Would you like to know what are the top-250 movies of all time? Or the best comedy shows that have ever hit the small screens? For all such answers, reviews, ratings, and trivia related to the world of movies and shows, people all over the world use IMDB, which is an online database of such information. While the information is updated by fans, the database itself is owned and operated by a subsidiary of Amazon. It was initially created as a database in 1990 and moved to the web in 1993. While anyone can access the information on the website, registration is a must in case you want to make edits to the facts or add reviews. In this blog, we take a look at how web scraping IMDB data is done using Python.
On top of various data points that are updated for both movies and small screen shows, IMDB also allows its users to add ratings and these ratings have formed the basis of multiple lists that are used by movie buffs and others to create their watch lists. While IMDB does not provide an API to query its data, it does allow you to download the data in textual format. You can also scrape the data using a DIY code.
How Is Web Scraping of IMDB Data Done?
We will be scraping 2 sets of data from IMDB
a). The IMDB top 250 movies
b). The IMDB top 250 television shows
We will be scraping certain data points for each movie or show on these lists. You may not want to scrape all the data at once, and hence we have provided the option to change the value of a parameter, to extract only the top n results.
You will require Python3.7 or higher along with the BeautifulSoup dependency and a text editor before we begin. Then you can run the code given below using the python command itself. No user input is required since we have hardcoded the links of the two lists that we mentioned earlier in the code.
In the code, we have 3 specific functions
a). get_top_rated_imdb_hits- This is where the execution starts. As input to this function, we pass the URL of the concerned list. It can be the movie list URL or the tv-shows list URL. We also pass the name of the file in which we want the result JSON and the number of top results that we want. We fetch certain data points such as the movie name and ratings that are available on the web page itself, and then call the get_extra_details function bypassing the movie/show specific URL to fetch extra data-points.
b). get_web_page_content- This function used to fetch the HTML content of the URL passed into it, and convert it to a BeautifulSoup object that can be easily parsed. This object is what this function returns.
c). get_extra_details- This function uses the movie or show-specific URL passed into it by the get_top_rated_imdb_hits function to fetch more details such as the summary, name of the top stars, and the director- information not available in the ranked-list webpage.
As you can see, we have called the function get_top_rated_imdb_hits twice, once with the movies URL and once with the tv shows URL. We have also passed the count as 2 as we want the data only for the top two candidates in both lists. Once this code runs, you will see two files created in your directory- “movies.json” and “tv_shows.json”.
The Data Points That We Extracted
For each movie or TV show, we extracted these data-points.
a). IMDB link for the specific show/ movie
One thing to note is that not all data points may be available for each movie or show but whichever is available will be scrapped. The JSON below shows the top 2 movies in the top-250 movies list in IMDB that we obtained on running the code above.
While we have scraped the data as it was and made minimal changes to the data itself. You can clean the data further to make the data points more usable. A few examples would be
a). Removing the brackets on the year.
b). Breaking Ratings into 2 separate data-points, the ratings and the number of people who submitted their ratings.
The JSON below shows the top 2 tv-shows that we extracted from the second webpage. Since many such web scrapers are available out there. Let us take a look at how we can scrape IMDB data from their website for different TV Shows. The below code is a detailed explanation of how it can be done.
While we have extracted only 2 from each list. You can allow the code to run for all the 250 shows or movies and create a massive JSON file. You can even store the data that you extract in a database. But for running the code on so many links. You will need to follow some best practices and keep some constraints in mind while web scraping IMDB data.
Constraints and Best Practices
In case you ran this code and changed the value of “nos” to say 250 and ran the code on all 250 movies and tv shows. There’s a high chance that the website will detect automated traffic from your IP and you will end up getting blocked. You will need to use tools like IP rotation. You can also create a wait time of a few seconds between scraping the HTML content of each URL.
As for the data that you scrape, even though most of its content created by volunteers. There can be certain restrictions on the commercial usage of the data. You need to follow the regulations wherever you are using data scraped from different web pages. This is how web scraping IMDB data using Python.
However, if you want a hassle-free web scraping experience where someone takes care of the data and you can focus on your core business model, our team at PromptCloud is at your service. We pride ourselves on our DaaS solution where we take care of everything. Right from scraping to accessing the scraped data.
If you liked the content above, we are sure you would love to explore our article on Web Scraping Flipkart Using Python.