{"categories":[{"label":"Information Analysis","url":"https://skillfed.io/packages/category/scientific-engineering-information-analysis"}],"enrichment":{"capability":"Modin is a drop-in replacement for pandas that distributes DataFrame operations across multiple CPU cores, enabling faster execution on larger datasets that would otherwise exhaust memory or run slowly with single-threaded pandas.","skillfed_tags":["dataframe-acceleration","distributed-computing","pandas-compatible"],"use_cases":["Accelerate data cleaning and transformation pipelines on multi-gigabyte CSV or Parquet files that pandas loads slowly.","Process datasets larger than available RAM by leveraging distributed memory across multiple cores.","Speed up exploratory data analysis workflows without rewriting pandas code for a different framework.","Scale batch analytics jobs that currently timeout or run out of memory with single-threaded pandas.","Parallelize read operations across multiple workers for faster I/O on large files."],"what_it_does":"Modin lets you run pandas code faster by automatically distributing operations across all available CPU cores instead of using a single thread. You import it exactly like pandas\u2014`import modin.pandas as pd` instead of `import pandas as pd`\u2014and your existing code runs unchanged but potentially much faster, especially on datasets larger than available memory or when pandas becomes a bottleneck.\n\nUnder the hood, Modin partitions your DataFrames and distributes computation across a cluster. It handles all the complexity of scheduling and data movement, so you don't need to rewrite your logic. The package depends on pandas, numpy, fsspec, psutil, packaging, and typing-extensions, and you choose which compute engine to install separately based on your environment.","worth_installing":"Yes, if you have multi-core hardware and pandas workflows on datasets large enough to benefit from parallelization. The low install friction, permissive license, and drop-in API make it a practical choice for accelerating existing code. However, the 316-day gap since the last release suggests slower maintenance cadence; verify that your specific pandas operations are fully supported before committing to production use, and test actual speedup on your hardware and data size."},"id":"modin","links":{"html":"https://skillfed.io/packages/modin","md":"https://skillfed.io/packages/modin.md","pypi":"https://pypi.org/project/modin/"},"maintenance":{"status":"aging"},"meta":{"latest_release":"2025-10-02","license_spdx":null,"license_treatment":"permissive","name":"modin","python_support":"supports_current","summary":"Modin: Make your pandas code run faster by changing one line of code."},"popularity":{"monthly_downloads":1441131,"position":3891,"tier":"top_5000"},"security":{"n_vulnerabilities":0},"version":"0.37.1"}