1.
Python and Data:What’s next?Wes McKinney@wesmckinnPyCon Singapore 2013-06-14

2.
Me2013: AnalyticsStartup in SF

3.
Book• Python essentials• NumPy• IPython• matplotlib• pandasPublished October 2012

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.
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.
Aside: vbench

7.
Tool inefficiencyimpedes innovation

8.
What cantell us about Python?

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.
Challenge:Keeping Python relevant

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.
Golden age for webvisualizationSVG

13.
Embracing the JavaScript• Build bridges, not walls• Some examples• IPython Notebook• RStudio• Rob Story’s pandas integrations• Chartkick

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.
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.
Big Data SQL

17.
Some thoughts• Web-friendliness: essential for survival• You can never be too productive• The data’s not getting any smaller

18.
Thanks!