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Sprinting Pandas (London Python)

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October 22, 2020

Sprinting Pandas (London Python)

3d644406158b4d440111903db1f62622?s=128

ianozsvald

October 22, 2020
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  1. Sprinting Pandas (live in London) @IanOzsvald – ianozsvald.com Ian Ozsvald

    London Python October 2020
  2.  Interim Chief Data Scientist  19+ years experience 

    Team coaching & public courses –I’m sharing from my Higher Performance Python course Introductions By [ian]@ianozsvald[.com] Ian Ozsvald 2nd Edition!
  3.  Pandas – Saving RAM to fit in more data

    – Calculating faster by dropping to Numpy  Advice for “being highly performant”  Has Covid 19 affected UK Company Registrations? Today’s goal By [ian]@ianozsvald[.com] Ian Ozsvald
  4. Strings are expensive and slow By [ian]@ianozsvald[.com] Ian Ozsvald

  5. Categoricals are cheap and fast! By [ian]@ianozsvald[.com] Ian Ozsvald Circa

    1% of previous memory cost
  6. Categoricals “.cat” accessor By [ian]@ianozsvald[.com] Ian Ozsvald

  7. Categoricals – over 10x speed up (on this data)! By

    [ian]@ianozsvald[.com] Ian Ozsvald
  8. Categoricals – index queries faster! By [ian]@ianozsvald[.com] Ian Ozsvald Circa

    500x speed-up!
  9. float64 is default and a bit expensive By [ian]@ianozsvald[.com] Ian

    Ozsvald
  10. float32 “half-price” and a bit faster By [ian]@ianozsvald[.com] Ian Ozsvald

  11. Make choices to save RAM By [ian]@ianozsvald[.com] Ian Ozsvald Including

    the index (previously we ignored it) we still save circa 50% RAM so you can fit in more rows of data
  12. “dtype_diet” gives you advice By [ian]@ianozsvald[.com] Ian Ozsvald

  13. Drop to NumPy if you know you can By [ian]@ianozsvald[.com]

    Ian Ozsvald Caveat – Pandas mean is not np mean, the fair comparison is to np nanmean which is slower – see my blog or PyDataAmsterdam 2020 talk for details
  14. NumPy vs Pandas overhead (ser.sum()) By [ian]@ianozsvald[.com] Ian Ozsvald 25

    files, 83 functions Very few NumPy calls! Thanks! https://github.com/ianozsvald/callgraph_james_powell
  15. Overhead... By [ian]@ianozsvald[.com] Ian Ozsvald

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

    functions Many fewer Pandas calls (but still a lot!)
  17. Is Pandas unnecessarily slow – NO! By [ian]@ianozsvald[.com] Ian Ozsvald

    https://github.com/pandas-dev/pandas/issues/34773 - the truth is a bit complicated!
  18.  Install optional (but great!) Pandas dependencies – bottleneck –

    numexpr  Investigate https://github.com/ianozsvald/dtype_diet  Investigate my ipython_memory_usage (PyPI/Conda) Being highly performant By [ian]@ianozsvald[.com] Ian Ozsvald https://pandas.pydata.org/pandas-docs/stable/user_guide/enhancingperf.html
  19. Pure Python is “slow” and expressive By [ian]@ianozsvald[.com] Ian Ozsvald

    Deliberately poor function – pretend this is clever but slow!
  20. Compile to Numba judiciously By [ian]@ianozsvald[.com] Ian Ozsvald Near 10x

    speed-up!
  21. Parallelise with Dask for multi-core By [ian]@ianozsvald[.com] Ian Ozsvald 

    Make plain-Python code multi-core  Note I had to drop text index column due to speed-hit  Data copy cost can overwhelm any benefits so (always) profile & time
  22.  Mistakes slow us down (PAY ATTENTION!) – Try nullable

    Int64 & boolean, forthcoming Float64 – Write tests (unit & end-to-end) – Lots more material & my newsletter on my blog IanOzsvald.com – Time saving docs: Being highly performant By [ian]@ianozsvald[.com] Ian Ozsvald
  23.  Memory mapped & lazy computation – New string dtype

    (RAM efficient)  Modin sits on Pandas, new “algebra” for dfs – Drop in replacement, easy to try Vaex / Modin By [ian]@ianozsvald[.com] Ian Ozsvald See talks on my blog:
  24.  You have a huge dataset on a single harddrive

     Memory mapped files (HDF5) are best  Numpy types and simpler Pandas-like functions  Investment – similar but different API to Pandas When to try Vaex By [ian]@ianozsvald[.com] Ian Ozsvald https://github.com/vaexio/vaex/issues/968
  25.  You want Pandas but ran out of RAM on

    1 machine  You want multi-machine cluster scalability  You want multi-core support for operations like groupby on parallelisable datasets  Investment – quick start then a learning curve When to try Dask By [ian]@ianozsvald[.com] Ian Ozsvald
  26.  You want all of Pandas  You have lots

    of RAM and many CPUs  You’re doing groupby operations on many columns  Investment – easy to try When to try Modin By [ian]@ianozsvald[.com] Ian Ozsvald https://github.com/modin-project/modin/issues/1390
  27. Covid 19’s effect on UK Economy? By [ian]@ianozsvald[.com] Ian Ozsvald

    Sharp decline in corporate registration after Lockdown – then apparent surge (perhaps just backed-up paperwork?). Will the recovery “last”? All open data, you can do similar things!
  28.  Make it right then make it fast  Think

    about being performant  See blog for my classes  I’d love a postcard if you learned something new! Summary By [ian]@ianozsvald[.com] Ian Ozsvald
  29. Be faster by learning new approaches By [ian]@ianozsvald[.com] Ian Ozsvald