Literate Programming And Notebooks

David Smith sums up a debate on notebooks versus literate programming:

There’s no video yet available of Joel’s talk, but you can guess the theme of that opening slide, and walking through the slides conveys the message well, I think. Yuhui Xie, author and creator of the rmarkdown package, provides a detailed summary and response to Joel’s talk, where he lists Joel’s main critiques of Notebooks:

  1. Hidden state and out-of-order execution

  2. Notebooks are difficult for beginners

  3. Notebooks encourage bad habits

  4. Notebooks discourage modularity and testing

  5. Jupyter’s autocomplete, linting, and way of looking up the help are awkward

  6. Notebooks encourage bad processes

  7. Notebooks hinder reproducible + extensible science

  8. Notebooks make it hard to copy and paste into Slack/Github issues

  9. Errors will always halt execution

  10. Notebooks make it easy to teach poorly

  11. Notebooks make it hard to teach well

Read the whole thing.  I agree with some of these points, but disagree with a few on the list.

Related Posts

Building Credit Scorecards

Andre Violante uses SAS to build credit scorecards and analyze credit data: For this analysis I’m using the SAS Open Source library called SWAT (Scripting Wrapper for Analytics Transfer) to code in Python and execute SAS CAS Action Sets. SWAT acts as a bridge between the python language to CAS Action Sets. CAS Action Sets are synonymous to libraries […]

Read More

A Functional Approach To PySpark

Tristan Robinson shows us how we can implement a transform function which makes Python code look a little bit more functional: After a small bit of research I discovered the concept of monkey patching (modifying a program to extend its local execution) the DataFrame object to include a transform function. This function is missing from […]

Read More

Categories

September 2018
MTWTFSS
« Aug Oct »
 12
3456789
10111213141516
17181920212223
24252627282930