Metadata-Version: 2.1
Name: rotten_tomatoes_scraper
Version: 1.1.2
Summary: How to scrape Rotten Tomatoes website using an easy interface.
Home-page: https://github.com/pdrm83/rotten_tomatoes_scraper
Author: Pedram Ataee
Author-email: pedram.ataee@gmail.com
License: MIT
Description: [![license](https://img.shields.io/badge/license-MIT-success)](https://github.com/pdrm83/Rotten_Tomatoes_Scraper/blob/master/LICENSE)
        
        # Rotten Tomatoes Scraper 
        You can extract information about **movies** and **actors** that are listed on the Rotten Tomatoes website using this 
        module. Each movie has different metadata such as *Rating*, *Genre*, *Box Office*, *Studio*, and *Scores*. The 
        **Genre** has 20+ subcategories that also gives you more granular information on a movie. These metadata can be helpful 
        for many data science projects. For actors you can extract movies listed in **highest-rated** or **filmography** 
        sections depending on your need. This module uses the BeautifulSoup package to parse HTML documents. 
        
        ## Module
        The module requires the following libraries:
        
        * bs4
        * re
        * requests
        * urllib
        
        ## Install
        
        It can be installed using pip:
        ```python
        pip3 install rotten_tomatoes_scraper
        ```
        
        ## Usage
        This module contains two classes: **MovieScraper** and **CelebrityScraper**
        
        You can use *CelebrityScraper* to extract the complete list of movies that a celebrity participated by calling 
        `extract_metadata` method and using `section='filmography'`. Plus, you can also extract the list of top ranked movies 
        by using the same method and `section='highest'`. 
        
        ```python
        from rotten_tomatoes_scraper.rt_scraper import CelebrityScraper
        
        celebrity_scraper = CelebrityScraper(celebrity_name='jack nicholson')
        celebrity_scraper.extract_metadata(section='highest')
        movie_titles = celebrity_scraper.metadata['movie_titles']
        
        print(movie_titles)
        ['Kubrick by Kubrick (Kubrick par Kubrick)', 'On a Clear Day You Can See Forever', 'The Shooting']
        ```
        
        You can also use *MovieScraper* to extract metadata of movies. If you want to find out what movie genres an actor has 
        played in, you can, first, extract the list of movies that he or she participated using `CelebrityScraper`. Then, you 
        must instantiate the `MovieScraper` and feed the list of movies to the `extract_metada` method. 
        
        ```python
        from rotten_tomatoes_scraper.rt_scraper import MovieScraper
        
        movie_url = 'https://www.rottentomatoes.com/m/marriage_story_2019'
        movie_scraper = MovieScraper(movie_url=movie_url)
        movie_scraper.extract_metadata()
        
        print(movie_scraper.metadata)
        {'score_rotten': '94%', 'score_audience': '85%', 'Rating': 'R', 'Genre': ['Drama'], 'Studio': 'Netflix'}
        ```
        
        You can feed `movie_url` or `movie_title` to extract the movie metadata. You can see the code below. 
        
        ```python
        from rotten_tomatoes_scraper.rt_scraper import MovieScraper
        
        movie_scraper = MovieScraper(movie_title='VICKY CRISTINA BARCELONA')
        movie_scraper.extract_metadata()
        
        print(movie_scraper.metadata)
        {'score_rotten': '81%', 'score_audience': '74%', 'Rating': 'PG-13', 'Genre': ['Comedy', 'Drama', 'Romance'], 'Box Office': 23164041, 'Studio': 'The Weinstein Co.'}
        ```
        
        This module doesn't give you a full access to all the metadata that you may find in Rotten Tomatoes website. However,
        you can easily use it to extract the most important ones.
        
        And, that's pretty much it!
        
Platform: UNKNOWN
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.7
Description-Content-Type: text/markdown
