipped = zip(a,b)
for i in zipped:
print(i)
print('\n')
shor_z = zip(a,c)
for j in shor_z:#取最短
print(j)z=list(zip(a,b))
zlist(zip(*z))#转为list能看见pandas merge/rename
pd.merge()通过键合并
a=pd.DataFrame({'lkey':['foo','foo','bar','bar'],'value':[1,2,3,4]})
a
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| lkey | value | |
|---|---|---|
| 0 | foo | 1 |
| 1 | foo | 2 |
| 2 | bar | 3 |
| 3 | bar | 4 |
for index,row in a.iterrows():
print(index)
print('*****')
print(row)b=pd.DataFrame({'rkey':['foo','foo','bar','bar'],'value':[5,6,7,8]})
b
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| rkey | value | |
|---|---|---|
| 0 | foo | 5 |
| 1 | foo | 6 |
| 2 | bar | 7 |
| 3 | bar | 8 |
pd.merge(a,b,left_on='lkey',right_on='rkey',how='left')
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| lkey | value_x | rkey | value_y | |
|---|---|---|---|---|
| 0 | foo | 1 | foo | 5 |
| 1 | foo | 1 | foo | 6 |
| 2 | foo | 2 | foo | 5 |
| 3 | foo | 2 | foo | 6 |
| 4 | bar | 3 | bar | 7 |
| 5 | bar | 3 | bar | 8 |
| 6 | bar | 4 | bar | 7 |
| 7 | bar | 4 | bar | 8 |
pd.merge(a,b,left_on='lkey',right_on='rkey',how='inner')
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}
.dataframe tbody tr th {
vertical-align: top;
}
| lkey | value_x | rkey | value_y | |
|---|---|---|---|---|
| 0 | foo | 1 | foo | 5 |
| 1 | foo | 1 | foo | 6 |
| 2 | foo | 2 | foo | 5 |
| 3 | foo | 2 | foo | 6 |
| 4 | bar | 3 | bar | 7 |
| 5 | bar | 3 | bar | 8 |
| 6 | bar | 4 | bar | 7 |
| 7 | bar | 4 | bar | 8 |
pd.rename()对行列重命名
dframe= pd.DataFrame(np.arange(12).reshape((3, 4)),
index=['NY', 'LA', 'SF'],
columns=['A', 'B', 'C', 'D'])
dframe
.dataframe thead tr:only-child th {
text-align: right;
}
.dataframe thead th {
text-align: left;
}
.dataframe tbody tr th {
vertical-align: top;
}
| A | B | C | D | |
|---|---|---|---|---|
| NY | 0 | 1 | 2 | 3 |
| LA | 4 | 5 | 6 | 7 |
| SF | 8 | 9 | 10 | 11 |
dframe.rename(columns={'A':'alpha'})
.dataframe thead tr:only-child th {
text-align: right;
}
.dataframe thead th {
text-align: left;
}
.dataframe tbody tr th {
vertical-align: top;
}
| alpha | B | C | D | |
|---|---|---|---|---|
| NY | 0 | 1 | 2 | 3 |
| LA | 4 | 5 | 6 | 7 |
| SF | 8 | 9 | 10 | 11 |
dframe
.dataframe thead tr:only-child th {
text-align: right;
}
.dataframe thead th {
text-align: left;
}
.dataframe tbody tr th {
vertical-align: top;
}
| A | B | C | D | |
|---|---|---|---|---|
| NY | 0 | 1 | 2 | 3 |
| LA | 4 | 5 | 6 | 7 |
| SF | 8 | 9 | 10 | 11 |
dframe.rename(columns={'A':'alpha'},inplace=True)
dframe
.dataframe thead tr:only-child th {
text-align: right;
}
.dataframe thead th {
text-align: left;
}
.dataframe tbody tr th {
vertical-align: top;
}
| alpha | B | C | D | |
|---|---|---|---|---|
| NY | 0 | 1 | 2 | 3 |
| LA | 4 | 5 | 6 | 7 |
| SF | 8 | 9 | 10 | 11 |
pandas datetime格式
pandas to_datetime()转为datetime格式
Wordcloud
wordcloud词云模块：
1.安装：在conda cmd中输入conda install -c conda-forge wordcloud
2.步骤：读入背景图片，文本，实例化Wordcloud对象wc，
wc.generate(text)产生云图，plt.imshow()显示图片参数：
mask：遮罩图，字的大小布局和颜色都会依据遮罩图生成
background_color：背景色，默认黑
max_font_size：最大字号
nltk简单介绍
from nltk.corpus import stopwords
如果stopwords报错没有安装，可以在anaconda cmd中import nltk;nltk.download()
在弹出窗口中选择corpa,stopword,刷新并下载
同理，在models选项卡中选择Punkt Tokenizer Model刷新并下载，可安装nltk.word_tokenize()分词：
nltk.sent_tokenize(text) #对文本按照句子进行分割
nltk.word_tokenize(sent) #对句子进行分词
stopwords:个人理解是对表述不构成影响，大量存在，且可以直接过滤掉的词
参考文章：
what’s my score
TMDB means per genre
新手学习，欢迎指教！