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Making Pandas Fly

Making Pandas Fly

Talk at PyDataBudapest on preprocessing data with Dask and then making Pandas run much faster and use less RAM: https://ianozsvald.com/2020/04/27/flying-pandas-and-making-pandas-fly-virtual-talks-this-weekend-on-faster-data-processing-with-pandas-modin-dask-and-vaex/


April 27, 2020

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  2.  Preparing data with Dask  Pandas – Saving RAM

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  3.  10 million rows “probably fine” but needs 10s GB

    RAM  Probably only single core, built for in-RAM computation  Complex 10yr codebase, hard to optimise When does Pandas get smelly? By [ian]@ianozsvald[.com] Ian Ozsvald
  4.  Mature project, Array (NumPy), Bag (list-like)  Distributed dataframe

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  5.  “Slower” than Pandas but happily works for 100GBs+ 

    Lots of docs & help on StackOverflow  Great for 1 or n machines for bigger-than-RAM tasks  Give Workers lots of RAM (else they die!) Dask Distributed DataFrame By [ian]@ianozsvald[.com] Ian Ozsvald
  6.  Get more into RAM with smaller dtypes  Use

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  7. Overhead with ser.values.sum() By [ian]@ianozsvald[.com] Ian Ozsvald 18 files, 51

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  8.  Dask on Bigger Data, Modin if in RAM. Vaex

    for many strings  See blog for my classes  I’d love a postcard if you learned something new Summary By [ian]@ianozsvald[.com] Ian Ozsvald
  9.  “New” project (not “Pandas”)  Memory mapped, virtual columns

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  10.  A new “algebra” for DataFrames, reimplemented functions & Pandas

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