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Obviously it's pretty hard to make general criticism of the Notebook GUI. This is especially without comparing to a specific other user interface for data scie

by ktpsns 7y ago

Obviously it's pretty hard to make general criticism of the Notebook GUI. This is especially without comparing to a specific other user interface for data scientists, such as a traditional REPL terminal, or some other command line tools? The Python world gives a good example about the sheer complexity of a notebook infrastructure. The is IPython, there is Jupyter Notebook, JupyterLab. There is even stuff like the SageMathCloud (nowadays called CoCalc) which is basically a web GUI to a VPS combining command lines and various notebooks. And hell, most of these web based interfaces try to make sharing easy. Mabye we should start comparing these (mostly OSS) tools to the traditional notebook GUIs of Matlab and Mathematica, something we used in the 90s and 2000s. From my feeling, they were more robust, could handle large data better, but they lack all the tooling we get for free in the web.

nl 7y ago
Jupyter Notebook/Jupyter Lab has replaced IPython as the notebook front end. I suspect 90%+ of Python Notebook work is done in Jupyter/Jupyter Lab (or things built on it like Google Collab/Kaggle Kernels). traditional notebook GUIs of Matlab and Mathematica, something we used in the 90s and 2000s. From my feeling, they were more robust, could handle large data better I've done 10s of terabyte analysis on Jupyter (Spark backend) and I personally know people doing petabyte work on it so this seems doubtful.
- ktpsns 7y ago
  You probably were careful enough to understand the limits of the Jupyter server and client (frontend). It's easy to screw up a terminal application in data science when dumping a large array. Many REPLs cannot handle this properly (and CTRL+C won't work). It's easy to test this: What does your favourite notebook do when you call some command such as (pseudocode/python here) print(range(int(1e7))) # or 1e8 In this particular example, the python CLI seems to handle keyboard interrupts fine when the terminal (or RAM) is flooded.

by ktpsns 7y ago

nl 7y ago

Jupyter Notebook/Jupyter Lab has replaced IPython as the notebook front end. I suspect 90%+ of Python Notebook work is done in Jupyter/Jupyter Lab (or things built on it like Google Collab/Kaggle Kernels). traditional notebook GUIs of Matlab and Mathematica, something we used in the 90s and 2000s. From my feeling, they were more robust, could handle large data better I've done 10s of terabyte analysis on Jupyter (Spark backend) and I personally know people doing petabyte work on it so this seems doubtful.

ktpsns 7y ago
You probably were careful enough to understand the limits of the Jupyter server and client (frontend). It's easy to screw up a terminal application in data science when dumping a large array. Many REPLs cannot handle this properly (and CTRL+C won't work). It's easy to test this: What does your favourite notebook do when you call some command such as (pseudocode/python here) print(range(int(1e7))) # or 1e8 In this particular example, the python CLI seems to handle keyboard interrupts fine when the terminal (or RAM) is flooded.