Animated chart presentations in Jupyter notebooks

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After releasing our open-source tool to build animated charts in Jupyter and other computational notebooks ( https://news.ycombinator.com/item?id=30895975 ), we got two requests from data scientists: 1. They lacked the opportunity to use these charts to present their findings and control the animation better than just (re)running notebook cells. 2. They had challenges using our generic chart-building engine - they are more acquainted with picking a chart type and using the parameters specific to that type. So we decided to build a new extension that enables our users to add the charts to slides and thus create interactive, animated presentations they can present right from the notebook. The presentation can be controlled with buttons beneath the charts, keyboard shortcuts (arrows, PgUp, PgDn, Home, End), or a clicker. We also added the capability to export these presentations to HTML to share them with others who don't use notebooks. Example - scroll to the bottom for the presentation: https://vizzuhq.github.io/ipyvizzu-story/examples/demo/ipyvi... . We tackled the other challenge by introducing chart presets: users can now pick from the 40+ pre-defined chart types. https://ipyvizzu.vizzuhq.com/examples/examples.html#Preset-c... Everything is released under the Apache 2.0 license and is available for JavaScript developers as an open-source data storytelling solution. Technically, in the Jupyter version, it's the JS lib being called from the notebook with a Python interface - similarly to what Plotly does. More info: https://github.com/vizzuhq/vizzu-ext-js-story & https://lib.vizzuhq.com/latest/ We'd love to know more about how our technology can be put to good use, so if you have any feedback or suggestions, we're here to listen.

Visit product View on Hacker NewsAug 25, 2022

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Product HuntOn track for Day 1 leaderboard · Strong signals: user, new, presentations · Missing: mac, agents, macos

88%88% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Indie HackersFits the IH revenue-focused audience · Strong signals: para · Missing: supports, reddit linkedin, podcasting

76%76% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released

63%63% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

AppSumoMay struggle as an AppSumo deal · Strong signals: interface, users · Missing: plus, platform, intuitive

50%50% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: users, para · Missing: mobile apps, ios, personal

47%47% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: arr, active · Missing: mrr, revenue, profit

11%11% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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