IMDB Top 250 Scrape
A Selenium workflow that collects ranked movie details from a dynamically rendered page and prepares them for analysis.
The question
Reliably extract structured movie data from a JavaScript-rendered ranking whose content is not fully available in the initial HTML.
The approach
I automated Chrome with Selenium, waited for dynamic elements, looped through movie cards, extracted ranking, title, year, duration, genre, rating, and votes, handled missing values, and assembled the results with Pandas.
The outcome
The script turns the complete ranking into a clean CSV that is immediately usable for film, rating, genre, popularity, or time-series analysis.
Analysis questions
What the work needed to answer.
- 01
How can a browser-rendered ranking be collected reliably?
- 02
Which fields make the dataset useful beyond a simple title list?
- 03
How should missing ratings or metadata be handled?
- 04
How can the collected records be prepared for analysis?
Method
From raw data to a useful answer.
Automate the browser
Used Selenium WebDriver to load the JavaScript-rendered ranking.
Wait for content
Applied explicit waits so extraction begins only after the movie elements are available.
Extract safely
Collected seven fields per movie and assigned a clear fallback when an optional element was absent.
Structure and export
Built a Pandas DataFrame from the records and exported an analysis-ready CSV.
Results
What the analysis revealed.
Static requests are not enough
The ranking depends on browser-rendered content, so Selenium is appropriate for dependable extraction.
Explicit waits improve reliability
Waiting for target elements prevents timing errors that occur when the page loads more slowly than expected.
Fallbacks preserve the dataset
Representing absent fields as “Not Rated” keeps the row without hiding the missing value.
The schema supports many analyses
Rank, title, year, duration, genre, rating, and votes enable comparisons across eras, genres, and popularity.