dtale VS best-of-generator

[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

btw. If you like to keep track on how we might implement your suggestion, you can also open an issue here with your suggestions: https://github.com/best-of-lists/best-of-generator/issues/new/choose

Good point. Our goal is actually to get to an automated scoring system that can reflect not just popularity, but also lots of other qualitative factors for libraries. With our initial release, we are already taking many different factors into account, not only stars: https://github.com/best-of-lists/best-of-generator#project-quality-score . But there is a lot to improve, and we are working on an improved version of the calculation.

Welcome to the post about the Top 5 Python ML Model Interpretability libraries! You ask yourself how we selected the libraries? Well, we took them from our Best of Machine Learning with Python list. All libraries on this best-of list are automatically ranked by a quality score based on a variety of metrics, such as GitHub stars, code activity, used license and other factors. You can find more details in the best-of-generator repo.