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PyCon Singapore 2013 Keynote

  1. 1. Python and Data: What’s next? Wes McKinney @wesmckinn PyCon Singapore 2013-06-14
  2. 2. Me 2013: Analytics Startup in SF
  3. 3. Book • Python essentials • NumPy • IPython • matplotlib • pandas Published October 2012
  4. 4. Some context • 2007 to 2013 • NumPy, SciPy mature • IPython Notebook • Key libraries/tools developed: scikit- learn, statsmodels, PyCUDA, ... • pandas helps make Python a desirable data preparation language
  5. 5. pandas • Fast structured data manipulation tools for Python with nice API • Goal: make Python a halfway decent language for data preparation / statistical analysis • Sometimes say:“R data frames in Python” • Fast-growing user base / community
  6. 6. Aside: vbench
  7. 7. Tool inefficiency impedes innovation
  8. 8. What can tell us about Python?
  9. 9. Some Trends • Decline of Desktop, Rise of Web/Cloud • SVG / HTML5 Canvas / WebGL Tech • Big Data • JIT-compile all the things • Democratize all the things
  10. 10. Challenge: Keeping Python relevant
  11. 11. Data on the Web • Nirvana: ubiquitous, easy data analysis • Challenges • JavaScript: weak language for implementing analytics • Computation needs to run “close” to data • Maintaining interactivity
  12. 12. Golden age for web visualization SVG
  13. 13. Embracing the JavaScript • Build bridges, not walls • Some examples • IPython Notebook • RStudio • Rob Story’s pandas integrations • Chartkick
  14. 14. In search of the perfect “data language” • Minimal syntax overhead • Domain-specific data types that all support missing (NA) values • Rich built-in prep-related operations • E.g. set logic, group by, sorting, binning, indexing • Integrate within a larger application
  15. 15. JIT compiler tech • LLVM: growing in popularity • Rolling a new, fast compute engine much easier than it used to be • But: not sure compiling Python code is the optimal long-term strategy
  16. 16. Big Data SQL
  17. 17. Some thoughts • Web-friendliness: essential for survival • You can never be too productive • The data’s not getting any smaller
  18. 18. Thanks!

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