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- Scale your pandas workflow by changing a single line of code — Modin 0. . . .
Modin uses Ray, Dask or Unidist to provide an effortless way to speed up your pandas notebooks, scripts, and libraries Unlike other distributed DataFrame libraries, Modin provides seamless integration and compatibility with existing pandas code
- modin · PyPI
Modin: Make your pandas code run faster by changing one line of code What is Modin? Modin is a drop-in replacement for pandas While pandas is single-threaded, Modin lets you instantly speed up your workflows by scaling pandas so it uses all of your cores
- Modin - Scale Your pandas Workflows
Scale your Pandas workflows by changing a single line of code In [1]: In [2]: df = pd read_csv (" path to file") df describe ()
- Introducing Modin: A Step-by-Step Guide to Accelerating Pandas
Developed by Devin Petersohn during his work in the RISELab at UC Berkeley, it is a drop-in replacement for the extensively used Python* library, Pandas Being fully compatible with Pandas, Modin has wide coverage of the Pandas API
- GitHub - modin-project modin: Modin: Scale your Pandas workflows by . . .
Modin transparently distributes the data and computation so that you can continue using the same pandas API while working with more data faster Because it is so light-weight, Modin provides speed-ups of up to 4x on a laptop with 4 physical cores
- Modin download | SourceForge. net
Modin uses Ray, Dask or Unidist to provide an effortless way to speed up your pandas notebooks, scripts, and libraries Unlike other distributed DataFrame libraries, Modin provides seamless integration and compatibility with existing pandas code
- Getting Started — Modin 0+untagged. 50. gbce3707 documentation
Modin is more than 30X faster at applying a single column of data, operating on 130+ million rows in a second In short, Modin provides orders of magnitude speed up over pandas for a variety of operations out of the box
- Modin – How to speedup pandas by changing one line of code
Modin is a python library that can be used to handle large datasets using parallelisation The syntax is similar to pandas and its astounding performance has made it a promising solution
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