Thanks! I've been keeping an eye on that thread and agree, there is a lot 
of overlap.

On Tuesday, September 18, 2018 at 8:56:34 AM UTC-7, Michael Milligan wrote:
>
> FYI you might take a look at the Jupyterlab variable inspector work being 
> tracked here: https://github.com/jupyterlab/jupyterlab/issues/443
>
> Seems like there could be some overlap with what you are trying to do.
>
> Cheers,
> Michael
>
> On Mon, Sep 17, 2018 at 7:42 PM, Adam Rule <[email protected] <javascript:>
> > wrote:
>
>> Great point Brian. For prototyping's sake monkey patching should be 
>> enough to test if the interaction is valuable or not.
>>
>>
>> On Monday, September 17, 2018 at 4:45:19 PM UTC-7, ellisonbg wrote:
>>>
>>> I think it could be regular python code that uses the Jupyter display 
>>> system to display the information. The challenge is to figure out how to 
>>> detect the pandas function calls and add the needed logic before and after. 
>>> A good starting point might be to just monkey patch the relevant pandas 
>>> calls and wrap them in the logic you need. That would allow you to get 
>>> started quickly and explore the problem space.
>>>
>>> On Mon, Sep 17, 2018 at 3:40 PM Adam Rule <[email protected]> wrote:
>>>
>>>> I have noticed that a number of Jupyter users call df.head(), df.shape, 
>>>> df.describe(), or something similar almost every time they load or 
>>>> manipulate a dataframe to inspect what their manipulation did. I would 
>>>> like 
>>>> to develop an extension or kernel magic that prints useful information to 
>>>> a 
>>>> cell's output based on the pandas function called in that cell. For 
>>>> example, running pd.read_csv() might automatically print the shape and 
>>>> column names of the loaded dataframe and df.drop_duplicates() might 
>>>> automatically print how many duplicates were dropped and how many unique 
>>>> rows remain.
>>>>
>>>> How might I architect such an extension (e.g., a JupyterLab extension, 
>>>> an iPython kernel magic, or something else)? I think I would need to 
>>>> detect 
>>>> when certain pandas functions are about to be run by the kernel and gather 
>>>> information about the dataframe immediately before and after execution. 
>>>> Would that even be feasible?
>>>>
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>>>
>>>
>>> -- 
>>> Brian E. Granger
>>> Associate Professor of Physics and Data Science
>>> Cal Poly State University, San Luis Obispo
>>> @ellisonbg on Twitter and GitHub
>>> [email protected] and [email protected]
>>>
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>
>
>
> -- 
> Michael Milligan, Ph.D.         | Supercomputing Institute
> Assistant Director for          | University of Minnesota
>    Application Development      | [email protected] <javascript:>
> www.msi.umn.edu/staff/milligan  | Phone: 612-624-8857
>

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