Try Perspective, especially if you want users to be able to interact with the visualizations and update them - including re-aggregating the data https://perspective.finos.org/
Ask HN: What's the best charting library for customer-facing dashboards?
51–60 of 64 posts
Re: Ask HN: What's the best charting library for customer-facing dashboards?
#52We use echarts at https://evidence.dev and have been quite happy with it. We do a lot of embedded analytics and it's worked well for us.
Re: Ask HN: What's the best charting library for customer-facing dashboards?
#53Plotly is based on D3. Has both open-source version and paid option.
Re: Ask HN: What's the best charting library for customer-facing dashboards?
#54If you need really custom charts and use react, I can recommend Visx. It’s a small wrapper around d3, that doesn’t really try to create their own abstractions, but mostly uses d3 conventions.
We use them for all our charting needs (quite a bit!) at re-cap.com.
Re: Ask HN: What's the best charting library for customer-facing dashboards?
#55Plotly offers more power and flexibility than chart.js and provides a much simpler API than D3 (it has D3 and webgl renderers).
The ecosystem is broad and includes React, Angular and other wrappers and language-interfaces for Python, Rust, Go, Scala and many others (incl Common Lisp).
If you start plotting a lot of data it can grow with you since it supports typed arrays and webgl rendering without undue boilerplate.
Disclaimer... I work for Plotly
Re: Ask HN: What's the best charting library for customer-facing dashboards?
#56Apache Echarts. You run into constraints with most others after a point.
Re: Ask HN: What's the best charting library for customer-facing dashboards?
#57Plotly offers more power and flexibility than chart.js and provides a much simpler API than D3 (it has D3 and webgl renderers). The ecosystem is broad and includes React, Angular and other wrappers and language-interfaces for Python, Rust, Go, Scala and many others (incl Common Lisp). If you start plotting a lot of data it can grow with you since it supports typed arrays and webgl rendering without undue boilerplate.…
I like Plotly’s interactivity, but the Python API would be so much better if it was typed. I need to google which attributes to change to get anything done all the time. Copilot helps a bit but also constantly hallucinates plausible but not implemented plotly settings.
I also failed when trying to create a nice violin plot as implemented with matplotlib in the Shap library.
That said I tried using other charting libraries but Plotly’s interactivity is a killer feature others don’t offer in the same way.
Re: Ask HN: What's the best charting library for customer-facing dashboards?
#58If you have millions of data points and require real time performance then go for a paid solution like SciChart: https://www.scichart.com/
Re: Ask HN: What's the best charting library for customer-facing dashboards?
#59https://www.chartjs.org/ It's pretty easy to configure and understand. It's the 80/20 principal of charts, it is 80% of D3 functionality for 20% the effort.
Ditto here. I thought I'd try Apex Charts on my most recent project and regret it: - mobile support works, but is poorly done - very heavy library to load - RAM intensive; a few charts w/ lots (e.g. ~100) data points spirals out of control (e.g. memory spikes from 60 MB to 700 MB). On mobile this basically guarantees that the tab crashes. - UI interactions feel laggy and lack responsiveness (e.g. panning is pure gues…
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Re: Ask HN: What's the best charting library for customer-facing dashboards?
#60Earlier quoted context omitted.
Echarts isn’t hard to set up anymore now that GPT-4 takes care of the complexity. Charts that used to take me days to set up and data-wrangle now only take minutes. I suppose this applies to other libraries but I like echarts.
Can you share some of your prompts that you have used?
‘Using echarts and provided DATA, demonstrate how to convert a table of rows such that the “value” is aggregated and shown on a bar chart as the sum of all “values” for each “timestamp”
DATA: {json array}’
Dumping your data into the context window tends to help specify the task and focus the AI on the data structures to use.