May 05, 2020


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

    functions Many fewer Pandas calls (but still a lot!)
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     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
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     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
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     “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 minutes-with-vaex-and-scikit-learn-on-your-9e2968e6f385
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     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
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     Mature project, Array (NumPy), Bag (list-like)  Distributed dataframe

    for Pandas – row blocks, not cols Dask Distributed DataFrame By [ian]@ianozsvald[.com] Ian Ozsvald
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     “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