productboard
Canny.io
Aha!
UserVoice
ProdPad
Featurebase
Upvoty
Frill
Matplotlib
Pandas
NumPy
Seaborn
D3.js
Plotly
GnuPlot
Jupyter
productboard MatplotlibBased on our record, Matplotlib seems to be a lot more popular than productboard. While we know about 114 links to Matplotlib, we've tracked only 4 mentions of productboard. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
Admittedly, this is an issue with organization and can be solved with thorough cleanups, but I suspect that may disrupt the usual flow of non-PM people more. I am thinking of using a separate tool like craft.io or productboard.com to highlight strategies, roadmaps, cross-team initiatives, discoveries, etc. With a possible link to JIRA somehow. Has anyone ever tried this? Source: about 4 years ago
Recently my friend at Productboard noticed an interesting bug in one of our services. For some reason our code responsible for calculating how many days our customers' features spend in certain states (Idea, Discovery, Delivery, etc) in some cases would give us wrong results. - Source: dev.to / about 4 years ago
ProductboardProductboard helps us capture user feedback from email, Slack, Zendesk, our public-facing product portal etc. And see what users need the most. We also use it for prioritizing product objectives, release planning, roadmappingโฆ. Source: almost 5 years ago
I use ProductBoard. It's fairly expensive but pretty great. I gather requirements into PB and use the inbuilt editor to flesh them out. When a story is ready I push a button and it ends up in Trello (but you can add your own integrations; there's one for github for example). The integrations aren't perfect but I love it. Used it in my last job and brought it in at my current job. https://productboard.com. - Source: Hacker News / about 5 years ago
In February, an AI agent named MJ Rathbun submitted a pull request to matplotlib โ the Python plotting library used by half the scientific computing world. Scott Shambaugh, a volunteer maintainer, rejected it. Standard code review. Nothing unusual. - Source: dev.to / 5 months ago
Numbers are useful, but sometimes itโs easier to spot patterns when you can actually see your data. Pandas works seamlessly with Matplotlib, a popular Python library for creating visualizations. Together, they make it easy to turn raw numbers into clear charts. - Source: dev.to / 8 months ago
We are storing the results in JSON files, which we combine, analyze and visualize using matplotlib in Python. Here's the structure of a benchmark result file:. - Source: dev.to / 8 months ago
NetworkX and Matplotlib were used to visualize the graph structure of the agent. - Source: dev.to / 9 months ago
The book introduces the core libraries essential for working with data in Python: particularly IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and related packages Familiarity with Python as a language is assumed; if you need a quick introduction to the language itself, see the free companion project, Aโฆ. - Source: dev.to / 10 months ago
Canny.io - Canny helps you collect and organize feature requests to better understand customer needs and prioritize your roadmap.
Pandas - Pandas is an open source library providing high-performance, easy-to-use data structures and data analysis tools for the Python.
Aha! - Aha! is the new way to create visual product roadmaps. Web-based product management tools and roadmapping software for agile product managers.
NumPy - NumPy is the fundamental package for scientific computing with Python
UserVoice - UserVoice integrates easy-to-use feedback, helpdesk, and knowledge base management tools in one platform that empowers users to speak and companies to understand.
Seaborn - Seaborn is a Python data visualization library that uses Matplotlib to make statistical graphics.