Theres a lot of room for custom widgets to be developed, this will take a lot for taipy to compete with streamlit ; generally streamlited apps gets transformed into either reactified or any popular frontend fw with django or fast api as backend for becoming prod ready I have explored many class A frameworks from python, none of them provides a excel /table copy paste directly into browser dataframe editorwidget other than streamlit.
Very nicely structured content. Loved that you kept it to the point and didn't add fillers to drag out the duration of the video. High production quality!!
I tried this dude, this has the same issues with text input that react native had few months ago! It's not that great and it can never be on production as how my experience went
Agreed, I also love Streamlit. In fact, comparing it with Streamlit does say a lot about Streamlit. But if I think beyond those use cases, that’s when I will look for an alternative.
There is a shameless cheating @ 9:32 what the hell is Decimator object that reduces number of points from 1 million to 500 !!! Why there is no equivalent decimation in streamlit?
@@DataSciencewithHarshit Thanks for the reply, but that decimation support is literally a one-liner. That has nothing to do with web framework. Here is the equivalent cheat in streamlit: ```python df = df.iloc[::len(df)//500][:500] # Decimation ``` I tried it out, streamlit is reduced to 1 second delay, still not as fast as taipy. You can still prove your point while being fair, don't make streamlit plot 1 million points and decimate the task to 500 points in taipy.
@DataSciencewithHarshit I think it just clicked why Decimator object is a thing. My one-line python equivalent still means that there is round-trip to the backend to resove the reaction to user input. While the decimator object wraps a javascript solution that is front-end resolved, so, no frontend-backend-frontend trip is involved, making it faster. I will consider this solution for data intensive apps.
So, what do you think about Taipy?
Amazing
Business License fee is expensive compared to available alternatives
Theres a lot of room for custom widgets to be developed, this will take a lot for taipy to compete with streamlit ; generally streamlited apps gets transformed into either reactified or any popular frontend fw with django or fast api as backend for becoming prod ready
I have explored many class A frameworks from python, none of them provides a excel /table copy paste directly into browser dataframe editorwidget other than streamlit.
Very nicely structured content. Loved that you kept it to the point and didn't add fillers to drag out the duration of the video. High production quality!!
Streamlit is not designed for production. It is exactly fir prototyping, fail fast, iterate fast. Once it sticks, go proper full stack!
Well, now I am using streamlit to do an AI chatbot application. Now wish to be designed for production, do you have any suggestions?
Amazing presentation 🎉
I tried this dude, this has the same issues with text input that react native had few months ago! It's not that great and it can never be on production as how my experience went
What about vs. Anvil?
How's this compared to Shiny for Python
Nice! This makes it so much easier
will try it for one of my dashboards. Very nice tutorial.
Great ..we’ll try this Thanks
I love streamlit easy to understand without any such hard coding required , client needs output
Designer app se koi Lena Dena Hota use
Agreed, I also love Streamlit. In fact, comparing it with Streamlit does say a lot about Streamlit. But if I think beyond those use cases, that’s when I will look for an alternative.
There is a shameless cheating @ 9:32 what the hell is Decimator object that reduces number of points from 1 million to 500 !!!
Why there is no equivalent decimation in streamlit?
This is already explained in the video, watch till the end. The whole point is the support for such algos within Taipy.
@@DataSciencewithHarshit
Thanks for the reply, but that decimation support is literally a one-liner. That has nothing to do with web framework.
Here is the equivalent cheat in streamlit:
```python
df = df.iloc[::len(df)//500][:500] # Decimation
```
I tried it out, streamlit is reduced to 1 second delay, still not as fast as taipy.
You can still prove your point while being fair, don't make streamlit plot 1 million points and decimate the task to 500 points in taipy.
@DataSciencewithHarshit
I think it just clicked why Decimator object is a thing. My one-line python equivalent still means that there is round-trip to the backend to resove the reaction to user input. While the decimator object wraps a javascript solution that is front-end resolved, so, no frontend-backend-frontend trip is involved, making it faster.
I will consider this solution for data intensive apps.
NIce, very nice , CRUD possible ?
Yes, check their gallery for more examples. You will have to hitch it up with a DB for CRUD.
@@DataSciencewithHarshit Awesome ,thank you
Looks great ! thank you!
Streamlit more easy to write code
Streamlit is free though
But flask is not for production.
Have you checked which all companies use Flask in production?
what is it for then