dtale VS mito

[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

2. The report you're sending out for display is _expected_ in an Excel format. The two main reasons for this are just organizational momentum, or that you want to let the receiver conduct additional ad-hoc analysis (Excel is best for this in almost every org).

The way we've sliced this problem space is by improving the interfaces that users can use to export formatting to Excel. You can see some of our (open-core) code here [2]. TL;DR: Mito gives you an interface in Jupyter that looks like a spreadsheet, where you can apply formatting like Excel (number formatting, conditional formatting, color formatting) - and then Mito automatically generates code that exports this formatting to an Excel. This is one of our more compelling enterprise features, for decision makers that work with non-expert Python programmers - getting formatting into Excel is a big hassle.

[1] https://trymito.io

[2] https://github.com/mito-ds/mito/blob/dev/mitosheet/mitosheet...

3. So you do want to do code-gen, does it make sense to do it in a chat interface, or can we do better?

As a Figma user, I'd answer these in the following way:

> Why is it necessary to generate code in the first place?

Because mockups aren't your production website, and your production website is written in code. But maybe this is just for now?

I'm sure some high-up PM at Figma has this as their goal - mockup the website in Figma, it generates the code for a website (you don't see this code!), and then you can click deploy _so easily_. Who wants to bet that hosting services like Vercel etc reach out to Figma once a week to try and pitch them...

In the meantime, while we have websites that don't fit neatly inside Figma constraints, while developers are easier to hire than good designers (in my experience), while no-code tools are continually thought of as limiting and a bad long-term solution -- Figma code export is good.

> Why is just writing the code by the hand not the best solution?

For the majority of us full-stack devs who have written >0 CSS but are less than masters, I'll leave this as self-evident.

> So you do want to do code-gen, does it make sense to do it in a chat interface, or can we do better?

In the case of Figma, if they were a new startup with no existing product and they were trying to "automation UI creation" -- v1 of their interface probably would be a "describe your website" and then we'll generate the code for it.

This would probably suck. What if you wanted to easily tweak the output? What if you had trouble describing what you wanted, but you could draw it (ok, OpenAI vision might help on this one)? What if you had experience with existing design tools you could use to augment the AI. A chat interface is not the best interface for design work.

ChatGPT-style code-generation is like v0.1. Github Copilot is an example of next step - it's not just a chat interface, it's something a bit more integrated into an environment that make sense in the context of the work you're doing. For design work, a canvas (literally! [2]) like Figma is well-suited as an environment for code-gen that can augment (and maybe one day replace) the programmers working on frontend. For tabular data work, we think a spreadsheet is the interface where users want to be, and the interface it makes sense to bring code-gen to.

Any thoughts appreciated!

[1] https://trymito.io, https://github.com/mito-ds/mito

I think the biggest area for growth for LLM based tools for data analysis is around helping users _understand what edits they actually made_.

I'm a co-founder of a non-AI data code-gen tool for data analysis -- but we also have a basic version of an LLM integration. The problem we see with tooling like Pandas AI (in practice! with real users at enterprises!) is that users make an edit like "remove NaN values" and then get a new dataframe -- but they have no way of checking if the edited dataframe is actually what they want. Maybe the LLM removed NaN values. Maybe it just deleted some random rows!

The key here: how can users build an understanding of how their data changed, and confirm that the changes made by the LLM are the changes they wanted. In other words, recon!

We've been experimenting more with this recon step in the AI flow (you can see the final PR here: https://github.com/mito-ds/monorepo/pull/751). It takes a similar approach to the top comment (passing a subset of the data to the LLM), and then really focuses in the UI around "what changes were made." There's a lot of opportunity for growth here, I think!

Any/all feedback appreciated :)

Mito — spreadsheet inside notebooks

You can see the open source code here.

Here is the Github again.

Mito is an open source Python package that allows the user to call an interactive spreadsheet into their Python environment. Each edit made in the spreadsheet generates the equivalent Python.

Mito is an open source Python tool that allows you to call a spreadsheet into your Python environment. Each edit you make in the spreadsheet generates the equivalent Python for you. This allows users to access Python with the spreadsheet skills they already have. Here is the Github