| | ---
|
| | license: cc0-1.0
|
| | task_categories:
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| | - text-classification
|
| | - text-generation
|
| | language:
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| | - en
|
| | tags:
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| | - movies
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| | - letterboxd
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| | - web-scraping
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| | - recommendation-systems
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| | - cinema
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| | - film-analysis
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| | size_categories:
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| | - 10K<n<100K
|
| | ---
|
| |
|
| | # Letterboxd Movies Dataset
|
| |
|
| | ## Dataset Description
|
| |
|
| | A comprehensive dataset of movies scraped from Letterboxd, including genres, ratings, runtime, countries, and detailed movie characteristics.
|
| |
|
| | This dataset contains **16246 movies** with **28 features** each, scraped from Letterboxd. It's perfect for:
|
| |
|
| | - 🎬 Movie recommendation systems
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| | - 📊 Film industry analysis
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| | - 🤖 Machine learning projects
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| | - 📈 Rating prediction models
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| | - 🔍 Movie discovery algorithms
|
| |
|
| | ## Dataset Structure
|
| |
|
| | ### Features
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| |
|
| | | Column | Type | Description |
|
| | |--------|------|-------------|
|
| | | title | object | Movie title |
|
| | | year | float64 | Movie year |
|
| | | decade | int64 | Movie decade |
|
| | | decade_category | object | Movie decade category |
|
| | | movie_era | object | Movie movie era |
|
| | | age_years | float64 | Movie age years |
|
| | | age_category | object | Movie age category |
|
| | | runtime | float64 | Movie runtime |
|
| | | runtime_category | object | Movie runtime category |
|
| | | runtime_percentile | object | Movie runtime percentile |
|
| | | is_feature_length | bool | Movie is feature length |
|
| | | genres | object | Movie genres |
|
| | | primary_genre | object | Movie primary genre |
|
| | | genre_count | float64 | Movie genre count |
|
| | | genre_diversity | object | Movie genre diversity |
|
| | | is_single_genre | bool | Movie is single genre |
|
| | | country | object | Movie country |
|
| | | country_category | object | Movie country category |
|
| | | language | object | Movie language |
|
| | | is_english | bool | Movie is english |
|
| | | title_length | int64 | Movie title length |
|
| | | title_complexity | object | Movie title complexity |
|
| | | word_count | int64 | Movie word count |
|
| | | title_runtime_ratio | float64 | Movie title runtime ratio |
|
| | | runtime_efficiency | object | Movie runtime efficiency |
|
| | | production_scale | object | Movie production scale |
|
| | | is_recent | bool | Movie is recent |
|
| | | is_classic | bool | Movie is classic |
|
| |
|
| |
|
| | ### Statistics
|
| |
|
| | - **Total Records**: 16,246
|
| | - **File Size**: 3.41 MB
|
| | - **Data Quality**: 99.1% complete
|
| | - **Year Range**: 1888 - 2029
|
| | - **Average Runtime**: 108.5 minutes
|
| |
|
| | ## Usage
|
| |
|
| | ```python
|
| | from datasets import load_dataset
|
| |
|
| | # Load the dataset
|
| | dataset = load_dataset("codealchemist01/letterboxd-movies")
|
| |
|
| | # Access the data
|
| | df = dataset['train'].to_pandas()
|
| | print(df.head())
|
| | ```
|
| |
|
| | ## Data Collection
|
| |
|
| | The data was collected through web scraping of Letterboxd.com using ethical scraping practices:
|
| | - Respectful rate limiting
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| | - Robots.txt compliance
|
| | - No personal user data collected
|
| |
|
| | ## Citation
|
| |
|
| | If you use this dataset in your research, please cite:
|
| |
|
| | ```
|
| | @dataset{letterboxd_movies_2025,
|
| | title={Letterboxd Movies Dataset},
|
| | author={Kutay Ahin},
|
| | year={2025},
|
| | url={https://huggingface.co/datasets/codealchemist01/letterboxd-movies}
|
| | }
|
| | ```
|
| |
|
| | ## License
|
| |
|
| | This dataset is released under the CC0 1.0 Universal License.
|
| |
|
