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May 05, 2020

More Decks by ianozsvald

Other Decks in Technology


  1.  Interim Chief Data Scientist  19+ years experience 

    Team coaching & public courses – Higher Performance! Introductions By [ian]@ianozsvald[.com] Ian Ozsvald 2nd Edition M ay 2020
  2.  Pandas – Saving RAM – Calculating faster by dropping

    to Numpy & Numba  A brief look at Modin for in-RAM faster Pandas ops  What does Covid 19 do to the (UK) economy? Today’s goal By [ian]@ianozsvald[.com] Ian Ozsvald
  3. Overhead with ser.values.sum() By [ian]@ianozsvald[.com] Ian Ozsvald 18 files, 51

    functions Many fewer Pandas calls (but still a lot!)
  4.  A new “algebra” for DataFrames, reimplemented functions & Pandas

    fallback  Young project, drop-in replacement  Uses Ray for parallel computation  Easy to experiment with Modin By [ian]@ianozsvald[.com] Ian Ozsvald
  5.  Modin if big df, else check your Pandas choices.

    Swifter multicore  See blog for my classes, also Thoughts & Jobs email list  I’d love a postcard if you learned something new Summary By [ian]@ianozsvald[.com] Ian Ozsvald
  6.  “New” project (not “Pandas”)  Memory mapped, virtual columns

    & lazy computation  New string dtype (RAM efficient)  See article (single laptop, billions of samples) -> Vaex By [ian]@ianozsvald[.com] Ian Ozsvald https://towardsdatascience.com/ml-impossible-train-a-1-billion-sample-model-in-20- minutes-with-vaex-and-scikit-learn-on-your-9e2968e6f385
  7.  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
  8.  Mature project, Array (NumPy), Bag (list-like)  Distributed dataframe

    for Pandas – row blocks, not cols Dask Distributed DataFrame By [ian]@ianozsvald[.com] Ian Ozsvald https://dask.readthedocs.io/en/latest/dataframe.html
  9.  “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