Fastplotlib: GPU-accelerated, fast, and interactive plotting library
1–10 of 190 posts
Re: Fastplotlib: GPU-accelerated, fast, and interactive plotting library
#2Seems like a nice library, but I have a hard time seeing myself using it over plotly. The plotly express API is just so simple and easy. For example, here's the docs for the histogram plot: https://plotly.com/python/histograms/
This code gives you a fully interactive, and performant, histogram plot:
```python
import plotly.express as px df = px.data.tips() fig = px.histogram(df, x="total_bill") fig.show()
```
Re: Fastplotlib: GPU-accelerated, fast, and interactive plotting library
#3>sine_wave.colors[::3] = "red"
I never knew I needed this until now
Re: Fastplotlib: GPU-accelerated, fast, and interactive plotting library
#4Seems like a nice library, but I have a hard time seeing myself using it over plotly. The plotly express API is just so simple and easy. For example, here's the docs for the histogram plot: https://plotly.com/python/histograms/ This code gives you a fully interactive, and performant, histogram plot: ```python import plotly.express as px df = px.data.tips() fig = px.histogram(df, x="total_bill") fig.show() ```
[deleted]
Re: Fastplotlib: GPU-accelerated, fast, and interactive plotting library
#5>sine_wave.colors[::3] = "red" I never knew I needed this until now
[deleted]
Re: Fastplotlib: GPU-accelerated, fast, and interactive plotting library
#6Very cool effort. That said, and it's probably because of the kind of work that I do, but I have almost never found the four challenges to be any kind of a problem for me. Although I do think there is some kind of contradiction there. Plotting (exploratory data analyis ("EDA"), really) is all about distilling key insights and finding features hidden in data. But you have to some kind of intuition about where the needle in the haystack is. IME, throwing up a ton of plots and being able to scrub around in them never seems to provide much insight. It's also very fast---usually the feedback loop is like "make a plot, go away and think about it for an hour, decide what plot I need to make next, repeat". If there is too much data on the screen it defeats the point of EDA a little bit.
For me, matplotlib still reigns supreme. Rather than a fancy new visualization framework, I'd love for matplotlib to just be improved (admittedly, fastplotlib covers a different set of needs than what matplotlib does... but the author named it what they named it, so they have invited comparison. ;-) ).
Two things for me at least that would go a long way:
1) Better 3D plotting. It sucks, it's slow, it's basically unusable, although I do like how it looks most of the time. I mainly use PyVista now but it sure would be nice to have the power of a PyVista in a matplotlib subplot with a style consistent with the rest of matplotlib.
2) Some kind of WYSIWYG editor that will let you propagate changes back into your plot easily. It's faster and easier to adjust your plot layout visually rather than in code. I'd love to be able to make a plot, open up a WYSIWYG editor, lay things out a bit, and have those changes propagate back to code so that I can save it for all time.
(If these features already exist I'll be ecstatic ;-) )
Re: Fastplotlib: GPU-accelerated, fast, and interactive plotting library
#7Really nice post introducing your library.
When would you reach for a different library instead of fastplotlib?
How does this deal with really large datasets? Are you doing any type of downsampling?
How does this work with pandas? I didn't see it as a requirement in setup.py
Does this work in Jupyter notebooks? What about marimo?
Re: Fastplotlib: GPU-accelerated, fast, and interactive plotting library
#8GPU all the things! GPU-accelerated Tableau would be incredible.
Re: Fastplotlib: GPU-accelerated, fast, and interactive plotting library
#9Very cool effort. That said, and it's probably because of the kind of work that I do, but I have almost never found the four challenges to be any kind of a problem for me. Although I do think there is some kind of contradiction there. Plotting (exploratory data analyis ("EDA"), really) is all about distilling key insights and finding features hidden in data. But you have to some kind of intuition about where the need…
I'd be curious to hear more about your EDA workflow.
What I want for EDA is a tool that let's me quickly toggle between common views of the dataset. I run through the same analysis over and over again, I don't want to type the same commands repeatedly. I have my own heuristics for which views I want, and I want a platform that lets me write functions that express those heuristics. I want to build the inteligence into the tool instead of having to remember a bunch of commands to type on each dataframe.
For manipulating the plot, I want a low-code UI that lets me point and click the operations I want to use to transform the dataframe. The lowcode UI should also emit python code to do the same operations (so you aren't tied to a low-code system, you just use it as a faster way to generate code then typing).
I have built the start of this for my open source datatable UX called Buckaroo. But it's for tables, not for plotting. The approach could be adapted to plotting. Happy to collaborate.
Re: Fastplotlib: GPU-accelerated, fast, and interactive plotting library
#10I know 3D is in the roadmap. Once the basic functionality is in place, it would be great to also consider integrating molecular visualization or at least provide enough fast primitives to simplify the integration of molecular visualization tools with this library.