How to scrape media and entertainment data from MUBI using the Minexa.ai extension
- Minexa.ai
- Jul 2
- 3 min read
MUBI's all-films catalogue is one of the most consistently structured film listing pages on the web, which makes it a reliable source for building media datasets covering curated cinema from dozens of countries and decades.
This guide walks through how to extract structured film data from mubi.com/en/films?all_films=true using the Minexa.ai Chrome extension, with no code required.
Watch the tutorial first
The video below covers the full extraction from start to export.
Step 1: Open the extension on the MUBI films page
Navigate to the MUBI all films page and open the Minexa.ai extension from your Chrome toolbar. The extension loads and detects the current page automatically.
Once the extension is open, click 'I'm on the right page' to confirm you want to extract from this URL.
Step 2: Review pagination and choose scraping mode
The extension detects the pagination logic on the page and shows the available options. Review the detected next-page behavior and click Continue.
Next, choose whether to scrape the list only or the list plus each film's detail page. For catalogue-level data, the list mode is sufficient.
Step 3: Select scraping scenario and highlight the container
Choose simple scraping mode unless you need custom interactions. Then hover over the film listing area until the full card grid is highlighted, and click to select it as the data container.
Step 4: Review extracted fields
After clicking 'Create Scraper', Minexa.ai automatically identifies all data points within the selected container. You can browse through the extracted columns using the next and previous navigation.
What the extracted fields contain
The film_title field captures the uppercase title as it appears in the h3 element on each card, for example PERFECT DAYS or IN THE MOOD FOR LOVE.
The director_country_year field returns a structured array of span-typed objects. Each array holds the director name, the country of origin, and the release year as three separate values. This makes it straightforward to split them into individual columns during post-processing.
The rating_percentage field surfaces a CSS background-position string such as '58.996% 27.723%'. This encodes the sprite position of the rating image rather than a numeric score, so if you need a comparable rating value you will need to interpret or normalize it separately.
The movie_details field is the most comprehensive column. It returns a traversable array covering the image alt text, the full CDN image URL, the video element ID used for trailer previews, the film href path, the synopsis paragraph text, and the card title, all within a single field. This is useful when you want to pull multiple attributes from a single pass without requesting detail pages.
The film_link field gives the relative path to each film's page, for example /en/gb/films/perfect-days, which you can combine with the base domain to build full URLs for downstream deep extraction.
The css_class field captures the unique per-card CSS class string. Because MUBI applies distinct class identifiers per card, this field can help you identify layout variants or card types across the listing when processing the output.
Sample extracted data
[
{
"film_title": "PERFECT DAYS",
"release_year": "2023",
"director_country_year": ["Wim Wenders", "Japan", "2023"],
"film_link": "/en/gb/films/perfect-days",
"rating_percentage": "58.996% 27.723%"
},
{
"film_title": "IN THE MOOD FOR LOVE",
"release_year": "",
"director_country_year": ["Wong Kar Wai", "Hong Kong", "2000"],
"film_link": "/en/gb/films/in-the-mood-for-love",
"rating_percentage": "50.139% 25.248%"
},
{
"film_title": "CLOSE",
"release_year": "2022",
"director_country_year": ["Lukas Dhont", "Belgium", "2022"],
"film_link": "/en/gb/films/close-2022",
"rating_percentage": "25.766% 40.594%"
}
]Step 5: Export your data
Once the scraping job completes, the results appear in a table view. From there you can export to Excel or JSON directly.
You can also connect the output to Google Sheets or set up a recurring schedule directly from the job summary screen.
If you want to go further and pull synopsis text, trailer video IDs, or full image URLs at scale, the movie_details array gives you all of that without needing to visit each film page individually.
Get started with the Minexa.ai Chrome extension to run this extraction yourself.
Comments