getMoviesByGenre($id, [$page], [$lang]) Movies & TV Series getTopRated([$page], [$type], [$lang]) getPopular([$page], [$type], [$lang]) Movies searchMovie($query, [$page], [$adult], [$lang]) getMovie($id, [$lang]) getMovieCast($id) getMovieImages($id, [$lang]) ...
head() #提取导演 def get_directors(x): for i in x: if i['job'] == 'Director': return i['name'] credits['crew'] = credits['crew'].apply(get_directors) #将字段‘crew’改为‘director’ credits.rename(columns={'crew':'director'}, inplace = True) #movies:json数据解析 json_cols...
Search movies by title, genre, and more. Get recommendations based on movies or TV shows. Access TV show details and seasons. Prerequisites n8ninstalled (Node.js required) TMDb API Key (available through TMDb account) Installation Open n8n and navigate toSettings>Community Nodes. ...
Convert genre Ids to genre names (unless you are calling the genre function to get a list of id/genre combinations.) Return a curated set of items from the call. The returned object shape is the same as the Raw calls: {data:// The meat of the return from TMDB API call.apiCall:<st...
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 im
credits.rename(columns={<!--->'crew':'director'},inplace=True)#movies:json数据解析 json_cols=['genres','keywords','spoken_languages','production_companies','production_countries']foriinjson_cols:movies[i]=movies[i].apply(json.loads)defget_names(x):return','.join([i['name']foriinx]...
Before we can make a request using the API, the very first thing we have to do is to register and get our API Key from themoviedb.org. The Movie Database (TMDb API) We will build a great movie app. Here are the main features of the app that we will build: List of movies by ...
中使用_concat回显mysql数据 、 这是的:// Some Stuff Here Batman Begins - 7.5 The LordLEFT JOIN cast ON cast.cast_tmdb_id=tmdb_movies.tmdb_id LEFTJOIN recommendations ON recommendations.recommendation 浏览2提问2017-07-05得票数 2 回答已采纳 点击加载...
get_ipython().run_line_magic('matplotlib', 'inline') # In[43]: #导入电影数据 movies_file = r'E:\Data analysis\kaggle\tmdb_5000_movies.csv ' credits_file = r'E:\Data analysis\kaggle\tmdb_5000_credits.csv ' credits = pd.read_csv(credits_file) ...
从Kaggle平台上下载2个原始数据集:tmdb_5000_movies.csv和tmdb_5000_credits.csv,前者存放电影的基本信息,后者存放电影的演职员名单。 导入后通过对数据的查看,筛选出本次重点研究的变量: 本次分析用到的变量 三. 数据清洗 数据清洗主要分三步:1.数据预处理;2.特征提取;3.特征选取。