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Python and Data:What’s next?Wes McKinney@wesmckinnPyCon Singapore 2013-06-14
Me2013: AnalyticsStartup in SF
Book• Python essentials• NumPy• IPython• matplotlib• pandasPublished October 2012
Some context• 2007 to 2013• NumPy, SciPy mature• IPython Notebook• Key libraries/tools developed: scikit-learn, statsmodel...
pandas• Fast structured data manipulation tools forPython with nice API• Goal: make Python a halfway decent languagefor da...
Aside: vbench
Tool inefficiencyimpedes innovation
What cantell us about Python?
Some Trends• Decline of Desktop, Rise of Web/Cloud• SVG / HTML5 Canvas / WebGL Tech• Big Data• JIT-compile all the things•...
Challenge:Keeping Python relevant
Data on the Web• Nirvana: ubiquitous, easy data analysis• Challenges• JavaScript: weak language for implementinganalytics•...
Golden age for webvisualizationSVG
Embracing the JavaScript• Build bridges, not walls• Some examples• IPython Notebook• RStudio• Rob Story’s pandas integrati...
In search of the perfect“data language”• Minimal syntax overhead• Domain-specific data types that all supportmissing (NA) v...
JIT compiler tech• LLVM: growing in popularity• Rolling a new, fast compute engine mucheasier than it used to be• But: not...
Big Data SQL
Some thoughts• Web-friendliness: essential for survival• You can never be too productive• The data’s not getting any smaller
Thanks!
PyCon Singapore 2013 Keynote
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PyCon Singapore 2013 Keynote

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by Wes McKinney

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PyCon Singapore 2013 Keynote

  1. 1. Python and Data:What’s next?Wes McKinney@wesmckinnPyCon Singapore 2013-06-14
  2. 2. Me2013: AnalyticsStartup in SF
  3. 3. Book• Python essentials• NumPy• IPython• matplotlib• pandasPublished 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 desirabledata preparation language
  5. 5. pandas• Fast structured data manipulation tools forPython with nice API• Goal: make Python a halfway decent languagefor data preparation / statistical analysis• Sometimes say:“R data frames in Python”• Fast-growing user base / community
  6. 6. Aside: vbench
  7. 7. Tool inefficiencyimpedes innovation
  8. 8. What cantell 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 implementinganalytics• Computation needs to run “close” to data• Maintaining interactivity
  12. 12. Golden age for webvisualizationSVG
  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 supportmissing (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 mucheasier than it used to be• But: not sure compiling Python code is theoptimal 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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