Our main target user is a professional python data scientist that wants to be faster at data wrangling and visualization so that they can focus on understanding the data instead of coding the same pandas commands over and over again. That’s why bamboolib has both data transformation and exploration features included. In the future, we will provide more sophisticated features from which also more experienced data scientists can profit.

Yet, I definitely agree that bamboolib especially nicely suits pandas learners and non-coders. Would be happy to have a direct exchange on your ideas on this topic (feel free to pm me at tobiaskrabel at gmail dot com).

I have a similar problem with my product which offers machine learning as a service. It offers complex data science features (building ML models) for non-coders. From my experience, I can tell that it is hard to find users that don't have enough technical background to train the model by themself and enough background to understand what to do with ML model.

Interesting. So on which platform are you providing that service? What does your product look like (maybe share a link?)? And did you find a "solution" to your problem of finding a large enough audience?

This doesn't accelerate professional data scientists.

To me the only use of this tool is to make available the more complex uses of pandas to individuals without the background/understanding of how to wield those.

But without that understanding, giving those people a UI of functions they have no understanding of is just a recipe for disaster.

All these tools that aim to lower the barrier to entry for data science without fully automating it are doomed to fail because they have no audience. The market for analysts who aren't also software engineers is shrinking to 0.

> But without that understanding, giving those people a UI of functions they have no understanding of is just a recipe for disaster.

I can see use of the UI in a classroom setting to bridge the learning gap for people that are pretty proficient with the utility libraries like pandas offer, but lack the experience with Python and reading documentation at this point in time. I honestly fail to understand this critique, that sounds like saying we should ban Excel because people could use it to calculate something that doesn't make sense. It's not like pandas does something magical and every half decent Excel user understands the functionality behind the buttons I see in the bamboo demo linked here.

> The market for analysts who aren't also software engineers is shrinking to 0.

While I certainly get where this perspective might be coming from, I find it unlikely to be true. The recent acquisition of Tableau and growth of similar no-code tools shows that it's untrue from a business perspective (just read one of the HN threads on these topics, plenty of non-SE people making good use of them). Even from the code perspective, outside of production most of the data analysis code I see hardly shows any signs of good software engineering practice and yet fulfills the task it is written for.

Actually, all our initial users are professional data scientists who work at least 10-20h per week with pandas. This is also reflected in the pricing.

They like the opportunity to not always have to write the code and just reach it via the UI. So they have a smooth workflow without inspecting the data in Libre Office/Excel or having to google so often.

However, what they especially like are the data exploration and visualization features because they save them a lot of time. You can see a video of those here: https://www.youtube.com/watch?v=I0a58h1OCcg