├── README.md ├── python大作业截图 ├── ##查看creditss中数据.PNG ├── TOP9电影风格.PNG ├── 两个数据框中的title列重复了，删除credits中的title列.PNG ├── 判断数据是否有缺失值.PNG ├── 取出主演，导演的数据.PNG ├── 可视化两公司电影风格比例.PNG ├── 可视化两公司票房收入对比.PNG ├── 可视化收益列和收益率列.PNG ├── 可视化票房收入相关性.PNG ├── 增加收益列和收益率列.PNG ├── 导入包.PNG ├── 找出runtime缺失行数并填充.PNG ├── 找出缺失值并填充.PNG ├── 查看creditss中所有列名.PNG ├── 查看moives中数据.PNG ├── 查看movies中所有列名，以字典形式存储.PNG ├── 查看数据信息，每个字段数据量.PNG ├── 电影评分预测.PNG ├── 电影风格数量图.PNG ├── 相关系数矩阵热力图.PNG ├── 票房和属性的相关性代码.PNG ├── 聚类分析代码图.PNG ├── 解析json数据.PNG ├── 转化为时间.PNG ├── 速度与激情8的预测分数.PNG └── 速度与激情评分.PNG ├── 吴清典_数据分析课程论文 ├── python大作业代码截图 │ ├── ##查看creditss中数据.PNG │ ├── TOP9电影风格.PNG │ ├── 两个数据框中的title列重复了，删除credits中的title列.PNG │ ├── 判断数据是否有缺失值.PNG │ ├── 取出主演，导演的数据.PNG │ ├── 可视化两公司电影风格比例.PNG │ ├── 可视化两公司票房收入对比.PNG │ ├── 可视化收益列和收益率列.PNG │ ├── 可视化票房收入相关性.PNG │ ├── 增加收益列和收益率列.PNG │ ├── 导入包.PNG │ ├── 找出runtime缺失行数并填充.PNG │ ├── 找出缺失值并填充.PNG │ ├── 查看creditss中所有列名.PNG │ ├── 查看moives中数据.PNG │ ├── 查看movies中所有列名，以字典形式存储.PNG │ ├── 查看数据信息，每个字段数据量.PNG │ ├── 电影评分预测.PNG │ ├── 电影风格数量图.PNG │ ├── 相关系数矩阵热力图.PNG │ ├── 票房和属性的相关性代码.PNG │ ├── 聚类分析代码图.PNG │ ├── 解析json数据.PNG │ ├── 转化为时间.PNG │ ├── 速度与激情8的预测分数.PNG │ └── 速度与激情评分.PNG ├── 分析代码 │ ├── TMDB-5000-MovieAnalysis.py │ ├── movie_Creditnumber.py │ ├── 分类测试.py │ └── 电影类型比例.py ├── 分析绘图 │ ├── Company Picture.png │ ├── Universal vs Paramount by revenue.png │ ├── film count by year.png │ ├── film genre by year.png │ ├── keywords.png │ ├── profit by genres.png │ ├── revenue.png │ ├── 数据列名.png │ ├── 数据列名.psd │ ├── 电影类型所占比例.png │ ├── 相关系数矩阵.png │ └── 聚类分析popularity和vote_count.png └── 数据集 │ ├── tmdb-movie-metadata.zip │ ├── tmdb_5000_credits.csv │ └── tmdb_5000_movies.csv └── 结论总结 └── Python期末大作业.docx /README.md: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Aoshika123/Python-TMDB-5000-MovieAnalysis/HEAD/README.md -------------------------------------------------------------------------------- /python大作业截图/##查看creditss中数据.PNG: 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