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Jupyter Package Manager – Manage Python Packages Directly in JupyterLab

Hacker News

Jupyter Package Manager – Manage Python Packages Directly in JupyterLab

Hi HN, Jupyter Package Manager simplifies managing Python packages directly within your notebooks without the need to leave your coding environment. Key Features: - List Packages: Quickly view all packages installed for the currently open notebook. - Filter Packages: Easily search and filter through packages by name. - Install New Packages: Install any package you need directly from the notebook interface. - Remove Packages: Effortlessly remove packages that are no longer needed. - Dark Theme: Enjoy a comfortable dark mode. The extension leverages pip under the hood, making it a seamless addition to your JupyterLab workflow. This package is a JupyterLab extension - just install it with pip to get started. By default, it will be available in MLJAR Studio, a desktop app for notebooks that I'm working on. I hope that Jupyter Package Manager will be a great help for beginners and for people learning Python. I'd love to hear your feedback or any suggestions for improvement. Thanks for taking a look!

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1points
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Indie HackersFits the IH revenue-focused audience · Strong signals: started · Missing: supports, reddit linkedin, podcasting
73%73% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Product HuntOn track for Day 1 leaderboard · Strong signals: new, coding, open · Missing: mac, agents, macos
61%61% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released
55%55% 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 · Missing: plus, platform, intuitive
37%37% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
29%29% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
12%12% 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.

Incorrect prediction on native model

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