PyCon APAC 2016 Keynote
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Wes McKinney gave the keynote presentation at PyCon APAC 2016 in Seoul. He discussed his work on Python data analysis tools like pandas, Apache Arrow, and Feather. He also talked about open source sustainability and governance. McKinney is working on the second edition of his book Python for Data Analysis, which is scheduled for release in 2017.
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PyCon APAC 2016 Keynote
- 7. My story â˘âŻ Accidental soMware developer â˘âŻ 2007: My ďŹrst job (ďŹnancial research analyst) â˘âŻ I started wriPng Python libraries to do my own work beQer â˘âŻ Soon I was helping my colleagues work beQer, too
- 8. Tools
- 9. Tools
- 13. Sustainable open source â˘âŻ How to keep contributors from drowning / burning out? â˘âŻ How to fund the work? â˘âŻ How to protect and serve the community?
- 14. The Grind
- 16. pandas, the open source project â˘âŻ Parts of code date back to April 2008 â˘âŻ Over 600 unique contributors on GitHub â˘âŻ AcPve project maintainers range from 4-7 people â˘âŻ > 6900 Closed Issues â˘âŻ > 5100 Pull Requests
- 18. April 7, 2014
- 23. Pythonâs normalizaPon in industry â˘âŻ Python has become a leading language instead of something âexperimentalâ or âriskyâ â˘âŻ Many businesses founded on the growth of the Python user base â˘âŻ See Paul Grahamâs 2004 essay âThe Python Paradoxâ â how things have changed!
- 26. Consensus
- 27. Some example governance documents â˘âŻ NumPy (see the docs) â˘âŻ IPython / Jupyter governance ââŻgithub.com/jupyter/governance â˘âŻ pandas ââŻgithub.com/pydata/pandas-governance ââŻModeled aMer Jupyter governance
- 30. conda-forge â˘âŻ Community-curated conda package channel (hosted on anaconda.org) â˘âŻ Reproducible build infrastructure (Docker + Circle CI + Travis CI + Appveyor) â˘âŻ Automated GitHub helper tools conda config --add channels conda-forge
- 32. Why pandas 2.0? â˘âŻ Some changes diďŹcult/impossible to do in an incremental way â˘âŻ pandasâs relaPonship with the ecosystem has evolved over the last 5 years â˘âŻ Make pandas ââŻFaster and use less memory ââŻFix long-standing limitaPons / inconsistencies ââŻEasier interoperability / extensibility
- 34. High Performance Sharing & Interchange Today With Arrow â˘âŻ Each system has its own internal memory format â˘âŻ 70-80% CPU wasted on serialization and deserialization â˘âŻ Similar functionality implemented in multiple projects â˘âŻ All systems utilize the same memory format â˘âŻ No overhead for cross- system communication â˘âŻ Projects can share functionality (eg, Parquet- to-Arrow reader)