Talk on PyData Paris 2016 about how we managed to move from stock movements information to pivot tables inside our Jupyter kernel for ERP5.

1. Wendelin: From Stock Movements To Pivot Tables
Inside Jupyter
Douglas Camata
douglas.camata (at) nexedi (dot) com
@douglascamata (mostly everywhere)

2. Details
Agenda
Who is Nexedi?
What is ERP5?
What is Wendelin?
ERP5 + Wendelin + Jupyter
PivotTableJs
Future

3. Details
Nexedi: Largest OSS Publisher In Europe

4. Details
Many Different Clients, All Over The Globe

5. Details
ERP5: The Most Powerful Open-Source ERP

6. Details
Wendelin Exanalytics Core: 100% Open-Source

7. Details
Wendelin Options

8. Details
Wendelin And Jupyter

9. Details
Advantages
Rapid prototyping (productivity++)
Code executed in ERP5 context
Copy and paste to ERP5
Fast access to famous scientific libraries
Rich visualisation of information
Customisation through Jupyter extensions

10. Details
ERP5 Jupyter Kernel Architecture

11. Details
Rich Object Representation

13. Details
Support For Multiple Processes: The Environment
Object

14. Details
Automatic "Import Fixer" Using AST Transformers

15. Details
Automatic "Import Fixer' Using AST Transformers

16. Details
PivotTableJs

17. Details
PivotTableJs Integration In ERP5

18. Details
PivotTableJs ERP5: Demo

19. Details
The Future
RestrictedPython kernel (improved security and
anonymous users)
Patch PivotTableJs to query ERP5 when filtering &
processing (removes limitations of client side
Javascript data manipulation)

20. Details
Wendelin: From Stock Movements To Pivot Tables
Inside Jupyter
Questions?
Douglas Camata
douglas.camata (at) nexedi (dot) com
@douglascamata (mostly everywhere)

21. Details
Wendelin: From Stock Movements To Pivot Tables
Inside Jupyter
Thank you!
Douglas Camata
douglas.camata (at) nexedi (dot) com
@douglascamata (mostly everywhere)