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MODIN
Drop-in parallel pandas — scale DataFrames across cores and clusters
Apache-2.0
ABOUT
Data scientists and ML engineers hit performance and memory walls when processing datasets that exceed a single machine's RAM or CPU capacity with pandas. Modin eliminates this bottleneck by transparently parallelizing DataFrame operations across the available hardware — from multi-core laptops to multi-node Ray clusters — without requiring users to learn distributed computing APIs. The same pandas code automatically scales to larger-than-memory and multi-terabyte datasets.
INSTALL
pip install "modin[ray]"INTEGRATION GUIDE
1. Process large CSV and Parquet datasets that exceed single-machine memory by distributing across cluster workers
2. Parallelize data cleaning, feature engineering, and ETL operations across all CPU cores without API changes
3. Scale pandas-based ML preprocessing pipelines from a laptop to a production Ray cluster seamlessly
4. Handle multi-terabyte joins, aggregations, and group-by operations that would OOM on standard pandas
5. Accelerate exploratory data analysis on large datasets by splitting work across distributed workers
TAGS
dataframepandasparalleldistributedraydaskdata-processingpython