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Scraping Imdb Rating Movies


IMDb (an acronym for Internet Movie Database)[1] is an online database of information related to films, television programs, home videos, video games, and streaming content online – including cast, production crew and personal biographies, plot summaries, trivia, ratings, and fan and critical reviews. An additional fan feature, message boards, was abandoned in February 2017. Originally a fan-operated website, the database is now owned and operated by, Inc., a subsidiary of Amazon.

As of December 2020, IMDb has approximately 7.5 million titles (including episodes) and 10.4 million personalities in its database,[2] as well as 83 million registered users.

IMDb began as a movie database on the Usenet group "rec.arts.movies" in 1990 and moved to the web in 1993.


Websites can restrict or ban scraping data from their website. Users can be subject to legal ramifications depending on where and how you attempt to scrape information. Websites usually describe this in their terms of use and in their robots.txt file found at their site, which usually looks something like this: So scrape responsibly, and respect the robots.txt.

What’s Web Scraping?

Web scraping consists of gathering data available on websites. This can be done manually by a human or by using a bot.
A bot is a program you build that helps you extract the data you need much quicker than a human’s hand and eyes can.

What Are We Going to Scrape?

It’s essential to identify the goal of your scraping right from the start. We don’t want to scrape any data we don’t actually need.
For this project, we’ll scrape data from IMDb’s “Top 1,000” movies, specifically the top 50 movies on this page. Here is the information we’ll gather from each movie listing:

  1. The title
  2. The year it was released
  3. Movie Genre
  4. Movie Certificate
  5. How long the movie is
  6. IMDb’s rating of the movie
  7. The Metascore of the movie
  8. How many votes the movie got
  9. The U.S. gross earnings of the movie


  • Requests will allow us to send HTTP requests to get HTML files
  • BeautifulSoup will help us parse the HTML files
  • pandas will help us assemble the data into a DataFrame to clean and analyze it
  • NumPy will add support for mathematical functions and tools for working with arrays