dtale VS dtale-desktop

[D-Tale](https://github.com/man-group/dtale) has recently released version 3.2.0 on pypi & conda-forge: ``` pip install -U dtale conda install dtale -c conda-forge ``` But if you want to take it one step further you can now integrate it with [ArcticDB](https://github.com/man-group/ArcticDB): ``` pip install -U dtale[arcticdb] ``` This allows you the ability to navigate your libraries of datasets saved to your ArcticDB database! But the best part is that all the reads are occuring directly against ArcticDB so some of the memory constraints you may have been hit with before are now a thing of the past. Here's a full write up how to use this functionality along with a quick demo: https://github.com/man-group/dtale/blob/master/docs/arcticdb/ARCTICDB\_INTEGRATION.md Hope this helps & please support open-source by throwing your star on the [repo](https://github.com/man-group/dtale). Thanks! 🙏

I've looked into external tooling, libs such as dtale, which feel overly complicated for my use case (but I'm open to alternatives). What I would like to have instead is something akin to Spyder's variable viewer, which allows sorting by column. VSCode goes a step further and also provides the ability to filter the dataframe.

D-Tale: A Python library that provides an interactive web-based interface for data exploration and analysis.

Try this: https://github.com/man-group/dtale

https://github.com/man-group/dtale

I find that I'm actually a lot faster using basic Pandas methods to get the data I want in exactly the form I want it.

If I really want to show everything, I just use:

'''

Just added "Time Series Analysis" in v1.60.1 of D-Tale on pypi & conda-forge: pip install -U dtale conda install dtale -c conda-forge This feature provides a quick and easy way to visualize the usage of the following time series analysis function in statsmodels:

Your data is stored in memory so the size of your dataframe is limited to the memory of your machine. That being said we’ve allowed users to swap out the machanism which stores the data so you can use something like Redis or Shelve to allieviate memory. Here’s some documentation: https://github.com/man-group/dtale/blob/master/docs/GLOBAL_STATE.md

It can be run both locally and as a web service -- the demo site is actually running on a kubernetes cluster. When run as a web service, it uses websocket connections to push real-time updates, ensuring that all connected users see the same thing. There are a large number of settings which can be used to configure exactly how it behaves, documented [here](https://github.com/phillipdupuis/dtale-desktop#settings).

Also, if you want to you can run it as a web service -- the demo site is actually running on a kubernetes cluster. When run as a web service, it uses websocket connections to push real-time updates, ensuring that all connected users see the same thing. There are a large number of settings which can be used to configure exactly how it behaves, documented here.

Inspiration taken from here: https://github.com/phillipdupuis/dtale-desktop/blob/master/dtale_desktop/source_code_tools.py#L20, in that case it's being used to execute text users write in a web browser as python code (text sent to server > server writes it to tempfile, turns the tempfile into a module, executes the code in the module, sends the result of executing that code back)